Metabolic phenotype prediction from genotyping data: a bottleneck for the implementation of pharmacogenetics in drug development and clinical practice

Relevance of evaluation of drug metabolizing enzymes capacity (phenotyping)

Besides several adverse drug reactions and tissue-tumor markers, variability in drug metabolism is so far the cornerstone in interindividual variability in drug response and pharmacogenetics. Metabolizing enzymes account for 80% of the genes/enzymes that are mentioned for pharmacogenetic purpose on current drug labels [1]; 32% of European Medicines Agency (EMA)-authorized drugs contain pharmacogenetic information on the drug label [2].

According to drug regulatory recommendations [1], metabolic phenotyping is a crucial step for both phases of drug development: preclinical and clinical. In addition, the metabolic phenotype plays an important role for clinical implementation and pharmacovigilance throughout the study of pharmacokinetic drug-drug interactions and individualization of drug dosages.

In conclusion, metabolic phenotyping is imperative for drug development: drug metabolism and pharmacogenetics are the main constituents of both the preclinical and clinical phases of drug development. Moreover, it is the key aspect for the use of pharmacogenetics in most pharmacovigilance or clinical implementation programs. For example, EMA has recently published an extensive Guideline of Pharmacogenetics and Pharmacokinetics for drug development and another one for pharmacogenetics and pharmacovigilance [1].

Lack of correlation between pheno- and genotypes (“measured” and “predicted” phenotypes)

Although the hydroxylation capacity of CYP450 enzymes is frequently assumed from genotyping (i.e. phenotype predicted from genotype –gPMs gUMs–), the actual enzyme hydroxylation capacity “metabolic phenotype” does not always correlate with the “predicted phenotype”, as demonstrated by the large degree of variability observed within any particular genotype group, especially the so-called ultra-rapid metabolizers (UMs) [3]. Moreover, drug-drug interactions, other genes that influence pharmacokinetics, as well as environmental factors, endogenous metabolism, and morbility state influence the enzyme activity. Consequently, the gene-environment interaction results in the actual metabolic phenotype. Considering the aforementioned lack of accuracy, the phenotype-genotype relationship of the main CYP450 enzymes should be further clarified [4].

Currently, enzyme hydroxylation capacity or metabolic phenotype is mostly deduced from genotyping data until the point that these two measures are considered to be the same. A recent worldwide drug regulatory consensus advised against the use of codeine for children who are CYP2D6 UMs [5]. However, these guidelines do not give an explanation on how to identify a UM, which is a concept related to a very high hydroxylation capacity. It is widely assumed that a UM is a subject with a multiplication of the gene, but there are also individuals without gene multiplication who are UMs, or individuals with a very high metabolic capacity but no gene multiplication. Therefore, the recommendations of the drug regulatory agencies cannot be accurately/precisely defined and implemented.

Although genotyping is just a tool to measure the metabolic phenotype, it has been also considered the metabolic phenotype itself, which has led to plenty of inconsistencies and confusion about how to use this tool.

For example, with the use of CYP2D6 genotyping methods, about 40% of the UMs that were identified by measuring the metabolic phenotype showed gene multiplications [3]. Furthermore, using only biological molecular methods, the extensive metabolic phenotypes might be misinterpreted because a subject that genotypically has metabolic capacity (classified as extensive metabolizer) may become a poor metabolizer due to phenocopy [4]. The cause of this phenocopy might be an interaction with exogenouos substrates (e.g. drug treatment) or endogenous substrates; moreover, a potential relationship with disease vulnerability has also been described (i.e. suicide [6]). We have reported that 80% of psychiatric patients during drug treatment were poor metabolizers, although the frequency of geneotypically determined poor metabolizers was, as expected, in Caucasians to be 5%–10% [7]. Furthermore, we have shown that the capacity of phenocopy is genetically determined, because the phenocopy capacity was negatively correlated with the number of active genes [8]. Also, inflammation processes may lead to phenocopy due to interaction with endogenous products [9].

Evaluation of metabolic phenotypes (“measured phenotypes”)

Metabolic enzyme hydroxylation capacity can be measured, but a consensus proposal for phenotyping methods and interpretation needs to be developed, using a test drug single/multiple cocktail approach for healthy volunteers [10], and for patients undergoing drug treatment the ratio between parent drug and main metabolite [11, 12]. Also, new approaches for both patients and healthy volunteers, such as use of endogenous metabolism or metabolomics [13], could be developed.

Future outlook

In conclusion, the pharmacogenetic guidelines and clinical recommendations should be based on actual enzyme hydroxylation capacity (metabolic phenotype) instead of metabolic capacity predicted from genotyping. Thus, despite knowing the metabolic phenotype, it remains to be known, first, if there exists a polymorphic metabolic phenotype for each enzyme, that is, if poor metabolizer and UM have been identified in human studies independently of the description of their genetic bases. Second, what clinical information is known linked to those metabolic phenotypes in terms of drug dosages, drug-drug interactions, and adverse reactions, that is, what clinical phenotypes have been associated with the metabolic phenotypes in humans. Finally, what methods are used to measure metabolic phenotypes; specifically considering that the most commonly used is molecular biology, a consensus proposal about the use of genotyping data for predicting metabolic phenotypes should be outlined.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.