Skip to content
Publicly Available Published by De Gruyter October 2, 2014

Applications of immunochemistry in human health: advances in vaccinology and antibody design (IUPAC Technical Report)

Reinhild Klein, Douglas M. Templeton and Michael Schwenk

Abstract

This report discusses the history and mechanisms of vaccination of humans as well as the engineering of therapeutic antibodies. Deeper understanding of the molecular interactions involved in both acquired and innate immunity is allowing sophistication in design of modified and even synthetic vaccines. Recombinant DNA technologies are facilitating development of DNA-based vaccines, for example, with the recognition that unmethylated CpG sequences in plasmid DNA will target Toll-like receptors on antigen-presenting cells. Formulations of DNA vaccines with increased immunogenicity include engineering into plasmids with “genetic adjuvant” capability, incorporation into polymeric or magnetic nanoparticles, and formulation with cationic polymers and other polymeric and non-polymeric coatings. Newer methods of delivery, such as particle bombardment, DNA tattooing, electroporation, and magnetic delivery, are also improving the effectiveness of DNA vaccines. RNA-based vaccines and reverse vaccinology based on gene sequencing and bioinformatic approaches are also considered. Structural vaccinology is an approach in which the detailed molecular structure of viral epitopes is used to design synthetic antigenic peptides. Virus-like particles are being designed for vaccine deliveries that are based on structures of viral capsid proteins and other synthetic lipopeptide building blocks. A new generation of adjuvants is being developed to further enhance immunogenicity, based on squalene and other oil–water emulsions, saponins, muramyl dipeptide, immunostimulatory oligonucleotides, Toll-like receptor ligands, and lymphotoxins. Finally, current trends in engineering of therapeutic antibodies including improvements of antigen-binding properties, pharmacokinetic and pharmaceutical properties, and reduction of immunogenicity are discussed. Taken together, understanding the chemistry of vaccine design, delivery and immunostimulation, and knowledge of the techniques of antibody design are allowing targeted development for the treatment of chronic disorders characterized by continuing activation of the immune system, such as autoimmune disorders, cancer, or allergies that have long been refractory to conventional approaches.

1 Introduction

Advances in immunochemistry have made a major contribution to our current state of health and longevity by allowing development of novel agents useful in immunotherapy. Immunotherapeutic agents are defined as substances that prevent and/or treat diseases by inducing/enhancing, modulating, or suppressing immune responses [1]. Infectious diseases have always been one of the most important burdens for mankind, and the development of vaccines has been a great step in the reduction and even elimination of some of these diseases. However, there still exists an inequity between rich and poor countries in life expectancy which is related to infections that dominate the causes of premature death in developing countries [2]. There is, therefore, an urgent need worldwide for the development not only of financial programs for those countries, but also for the generation of new vaccines against both old and newly emerging infectious agents [2]. The decrease in infectious disorders in industrialized countries has been, however, followed by a rise in the incidence of chronic inflammatory disorders associated with an enhanced activation of the immune system such as allergies and autoimmune or autoinflammatory diseases [3]. The reasons for this increase are still not completely understood. Those diseases have been treated until now with anti-inflammatory or immunosuppressive drugs, which affect, however, rather nonspecifically the immune system and can act also on other organs, resulting in a wide spectrum of adverse reactions, and even life-threatening consequences can occur. Anti-inflammatory drugs include nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and biologic or nonbiologic disease-modifying drugs (DMDs) (Fig. 1) [4]. The first two classes are used mostly to relieve symptoms, whereas DMDs are distinguished by their ability to reduce or prevent tissue damage caused by the inflammatory attack, especially when used early in the course of the disease. Nonbiologic DMDs, such as low-dose methotrexate, suppress various inflammatory processes in multiple types of immune cells. Biologic DMDs are genetically engineered recombinant proteins (i.e., humanized antibodies) that target specific inflammatory molecules or their receptors or signalling pathways. Immunosuppressive drugs are a rather heterogeneous class consisting, for instance, of antimetabolites (azathioprine, methotrexate, mycophenolate-mofetil, leflunomide), alkylating agents (cyclophosphamide), calcineurin inhibitors (cyclosporin, tacrolimus), or mTOR (mechanistic target of rapamycin) inhibitors (everolimus, sirolimus) which interact directly with immunocompetent cells. Improved understanding of the inflammatory response has led in recent years to the development of more specific agents in the treatment of chronic inflammatory disorders, especially antibodies reacting with mediators or surface molecules of immunocompetent cells.

Fig. 1 Targets for immunotherapeutic drugs within the inflammatory cascade. Inflammation is initiated by pathogen- or danger-associated molecular patterns which react with pattern recognition receptors, such as TLRs and NLRs. Binding to these receptors, which couple to several signal transduction pathways, activate transcription factors as, for instance, NFκB or AP-1. These factors induce the expression of a large number of genes that exert antimicrobial activities, e.g., generate ROI and RNI. Chemokines regulate the recruitment of additional immune cells. Production of prostaglandins also regulates pro- and anti-inflammatory cell functions. Expression of inflammatory cytokines provides a feed-forward loop for amplification of the initial response. Already during the inflammatory response anti-inflammatory mechanisms are activated, as, for instance, IL-10, to start the resolution program. Existing or potential targets for therapeutic intervention in the inflammatory pathway are indicated (1–7) (adapted from [4]). Ab, antibody; AP-1, activating protein-1; Cox2, cyclooxygenase 2; GR, glucocorticoid receptor; HDM, histone demethylase; HMT, histone methyltransferase; IKK, IκB kinase; IL, interleukin; JAK, Janus kinase; JNK, c-Jun N-terminal kinase; MAP, mitogen-activated protein kinase; MCP1, monocyte chemotactiv protein-1; NFκB, nuclear factor κB; NLR, NOD-like receptor; NSAIDs, non-steroidal anti-inflammatory drugs; RNI, reactive nitrogen intermediates; ROI, reactive oxygen intermediate; TLR, Toll-like receptor; TNFR, tumor necrosis factor receptor.

Fig. 1

Targets for immunotherapeutic drugs within the inflammatory cascade. Inflammation is initiated by pathogen- or danger-associated molecular patterns which react with pattern recognition receptors, such as TLRs and NLRs. Binding to these receptors, which couple to several signal transduction pathways, activate transcription factors as, for instance, NFκB or AP-1. These factors induce the expression of a large number of genes that exert antimicrobial activities, e.g., generate ROI and RNI. Chemokines regulate the recruitment of additional immune cells. Production of prostaglandins also regulates pro- and anti-inflammatory cell functions. Expression of inflammatory cytokines provides a feed-forward loop for amplification of the initial response. Already during the inflammatory response anti-inflammatory mechanisms are activated, as, for instance, IL-10, to start the resolution program. Existing or potential targets for therapeutic intervention in the inflammatory pathway are indicated (1–7) (adapted from [4]). Ab, antibody; AP-1, activating protein-1; Cox2, cyclooxygenase 2; GR, glucocorticoid receptor; HDM, histone demethylase; HMT, histone methyltransferase; IKK, IκB kinase; IL, interleukin; JAK, Janus kinase; JNK, c-Jun N-terminal kinase; MAP, mitogen-activated protein kinase; MCP1, monocyte chemotactiv protein-1; NFκB, nuclear factor κB; NLR, NOD-like receptor; NSAIDs, non-steroidal anti-inflammatory drugs; RNI, reactive nitrogen intermediates; ROI, reactive oxygen intermediate; TLR, Toll-like receptor; TNFR, tumor necrosis factor receptor.

The aim of this review is to present recent advances in the prevention of infectious disorders (i.e. in vaccination) and in the specific therapy of chronic disorders, including cancer, with special emphasis on engineering of therapeutic antibodies.

2 Vaccination

2.1 History

Vaccines have been a major innovation in the history of mankind, and they are an important factor contributing to the increase in our lifespan. Vaccination is a very old medical practice that started in Asia using material from smallpox lesions to transmit a mild infection and thereby protect against more serious disease [5, 6]. The term was created by Edward Jenner who introduced this practice in 1796 in Western medicine using infected materials from cows (Latin: vacca) to immunize against smallpox [7]. However, it was not until the 1880s that an understanding of infectious diseases and the action of vaccines became better known, with the establishment of the “germ theory of disease” by Louis Pasteur and Robert Koch. Louis Pasteur started the rational development of vaccines and established the basic rules of vaccinology “isolate, inactivate and inject the microorganism (vaccinate)” [8, 9]. These rules were followed for a century by vaccine developers, leading to the global reduction or even elimination of many of the devastating infectious disorders such as smallpox, diphtheria, tetanus, poliomyelitis, pertussis, measles, mumps, and rubella [10, 11].

In 1918 Karl Landsteiner, a clinical immunochemist, found that also small, non-immunogenic molecules (haptens) became able to induce a specific antibody response when they were artificially linked to larger “carrier” molecules. However, this “hapten-carrier” concept was transferred into the clinic only in 1980 when polysaccharides of Haemophilus influenzae type B, which are poorly immunogenic in young children, were linked to tetanus toxoid.

The traditional vaccines (or first-generation vaccines) were developed using at least one of the following approaches: killed (inactivated) vaccines, live attenuated vaccines, or subunit vaccines including the protein-conjugated capsular polysaccharides, toxoids, cell-free extracts, recombinant proteins, and stand-alone capsular polysaccharides (Table 1).

Table 1

Schematic presentation of human vaccines produced over time and the major disciplines involved in their development (adapted from [12]).

YearAttenuated or killed bacteriaViral vaccinesVaccines based on
Genetic engineeringImmunological principles
1880CholeraRabies
Anthrax
Tetanus
1900Typhoid
Diphtheria
Pertussis
1920TuberculosisTetanus toxoid
1940Yellow fever
Influenza
Japanese encephalitis
Polio (killed)
1960Meningococcal polysaccharideAdenovirus
PneumoniaPolio (live)
MeningitisMeasles
Mumps
Rubella
Chicken pox
Rabies
1980Hepatitis AHepatitis BHiB conjugate
Rabies
1995 Craig Venter publishes the genome of the first free-living organism
2000RotavirusPneumococcal conjugate
Lyme diseaseHepatitis B-AS04
Human papilloma virus (HPV)Meningococcal conjugate
2010HPV-AS04
Prostate cancer (Provenge)

AS04, adjuvant formulation containing the TLR4 stimulator monophosphoryl lipid A.

Inactivation and attenuation of pathogens was the first choice for many years, but the difficulty in cultivating some microorganisms in vitro and the fact that even attenuation may result in detrimental or unwanted immune responses showed that these approaches were impractical in some instances. The increasing knowledge in molecular biology enabled the development of new techniques. Powerful technologies became available in 1995 when Craig Venter published the genome of the first free-living organism [13, 14]. Thus, there has been much interest in developing vaccines based on the injection of antigen-expressing DNA plasmids (DNA vaccine platform). Furthermore, it became possible to identify by computer analysis putative surface-exposed proteins on bacteria in a reverse manner, starting from the genome rather than the microorganism (Table 1). This new approach was termed “reverse vaccinology” [15] (recombinant antigen platform) (Fig. 2). Advances in structural biology have now led to an increase in knowledge about how antibodies recognize vaccine antigens. The crystal structure of a pathogen-derived antigen bound to a cognate protective antibody reveals the folded antigen conformation against which a protective humoral immune response was elicited. This “structural vaccinology” uses the 3D structural information to rationally design novel and improved vaccine antigens [16].

Fig. 2 Schematic representation of the essential steps of vaccine development by the conventional approach and by reverse vaccinology (adapted from [15]).

Fig. 2

Schematic representation of the essential steps of vaccine development by the conventional approach and by reverse vaccinology (adapted from [15]).

A further breakthrough in vaccine development occurred in 1923 with the observation by Glenny and Hopkins [17] that diphtheria toxoids induce a vastly greater anti-toxin (antibody) response when injected together with aluminum sulfate or potash, as compared to the unaccompanied toxins. The authors postulated that this was due to the slow release of the antigen into the tissue, thereby providing prolonged stimulation of immunocompetent cells [18]. This property of alum in “helping” (Latin: adjuvare) the immune system to respond to an absorbed antigen inspired the term “adjuvant”. Several adjuvants, as for instance Freund’s complete and incomplete adjuvants, are used nowadays in experimental immunology. Their mode of action is not yet fully understood, but its further investigation is opening up interesting new insights into the function of the innate immune system.

Vaccination historically aimed at the prevention and treatment of infectious diseases. However, there is presently a strong interest in developing vaccines for the treatment of other types of diseases, including allergy, autoimmune diseases, and cancer.

2.2 Mechanisms of vaccination

The mechanisms by which vaccines activate the immune system are now becoming increasingly well understood at the molecular and cellular levels [19]. The protective immune response induced by all current killed and subunit vaccines relies solely on the production and maintenance of specific antibodies [20, 21], which protect by recognizing specific epitopes on the surfaces of bacteria or viruses. On the other hand, live-attenuated vaccines can mobilize both the cellular and humoral arms of the immune response, and generally induce more-prolonged immunity.

2.2.1 Antigen-presenting cells

Antigen-presenting cells (APCs), and especially dendritic cells (DCs), are the sentinel cells in the body that can sense invading microorganisms and alert other cells of the immune system [22]. They recognize microbial products (pathogen-associated molecular patterns; PAMPs) by pattern recognition receptors (PRRs), and this process leads to their maturation and migration to the lymph nodes. Examples of common PAMPs are double-stranded RNA, lipopolysaccharide (LPS) components of the cell wall of Gram-negative bacteria, and DNA sequences containing unmethylated cytosine-guanine DNA sequences. These molecules are all scarce or absent in the host and abundant in viruses or bacteria. Hence, they have evolved to provide the first “danger signal” to be perceived by the host immune system upon infection. This signal is indispensable for mounting a B- or T-cell response. Innate immune signals modulate not only the magnitude of the adaptive immune response but also the quality of this response. For instance, secretion of pro-inflammatory cytokines such as interleukin (IL)-12 will drive Th1-cell polarization and subsequent interferon (IFN)-γ secretion by T cells. Different signals are required to initiate an immune response (Fig. 3), namely, an antigen recognition and APC activation signal (signal 0), an antigen presentation signal (signal 1), and co-stimulation (signal 2).

Fig. 3 Schematic representation of the initiation of a T-helper (TH) cell response by an antigen-presenting cell (APC) via three signals (adapted from [20]). Signal 0 is mandatory to activate APCs and is mostly induced through the recognition of pathogen-associated molecular patterns (PAMPs) by pathogen-recognition receptors (PRRs) as, for instance, Toll-like receptors (TLRs). Signal 1 is triggered by specific peptide presentation by class II major histocompatibility (MHC) molecules to the T-cell receptor (TCR). However, unless another signal is given, anergy and abortive responses are induced. To avoid anergy, the co-stimulatory signal 2 is needed, through receptor-ligand interaction between APCs and T-cell antigens, such as CD40-CD40L, CD80 or CD86-CD28, and others.

Fig. 3

Schematic representation of the initiation of a T-helper (TH) cell response by an antigen-presenting cell (APC) via three signals (adapted from [20]). Signal 0 is mandatory to activate APCs and is mostly induced through the recognition of pathogen-associated molecular patterns (PAMPs) by pathogen-recognition receptors (PRRs) as, for instance, Toll-like receptors (TLRs). Signal 1 is triggered by specific peptide presentation by class II major histocompatibility (MHC) molecules to the T-cell receptor (TCR). However, unless another signal is given, anergy and abortive responses are induced. To avoid anergy, the co-stimulatory signal 2 is needed, through receptor-ligand interaction between APCs and T-cell antigens, such as CD40-CD40L, CD80 or CD86-CD28, and others.

2.2.2 Activation of the innate immune system

The re-evaluation of the importance of the innate immunity began with the discovery of the family of Toll-like receptors (TLRs) and the demonstration that these innate, germline-encoded immune receptors, permanently present on a variety of cells and tissues, were specific for conserved PAMPs. The TLRs were first discovered in Drosophila through a screen designed to identify molecules involved in fungal resistance [23]. The human homologue was identified and then a genetic screen to identify genes involved in the recognition of the Gram-negative bacterial cell wall component, LPS, identified the human TLR4 gene, which is essential for LPS signalling [24, 25]. As already described in detail by Robinson and Moehle (this issue), TLRs are members of a family of PRRs, which also includes C-type lectin-like receptors (CLRs), cytosolic nucleotide oligomerization domain-like receptors, cytosolic nucleotide oligomerization domain-like receptors, retinoic acid-inducible gene-based-I-like receptors (RIG-Is), and melanoma differentiation-associated gene 5 (MDA5) [26–31]. These receptors bind microbial ligands (“danger signals”) including cell wall components, lipoproteins, proteins, lipopolysaccharides, and DNA and RNA of bacteria, viruses, protozoa, and fungi to trigger different types of immune responses [32, 33]. Recently discovered PRRs include the NACHT, LRR and PYD domains-containing protein (NALP) family, which form the inflammasome and recognize cytoplasmic peptidoglycan, crystalline substances such as uric acid and alum, and general cellular stress from reactive oxygen species [34, 35].

RIG-I responds to RNA viruses, including paramyxoviruses, influenza virus, and Japanese encephalitis virus, whereas MDA5 detects picorna-viruses. The ligand for RIG-I was recently shown to be triphosphatase RNA (3pRNA) [20, 36–40]. HIN-200 (hematopoietic interferon-inducible nuclear antigens with 200 amino acid repeats) and absent-in-melanoma 2 (AIM2) families of proteins recognize cytoplasmic DNA [39, 41].

Further cytosolic sensors are nucleotide-binding oligomerization domain proteins (NODs) and NOD-like receptors (NLRs). NOD1 and NOD2 detect distinct substructures from bacterial peptidoglycan: NOD2 detects muramyl dipeptide (MDP) from Gram-negative and -positive bacteria, and NOD1 senses meso-diaminopimelic acid (meso-DAP), which is found in Gram-negative bacteria and in Gram-positive rods [42]. NLRs sense danger-associated host components such as uric acid crystals [43].

Other receptors expressed by APC and involved in antigen capture and recognition are scavenger receptors, CLRs, and triggering receptors expressed on myeloid cells (TREM). Scavanger receptors bind polyanionic ligands and can internalize not only pathogens but also host components such as apoptotic cells and modified low-density lipoproteins [38]. CLRs, including dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) and the mannose receptor, can bind a wide range of viruses, bacteria, and fungi through recognition of sugar moieties, such as N-acetyl-glucosamine, mannose, N-acetyl-mannosamine, fucose, and glucose [44]. The first role of scavengers and CLRs is not in pathogen recognition, and pathogens seem to be able to use these host-host-recognition systems for their own benefit. In contrast to TLRs, which have as primary function the discrimination between self and non-self, targeting scavengers and CLRs require a co-stimulatory signal to induce immunity rather than tolerance.

2.2.3 Activation of the adaptive immune system

This newly discovered variety of innate immune receptors can dictate the expression or activation of distinct intracellular transcription factors in immune cells, which ultimately induce the production of immunomodulatory molecules including cytokines, leukotrienes, prostaglandins, and chemokines. These molecules promote activation and recruitment of cells to the site of infection and also dictate subtle or dramatic differences in the activation of specific T-helper (TH) subsets and the subsequent adaptive immune response. Moreover, activation of innate immune pathways often up-regulates molecules involved in antigen presentation, thereby accelerating and strengthening the development of an adaptive immune response. Manipulation of innate pathways by incorporating selected innate stimulators into vaccine adjuvants may enable the generation of vaccines that induce much safer, more effective, and sustained immune responses to specific infections.

After activation of APC, migration to and entry into the lymph nodes, peptide fragments comprising T-cell epitopes are displayed in complexes with major histocompatibility complex (MHC) class I and MHC class II proteins on the cell surface, where they are recognized by T-cell receptors (TCRs) on T cells [45]. Activated T cells then provide crucial help to B cells during the maturation phase of the adaptive immune response. Surface-bound antigens on DCs are also screened by B cells. However, vaccines can also directly activate B cells, because repetitive multivalent display of B-cell epitopes across a bacterial or viral surface can cross-link cell-surface B-cell receptors leading to B-cell activation [46, 47]. Differentiation of B cells into plasma cells after activation is a final and important step in the activation cascade, because these plasma cells are able to produce the required antibodies and release them into the blood.

With an understanding of the above processes, it is now clear that new approaches to vaccine development should take into consideration the induction of strong cellular responses, including production of TH cells and sometimes cytotoxic T lymphocytes (CTLs), in addition to antibodies.

2.3 The DNA vaccine platform

2.3.1 General aspects

DNA vaccination, or genetic vaccination, is the common name for several vaccination methods that induce immunity by transfecting host cells with DNA that encodes an antigen, rather than by injecting the antigen itself in the form of protein or peptide; i.e. in DNA vaccines the antigen is replaced by its blueprint. After administration and incorporation, DNA causes the expression of the protein that induces the immune response (Fig. 4). Nearly 30 years ago it was first reported that plasmids encoding insulin could be expressed in vivo [48]. In 1990 it was shown that purified plasmid DNA (pDNA) coding for β-galactosidase transfected muscle cells in vivo when injected intramuscularly into mice, resulting in the expression of the plasmid’s marker genes in muscle cells [49]. Two years later it became evident that pDNA delivered into the skin or muscle induced antibody reactions to viral and non-viral antigens [50–53].

Fig. 4 Schematic representation of antigen expression and presentation upon pDNA vaccination (adapted from [60]). pDNA is taken up by cells via endocytosis (or cytosolic uptake). After endosomal escape, cytosolic trafficking, and nuclear entry, the pDNA can be transcribed into mRNA, followed by intracellular translation of the antigen. For T-lymphocyte activation, antigens have to be presented in the context of MHC class I or MHC class II molecules in the presence of co-stimulatory molecules (see also Fig. 3). Since non-APCs cannot induce T-cell activation, extracellular release or cell death can lead to an uptake of the antigen by APC. Antigens produced by direct transfection of APCs are presented by MHC I. Antigen present in the cytoplasm of APC probably because of endosomal escape can also enter the MHC class I pathway (“cross-presentation”).

Fig. 4

Schematic representation of antigen expression and presentation upon pDNA vaccination (adapted from [60]). pDNA is taken up by cells via endocytosis (or cytosolic uptake). After endosomal escape, cytosolic trafficking, and nuclear entry, the pDNA can be transcribed into mRNA, followed by intracellular translation of the antigen. For T-lymphocyte activation, antigens have to be presented in the context of MHC class I or MHC class II molecules in the presence of co-stimulatory molecules (see also Fig. 3). Since non-APCs cannot induce T-cell activation, extracellular release or cell death can lead to an uptake of the antigen by APC. Antigens produced by direct transfection of APCs are presented by MHC I. Antigen present in the cytoplasm of APC probably because of endosomal escape can also enter the MHC class I pathway (“cross-presentation”).

Recombinant DNA technologies have facilitated the preparation of plasmids as compared with conventional vaccines. Plasmids are considered replicons, which are capable of replicating autonomously within a suitable host. Basically, a DNA vaccine consists of a circular double-stranded bacterial DNA (the plasmid) in which a few eukaryotic and synthetic sequences have been inserted. These are limited to the antigen sequence, a ubiquitous enhancer-promoter driving its expression, and a transcription termination site in order to minimize the possibility of plasmid integration into host chromosomes. To permit insertion of the sequence coding for the antigen, the plasmid should also contain a synthetic 100-base pair DNA sequence (the multiple cloning site). The presence of a “relaxed origin of replication” and an antibiotic-resistance gene allows efficient plasmid replication in bacterial cells and their selection, two key aspects for large-scale plasmid production [54].

The first of several phase I trials, conducted almost two decades ago, evaluated the efficacy of a DNA vaccine targeting human immunodeficiency virus type I (HIV-1) [55]. Other studies followed for other HIV-1 antigens, influenza, human papillomavirus (HPV), hepatitis, and malaria. The vaccines were safe and well tolerated in small animals. However, it emerged that the amount of protein expressed, its persistence, and the nature of antigen presentation are key variables that determine the efficacy of the protection thus elicited. Disappointingly, the animal findings were not recapitulated in the induction of similar immunity in human clinical trials, and DNA vaccines had to be re-examined to understand their mechanism of action. Thus, the induced antibody titres were very low or absent, CD8+ T-cell responses were sporadic, and CD4+ T-cell responses were of low frequency. Therefore, no DNA vaccines are registered for use in humans today, although some are registered for veterinary use (e.g., a prophylactic West Nile virus DNA vaccine for horses and a therapeutic DNA vaccine for melanoma in dogs [56–59]), and a number of reports show efficacy of DNA vaccination in non-human primates [60]. The research effort on the DNA vaccination platform is, therefore, ongoing, driven in part by the fact that based on these studies several advantageous attributes of DNA vaccines have become clear (Table 2).

Table 2

Advantages and disadvantages of pDNA vaccine [61].

Advantages
DNA is inexpensive compared to isolated proteins or organisms used for conventional vaccines.
DNA vaccines can result in longer-lasting production of the antigenic protein; thereby, booster shots are no longer required.
DNA vaccines produce stronger immune responses than conventional vaccines.
Stability of vaccine for storage and shipping.
Subunit vaccination with no risk for infection.
Ease of development and production.
Disadvantages
Testing results have been favourable in small animals but less impressive in larger animals (including humans).
DNA uptake to cells apparently decreases with increased particle size.
Extended immunostimulation could lead to chronic inflammation or autoantibody production.
Limited to protein immunogens (not useful for non-protein-based antigens such as bacterial polysaccharides).
Risk of affecting genes controlling cell growth.

