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In Silico Models for Acute Systemic Toxicity

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1425))

Abstract

In this chapter, we give an overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review the availability of structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

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References

  1. Combes R, Grindon C, Cronin MTD, Roberts D, Garrod J (2008) Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation. ATLA 36(Suppl 1):91–109

    CAS  PubMed  Google Scholar 

  2. Devillers J, Devillers H (2009) Prediction of acute mammalian toxicity from QSARs and interspecies correlations. SAR QSAR Environ Res 20:467–500

    Article  CAS  PubMed  Google Scholar 

  3. Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MN (2015) In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect. Mini Rev Med Chem 15(8):677–686

    Article  CAS  PubMed  Google Scholar 

  4. Lapenna S, Fuart-Gatnik M, Worth A (2010) Review of QSAR models and software tools for predicting acute and chronic systemic toxicity, JRC technical report EUR 24639 EN. Publications Office of the European Union, Luxembourg, http://publications.jrc.ec.europa.eu/repository/

    Google Scholar 

  5. Tsakovska I, Lessigiarska I, Netzeva T, Worth Andrew P (2008) A mini review of mammalian toxicity (Q)SAR models. QSAR Comb Sci 27(1):41–48

    Article  CAS  Google Scholar 

  6. OECD (2001) Guideline for testing of chemicals, 420, acute oral toxicity—fixed dose method. OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris

    Google Scholar 

  7. European Union (2008a) Regulation (EC) No 440/2008 of 30 May 2008 laying down test methods pursuant to Regulation (EC) No 1907/2006 of the European Parliament and of the Council on the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Off J Eur Union L142

    Google Scholar 

  8. OECD (2001) Guideline for testing of chemicals, 423, acute oral toxicity—acute toxic class method. OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris

    Google Scholar 

  9. OECD (2001) Guideline for testing of chemicals, 425, acute oral toxicity—up-and-down procedure. OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris

    Google Scholar 

  10. OECD (1987) Guideline for testing of chemicals, 420, acute dermal toxicity. OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris

    Google Scholar 

  11. OECD (2009) Guideline for testing of chemicals, 403, acute inhalation toxicity. OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris

    Google Scholar 

  12. European Union (2014) Commission Regulation (EU) No 260/2014 of 24 January 2014 amending, for the purpose of its adaptation to technical progress, Regulation (EC) No 440/2008 laying down test methods pursuant to Regulation (EC) No 1907/2006 of the European Parliament and of the Council on the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Off J Eur Union L81:1–253

    Google Scholar 

  13. OECD (2009) Guideline for testing of chemicals, 436, acute inhalation toxicity—acute toxic class method. OECD guidelines for the testing of chemicals, Section 4. OECD Publishing, Paris

    Google Scholar 

  14. European Union (2009) Regulation (EC) No 1223/2009 of the European Parliament and the Council of 30 November 2009 on cosmetic products. Off J Eur Union L342:59–209

    Google Scholar 

  15. European Union (2008b) Regulation (EC) No 1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006. Off J Eur Union L353

    Google Scholar 

  16. European Union (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 and 2000/21/EC. Off J Eur Union L396:1–849

    Google Scholar 

  17. European Union (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 

  18. European Union (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 

  19. ICH (2009) International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use. Guidance on nonclinical safety studies for the conduct of human clinical trials and marketing authorization for pharmaceuticals M3(R2). Recommended for adoption at step 4 of the ICH process on 11 June 2009

    Google Scholar 

  20. Chapman K, Creton S, Kupferschmidt H, Bond GR, Wilks MF, Robinson S (2010) The value of acute toxicity studies to support the clinical management of overdose and poisoning: a cross-discipline consensus. Regul Toxicol Pharmacol 58:354–359

    Article  PubMed  Google Scholar 

  21. Robinson S, Delongeas JL, Donald E, Dreher D, Festag M, Kervyn S, Lampo A, Nahas K, Nogues V, Ockert D, Quinn K, Old S, Pickersgill N, Somers K, Stark C, Stei P, Waterson L, Chapman K (2008) A European pharmaceutical company initiative challenging the regulatory requirement for acute toxicity studies in pharmaceutical drug development. Regul Toxicol Pharmacol 50:345–352

