Skip to main content
SpringerLink
Log in

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

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

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

Abstract

Many regulatory contexts require the evaluation of repeated-dose toxicity (RDT) studies conducted in laboratory animals. The main outcome of RDT studies is the identification of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL) that are normally used as point of departure for the establishment of health-based guidance values. Since in vivo RDT studies are expensive and time-consuming, in silico approaches could offer a valuable alternative. However, NOAEL and LOAEL modeling suffer some limitations since they do not refer to a single end point but to several different effects, and the doses used in experimental studies strongly influence the results. Few attempts to model NOAEL and LOAEL have been reported. The available database and models for the prediction of NOAEL and LOAEL are reviewed here.

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. Scientific Committee on Consumer Safety (SCCS) (2012) The SCCS’s notes of guidance for the testing of cosmetic ingredients and their safety evaluation. http://ec.europa.eu/health/scientific_committees/consumer_safety/docs/sccs_s_006.pdf. Accessed 08 June 2015

  2. Worth A, Barroso J, Bremer S et al (2014) Alternative methods for regulatory toxicologya state-of the-art review. JRC science and policy reports. Report EUR 26797 EN

    Google Scholar 

  3. Steinmetz KL, Spack EG (2009) The basics of preclinical drug development for neurodegenerative disease indications. Neurology 9:S2

    PubMed  PubMed Central  Google Scholar 

  4. Dorado MA, Engelhardt JA (2005) The no-observed adverse level in drug safety evaluations: use, issue, and definition(s). Regul Toxicol Pharmacol 42:265–274

    Google Scholar 

  5. European Medicines Agency (2010) Guideline on repeated dose toxicity. Committee for Human Medicinal Products. Reference number CPMP/SWP/1042/99 Rev

    Google Scholar 

  6. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) (2010) Guidance on nonclinical safety studies for the conduct of human clinical trials and marketing authorization for pharmaceuticals M3(R2). http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002941.pdf. Accessed 06 June 2015

  7. European Commission (2006) Regulation (EC) No 1907/2006 of the European Parliament and the council of 18 December 2006 concerning the registration, evaluation, authorisation and restriction of chemicals (REACH), establishing a European Chemicals Agency, amending directive 1999/45/EC and repealing council regulation (EEC) No 793/93 and commission regulation (EC) No 1488/94 as well as council directive 76/769/EEC and commission directives 91/155/EEC, 93/67/EEC, 93/105/EC and2000/21/EC. Off J Eur Union L396:1–849

    Google Scholar 

  8. European Commission (2009) Regulation (EC) no 1107/2009 of the European Parliament and of the council of 21 October 2009 concerning the placing of plant protection products on the market and repealing council directives 79/117/EEC and 91/414/EEC. Off J Eur Union L309:1–47

    Google Scholar 

  9. European Commission (2013) Regulation (EU) No 283/2013 of 1 March 2013 setting out the data requirements for active substances, in accordance with regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market

    Google Scholar 

  10. Union E (2012) Regulation (EU) no 528/2012 of the European Parliament and of the council of 22 May 2012 concerning the making available on the market and use of biocidal products. Off J Eur Union L167:1–116

    Google Scholar 

  11. European Commission. Commission implementing regulation (EU) No 503/2013 of 3 April 2013 on applications for authorisation of genetically modified food and feed in accordance with Regulation (EC) No 1829/2003 of the European Parliament and of the Council OJ L 157, 8.6.2013, pp 1–48

    Google Scholar 

  12. European Commission. Commission regulation (EC) No 429/2008 of 25 April 2008 on detailed rules for the implementation of Regulation (EC) No 1831/2003 of the European Parliament and of the Council as regards the preparation and the presentation of applications and the assessment and the authorisation of feed additives OJ L, 133 (2008), pp 1–65

    Google Scholar 

  13. European Commission. Commission implementing regulation (EU) 2017/2469 of 20 December 2017 laying down administrative and scientific requirements for applications referred to in article 10 of regulation (EU) 2015/2283 of the European Parliament and of the council on novel foods. OJ L, 351 (2017), pp 64–71

    Google Scholar 

  14. European Commission. Commission Regulation (EU) No 234/2011 of 10 March 2011 implementing regulation (EC) no 1331/2008 of the European Parliament and of the council establishing a common authorisation procedure for food additives, food enzymes and food flavourings. OJ L, 64 (2011), pp 15–24

    Google Scholar 

  15. European Commission (2009) Regulation (EC) no 1223/2009 of the European Parliament and of the council of 30 November 2009 on cosmetic products