2.3.2 Detailed mechanisms of DNA vaccination

To induce immunity successfully, the DNA has to be taken up, the encoded protein expressed, and then protein-derived fragments presented on the surface of appropriate cells to the responding T and B cells [62]. Three principal mechanisms of antigen presentation are hypothesized: (1) somatic cell transfection (e.g. of keratinocytes or myocytes), (2) APC transfection, and (3) uptake of secreted antigen and presentation by professional APCs through cross-priming pathways (Fig. 4). For instance, following intramuscular injection, DNA is taken up by myocytes. However, myocytes cannot prime T cells since they do not normally express MHC class II or costimulatory molecules [49, 63]; but it became clear that APC can ingest the antigen produced by myocytes and then prime the ensuing immune response [64]. Interestingly, these APCs primed not only CD4+ T cells and B cells as part of the humoral response, but also a strong cellular response consisting of CD8+ T cells; i.e., the antigen fragments derived from the exogenous antigen by ingestion and degradation are not only loaded onto MHC class II molecules, which are responsible for CD4+ T cell activation, but also onto MHC class I molecules leading to CD8+ T cell stimulation. This mechanism is referred to as “cross-presentation” (Fig. 4).

As mentioned above, however, maturation of APC requires danger signals that are recognized by PRRs. DNA vaccines have long been thought to have such adjuvant potency in their own right. Because of the bacterial origin of unmethylated CpG sequences, they are abundant in pDNA, and they bind to the Toll-like receptor 9 (TLR9) on APC. Indeed, it has become evident that activation via TLR9 increases the potency of DNA vaccines to some extent, although it is not absolutely necessary for the generation of immune responses elicited by these vaccines [65, 66]; i.e., other danger signals are generated during transfection of host tissue. To cite one example, in keratinocytes the presence of pDNA in the cytoplasm can be detected by other molecular sensors such as DNA-dependent activator of IFN-regulatory factors (DAI) and AIM-2, resulting in the activation of a cell stress signalling complex named the “inflammasome” and leading to the production of immunogenic cytokines such as IL-1 and IL-18.

Novel strategies have been developed (“second-generation” DNA vaccines) in order to enhance transfection efficiency, optimize the antigens encoded by the plasmids (thereby increasing the antigen expression on a per cell basis), and improve formulation to include molecular adjuvants [67]. DNA vaccination research now focuses on optimisation of the delivery methods, carrier molecules, and genetic optimisation of the construct used.

2.3.3 Formulations and molecular adjuvants

Initially, viruses were tested for their potential as DNA-vaccine gene-delivery vehicles. However, it emerged that some people had a pre-existing immunity against the viral vector, thus decreasing the efficacy of the vaccine. And, vector-specific immunity can also result in more serious side effects [68–71]. This has led to the investigation of non-viral-based approaches.

Thus, to improve DNA vaccine immunogenicity, its formulation in microparticles or liposomes [67], or alternatively the inclusion in the same plasmid of additional plasmids or inserts that encode molecular adjuvants (“genetic adjuvants”) have been shown to be helpful. A genetic adjuvant is a protein with adjuvant properties that is encoded by the pDNA and hence is co-expressed with the antigen, bolstering the immune response towards this antigen. Examples are IL-12 or IL-15, IL-28B, the use of granulocyte macrophage colony-stimulating factor (GM-CSF) or high-mobility-group-protein B1 (HMGB1) [72–84].

2.3.4 Particulate carriers

The concept of delivering DNA with particles arose from the observation that 40–50-nm particles localize preferentially to dendritic cells in the draining lymph node after intradermal injection, and that they are capable of stimulating strong levels of immunity when conjugated with protein antigen [85]. From several studies, it emerged that nanoparticles in the viral size range (around 40 nm), rather than bacterial-sized microparticles (>1000 nm), are superior DNA vaccine carriers, capable of inducing high levels of CD8+ T cells as well as antibodies against the pDNA-encoded antigen [62, 86]. Binding of pDNA to carboxylated polystyrene particles via a poly-l-lysine (PLL) linker offering cationic charges that mediate electrostatic binding to the negatively charged DNA strongly enhanced the uptake of DNA by bone-marrow-derived DCs in vitro, and the ability of the DNA to induce potent cellular and humoral immune responses to the encoded antigen in vivo [86].

The global use of nanoparticle-based vaccines requires, however, the development of safer formulations. Rapid progress has been made on two large families of biocompatible and biodegradable nanoparticle-based DNA vectors: those based on degradable polymers such as polycaprolactone (PCL), polyvinylpyrrolidone (PVP), polyesters – particularly polylactic acid (PLA) and poly(lactic-co-glycolic acid) (PLGA) – and those based on a core of iron oxide, namely, maghemite (γ-Fe2 O3) or magnetide (Fe3 O4).

2.3.4.1 Nanospheres and nanocapsules

Polymers are attracting attention as potential controlled-delivery carriers for introducing DNA into cells, for several reasons. These include the ability (1) to protect DNA payload from extracellular degradation, (2) to accommodate large size plasmids and other immunostimulatory agents, (3) to offer a phagocytosis-based passive targeting to APCs, and (4) to be conjugated with appropriate functionalities to enhance target delivery and uptake [62]. Polymeric nanostructures are colloidal carriers ranging in size typically from 1 to 100 nm. They are divided into nanospheres and nanocapsules. Nanospheres consist of polymeric matrices, while nanocapsules are vesicular systems with a polymeric shell and an inner core. Encapsulation of DNA protects it from nuclease degradation, and the controlled DNA delivery system can be designed to exhibit varying degradation times and release kinetics of DNA for prolonged gene expression over a desired duration. The most appealing advantages of polymeric systems are their biocompatibility, ease of formulation, and biodegradability to provide an attractive scaffold for sustained release of DNA. Formulated or encapsulated therapeutic agents have been shown to exhibit greater therapeutic efficacy compared to unformulated biomolecules [87].

2.3.4.2 Cationic polymers

Cationic polymers such as, polyethyleneimines (PEIs), polyamidoamine dendrimers (PAMAMs), and chitosan have been applied as carriers for complexing DNA in polyplexes of defined virus-like sizes [88–91]. When applied to cells, the positively charged polyplexes mediate transfection via a multistage process that includes cationic binding to the negatively charged cell membrane to facilitate entrance into the cytoplasm [90, 92]. The high density of amino groups confers significant buffering capacity to these cationic polymers, especially in the endosome where the pH decreases from 7 to 5. The “proton sponge effect” explains the high transfection efficiencies obtained with cationic polymers that appear to function as an endosomolytic reagent. However, the high number of positive charges leads to greater toxicity. Coupling to poly(ethylene glycol) (PEG) to mask the surface charge of PEI/pDNA polyplexes is a popular approach to lower the cellular toxicity by reducing the nonspecific interactions of polyplexes in the bloodstream [93].

2.3.4.3 Micelles

Micelles are nanosized and spherical capsules with a hydrophobic interior (core) and a hydrophilic exterior (shell), which form by the self-assembly of block or graft polymers in aqueous media. Their individual size is normally <100 nm in diameter and is designed to be thermodynamically stable and biocompatible so they may circulate for prolonged periods in the blood. The micellar delivery systems including graft, diblock, or multiblock copolymers possess numerous advantages over liposomes, and have attracted attention as promising gene carriers [62, 94, 95]; indeed, several micelle gene carriers are being studied in preclinical and clinical trials [62, 94]. However, further improvement of polyplex functionality is necessary for enhanced therapeutic efficacy with reduced adverse side effects before these agents can be translated into practical pharmaceutical agents [96–98].

2.3.4.4 Magnetic nanoparticles

Association of DNA with superparamagnetic iron oxide nanoparticles, as for instance maghemite (γ-Fe2 O2) or magnetite (Fe3 O4), is the basic principle of magnetically guided gene transfection (magnetofection; see Section 3.5). Both have been classified as biocompatible and nontoxic by the U.S. Food and Drug Administration (FDA) [99]. However, the use of bare magnetic nanoparticles (MNPs) in vivo could be detrimental if aggregation, oxidation, acidic leaching, or instability at physiological conditions occurred. Therefore, the surfaces of MNPs have been modified to improve stability and biocompatibility. Moreover, these modifications provide surface sites for conjugation of ligands that can specifically bind to the receptors on the target sites, and other functionalities such as fluorescence for optical tracking. Non-polymeric or polymeric coating of MNPs is possible.

For nonpolymeric coating, for instance, silica can be used. Its good mechanical strength and heat resistance renders silica ideal for protecting the magnetic cores, while its surfaces can be easily functionalized via silane chemistry using organosilanes for covalent attachment of ligands, due to the availability of hydroxyl groups. Functional silanes have been used for bioconjugation of proteins, enzymes, antibodies, cell-targeting agents, and DNA (for references, see [62]).

Synthetic or natural polymers can be chemically attached or physically adsorbed onto MNP surfaces, thus creating steric forces to balance the magnetic and Van der Waals attractive forces and improve stability in liquid media. Synthetic PEG is the most widely used polymeric coating for in vivo application due to its biocompatibility, protein-resistance, hydrophilicity, and high surface mobility leading to high steric exclusion [100]. PEG coating could help improve circulation lifetime and bioavailability by decreasing immunogenicity and renal clearance rate, thus providing a “stealth” shielding effect [101]. PEG-coated MNPs show an excellent solubility and stability in physiological saline solutions [102], and they can be internalized without evidence for being toxic to the cells [103]. A commonly used natural polymer is dextran, which is composed exclusively of α-d-glucopyranosyl units with varying degrees of chain length and branching, including unsubstituted dextran, monosubstituted carboxydextran, and polysubstituted polycarboxymethyldextran.

The main advantages of non-polymeric coatings are negligible swelling and porosity changes with pH, and resistance to microbial attack. However, polymeric coatings are more biocompatible and suitable for in vivo applications than non-polymeric coating [104], but they have low intrinsic stability at higher temperatures, which may be a disadvantage considering the fact that the iron oxide cores might induce heating effects upon the application of an external magnetic field.

2.3.4.5 Receptor-mediated uptake

There is now another approach for enhancing DNA delivery targeting its uptake by APC, namely, receptor-mediated uptake which allows site-specific gene delivery to target cells. Several ligands binding to cell surface receptors have been targeted for DNA delivery. Such receptors include those for mannose, low-density lipoprotein, transferrin, neurotensin, asialoglycoprotein, C-type lectin, and chemokine receptor [105–109].

2.3.5 Approaches to delivery

The low immunogenicity of DNA vaccines, especially in larger animals and humans, has been postulated to be at least in part due to ineffective uptake of plasmids by cells, probably because of inefficient delivery. Two routes of administration are commonly used for delivering DNA vaccines, intramuscular (IM) and intradermal (ID). IM administration results in the highest levels of antigen expression but may not be the most immunogenic, since the frequency of APCs in muscle tissue is rather low. In contrast, ID delivery of DNA vaccines does not lead to the amount of specific protein production that is obtained upon IM injection, but may be much more immunogenic, since the skin is rich in APCs ready to take up and present antigens [60].

Several physical methods of delivery have been explored to increase the transfection efficiency of DNA vaccines, including needle-free approaches such as particle bombardement and high-pressure delivery, dermal patches and DNA tattooing, electroporation (EP), and magnetic transfection:

  • Particle bombardment uses a highly pressurized stream to deliver vaccine plasmids on microscopic metal beads.

  • High-pressure mediated delivery is conceptually similar to particle bombardement in delivering vaccines by forcing liquid through a tiny orifice to create a fine, high-pressure stream that penetrates the skin [110].

  • Dermal patches are coated with multiple antigen or adjuvant-encoding plasmids and a synthetic polymer that forms pathogen-like nanoparticles [111].

  • DNA tattooing as a strategy for intradermal administration has been shown to be highly immunogenic in mice and non-human primates probably due to the abundance of danger signals that are generated in the damaged skin upon mechanical disruption by the tattoo needles [112, 113].

  • Electroporation is another promising physical method of delivery; it uses short electrical pulses to destabilise cell membranes. This method leads to the formation of transient pores, which increases the uptake of pDNA by cells [114–117]. Some of these devices have already been tested in the clinic [118, 119].

  • Magnetic transfection efficacy was 350-fold as compared to conventional transfection as shown in experiments delivering the luciferase reporter gene to target cells. The magnitude of transgene-expression after 10 min under magnetic fields was comparable to 4 h in the absence of a magnetic field [120]. Thus, magnetically guided gene transfection has been shown to improve both the efficacy and the rate of delivery, but direct comparisons with the effects of electrical field or hydrostatic pressure have not yet been performed. This method of delivery is less complicated than gene gun or other needle-free delivery devices as it does not require a specially constructed device for injection. It is also better tolerated by live animals than the use of an electric field in electroporation that could induce additional pain or distress during application. Furthermore, MNPs have the potential to improve the efficacy of delivery as the applied magnetic field can help to distribute the particles over the surrounding tissue and to direct rapidly (within a few minutes) the full dose onto the target cells, thus minimizing DNA degradation due to opsonization or interactions with the immune system [121].

2.3.6 Safety and tolerability of DNA vaccines

It has been postulated that DNA vaccines are safer and more stable than are conventional vaccine approaches. Plasmids are nonlive and nonreplicating, which leaves little risk for reversion to a disease-causing state or secondary infection. The original concerns associated with the DNA platform were the potential for genomic integration and development of anti-DNA immune responses. Although genomic integration could be confirmed in animal studies, integration rates were always significantly lower than the spontaneous integration rate [60, 122–125]. Also, induction of an anti-DNA immune response has not been reported.

2.4 RNA vaccines

In 1990, when it was first shown that injection of pDNA and messenger RNA (mRNA) into a skeletal muscle resulted in the expression of the encoded protein [49], the high production costs and lower stability of mRNA presented a limitation for the broad application of RNA vaccination. These obstacles have now largely been surmounted, and recently there has been a revival in the use of nonamplifying mRNA vaccines for gene therapy, allergy, and especially for cancer [126–129]. Moreover, it has been postulated that RNA vaccines may be a safer and more potent alternative to pDNA because there is no risk of genomic integration [130]. Nevertheless, there is still a need for appropriate delivery systems [126, 131].

2.5 Recombinant antigen platforms

2.5.1 Reverse vaccinology

Reverse vaccinology starts from the genomic sequence and predicts those antigens that are most likely to be vaccine candidates [15] (Fig. 2). In principle, the genome sequence provides a catalogue of all protein antigens that the pathogen can express at any time. This approach, therefore, allows not only the identification of all the antigens seen by the conventional methods, but also the discovery of novel antigens that work on a totally different paradigm. Furthermore, problems related to noncultivable microorganisms can be avoided. The feasibility of the approach relies heavily on the availability of a high-throughput system to screen protective immunity, which is, however, still the rate-limiting step of reverse vaccinology. The other limit of this approach is the inability to identify non-protein antigens such as polysaccharides or glycolipids, which represent new promising vaccine candidates.

The first example of the successful application of reverse vaccinology was serogroup B Neisseria meningitidis (meningococcus; MenB), which causes 50 % of the meningococcal meningitis worldwide but has been refractory to vaccine development because its capsular polysaccharide is identical to a human self-antigen (α2-8-linked polysialic acid present in many tissues), whereas the bacterial surface proteins are extremely variable. Therefore, fragments of DNA were screened by computer analysis, and over 600 novel genes were predicted to code for surface-bound or exported proteins. Three hundred and fifty of them were successfully expressed, purified, and used to immunize mice. The sera obtained were used to test for the ability to induce complement-mediated in vitro killing of bacteria, a test that correlates with vaccine efficacy in humans. Thus, 85 novel surface-exposed proteins were discovered and 25 of them were shown to induce bactericidal antibodies [132]. Antigens inducing the best and broadest bactericidal activity were selected and inserted into prototype vaccines that were able to induce protective immunity against most of the MenB strains in mice [133].

This classical reverse vaccinology has been applied subsequently to many other bacterial pathogens, for instance, group A and group B streptococcus, antibiotic-resistant Staphylococcus aureus, Porphyromonas gingivalis, Chlamydia pneumoniae, Bacillus anthracis, and several others [134].

However, in the case of Streptococcus agalactiae (group B Streptococcus), it became evident that the sequenced strain differed from that of 19 other strains tested and that there was substantial genetic heterogeneity even among strains with the same serotype, and in particular between genes that are expected to play a role in disease, such as transcriptional regulatory and surface proteins [135]; i.e., sequencing the genome of only one strain is not enough to provide the information needed for a development of a universal vaccine. This resulted in development of comparative reverse vaccinology; sequencing six further genome sequences of S. agalactiae led to the information that 1806 genes were shared by all strains of S. agalactiae, representing the “core genome” that corresponds to approximately 80 % of the average number of genes encoded in each strain. A universal vaccine against S. agalactiae was then provided by comparative genome analysis. Computational algorithms predicted 589 surface-associated proteins, of which 396 belong to the core genome and 193 were absent in at least one strain [136]. Each of these proteins was tested for protection, and four antigens were able to elicit protective immune responses in the animal model. However, none of these protective antigens were universal. A cocktail combining the four best candidates conferred 59–100 % protection against a panel of 12 S. agalactiae isolates including the major serotypes as well as two strains from a less common serotype [136–139].

After the first success in inducing protective immunity, similar observations were also made for the above-mentioned MenB; over the following years, the four most immunogenic and conserved antigens were selected and incorporated into a vaccine, which has undergone clinical trials in more than 7500 infants, toddlers, adolescents, and adults. The results showed that 92–97 % of participants had protective antibodies to test strains after one dose and even 99–100 % after two or three doses, and that the vaccine can protect against 77 % of more than 800 genetically diverse disease-causing MenB strains that have been isolated in Europe [140]. The comparative genome analysis provided, therefore, new concepts in delivering universal vaccines by the reverse vaccinology approach, even for microorganisms in which a high variability is observed.

In a next stage, it emerged that in order to obtain an effective vaccine for pathogenic strains it may be important to sequence also a non-pathogenic strain genome, because this could provide information necessary for the identification of antigens that could make the difference in pathogenesis, responsible for the most strict host–pathogen interactions. In a subtractive comparative genome analysis, genes conserved between pathogenic and non-pathogenic strains of the same or even related species could be discarded, reducing the number of candidates and the time for the delivery of a vaccine [134]. Furthermore, this approach may be helpful to develop vaccines against pathogenic E. coli bacteria, leaving non-pathogenic E. coli strains untouched [141].

2.5.2 Structural vaccinology

Advances in structural biology provide an understanding of the structural basis of immunodominant or immunosilent antigens, and enable the rational design of peptide mimetics of bacterial epitopes [16]. Identification of broadly neutralizing antibodies and the determination of the structure of their binding sites at the molecular level using crystallographic methods paves the way for rational design of novel and improved vaccine antigens [142–148]. Structure-based design of antiviral therapeutics enables the engineering of multiple immunodominant epitopes in one molecule to induce broad immune responses against different protein variants. This approach has meanwhile led to the development of drugs directed toward the active sites of the HIV-1 protease and influenza neuraminidase. Structural vaccinology also includes epitope mimetics, epitope grafting, and synthesis of virus-like particles.

2.5.2.1 Epitope mimetics

Protein epitope mimetics are rapidly gaining prominence as a source of novel leads in drug and vaccine research. They are designed to mimic the 3D surface regions of peptides and proteins recognized by biological receptors. Therefore, also opportunities arise for their use in the structure-based design of synthetic vaccines, targeting a wide range of infectious diseases and chronic human health problems such as allergies, Alzheimer disease, and cancer. Knowing today at a structural level how antibodies recognize protective epitopes on pathogens heralds a new era of structural vaccinology, where this information can be exploited in rational structure-based approaches to vaccine design [16]. For instance, antibodies to HIV, HCV, and influenza are serving as templates for antigen design [149–152].

The majority of antibody responses are directed at structural epitopes, which are difficult to recapitulate with synthetic peptides because they are typically discontinuous epitopes, formed by protein folding and, thus, are composed of amino acid residues that are often spatially close but separated by great distances within the linear protein sequence [153]. Immunoaffinity selection of random peptides offers an alternative strategy to characterize those discontinuous epitopes and offers an unbiased method to screen for epitope mimetics (mimotopes) [154] that can define antibody targets and serve directly as immunogens. To apply this strategy effectively, it is necessary to isolate many random peptides with unique sequences because each immuno-selected peptide will carry only partial homology for the original target. Through the use of computational modelling, it is possible to derive a consensus sequence from the selected peptides and identify the target epitope [155]. The use of linear random peptides for this strategy is limited because the conformational space available to linear peptides is great, allowing the linear peptides to assume a large array of conformations. Constraining the peptide by cyclization reduces the field of conformational possibilities for the molecule and results in the peptide adopting the most favourable conformation [156]. Further, the constrained nature of these peptides causes them to adopt tertiary structure enabling them to mimic conformational epitopes. Cyclization can be achieved simply by the incorporation of Cys residues at the N- and C-termini of a given peptide.

It has been shown that many of the protective epitopes of human pathogens contain loop, β-hairpin, or α-helical motifs. Conformationally constrained synthetic epitope mimetics based on these structures may be useful as immunogens in vaccine design.

A number of different technologies for the stabilization of helical conformations in peptides have been developed [157]. Helical conformations can be stabilized through the insertion of amino acids with restricted conformational space, such as α-methylated amino acids [e.g. an α-aminoisobutyric (Aib) residue], by side-chain cross-linking or “stapling”, and the use of helix caps and hydrogen bond surrogates. Some of these approaches have been explored already in vaccine design efforts, for example, the use of Aib residues to favour helical turns, hydrazone crosslinks as hydrogen bond surrogates, Freidinger-like lactams and pseudoprolines to stabilize turns, and cross-linked side-chains to stabilize helical epitopes [158–165]. β-Hairpin mimetics might also be very useful in synthetic vaccine design. For example, the HIV-1 envelope glycoprotein gp120 that becomes exposed on the viral surface once the CD4 receptor on target cells binds to the viral gp120 glycoprotein contains a highly immunogenic region represented by the β-hairpin V3 loop. The tip of the V3 loop is then able to dock with the cellular chemokine co-receptor (CXCR4 or CCR5), which ultimately leads to virus entry into the cell. Several crystal structures are now available for neutralizing antibody fragments bound to peptides derived from the HIV-1-gp120 V3 loop [166–170].

2.5.2.2 Epitope grafting

Epitope grafting is the transplantation of immunodominant epitopes onto scaffold proteins in an attempt to improve their presentation. Thus, immunogens designed by epitope transplantation have elicited structure-specific responses targeting a snake toxin, the yeast transcription factor GCN4, the severe acute respiratory syndrome coronavirus S glycoprotein, and the human immunodeficiency virus 2F5 and 4E10 epitopes [171–174]. However, the ability to graft a motif onto a scaffold is still limited by the complexity of the motif and the availability of scaffolds with suitable structures.

Epitope grafting uses the tools of protein engineering; the epitope of interest is transferred onto a new protein scaffold (perhaps more stable, easier to produce, or more immunogenic) that can display the epitope in the correctly folded conformation. Several recent examples document how structure-based methods and modelling can allow prediction of proteins that might be useful as scaffolds for the newly grafted epitopes [148, 171, 175–182]. In some cases, however, it might still be technically difficult to produce the correctly folded protein subunit vaccine. Moreover, other non-protective epitopes on the surface of a recombinant protein may still dominate the immune response, thereby deflecting attention away from the protective epitope and leading to a poorly effective vaccine. Also, co-administration with an adjuvant will again be required to boost immunogenicity (see below).

An alternative approach is to exploit advances in synthetic peptide and protein engineering, which use the tools of organic and peptide chemistry for the production of folded proteins and related epitope mimetics. Conformational flexibility is one key parameter that must be addressed in the design of synthetic epitope mimetics. The use of flexible peptides as immunogens often elicits antibodies that bind weakly to conformational epitopes in folded proteins. However, antibodies that bind tightly to an antigen are usually required to protect against infection, and their efficient production in an immune response will require the use of correctly folded epitope mimetics [183].

2.5.2.3 Virus-like particles

Virus-like particles (VLPs) and related artificial nanoparticles are a further promising approach to vaccine delivery. VLPs may integrate the key immunostimulatory signals in one nano-sized particle, resulting in potent immunological activity. VLPs are typically made of viral capsid proteins that self-assemble into particulate structures closely resembling the natural viruses from which they are derived. They lack genetic material and so are non-infectious and replication-incompetent. Examples of VLP-based vaccines are the hepatitis B vaccine made from the surface antigen, and the human papillomavirus vaccine made from the L1 surface protein, which both spontaneously form VLPs in solution [184–186]. Recently, several VLP-based vaccine candidates have entered clinical investigations with the aim of developing further VLP-based vaccines [187–189].

A crucial factor in the construction of new VLPs is the choice of the expression system. The analysis of the published reports on the successful construction of 174 unique VLPs indicates that bacterial systems are used in 28 % of cases, particularly for the production of bacterial and plant VLPs. The yeast (20 %) and insect systems (28 %) are more universal, and these expression hosts have been used successfully for the construction of VLPs from different sources. Plant (9 %) and mammalian (15 %) hosts are typically used to insure the production of VLPs with specific properties [188].

