Skip to main content
SpringerLink
Log in
Book cover

In Silico Methods for Predicting Drug Toxicity pp 57–83Cite as

Pharmacokinetic Tools and Applications

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2425))

Abstract

Drug toxicity, as well as therapeutic activity, is contingent upon the parent drug, or a derivative thereof, reaching the relevant site of action in the body, at sufficient concentration, over a given period of time. Thus, the potential to truly elicit an effect is governed by both the intrinsic activity/toxicity of the drug (or its transformation products) and its pharmacokinetic profile. As the pharmaceutical industry has become increasingly aware of the role of pharmacokinetics in determining drug activity and toxicity, the range of software, both freely available and commercial, to predict relevant properties has proliferated. Such tools can be considered on three different levels, applicable at different stages within the drug development process and providing increasing detail and relevance of information. Level (i) is the prediction of fundamental physicochemical properties that can be used to screen vast virtual libraries of potential candidates. Level (ii), predicting the individual absorption, distribution, metabolism, and excretion (ADME) characteristics of potential drugs, can also be applied to many compounds simultaneously. Level (iii), predicting the concentration–time profile of a drug in blood or specific tissues/organs for individuals or a population, is the most sophisticated level of prediction, applied to fewer candidates. In this chapter, in silico tools for predicting ADME-relevant properties, across these three levels, and the applications of this information, are described using exemplar, freely available resources. Further resources are signposted but not all are considered in detail as the purpose here is more to provide an introduction to the capabilities and practicalities of the tools, rather than to provide an exhaustive review of all the tools available.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Prentis RA, Lis Y, Walker SR (1988) Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964–1985). Br J Clin Pharmac 25:387–396

    Article  CAS  Google Scholar 

  2. Kerns EH, Di L (2008) Drug-like properties: concepts, structure design and methods: from ADME to toxicity optimization. Elsevier, Burlington, USA

    Google Scholar 

  3. Waring MJ, Arrowsmith J, Leach AR et al (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 14:475–448

    Article  CAS  Google Scholar 

  4. Sanz F, Pognan F, Steger-Hartmann T et al (2017) Legacy data sharing to improve drug safety assessment: the eTOX project. Nat Rev Drug Discov 16:811–812

    Article  CAS  Google Scholar 

  5. Pognan F, Steger-Hartmann T, Diaz C (2021) The eTRANSAFE project on translational safety assessment through integrative knowledge management: achievements and perspectives. Pharmaceuticals 14:237

    Article  Google Scholar 

  6. Cherkasov A, Muratov EM, Fourches D et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  Google Scholar 

  7. Ferreira LLG, Andricopulo AD (2019) ADMET modeling approaches in drug discovery. Drug Discov Today 24:1157–1165

    Article  CAS  Google Scholar 

  8. Hemmerich J, Ecker GF (2020) In silico toxicology: from structure–activity relationships towards deep learning and adverse outcome pathways. WIREs Comput Mol Sci 10:e1475

    Article  CAS  Google Scholar 

  9. Madden JC, Enoch SJ, Paini A et al (2020) A review of in silico tools as alternatives to animal testing: principles, resources and applications. Altern Lab Anim 48:146–172

    Article  Google Scholar 

  10. Wang Y, Xing J, Xu Y (2015) In silico ADME/T modelling for rational drug design. Q Rev Biophys 48:488–515

    Article  Google Scholar 

  11. Madden JC, Pawar G, Cronin MTD et al (2019) In silico resources to assist in the development and evaluation of physiologically-based kinetic models. Comp Tox 11:33–49

    Google Scholar 

  12. Kar S, Leszczynski J (2020) Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin Drug Disc 15(12):1473–1487. https://doi.org/10.1080/17460441.2020.1798926

    Article  CAS  Google Scholar 

  13. Pawar G, Madden JC, Ebbrell D et al (2019) In silico toxicology data resources to support read-across and (Q)SAR. Front Pharmacol 10:561

    Article  CAS  Google Scholar 

  14. Mostrag-Szlichtyng A and Worth A (2010) Review of QSAR models and software tools for predicting biokinetic properties. JRC scientific and technical reports EUR 24377 EN—2010. https://doi.org/10.2788/94537

  15. Card ML, Gomez-Alvarez V, Lee W-H et al (2017) History of EPI Suite™ and future perspectives on chemical property estimation in US toxic substances control act new chemical risk assessments. Environ Sci Process Impacts 19:203–212

    Article  CAS  Google Scholar 

  16. Lipinski CA, Lombardo F, Dominy BW et al (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliver Rev 23:3–25

    Article  CAS  Google Scholar 

  17. Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) A knowledge based approach in designing combinatorial and medicinal chemistry libraries for drug discovery: 1. Qualitative and quantitative characterization of known drug databases. J Comb Chem 1:55–68

