In silico toxicology methods in drug safety assessment

  • Danijela Djukić-Ćosić University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Katarina Baralić University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Dragica Jorgovanović University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Katarina Živančević University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Dragana Javorac University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Nikola Stojilković University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Biljana Radović University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Djurdjica Marić University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Marijana Ćurčić University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Aleksandra Buha Djordjević University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Zorica Bulat University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Evica Antonijević Miljaković University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
  • Biljana Antonijević University of Belgrade – Faculty of Pharmacy, Department of Toxicology “Akademik Danilo Soldatović”
Keywords: software, databases, drug preclinical trials, drug impurities, safety assessment

Abstract


While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and are currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.

References

Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci. 2019;40(9):624–635.

Drwal MN, 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(W1):53–58.

Kar S, Leszczynski J. Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin Drug Discov. 2020;15(12):1473–1487.

Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci. 2018;165(1):198-212.

Yang H, Sun L, Li W, Liu G, Tang Y. In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem. 2018;20(6):30.

Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT et al. In silico toxicology protocols. Regul Toxicol Pharmacol. 2018;96:1-17.

Shegokar R. Preclinical testing—Understanding the basics first. In Drug Delivery Aspects 2020;19-32. Amsterdam: Elsevier.

Javorac D, Baralić K, Bulat Z, Đukić-Ćosić D, Antonijević B. In silico metodologija u toksikologiji - softveri za predviđanje toksičnosti. Arh Farm. 2019;69(1):28-38. (Serbian)

Kandárová H, Letašiová S. Alternative methods in toxicology: pre-validated and validated methods. Interdiscip Toxicol. 2011;4(3):107-113.

Russell MS, Burch RL. The principles of humane experimental technique. London: Methuen; 1959. 238 p.

European Commission (EC), Directive of 27 July 1976 on the Approximation of the Laws of the Member States Relating to Cosmetic Products (76/768/EEC), 1976. Available from: http://ec.europa.eu/consumers/sectors/cosmetics/documents/ directive/

European Commission (EC), Regulation No 1907/2006. 2006. Available from: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32006R 1907:EN:NOT

Raunio H. In silico toxicology–non-testing methods. Front Pharmacol. 2011;2:33.

Valerio LG. In silico toxicology models and databases as FDA Critical Path Initiative toolkits. Hum Genomics. 2011;5(3):1-8.

Benigni R, Bassan A, Pavan M. In silico models for genotoxicity and drug regulation. Expert Opin Drug Metab Toxicol. 2020;16(8):651–62.

Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, et al. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem. doi:10.3389/fchem.2020.00726

Valerio LG. In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol. 2009;241(3):356–370.

Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012;17(1–2):44–55.

Tsaioun K, Blaauboer BJ, Hartung T. (2016). Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. Altex. 2016;33(4):343–358.

Basketter D, Clewell H, Kimber I, Rossi A. WBI Studies Repository A Roadmap for the Development of Alternative (Non-Animal) Methods for Systemic Toxicity Testing. Altex. 2012;29(1): 3–91.

Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, et al. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. doi: 10.1093/nar/gkab255

Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf and Model. 2012;52(11):3099–3105.

Schyman P, Liu R, Desai V, Wallqvist A. vNN web server for ADMET predictions. Front Pharmacol. doi: 10.3389/fphar.2017.00889

Diaza RG, Manganelli S, Esposito A, Roncaglioni A, Manganaro A, Benfenati E. Comparison of in silico tools for evaluating rat oral acute toxicity. SAR QSAR Environ Res. 2015;26(1):1–27.

Piparo EL, Worth A. Review of QSAR Models and Software Tools for predicting Developmental and Reproductive Toxicity. In JRC Scientific and Technical Reports: Vol. EUR 24522, 2010. https://doi.org/10.2788/9628

Lassalle Y, Jellouli H, Ballerini L, Souissi Y, Nicol É, Bourcier S, et al. Ultraviolet-vis degradation of iprodione and estimation of the acute toxicity of its photodegradation products. J Chromatogr A. 2014:1371:146–153.

Venkatapathy R, Moudgal CJ, Bruce RM. Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction. J Chem Inform Comput Sci. 2004; 44(5):1623–1629.

Walker JM. In Silico Methods for Predicting Drug Toxicity. 2016;1425:163–176. https://doi.org/10.1007/978-1-4939-3609-0

Benfenati E. In Silico Methods for Predicting Drug Toxicity Methods in Molecular Biology, Humana Press; 2016. 1425 p.

Lowe PJ, Hijazi Y, Luttringer O, Yin H, Sarangapani R, Howard D. On the anticipation of the human dose in first-in-man trials from preclinical and prior clinical information in early drug development. Xenobiotica, 2007;37(10–11):1331–1354.

Bitsch A, Jacobi S, Melber C, Wahnschaffe U, Simetska N, Mangelsdorf I. (2006). REPDOSE: A database on repeated dose toxicity studies of commercial chemicals-A multifunctional tool. Regul Toxicol Pharmacol. 2006;46(3):202–210.