There is now also great interest in chemical approaches to VLP-like nanoparticles for use as vaccine carriers [190]. One further idea is to engineer the viral capsid proteins so that protective foreign epitopes can be inserted and displayed on the surface of the particle. Alternatively, a chemical coupling approach can be taken to conjugate epitopes to VLPs. One chemical approach reported recently exploits the unique chemical and physical properties of designed synthetic lipopeptide building blocks that spontaneously self-assemble in aqueous buffers into homogeneous nanoparticles in the 20–30-nm size range, called synthetic VLPs (SVLPs) [191]. Synthetic lipopeptides containing a “virally derived” coiled-coil (CC) sequence coupled through a linker at its N-terminus to a phospholipid are also able to spontaneously self-assemble in aqueous buffers into homogeneous nanoparticles [192]. This can be due to an association of the CCs in each lipopeptide building block into parallel trimeric helical bundles. The aggregation of lipid chains at the N-termini of multiple helical bundles then drives particle formation, with the lipid chains buried in the core of the particle (Fig. 5) [193]. Typically, around 24 three-helix lipopeptide CC bundles radiate outward from the lipid core into solution, generating a spherical-like particle about 20 nm in diameter. These particles have a size and shape similar to those of some recombinant VLP capsids, but are completely of synthetic origin (synthetic VLPs, SVLPs). A synthetic antigen can then be coupled to the lipopeptide building blocks so that after self-assembly into SVLPs, multiple copies of the antigen are displayed over the outer exposed surface of the nanoparticle (Fig. 5). The dimensions (20–30 nm diameter) and composition (peptide + lipid) of these nanoparticles resemble those of some natural viruses, but the SVLPs are produced by chemical synthesis. SVLPs are rapidly bound to dendritic cells and are then internalized using multiple endocytotic routes [183]. Processing then occurs more slowly by proteolytic cleavage of the lipopeptides. The processing is highly effective as evidenced by the strong immune responses induced by SVLPs in small animals, without the need for external adjuvants [191, 192, 194].

Fig. 5 Engineered synthetic virus-like particles (SVLPs) and their use in vaccine delivery (according to [191]). SVLP formation from lipopeptide building blocks.

Fig. 5

Engineered synthetic virus-like particles (SVLPs) and their use in vaccine delivery (according to [191]). SVLP formation from lipopeptide building blocks.

Immunopotentiating reconstituted influenza virosomes (IRIVs) are proteoliposomes composed of phospholipids, influenza hemagglutinin (HA), and a selected target antigen [195]. APCs take up the virosomes by HA receptor-mediated endocytosis. IRIV is registered as a component of the hepatitis A vaccine in Europe, Asia, and South America. In clinical trials, the IRIV vaccine generated a faster immune response and fewer injection site adverse reactions compared to a conventional alum-containing vaccine [196]. Similar to VLPs, IRIVs are also taken up by APCs by receptor-mediated endocytosis, and have been shown to stimulate both cellular and humoral immune responses [197].

2.6 Adjuvants

2.6.1 General aspects

Adjuvants are necessary to enhance the activation of APCs, especially when only parts or subunits of pathogens are used as vaccines. Thus, attenuated or killed whole-organism vaccines can induce long-lasting immunity, often lifelong, with few or no booster vaccinations required to maintain protective immunity. However, a complication with some of these vaccines is that they can be quite reactogenic, induce mild disease, and in severe cases revert to virulence. In contrast, the above-described “subunit” vaccines are much safer and generally have fewer side effects than whole-cell or virus-based vaccines; however, they also tend to be less immunogenic, requiring multiple doses and periodic booster shots.

Adjuvants can act on all three signals required for the induction of an immune response (see also Fig. 3). They have different functions and activities, including carrier/depot or targeting functions and immunostimulant and/or immunomodulatory activity. The current challenge facing adjuvant research is, therefore, to find the “perfect mix”, i.e., an optimal, safe formulation, the different components of which will not only be additive but synergistic, and which will eventually drive the desired immune response. Adjuvants can be classified according to their component sources, physicochemical properties, or mechanisms of action (Table 3). Two classes of adjuvants commonly found in modern vaccines include immunostimulants that directly act on the immune system to increase responses to antigens, for instance, TLR ligands, cytokines, saponins, and bacterial endotoxins; and vehicles that present vaccine antigens to the immune system in an optimal manner, including controlled release and depot delivery systems to increase the specific immune response, such as mineral salts, emulsions, liposomes, virosomes, biodegradable polymer microspheres, and so-called immune-stimulating complexes (ISCOMs) [10, 199].

Table 3

Mechanisms of adjuvants in eliciting an immune response [198].

Sustained release of antigen at the site of injection (depot effect).
Up-regulation of cytokines and chemokines.
Cellular recruitment at the site of injection.
Increase in antigen uptake and presentation to antigen-presenting cells (APCs).
Activation and maturation of APCs (increased expression of MHC class II and co-stimulatory molecules) and migration to the draining lymph nodes.
Activation of inflammasomes.

Criteria involved in selecting the formulation for a given vaccine include the nature of the antigenic components, the type of immune response desired, the preferred route of delivery, avoidance of significant adverse effects, and stability of the vaccine. The optimally formulated adjuvant will be safe, stable before administration, readily biodegraded or eliminated, able to promote an antigen-specific immune response, and inexpensive to produce, and it will be well defined chemically and physically to facilitate quality control that will ensure reproducible manufacturing and activity.

A web-based vaccine adjuvant base, Vaxjo, curates, stores, and analyses vaccine adjuvants and their usages in vaccine developments [200].

2.6.2 Adjuvants approved for human vaccines

Adjuvants in approved human vaccines include alum, several oil-in-water emulsions, MPL® (a glycolipid), and cholera toxin (Table 4).

Table 4

Vaccine adjuvants licensed for human use and mechanisms of action (examples).

Adjuvant name (year license)Adjuvant classComponentsProposed mechanisms of actionImmune response activatedLicensed vaccines
Alum (1924)Mineral saltsAluminum phosphate or aluminium hydroxideNo depot effect

NLRP3 activation in vivo?
↑ antibody responses

↑ Th2 responses
Several human vaccines (DTap, Hib, hepatitis A, hepatitis B etc.
Independent of TLR signallingPoor Th1 responses
↑ Local cytokines and chemokines
↑ Cell recruitment (eosinophils, monocytes, macrophages)
↑ Antigen presentation
MF59 (Novartis; 1997)Oil-in-water emulsionSqualene, polysorbate 80 (Tween 80; ICI Americas), sorbitan trioleate (Span 85; Croda International)No depot effect

NLRP3 independent but ASC dependent

Independent of TLR signalling but MyD88-dependent for Ab responses
Balanced Th1 and Th2 responsesInfluenza vaccine, H5N1 pre-pandemic vaccine, H1N1 pandemic vaccines
↑ Local cytokines and chemokines
↑ Cell recruitment (neutrophils, macrophages, monocytes)
↑ Antigen uptake
Activate muscle cells
↑ Antigen-loaded neutrophils and monocytes in draining lymph nodes
AS03 (GlaxoSmithKline; 2009)Oil-in-water emulsionSqualene, Tween80, α-tocopherolSpatio-temporal co-localization with antigen

Transient ↑ cytokines locally and in draining lymph nodes

↑ Cell recruitment (granulocytes and monocytes)

↑ Antigen-loaded monocytes in draining lymph nodes
↑ Antibody responses

↑ Immune memory
Pandemic flu vaccine
AS04 (GlaxoSmithKline; 2005)Alum-adsorbed TLR4 agonistAluminum hydroxide, monophosphoryl lipid A (MPL)MPL signals through TLR4 to activate APCs

↑ Local cytokines and chemokines
↑ Antibody responses

↑ Th1 responses
Human papilloma virus (HPV), hepatitis B virus
↑Cell recruitment (DCs and monocytes)
↑ Antigen-loaded DCs and monocytes in draining lymph nodes
Virosomes (Berna Biotech; 2000)LiposomesLipids, hemagglutininAntigen delivery vehicle↑ Antibody responsesInfluenza vaccines and hepatitis A vaccine
Bind APCs and induce receptor-mediated endocytosis
Escape endosomal degradation↑ CTL responses
Antigen presentation via MHC class II and MHC class I to CD4+ and CD8+ T cells, respectively
Immunopotentiator

Alum is a generic term for aluminum salt-based adjuvants and noncrystalline gels based on aluminum oxyhydroxide, aluminum hydroxyphosphate, or various proprietary salts such as aluminum hydroxyl-sulfate. Formulation is achieved through adsorption of antigens onto highly charged aluminum particles. Depending on the antigen, the appropriate aluminum adjuvant is selected to maintain antigen immunogenicity and to obtain maximum adjuvant effect. The mechanisms of action of the aluminum salts include depot formation facilitating continuous antigen release, particulate structure formation promoting antigen phagocytosis by APC, and induction of inflammation resulting in recruitment and activation of macrophages and increased MHC class II expression and antigen presentation [201]. Alum has been shown to boost humoral immunity by providing Th2 cell help to follicular B cells [202]. The advantages of aluminum adjuvants include their safety record, augmentation of antibody responses, antigen stabilization, and relatively simple formulation for large-scale production. The major limitations include their inability to elicit cell-mediated Th1 or CTL responses that are required to control most intracellular pathogens such as those that cause tuberculosis, malaria, leishmaniasis, leprosy, and AIDS [203]. Moreover, vaccines containing alum cannot be frozen because this leads to loss of potency, and it can induce granulomas at the injection site.

Oil and water emulsions are used in Europe as adjuvants in influenza vaccines, such as MF59, which consists of an oil (squalene)-in-water nano-emulsion composed of <250-nm droplets [204, 205]. It is believed to act through a depot effect and direct stimulation of cytokine and chemokine production by monocytes, macrophages, and granulocytes [206]. Like alum, MF59 does not induce increased CD4+ Th1 immune responses, but because of its ability to increase the levels of functional haemagglutination-inhibiting antibodies and CD8+ T-cell responses, it has the potential for use in pandemic influenza vaccines [207, 208].

MPL is a nontoxic derivative of the LPS of Salmonella minnesota and is a potent stimulator of Th1 responses. LPS consists of two basic structures: a hydrophilic polysaccharide and a hydrophobic lipid moiety (called lipid A) [209]. Lipid A derivatives are only biologically active in aggregate forms [210]. Thus, structural modifications to the lipid A molecule alter the shape and structural order of the lipid, which in turn influence its aggregation behaviour and resultant biological activity [209, 210]. MPL is a TLR-4 agonist. An aqueous formulation of MPL and alum, AS04, results in higher levels of specific antibody and efficacy with fewer injections. MPL is licensed in Europe for allergy treatment because of its ability to down-modulate Th2 responses to allergens. It has been given to thousands of individuals in several formulations and found to be a safe, well-tolerated, and potent adjuvant component.

Cholera toxin B subunit (CTB) is used to enhance mucosal immune responses of orally delivered vaccines. The naturally occurring cholera toxin belongs to the AB class of bacterial toxins. It consists of a pentameric B oligomer that binds to ganglioside (GM-1) receptors on the surface of intestinal epithelial cells and an enzymatically active A subunit that is responsible for the toxicity. The recombinant CTB contains only the nontoxic B component of the cholera enterotoxin. It is composed of five identical monomers tightly linked into a trypsin-resistant pentameric ring-like structure [211]. CTB can act as a mucosal adjuvant and enhance immunoglobulin A (IgA) production to co-administered or coupled antigens intranasally [211]. It is part of a licensed whole-cell, orally delivered cholera vaccine [212]. This vaccine has been shown to induce a high level of protection against cholera, but is short-lived [213].

2.6.3 Adjuvants in development

Additional adjuvants have been developed due to the shortcomings of aluminum adjuvants (failure to stimulate T-cell responses, including CTLs, loss of potency if frozen, and causing of granulomas at injection sites). In many instances, several adjuvants have been combined in one formulation to obtain synergistic or additive effects [199].

Montanides (ISA51, ISA720) are water-in-oil emulsions containing mannide-mono-oleate as an emulsifier. Montanides, similar in physical character to incomplete Freund’s adjuvant (IFA) but biodegradable, have been developed in response to safety concerns with IFA in animal studies [214, 215]. They induce a strong immune response and are available without requiring a license or contractual agreement, and they have been used in malaria, HIV, and cancer vaccine trials [216]. However, montanides are difficult to formulate because an extensive and costly emulsification procedure is required for each antigen. In several studies they have induced strong local reactions [217].

Saponins (Quil-A, ISCOM, QS-21) are triterpene glycosides isolated from plants. Quil-A is extracted from the bark of the Quillaja saponaria tree [218] and is composed of a heterogeneous mixture of triterpene glycosides that vary in their adjuvant activity and toxicity.

Saponins have been widely used as adjuvants in veterinary vaccines. Partially purified fractions of Quil-A have also been used in ISCOMs composed of antigen, phospholipids, cholesterol, and Quil-A fractions. ISCOMs are ∼40-nm cage-like particles trapping the protein antigen through hydrophobic interactions, whereas ISOMATRIX [219], which consists of preformed antigen-free particles, provides for more general applications by better accommodating non-hydrophobic antigens. Because of their particulate nature, ISCOMs are directly targeted to and more efficiently taken up by APC via endocytosis. Saponin-mediated targeting of DEC-205 (a macrophage mannose receptor family of C-type lectin endocytic receptors) on the surface of DC might account for higher uptake and more efficient presentation of antigens to T cells [220, 221]. Antigen processing can occur in the endosome for both MHC class I and class II presentation [222, 223].

MPL formulations. MPL-SE is the result of MPL mixed with squalene oil, excipients (inactive substances used as carriers for the active ingredient), and water to produce a stable oil-in-water emulsion. It is a promoter of Th1 responses and is currently being evaluated in several clinical trials to treat and prevent leishmaniasis. The Adjuvant System (AS) series AS01, AS02, and AS04 are proprietary formulations, several of which contain MPL, and QS21 that induces both strong humoral and Th1 responses [198, 224, 225].

Syntex adjuvant formulation (SAF) is an oil-in-water emulsion containing squalene, TweenTM80, and Pluronic TML121 (a nonionic block copolymer) in phosphate-buffered saline. SAF or SAF+threonyl-muramyl dipeptide were safe and effective in some preclinical studies [226–229]. It elicits both humoral and cell-mediated immune responses, but was found to cause severe local adverse reactions in a human HIV trial.

Muramyl dipeptide (MDP) is the minimal unit of the mycobacterial cell wall complex that generates the adjuvant activity of complete Freund’s adjuvant (CFA). Several synthetic analogs of MDP, such as muramyl tripeptide phosphatidylethanolamine (MTP-PtdEtn), have been generated, and they exhibit a wide range of adjuvant potency and side effects. Furthermore, MDP has poor stability [227].

Immunostimulatory oligonucleotides. Synthetic oligodeoxynucleotides, containing unmethylated CpG motifs, act through TLR-9 and induce activation of DC and secretion of pro-inflammatory cytokines such as tumour necrosis factor (TNF)-α, IL-1, and IL-6. TLR-9 activation also leads to secretion of the proinflammatory cytokines IFNα, IFNγ, and IL-12. CpGs are extremely efficient inducers of Th1 immunity and CTL responses and induce protection against infectious disease, allergy, and cancer in mice and primate models [230, 231].

Other TLR ligands include synthetic compounds that induce the maturation and activation of professional APCs and the secretion of inflammatory cytokines and chemokines [232]. The small-molecule nucleoside analogues imiquimod and resiquimod are ligands for TLR-7 and TLR-7/8, respectively [233].

Escherichia coli heat-labile exotoxin is a potent mucosal adjuvant. The native lymphotoxin (LT) is composed of two subunits, LT-A and LT-B. The LT-B subunit has the affinity for the Gm1 gangliosides of nerves, which is probably responsible for the facial palsy seen when this molecule is administered nasally [234]. Another adjuvant under development for nasal administration contains the fully active LT-A component, with the LT-B component replaced by a LT B-cell binding sequence [235].

2.6.4 Adjuvants to enable future vaccines

During the past two decades, adjuvant development has shifted from empiricism to more focused research. Among the many immunostimulants and formulations available, researchers can now rationally identify, characterize, and combine those that will give vaccines the necessary help. The choice of adjuvant is guided by the nature of the antigen(s) and the known or expected correlates of protection, keeping in mind that the induction of an immune process is not a black or white process. The development of these sometimes complex products follows a long and often uncertain road, but we now have more basic and applied tools to analyse the potential issues and propose new solutions.

Ideally, new adjuvants and formulations would generate a protective immune response with a reduced number of administrations. This will result in rational knowledge-based selection of adjuvant systems for the development of new vaccines eliciting predominantly humoral and/or cellular responses.

Numerous challenges remain related to adjuvant development. In effect, it is unlikely that any single immunostimulant or delivery system will be sufficient to induce the broad and long-lasting immunity that is required for all new vaccines. Effective adjuvant systems are likely to require synergy between one or more immunostimulants, and a carrier or delivery system. In addition, it is often impossible to compare adjuvants analysed in different laboratories, or even within the same laboratory, because adjuvant formulation and characterization methods are not standardized. Furthermore, each antigen has a different intrinsic immunogenicity and interacts differently with immunostimulants and carriers, and no reliable algorithms exist to permit selection of optimal adjuvants based on physico-chemical or immunological properties of an antigen [199].

2.7 Vaccination in allergy and cancer

Paul Ehrlich postulated that cancer cells are eliminated by the immune system [236]. Nowadays, it is well known that cancer patients can harbour CD8+ and CD4+ T cells specific for cancer or for differentiation antigens expressed in their tumors. Furthermore, it has been observed that the presence of intratumoral CD3+ or CD8+ cytotoxic T cells correlate with prolonged patient survival [237]. These observations support the idea of generating therapeutic cancer vaccines. Three major types of cancer antigens that can be applied for vaccination can be distinguished: (1) neoantigens including mutations and viral antigens, (2) self proteins that are overexpressed by the tumor cells but are expressed at very low levels by other cells of adult tissue, and (3) tissue-specific (“differentiation”) antigens. These three types of antigens can be used for vaccination in all forms above described, i.e., as proteins, virus constructs, DNA, RNA, long peptides, or peptides representing exactly the natural HLA ligands on tumor cells [236, 238]. The literature on cancer vaccination including different tumor entities, techniques, and adjuvants as well as efficacy and immunological responses is expanding rapidly; it is beyond the scope of this paper to go into more details, but there are a number of excellent recent reviews dealing with this topic [54, 126, 236, 237, 239–244]. Interestingly, also in allergies, especially in type I allergy associated with immunoglobulin E (IgE)-mediated hypersensitivity reactions, vaccination can be useful. This seems paradoxical considering that allergic disorders are per se the consequence of hyperimmune responses. However, already in 1903 and 1911 it was shown that subcutaneous injection of grass pollen extracts in animals and humans induced a protective immune response [245, 246]. In 1935, it was shown that this protection was due to the development of allergen-blocking IgG antibodies [247]. However, the variations in quality of natural allergens and the risk that the administration of allergens to patients may induce severe side-effects hindered a more general application of allergy vaccination. Only since the availability of molecular biological and cloning techniques could epitopes involved in allergic inflammation be identified that can now be used in specific immunotherapies. Several novel therapeutic and prophylactic therapies against allergy are currently under investigation (for literature survey, see [248–251]).

2.8 Future prospects

Advances in biology result in the identification of new targets for vaccine research – not only in infectious disorders but also in cancer, allergies, and other chronic inflammatory disorders. The critical question for the development of successful vaccines in the future will be, which technology must be used to elicit a protective or therapeutic response towards specific pathogens/antigens. Therefore, it will be important to assess the impact of novel vaccine technologies in human trials and to correlate the outcome with immunological responses with the aim of identifying biomarkers of safety and efficacy. This will be necessary in order to identify the most promising vaccine candidates in early exploratory trials before proceeding to long, expensive efficacy trials that involve very large numbers of subjects.

3 Antibody engineering

3.1 History

In recent years, antibodies have evolved as important therapeutic agents in the treatment of a variety of chronic inflammatory disorders. Historically, early anti-inflammatory agents were developed long before their therapeutic targets were known. Many of these targets later turned out to be control points in relevant pathways and proved to be enzymes (as with aspirin and similarly acting nonsteroidal anti-inflammatory agents) or transcription-regulating factors (such as glucocorticoids); in some instances, the targets remained uncertain (as with gold salts). Interestingly, statins that are directed against hydroxymethylglutaryl CoA reductase, an enzyme not anticipated to have anything to do with inflammation, evolved as strong anti-inflammatory agents [252]. Nowadays, in an era when processes involved in inflammation can be more precisely defined, specific targets can be identified in advance leading to the development of anti-inflammatory agents directed, for instance, against specific cytokines (e.g. TNFα or IL-1β), receptors (e.g. those for cysteinyl leukotrienes), co-stimulation molecules, adhesion molecules (e.g. α4 integrin), or surface molecules of T or B cells.

Passive immunotherapy involving injection of serum from recovering patients or from immunized animals was used even before the availability of antimicrobial therapy in the mid-1940s and was further improved by the use of antitoxins raised in animals. In 1944, the fractionation of human immunoglobulin for the treatment of measles overcame the problem of serum sickness and introduced the concept of pooled immunoglobulin therapy.

Molecular techniques have now offered the opportunity for development and use of defined monoclonal antibodies (mAbs) against factors involved in the pathogenesis of diseases [1]. In 1975, Georges Köhler and César Milstein of the Medical Research Council Laboratory of Molecular Biology in Cambridge described a method of obtaining antigen-specific antibodies in large amounts [253]. Using this hybridoma technology, it became possible to produce specific high-affinity mAbs.

The arsenal of biological therapies available to treat chronic inflammatory diseases, especially autoimmune and autoinflammatory disorders as well as cancer, but also cardiovascular and neurological diseases, is quickly expanding as a result of better understanding of molecular mechanisms together with improved production capacity [4, 254]. These therapies include (by class): novel anti-TNFα blockers (fully humanized or pegylated), anti-interleukin (IL) agents (to IL-1, IL-6), B-cell-directed therapies (to CD20, CD22), interference with co-activation signaling (CTLA4-Ig), intravenous immunoglobulins (IVIGs), and antibodies to tumor antigens.

However, engineering of therapeutic proteins and especially of therapeutic antibodies has to deal with the problem that they are, to a variable extent, immunogenic [255]. Extrinsic and intrinsic factors may play a role: the presence of aggregates, adjuvant-like contaminations, co-medication of the patient, MHC variants, and immunological status of the patient or even cytokine releases are known as extrinsic factors [256]. Major intrinsic factors include the presence of glycosylation sites of particular carbohydrate side chains both in variable and constant regions of the immunoglobulin and other post-translational modifications of antibodies such as glycation, deamidation, and oxidation of amino acid side chains, all of which may affect the recognition of epitopes by receptors of the immune system (Fig. 6).

Fig. 6 Factors influencing immunogenicity of therapeutic antibodies (according to [350]).

Fig. 6

Factors influencing immunogenicity of therapeutic antibodies (according to [350]).

3.2 Role of immunoglobulins

IVIG, a preparation of human polyclonal antibodies pooled from several healthy donors, has been used for a variety of conditions including immunodeficiency, infectious diseases, sepsis, autoimmune disorders, and inflammatory diseases. The goals of treatment with IVIG are (1) replacement therapy in humoral immunodeficiency disorder, (2) protection of the recipient against infection, and/or (3) suppression of inflammatory and immune-mediated processes. Their mode of action, especially in chronic inflammatory disorders, is, however, still a matter of debate [257, 258].

Therapy with hyperimmune serum immunoglobulins continues to be used for the prophylaxis or treatment of various bacterial and viral diseases such as those caused by cytomegalovirus, respiratory syncytial virus, and hepatitis B virus. Antibodies against toxins are used for the treatment and prophylaxis of bacterial diseases such as tetanus, botulism, and diphtheria [259]. Antivenoms used for stings and snakebites are examples of the therapeutic application of immunoglobulins in noninfectious diseases [260].

3.3 Engineering of monoclonal antibodies (mAbs)

Muronomab-CD3, a murine-derived mAb used to prevent organ allograft rejection, was the first therapeutic antibody approved for clinical use; this was in 1986. This was followed by the development of humanized chimeric mAbs, and most of the recently approved biologics are fully human mAbs. Nowadays, a large spectrum of therapeutic antibodies is either already used clinically, undergoing clinical trials, or is in the pipeline. Cancer and immunologic disorders continue to be the focus of investigational therapeutic mAbs (estimated at >500 currently across various stages of development) [261].

The aim of engineering of antibodies for therapeutic purposes is improvement of their antigen-binding properties, pharmacokinetics, pharmaceutical properties, and immunogenicity.

The quality of a therapeutic antibody depends upon several features [262]: the epitope to which the antibody binds, its affinity to the target, its pharmacokinetics, the effector function of the constant region (Fc region), and the safety profile of the antibody including its level of immunogenicity.

These various properties of mAbs can be improved by antibody engineering and optimization technologies that, in the case of whole IgG antibodies, can be classified into two categories: variable region engineering and Fc engineering. The variable region is responsible for the antigen-binding properties of IgG antibodies, but it can also influence their pharmacokinetics, pharmaceutical properties, and immunogenicity. The Fc region is responsible for the effector functions and also the pharmacokinetics. For applications where epitope binding is sufficient for the desired therapeutic effect, such as virus neutralization or receptor blocking, small antibody fragments can be used. The smallest antigen-binding fragment of an immunoglobulin that maintains its complete antigen-binding site is the Fv fragment, which consists only of protein variable (V) regions. A soluble and flexible amino acid peptide linker is used to connect the V regions to form an scFv (single-chain fragment variable) fragment for stabilization of the molecule [263], or the constant (C) domains may be linked to the V regions to obtain a Fab fragment (see Fig. 1 in Gubala et al., this issue). Today, scFv and Fab are the most widely used antibody fragments which are produced in prokaryotes [264].