    Article  CAS  Google Scholar 

  18. Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aid Mol Des 14:251–264

    Article  CAS  Google Scholar 

  19. Veber DF, Johnson SR, Cheng H-Y et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623

    Article  CAS  Google Scholar 

  20. Varma MVS, Obach RS, Rotter C et al (2010) Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem 53:1098–1108

    Article  CAS  Google Scholar 

  21. Potts RO, Guy RH (1992) Predicting skin permeability. Pharm Res 9:662–669

    Article  Google Scholar 

  22. Patel M, Chilton ML, Sartini A et al (2018) Assessment and reproducibility of quantitative structure–activity relationship models by the nonexpert. J Chem Inf Model 58:673–682

    Article  CAS  Google Scholar 

  23. Przybylak KR, Madden JC, Covey-Crump E et al (2018) Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opin Drug Met 14:169–181

    Article  CAS  Google Scholar 

  24. Dong J, Wang N-N, Yao Z-J et al (2018) ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform 10:29

    Article  Google Scholar 

  25. Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072

    Article  CAS  Google Scholar 

  26. Daina A, Zoete V (2016) A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 11:1117–1121

    Article  CAS  Google Scholar 

  27. Egan WJ, Merz KM Jr, Baldwin JJ (2000) Prediction of drug absorption using multivariate statistics. J Med Chem 43:3867–3877

    Article  CAS  Google Scholar 

  28. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717

    Article  Google Scholar 

  29. Yang H, Lou C, Sun L et al (2019) admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35:1067–1069

    Article  CAS  Google Scholar 

  30. Schyman P, Liu R, Desai V et al (2017) vNN web server for ADMET predictions. Front Pharmacol 8:889

    Article  Google Scholar 

  31. Organisation for Economic Co-operation and Development (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models, Series on Testing and Assessment No. 69. Paris

    Google Scholar 

  32. Yang M, Chen J, Xu L et al (2018) A novel adaptive ensemble classification framework for ADME prediction. RSC Adv 8:11661–11683

    Article  CAS  Google Scholar 

  33. Tian S, Djoumbou-Feunang Y, Greiner R et al (2018) CypReact: a software tool for in silico reactant prediction for human cytochrome P450 enzymes. J Chem Inf Model 58:1282–1291

    Article  CAS  Google Scholar 

  34. Ridder L, Wagener M (2008) SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3:821–832

    Article  CAS  Google Scholar 

  35. Zaretzki J, Matlock M, Swamidass SJ (2013) XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks. J Chem Inf Model 53:3373–3383

    Article  CAS  Google Scholar 

  36. Stepan AF, Walker DP, Bauman J et al (2011) Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol 24:1345–1410

    Article  CAS  Google Scholar 

  37. Bois FY, Brochot C (2016) In: Benfenati E (ed) Modelling pharmacokinetics in in silico methods for predicting drug toxicity. Humana Press, Springer, New York

    Google Scholar 

  38. Kuepfer L, Niederalt C, Wendl T et al (2016) Applied concepts in PBPK modeling: how to build a PBPK/PD model. CPT Pharmacometrics Syst Pharmacol 5:516–531

    Article  CAS  Google Scholar 

  39. Eissing T, Kuepfer L, Becker C et al (2011) A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Pharmacol 2:4

    Google Scholar 

  40. Peters SA (2008) Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin Pharmacokinet 47:261–275

    Article  CAS  Google Scholar 

  41. Pendse N, Efremenko AY, Hack CE et al (2020) Population life-course exposure to health effects model (PLETHEM): an R package for PBPK modeling. Comput Toxicol 13:100115

    Article  Google Scholar 

  42. Mallick P, Song G, Efremenko AY et al (2020) Physiologically based pharmacokinetic modeling in risk assessment: case study with pyrethroids. Toxicol Sci 176:460–469

    Article  CAS  Google Scholar 

  43. Punt A, Pinckaers N, Peijnenburg A et al (2021) Development of a web-based toolbox to support quantitative in-vitro-to-in-vivo extrapolations (QIVIVE) within nonanimal testing strategies. Chem Res Toxicol 34(2):460–472. https://doi.org/10.1021/acs.chemrestox.0c00307

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The funding of the European Partnership for Alternative Approaches to Animal Testing (EPAA) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK

    Judith C. Madden & Courtney V. Thompson

Authors
  1. Judith C. Madden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Courtney V. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Judith C. Madden .

Editor information

Editors and Affiliations

  1. Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy

    Emilio Benfenati

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Madden, J.C., Thompson, C.V. (2022). Pharmacokinetic Tools and Applications. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1960-5_3

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1959-9

  • Online ISBN: 978-1-0716-1960-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation