Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter August 20, 2019

Prediction of toxicity of secondary metabolites

Ricardo Bruno Hernández-Alvarado, Abraham Madariaga-Mazón and Karina Martinez-Mayorga
From the journal Physical Sciences Reviews

Abstract

The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.

Acknowledgements

This work was supported by Instituto de Química-UNAM, and DGAPA-UNAM (PAPIIT IN210518). The authors thank ChemAxon and Lhasa Limited, for kindly providing academic licenses of their software.

References

[1] Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007;13:3494–504.1822078610.2174/138161207782794257Search in Google Scholar

[2] Tropsha A, Wang SX. QSAR modeling of GPCR ligands: methodologies and examples of applications. In: Bourne H, Horuk R, Kuhnke J, Michel H, editors. GPCRs: from deorphanization to lead structure identification. Berlin: Springer, 2007:49–74.Search in Google Scholar

[3] Veith GD. On the nature, evolution and future of quantitative structure-activity relationships (QSAR) in toxicology. SAR QSAR Environ Res. 2004;15:323–30.1566969210.1080/10629360412331297380Search in Google Scholar

[4] Demchuk E, Ruiz P, Chou S, Fowler BA. SAR/QSAR methods in public health practice. Toxicol Appl Pharmacol. 2011;254:192–7.2103476610.1016/j.taap.2010.10.017Search in Google Scholar

[5] Ruiz P, Begluitti G, Tincher T, Wheeler J, Mumtaz M. Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products. Molecules. 2012;17:8982–9001.10.3390/molecules1708898222842643Search in Google Scholar

[6] Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD. 2014. DOI: 10.1787/9789264085442-en.Search in Google Scholar

[7] Gómez-Jiménez G, Gonzalez-Ponce K, Castillo-Pazos DJ, Madariaga-Mazón A, Barroso-Flores J, Cortés-Guzman F, et al. The OECD principles for (Q)SAR models in the context of knowledge discovery in databases (KDD). In: Karabencheva-Christova T, Christov C, editor(s). Advances in protein chemistry and structural biology Vol. 113. Amsterdam: Elsevier, 2018:85–117.30149907Search in Google Scholar

[8] Pu L, Naderi M, Liu T, Wu H, Mukhopadhyay S, Brylinski M. eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol. 2019;20:2.10.1186/s40360-018-0282-630621790Search in Google Scholar

[9] Mishra N, Singla D, Agarwal S, Consortium OSDD, Raghava GPS. ToxiPred: a server for prediction of aqueous toxicity of small chemical molecules in T. Pyriformis. J Transl Toxicol. 2014;1:21–7.Search in Google Scholar

[10] Drwal , Banerjee P, Dunkel M, Wettig MR, Preissner R. ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res. 2014;42:53–8.10.1093/nar/gku401Search in Google Scholar

[11] Wexler P. TOXNET: an evolving web resource for toxicology and environmental health information. Toxicol. 2001;157:3–10.10.1016/S0300-483X(00)00337-1Search in Google Scholar

[12] Richard A, Williams C. DSSTox chemical-index files for exposure-related experiments in arrayexpress and gene expression omnibus: enabling toxico-chemogenomics data linkages. Bioinformatics. 2009;25:692–4.10.1093/bioinformatics/btp04219158160Search in Google Scholar

[13] Richard A, Judson RS, Houck KA, Grulke CM, Volarath P, Thillainadarajah I, et al. ToxCast chemical landscape: paving the road to twenty-first century toxicology. Chem Res Toxicol. 2016;26:1225–51.Search in Google Scholar

[14] Judson R, Richard A, Dix D, Houck K, Elloumi F, Martin M, et al. ACToR–aggregated computational toxicology resource. Toxicol Appl Pharmacol. 2008;233:7–13.10.1016/j.taap.2007.12.03718671997Search in Google Scholar