3.3.1 Antigen-binding properties

The primary role of the antibody V region is to bind to the target antigen. Two important antigen-binding properties are the affinity and the specificity, and engineering these properties has much improved the quality of therapeutic antibodies.

3.3.1.1 Affinity of antibodies

Currently there are three approaches available for in vitro affinity maturation and generation of variants of the original antibody: random mutagenesis, targeted mutagenesis, and the shuffling approach (Table 5). The random mutagenesis approach utilizes E. coli mutator bacterial strains, saturation mutagenesis, or error-prone PCR [265–267]. Targeted mutagenesis uses alanine-scanning or site-directed mutagenesis, such as look-through mutagenesis [268]. The shuffling approach includes DNA-, light chain-, or complementarity-determining region (CDR; see below) shuffling [269–272].

Table 5

Engineering of therapeutic IgG antibodies (according to [262]).

EngineeringEffect
Antigen-binding properties
 Affinity maturation
  Random mutagenesisImproving therapeutic efficacy
  Targeted mutagenesis
  Shuffling approachesDecreasing minimum effective plasma antibody concentration
  In silico approaches
 Altering specificity
  Random mutagenesisReducing the cross reactivity to other antigens
  Targeted mutagenesisBroadening the specificity to related antigens
Improving the non-human species cross-reactivity
Binding to two different antigens by single binding site
Pharmacokinetics
 Isoelectric point engineering
  Lowering isoelectric pointReducing the nonspecific clearance
 Engineering pH dependency
  Rapid dissociation in acidic conditionReducing the antigen-mediated clearance
Binding to multiple antigen by single antibody
Pharmaceutical properties
 Thermal stability improvement
  Optimizing hydrophobic core and charge cluster residuesRetaining biological activity during storage
  Optimizing conserved residuesMinimizing aggregation not to increase immunogenicity
  Removing hydrophobic surface residuesImproving expression yield
 Solubility improvement
  Reducing the surface hydrophobicityEnabling high concentration formulation
  Introducing a N-linked carbohydrate
  Modifying the isoelectric point
 Chemical stability improvement
  Avoiding deamidation and isomerizationRetaining biological activity during storage
  Avoiding Met and Trp oxidationFacilitating quality control in manufacturing
  Avoiding glycosylationFacilitating quality control in manufacturing
Immunogenicity
 Humanization
  CDR graftingReducing immunogenicity
  SDR grafting
  Recombinant antibody libraries
 Deimmunization
  In silico prediction of T-cell epitopesReducing immunogenicity
  In vitro analysis of T-cell epitopes
  Introducing regulatory T-cell epitopes

An antibody with high affinity is selected from these variants of the parent antibody by display panning technologies. The most general display technology for affinity maturation is phage display using pIII fusion protein or pIX fusion protein [273, 274].

A variety of other display methods have been reported such as ribosome display, yeast surface display, E. coli surface display, and mRNA display [262, 275–277]. However, these combinatorial approaches to antibody affinity maturation require a large library size, which is often limited by the transformation steps following assembly and ligation [278]. Ribosome display, a cell-free system, allows the generation of large libraries with up to 1014 members, and antibodies with picomolar affinity have been identified [279]. The advantages of yeast surface display are that the eukaryotic system offers post-translational modification and processing machinery similar to that of mammals. Screening the randomly mutagenized library of a parent antibody in the yeast surface display provided a highest-affinity variant with a dissociation constant of 48 fM, which was a 10 000-fold improvement over the original antibody [280].

Meanwhile, in silico approaches for affinity maturation using computational design have been reported [281–283].

Improving the affinity of a therapeutic antibody to its target antigen may help to decrease the minimum concentration at which the antibody is effective in plasma in vivo and, therefore, permits a reduction of dose or dosing interval [284–287]. However, due to the stoichiometric binding of the antibody to the antigen, the minimum effective concentration of the antibody cannot be lowered below the equilibrium concentration of the soluble antigen, even if the antibody had infinite affinity [288]. Moreover, since the antigen is continuously produced in vivo, which may be different from the in vitro situation, the antibody dosage cannot be lowered below the amount of antigen produced between doses. Therefore, if the equilibrium concentration or turnover of the antigen in vivo is very high, affinity maturation may have a limited effect on reducing the antibody dose or dosing intervals.

Affinity maturation of antibodies targeting a solid tumor has to consider that antibody-based molecules with extremely high affinity seem to have impaired tumor penetration properties as shown in one study [289]. Diffusion of the highest-affinity scFv was limited to the peripheral tumor space adjacent to the blood vessels while the lowest-affinity scFv diffused uniformly throughout the tumor interior [289]. On the other hand, antibodies with higher affinity promote stronger antibody-dependent cellular cytotoxicity (ADCC) than the lower-affinity variants and after internalization by target cells they show highest cytotoxicity for tumor cells [290, 291]. The fact that the affinity of a therapeutic antibody to solid tumors influences its ability to diffuse throughout the tumor, its cytotoxic effect on tumor cells, and its effect on ADCC activity, renders it difficult to predict its in vivo antitumor efficacy.

3.3.1.2 Specificity of antibodies

Binding specificity is another important factor for therapeutic antibodies. Engineering specificity includes reducing the cross-reactivity to other antigens, broadening the specificity to related antigens, and improving the cross-reactivity with nonhuman species [292–301]. This can be achieved by the same mutagenesis and display technologies used for affinity maturation as phage display technology or computational design approach.

Bispecific antibodies represent one of the promising second-generation antibody therapies. Various formats for bispecific antibodies, such as dual variable domain immunoglobulin (DVD-Ig) and IgG-like bispecific antibody (IgG-scFv) have been reported [302–304], and whole IgG-type bispecific antibody using a common light chain is one of the promising formats from the standpoint of manufacturability.

3.3.2 Improving the pharmacokinetics

IgG antibodies tend to have a longer half-life (about 21–29 days; [305, 306]) than other therapeutic proteins due to neonatal receptor for Fc (FcRn)-mediated recycling [307]. Further improvement of their pharmacokinetics may, however, help to lower the dose, to enable the development of a subcutaneous formulation, and to prolong the dosing interval, which would be more convenient for patients with chronic inflammatory disorders [262]. The clearance of IgG antibodies (i.e. their disappearance from the circulation) can be due to two different pathways: nonspecific clearance and antigen-mediated clearance [308]. Nonspecific clearance of IgG could be improved by an Fc-engineering approach to increase the Fc/FcRn interaction [307, 309–311]. Several approaches for improving the two clearance pathways have been reported (Table 5).

3.3.2.1 Reduction of the nonspecific clearance of IgG antibodies

Immunoglobulins and IgG complexes are eliminated by the mononuclear phagocytic system. Their nonspecific clearance is due to a nonspecific uptake (by pinocytosis) of IgG antibodies into the cellular endosomal compartment followed by lysosomal degradation of the non-FcRn-bound antibody [312].

Most human or humanized mAbs obtained by standard methods have a rather high isoelectric point. It has been shown that the nonspecific uptake of an antibody can, for instance, be reduced by electrostatic repulsion between a low isoelectric point antibody and a negatively charged cell surface [313]. Thus, lowering the isoelectric point of Fv while maintaining the affinity to the antigen reduced the clearance of the original antibody by 3.1-fold. This improvement seems to be comparable to, or even better than, the reported Fc engineering approach [310].

3.3.2.2 Reduction of the antigen-mediated clearance of IgG antibodies

Antigen-mediated clearance contributes to the total clearance when IgG antibodies target membrane-bound antigens with an internalizing property; i.e., the antibody bound to the membrane-bound antigen is internalized as an antigen–antibody complex followed by lysosomal degradation of that complex [308]. In a recent approach, lowering pH in the endosome (pH 6.0) by introducing histidine residues into the antigen-binding site of an anti-IL-6 receptor antibody lead to a rapid dissociation of the soluble IL-6R from the antibody in the acidic endosome; this pH-dependent process enables selective lysosomal degradation of the previously antibody-bound soluble IL-6R, and FcRn-mediated recycling of the free therapeutic IL-6R antibody back to the plasma [313]. Since this antibody maintained its binding affinity to IL-6R in the plasma (pH 7.4), it can bind to another antigen: i.e., pH-dependent binding would enable a single antibody molecule to bind repeatedly to multiple antigens, potentially leading to reduced antibody dose or dosing interval.

3.3.3 Improvement of pharmaceutical properties

The varying pharmacological properties of IgG antibodies such as thermal or chemical stability, solubility, and heterogeneity are determined by the difference in the constant region sequences (i.e. the IgG isotype) [314]. However, the variable region sequence, which comprises only one-third of the whole IgG molecule, also has a significant effect on these pharmaceutical properties, which can be improved by several engineering technologies (Table 5).

Within the VH and VL domains, three particular variable segments can be identified, the socalled hypervariable or complementarity-determining regions (CDRs) 1–3. CDRs from both VH and VL domains contribute to the antibody-binding site, i.e., the combination of the heavy and light chains determine the final antigen specificity. The four regions between the CDRs, which comprise the rest of the V-domain, are termed the “framework regions” (FRs 1–4).

3.3.3.1 Engineering to improve thermal stability

Poor thermal stability may cause various problems such as aggregation, resulting in reduced biological activity, increased immunogenicity, and low levels of expression [255, 265, 315–318]. Engineering the stability of the antibody variable region has mainly focused on scFv due to its inherently poor stability. Stability of scFv can be improved by optimizing the hydrophobic core residues, charge cluster residues, residues which determine the framework subclasses, conserved proline and glycine residues, hydrophobic surface residues, and VH/VL interface residues using either a rational mutagenesis approach or a library approach [265, 318–321].

Germline families of the heavy chain have a significant effect on the stability and the expression level of scFv [322]. Exploring and analyzing the complex relationship between CDRs and FRs improved biophysical properties of immunoglobulin domains and fragments during the process of molecular engineering [318, 323]. Thus, the stability of an anti-peptide scFv was improved by 20.9 kJ/mol and the expression yield 4-fold when particular amino acid residues (six in total alone or in combination) between the VH domains of human germline families 2, 4, and 6 were exchanged [322, 324]. However, the heavy chains derived from germline families VH2, VH4, and VH6 tend to have lower stability and expression yield than VH1, VH3, and VH5. Especially, the VH3 germline appears to have favorable properties. Although selecting the VH3 germline framework seems to be a promising approach for generating high-stability scFv, CDR grafting of murine CDRs into a VH3 germline does not always result in stable scFv [324]. This is presumably because the compatibility of CDR with the framework residues is important for the overall stability of scFv.

As an alternative to rational structure-based strategies, it is possible to stabilize antibodies or antibody fragments by designing of libraries of mutants that are screened according to the correlation of stability and biological activity [325]. Expression of such libraries can be done by phage or ribosome display, and the selection of more stable molecules is achieved by using stringent conditions, including high temperature, presence of denaturing agents such as guanidine hydrochloride, addition of proteases, or the use of reducing agents such as dithiotreitol [326, 327].

Although engineering the scFv to improve thermal stability is being extensively studied, stability engineering of the Fab domain in IgG antibodies has not been reported [262]. This seems to be due to the fact that Fab often already has acceptable stability for therapeutic development because of the stabilization effect of the CH1/CL domain present in the Fab [328]. Analysis of the melting temperature of 17 different Fab from different human or humanized antibodies revealed that the melting temperature of these antibodies ranged from 57 to 82 °C [329].

3.3.3.2 Engineering to improve chemical stability

The efficacy of therapeutic antibodies is often reduced by chemical degradations such as deamidation, isomerization, succinimide formation, methionine and tryptophan oxidation, and cysteinylation of unpaired cysteine in the CDR region [330–334], and antibodies with such tendencies should be, therefore, avoided as clinical candidate molecules. Among various degradation pathways, asparagine deamidation and isomerization in the CDRs seems to play a major role. It can be avoided by substituting the asparagine residue itself with other amino acids that maintain the antigen-binding capability. But in some cases, the asparagine residue in the CDR might be critical for its antigen binding and may not be replaceable. Since the rate of deamidation is mainly determined by the amino acid neighboring asparagine at the C-terminal site and is most rapid when this amino acid is glycine or serine, instead of the asparagine residues the glycine can be substituted with arginine, to remove the potentially deamidating site from the parent antibody while maintaining antigen binding [335]. Methionine or tryptophan oxidation in the CDRs could also be avoided by substituting oxidizing residues with other amino acids which do not undergo oxidative degradation and which maintain the binding capacity, although there has been no such report to date.

3.3.3.3 Engineering to improve solubility and viscosity

Subcutaneous delivery of antibody therapeutics is preferable for chronic disease. Since the volume for a single subcutaneous injection is generally limited to <1.5 mL, subcutaneous injection often requires a formulation with high antibody concentration (e.g. >100 mg per mL) [336]. Major difficulties for achieving the necessary concentration are presented by stability, solubility, and viscosity [337].

The development of high concentration formulations for subcutaneous injection may cause high viscosity of therapeutic antibodies. Different IgG1 antibodies have different viscosity, which could arise from the different variable region sequences, and electrostatic interaction between Fab sequences seems to play a role in increased viscosity in some antibodies [337, 338].

The solubility of IgG antibody is generally high (>100 mg per mL), but extremely low solubility has been reported in cryoglobulin IgG antibodies and some therapeutic IgG antibodies [339, 340]. An attempt was made to reduce surface hydrophobicity by rationally mutating the residues exposed at the surface of the variable region; another strategy was to introduce an N-linked carbohydrate in the variable region [339, 341]. However, neither strategy was always successful.

A recent study compared and mutated the sequences of two related antibody domains – one aggregation-prone and the other was its aggregation-resistant counterpart – to identify “aggregation hotspots” [255, 342]. By swapping and mutagenesis analysis of CDR loops, an aggregation-prone human VH segment was converted into an aggregation-resistant one. Only CDR1 in the VH segment appeared crucial since it contained the only “hotspot” identified as regulating the reversible folding of VH variants via the presence or absence of charged residues; a triad of charged mutations was thus identified.

3.3.3.4 Engineering to improve heterogeneity

Heterogeneities deriving from post-translational modifications, such as glycosylation, N-terminal pyroglutamine cyclization, and C-terminal clipping, need to be controlled to maintain the product quality [343, 344], but is rather expensive. Glycosylation sites are located in the Fc but also in the Fv region [345, 346]. The most common N-glycosylation site in the variable region is CDR2 of the heavy chain, but other potential N-glycosylation sites can be generated by somatic mutations [347]. The presence of glycosylation in the variable region may have either positive of negative effects on the antigen binding, and the difference in the carbohydrate structure could also influence the pharmacokinetics [348, 349].

3.3.4 Reduction of immunogenicity

Clinical utility, pharmacokinetics, and efficacy of the therapeutic antibodies can be limited by their immunogenicity, when the therapeutic antibodies themselves are recognized as foreign to the body, leading to the production of anti-drug antibodies (ADAs), which can sometimes even lead to serious side-effects. The most important factors influencing immunogenicity of therapeutic antibodies are their properity to aggregate and the presence of effector T-cell epitopes in the therapeutic protein that may be recognized by the host’s immune system. Various human or humanized antibodies and deimmunization technologies to minimize the number of effector T-cell epitopes have been reported for reducing immunogenicity of therapeutic antibodies (Tables 5 and 6).

Table 6

Methods for aggregate anaylsis in therapeutic protein development [350, 351].

MethodSize rangePrinciple
Size exclusion chromatography (SEC): native and denaturing5–50 nmSeparation through porous matrix by molecular sieving
Analytical ultracentrifugation (AUC)1 nm–0.1 μmSedimentation rate in response to centrifugal force
Flow field-flow fractionation1 nm–1 μmSeparation by flow retention based on diffusion coefficient
Asymmetrical flow field-flow fractionation (AF4)1 nm–few μmSeparation by size through flow retention
Dynamic light scattering0.5 nm–10 μmFluctuation of scattered light intensity
Mass spectrometryAtomic resolution-MDaMass/charge detection of ionized molecules in a field
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)kDa-MDaSeparation of denatured proteins in a gel by size in an electric field
Native PAGEkDa-MDaSeparation by size/charge in an electric field
Capillary-SDS electrophoresiskDa-MDaSeparation in a capillary by size in an electric field
Optical microscopy1 μm–mmMicroscopy-aided visualization of particles
Electron microscopynm–mmVisualization of protein particles and detection of chemical composition at high resolution
Fluorescence microscopy>1 μm–mmProtein fluorescence is induced and detected
3.3.4.1 Role and engineering of aggregation

Protein aggregation can augment a protein-specific immune response and can lead to formation of antibodies directed against protein therapeutics [316, 352]. Minimizing the risk of aggregation of biotherapeutics may, therefore, reduce the risk of immunogenicity.

Aggregation is a broad term, encompassing the interactions which result in the self-association of protein molecules into assemblies other than their native quaternary structures [350, 353]. The aggregates can range considerably in size, from dimers up to subvisible and even visible particles; they can involve covalent or non-covalent linkages, be ordered or disordered in structure, be soluble or insoluble, and their formation can be reversible or irreversible. Aggregation of therapeutic proteins during bioprocessing is facilitated when they are partially unfolded.

Antibody fragments and mAbs are susceptible to aggregation upon exposure to a variety of stresses. Therapeutic mAbs are typically derived from mammalian cell culture [264], and the process involves over-expression of recombinant proteins in cultures that can result in aggregation [354]. The high antibody concentrations necessary for subcutaneous therapeutic delivery (50–200 mg/mL), subsequent long storage times, as well as purification steps involving elevated temperatures, high ionic strength, mechanical stress (as, e.g. freeze–thaw cycles), attraction to hydrophobic interfaces, and pH values far from neutrality can lead to a loss of native structure that facilitates aggregation [355–360]. Thus, additives that maintain native protein structure during purification have been used to reduce protein aggregation [361].

Large aggregates with native conformation can form through adsorption to microparticles, or at high concentration through “salting out”. This phenomenon is caused by protein exceeding the solubility limit above that required to cause precipitation as the salt concentration increases [362].

Native protein stability can also be compromised by physical and chemical stressors. Since aggregation of protein in both native and non-native conformations can occur at many stages of manufacturing and purification – including protein expression, purification, and storage – characterization of the protein is required at each stage to ensure batch-to-batch uniformity and overall quality [350, 363]. Several reviews describe in detail the engineering of aggregation-resistant antibodies [255, 364].

3.3.4.2 Stages in the development of fully human therapeutic antibodies

As already mentioned, the first therapeutic antibody approved for clinical use was a murine-derived mAb, muronomab-CD3 [261]. Historically, chimerization and then humanization were the two subsequent molecular engineering processes described and applied to mAbs intended for therapeutic purposes [365, 366]. Developed in the 1980s, these techniques are still used because they reduce the potential for the induction of human anti-mouse antibodies (HAMAs) by the host, which reduces therapeutic efficacy [367]. Nevertheless, these chimeric and to a lesser extent humanized mAbs are still associated with a risk of eliciting an immune response [368]. To address this problem, in addition to murine CDR grafting onto acceptor human frameworks, a variety of other humanization methods have been reported that ensure more accurate grafting of the residues associated with the binding properties.

3.3.4.2.1 Murine monoclonal antibodies

Fully murine mAbs were developed by hybridoma technology fusing immortal mouse myeloma cells and splenic B cells from hyperimmune animals during the 1970s [253]. However, application of the murine anti-CD3 mAb muromonab led to the clinical experience of a strong host immune response and poor pharmacokinetics, thus reducing its efficacy with long-term and repeated administration [369]. Thus, HAMA responses resulted in systemic reactions (limiting the utility of murine mAbs to essentially single-use applications). Muronomab-CD3 binds to the CD3ε receptor subtype, which leads to nonspecific T-cell activation and a release of cytokines that results in flu-like symptoms such as fever, chills, and gastrointestinal irritation [370]. Its second effect is to block binding of the TCR to antigen. Clinical experience with muronomab-CD3 first suggested the importance of concomitant use of immunosuppressive drugs in limiting HAMA responses, although HAMAs can occur even in the context of intense immunosuppression [371]. An immune response mounted against the muronomab-CD3 antibodies inhibits muronomab-CD3 binding to CD3 and can lead to treatment failure. In the absence of neutralizing host anti-idiotype antibodies, muronomab-CD3 has a harmonic mean half-life of 18 h [372]. Once patients become sensitized, muronomab-CD3 may be cleared from the circulation within a few hours [371]. Of equal concern, circulating IgE against muronomab-CD3 can lead to life-threatening anaphylactic reactions on subsequent treatment [372].

These initial disappointments provided the impetus for the ongoing improvements in mAb technology that began with the generation of chimeric and then humanized mAbs and has culminated with the recent development of full human mAbs. Investigators then sought to develop recombinant DNA strategies that would result in more humanized, less immunogenic mAbs (Fig. 7).

Fig. 7 Reduction of immunogenicity of therapeutic antibodies by humanization (according to [261]).

Fig. 7

Reduction of immunogenicity of therapeutic antibodies by humanization (according to [261]).

3.3.4.2.2 Chimeric antibodies

In a chimeric antibody, the variable region of the antibody has mouse sequences and the remaining sequences are human. The content of human sequences is intended to reduce the recognition of the antibody as “foreign”. Thus, several chimeric mAbs have been associated with markedly fewer anti-antibody responses compared with murine mAbs [369]. Nevertheless, the development of the human anti-chimeric antibody (HACA) response remains a potentially significant problem that requires close monitoring and, in some instances, concomitant treatment with corticosteroids or discontinuation of therapy. HACA response rates of up to 61 % have been reported with infliximab (Remicade) and have been associated with shorter duration of effect and increased risk for infusion reactions [373].

3.3.4.2.3 Humanized antibodies

The next phase of development was to generate humanized mAbs by substituting rodent sequences with human sequences except for those found within the antigen-binding CDRs [369, 374]. It was thought that such a substitution would remove much of the immunogenicity remaining with chimeric antibodies. In reality, however, human anti-human antibody (HAHA) responses are now observed although with a reduced incidence compared with HAMA or HACA. Alemtuzumab (Campath-IH) has a reported HAHA incidence rate of 1.9 %, and trastuzumab (Herceptin) has a reported rate of only 0.1 %, but other humanized antibodies such as daclizumab have HAHA rates as high as 34 % [369]. Furthermore, humanization may be complicated by the fact that FRs contribute significantly to antibody affinity, so simply grafting CDRs onto a generic framework is not sufficient. To restore binding, it is often necessary to conserve some rodent sequence in the framework residues [374–376].

On the assumption that the germline sequences will not activate effector T cells in broader populations [368, 377, 378], human germline frameworks have been used as templates for CDR grafting. Those antibodies may, however, still contain several T-cell epitopes in the nonhuman CDRs and the CDR/framework junction regions. The next approach was, therefore, the use of specificity-determining residue (SDR) grafting, in which only the SDRs (not whole CDRs) are grafted onto the germline antibodies. These SDR-grafted humanized antibodies indeed showed lower immunogenicity than CDR-grafted humanized antibodies [379–382].

3.3.4.2.4 Fully human mAbs

Initially, the production of fully human mAbs was hampered by challenges related to a lack of a stable human myeloma fusion partner and concerns about human immunization. Generation of fully human therapeutic mAbs was made possible by the development of phage-display platforms and, more recently, by transgenic mouse platforms [383–386].

Bernett et al. developed a novel and sophisticated method of humanization that results in fully human mAbs from parent murine sequences [255, 387]. The crucial and central point of their strategy is the rational molecular engineering of residues within and proximal to CDRs, together with the optimization of the variable domain interface. This was achieved by successive and iterative explorations of the human germline repertoire using semi-automated computational methods, to progressively select functional humanized mAbs with the highest level of humanness. Thus, three mAbs targeting three different human antigens (CD25, vascular endothelial growth factor [VEGF], and TNFα) were used to validate their molecular engineering, resulting in the introduction of 59, 46, and 45 substitutions in the parent murine sequences, respectively. The resulting fully human Abs retained the potency of the corresponding chimeric mAbs and had in vitro activity comparable to that of their respective marketed drugs (i.e. daclizumab, bevacizumab, and infliximab).

A strategy for development of fully human antibodies relies on transgenic mice [369], in which the murine immunoglobulin genes have been disrupted and replaced with human immunoglobulin gene clusters [388, 389]. However, since the human-heavy, lambda-light, and kappa-light chain loci, located on chromosomes 14, 22, and 2, respectively, are arranged in large clusters spanning more than 1 Mbase each [390], conventional transgenic strategies had to be adapted. Several transgenic mouse strains have been created that can produce fully human antibodies (see Fig. 8 regarding the XenoMouse):

Fig. 8 The XenoMouse technology for the production of fully human monoclonal antibodies (according to [261]).

Fig. 8

The XenoMouse technology for the production of fully human monoclonal antibodies (according to [261]).

The HuMab mouse (GenPharm, Canada) with four distinct genetic modifications [391]: (1) deletion of the endogenous H-chain J gene segments preventing VDJ rearrangement and expression of murine IgM; (2) deletion of the κL-chain J and C segments; (3) transgenic for a human-heavy chain minilocus containing 4 VH gene segments, 16 D segments, 6 JH segments, Cμ and Cγ1; (4) combination of plasmids and a 450-kilobase yeast artificial chromosome (YAC) comprising most Vk sequences, VH sequences as well as all 5 Jk and Ck.

This strain retains a large proportion of the diversity of human Vk sequences, but VH sequences are more poorly represented.