    Article  PubMed  Google Scholar 

  22. Prieto P, Burton J, Graepel R, Price A, Whelan M, Worth A (2014) EURL ECVAM strategy to replace, reduce and refine the use of animals in the assessment of acute mammalian systemic toxicity, JRC report EUR 26797 EN. Publications Office of the European Union, Luxembourg, http://publications.jrc.ec.europa.eu/repository/

    Google Scholar 

  23. Fuart-Gatnik M, Worth AP (2010) Review of software tools for toxicity prediction. JRC report EUR 24489 EN. Publications Office of the European Union. http://publications.jrc.ec.europa.eu/repository/

  24. Chakravarti SK, Saiakhov RD, Klopman G (2012) Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts. J Chem Inform Model 52(10):2609–2618

    Article  CAS  Google Scholar 

  25. Tunkel J, Mayo K, Austin C, Hickerson A, Howard P (2005) Practical considerations on the use of predictive models for regulatory purposes. Environ Sci Technol 39(7):2188–2199

    Article  CAS  PubMed  Google Scholar 

  26. Vedani A, Dobler M, Smieško M (2012) VirtualToxLab—a platform for estimating the toxic potential of drugs, chemicals and natural products. Toxicol Appl Pharmacol 261(2):142–153

    Article  CAS  PubMed  Google Scholar 

  27. Roberts G, Myatt GJ, Johnson WP, Cross KP, Blower PE Jr (2000) LeadScope: software for exploring large sets of screening data. J Chem Inf Comput Sci 40(6):1302–1314

    Article  CAS  PubMed  Google Scholar 

  28. Halle W (2003) The registry of cytotoxicity: toxicity testing in cell cultures to predict acute toxicity (LD50) and to reduce testing in animals. Altern Lab Anim 31:89–198

    CAS  PubMed  Google Scholar 

  29. Kinsner-Ovaskainen A, Rzepka R, Rudowski R, Coecke S, Cole T, Prieto P (2009) Acutoxbase, an innovative database for in vitro acute toxicity studies. Toxicol In Vitro 23(3):476–485

    Article  CAS  PubMed  Google Scholar 

  30. Kinsner-Ovaskainen A, Prieto P, Stanzel S, Kopp-Schneider A (2013) Selection of test methods to be included in a testing strategy to predict acute oral toxicity: an approach based on statistical analysis of data collected in phase 1 of the ACuteTox project. Toxicol In Vitro 27(4):1377–1394

    Article  CAS  PubMed  Google Scholar 

  31. Prieto P, Kinsner-Ovaskainen A, Stanzel S, Albella B, Artursson P, Campillo N, Cecchelli R, Cerrato L, Díaz L, Di Consiglio E, Guerra A, Gombau L, Herrera G, Honegger P, Landry C, O’Connor JE, Páez JA, Quintas G, Svensson R, Turco L, Zurich MG, Zurbano MJ, Kopp-Schneider A (2013) The value of selected in vitro and in silico methods to predict acute oral toxicity in a regulatory context: results from the European Project ACuteTox. Toxicol In Vitro 27(4):357–376

    Article  Google Scholar 

  32. Hoffmann S, Kinsner-Ovaskainen A, Prieto P, Mangelsdorf I, Bieler C, Cole T (2010) Acute oral toxicity: variability, reliability, relevance and interspecies comparison of rodent LD50 data from literature surveyed for the ACuteTox project. Regul Toxicol Pharmacol 58:395–407

    Article  CAS  PubMed  Google Scholar 

  33. Fonger GC, Hakkinen P, Jordan S, Publicker S (2014) The National Library of Medicine’s (NLM) Hazardous Substances Data Bank (HSDB): background, recent enhancements and future plans. Toxicology 325:209–216

    Article  CAS  PubMed  Google Scholar 

  34. Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A (2009) Quantitative structure–activity relationship modeling of rat acute toxicity by oral exposure. Chem Res Toxicol 22(12):1913–1921

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A (2009) A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environ Health Perspect 117(8):1257–1264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  37. Norlén H, Berggren E, Whelan M, Worth A (2012) An investigation into the use of computational and in vitro methods for acute systemic toxicity prediction, JRC report EUR 25473 EN. Publications Office of the European Union, Luxembourg, http://publications.jrc.ec.europa.eu/repository/

    Google Scholar 

  38. Lessigiarska I, Worth AP, Netzeva TI, Dearden JC, Cronin MTD (2006) Quantitative structure-activity-activity and quantitative structure-activity investigations of human and rodent toxicity. Chemosphere 65(10):1878–1887