[15] Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K, Baker NC, et al. The CompTox chemistry dashboard: a community data resource for environmental chemistry. J Chem inform. 2017;9:61.2918506010.1186/s13321-017-0247-6Search in Google Scholar

[16] Fay M, Donohue J, De Rosa C. ATSDR evaluation of health effects of chemicals. VI. Di(2-ethylhexyl)phthalate. Agency for Toxic Substances and Disease Registry. Toxicol Ind HealthToxicol. 1999;15:651–746.10.1177/074823379901500801Search in Google Scholar

[17] Review of EPA´s Integrated Risk Information System (IRIS) Process. EPA 2014. DOI: 10.17226/18764.Search in Google Scholar

[18] Schmidt U, Struck S, Gruening B, Hossbach J, Jaeger IS, Parol R, et al. SuperToxic: a comprehensive database of toxic compounds. Nucleic Acids Res. 2008;37:D295–9.19004875Search in Google Scholar

[19] Pineda S, Chaumeil P, Kunert A, Kaas Q, Thang M, Le L, et al. ArachnoServer 3.0: an online resource for automated discovery, analysis and annotation of spider toxins. Bioinformatics. 2018;34:1074–6. DOI: 10.1093/bioinformatics/btx661.29069336Search in Google Scholar

[20] Bhatia S, Schultz T, Roberts D, Shen J, Kromidas L, Api AM. Comparison of Cramer classification between Toxtree, the OECD QSAR Toolbox and expert judgment. Regul Toxicol Pharmacol. 2015;71:52–62.10.1016/j.yrtph.2014.11.00525460032Search in Google Scholar

[21] Sushko I, Salmina E, Potemkin VA, Poda G, Tetko IV. ToxAlerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. J Chem Inf Model. 2012;52:2310–16.2287679810.1021/ci300245qSearch in Google Scholar

[22] Loizou G, Hogg A. MEGen: a physiologically based pharmacokinetic model generator. Front Pharmacol. 2011;2:56.22084631Search in Google Scholar

[23] Bessems JG, Loizou G, Krishnan K, Clewell HJ, Bernasconi C, Bois F, et al. PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment. Regul Toxicol Pharmacol. 2014;68:119–39.10.1016/j.yrtph.2013.11.00824287156Search in Google Scholar

[24] Gini G, Franchi AM, Manganaro A, Golbamaki A, Benfenati E. ToxRead: a tool to assist in read across and its use to assess mutagenicity of chemicals. SAR QSAR Environ Res. 2014;25:999–1011.10.1080/1062936X.2014.97626725511972Search in Google Scholar

[25] Chaudhry Q, Piclin N, Cotterill J, Pintore M, Price NR, Chrétien JR, et al. Global QSAR models of skin sensitisers for regulatory purposes. Chem Cent J. 2010;4:S5.2067818410.1186/1752-153X-4-S1-S5Search in Google Scholar

[26] Gramatica P, Cassani S, Chirico N. QSARINS-chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem. 2014;35:1036–44.10.1002/jcc.2357624599647Search in Google Scholar

[27] Capuzzi SJ, Kim IS-J, Lam WI, Thornton TE, Muratov EN, Pozefsky D, et al. Chembench: a publicly accessible, integrated cheminformatics portal. J Chem Inf Model. 2017;57:105–8.2804554410.1021/acs.jcim.6b00462Search in Google Scholar

[28] Ruusmann V, Sild S, Maran U. QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models. J Cheminform. 2015;7:32.10.1186/s13321-015-0082-626110025Search in Google Scholar

[29] LoPachin RM, Gavin T. Molecular mechanisms of aldehyde toxicity: a chemical perspective. Chem Res Toxicol. 2014;27:1081–91.10.1021/tx500104624911545Search in Google Scholar

[30] Mehta R, Schrader TJ. Carcinogenic substances in food: mechanisms. In: Caballero B, Finglas P, Toldra F, editor(s). Encyclopedia of food sciences and nutrition. USA: Academic Press, 2005:117.Search in Google Scholar

[31] Matsumoto M, Kano H, Suzuki M, Katagiri T, Umeda Y, Fukushima S. Carcinogenicity and chronic toxicity of hydrazine monohydrate in rats and mice by two-year drinking water treatment. Regul Toxicol Pharmacol. 2016;76:63–73.10.1016/j.yrtph.2016.01.00626774757Search in Google Scholar

[32] Vale A, Lotti M. Organophosphorus and carbamate insecticide poisoning. In: Lotti M, Bleecker M, editor(s). Handbook of clinical neurology Vol. 131. Amsterdam: Elsevier, 2015:149–68.26563788Search in Google Scholar

[33] Müller L, Mauthe RJ, Riley CM, Andino MM, De Antonis D, Beels C, et al. A rationale for determining, testing, and controlling specific impurities in pharmaceuticals that possess potential for genotoxicity. Regul Toxicol Pharmacol. 2006;44:198–211.1641254310.1016/j.yrtph.2005.12.001Search in Google Scholar

[34] Kovacic P, Somanathan R. Nitroaromatic compounds: environmental toxicity, carcinogenicity, mutagenicity, therapy and mechanism. J Appl Toxicol. 2014;34:810–24.10.1002/jat.298024532466Search in Google Scholar

[35] Reddy AV, Jaafar J, Umar K, Majid ZA, Aris AB, Talib J, et al. Identification, control strategies, and analytical approaches for the determination of potential genotoxic impurities in pharmaceuticals: a comprehensive review. J Sep Sci. 2015;38:764–79.10.1002/jssc.20140114325556762Search in Google Scholar

[36] Gehring R, Van Der Merwe D. Toxicokinetic-toxicodynamic modeling. In: Gupta R, editor. Biomarkers in toxicology. USA: Academic Press, 2014.Search in Google Scholar

[37] Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016;6:147–72.10.1002/wcms.124027066112Search in Google Scholar

[38] Ding B, Hua C, Kepert CJ, D’Alessandro DM. Influence of structure–activity relationships on through-space intervalence charge transfer in metal–organic frameworks with cofacial redox-active units. Chem Sci. 2019;10:1392–400.3080935610.1039/C8SC01128ASearch in Google Scholar

[39] Romero-Estudillo I, Viveros-Ceballos JL, Cazares-Carreño O, González-Morales A, Flores de Jesus B, López-Castillo M, et al. Synthesis of new α-aminophosphonates: evaluation as anti-inflammatory agents and QSAR studies. Bioorg Med Chem. 2018;27:2376–86.30635220Search in Google Scholar

[40] Begam BF, Kumar JS. Computer assisted QSAR/QSPR approaches – a review. Indian J Sci Technol. 2016;9:8.Search in Google Scholar

[41] Yousefinejad S, Hemmateenejad B. Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom Intell Lab Syst. 2015;149:177–204.10.1016/j.chemolab.2015.06.016Search in Google Scholar

[42] Medina-Franco JL, Navarrete-Vázquez G, Méndez-Lucio O. Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry. Future Med Chem. 2015;7:1197–211.10.4155/fmc.15.5126132526Search in Google Scholar

[43] Maggiora GM. On outliers and activity cliffs – why QSAR often disappoints. J Chem Inf Model. 2006;46:1535.1685928510.1021/ci060117sSearch in Google Scholar

[44] Dimova D, Bajorath J. Advances in activity cliff research. Mol Inform. 2016;35:181–91.10.1002/minf.20160002327492084Search in Google Scholar

[45] Szczepek M, Brondani V, Büchel J, Serrano L, Segal DJ, Cathomen T. Structure-based redesign of the dimerization interface reduces the toxicity of zinc-finger nucleases. Nat Biotechnol. 2007;25:786–93.1760347610.1038/nbt1317Search in Google Scholar