The “TransChromo” (TC) mouse created by a microcell-mediated chromosome transfer [369] (Kirin Brewery Company, Japan), which facilitated the introduction of the largest fraction of the human germline repertoire [385]. In this approach, human fibroblast-derived microcells are fused with mouse embryonic stem cells resulting in pluripotent cell lines having a single human chromosome or chromosome fragment – including a centromere and both telomeres – that replicates and assorts during cell division without insertion into an endogenous mouse chromosome. Thus, cells were created bearing a fragment of chromosome 2 or an entire chromosome 14 or 22. Initially, only chimeric mice bearing the small chromosome 2 fragment were able to pass the minichromosome on to their progeny. Later, retention of a second minichromosome, a fragment of chromosome 14, was achieved, and the double-TC mice were crossed onto a double-immunoglobulin knockout background.

Since problems with the stability of the κL-chain-bearing chromosome 2 still hindered production of fully human mAbs, the TC mouse was crossed against the HuMab mouse creating the KM mouse (Kirin Brewery Company and Mederex), which combines the VH diversity of the TC mouse’s more stable chromosome 14 fragment with the Vκ diversity of the HuMab mouse’s YAC transgene. One such product derived from this technology is zanolinumab (HuMax-CD4; Genmab A/S, Denmark), which is currently in phase 3 clinical trials for cutaneous T-cell lymphoma.

The XenoMouse (Abgenix) (Fig. 8) used yeast artificial chromosome (YAC) transfer: a total of four YACs representing ∼1 Mbase of heavy chain (∼66 VH genes, Cμ, Cδ, and Cγ2 constant regions) were “stitched” together by homologous recombination in yeast to create a single YAC (yH2) [388, 392]. The same procedure was then used to combine three YACs (∼800 kb) of κ-sequence (32 Vκ genes, Cκ, and Jκ) to create gκk2 [392]. These extremely large YACs were then transferred into recipient cells by fusion of yeast spheroplasts to murine embryonic stem cells to create chimeric mice. These were then crossed against DI mice, which are homozygous for targeted disruptions of mouse-heavy and κ-light chains [393]. The resulting final product, XenoMouse II, produces significant levels of human μ-, γ2-, and γ-light chains, displaying a diversity of variable chain genes used [392]. Several XenoMouse-derived antibodies are in late-phase clinical trials (e.g. panitumumab, a mAb against EGFR; denosumab, a mAb targeting RANKL; CD675, 206, an anti-cytotoxic T-lymphocyte antigen [CTLA]-4 mAb).

The advantage of fully human antibodies over humanized antibodies with regard to immunogenicity remains, however, is still controversial. Because each fully human therapeutic antibody has its own inherent immunogenicity caused by its particular sequence [369], this immunogenicity is to be determined in part by the sequence of its CDRs. Adalimumab, a fully human antibody, has been reported to induce an antibody production in a subset of patients (5–89) % that varies depending on the situation, and a reduction in therapeutic efficacy was observed in the antibody-positive patients [394–397]. This significant immunogenicity of adalimumab was supported by the presence of effector T-cell epitopes in regions containing a CDR sequence determined by an in vitro helper CD4+ T-cell assay.

3.3.4.3 In silico and in vitro deimmunization technologies

As demonstrated above, effector T-cell epitopes may be present in the humanized or even in fully human antibody and additional T-cell epitopes may also be introduced during the engineering processes. The existence of such effector T-cell epitopes in each antibody sequence can be predicted by several in silico tools such as Epibase (AlgoNomics NV), iTope/TCED (Antitope Ltd.), and EpiMatrix (EpiVax Inc.) [398–402]. Selecting a sequence with the minimum number of effector T-cell epitopes (i.e. deimmunization) may, therefore, allow reduction of the potential immunogenicity of the therapeutic antibody.

Additionally, in vitro CD4+ helper T-cell assays can be used to identify effector T-cell epitopes [395]; a disadvantage of this in vitro assay is, however, its low throughput. A combination of both procedures (i.e. minimizing the number of effector T-cell epitopes by in silico tools and subsequent confirmation of the potential immunogenicity by in vitro assessments) might, therefore, be a realistic deimmunization procedure.

Another interesting approach is the introduction of a regulatory T-cell epitope (Tregitope) in the Fc and variable region of the antibody, which may serve as a suppressor of immune response also leading to deimmunization [403, 404].

3.3.5 Screening for immunogenicity and aggregation

Some techniques for analysis of protein particles and aggregates of different sizes are given in Table 6 (see literature cited in [351]). However, there is still a lack of knowledge regarding the size and type of aggregates that can induce immunogenicity, and currently there is no single method available for detection of the whole size range of aggregates that may arise from bioprocessing.

In contrast, immunogenicity testing is now a key component of biotherapeutic drug development. The formation of neutralizing antibodies can affect safety and efficacy, but non-neutralizing antibodies can also be a concern due to effects on half-life and biodistribution [405]. Techniques for investigating the presence of anti-antibody response include immunoassays that can identify antibodies capable of binding to antigen and bioassays that can distinguish between neutralizing and non-neutralizing antibodies.

3.4 Future prospects

Although a spectrum of therapeutic antibodies has been developed in recent years, only some of them are clinically useful and acceptably safe. Furthermore, it has to be pointed out that despite all the successes of disease-modifying drugs and biologics, for the treatment of chronic inflammatory diseases multipronged therapeutic approaches are needed [406]. Anti-inflammatory components, including therapeutic antibodies directed towards various factors of the inflammatory response, may synergize especially when they interrupt positive feedback loops that drive the disease. The development of integrated therapeutic strategies should, therefore, rely on the concept that each intervention targets at least one pathway, not just a single-molecule target.

4 Membership of sponsoring body

Membership of the IUPAC Chemistry and Human Health Division Committee for the period 2014–2015 is as follows:

President: T. J. Perun (USA); Vice President: R. Cornelis (Belgium); Secretary: M. Schwenk (Germany); Titular Members: E. Differding (Belgium); J. Fischer (Hungary); V. Gubala (Slovakia); P. Illing (UK); L. Johnston (Canada); H. Møller Johannessen (Denmark); W. A. Temple (New Zealand); Associate Members: Vincenzo Abbate (UK); M. Kiilunen (Finland); Y. C. Martin (USA); S. Mignani (France); D. Rotella (USA); National Representatives: N. Nahar (Bangladesh); M.-X. Wang (China/Beijing); R. Jih-Ru Hwu (China/Taipei); S. Alihodžić (Croatia); A. Rahatgoanker (India); G. Bee Teh (Malaysia); R. Leurs (Netherlands); B. Haug (Norway); S. Bachurin (Russia); P. Ploypradith (Thailand).


Article note

Sponsoring body: IUPAC Chemistry and Human Health Division; see more details on p. 1608.



Corresponding author: Reinhild Klein, Department of Internal Medicine II, University of Tuebingen, Otfried-Mueller-Strasse 10, D 72076 Tuebingen, Germany, e-mail:

References

[1] L. Shahani, S. Singh, N. M. Khardori. Med. Clin. North Am. 96, ix, 421 (2012).10.1016/j.mcna.2012.04.001Search in Google Scholar

[2] G. J. Nossal. Ann. NY Acad. Sci. 1283, 1 (2013).10.1111/nyas.12035Search in Google Scholar

[3] J. F. Bach. N. Engl. J. Med. 347, 911 (2002).10.1056/NEJMra020100Search in Google Scholar

[4] I. Tabas, C. K. Glass. Science 339, 166 (2013).10.1126/science.1230720Search in Google Scholar

[5] F. Fenner. Aust. J. Exp. Biol. Med. Sci. 63 (Pt. 6), 607 (1985).10.1038/icb.1985.64Search in Google Scholar

[6] A. Sette, R. Rappuoli. Immunity 33, 530 (2010).10.1016/j.immuni.2010.09.017Search in Google Scholar

[7] E. Jenner. The Origin of the Vaccines Inoculation, Shury, London (1801).Search in Google Scholar

[8] L. Pasteur. C. R. Acad. Sci. Paris 91, 673 (1880).Search in Google Scholar

[9] R. Rappuoli. Nat. Med. 10, 1177 (2004).10.1038/nm1129Search in Google Scholar

[10] I. Delany, R. Rappuoli, E. De Gregorio. EMBO Mol. Med. 6, 708 (2014).Search in Google Scholar

[11] P. A. Offit. Vaccinated: One Man’s Quest to Defeat the World’s Deadliest Diseases, Harper Collins, New York (2007).Search in Google Scholar

[12] M. de Veer, E. Meeusen. Discov. Med. 12, 195 (2011).Search in Google Scholar

[13] R. D. Fleischmann, M. D. Adams, O. White, R. A. Clayton, E. F. Kirkness, A. R. Kerlavage, C. J. Bult, J. F. Tomb, B. A. Dougherty, J. M. Merrick, K. McKenney, G. Sutton, W. FitzHugh, C. Fields, J. D. Gocayne, J. Scott, R. Shirley, L.-I. Liu, A. Glodek, J. M. Kelley, J. F. Weidman, C. A. Philips, T. Spriggs, E. Hedblom, M. D. Cotton, T. R. Utterback, M. C. Hanna, D. T. Nguyen, D. M. Saudek, R. C. Brandon, L. D. Fine, J. L. Fritchman, J. L. Fuhrmann, N. S. M. Geoghagen, C. L. Gnehm, L. A. McDonald, K. V. Small, C. M. Fraser, H. O. Smith, J. C. Venter. Science 269, 496 (1995).10.1126/science.7542800Search in Google Scholar

[14] H. O. Smith, J. F. Tomb, B. A. Dougherty, R. D. Fleischmann, J. C. Venter. Science 269, 538 (1995).10.1126/science.7542802Search in Google Scholar

[15] R. Rappuoli. Curr. Opin. Microbiol. 3, 445 (2000).10.1016/S1369-5274(00)00119-3Search in Google Scholar

[16] P. R. Dormitzer, G. Grandi, R. Rappuoli. Nat. Rev. Microbiol. 10, 807 (2012).Search in Google Scholar

[17] A. T. Glenny, B. E. Hopkins. Br. J. Exp. Pathol. 4, 283 (1923).Search in Google Scholar

[18] A. T. Glenny. Br. Med. J. 2, 244 (1930).10.1136/bmj.2.3632.244Search in Google Scholar

[19] B. Pulendran, R. Ahmed. Nat. Immunol. 12, 509 (2011).Search in Google Scholar

[20] B. Guy. Nat. Rev. Microbiol. 5, 505 (2007).10.1038/nrmicro1681Search in Google Scholar

[21] R. M. Zinkernagel, H. Hengartner. Immunol. Rev. 211, 310 (2006).Search in Google Scholar

[22] S. F. Gonzalez, S. E. Degn, L. A. Pitcher, M. Woodruff, B. A. Heesters, M. C. Carroll. Ann. Rev. Immunol. 29, 215 (2011).Search in Google Scholar

[23] B. Lemaitre, E. Nicolas, L. Michaut, J. M. Reichhart, J. A. Hoffmann. Cell 86, 973 (1996).10.1016/S0092-8674(00)80172-5Search in Google Scholar

[24] R. Medzhitov, P. Preston-Hurlburt, C. A. Janeway Jr. Nature 388, 394 (1997).10.1038/41131Search in Google Scholar PubMed

[25] A. Poltorak, X. He, I. Smirnova, M. Y. Liu, C. Van Huffel, X. Du, D. Birdwell, E. Alejos, M. Silva, C. Galanos, M. Freudenberg, P. Ricciardi-Castagnoli, B. Layton, B. Beutler. Science 282, 2085 (1998).10.1126/science.282.5396.2085Search in Google Scholar PubMed

[26] L. A. Carneiro, L. H. Travassos, S. E. Girardin. Ann. Med. 39, 581 (2007).Search in Google Scholar

[27] T. Kawai, S. Akira. Nat. Immunol. 11, 373 (2010).Search in Google Scholar

[28] E. P. McGreal, J. L. Miller, S. Gordon. Curr. Opin. Immunol. 17, 18 (2005).Search in Google Scholar

[29] K. Onomoto, M. Yoneyama, T. Fujita. Curr. Opin. Microbiol. Immunol. 316, 193 (2007).Search in Google Scholar

[30] O. Takeuchi, S. Akira. Immunol. Rev. 220, 214 (2007).Search in Google Scholar

[31] T. Kawai, S. Akira. Semin. Immunol. 19, 24 (2007).Search in Google Scholar

[32] E. M. Palsson-McDermott, L. A. O’Neill. Biochem. Soc. Trans. 35, 1437 (2007).Search in Google Scholar

[33] A. Pashine, N. M. Valiante, J. B. Ulmer. Nat. Med. 11, S63 (2005).10.1038/nm1210Search in Google Scholar PubMed

[34] F. Bauernfeind, A. Ablasser, E. Bartok, S. Kim, J. Schmid-Burgk, T. Cavlar, V. Hornung. Cell. Mol. Life Sci. 68, 765 (2011).Search in Google Scholar

[35] L. Franchi, T. Eigenbrod, R. Munoz-Planillo, G. Nunez. Nat. Immunol. 10, 241 (2009).Search in Google Scholar

[36] V. Hornung, J. Ellegast, S. Kim, K. Brzozka, A. Jung, H. Kato, H. Poeck, S. Akira, K. K. Conzelmann, M. Schlee, S. Endres, G. Hartmann. Science 314, 994 (2006).10.1126/science.1132505Search in Google Scholar PubMed

[37] H. Kato, O. Takeuchi, S. Sato, M. Yoneyama, M. Yamamoto, K. Matsui, S. Uematsu, A. Jung, T. Kawai, K. J. Ishii, O. Yamaguchi, K. Otsu, T. Tsujimura, C. S. Koh, C. Reis e Sousa, Y. Matsuura, T. Fujita, S. Akira. Nature 441, 101 (2006).10.1038/nature04734Search in Google Scholar PubMed

[38] L. Peiser, S. Mukhopadhyay, S. Gordon. Curr. Opin. Immunol. 14, 123 (2002).Search in Google Scholar

[39] A. Pichlmair, C. Lassnig, C. A. Eberle, M. W. Gorna, C. L. Baumann, T. R. Burkard, T. Burckstummer, A. Stefanovic, S. Krieger, K. L. Bennett, T. Rulicke, F. Weber, J. Colinge, M. Muller, G. Superti-Furga. Nat. Immunol. 12, 624 (2011).Search in Google Scholar

[40] A. Pichlmair, O. Schulz, C. P. Tan, T. I. Naslund, P. Liljestrom, F. Weber, C. Reis e Sousa. Science 314, 997 (2006).10.1126/science.1132998Search in Google Scholar PubMed

[41] T. L. Roberts, A. Idris, J. A. Dunn, G. M. Kelly, C. M. Burnton, S. Hodgson, L. L. Hardy, V. Garceau, M. J. Sweet, I. L. Ross, D. A. Hume, K. J. Stacey. Science 323, 1057 (2009).10.1126/science.1169841Search in Google Scholar PubMed

[42] S. Traub, S. von Aulock, T. Hartung, C. Hermann. J. Endotoxin Res. 12, 69 (2006).Search in Google Scholar

[43] E. Meylan, J. Tschopp, M. Karin. Nature 442, 39 (2006).10.1038/nature04946Search in Google Scholar

[44] M. J. Robinson, D. Sancho, E. C. Slack, S. LeibundGut-Landmann, C. Reis e Sousa. Nat. Immunol. 7, 1258 (2006).Search in Google Scholar

[45] D. R. Fooksman, S. Vardhana, G. Vasiliver-Shamis, J. Liese, D. A. Blair, J. Waite, C. Sacristan, G. D. Victora, A. Zanin-Zhorov, M. L. Dustin. Ann. Rev. Immunol. 28, 79 (2010).Search in Google Scholar

[46] F. D. Batista, N. E. Harwood. Nat. Rev. Immunol. 9, 15 (2009).Search in Google Scholar

[47] N. E. Harwood, F. D. Batista. Ann. Rev. Immunol. 28, 185 (2010).Search in Google Scholar

[48] T. W. Dubensky, B. A. Campbell, L. P. Villarreal. Proc. Natl. Acad. Sci. USA 81, 7529 (1984).10.1073/pnas.81.23.7529Search in Google Scholar

[49] J. A. Wolff, R. W. Malone, P. Williams, W. Chong, G. Acsadi, A. Jani, P. L. Felgner. Science 247, 1465 (1990).10.1126/science.1690918Search in Google Scholar

[50] E. F. Fynan, R. G. Webster, D. H. Fuller, J. R. Haynes, J. C. Santoro, H. L. Robinson. Proc. Natl. Acad. Sci. USA 90, 11478 (1993).10.1073/pnas.90.24.11478Search in Google Scholar

[51] D. C. Tang, M. DeVit, S. A. Johnston. Nature 356, 152 (1992).10.1038/356152a0Search in Google Scholar

[52] J. B. Ulmer, J. J. Donnelly, S. E. Parker, G. H. Rhodes, P. L. Felgner, V. J. Dwarki, S. H. Gromkowski, R. R. Deck, C. M. DeWitt, A. Friedman, L. A. Hawe, K. R. Leander, D. Martinez, H. C. Perry, J. W. Shiver, D. L. Montgomery, M. A. Liu. Science 259, 1745 (1993).10.1126/science.8456302Search in Google Scholar

[53] B. Wang, M. G. Agadjanyan, V. Srikantan, K. E. Ugen, W. Hall, M. H. Kaplan, K. Dang, W. V. Williams, D. B. Weiner. AIDS Res. Hum. Retroviruses 9, 849 (1993).10.1089/aid.1993.9.849Search in Google Scholar

[54] M. Iezzi, E. Quaglino, A. Amici, P. L. Lollini, G. Forni, F. Cavallo. Oncoimmunology 1, 316 (2012).10.4161/onci.19127Search in Google Scholar

[55] R. R. MacGregor, J. D. Boyer, K. E. Ugen, K. E. Lacy, S. J. Gluckman, M. L. Bagarazzi, M. A. Chattergoon, Y. Baine, T. J. Higgins, R. B. Ciccarelli, L. R. Coney, R. S. Ginsberg, D. B. Weiner. J. Infect. Dis. 178, 92 (1998).Search in Google Scholar

[56] P. J. Bergman, J. McKnight, A. Novosad, S. Charney, J. Farrelly, D. Craft, M. Wulderk, Y. Jeffers, M. Sadelain, A. E. Hohenhaus, N. Segal, P. Gregor, M. Engelhorn, I. Riviere, A. N. Houghton, J. D. Wolchok. Clin. Cancer Res. 9, 1284 (2003).Search in Google Scholar

[57] B. S. Davis, G. J. Chang, B. Cropp, J. T. Roehrig, D. A. Martin, C. J. Mitchell, R. Bowen, M. L. Bunning. J. Virol. 75, 4040 (2001).Search in Google Scholar

[58] A. M. Kilpatrick, A. P. Dupuis, G. J. Chang, L. D. Kramer. Vector Borne Zoonotic Dis. 10, 377 (2010).Search in Google Scholar

[59] J. C. Liao, P. Gregor, J. D. Wolchok, F. Orlandi, D. Craft, C. Leung, A. N. Houghton, P. J. Bergman. Cancer Immun. 6, 8 (2006).Search in Google Scholar

[60] A. D. Bins, J. H. van den Berg, K. Oosterhuis, J. B. Haanen. Neth. J. Med. 71, 109 (2013).Search in Google Scholar

[61] A. Ghanem, R. Healey, F. G. Adly. Anal. Chim. Acta 760, 1 (2013).10.1016/j.aca.2012.11.006Search in Google Scholar

[62] S. D. Xiang, C. Selomulya, J. Ho, V. Apostolopoulos, M. Plebanski. Wiley Interdisc. Rev. Nanomed. Nanobiotechnol. 2, 205 (2010).Search in Google Scholar

[63] R. Hohlfeld, A. G. Engel. Immunol. Today 15, 269 (1994).10.1016/0167-5699(94)90006-XSearch in Google Scholar

[64] E. Raz, D. A. Carson, S. E. Parker, T. B. Parr, A. M. Abai, G. Aichinger, S. H. Gromkowski, M. Singh, D. Lew, M. A. Yankauckas, S. M. Baird, G. H. Rhodes. Proc. Natl. Acad. Sci. USA 91, 9519 (1994).10.1073/pnas.91.20.9519Search in Google Scholar

[65] B. Spies, H. Hochrein, M. Vabulas, K. Huster, D. H. Busch, F. Schmitz, A. Heit, H. Wagner. J. Immunol. 171, 5908 (2003).Search in Google Scholar

[66] D. Tudor, C. Dubuquoy, V. Gaboriau, F. Lefevre, B. Charley, S. Riffault. Vaccine 23, 1258 (2005).10.1016/j.vaccine.2004.09.001Search in Google Scholar

[67] M. A. Kutzler, D. B. Weiner. Nat Rev. Genetics 9, 776 (2008).10.1038/nrg2432Search in Google Scholar

[68] S. P. Buchbinder, D. V. Mehrotra, A. Duerr, D. W. Fitzgerald, R. Mogg, D. Li, P. B. Gilbert, J. R. Lama, M. Marmor, C. Del Rio, M. J. McElrath, D. R. Casimiro, K. M. Gottesdiener, J. A. Chodakewitz, L. Corey, M. N. Robertson. Lancet 372, 1881 (2008).10.1016/S0140-6736(08)61591-3Search in Google Scholar

[69] N. Frahm, A. C. DeCamp, D. P. Friedrich, D. K. Carter, O. D. Defawe, J. G. Kublin, D. R. Casimiro, A. Duerr, M. N. Robertson, S. P. Buchbinder, Y. Huang, G. A. Spies, S. C. De Rosa, M. J. McElrath. J. Clin. Invest. 122, 359 (2012).Search in Google Scholar

[70] L. Gudmundsdotter, C. Nilsson, A. Brave, B. Hejdeman, P. Earl, B. Moss, M. Robb, J. Cox, N. Michael, M. Marovich, G. Biberfeld, E. Sandstrom, B. Wahren. Vaccine 27, 4468 (2009).10.1016/j.vaccine.2009.05.018Search in Google Scholar PubMed PubMed Central

[71] S. R. Walsh, M. S. Seaman, L. E. Grandpre, C. Charbonneau, K. E. Yanosick, B. Metch, M. C. Keefer, R. Dolin, L. R. Baden. Vaccine 31, 114 (2012).10.1016/j.vaccine.2012.10.093Search in Google Scholar PubMed PubMed Central

[72] J. D. Boyer, T. M. Robinson, M. A. Kutzler, R. Parkinson, S. A. Calarota, M. K. Sidhu, K. Muthumani, M. Lewis, G. Pavlakis, B. Felber, D. Weiner. J. Med. Primatol. 34, 262 (2005).Search in Google Scholar

[73] M. A. Egan, S. Y. Chong, S. Megati, D. C. Montefiori, N. F. Rose, J. D. Boyer, M. K. Sidhu, J. Quiroz, M. Rosati, E. B. Schadeck, G. N. Pavlakis, D. B. Weiner, J. K. Rose, Z. R. Israel, S. A. Udem, J. H. Eldridge. AIDS Res. Hum. Retroviruses 21, 629 (2005).10.1089/aid.2005.21.629Search in Google Scholar PubMed

[74] C. S. Eickhoff, J. R. Vasconcelos, N. L. Sullivan, A. Blazevic, O. Bruna-Romero, M. M. Rodrigues, D. F. Hoft. PLoS Negl. Trop. Dis. 5, e983 (2011).10.1371/journal.pntd.0000983Search in Google Scholar PubMed PubMed Central

[75] P. Fagone, D. J. Shedlock, H. Bao, O. U. Kawalekar, J. Yan, D. Gupta, M. P. Morrow, A. Patel, G. P. Kobinger, K. Muthumani, D. B. Weiner. Gene Ther. 18, 1070 (2011).Search in Google Scholar

[76] B. Ferraro, M. P. Morrow, N. A. Hutnick, T. H. Shin, C. E. Lucke, D. B. Weiner. Clin. Infect. Dis. 53, 296 (2011).Search in Google Scholar

[77] M. Katae, Y. Miyahira, K. Takeda, H. Matsuda, H. Yagita, K. Okumura, T. Takeuchi, T. Kamiyama, A. Ohwada, Y. Fukuchi, T. Aoki. Infect. Immun. 70, 4833 (2002).Search in Google Scholar

[78] L. Lai, D. Vodros, P. A. Kozlowski, D. C. Montefiori, R. L. Wilson, V. L. Akerstrom, L. Chennareddi, T. Yu, S. Kannanganat, L. Ofielu, F. Villinger, L. S. Wyatt, B. Moss, R. R. Amara, H. L. Robinson. Virology 369, 153 (2007).10.1016/j.virol.2007.07.017Search in Google Scholar PubMed PubMed Central

[79] P. T. Loudon, E. J. Yager, D. T. Lynch, A. Narendran, C. Stagnar, A. M. Franchini, J. T. Fuller, P. A. White, J. Nyuandi, C. A. Wiley, M. Murphey-Corb, D. H. Fuller. PloS One 5, e11021 (2010).10.1371/journal.pone.0011021Search in Google Scholar PubMed PubMed Central

[80] M. P. Morrow, J. Yan, P. Pankhong, B. Ferraro, M. G. Lewis, A. S. Khan, N. Y. Sardesai, D. B. Weiner. Clin. Vaccine Immunol. 17, 1493 (2010).Search in Google Scholar