Dearden CJ. In silico prediction of drug toxicity. J. Comput. Aided Mol Des. 2003;17(5):119–127.

Patlewicz G, Rodford R, Walker JD. Quantitative structure-activity relationships for predicting mutagenicity and carcinogenicity. Environ Toxicol Chem. 2003;22(8):1885–1893.

Baralić K, Javorac D, Antonijević E, Buha-Djordjević A, Ćurčić M, Djukić-Ćosić D, et al. Relevance and evaluation of the benchmark dose in toxicology. Arh Farm. 2020;70(3):130–141.

Hardy A, Benford D, Halldorsson T, Jeger MJ, Knutsen KH, More S, et al. Update: use of the benchmark dose approach in risk assessment. EFSA Journal. 2017;15(1):1–41.

Edwards SW, Tan YM, Villeneuve DL, Meek ME, McQueen CA. (2016). Adverse Outcome Pathways-Organizing Toxicological Information to Improve Decision Making. J pharmacol exper ther. 2016;356(1):170-181.

Issa NT, Wathieu H, Ojo A, Byers SW, Dakshanamurthy S. Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools. Curr Drug Metab. 2017;18(6):556–565.

Kleinstreuer NC, Sullivan K, Allen D, Edwards S, Mendrick DL, Embry M, et al. Adverse outcome pathways: From research to regulation scientific workshop report. Regul Toxicol Pharmacol. 2016;76:39–50.

Ives C, Campia I, Wang R-L, Wittwehr C, Edwards S. Creating a Structured Adverse Outcome Pathway Knowledgebase via Ontology-Based Annotations. Appl In Vitro Toxicol. 2017;3(4):298–311.

Oki NO, Edwards SW. An integrative data mining approach to identifying adverse outcome pathway signatures. Toxicology.2016; 350:49–61.

Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Wiegers TC, et al. Comparative Toxicogenomics Database (CTD): Update 2021. Nucleic Acids Res. 2021;49:1138–1143.

Meng Q, Richmond-Bryant J, Lu SE, Buckley B, Welsh WJ, Whitsel EA, et al. Cardiovascular outcomes and the physical and chemical properties of metal ions found in particulate matter air pollution: A QICAR study. Environ Health Perspect. 2013;121(5):558–564.

Cheng A. In Silico Prediction of Hepatotoxicity. Curr Comput Aided-Drug Des. 2009; 5(2):122–127.

Luo G, Shen Y, Yang L, Lu A, Xiang Z. A review of drug ‑ induced liver injury databases. Arch Toxicol. 2017;91(9):3039-3049.

Fraser K, Bruckner DM, Dordick JS. Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies. Chem Res Toxicol. 2018;31(6):412–430.

López-Massaguer O, Pastor M, Sanz F, Carbonell P. Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data. Methods Mol Biol. 2018;1800:505–518.

Cases M, Briggs K, Steger-Hartmann T, Pognan F, Marc P, Kleinöder T, et al. The eTOX data-sharing project to advance in Silico drug-induced toxicity prediction. Int J Mol Sci. 2014;15(11):21136–21154.

Pinches MD, Thomas R, Porter R, Camidge L, Briggs K. Curation and analysis of clinical pathology parameters and histopathologic findings from eTOXsys, a large database project (eTOX) for toxicologic studies. Regul Toxicol Pharmacol. 2019;107:104396.

Tang Y. In silico Prediction of Chemical Ames Mutagenicity, Journal of chemical information and modeling. 2012;26;52(11):2840-7.

Greene N, Fisk L, Naven RT, Note RR, Patel ML, Pelletier DJ. Developing structure-activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol. 2010;23(7):1215–1222.

Cerruela G, Nicolás G, Luque I, Miguel R. An ensemble approach for in silico prediction of Ames mutagenicity. J Math Chem. 2018;56(7):2085–2098.

Marzo M, Kulkarni S, Manganaro A, Roncaglioni A, Wu S, Barton-Maclaren TS, et al. Integrating in silico models to enhance predictivity for developmental toxicity. Toxicology. 2016;370:127–137.

Walsh DB, Claxton LD. Computer-assisted structure-activity relationships of nitrogenous cyclic compounds tested in Salmonella assays for mutagenicity. Mutation Research/Environmental Mutagenesis and Related Subjects. 1987;182(2):55–64.

Lewis DFV, Ioannides C, Parke, DV. A prospective toxicity evaluation (COMPACT) on 40 chemicals currently being tested by the national toxicology program. Mutagenesis. 1990;5(5):433–435.

Mekenyan O, Dimitrov S, Schmieder P, Veith G. (2003). In silico modelling of hazard endpoints: Current problems and perspectives. SAR QSAR Environ Res. 2003;14(5–6):361–371.