    Google Scholar 

  16. Sand F, Parham CJ, Portier R et al (2017) Comparison of points of departure for health risk assessment based on high-throughput screening data. Environ Health Perspect 125(4):623–633

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  18. Tsakovska I, Lessigiarska I, Netzeva T, Worth AP (2007) A mini review of mammalian toxicity (Q)SAR models. QSAR Comb Sci 27:41–48

    Google Scholar 

  19. Przybylak KR, Madden JC, Cronin MTD et al (2012) Assessing toxicological data quality: basic principles, existing schemes and current limitations. SAR QSAR Environ Res 23:435–459

    CAS  PubMed  Google Scholar 

  20. Bitsch A, Jacobi S, Melber C et al (2006) RepDose: a database on repeated dose toxicity studies of commercial chemicals—a multifunctional tool. Regul Toxicol Pharmacol 46:202–210

    CAS  PubMed  Google Scholar 

  21. https://www.oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm

  22. Munro IC, Ford RA, Kennepohl E et al (1996) Correlation of structural class with no-observed- effect levels: a proposal for establishing a threshold of concern. Food Chem Toxicol 34:829–867

    CAS  PubMed  Google Scholar 

  23. Cramer GM, Ford RA, Hall RL (1978) Estimation of toxic hazard—a decision tree approach (and errata sheet). Food Cosmet Toxicol 16:255–276

    CAS  PubMed  Google Scholar 

  24. Hayashi M, Sakuratani Y (2011) Development of an evaluation support system for estimating repeated dose toxicity of chemicals based on chemical structure. In: Wilson AGE (ed) New horizons in predictive toxicology. Current status and application. RSC Publishing, Cambridge

    Google Scholar 

  25. Persad AS, Cooper GS (2008) Use of epidemiologic data in integrated risk information system (IRIS) assessments. Toxicol Appl Pharmacol 233:137–145

    CAS  PubMed  Google Scholar 

  26. Anzali S, Berthold MR, Fioravanzo E et al (2012) Development of computational models for the risk assessment of cosmetic ingredients. IFSCC Mag 15:249–255

    CAS  Google Scholar 

  27. Martin MT, Judson RS, Reif DM et al (2009) Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database. Environ Health Perspect 117:392–399

    CAS  PubMed  Google Scholar 

  28. Watford S, Pham LL, Wignall J et al (2019) ToxRefDB version 2.0: improved utility for predictive and retrospective toxicology analyses. Reprod Toxicol 89:145–158

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Dorne JL, Richardson J, Livaniou A et al (2021) EFSA’s OpenFoodTox: an open source toxicological database on chemicals in food and feed and its future developments. Environ Int 146:106293

    CAS  PubMed  Google Scholar 

  30. Yang C, Cheeseman M, Rathman J et al (2020) A new paradigm in threshold of toxicological concern based on chemoinformatics analysis of a highly curated database enriched with antimicrobials. Food Chem Toxicol 143:111561

    CAS  PubMed  Google Scholar 

  31. Feigenbaum A, Pinalli R, Giannetto M et al (2015) Reliability of the TTC approach: learning from inclusion of pesticide active substances in the supporting database. Food Chem Toxicol 75:24–38

    CAS  PubMed  Google Scholar 

  32. Yang C, Rathman JF, Magdziarz T et al (2021) Do similar structures have similar no observed adverse effect level (NOAEL) values? Exploring chemoinformatics approaches for estimating NOAEL bounds and uncertainties. Chem Res Toxicol 34(2):616–633

    CAS  PubMed  Google Scholar 

  33. Gadaleta D, Marzo M, Toropov A et al (2021) Integrated in silico models for the prediction of no-observed-(adverse)-effect levels and lowest-observed-(adverse)-effect levels in rats for sub-chronic repeated-dose toxicity. Chem Res Toxicol 34(2):247–257

    CAS  PubMed  Google Scholar 

  34. Pradeep P, Friedman KP, Judson R (2020) Structure-based QSAR models to predict repeat dose toxicity points of departure. Comput Toxicol 16:100139

    Google Scholar 

  35. Helma C, Vorgrimmler D, Gebele D et al (2018) Modeling chronic toxicity: a comparison of experimental variability with (Q) SAR/read-across predictions. Front Pharmacol 9:413

    PubMed  PubMed Central  Google Scholar 

  36. Mazzatorta P, Estevez MD, Coulet M et al (2008) Modeling oral rat chronic toxicity. J Chem Inf Model 48:1949–1954

    CAS  PubMed  Google Scholar 

  37. Maunz A, Gütlein M, Rautenberg M et al (2013) Lazar: a modular predictive toxicology framework. Front Pharmacol 4:38

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Toropova AP, Toropov AA, Marzo M et al (2017) The application of new HARD-descriptor available from the CORAL software to building up NOAEL models. Food Chem Toxicol 112:544–550