[81] M. P. Morrow, J. Yan, P. Pankhong, D. J. Shedlock, M. G. Lewis, K. Talbott, R. Toporovski, A. S. Khan, N. Y. Sardesai, D. B. Weiner. Mol. Ther. 18, 1714 (2010).Search in Google Scholar

[82] H. L. Robinson, D. C. Montefiori, F. Villinger, J. E. Robinson, S. Sharma, L. S. Wyatt, P. L. Earl, H. M. McClure, B. Moss, R. R. Amara. Virology 352, 285 (2006).10.1016/j.virol.2006.02.011Search in Google Scholar

[83] L. R. Smith, M. K. Wloch, M. Ye, L. R. Reyes, S. Boutsaboualoy, C. E. Dunne, J. A. Chaplin, D. Rusalov, A. P. Rolland, C. L. Fisher, M. S. Al-Ibrahim, M. L. Kabongo, R. Steigbigel, R. B. Belshe, E. R. Kitt, A. H. Chu, R. B. Moss. Vaccine 28, 2565 (2010).10.1016/j.vaccine.2010.01.029Search in Google Scholar

[84] K. Q. Xin, K. Hamajima, S. Sasaki, T. Tsuji, S. Watabe, E. Okada, K. Okuda. Vaccine 17, 858 (1999).10.1016/S0264-410X(98)00271-0Search in Google Scholar

[85] T. Fifis, A. Gamvrellis, B. Crimeen-Irwin, G. A. Pietersz, J. Li, P. L. Mottram, I. F. McKenzie, M. Plebanski. J. Immunol. 173, 3148 (2004).Search in Google Scholar

[86] G. Minigo, A. Scholzen, C. K. Tang, J. C. Hanley, M. Kalkanidis, G. A. Pietersz, V. Apostolopoulos, M. Plebanski. Vaccine 25, 1316 (2007).10.1016/j.vaccine.2006.09.086Search in Google Scholar

[87] C. W. Pouton, L. W. Seymour. Adv. Drug Deliv. Rev. 46, 187 (2001).Search in Google Scholar

[88] G. Borchard. Adv. Drug Deliv. Rev. 52, 145 (2001).10.1016/S0169-409X(01)00198-3Search in Google Scholar

[89] O. Boussif, F. Lezoualc’h, M. A. Zanta, M. D. Mergny, D. Scherman, B. Demeneix, J. P. Behr. Proc. Natl. Acad. Sci. USA 92, 7297 (1995).10.1073/pnas.92.16.7297Search in Google Scholar

[90] U. Lungwitz, M. Breunig, T. Blunk, A. Gopferich. Eur. J. Pharm. Biopharm. 60, 247 (2005).Search in Google Scholar

[91] E. Wagner, M. Ogris, W. Zauner. Adv. Drug Deliv. Rev. 30, 97 (1998).Search in Google Scholar

[92] H. Petersen, K. Kunath, A. L. Martin, S. Stolnik, C. J. Roberts, M. C. Davies, T. Kissel. Biomacromolecules 3, 926 (2002).10.1021/bm025539zSearch in Google Scholar

[93] G. P. Tang, J. M. Zeng, S. J. Gao, Y. X. Ma, L. Shi, Y. Li, H. P. Too, S. Wang. Biomaterials 24, 2351 (2003).10.1016/S0142-9612(03)00029-2Search in Google Scholar

[94] M. Oishi, K. Kataoka, Y. Nagasaki. Bioconjugate Chem. 17, 677 (2006).Search in Google Scholar

[95] T. Schnitzler, A. Herrmann. Acc. Chem. Res. 45, 1419 (2012).Search in Google Scholar

[96] M. Sanjoh, K. Miyata, R. J. Christie, T. Ishii, Y. Maeda, F. Pittella, S. Hiki, N. Nishiyama, K. Kataoka. Biomacromolecules 13, 3641 (2012).10.1021/bm301095aSearch in Google Scholar PubMed

[97] R. R. Sawant, A. M. Jhaveri, V. P. Torchilin. Adv. Drug Deliv. Rev. 64, 1436 (2012).Search in Google Scholar

[98] R. R. Sawant, S. K. Sriraman, G. Navarro, S. Biswas, R. A. Dalvi, V. P. Torchilin. Biomaterials 33, 3942 (2012).10.1016/j.biomaterials.2011.11.088Search in Google Scholar PubMed PubMed Central

[99] M. Lattuada, T. A. Hatton. Langmuir 23, 2158 (2007).10.1021/la062092xSearch in Google Scholar

[100] S. Laurent, D. Forge, M. Port, A. Roch, C. Robic, L. Vander Elst, R. N. Muller. Chem. Rev. 108, 2064 (2008).Search in Google Scholar

[101] J. M. Harris, N. E. Martin, M. Modi. Clin. Pharmacokinetics 40, 539 (2001).10.2165/00003088-200140070-00005Search in Google Scholar

[102] E. A. Schellenberger, A. Bogdanov Jr., D. Hogemann, J. Tait, R. Weissleder, L. Josephson. Mol. Imaging 1, 102 (2002).10.1162/153535002320162769Search in Google Scholar

[103] A. K. Gupta, S. Wells. IEEE Trans. Nanobiosci. 3, 66 (2004).Search in Google Scholar

[104] A. H. Lu, E. L. Salabas, F. Schuth. Angew. Chem. 46, 1222 (2007).Search in Google Scholar

[105] A. Behnecke, W. Li, L. Chen, A. Saxon, K. Zhang. J. Allergy Clin. Immunol. 124, 108 (2009).Search in Google Scholar

[106] A. Egorova, A. Kiselev, M. Hakli, M. Ruponen, V. Baranov, A. Urtti. J. Gene Med. 11, 772 (2009).Search in Google Scholar

[107] M. Shiota, L. Shamsur, S. Kawahara, R. Wadhwa, Y. Ikeda. Chem. Asian J. 4, 1318 (2009).Search in Google Scholar

[108] C. K. Tang, J. Lodding, G. Minigo, D. S. Pouniotis, M. Plebanski, A. Scholzen, I. F. McKenzie, G. A. Pietersz, V. Apostolopoulos. Immunology 120, 325 (2007).10.1111/j.1365-2567.2006.02506.xSearch in Google Scholar

[109] C. K. Tang, K. C. Sheng, V. Apostolopoulos, G. A. Pietersz. Exp. Rev. Vaccines 7, 1005 (2008).10.1586/14760584.7.7.1005Search in Google Scholar

[110] R. J. Mumper, Z. Cui. Methods 31, 255 (2003).10.1016/S1046-2023(03)00138-5Search in Google Scholar

[111] J. Lisziewicz, S. A. Calarota, F. Lori. Exp. Opin. Biol. Ther. 7, 1563 (2007).Search in Google Scholar

[112] A. D. Bins, A. Jorritsma, M. C. Wolkers, C. F. Hung, T. C. Wu, T. N. Schumacher, J. B. Haanen. Nat. Med. 11, 899 (2005).Search in Google Scholar

[113] B. E. Verstrepen, A. D. Bins, C. S. Rollier, P. Mooij, G. Koopman, N. C. Sheppard, Q. Sattentau, R. Wagner, H. Wolf, T. N. Schumacher, J. L. Heeney, J. B. Haanen. Vaccine 26, 3346 (2008).10.1016/j.vaccine.2008.03.091Search in Google Scholar PubMed

[114] L. A. Hirao, L. Wu, A. S. Khan, D. A. Hokey, J. Yan, A. Dai, M. R. Betts, R. Draghia-Akli, D. B. Weiner. Vaccine 26, 3112 (2008).10.1016/j.vaccine.2008.02.036Search in Google Scholar PubMed PubMed Central

[115] G. Otten, M. Schaefer, B. Doe, H. Liu, I. Srivastava, J. zur Megede, D. O’Hagan, J. Donnelly, G. Widera, D. Rabussay, M. G. Lewis, S. Barnett, J. B. Ulmer. Vaccine 22, 2489 (2004).10.1016/j.vaccine.2003.11.073Search in Google Scholar PubMed

[116] M. Rosati, A. Valentin, R. Jalah, V. Patel, A. von Gegerfelt, C. Bergamaschi, C. Alicea, D. Weiss, J. Treece, R. Pal, P. D. Markham, E. T. Marques, J. T. August, A. Khan, R. Draghia-Akli, B. K. Felber, G. N. Pavlakis. Vaccine 26, 5223 (2008).10.1016/j.vaccine.2008.03.090Search in Google Scholar PubMed PubMed Central

[117] A. V. Titomirov, S. Sukharev, E. Kistanova. Biochim. Biophys. Acta 1088, 131 (1991).10.1016/0167-4781(91)90162-FSearch in Google Scholar

[118] M. L. Bagarazzi, J. Yan, M. P. Morrow, X. Shen, R. L. Parker, J. C. Lee, M. Giffear, P. Pankhong, A. S. Khan, K. E. Broderick, C. Knott, F. Lin, J. D. Boyer, R. Draghia-Akli, C. J. White, J. J. Kim, D. B. Weiner, N. Y. Sardesai. Sci. Trans. Med. 4, 155ra138 (2012).Search in Google Scholar

[119] L. Chudley, K. McCann, A. Mander, T. Tjelle, J. Campos-Perez, R. Godeseth, A. Creak, J. Dobbyn, B. Johnson, P. Bass, C. Heath, P. Kerr, I. Mathiesen, D. Dearnaley, F. Stevenson, C. Ottensmeier. Cancer Immunol. Immunother. 61, 2161 (2012).Search in Google Scholar

[120] F. Krotz, H. Y. Sohn, T. Gloe, C. Plank, U. Pohl. J. Vasc. Res. 40, 425 (2003).Search in Google Scholar

[121] X. F. Zhou, B. Liu, X. H. Yu, X. Zha, X. Z. Zhang, X. Y. Wang, Y. H. Jin, Y. G. Wu, C. L. Jiang, Y. Chen, Y. Chen, Y. M. Shan, J. Q. Liu, W. Kong, J. C. Shen. Small 3, 1707 (2007).10.1002/smll.200700151Search in Google Scholar PubMed

[122] R. Pal, Q. Yu, S. Wang, V. S. Kalyanaraman, B. C. Nair, L. Hudacik, S. Whitney, T. Keen, C. L. Hung, L. Hocker, J. S. Kennedy, P. Markham, S. Lu. Vaccine 24, 1225 (2006).10.1016/j.vaccine.2005.07.112Search in Google Scholar PubMed

[123] S. Manam, B. J. Ledwith, A. B. Barnum, P. J. Troilo, C. J. Pauley, L. B. Harper, T. G. Griffiths, 2nd, Z. Niu, L. Denisova, T. T. Follmer, S. J. Pacchione, Z. Wang, C. M. Beare, W. J. Bagdon, W. W. Nichols. Intervirology 43, 273 (2000).10.1159/000053994Search in Google Scholar PubMed

[124] R. L. Sheets, J. Stein, T. S. Manetz, C. Duffy, M. Nason, C. Andrews, W. P. Kong, G. J. Nabel, P. L. Gomez. Toxicol. Sci. 91, 610 (2006).Search in Google Scholar

[125] Z. Wang, P. J. Troilo, X. Wang, T. G. Griffiths, S. J. Pacchione, A. B. Barnum, L. B. Harper, C. J. Pauley, Z. Niu, L. Denisova, T. T. Follmer, G. Rizzuto, G. Ciliberto, E. Fattori, N. L. Monica, S. Manam, B. J. Ledwith. Gene Ther. 11, 711 (2004).Search in Google Scholar

[126] A. Bringmann, S. A. Held, A. Heine, P. Brossart. J. Biomed. Biotechnol. 2010, 623687 (2010).Search in Google Scholar

[127] S. Pascolo. Handbook Exp. Pharmacol. 183, 221 (2008).10.1007/978-3-540-72167-3_11Search in Google Scholar PubMed

[128] G. Tavernier, O. Andries, J. Demeester, N. N. Sanders, S. C. De Smedt, J. Rejman. J. Controlled Release 150, 238 (2011).10.1016/j.jconrel.2010.10.020Search in Google Scholar PubMed

[129] R. Weiss, S. Scheiblhofer, E. Roesler, E. Weinberger, J. Thalhamer. Exp. Rev. Vaccines 11, 55 (2012).10.1586/erv.11.168Search in Google Scholar PubMed

[130] J. B. Ulmer, P. W. Mason, A. Geall, C. W. Mandl. Vaccine 30, 4414 (2012).10.1016/j.vaccine.2012.04.060Search in Google Scholar PubMed

[131] A. J. Geall, A. Verma, G. R. Otten, C. A. Shaw, A. Hekele, K. Banerjee, Y. Cu, C. W. Beard, L. A. Brito, T. Krucker, D. T. O’Hagan, M. Singh, P. W. Mason, N. M. Valiante, P. R. Dormitzer, S. W. Barnett, R. Rappuoli, J. B. Ulmer, C. W. Mandl. Proc. Natl. Acad. Sci. USA 109, 14604 (2012).10.1073/pnas.1209367109Search in Google Scholar PubMed PubMed Central

[132] M. Pizza, V. Scarlato, V. Masignani, M. M. Giuliani, B. Arico, M. Comanducci, G. T. Jennings, L. Baldi, E. Bartolini, B. Capecchi, C. L. Galeotti, E. Luzzi, R. Manetti, E. Marchetti, M. Mora, S. Nuti, G. Ratti, L. Santini, S. Savino, M. Scarselli, E. Storni, P. Zuo, M. Broeker, E. Hundt, B. Knapp, E. Blair, T. Mason, H. Tettelin, D. W. Hood, A. C. Jeffries, N. J. Saunders, D. M. Granoff, J. C. Venter, E. R. Moxon, G. Grandi, R. Rappuoli. Science 287, 1816 (2000).10.1126/science.287.5459.1816Search in Google Scholar PubMed

[133] M. M. Giuliani, J. Adu-Bobie, M. Comanducci, B. Arico, S. Savino, L. Santini, B. Brunelli, S. Bambini, A. Biolchi, B. Capecchi, E. Cartocci, L. Ciucchi, F. Di Marcello, F. Ferlicca, B. Galli, E. Luzzi, V. Masignani, D. Serruto, D. Veggi, M. Contorni, M. Morandi, A. Bartalesi, V. Cinotti, D. Mannucci, F. Titta, E. Ovidi, J. A. Welsch, D. Granoff, R. Rappuoli, M. Pizza. Proc. Natl. Acad. Sci. USA 103, 10834 (2006).10.1073/pnas.0603940103Search in Google Scholar PubMed PubMed Central

[134] D. G. Moriel, M. Scarselli, L. Serino, M. Mora, R. Rappuoli, V. Masignani. Hum. Vaccines 4, 184 (2008).10.4161/hv.4.3.6313Search in Google Scholar PubMed

[135] H. Tettelin, V. Masignani, M. J. Cieslewicz, J. A. Eisen, S. Peterson, M. R. Wessels, I. T. Paulsen, K. E. Nelson, I. Margarit, T. D. Read, L. C. Madoff, A. M. Wolf, M. J. Beanan, L. M. Brinkac, S. C. Daugherty, R. T. DeBoy, A. S. Durkin, J. F. Kolonay, R. Madupu, M. R. Lewis, D. Radune, N. B. Fedorova, D. Scanlan, H. Khouri, S. Mulligan, H. A. Carty, R. T. Cline, S. E. Van Aken, J. Gill, M. Scarselli, M. Mora, E. T. Iacobini, C. Brettoni, G. Galli, M. Mariani, F. Vegni, D. Maione, D. Rinaudo, R. Rappuoli, J. L. Telford, D. L. Kasper, G. Grandi, C. M. Fraser. Proc. Natl. Acad. Sci. USA 99, 12391 (2002).10.1073/pnas.182380799Search in Google Scholar PubMed PubMed Central

[136] D. Maione, I. Margarit, C. D. Rinaudo, V. Masignani, M. Mora, M. Scarselli, H. Tettelin, C. Brettoni, E. T. Iacobini, R. Rosini, N. D’Agostino, L. Miorin, S. Buccato, M. Mariani, G. Galli, R. Nogarotto, V. Nardi-Dei, F. Vegni, C. Fraser, G. Mancuso, G. Teti, L. C. Madoff, L. C. Paoletti, R. Rappuoli, D. L. Kasper, J. L. Telford, G. Grandi. Science 309, 148 (2005).10.1126/science.1109869Search in Google Scholar PubMed PubMed Central

[137] M. Mora, C. Donati, D. Medini, A. Covacci, R. Rappuoli. Curr. Opin. Microbiol. 9, 532 (2006).Search in Google Scholar

[138] H. Tettelin, D. Riley, C. Cattuto, D. Medini. Curr. Opin. Microbiol. 11, 472 (2008).Search in Google Scholar

[139] I. Margarit, C. D. Rinaudo, C. L. Galeotti, D. Maione, C. Ghezzo, E. Buttazzoni, R. Rosini, Y. Runci, M. Mora, S. Buccato, M. Pagani, E. Tresoldi, A. Berardi, R. Creti, C. J. Baker, J. L. Telford, G. Grandi. J. Infect. Dis. 199, 108 (2009).Search in Google Scholar

[140] N. J. Carter. BioDrugs: Clin. Immunother., Biopharm. Gene Ther. 27, 263 (2013).Search in Google Scholar

[141] D. G. Moriel, I. Bertoldi, A. Spagnuolo, S. Marchi, R. Rosini, B. Nesta, I. Pastorello, V. A. Corea, G. Torricelli, E. Cartocci, S. Savino, M. Scarselli, U. Dobrindt, J. Hacker, H. Tettelin, L. J. Tallon, S. Sullivan, L. H. Wieler, C. Ewers, D. Pickard, G. Dougan, M. R. Fontana, R. Rappuoli, M. Pizza, L. Serino. Proc. Natl. Acad. Sci. USA 107, 9072 (2010).10.1073/pnas.0915077107Search in Google Scholar PubMed PubMed Central

[142] D. R. Burton, P. Poignard, R. L. Stanfield, I. A. Wilson. Science 337, 183 (2012).10.1126/science.1225416Search in Google Scholar PubMed PubMed Central

[143] P. D. Kwong, J. R. Mascola. Immunity 37, 412 (2012).10.1016/j.immuni.2012.08.012Search in Google Scholar PubMed PubMed Central

[144] J. F. Scheid, H. Mouquet, B. Ueberheide, R. Diskin, F. Klein, T. Y. Oliveira, J. Pietzsch, D. Fenyo, A. Abadir, K. Velinzon, A. Hurley, S. Myung, F. Boulad, P. Poignard, D. R. Burton, F. Pereyra, D. D. Ho, B. D. Walker, M. S. Seaman, P. J. Bjorkman, B. T. Chait, M. C. Nussenzweig. Science 333, 1633 (2011).10.1126/science.1207227Search in Google Scholar PubMed PubMed Central

[145] L. M. Walker, S. K. Phogat, P. Y. Chan-Hui, D. Wagner, P. Phung, J. L. Goss, T. Wrin, M. D. Simek, S. Fling, J. L. Mitcham, J. K. Lehrman, F. H. Priddy, O. A. Olsen, S. M. Frey, P. W. Hammond, G. P. I. Protocol, S. Kaminsky, T. Zamb, M. Moyle, W. C. Koff, P. Poignard, D. R. Burton. Science 326, 285 (2009).10.1126/science.1178746Search in Google Scholar PubMed PubMed Central

[146] T. Zhou, I. Georgiev, X. Wu, Z. Y. Yang, K. Dai, A. Finzi, Y. D. Kwon, J. F. Scheid, W. Shi, L. Xu, Y. Yang, J. Zhu, M. C. Nussenzweig, J. Sodroski, L. Shapiro, G. J. Nabel, J. R. Mascola, P. D. Kwong. Science 329, 811 (2010).10.1126/science.1192819Search in Google Scholar PubMed PubMed Central

[147] A. Nuccitelli, R. Cozzi, L. J. Gourlay, D. Donnarumma, F. Necchi, N. Norais, J. L. Telford, R. Rappuoli, M. Bolognesi, D. Maione, G. Grandi, C. D. Rinaudo. Proc. Natl. Acad. Sci. USA 108, 10278 (2011).10.1073/pnas.1106590108Search in Google Scholar

[148] M. Scarselli, B. Arico, B. Brunelli, S. Savino, F. Di Marcello, E. Palumbo, D. Veggi, L. Ciucchi, E. Cartocci, M. J. Bottomley, E. Malito, P. Lo Surdo, M. Comanducci, M. M. Giuliani, F. Cantini, S. Dragonetti, A. Colaprico, F. Doro, P. Giannetti, M. Pallaoro, B. Brogioni, M. Tontini, M. Hilleringmann, V. Nardi-Dei, L. Banci, M. Pizza, R. Rappuoli. Sci. Trans. Med. 3, 91ra62 (2011).Search in Google Scholar

[149] D. C. Ekiert, R. H. Friesen, G. Bhabha, T. Kwaks, M. Jongeneelen, W. Yu, C. Ophorst, F. Cox, H. J. Korse, B. Brandenburg, R. Vogels, J. P. Brakenhoff, R. Kompier, M. H. Koldijk, L. A. Cornelissen, L. L. Poon, M. Peiris, W. Koudstaal, I. A. Wilson, J. Goudsmit. Science 333, 843 (2011).10.1126/science.1204839Search in Google Scholar

[150] M. Law, T. Maruyama, J. Lewis, E. Giang, A. W. Tarr, Z. Stamataki, P. Gastaminza, F. V. Chisari, I. M. Jones, R. I. Fox, J. K. Ball, J. A. McKeating, N. M. Kneteman, D. R. Burton. Nat. Med. 14, 25 (2008).Search in Google Scholar

[151] S. W. Kaldor, V. J. Kalish, J. F. Davies, 2nd, B. V. Shetty, J. E. Fritz, K. Appelt, J. A. Burgess, K. M. Campanale, N. Y. Chirgadze, D. K. Clawson, B. A. Dressman, S. D. Hatch, D. A. Khalil, M. B. Kosa, P. P. Lubbehusen, M. A. Muesing, A. K. Patick, S. H. Reich, K. S. Su, J. H. Tatlock. J. Med. Chem. 40, 3979 (1997).Search in Google Scholar

[152] C. U. Kim, W. Lew, M. A. Williams, H. Liu, L. Zhang, S. Swaminathan, N. Bischofberger, M. S. Chen, D. B. Mendel, C. Y. Tai, W. G. Laver, R. C. Stevens. J. Am. Chem. Soc. 119, 681 (1997).Search in Google Scholar

[153] M. H. V. Van Regenmortel. Methods 9, 465 (1996).10.1006/meth.1996.0054Search in Google Scholar

[154] H. M. Geysen, S. J. Rodda, T. J. Mason. Mol. Immunol. 23, 709 (1986).Search in Google Scholar

[155] G. F. Denisova, D. A. Denisov, J. L. Bramson. Immunome Res. 6 (Suppl. 2), S6 (2010).10.1186/1745-7580-6-S2-S6Search in Google Scholar

[156] K. D. Kopple, A. Go, D. R. Pilipauskas. J. Am. Chem. Soc. 97, 6830 (1975).Search in Google Scholar

[157] K. Estieu-Gionnet, G. Guichard. Exp. Opin. Drug Discov. 6, 937 (2011).Search in Google Scholar

[158] F. M. Brunel, M. B. Zwick, R. M. Cardoso, J. D. Nelson, I. A. Wilson, D. R. Burton, P. E. Dawson. J. Virol. 80, 1680 (2006).Search in Google Scholar

[159] E. Cabezas, M. Wang, P. W. Parren, R. L. Stanfield, A. C. Satterthwait. Biochemistry 39, 14377 (2000).10.1021/bi0003691Search in Google Scholar

[160] R. M. Cardoso, F. M. Brunel, S. Ferguson, M. Zwick, D. R. Burton, P. E. Dawson, I. A. Wilson. J. Mol. Biol. 365, 1533 (2007).Search in Google Scholar

[161] J. B. Ghiara, D. C. Ferguson, A. C. Satterthwait, H. J. Dyson, I. A. Wilson. J. Mol. Biol. 266, 31 (1997).Search in Google Scholar

[162] G. B. McGaughey, M. Citron, R. C. Danzeisen, R. M. Freidinger, V. M. Garsky, W. M. Hurni, J. G. Joyce, X. Liang, M. Miller, J. Shiver, M. J. Bogusky. Biochemistry 42, 3214 (2003).10.1021/bi026952uSearch in Google Scholar

[163] R. Stanfield, E. Cabezas, A. Satterthwait, E. Stura, A. Profy, I. Wilson. Structure 7, 131 (1999).10.1016/S0969-2126(99)80020-3Search in Google Scholar

[164] Y. Tian, C. V. Ramesh, X. Ma, S. Naqvi, T. Patel, T. Cenizal, M. Tiscione, K. Diaz, T. Crea, E. Arnold, G. F. Arnold, J. W. Taylor. J. Peptide Res. 59, 264 (2002).Search in Google Scholar

[165] A. Wittelsberger, M. Keller, L. Scarpellino, L. Patiny, H. Acha-Orbea, M. Mutter. Angew. Chem. 39, 1111 (2000).Search in Google Scholar

[166] V. Burke, C. Williams, M. Sukumaran, S. S. Kim, H. Li, X. H. Wang, M. K. Gorny, S. Zolla-Pazner, X. P. Kong. Structure 17, 1538 (2009).10.1016/j.str.2009.09.012Search in Google Scholar PubMed PubMed Central

[167] R. Pantophlet, R. O. Aguilar-Sino, T. Wrin, L. A. Cavacini, D. R. Burton. Virology 364, 441 (2007).10.1016/j.virol.2007.03.007Search in Google Scholar PubMed PubMed Central

