Skip to main content
SpringerLink
Log in

In Silico Models for Hepatotoxicity

  • Protocol
  • First Online:
In Silico Methods for Predicting Drug Toxicity

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

Abstract

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

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

Access this chapter

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

Institutional subscriptions

References

  1. Zimmerman HJ (1999) Hepatotoxicity: the adverse effects of drugs and other chemicals on the liver. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  2. Stickel F, Kessebohm K, Weimann R, Seitz HK (2011) Review of liver injury associated with dietary supplements. Liver Int 31:595–605

    CAS  PubMed  Google Scholar 

  3. Hodgson E, Meyer SA (2018) 2.22 - Metabolism and hepatotoxicity of pesticides. In: McQueen CA (ed) Comprehensive toxicology, 3rd edn. Elsevier, Oxford, pp 538–574. https://doi.org/10.1016/B978-0-12-801238-3.02109-7

    Chapter  Google Scholar 

  4. Malaguarnera G, Cataudella E, Giordano M, Nunnari G, Chisari G, Malaguarnera M (2012) Toxic hepatitis in occupational exposure to solvents. World J Gastroenterol: WJG 18(22):2756

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Weaver RJ, Valentin J-P (2019) Today’s challenges to de-risk and predict drug safety in human “mind-the-gap”. Toxicol Sci 167(2):307–321. https://doi.org/10.1093/toxsci/kfy270

    Article  CAS  PubMed  Google Scholar 

  6. Przybylak KR, Cronin MTD (2012) In silico models for drug-induced liver injury - current status. Expert Opin Drug Metab Toxicol 8:201–217

    CAS  PubMed  Google Scholar 

  7. Meunier L, Larrey D (2019) Drug-induced liver injury: biomarkers, requirements, candidates, and validation. Front Pharmacol 10:8. https://doi.org/10.3389/fphar.2019.01482

    Article  CAS  Google Scholar 

  8. Holt MP, Ju C (2006) Mechanisms of drug-induced liver injury. AAPS J 8(1):E48–E54. https://doi.org/10.1208/aapsj080106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kaplowitz N (2005) Idiosyncratic drug hepatotoxicity. Nat Rev Drug Discov 4:489–499

    CAS  PubMed  Google Scholar 

  10. Kuna L, Bozic I, Kizivat T, Bojanic K, Mrso M, Kralj E, Smolic R, Wu GY, Smolic M (2018) Models of drug induced liver injury (DILI) - current issues and future perspectives. Curr Drug Metab 19(10):830–838. https://doi.org/10.2174/1389200219666180523095355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sistare FD, Mattes WB, LeCluyse EL (2017) The promise of new technologies to reduce, refine, or replace animal use while reducing risks of drug induced liver injury in pharmaceutical development. ILAR J 57(2):186–211

    Google Scholar 

  12. Atienzar FA, Nicolas J-M (2018) Prediction of human liver toxicity using in vitro assays: limitations and opportunities. In: Chen M, Will Y (eds) Drug-induced liver toxicity. Springer New York, New York, NY, pp 125–150. https://doi.org/10.1007/978-1-4939-7677-5_7

    Chapter  Google Scholar 

  13. Egan WJ, Zlokarnik G, Grootenhuuis PDJ (2004) In silico prediction of drug safety: despite progress there is abundant room for improvement. Drug Discov Today Technol 1:381–387

    CAS  PubMed  Google Scholar 

  14. Benigni R, Bossa C (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat Res 659:248–261

    CAS  PubMed  Google Scholar 

  15. Patlewicz G, Dimitrov S, Low LK, Kern PS, Dimitrova G, Comber M, Aptula AO, Phillips RD, Niemela J, Madsen C, Wedebye EB, Roberts DW, Bailey PT, Mekenyan O (2007) TIMES-SS - a promising tool for the assessment of skin sensitisation hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity. Regul Toxicol Pharmacol 48:225–239

    CAS  PubMed  Google Scholar 

  16. Ford KA (2016) Refinement, reduction, and replacement of animal toxicity tests by computational methods. ILAR J 57(2):226–233. https://doi.org/10.1093/ilar/ilw031