    Article  CAS  PubMed  Google Scholar 

  39. Raevsky OA, Grigor’ev VJ, Modina AE, Worth AP (2010) Prediction of acute toxicity to mice by the arithmetic mean toxicity (AMT) modelling approach. SAR QSAR Environ Res 21(1):265–275

    Article  CAS  PubMed  Google Scholar 

  40. Chavan S, Nicholls IA, Karlsson BC, Rosengren AM, Ballabio D, Consonni V, Todeschini R (2014) Towards global QSAR model building for acute toxicity: Munro database case study. Int J Mol Sci 15(10):18162–18174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lu J, Peng J, Wang J, Shen Q, Bi Y, Gong L, Zheng M, Luo X, Zhu W, Jiang H, Chen K (2014) Estimation of acute oral toxicity in rat using local lazy learning. J Cheminform 6:26

    Article  PubMed  PubMed Central  Google Scholar 

  42. Low Y, Sedykh A, Fourches D, Golbraikh A, Whelan M, Rusyn I, Tropsha A (2013) Integrative chemical-biological read-across approach for chemical hazard classification. Chem Res Toxicol 26(8):1199–1208

    Article  CAS  PubMed  Google Scholar 

  43. Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A (2011) Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ Health Perspect 119(3):364–370

    Article  CAS  PubMed  Google Scholar 

  44. Ekwall B, Barile FA, Castano A et al (1998) MEIC evaluation of acute systemic toxicity. Part V. I. The prediction of human toxicity by rodent LD50 values and results from 61 in vitro methods. Alternatives to laboratory animals. ATLA 26(Suppl 2):617–658

    PubMed  Google Scholar 

  45. Spielmann H, Genshow E, Liebsch M, Halle W (1999) Determination of the starting dose for acute oral toxicity (LD50) testing in the up and down procedure (UDP) from cytotoxicity data. Altern Labor Anim ATLA 27(6):957–966

    CAS  Google Scholar 

  46. Clemedson C, Ekwall B (1999) Overview of the final MEIC results: I. The in vitro–in vitro evaluation. Toxicol In Vitro 13(4-5):657–663

    Article  CAS  PubMed  Google Scholar 

  47. EURL ECVAM (2013) EURL ECVAM recommendation on the 3T3 neutral red uptake cytotoxicity assay for acute oral toxicity testing (2013) JRC technical report EUR 25946 EN. Publications Office of the European Union, Luxembourg, https://eurl-ecvam.jrc.ec.europa.eu/eurl-ecvam-recommendations

    Google Scholar 

  48. Rovida C, Alépée N, Api AM, Basketter DA, Bois FY et al (2015) Integrated Testing Strategies (ITS) for safety assessment. ALTEX Altern Anim Exp 32(1):25–40

    Google Scholar 

  49. 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

    Article  CAS  PubMed  Google Scholar 

  50. Lee S, Kang Y, Park H, Dong M, Shin J, No K (2013) Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites. Chem Res Toxicol 26(11):1652–1659

    Article  CAS  PubMed  Google Scholar 

  51. Myshkin E, Brennan R, Khasanova T, Sitnik T, Serebriyskaya T, Litvinova E, Guryanov A, Nikolsky Y, Nikolskaya T, Bureeva S (2012) Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical histopathology annotations. Chem Biol Drug Des 80:406–416

    Article  CAS  PubMed  Google Scholar 

  52. Munday R, Smith BL, Munday CM (2007) Structure-activity relationships in the haemolytic activity and nephrotoxicity of derivatives of 1,2- and 1,4-naphthoquinone. J Appl Toxicol 27(3):262–269

    Article  CAS  PubMed  Google Scholar 

  53. Jolivette LJ, Anders MW (2002) Structure-activity relationship for the biotransformation of haloalkenes by rat liver microsomal glutathione transferase 1. Chem Res Toxicol 15(8):1036–1041

    Article  CAS  PubMed  Google Scholar 

  54. Makhaeva GF, Radchenko EV, Palyulin VA, Rudakova EV, Aksinenko AY, Sokolov VB, Zefirov NS, Richardson RJ (2013) Organophosphorus compound esterase profiles as predictors of therapeutic and toxic effects. Chem Biol Interact 203(1):231–237

    Article  CAS  PubMed  Google Scholar 

  55. Makhaeva GF, Radchenko EV, Baskin II, Palyulin VA, Richardson RJ, Zefirov NS (2012) Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer’s disease. SAR QSAR Environ Res 23(7–8):627–647