[168] O. Rosen, M. Sharon, S. R. Quadt-Akabayov, J. Anglister. Proc. Natl. Acad. Sci. USA 103, 13950 (2006).10.1073/pnas.0606312103Search in Google Scholar PubMed PubMed Central

[169] R. L. Stanfield, M. K. Gorny, C. Williams, S. Zolla-Pazner, I. A. Wilson. Structure 12, 193 (2004).10.1016/j.str.2004.01.003Search in Google Scholar PubMed

[170] R. L. Stanfield, M. K. Gorny, S. Zolla-Pazner, I. A. Wilson. J. Virol. 80, 6093 (2006).Search in Google Scholar

[171] M. L. Azoitei, Y. E. Ban, J. P. Julien, S. Bryson, A. Schroeter, O. Kalyuzhniy, J. R. Porter, Y. Adachi, D. Baker, E. F. Pai, W. R. Schief. J. Mol. Biol. 415, 175 (2012).Search in Google Scholar

[172] E. Drakopoulou, S. Zinn-Justin, M. Guenneugues, B. Gilqin, A. Menez, C. Vita. J. Biol. Chem. 271, 11979 (1996).Search in Google Scholar

[173] S. M. Lu, R. S. Hodges. J. Biol. Chem. 277, 23515 (2002).Search in Google Scholar

[174] B. Tripet, D. J. Kao, S. A. Jeffers, K. V. Holmes, R. S. Hodges. J. Struct. Biol. 155, 176 (2006).Search in Google Scholar

[175] M. L. Azoitei, B. E. Correia, Y. E. Ban, C. Carrico, O. Kalyuzhniy, L. Chen, A. Schroeter, P. S. Huang, J. S. McLellan, P. D. Kwong, D. Baker, R. K. Strong, W. R. Schief. Science 334, 373 (2011).10.1126/science.1209368Search in Google Scholar PubMed

[176] B. E. Correia, Y. E. Ban, D. J. Friend, K. Ellingson, H. Xu, E. Boni, T. Bradley-Hewitt, J. F. Bruhn-Johannsen, L. Stamatatos, R. K. Strong, W. R. Schief. J. Mol. Biol. 405, 284 (2011).Search in Google Scholar

[177] S. J. Fleishman, T. A. Whitehead, D. C. Ekiert, C. Dreyfus, J. E. Corn, E. M. Strauch, I. A. Wilson, D. Baker. Science 332, 816 (2011).10.1126/science.1202617Search in Google Scholar PubMed PubMed Central

[178] J. Holm, M. Ferreras, H. Ipsen, P. A. Wurtzen, M. Gajhede, J. N. Larsen, K. Lund, M. D. Spangfort. J. Biol. Chem. 286, 17569 (2011).Search in Google Scholar

[179] J. S. McLellan, M. Pancera, C. Carrico, J. Gorman, J. P. Julien, R. Khayat, R. Louder, R. Pejchal, M. Sastry, K. Dai, S. O’Dell, N. Patel, S. Shahzad-ul-Hussan, Y. Yang, B. Zhang, T. Zhou, J. Zhu, J. C. Boyington, G. Y. Chuang, D. Diwanji, I. Georgiev, Y. D. Kwon, D. Lee, M. K. Louder, S. Moquin, S. D. Schmidt, Z. Y. Yang, M. Bonsignori, J. A. Crump, S. H. Kapiga, N. E. Sam, B. F. Haynes, D. R. Burton, W. C. Koff, L. M. Walker, S. Phogat, R. Wyatt, J. Orwenyo, L. X. Wang, J. Arthos, C. A. Bewley, J. R. Mascola, G. J. Nabel, W. R. Schief, A. B. Ward, I. A. Wilson, P. D. Kwong. Nature 480, 336 (2011).10.1038/nature10696Search in Google Scholar PubMed PubMed Central

[180] G. Ofek, F. J. Guenaga, W. R. Schief, J. Skinner, D. Baker, R. Wyatt, P. D. Kwong. Proc. Natl. Acad. Sci. USA 107, 17880 (2010).10.1073/pnas.1004728107Search in Google Scholar PubMed PubMed Central

[181] S. K. Sia, P. S. Kim. Proc. Natl. Acad. Sci. USA 100, 9756 (2003).10.1073/pnas.1733910100Search in Google Scholar PubMed PubMed Central

[182] R. L. Stanfield, J. P. Julien, R. Pejchal, J. S. Gach, M. B. Zwick, I. A. Wilson. J. Mol. Biol. 414, 460 (2011).Search in Google Scholar

[183] J. A. Robinson. J. Peptide Sci. 19, 127 (2013).10.1002/psc.2482Search in Google Scholar PubMed PubMed Central

[184] I. H. Frazer. Nat. Rev. Immunol. 4, 46 (2004).10.1038/nri1260Search in Google Scholar PubMed

[185] R. Kirnbauer, F. Booy, N. Cheng, D. R. Lowy, J. T. Schiller. Proc. Natl. Acad. Sci. USA 89, 12180 (1992).10.1073/pnas.89.24.12180Search in Google Scholar PubMed PubMed Central

[186] P. Valenzuela, A. Medina, W. J. Rutter, G. Ammerer, B. D. Hall. Nature 298, 347 (1982).10.1038/298347a0Search in Google Scholar PubMed

[187] A. Roldao, M. C. Mellado, L. R. Castilho, M. J. Carrondo, P. M. Alves. Exp. Rev. Vaccines 9, 1149 (2010).10.1586/erv.10.115Search in Google Scholar

[188] A. Zeltins. Mol. Biotechnol. 53, 92 (2013).10.1007/s12033-012-9598-4Search in Google Scholar

[189] L. Buonaguro, M. Tagliamonte, M. L. Tornesello, F. M. Buonaguro. Exp. Rev. Vaccines 10, 1569 (2011).10.1586/erv.11.135Search in Google Scholar

[190] M. L. De Temmerman, J. Rejman, J. Demeester, D. J. Irvine, B. Gander, S. C. De Smedt. Drug Discov. Today 16, 569 (2011).10.1016/j.drudis.2011.04.006Search in Google Scholar

[191] A. Ghasparian, T. Riedel, J. Koomullil, K. Moehle, C. Gorba, D. I. Svergun, A. W. Perriman, S. Mann, M. Tamborrini, G. Pluschke, J. A. Robinson. Chembiochem 12, 100 (2011).10.1002/cbic.201000536Search in Google Scholar

[192] F. Boato, R. M. Thomas, A. Ghasparian, A. Freund-Renard, K. Moehle, J. A. Robinson. Angew. Chem. 46, 9015 (2007).Search in Google Scholar

[193] A. W. Perriman, D. S. Williams, A. J. Jackson, I. Grillo, J. M. Koomullil, A. Ghasparian, J. A. Robinson, S. Mann. Small 6, 1191 (2010).10.1002/smll.200901186Search in Google Scholar

[194] T. Riedel, A. Ghasparian, K. Moehle, P. Rusert, A. Trkola, J. A. Robinson. Chembiochem 12, 2829 (2011).10.1002/cbic.201100586Search in Google Scholar

[195] R. Gluck, K. G. Burri, I. Metcalfe. Curr. Drug Deliv. 2, 395 (2005).Search in Google Scholar

[196] B. R. Holzer, C. Hatz, D. Schmidt-Sissolak, R. Gluck, B. Althaus, M. Egger. Vaccine 14, 982 (1996).10.1016/0264-410X(96)00042-4Search in Google Scholar

[197] R. Gluck, C. Moser, I. C. Metcalfe. Exp. Opin. Biol. Ther. 4, 1139 (2004).Search in Google Scholar

[198] S. Awate, L. A. Babiuk, G. Mutwiri. Frontiers Immunol. 4, 114 (2013).Search in Google Scholar

[199] S. G. Reed, S. Bertholet, R. N. Coler, M. Friede. Trends Immunol. 30, 23 (2009).Search in Google Scholar

[200] S. Sayers, G. Ulysse, Z. Xiang, Y. He. J. Biomed. Biotechnol. 2012, 831486 (2012).Search in Google Scholar

[201] M. Ulanova, A. Tarkowski, M. Hahn-Zoric, L. A. Hanson. Infect. Immun. 69, 1151 (2001).Search in Google Scholar

[202] J. M. Brewer, M. Conacher, C. A. Hunter, M. Mohrs, F. Brombacher, J. Alexander. J. Immunol. 163, 6448 (1999).Search in Google Scholar

[203] R. Edelman. Mol. Biotechnol. 21, 129 (2002).10.1385/MB:21:2:129Search in Google Scholar

[204] G. Ott, G. L. Barchfeld, D. Chernoff, R. Radhakrishnan, P. van Hoogevest, G. Van Nest. Pharm. Biotechnol. 6, 277 (1995).Search in Google Scholar

[205] M. Wadman. Nature 438, 23 (2005).10.1038/438023aSearch in Google Scholar

[206] A. Seubert, E. Monaci, M. Pizza, D. T. O’Hagan, A. Wack. J. Immunol. 180, 5402 (2008).Search in Google Scholar

[207] A. Podda, G. Del Giudice. Exp. Rev. Vaccines 2, 197 (2003).10.1586/14760584.2.2.197Search in Google Scholar

[208] K. Radosevic, A. Rodriguez, R. Mintardjo, D. Tax, K. L. Bengtsson, C. Thompson, M. Zambon, G. J. Weverling, F. Uytdehaag, J. Goudsmit. Vaccine 26, 3640 (2008).10.1016/j.vaccine.2008.04.071Search in Google Scholar

[209] K. Brandenburg, A. Wiese. Curr. Top. Med. Chem. 4, 1127 (2004).Search in Google Scholar

[210] M. Mueller, B. Lindner, S. Kusumoto, K. Fukase, A. B. Schromm, U. Seydel. J. Biol. Chem. 279, 26307 (2004).Search in Google Scholar

[211] H. Y. Wu, M. W. Russell. Vaccine 16, 286 (1998).10.1016/S0264-410X(97)00168-0Search in Google Scholar

[212] E. T. Ryan, S. B. Calderwood. Clin. Infect. Dis. 31, 561 (2000).Search in Google Scholar

[213] D. R. Hill, L. Ford, D. G. Lalloo. Lancet Infect. Dis. 6, 361 (2006).Search in Google Scholar

[214] J. Aucouturier, S. Ascarateil, L. Dupuis. Vaccine 24 (Suppl. 2), S2-44-45 (2006).10.1016/j.vaccine.2005.01.116Search in Google Scholar

[215] A. A. Scalzo, S. L. Elliott, J. Cox, J. Gardner, D. J. Moss, A. Suhrbier. J. Virol. 69, 1306 (1995).Search in Google Scholar

[216] R. T. Kenney, R. Edelman. Exp. Rev. Vaccines 2, 167 (2003).10.1586/14760584.2.2.167Search in Google Scholar

[217] Y. Wu, R. D. Ellis, D. Shaffer, E. Fontes, E. M. Malkin, S. Mahanty, M. P. Fay, D. Narum, K. Rausch, A. P. Miles, J. Aebig, A. Orcutt, O. Muratova, G. Song, L. Lambert, D. Zhu, K. Miura, C. Long, A. Saul, L. H. Miller, A. P. Durbin. PloS One 3, e2636 (2008).10.1371/journal.pone.0002636Search in Google Scholar

[218] C. R. Kensil, J. Y. Wu, S. Soltysik. Pharm. Biotechnol. 6, 525 (1995).Search in Google Scholar

[219] M. J. Pearse, D. Drane. Adv. Drug Deliv. Rev. 57, 465 (2005).Search in Google Scholar

[220] W. Jiang, W. J. Swiggard, C. Heufler, M. Peng, A. Mirza, R. M. Steinman, M. C. Nussenzweig. Nature 375, 151 (1995).10.1038/375151a0Search in Google Scholar

[221] K. Mahnke, M. Guo, S. Lee, H. Sepulveda, S. L. Swain, M. Nussenzweig, R. M. Steinman. J. Cell Biol. 151, 673 (2000).Search in Google Scholar

[222] C. V. Harding, D. S. Collins, J. W. Slot, H. J. Geuze, E. R. Unanue. Cell 64, 393 (1991).10.1016/0092-8674(91)90647-HSearch in Google Scholar

[223] R. S. van Binnendijk, C. A. van Baalen, M. C. Poelen, P. de Vries, J. Boes, V. Cerundolo, A. D. Osterhaus, F. G. UytdeHaag. J. Exp. Med. 176, 119 (1992).Search in Google Scholar

[224] A. M. Didierlaurent, S. Morel, L. Lockman, S. L. Giannini, M. Bisteau, H. Carlsen, A. Kielland, O. Vosters, N. Vanderheyde, F. Schiavetti, D. Larocque, M. Van Mechelen, N. Garcon. J. Immunol. 183, 6186 (2009).Search in Google Scholar

[225] S. Morel, A. Didierlaurent, P. Bourguignon, S. Delhaye, B. Baras, V. Jacob, C. Planty, A. Elouahabi, P. Harvengt, H. Carlsen, A. Kielland, P. Chomez, N. Garcon, M. Van Mechelen. Vaccine 29, 2461 (2011).10.1016/j.vaccine.2011.01.011Search in Google Scholar

[226] A. C. Allison, N. E. Byars. Res. Immunol. 143, 519 (1992).Search in Google Scholar

[227] F. Audibert, M. Parant, C. Damais, P. Lefrancier, M. Derrien, J. Choay, L. Chedid. Biochem. Biophys. Res. Commun. 96, 915 (1980).Search in Google Scholar

[228] N. E. Byars, A. C. Allison, M. W. Harmon, A. P. Kendal. Vaccine 8, 49 (1990).10.1016/0264-410X(90)90177-NSearch in Google Scholar

[229] N. E. Byars, G. Nakano, M. Welch, D. Lehman, A. C. Allison. Vaccine 9, 309 (1991).10.1016/0264-410X(91)90056-CSearch in Google Scholar

[230] M. J. Bevan. Nat. Rev. Immunol.4, 595 (2004).10.1038/nri1413Search in Google Scholar

[231] A. M. Harandi, J. Holmgren. Curr. Opin. Investigat. Drugs 5, 141 (2004).Search in Google Scholar

[232] S. Akira. Curr. Top. Microbiol. Immunol. 311, 1 (2006).Search in Google Scholar

[233] K. B. Gorden, K. S. Gorski, S. J. Gibson, R. M. Kedl, W. C. Kieper, X. Qiu, M. A. Tomai, S. S. Alkan, J. P. Vasilakos. J. Immunol. 174, 1259 (2005).Search in Google Scholar

[234] R. Zurbriggen, I. C. Metcalfe, R. Gluck, J. F. Viret, C. Moser. Exp. Rev. Vaccines 2, 295 (2003).10.1586/14760584.2.2.295Search in Google Scholar

[235] R. J. Salmond, J. A. Luross, N. A. Williams. Exp. Rev. Mol. Med. 4, 1 (2002).Search in Google Scholar

[236] H. G. Rammensee, H. Singh-Jasuja. Exp. Rev. Vaccines 12, 1211 (2013).10.1586/14760584.2013.836911Search in Google Scholar

[237] I. Mellman, G. Coukos, G. Dranoff. Nature 480, 480 (2011).10.1038/nature10673Search in Google Scholar

[238] K. Falk, O. Rotzschke, S. Stevanovic, G. Jung, H. G. Rammensee. Nature 351, 290 (1991).10.1038/351290a0Search in Google Scholar

[239] J. J. Jacobs, C. Snackey, A. A. Geldof, D. Characiejus, R. J. Van Moorselaar, W. Den Otter. Anticancer Res. 34, 2689 (2014).Search in Google Scholar

[240] I. Melero, G. Gaudernack, W. Gerritsen, C. Huber, G. Parmiani, S. Scholl, N. Thatcher, J. Wagstaff, C. Zielinski, I. Faulkner, H. Mellstedt. Nat. Rev. Clin. Oncol. 11, 509 (2014).Search in Google Scholar

[241] F. O. Nestle, A. Farkas, C. Conrad. Curr. Opin. Immunol. 17, 163 (2005).Search in Google Scholar

[242] S. A. Rosenberg. Nature 411, 380 (2001).10.1038/35077246Search in Google Scholar

[243] S. Anguille, E. L. Smits, E. Lion, V. F. van Tendeloo, Z. N. Berneman. Lancet Oncol. 15, e257 (2014).10.1016/S1470-2045(13)70585-0Search in Google Scholar

[244] M. M. O’Meara, M. L. Disis. Omics: J. Integrat. Biol. 15, 579 (2011).Search in Google Scholar

[245] W. P. Dunbar. Deutsche Med. Wochenschrift 9, 24 (1903).Search in Google Scholar

[246] L. Noon. Lancet Oncol. 1, 1572 (1911).10.1016/S0140-6736(00)78276-6Search in Google Scholar

[247] R. A. Cooke, J. H. Barnard, S. Hebald, A. Stull. J. Exp. Med. 62, 733 (1935).Search in Google Scholar

[248] F. J. Baird, A. L. Lopata. Frontiers Microbiol. 5, 365 (2014).Search in Google Scholar

[249] M. Focke-Tejkl, R. Valenta. Curr. Opin. Allergy Clin. Immunol. 12, 555 (2012).Search in Google Scholar

[250] B. Linhart, M. Narayanan, M. Focke-Tejkl, F. Wrba, S. Vrtala, R. Valenta. Clin. Exp. Allergy 44, 278 (2014).10.1111/cea.12216Search in Google Scholar PubMed PubMed Central

[251] B. Linhart, R. Valenta. Curr. Opin. Immunol. 24, 354 (2012).Search in Google Scholar

[252] M. K. Jain, P. M. Ridker. Nat. Rev. Drug Discov. 4, 977 (2005).Search in Google Scholar

[253] G. Kohler, C. Milstein. Nature 256, 495 (1975).10.1038/256495a0Search in Google Scholar PubMed

[254] Z. Rosman, Y. Shoenfeld, G. Zandman-Goddard. BMC Med. 11, 88 (2013).Search in Google Scholar

[255] F. Ducancel, B. H. Muller. mAbs 4, 445 (2012).10.4161/mabs.20776Search in Google Scholar PubMed PubMed Central

[256] S. K. Singh. J. Pharm. Sci. 100, 354 (2011).10.1002/jps.22276Search in Google Scholar PubMed

[257] A. Quick, R. Tandan. Curr. Rheumatol. Rep. 13, 192 (2011).Search in Google Scholar

[258] I. Schwab, F. Nimmerjahn. Nat. Rev. Immunol. 13, 176 (2013).Search in Google Scholar

[259] M. A. Keller, E. R. Stiehm. Clin. Microbiol. Rev. 13, 602 (2000).Search in Google Scholar

[260] C. E. Chan, A. H. Chan, B. J. Hanson, E. E. Ooi. Singapore Med. J. 50, 663; quiz 673 (2009).Search in Google Scholar

[261] I. N. Foltz, M. Karow, S. M. Wasserman. Circulation 127, 2222 (2013).10.1161/CIRCULATIONAHA.113.002033Search in Google Scholar PubMed

[262] T. Igawa, H. Tsunoda, T. Kuramochi, Z. Sampei, S. Ishii, K. Hattori. mAbs 3, 243 (2011).10.4161/mabs.3.3.15234Search in Google Scholar PubMed PubMed Central

[263] R. E. Bird, K. D. Hardman, J. W. Jacobson, S. Johnson, B. M. Kaufman, S. M. Lee, T. Lee, S. H. Pope, G. S. Riordan, M. Whitlow. Science 242, 423 (1988).10.1126/science.3140379Search in Google Scholar PubMed

[264] A. Frenzel, M. Hust, T. Schirrmann. Frontiers Immunol. 4, 217 (2013).Search in Google Scholar

[265] P. S. Chowdhury, I. Pastan. Nat. Biotechnol. 17, 568 (1999).Search in Google Scholar

[266] P. Martineau. Methods Mol. Biol. 178, 287 (2002).Search in Google Scholar

[267] W. P. Yang, K. Green, S. Pinz-Sweeney, A. T. Briones, D. R. Burton, C. F. Barbas 3rd. J. Mol. Biol. 254, 392 (1995).Search in Google Scholar

[268] A. Rajpal, N. Beyaz, L. Haber, G. Cappuccilli, H. Yee, R. R. Bhatt, T. Takeuchi, R. A. Lerner, R. Crea. Proc. Natl. Acad. Sci. USA 102, 8466 (2005).10.1073/pnas.0503543102Search in Google Scholar PubMed PubMed Central

[269] C. J. Chen, S. L. You, L. H. Lin, W. L. Hsu, Y. W. Yang. Jpn. J. Clin. Oncol. 32 (Suppl.), S66 (2002).10.1093/jjco/hye138Search in Google Scholar PubMed

[270] L. Jermutus, A. Honegger, F. Schwesinger, J. Hanes, A. Pluckthun. Proc. Natl. Acad. Sci. USA 98, 75 (2001).10.1073/pnas.98.1.75Search in Google Scholar PubMed PubMed Central

[271] J. D. Marks. Methods Mol. Biol. 248, 327 (2004).Search in Google Scholar

[272] R. Schier, J. Bye, G. Apell, A. McCall, G. P. Adams, M. Malmqvist, L. M. Weiner, J. D. Marks. J. Mol. Biol. 255, 28 (1996).Search in Google Scholar

[273] L. Shi, J. C. Wheeler, R. W. Sweet, J. Lu, J. Luo, M. Tornetta, B. Whitaker, R. Reddy, R. Brittingham, L. Borozdina, Q. Chen, B. Amegadzie, D. M. Knight, J. C. Almagro, P. Tsui. J. Mol. Biol. 397, 385 (2010).Search in Google Scholar

[274] H. Thie, B. Voedisch, S. Dubel, M. Hust, T. Schirrmann. Methods Mol. Biol. 525, xv, 309 (2009).10.1007/978-1-59745-554-1_16Search in Google Scholar PubMed

[275] P. S. Daugherty, G. Chen, M. J. Olsen, B. L. Iverson, G. Georgiou. Protein Eng. 11, 825 (1998).Search in Google Scholar

[276] J. Hanes, L. Jermutus, S. Weber-Bornhauser, H. R. Bosshard, A. Pluckthun. Proc. Natl. Acad. Sci. USA 95, 14130 (1998).10.1073/pnas.95.24.14130Search in Google Scholar

[277] R. W. Siegel. Methods Mol. Biol. 504, 351 (2009).10.1007/978-1-60327-569-9_20Search in Google Scholar

[278] C. Schaffitzel, J. Hanes, L. Jermutus, A. Pluckthun. J. Immunol. Methods 231, 119 (1999).10.1016/S0022-1759(99)00149-0Search in Google Scholar

[279] J. Hanes, C. Schaffitzel, A. Knappik, A. Pluckthun. Nat. Biotechnol. 18, 1287 (2000).Search in Google Scholar

[280] E. T. Boder, K. S. Midelfort, K. D. Wittrup. Proc. Natl. Acad. Sci. USA 97, 10701 (2000).10.1073/pnas.170297297Search in Google Scholar

[281] R. Barderas, J. Desmet, P. Timmerman, R. Meloen, J. I. Casal. Proc. Natl. Acad. Sci. USA 105, 9029 (2008).10.1073/pnas.0801221105Search in Google Scholar

[282] L. A. Clark, P. A. Boriack-Sjodin, J. Eldredge, C. Fitch, B. Friedman, K. J. Hanf, M. Jarpe, S. F. Liparoto, Y. Li, A. Lugovskoy, S. Miller, M. Rushe, W. Sherman, K. Simon, H. Van Vlijmen. Protein Sci. 15, 949 (2006).Search in Google Scholar

[283] S. M. Lippow, K. D. Wittrup, B. Tidor. Nat. Biotechnol. 25, 1171 (2007).Search in Google Scholar

[284] S. Johnson, S. D. Griego, D. S. Pfarr, M. L. Doyle, R. Woods, D. Carlin, G. A. Prince, S. Koenig, J. F. Young, S. B. Dillon. J. Infect. Dis. 180, 35 (1999).Search in Google Scholar

[285] J. A. Maynard, C. B. Maassen, S. H. Leppla, K. Brasky, J. L. Patterson, B. L. Iverson, G. Georgiou. Nat. Biotechnol. 20, 597 (2002).Search in Google Scholar

[286] W. S. Putnam, J. Li, J. Haggstrom, C. Ng, S. Kadkhodayan-Fischer, M. Cheu, Y. Deniz, H. Lowman, P. Fielder, J. Visich, A. Joshi, N. S. Jumbe. AAPS J. 10, 425 (2008).Search in Google Scholar

[287] H. Wu, D. S. Pfarr, S. Johnson, Y. A. Brewah, R. M. Woods, N. K. Patel, W. I. White, J. F. Young, P. A. Kiener. J. Mol. Biol. 368, 652 (2007).Search in Google Scholar

[288] P. Rathanaswami, S. Roalstad, L. Roskos, Q. J. Su, S. Lackie, J. Babcook. Biochem. Biophys. Res. Commun. 334, 1004 (2005).Search in Google Scholar

[289] G. P. Adams, R. Schier, A. M. McCall, H. H. Simmons, E. M. Horak, R. K. Alpaugh, J. D. Marks, L. M. Weiner. Cancer Res. 61, 4750 (2001).Search in Google Scholar

[290] R. J. Kyriakos, L. B. Shih, G. L. Ong, K. Patel, D. M. Goldenberg, M. J. Mattes. Cancer Res. 52, 835 (1992).Search in Google Scholar