    Article  CAS  PubMed  Google Scholar 

  17. Fraser K, Bruckner DM, Dordick JS (2018) Advancing predictive hepatotoxicity at the intersection of experimental, in silico, and artificial intelligence technologies. Chem Res Toxicol 31(6):412–430. https://doi.org/10.1021/acs.chemrestox.8b00054

    Article  CAS  PubMed  Google Scholar 

  18. Ivanov S, Semin M, Lagunin A, Filimonov D, Poroikov V (2017) In silico identification of proteins associated with drug-induced liver injury based on the prediction of drug-target interactions. Mol Inform 36(7):13. https://doi.org/10.1002/minf.201600142

    Article  CAS  Google Scholar 

  19. Saini N, Bakshi S, Sharma S (2018) In-silico approach for drug induced liver injury prediction: recent advances. Toxicol Lett 295:288–295. https://doi.org/10.1016/j.toxlet.2018.06.1216

    Article  CAS  PubMed  Google Scholar 

  20. Church RJ, Watkins PB (2018) In silico modeling to optimize interpretation of liver safety biomarkers in clinical trials. Exp Biol Med 243(3):300–307. https://doi.org/10.1177/1535370217740853

    Article  CAS  Google Scholar 

  21. Li AP (2002) A review of the common properties of drugs with idiosyncratic hepatotoxicity and the “multiple determinant hypothesis” for the manifestation of idiosyncratic drug toxicity. Chem Biol Interact 142:7–23

    CAS  PubMed  Google Scholar 

  22. Cheng A, Dixon SL (2003) In silico models for the prediction of dose-dependent human hepatotoxicity. J Comput Aided Mol Des 17:811–823

    CAS  PubMed  Google Scholar 

  23. Clark RD, Wolohan PRN, Hodgkin EE, Kelly HK, Sussman NL (2004) Modelling in vitro hepatotoxicity using molecular interaction fields and SIMCA. J Mol Graph Model 22:487–497

    CAS  PubMed  Google Scholar 

  24. Testa B, Turski L (2006) Virtual ADMET assessment in target selection and maturation, vol 6. IOS Press

    Google Scholar 

  25. Marchant CA, Fisk L, Note RR, Patel ML, Suarez D (2009) An expert system approach to the assessment of hepatotoxic potential. Chem Biodivers 6:2107–2114

    CAS  PubMed  Google Scholar 

  26. Cruz-Monteagudo M, Cordeiro MNDS, Borges F (2007) Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity. J Comput Chem 29:533–549

    Google Scholar 

  27. Ekins S, Williams AJ, Xu JJ (2010) A predictive ligand-based bayesian model for human drug-induced liver injury. Drug Metab Dispos 38:2302–2308

    CAS  PubMed  Google Scholar 

  28. Liu Z, Shi Q, Ding D, Kelly R, Fang H, Tong W (2011) Translating clinical findings into knowledge in drug safety evaluation-drug induced liver injury prediction system (DILIps). PLoS Comput Biol 7(12):e1002310

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz’min V, Fourches D, Zhu H, Rusyn I, Tropsha A (2011) Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 24:1251–1262

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Rodgers AD, AZhu H, Fourches D, Rusyn I, Tropsha A (2010) Modeling liver-realted adverse effects of drugs using K-nearest neighbor quantitative structure-activity relationship method. Chem Res Toxicol 23:724–732

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhu XW, Sedykh A, Liu SS (2014) Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information. J Appl Toxicol 34(3):281–288

    PubMed  Google Scholar 

  32. Mulliner D, Schmidt F, Stolte M, Spirkl HP, Czich A, Amberg A (2016) Computational models for human and animal hepatotoxicity with a global application scope. Chem Res Toxicol 29(5):757–767. https://doi.org/10.1021/acs.chemrestox.5b00465

    Article  CAS  PubMed  Google Scholar 

  33. Matthews EJ, Ursem CJ, Kruhlak NL, Benz RD, Sabaté DA, Yang C, Klopman G, Contrera JF (2009) Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: part B. Use of (Q) SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 54(1):23–42

    CAS  PubMed  Google Scholar 

  34. Chan K, Jensen NS, Silber PM, O’Brien PJ (2007) Structure-activity relationships for halobenzene induced cytotoxicity in rat and human hepatocytes. Chem Biol Interact 165:165–174

    CAS  PubMed  Google Scholar 

  35. Chen M, Hong H, Fang H, Kelly R, Zhou G, Borlak J, Tong W (2013) Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. Toxicol Sci 136(1):242–249

    CAS  PubMed  Google Scholar 

  36. Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A (2010) Cheminformatics analysis of assertions mined from the literature that describe drug-induced liver injury in different species. Chem Res Toxicol 23:171–183

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  38. Liew CY, Lim YC, Yap CW (2011) Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des 25(9):855

    CAS  PubMed  Google Scholar 

  39. Liu J, Mansouri K, Judson RS, Martin MT, Hong H, Chen M, Xu X, Thomas RS, Shah I (2015) Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chem Res Toxicol 28(4):738–751. https://doi.org/10.1021/tx500501h

    Article  CAS  PubMed  Google Scholar 

  40. Steinmetz FP, Mellor CL, Meinl T, Cronin MT (2015) Screening chemicals for receptor-mediated toxicological and pharmacological endpoints: using public data to build screening tools within a KNIME workflow. Mol Inform 34(2-3):171–178

    CAS  PubMed  Google Scholar 

  41. Tsakovska I, Al Sharif M, Alov P, Diukendjieva A, Fioravanzo E, Cronin MT, Pajeva I (2014) Molecular modelling study of the PPARγ receptor in relation to the mode of action/adverse outcome pathway framework for liver steatosis. Int J Mol Sci 15(5):7651–7666

    PubMed  PubMed Central  Google Scholar 

  42. Liu R, Yu X, Wallqvist A (2015) Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries. J Chem 7(1):4

    Google Scholar 

  43. Tralau T, Oelgeschlaeger M, Guertler R, Heinemeyer G, Herzler M, Hoefer T, Itter H, Kuhl T, Lange N, Lorenz N (2015) Regulatory toxicology in the twenty-first century: challenges, perspectives and possible solutions. Arch Toxicol 89(6):823–850

    CAS  PubMed  Google Scholar 

  44. Williams AJ, Tkachenko V, Lipinski C, Ekins S (2009) Free online resources enabling crowd-sourced drug discovery. Drug Discov World 10:33

    CAS  Google Scholar 

  45. Golbraikh A, Tropsha A (2000) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 5(4):231–243

    CAS  Google Scholar 

  46. Hasselgren C, Muthas D, Ahlberg E, Andersson S, Carlsson L, Noeske T, Stålring J, Boyer S (2013) Chemoinformatics and beyond: moving from simple models to complex relationships in phar-maceutical computational toxicology. Chemoinform Drug Discov 3:267–290

    Google Scholar 

  47. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth International Group, Belmont, CA

    Google Scholar 

  48. Hawkins D, Kass G (1982) Automatic interaction detection. In: Hawkins DH (ed) Topics in applied multivariate analysis. Cambridge University Press, Cambridge, pp 269–302

    Google Scholar 

  49. Dixon SL, Villar HO (1999) Investigation of classification methods for the prediction of activity in diverse chemical libraries. J Comput Aided Mol Des 13(5):533–545

    CAS  PubMed  Google Scholar 

  50. Xia X, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47(18):4463–4470

    CAS  PubMed  Google Scholar 

  51. Kim E, Nam H (2017) Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinformatics 18:10. https://doi.org/10.1186/s12859-017-1638-4

    Article  CAS  Google Scholar 

  52. Li X, Chen YJ, Song XR, Zhang Y, Li HH, Zhao Y (2018) The development and application of in silico models for drug induced liver injury. RSC Adv 8(15):8101–8111. https://doi.org/10.1039/c7ra12957b

    Article  CAS  Google Scholar 

  53. Zhu XW, Li SJ (2017) In silico prediction of drug-induced liver injury based on adverse drug reaction reports. Toxicol Sci 158(2):391–400. https://doi.org/10.1093/toxsci/kfx099

    Article  CAS  PubMed  Google Scholar 

  54. Orange book: approved drug products with therapeutic equivalence evaluations. http://www.accessdata.fda.gov/scripts/cder/ob/default.cfm

  55. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474

    CAS  PubMed  Google Scholar 

  56. Bajzelj B, Drgan V (2020) Hepatotoxicity modeling using counter-propagation artificial neural networks: handling an imbalanced classification problem. Molecules 25(3):16. https://doi.org/10.3390/molecules25030481

    Article  CAS  Google Scholar 

  57. He SB, Ye TY, Wang RY, Zhang CY, Zhang XL, Sun GB, Sun XB (2019) An in silico model for predicting drug-induced hepatotoxicity. Int J Mol Sci 20(8):17. https://doi.org/10.3390/ijms20081897

    Article  CAS  Google Scholar 

  58. Ancuceanu R, Hovanet MV, Anghel AI, Furtunescu F, Neagu M, Constantin C, Dinu M (2020) Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the DILIrank dataset. Int J Mol Sci 21(6):23. https://doi.org/10.3390/ijms21062114

    Article  CAS  Google Scholar 

  59. Irwin JJ, Shoichet BK (2005) ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182. https://doi.org/10.1021/ci049714+

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mora JR, Marrero-Ponce Y, Garcia-Jacas CR, Suarez Causado A (2020) Ensemble models based on QuBiLS-MAS features and shallow learning for the prediction of drug-induced liver toxicity: improving deep learning and traditional approaches. Chem Res Toxicol 33(7):1855–1873. https://doi.org/10.1021/acs.chemrestox.0c00030

    Article  CAS  PubMed  Google Scholar 

  61. Xu JJ, Henstock PV, Dunn MC, Smith AR, Chabot JR, de Graaf D (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol Sci 105(1):97–105

    CAS  PubMed  Google Scholar 

  62. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G, Bracken W (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56–67

    CAS  PubMed  Google Scholar 

  63. Olson H, Betton G, Stritar J, Robinson D (1998) The predictivity of the toxicity of pharmaceuticals in humans from animal data—an interim assessment. Toxicol Lett 102:535–538

    PubMed  Google Scholar 

  64. Farrell GC (1994) Drug-induced liver disease. Churchill Livingstone, New York

    Google Scholar 

  65. Allen TE, Goodman JM, Gutsell S, Russell PJ (2014) Defining molecular initiating events in the adverse outcome pathway framework for risk assessment. Chem Res Toxicol 27(12):2100–2112. https://doi.org/10.1021/tx500345j

    Article  CAS  PubMed  Google Scholar 

  66. https://aopwiki.org/

  67. Matthews EJ, Kruhlak NL, Benz RD, Sabate DA, Marchant CA, Contrera JF (2009) Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: part C: use of QSAR and an expert system for the estimation of the mechanism of action of drug-induced hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 54:43–65

    CAS  PubMed  Google Scholar 

  68. Ursem CJ, Kruhlak NL, Contrera JF, MacLaughlin PM, Benz RD, Matthews EJ (2009) Identification of structure–activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities. Regul Toxicol Pharmacol 54(1):1–22

    CAS  PubMed  Google Scholar 

  69. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13(34):3494–3504

    CAS  PubMed  Google Scholar 

  70. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6(1):343

    PubMed  PubMed Central  Google Scholar 

  71. Chen M, Vijay V, Shi Q, Liu Z, Fang H, Tong W (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today 16(15-16):697–703

    PubMed  Google Scholar 

  72. O’Brien P, Irwin W, Diaz D, Howard-Cofield E, Krejsa C, Slaughter M, Gao B, Kaludercic N, Angeline A, Bernardi P (2006) High concordance of drug-induced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch Toxicol 80(9):580–604

    PubMed  Google Scholar 

  73. Liu L, Fu L, Zhang JW, Wei H, Ye WL, Deng ZK, Zhang L, Cheng Y, Ouyang DF, Cao Q, Cao DS (2019) Three-level hepatotoxicity prediction system based on adverse hepatic effects. Mol Pharm 16(1):393–408. https://doi.org/10.1021/acs.molpharmaceut.8b01048

    Article  CAS  PubMed  Google Scholar 

  74. http://cosmostox.eu

  75. http://www.cosmostox.eu/what/knime/

  76. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Thiel K, Wiswedel B (2009) KNIME-the Konstanz information miner: version 2.0 and beyond. AcM SIGKDD Explor Newslett 11(1):26–31

    Google Scholar 

  77. Kavlock R, Chandler K, Houck K, Hunter S, Judson R, Kleinstreuer N, Knudsen T, Martin M, Padilla S, Reif D (2012) Update on EPA’s ToxCast program: providing high throughput decision support tools for chemical risk management. Chem Res Toxicol 25(7):1287–1302

    CAS  PubMed  Google Scholar 

  78. Uehara T, Ono A, Maruyama T, Kato I, Yamada H, Ohno Y, Urushidani T (2010) The Japanese toxicogenomics project: application of toxicogenomics. Mol Nutr Food Res 54(2):218–227

    CAS  PubMed  Google Scholar 

  79. Liu XB, Zheng DH, Zhong Y, Xia ZF, Luo H, Weng ZQ (2020) Machine-learning prediction of oral drug-induced liver injury (DILI) via multiple features and endpoints. Biomed Res Int 2020:10. https://doi.org/10.1155/2020/4795140

    Article  CAS  Google Scholar 

  80. Semenova E, Williams DP, Afzal AM, Lazic SE (2020) A Bayesian neural network for toxicity prediction. Comput Toxicol 16:100133. https://doi.org/10.1016/j.comtox.2020.100133

    Article  Google Scholar 

  81. Kotsampasakou E, Ecker GF (2017) Predicting drug-induced cholestasis with the help of hepatic transporters-an in silico modeling approach. J Chem Inf Model 57(3):608–615. https://doi.org/10.1021/acs.jcim.6b00518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Hewitt M, Enoch SJ, Madden JC, Przybylak KR, Cronin MTD (2013) Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action. Crit Rev Toxicol 43:537–558

    CAS  PubMed  Google Scholar 

  83. Toxmatch. https://ec.europa.eu/jrc/en/scientific-tool/toxmatch

  84. http://www.etoxproject.eu/

  85. Pizzo F, Lombardo A, Manganaro A, Benfenati E (2016) A new structure-activity relationship (SAR) model for predicting drug-induced liver injury, based on statistical and expert-based structural alerts. Front Pharmacol 7:15. https://doi.org/10.3389/fphar.2016.00442

    Article  CAS  Google Scholar 

  86. Alves VM, Muratov EN, Capuzzi SJ, Politi R, Low Y, Braga RC, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade CH, Kuz’min VE, Fourches D, Tropsha A (2016) Alarms about structural alerts. Green Chem 18(16):4348–4360. https://doi.org/10.1039/C6GC01492E

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Zhao LL, Russo DP, Wang WY, Aleksunes LM, Zhu H (2020) Mechanism-driven read-across of chemical hepatotoxicants based on chemical structures and biological data. Toxicol Sci 174(2):178–188. https://doi.org/10.1093/toxsci/kfaa005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Human and Natural Sciences Directorate, School of Science, Engineering and Environment, University of Salford, Manchester, UK

    Claire Ellison

  2. School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK

    Mark Hewitt

  3. Unilever Safety and Environmental Assurance Centre, Bedfordshire, UK

    Katarzyna Przybylak

Authors
  1. Claire Ellison
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Hewitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katarzyna Przybylak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark Hewitt .

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

Ellison, C., Hewitt, M., Przybylak, K. (2022). In Silico Models for Hepatotoxicity. 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_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1960-5_14

  • 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