    Article  CAS  PubMed  Google Scholar 

  56. Stenberg M, Hamers T, Machala M, Fonnum F, Stenius U, Lauy AA, van Duursen MB, Westerink RH, Fernandes EC, Andersson PL (2011) Multivariate toxicity profiles and QSAR modeling of non-dioxin-like PCBs—an investigation of in vitro screening data from ultra-pure congeners. Chemosphere 85(9):1423–1429

    Article  CAS  PubMed  Google Scholar 

  57. Estrada E, Molina E, Uriarte E (2001) Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents. SAR QSAR Environ Res 12(5):445–459

    Article  CAS  PubMed  Google Scholar 

  58. Yazal JE, Rao SN, Mehl A, Slikker W Jr (2001) Prediction of organophosphorus acetylcholinesterase inhibition using three dimensional quantitative structure-activity relationship (3D-QSAR) methods. Toxicol Sci 63(2):223–232

    Article  PubMed  Google Scholar 

  59. Hosoya J, Tamura K, Muraki N, Okumura H, Ito T, Maeno M (2011) A novel approach for a toxicity prediction model of environmental pollutants by using a quantitative structure-activity relationship method based on toxicogenomics. ISRN Toxicol 2011:515724

    PubMed  PubMed Central  Google Scholar 

  60. Sayes C, Ivanov I (2010) Comparative study of predictive computational models for nanoparticle-induced cytotoxicity. Risk Anal 30(11):1723–1734

    Article  PubMed  Google Scholar 

  61. Kafoury RM, Huang MJ (2005) Application of quantitative structure activity relationship (QSAR) models to predict ozone toxicity in the lung. Environ Toxicol 20(4):441–448

    Article  CAS  PubMed  Google Scholar 

  62. Tenorio-Borroto E, Peñuelas-Rivas CG, Vásquez-Chagoyán JC, Castañedo N, Prado-Prado FJ, García-Mera X, González-Díaz H (2014) Model for high-throughput screening of drug immunotoxicity—study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 72:206–220

    Article  CAS  PubMed  Google Scholar 

  63. Yuan J, Pu Y, Yin L (2013) Docking-based three-dimensional quantitative structure-activity relationship (3D-QSAR) predicts binding affinities to aryl hydrocarbon receptor for polychlorinated dibenzodioxins, dibenzofurans, and biphenyls. Environ Toxicol Chem SETAC 32(7):1453–1458

    CAS  Google Scholar 

  64. Hui-Ying X, Jian-Wei Z, Gui-Xiang H, Wei W (2010) QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs). Chemosphere 80(6):665–670

    Article  PubMed  Google Scholar 

  65. Crivori P, Pennella G, Magistrelli M, Grossi P, Giusti AM (2011) Predicting myelosuppression of drugs from in silico models. J Chem Inf Model 51(2):434–445

    Article  CAS  PubMed  Google Scholar 

  66. Wang S, Li Y, Xu L, Li D, Hou T (2013) Recent developments in computational prediction of HERG blockage. Curr Top Med Chem 13(11):1317–1326

    Article  CAS  PubMed  Google Scholar 

  67. Villoutreix BO, Taboureau O (2015) Computational investigations of hERG channel blockers: new insights and current predictive models. Adv Drug Deliv Rev 15:00022–00028

    Google Scholar 

  68. Anders MW, Dekant W (1998) Glutathione-dependent bioactivation of haloalkenes. Annu Rev Pharmacol Toxicol 38(1):501–537

    Article  CAS  PubMed  Google Scholar 

  69. Prieto and Kinsner-Ovaskainen (2015). Short commentary to Human in vivo database now on ACuteTox home page [Toxicol. In Vitro 27 (2013) 2350-2351]. Toxicol In Vitro. 2015 Mar;29(2):415

    Google Scholar 

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Acknowledgement

This work was partially funded by the European Community’s Seventh Framework Program (FP7/2007–2013) COSMOS Project under grant agreement no 266835 and from Cosmetics Europe. The authors are grateful to Dr. Pilar Prieto (JRC) for critically reviewing this work.

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Correspondence to Andrew P. Worth .

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Burton, J., Worth, A.P., Tsakovska, I., Diukendjieva, A. (2016). In Silico Models for Acute Systemic Toxicity. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3609-0_10

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  • DOI: https://doi.org/10.1007/978-1-4939-3609-0_10

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