[46] Allen CHG, Koutsoukas A, Cortés-Ciriano I, Murrell DS, Malliavin TE, Glen RC, et al. Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data. Toxicol Res (Camb). 2016;5:883–94.3009039710.1039/C5TX00406CSearch in Google Scholar

[47] Gleeson MP, Modi S, Bender A, Robinson RL, Kirchmair J, Promkatkaew M, et al. The challenges involved in modeling toxicity data in silico: a review. Curr Pharm Des. 2012;18:1266–91.10.2174/13816121279943635922316153Search in Google Scholar

[48] Kalgutkar A, Didiuk M. Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem Biodivers. 2009;6:2115–37.1993784810.1002/cbdv.200900055Search in Google Scholar

[49] Lasser KE, Allen PD, Woolhandler SJ, Himmelstein DU, Wolfe SM, Bor DH. Timing of new black box warnings and withdrawals for prescription medications. J Am Med Assoc. 2002;287:2215–20.10.1001/jama.287.17.2215Search in Google Scholar

[50] Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using deep learning. Front Environ Sci. 2016;3:80.Search in Google Scholar

[51] Glück J, Buhrke T, Frenzel F, Braeuning A, Lampen A. In silico genotoxicity and carcinogenicity prediction for food-relevant secondary plant metabolites. Food Chem Toxicol. 2018;116:298–306.2966036510.1016/j.fct.2018.04.024Search in Google Scholar

[52] Arvidson KB, Valerio LG, Diaz M, Chanderbhan RF. In silico toxicological screening of natural products. Toxicol Mech Methods. 2008;18:229–32.10.1080/1537651070185699120020917Search in Google Scholar

[53] Ruiz-Rodríguez MA, Vedani A, Flores-Mireles AL, Cháirez-Ramírez MH, Gallegos-Infante JA, González-Laredo RF. In silico prediction of the toxic potential of lupeol. Chem Res Toxicol. 2017;30:1562–71.10.1021/acs.chemrestox.7b0007028654752Search in Google Scholar

[54] Valerio LG, Arvidson KB, Chanderbhan RF, Contrera JF. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. Toxicol Appl Pharmacol. 2007;222:1–16.1748222310.1016/j.taap.2007.03.012Search in Google Scholar

[55] Münzner R, Renner HW. Mutagenicity testing of quinine with submammalian and mammalian systems. Toxicol. 1983;26:173–8.10.1016/0300-483X(83)90068-9Search in Google Scholar

[56] Ramel C, Alekperov UK, Ames BN, Kada T, Wattenberg LW. Inhibitors of mutagenesis and their relevance to carcinogenesis: report by ICPEMC expert group on antimutagens and desmutagens. Mutat Res Genet Toxicol. 1986;39:511–7.Search in Google Scholar

[57] Van Went GF. Mutagenicity testing of 3 hallucinogens: LSD, psilocybin and 9-THC using the micronucleus test. Experientia. 1978;34:342–5.Search in Google Scholar

[58] Noriega-Colima K, Martinez-Mayorga K, Madariaga-Mazon A. DiaNat-DB: una base de datos de agentes antidiabéticos de origen natural: generación y análisis d elas propiedades fisicoquímicas y estructurales. Available at: http://132.248.9.195/ptd2019/marzo/0786633/Index.html. 2018.Search in Google Scholar

[59] Onguéné A, Simoben C, Fotso G, Andrae-Marobela K, Khalid S, Ngadjiu B, et al. In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties. Comput Biol Chem. 2018;72:136–49.10.1016/j.compbiolchem.2017.12.00229277258Search in Google Scholar

[60] Ntie-Kang A, Telukunta KK, Döring K, Simoben CV, Moumbock AFA, Malange YI, et al. NANPDB: a resource for natural products from Northern African sources. J Nat Prod. 2017;80:2067–76.10.1021/acs.jnatprod.7b0028328641017Search in Google Scholar

Published Online: 2019-08-20

© 2019 Walter de Gruyter GmbH, Berlin/Boston