    PubMed  Google Scholar 

  39. Toropov AA, Toropova AP, Pizzo F et al (2015) CORAL: model for no observed adverse effect level (NOAEL). Mol Divers 19:563–575. https://doi.org/10.1007/s11030-015-9587-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Veselinovic JB, Veselinovic AM, Toropova AP et al (2016) The Monte Carlo technique as a tool to predict LOAEL. J Med Chem 30(116):71–75

    Google Scholar 

  41. Novotarskyi S, Abdelaziz A, Sushko Y et al (2016) ToxCast EPA in vitro to in vivo challenge: insight into the rank-I model. Chem Res Toxicol 29(5):768–775

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Hisaki T, Née Kaneko MA, Yamaguchi M et al (2015) Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients. J Toxicol Sci 40(2):163–180

    CAS  PubMed  Google Scholar 

  43. Chavan S, Friedman R, Nicholls IA (2015) Acute toxicity-supported chronic toxicity prediction: a k-nearest neighbor coupled read-across strategy. Int J Mol Sci 16(5):11659–11677

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Gadaleta D, Pizzo F, Lombardo A et al (2014) A k -NN algorithm for predicting oral sub-chronic toxicity in the rat. ALTEX 31:423–432

    PubMed  Google Scholar 

  45. Toropova AP, Toropov A, Veselinović JB et al (2014) QSAR as a random event. Environ Sci Pollut Res Int 22:8264–8271. https://doi.org/10.1007/s11356-014-3977-2

    Article  PubMed  Google Scholar 

  46. Sakuratani Y, Zhang H, Nishikawa S et al (2013) Hazard evaluation support system (HESS) for predicting repeated dose toxicity using toxicological categories. SAR QSAR Environ Res 24:351–363

    CAS  PubMed  Google Scholar 

  47. García-Domenech R, de Julián-Ortiz JV, Besalú E (2006) True prediction of lowest observed adverse effect levels. Mol Divers 10:159–168

    PubMed  Google Scholar 

  48. Julián-Ortiz JV, García-Domenech R, Gálvez J et al (2005) Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals. SAR QSAR Environ Res 16:263–272

    PubMed  Google Scholar 

  49. Matthews EJ, Kruhlak NL, Benz RD et al (2004) Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data 1. Curr Drug Discov Technol 1:61–76

    CAS  PubMed  Google Scholar 

  50. Mumtaz MM, Knau LA, Reisman DJ et al (1995) Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed adverse- effect level (LOAEL). Toxicol Lett 79:131–143

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  52. Tilaoui L, Schilter B, Tran LA, Mazzatorta P et al (2006) Integrated computational methods for prediction of the lowest observable adverse effect level of food-borne molecules. QSAR Comb Sci 26:102–108

    Google Scholar 

  53. Sand S, Victorin K, Filipsson AF (2008) The current state of knowledge on the use of the benchmark dose concept in risk assessment. J Appl Toxicol 28:405–421

    CAS  PubMed  Google Scholar 

  54. Vermeire TG, Baars AJ, Bessems JGM et al (2007) Toxicity testing for human health risk assessment. In: van Leeuwen CJ, Vermeire TG (eds) Risk assessment of chemicals, an introduction, 2nd edn. Springer, Dordrecht

    Google Scholar 

  55. Paparella M, Daneshian M, Hornek-Gausterer R et al (2013) Food for thought…uncertainty of testing methods-what do we (want to) know? ALTEX 30:131–144

    PubMed  Google Scholar 

  56. Haber LT, Dourson ML, Allen BC et al (2018) Benchmark dose (BMD) modeling: current practice, issues, and challenges. Crit Rev Toxicol 48(5):387–415

    PubMed  Google Scholar 

Download references

Disclaimer

The views expressed in this manuscript are the authors only and do not represent the views of the European Food Safety Authority.

Author information

Authors and Affiliations

  1. FEED Unit, European Food Safety Authority, Parma, Italy

    Fabiola Pizzo

  2. Istituto di Ricerche Farmacologiche “Mario Negri” IRCCS, Milan, Italy

    Domenico Gadaleta & Emilio Benfenati

Authors
  1. Fabiola Pizzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Domenico Gadaleta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emilio Benfenati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabiola Pizzo .

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

Pizzo, F., Gadaleta, D., Benfenati, E. (2022). In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs. 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_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1960-5_11

  • 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