[291] Y. Tang, J. Lou, R. K. Alpaugh, M. K. Robinson, J. D. Marks, L. M. Weiner. J. Immunol. 179, 2815 (2007).Search in Google Scholar

[292] P. Chames, S. Coulon, D. Baty. J. Immunol. 161, 5421 (1998).Search in Google Scholar

[293] O. Dubreuil, M. Bossus, M. Graille, M. Bilous, A. Savatier, M. Jolivet, A. Menez, E. Stura, F. Ducancel. J. Biol. Chem. 280, 24880 (2005).Search in Google Scholar

[294] S. Fagete, U. Ravn, F. Gueneau, G. Magistrelli, M. H. Kosco-Vilbois, N. Fischer. mAbs 1, 288 (2009).10.4161/mabs.1.3.8527Search in Google Scholar

[295] C. J. Farady, B. D. Sellers, M. P. Jacobson, C. S. Craik. Bioorg. Med. Chem. Lett. 19, 3744 (2009).Search in Google Scholar

[296] C. Garcia-Rodriguez, R. Levy, J. W. Arndt, C. M. Forsyth, A. Razai, J. Lou, I. Geren, R. C. Stevens, J. D. Marks. Nat. Biotechnol. 25, 107 (2007).Search in Google Scholar

[297] T. Korpimaki, E. C. Brockmann, O. Kuronen, M. Saraste, U. Lamminmaki, M. Tuomola. J. Agric. Food Chem. 52, 40 (2004).Search in Google Scholar

[298] T. Korpimaki, J. Rosenberg, P. Virtanen, T. Karskela, U. Lamminmaki, M. Tuomola, M. Vehniainen, P. Saviranta. J. Agric. Food Chem. 50, 4194 (2002).Search in Google Scholar

[299] T. Korpimaki, J. Rosenberg, P. Virtanen, U. Lamminmaki, M. Tuomola, P. Saviranta. Protein Eng. 16, 37 (2003).Search in Google Scholar

[300] B. J. McCarthy, A. S. Hill. J. Immunol. Methods 251, 137 (2001).10.1016/S0022-1759(00)00319-7Search in Google Scholar

[301] W. A. Werther, T. N. Gonzalez, S. J. O’Connor, S. McCabe, B. Chan, T. Hotaling, M. Champe, J. A. Fox, P. M. Jardieu, P. W. Berman, L. G. Presta. J. Immunol. 157, 4986 (1996).Search in Google Scholar

[302] J. S. Michaelson, S. J. Demarest, B. Miller, A. Amatucci, W. B. Snyder, X. Wu, F. Huang, S. Phan, S. Gao, A. Doern, G. K. Farrington, A. Lugovskoy, I. Joseph, V. Bailly, X. Wang, E. Garber, J. Browning, S. M. Glaser. mAbs 1, 128 (2009).10.4161/mabs.1.2.7631Search in Google Scholar PubMed PubMed Central

[303] C. Wu, H. Ying, S. Bose, R. Miller, L. Medina, L. Santora, T. Ghayur. mAbs 1, 339 (2009).10.4161/mabs.1.4.8755Search in Google Scholar PubMed PubMed Central

[304] J. Bostrom, S. F. Yu, D. Kan, B. A. Appleton, C. V. Lee, K. Billeci, W. Man, F. Peale, S. Ross, C. Wiesmann, G. Fuh. Science 323, 1610 (2009).10.1126/science.1165480Search in Google Scholar PubMed

[305] S. Mankarious, M. Lee, S. Fischer, K. H. Pyun, H. D. Ochs, V. A. Oxelius, R. J. Wedgwood. J. Lab. Clin. Med. 112, 634 (1988).Search in Google Scholar

[306] A. Morell, W. D. Terry, T. A. Waldmann. J. Clin. Invest. 49, 673 (1970).Search in Google Scholar

[307] T. Suzuki, A. Ishii-Watabe, M. Tada, T. Kobayashi, T. Kanayasu-Toyoda, T. Kawanishi, T. Yamaguchi. J. Immunol. 184, 1968 (2010).Search in Google Scholar

[308] M. A. Tabrizi, C. M. Tseng, L. K. Roskos. Drug Discov. Today 11, 81 (2006).10.1016/S1359-6446(05)03638-XSearch in Google Scholar

[309] W. F. Dall’Acqua, P. A. Kiener, H. Wu. J. Biol. Chem. 281, 23514 (2006).Search in Google Scholar

[310] P. R. Hinton, J. M. Xiong, M. G. Johlfs, M. T. Tang, S. Keller, N. Tsurushita. J. Immunol. 176, 346 (2006).Search in Google Scholar

[311] J. Zalevsky, A. K. Chamberlain, H. M. Horton, S. Karki, I. W. Leung, T. J. Sproule, G. A. Lazar, D. C. Roopenian, J. R. Desjarlais. Nat. Biotechnol. 28, 157 (2010).Search in Google Scholar

[312] E. D. Lobo, R. J. Hansen, J. P. Balthasar. J. Pharm. Sci. 93, 2645 (2004).Search in Google Scholar

[313] T. Igawa, S. Ishii, T. Tachibana, A. Maeda, Y. Higuchi, S. Shimaoka, C. Moriyama, T. Watanabe, R. Takubo, Y. Doi, T. Wakabayashi, A. Hayasaka, S. Kadono, T. Miyazaki, K. Haraya, Y. Sekimori, T. Kojima, Y. Nabuchi, Y. Aso, Y. Kawabe, K. Hattori. Nat. Biotechnol. 28, 1203 (2010).Search in Google Scholar

[314] H. Franey, S. R. Brych, C. G. Kolvenbach, R. S. Rajan. Protein Sci. 19, 1601 (2010).Search in Google Scholar

[315] S. Porter. J. Pharm. Sci. 90, 1 (2001).10.1002/1520-6017(200101)90:1<1::AID-JPS1>3.3.CO;2-BSearch in Google Scholar

[316] A. S. Rosenberg. AAPS J. 8, E501 (2006).10.1208/aapsj080359Search in Google Scholar

[317] W. Wang. Int. J. Pharm. 289, 1 (2005).Search in Google Scholar

[318] S. Ewert, A. Honegger, A. Pluckthun. Methods 34, 184 (2004).10.1016/j.ymeth.2004.04.007Search in Google Scholar

[319] J. A. Caravella, D. Wang, S. M. Glaser, A. Lugovskoy. Curr. Computer-aided Drug Des. (2010).Search in Google Scholar

[320] J. Dong, A. Sereno, W. B. Snyder, B. R. Miller, S. Tamraz, A. Doern, M. Favis, X. Wu, H. Tran, E. Langley, I. Joseph, A. Boccia, R. Kelly, K. Wortham, Q. Wang, L. Berquist, F. Huang, S. X. Gao, Y. Zhang, A. Lugovskoy, S. Martin, H. Gouvis, S. Berkowitz, G. Chiang, M. Reff, S. M. Glaser, K. Hariharan, S. J. Demarest. J. Biol. Chem. 286, 4703 (2011).Search in Google Scholar

[321] B. R. Miller, S. J. Demarest, A. Lugovskoy, F. Huang, X. Wu, W. B. Snyder, L. J. Croner, N. Wang, A. Amatucci, J. S. Michaelson, S. M. Glaser. Protein Eng., Des. Selection 23, 549 (2010).10.1093/protein/gzq028Search in Google Scholar

[322] S. Ewert, A. Honegger, A. Pluckthun. Biochemistry 42, 1517 (2003).10.1021/bi026448pSearch in Google Scholar

[323] L. Nieba, A. Honegger, C. Krebber, A. Pluckthun. Protein Eng. 10, 435 (1997).Search in Google Scholar

[324] A. Honegger, A. D. Malebranche, D. Rothlisberger, A. Pluckthun. Protein Eng., Des. Selection 22, 121 (2009).10.1093/protein/gzn077Search in Google Scholar

[325] A. Worn, A. Auf der Maur, D. Escher, A. Honegger, A. Barberis, A. Pluckthun. J. Biol. Chem. 275, 2795 (2000).Search in Google Scholar

[326] E. C. Brockmann, M. Cooper, N. Stromsten, M. Vehniainen, P. Saviranta. J. Immunol. Methods 296, 159 (2005).10.1016/j.jim.2004.11.008Search in Google Scholar

[327] S. Jung, A. Honegger, A. Pluckthun. J. Mol. Biol. 294, 163 (1999).Search in Google Scholar

[328] D. Rothlisberger, A. Honegger, A. Pluckthun. J. Mol. Biol. 347, 773 (2005).Search in Google Scholar

[329] E. Garber, S. J. Demarest. Biochem. Biophys. Res. Commun. 355, 751 (2007).Search in Google Scholar

[330] D. D. Banks, H. S. Gadgil, G. D. Pipes, P. V. Bondarenko, V. Hobbs, J. L. Scavezze, J. Kim, X. R. Jiang, V. Mukku, T. M. Dillon. J. Pharm. Sci. 97, 775 (2008).Search in Google Scholar

[331] J. Cacia, R. Keck, L. G. Presta, J. Frenz. Biochemistry 35, 1897 (1996).10.1021/bi951526cSearch in Google Scholar

[332] J. Vlasak, M. C. Bussat, S. Wang, E. Wagner-Rousset, M. Schaefer, C. Klinguer-Hamour, M. Kirchmeier, N. Corvaia, R. Ionescu, A. Beck. Anal. Biochem. 392, 145 (2009).Search in Google Scholar

[333] Z. Wei, J. Feng, H. Y. Lin, S. Mullapudi, E. Bishop, G. I. Tous, J. Casas-Finet, F. Hakki, R. Strouse, M. A. Schenerman. Anal. Chem. 79, 2797 (2007).Search in Google Scholar

[334] B. Yan, S. Steen, D. Hambly, J. Valliere-Douglass, T. Vanden Bos, S. Smallwood, Z. Yates, T. Arroll, Y. Han, H. Gadgil, R. F. Latypov, A. Wallace, A. Lim, G. R. Kleemann, W. Wang, A. Balland. J. Pharm. Sci. 98, 3509 (2009).Search in Google Scholar

[335] K. Nakano, T. Ishiguro, H. Konishi, M. Tanaka, M. Sugimoto, I. Sugo, T. Igawa, H. Tsunoda, Y. Kinoshita, K. Habu, T. Orita, M. Tsuchiya, K. Hattori, H. Yamada-Okabe. Anti-cancer Drugs 21, 907 (2010).10.1097/CAD.0b013e32833f5d68Search in Google Scholar

[336] S. J. Shire, Z. Shahrokh, J. Liu. J. Pharm. Sci. 93, 1390 (2004).Search in Google Scholar

[337] S. Kanai, J. Liu, T. W. Patapoff, S. J. Shire. J. Pharm. Sci. 97, 4219 (2008).Search in Google Scholar

[338] S. Yadav, S. J. Shire, D. S. Kalonia. J. Pharm. Sci. 99, 4812 (2010).Search in Google Scholar

[339] R. B. Pepinsky, L. Silvian, S. A. Berkowitz, G. Farrington, A. Lugovskoy, L. Walus, J. Eldredge, A. Capili, S. Mi, C. Graff, E. Garber. Protein Sci. 19, 954 (2010).Search in Google Scholar

[340] A. Tedeschi, C. Barate, E. Minola, E. Morra. Blood Rev. 21, 183 (2007).Search in Google Scholar

[341] S. J. Wu, J. Luo, K. T. O’Neil, J. Kang, E. R. Lacy, G. Canziani, A. Baker, M. Huang, Q. M. Tang, T. S. Raju, S. A. Jacobs, A. Teplyakov, G. L. Gilliland, Y. Feng. Protein Eng., Des. Selection 23, 643 (2010).10.1093/protein/gzq037Search in Google Scholar

[342] J. M. Perchiacca, M. Bhattacharya, P. M. Tessier. Proteins 79, 2637 (2011).10.1002/prot.23085Search in Google Scholar

[343] H. Liu, G. Gaza-Bulseco, D. Faldu, C. Chumsae, J. Sun. J. Pharm. Sci. 97, 2426 (2008).Search in Google Scholar

[344] W. Wang, S. Singh, D. L. Zeng, K. King, S. Nema. J. Pharm. Sci. 96, 1 (2007).Search in Google Scholar

[345] C. A. Abel, H. L. Spiegelberg, H. M. Grey. Biochemistry 7, 1271 (1968).10.1021/bi00844a004Search in Google Scholar

[346] J. F. Valliere-Douglass, C. M. Eakin, A. Wallace, R. R. Ketchem, W. Wang, M. J. Treuheit, A. Balland. J. Biol. Chem. 285, 16012 (2010).Search in Google Scholar

[347] D. Dunn-Walters, L. Boursier, J. Spencer. Mol. Immunol. 37, 107 (2000).Search in Google Scholar

[348] M. J. Coloma, R. K. Trinh, A. R. Martinez, S. L. Morrison. J. Immunol. 162, 2162 (1999).Search in Google Scholar

[349] A. Wright, M. H. Tao, E. A. Kabat, S. L. Morrison. EMBO J. 10, 2717 (1991).Search in Google Scholar

[350] K. D. Ratanji, J. P. Derrick, R. J. Dearman, I. Kimber. J. Immunotoxicol. 11, 99 (2013).Search in Google Scholar

[351] J. den Engelsman, P. Garidel, R. Smulders, H. Koll, B. Smith, S. Bassarab, A. Seidl, O. Hainzl, W. Jiskoot. Pharm. Res. 28, 920 (2011).Search in Google Scholar

[352] M. Sauerborn, V. Brinks, W. Jiskoot, H. Schellekens. Trends Pharmacol. Sci. 31, 53–59 (2010).10.1016/j.tips.2009.11.001Search in Google Scholar

[353] L. O. Narhi, J. Schmit, K. Bechtold-Peters, D. Sharma. J. Pharm. Sci. 101, 493 (2012).Search in Google Scholar

[354] A. Schrodel, A. de Marco. BMC Biochem. 6, 10 (2005).Search in Google Scholar

[355] M. E. Cromwell, E. Hilario, F. Jacobson. AAPS J. 8, E572 (2006).10.1208/aapsj080366Search in Google Scholar

[356] D. Gidalevitz, Z. Huang, S. A. Rice. Proc. Natl. Acad. Sci. USA 96, 2608 (1999).10.1073/pnas.96.6.2608Search in Google Scholar

[357] S. B. Hari, H. Lau, V. I. Razinkov, S. Chen, R. F. Latypov. Biochemistry 49, 9328 (2010).10.1021/bi100841uSearch in Google Scholar

[358] T. A. Horbett. Protein Eng. 2, 172 (1988).10.1093/protein/2.3.172Search in Google Scholar

[359] S. Kiese, A. Papppenberger, W. Friess, H. C. Mahler. J. Pharm. Sci. 97, 4347 (2008).Search in Google Scholar

[360] M. J. Treuheit, A. A. Kosky, D. N. Brems. Pharm. Res. 19, 511 (2002).Search in Google Scholar

[361] M. C. Manning, D. K. Chou, B. M. Murphy, R. W. Payne, D. S. Katayama. Pharm. Res. 27, 544 (2010).Search in Google Scholar

[362] A. L. Fink. Folding Des. 3, R9 (1998).10.1016/S1359-0278(98)00002-9Search in Google Scholar

[363] S. Frokjaer, D. E. Otzen. Nat. Rev. Drug Discov. 4, 298 (2005).Search in Google Scholar

[364] J. M. Perchiacca, P. M. Tessier. Ann. Rev. Chem. Biomol. Eng. 3, 263 (2012).Search in Google Scholar

[365] P. T. Jones, P. H. Dear, J. Foote, M. S. Neuberger, G. Winter. Nature 321, 522 (1986).10.1038/321522a0Search in Google Scholar PubMed

[366] S. L. Morrison, M. J. Johnson, L. A. Herzenberg, V. T. Oi. Proc. Natl. Acad. Sci. USA 81, 6851 (1984).10.1073/pnas.81.21.6851Search in Google Scholar

[367] R. W. Schroff, K. A. Foon, S. M. Beatty, R. K. Oldham, A. C. Morgan Jr. Cancer Res. 45, 879 (1985).Search in Google Scholar

[368] W. Y. Hwang, J. C. Almagro, T. N. Buss, P. Tan, J. Foote. Methods 36, 35 (2005).10.1016/j.ymeth.2005.01.004Search in Google Scholar

[369] L. M. Weiner. J. Immunother. 29, 1 (2006).10.1097/01.cji.0000192105.24583.83Search in Google Scholar

[370] C. Sgro. Toxicology 105, 23 (1995).10.1016/0300-483X(95)03123-WSearch in Google Scholar

[371] G. J. Jaffers, T. C. Fuller, A. B. Cosimi, P. S. Russell, H. J. Winn, R. B. Colvin. Transplantation 41, 572 (1986).10.1097/00007890-198605000-00004Search in Google Scholar

[372] D. Abramowicz, A. Crusiaux, M. Goldman. N. Engl. J. Med. 327, 736 (1992).Search in Google Scholar

[373] F. Baert, M. Noman, S. Vermeire, G. Van Assche, D. H. G, A. Carbonez, P. Rutgeerts. N. Engl. J. Med. 348, 601 (2003).Search in Google Scholar

[374] L. Riechmann, M. Clark, H. Waldmann, G. Winter. Nature 332, 323 (1988).10.1038/332323a0Search in Google Scholar

[375] J. Foote, G. Winter. J. Mol. Biol. 224, 487 (1992).Search in Google Scholar

[376] C. A. Kettleborough, J. Saldanha, V. J. Heath, C. J. Morrison, M. M. Bendig. Protein Eng. 4, 773 (1991).Search in Google Scholar

[377] T. Pelat, H. Bedouelle, A. R. Rees, S. J. Crennell, M. P. Lefranc, P. Thullier. J. Mol. Biol. 384, 1400 (2008).Search in Google Scholar

[378] P. Tan, D. A. Mitchell, T. N. Buss, M. A. Holmes, C. Anasetti, J. Foote. J. Immunol. 169, 1119 (2002).Search in Google Scholar

[379] N. R. Gonzales, E. A. Padlan, R. De Pascalis, P. Schuck, J. Schlom, S. V. Kashmiri. Mol. Immunol. 41, 863 (2004).Search in Google Scholar

[380] S. V. Kashmiri, R. De Pascalis, N. R. Gonzales, J. Schlom. Methods 36, 25 (2005).10.1016/j.ymeth.2005.01.003Search in Google Scholar

[381] M. Tamura, D. E. Milenic, M. Iwahashi, E. Padlan, J. Schlom, S. V. Kashmiri. J. Immunol. 164, 1432 (2000).Search in Google Scholar

[382] S. O. Yoon, T. S. Lee, S. J. Kim, M. H. Jang, Y. J. Kang, J. H. Park, K. S. Kim, H. S. Lee, C. J. Ryu, N. R. Gonzales, S. V. Kashmiri, S. M. Lim, C. W. Choi, H. J. Hong. J. Biol. Chem. 281, 6985 (2006).Search in Google Scholar

[383] L. L. Green. J. Immunol. Methods 231, 11 (1999).10.1016/S0022-1759(99)00137-4Search in Google Scholar

[384] H. R. Hoogenboom. Nat. Biotechnol. 23, 1105 (2005).10.1038/nbt1126Search in Google Scholar PubMed

[385] N. Lonberg. Nat. Biotechnol. 23, 1117 (2005).10.1038/nbt1135Search in Google Scholar PubMed

[386] N. Lonberg. Curr. Opin. Immunol. 20, 450 (2008).10.1016/j.coi.2008.06.004Search in Google Scholar PubMed

[387] M. J. Bernett, S. Karki, G. L. Moore, I. W. Leung, H. Chen, E. Pong, D. H. Nguyen, J. Jacinto, J. Zalevsky, U. S. Muchhal, J. R. Desjarlais, G. A. Lazar. J. Mol. Biol. 396, 1474 (2010).Search in Google Scholar

[388] L. L. Green, M. C. Hardy, C. E. Maynard-Currie, H. Tsuda, D. M. Louie, M. J. Mendez, H. Abderrahim, M. Noguchi, D. H. Smith, Y. Zeng, N. E. David, H. Sasai, D. Garza, D. G. Brenner, J. F. Hales, R. P. McGuinness, D. J. Capon, S. Klapholz, A. Jakobovits. Nat. Genet. 7, 13 (1994).Search in Google Scholar

[389] N. Lonberg, L. D. Taylor, F. A. Harding, M. Trounstine, K. M. Higgins, S. R. Schramm, C. C. Kuo, R. Mashayekh, K. Wymore, J. G. McCabe, D. Munoz-O’Regan, S. L. O’Donnell, E. S. G. Lapachet, T. Bengoechea, D. M. Fishwild, C. E. Carmack, R. M. Kay, D. Huszar. Nature 368, 856 (1994).10.1038/368856a0Search in Google Scholar PubMed

[390] F. Matsuda, K. Ishii, P. Bourvagnet, K. Kuma, H. Hayashida, T. Miyata, T. Honjo. J. Exp. Med. 188, 2151 (1998).Search in Google Scholar

[391] D. M. Fishwild, S. L. O’Donnell, T. Bengoechea, D. V. Hudson, F. Harding, S. L. Bernhard, D. Jones, R. M. Kay, K. M. Higgins, S. R. Schramm, N. Lonberg. Nat. Biotechnol. 14, 845 (1996).Search in Google Scholar

[392] M. J. Mendez, L. L. Green, J. R. Corvalan, X. C. Jia, C. E. Maynard-Currie, X. D. Yang, M. L. Gallo, D. M. Louie, D. V. Lee, K. L. Erickson, J. Luna, C. M. Roy, H. Abderrahim, F. Kirschenbaum, M. Noguchi, D. H. Smith, A. Fukushima, J. F. Hales, S. Klapholz, M. H. Finer, C. G. Davis, K. M. Zsebo, A. Jakobovits. Nat. Genet. 15, 146 (1997).Search in Google Scholar

[393] A. Jakobovits, G. J. Vergara, J. L. Kennedy, J. F. Hales, R. P. McGuinness, D. E. Casentini-Borocz, D. G. Brenner, G. R. Otten. Proc. Natl. Acad. Sci. USA 90, 2551 (1993).10.1073/pnas.90.6.2551Search in Google Scholar PubMed PubMed Central

[394] N. K. Bender, C. E. Heilig, B. Droll, J. Wohlgemuth, F. P. Armbruster, B. Heilig. Rheumatol. Int. 27, 269 (2007).Search in Google Scholar

[395] F. A. Harding, M. M. Stickler, J. Razo, R. B. DuBridge. mAbs 2, 256 (2010).10.4161/mabs.2.3.11641Search in Google Scholar PubMed PubMed Central

[396] T. R. Radstake, M. Svenson, A. M. Eijsbouts, F. H. van den Hoogen, C. Enevold, P. L. van Riel, K. Bendtzen. Ann. Rheumatic Dis. 68, 1739 (2009).Search in Google Scholar

[397] R. L. West, Z. Zelinkova, G. J. Wolbink, E. J. Kuipers, P. C. Stokkers, C. J. van der Woude. Aliment. Pharm. Ther. 28, 1122 (2008).Search in Google Scholar

[398] C. J. Bryson, T. D. Jones, M. P. Baker. BioDrugs: Clin. Immunother., Biopharm. Gene Ther. 24, 1 (2010).Search in Google Scholar

[399] A. S. De Groot, J. McMurry, L. Moise. Curr. Opin. Pharmacol. 8, 620 (2008).Search in Google Scholar

[400] E. Koren, A. S. De Groot, V. Jawa, K. D. Beck, T. Boone, D. Rivera, L. Li, D. Mytych, M. Koscec, D. Weeraratne, S. Swanson, W. Martin. Clin. Immunol. 124, 26 (2007).Search in Google Scholar

[401] L. C. Perry, T. D. Jones, M. P. Baker. Drugs R&D 9, 385 (2008).10.2165/0126839-200809060-00004Search in Google Scholar PubMed

[402] I. Van Walle, Y. Gansemans, P. W. Parren, P. Stas, I. Lasters. Exp. Opin. Biol. Ther. 7, 405 (2007).Search in Google Scholar

[403] A. S. De Groot, L. Moise, J. A. McMurry, E. Wambre, L. Van Overtvelt, P. Moingeon, D. W. Scott, W. Martin. Blood 112, 3303 (2008).10.1182/blood-2008-02-138073Search in Google Scholar PubMed PubMed Central

[404] C. A. Weber, P. J. Mehta, M. Ardito, L. Moise, B. Martin, A. S. De Groot. Adv. Drug Deliv. Rev. 61, 965 (2009).Search in Google Scholar

[405] G. Shankar, E. Shores, C. Wagner, A. Mire-Sluis. Trends Biotechnol. 24, 274 (2006).Search in Google Scholar

[406] C. Nathan, A. Ding. Cell 140, 871 (2010).10.1016/j.cell.2010.02.029Search in Google Scholar PubMed


Note

Republication or reproduction of this report or its storage and/or dissemination by electronic means is permitted without the need for formal IUPAC permission on condition that an acknowledgment, with full reference to the source, along with use of the copyright symbol ©, the name IUPAC, and the year of publication, are prominently visible. Publication of a translation into another language is subject to the additional condition of prior approval from the relevant IUPAC National Adhering Organization.


Received: 2013-10-22
Accepted: 2014-8-22
Published Online: 2014-10-2
Published in Print: 2014-10-21

©2014 IUPAC & De Gruyter

Scroll Up Arrow