Bhati S, Kaushik V, Singh J. In Silico Identification of Piperazine Linked Thiohydantoin Derivatives as Novel Androgen Antagonist in Prostate Cancer Treatment. Int J Pept Res Ther. 2019;25(3):845–860.

Sander T, Freyss J, Von Korff M, Rufener C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015;55(2):460–473.

Attwa MW, Kadi AA, Abdelhameed AS, Alhazmi HA. Metabolic stability assessment of new parp inhibitor talazoparib using validated lc–ms/ms methodology: In silico metabolic vulnerability and toxicity studies. Drug Des Devel Ther. 2020;14:783–793.

Lawal M, Olotu FA, Soliman MES. Across the blood-brain barrier: Neurotherapeutic screening and characterization of naringenin as a novel CRMP-2 inhibitor in the treatment of Alzheimer’s disease using bioinformatics and computational tools. Comput Biol Med. 2018;98:168–177.

Kruhlak NL, Contrera JF, Benz RD, Matthews EJ. Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Adv Drug Deliv Rev. 2007;59(1):43–55.

Sutter A, Amberg A, Boyer S, Brigo A, Contrera JF, Custel LL. Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol. 2013;67(1):39–52.

Pikul P, Jamrógiewicz M, Nowakowska J, Hewelt-Belka W, Ciura K. Forced degradation studies of ivabradine and in silico toxicology predictions for its new designated impurities. Front Pharmacol. 2016;7:1–12.

ICH Q3B(R) International Conferences on Harmonization, Draft Revised Guidance on Impurities in New Drug Products. Federal Register. 2000; 65(139):44791- 44797.

ICH Q3A(R) International Conferences on Harmonization, Draft Revised Guidance on Impurities in New Drug Substances. Federal Register. 2000;65(140):45085-45090.

ICH M7 International Conferences on Harmonization, Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk M7. 2014.

Jamrógiewicz M. Consequences of new approach to chemical stability tests to active pharmaceutical ingredients. Front Pharmacol. 2016;7:1–7.

Zhu Q, Li T, Wei X, Li J, Wang W. In silico and in vitro genotoxicity evaluation of descarboxyl levofloxacin, an impurity in levofloxacin’, Drug Chem Toxicol. 2014; 37(3): 311–315.

Nagulakonda NNM, Ananthula RS, Krishnamurthy T, Rao MRP, Rao GN. Quantification and in Silico Toxicity Assessment of Tazarotene and its Impurities for a Quality and Safe Drug Product Development. J Chromatogr Sci. 2019;57(7):625–635.

Fuart Gatnik M, Worth A P. Review of software tools for toxicity prediction, JRC Scientific and Technical Reports, 2010, pp. 1–22. doi: 10.2788/60101.

Rim KT. In silico prediction of toxicity and its applications for chemicals at work. Toxicol Environ Health Sci. 2020;12(3):191–202.

Mital P, Charmy K, Vivek V. An innovative impurity profiling of Avanafil using LC and LC-MS/MS with in-silico toxicity prediction. Arab J Chem. 2020;13(8):6493–6509.

Preethi L, Ganamurali N, Dhanasekaran D, Sabarathinam S. Therapeutic use of Guggulsterone in COVID-19 induced obesity (COVIBESITY) and significant role in immunomodulatory effect. Obes Med. 2021;24:100346.

Yordanova D, Schultz TW, Kuseva C, Tankova K, Ivanova H, Dermen I, et al. Automated and standardized workflows in the OECD QSAR Toolbox’, Comput Toxicol. 2019;10:89–104.

Han Y, Zhang J, Hu CQ, Zhang X, Ma B, Zhang P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front Pharmacol. 2019;10:1–12.

Klopman G. Artificial Intelligence Approach to Structure-Activity Studies. Computer Automated Structure Evaluation of Biological Activity of Organic Molecules. J. Am. Chem. Soc. 1984;106(24):315–7321.

Klopman G. MULTICASE 1. A Hierarchical Computer Automated Structure Evaluation Program. Quantitative Structure‐Activity Relationships. 1992;11(2):176–184.

Kavlock RJ, Ankley G, Blancato J, Breen M, Conolly R, Dix D, et al. Computational toxicology—a state of the science mini review. Toxicol Sci. 2008;103(1):14-27.

Baralić K, Jorgovanović D, Živančević K, Miljaković EA, Antonijević B, Djordjevic AB, et al. Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach. Toxicol Appl Pharmacol. 2020;406:115237.

Chen CY, Kao CL, Liu CM. The cancer prevention, anti-inflammatory and anti-oxidation of bioactive phytochemicals targeting the TLR4 signaling pathway. Int J Mol Sci. 2018;19:2729.

Davis AP, Grondin CJ, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, et al.The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic Acids Rea. 2015;43(D1):D914-D920.

Fioravanzo E, Bassan A, Pavan M, Mostrag-Szlichtyng A, Worth AP. Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities. SAR QSAR Environ Res. 2012;23(3–4):257–277.

Published
2021/08/27
Section
Review articles