Computational methods for the prediction of drug toxicity

Current Opinion in Drug Discovery and Development

Volumn 3, Issue 3, 2000, Pages 292-297

  Cronin, Mark T D ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Combinatorial libraries; Computer aided prediction; Expert system; Quantitative structure activity relationship; Toxicity

Indexed keywords

ANALYTIC METHOD; ARTICLE; COMPUTER ANALYSIS; DATA BASE; EXPERT SYSTEM; MEMBRANE PERMEABILITY; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; TOXICITY TESTING;

EID: 0033946802     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (45)

1. Benfenati E, Gini G: Computational predictive programs (expert systems) in toxicology. Toxicol(1997) 119:213-225. • This is a good review which compares the expert systems available for toxicity, and the methodological approaches that are utilized.
2. Benign! R, Richard AM: Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity. Methods Enzymol (1998) 14:264-276. • This is a good detailed review of expert systems and other approaches to predict toxicity.
3. Cronin MTD: Computer-aided prediction of drug toxicity in high-throughput screening. Pharm Pharmacol Comm (1998) 4(3):157-163.
4. Dearden JC, Barrait MD, Benigni R, Bristol DW, Combes RD, Cronin MTD, Judson PN, Payne MP, Richard AM, Tichy M, Worth AP, Youn'ck JJ: The development and validation of expert systems for predicting toxicity. Alt Lab Animals (1997)25:223-252. •• This is the report of a European Centre for the Validation of Alternative Methods (ECVAM) workshop. It forms an excellent review of the use of expert systems and structure-based approaches to predict toxicity. It also includes some assessments of the predicitivity of the systems. The full text can be viewed at httprfaltweb.jhsph.edu/science/pubs/ECVAM/ecvam24.htm
5. Hall AH: Computer modeling and computational toxicology in new chemical and pharmaceutical product development. Tbx/co/ieff (1998) 102-103:623-626.
6. Background information on the RTECS database can be viewed at httprfwww.cdc.gov/niosh/rtecs.html
7. Further details of the Toxicity database can be obtained from the MDL website. http://www.mdli.co.uk/
8. Further details of the TOXSYS software and database can be obtained from the Scivision website. httprfwww.scivision.com/ToxSys.html
9. Cronin MTD, Dearden JC: QSAR in toxicology 1. Prediction of aquatic toxicity. Quant Struct-Act Relat (1995) 14(1):1-7.
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11. Cronin MTD, Dearden JC: QSAR in toxicology 3. Prediction of chronic toxicities. Quant Struct-Act Relat (1995) 14(4):329-334.
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14. Seydel JK, Bürger H, Saxena AK, Coleman MD, Smith SN, Perns AD: Quantitative structure-activity and structuretoxicity relationships of 4-aminodiphenyl sulfone derivatives with anti-inflammatory activity. Quant Struct-Act Relat (1999)18(1 ):43-51.
15. Tuppurainen K: Frontier orbital energies, hydrophobicity and steric factors as physical QSAR descriptors of molecular mutagenicity. A review with a case study: MX compounds. Chemosphere (1999) 38(13):3015-3030. • This provides a good review of QSARs for the prediction of mutagenic effects. Particularly commendable is the continual effort in this paper to base QSARs on mechanisms of action and to relate the QSARs back to putative mechanisms. Such approaches to the QSAR modeling of all toxicity data are recommended.
16. Wang J, Lai L, Tang Y: Data mining of toxic chemicals: Structure patterns and QSAR. J Mol Model (1999) 5:252-262. •• This is an extremely interesting approach and such methodology may provide some real progress in extracting information from large uhvalidated databases such as RTECS (in these instances approaches such as those described in reference [15] are impractical).
17. Yang RSH, Thomas RS, Gustafson DL, Campain J, Benjamin SA, Verhaar HJM, Mumtaz MM: Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling. Environ Health Persp (1998) 106(Suppl 6):1385-1393.
18. Fouchécourt MO, Krishman K: Quantitative relationship between steady-state blood concentrations and structural features of aliphatic hydrocarbons. Toxicol Lett (1999) 110(3):177-182.
19. Cronin MTD, Dearden JC, Moss GP, Murray-Dickson G: Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships. EurJPharm Sc/(1999) 7(4):325-330.
20. Worth AP, Cronin MTD: Structure-permeability relationships for transcorneal penetration. Alt Lab Animals (2000) in press.
21. Buchwald P, Bodor N: Quantitative structure-metabolism relationships: Steric and non-steric effects in the enzymatic hydrolysis of noncongener carboxylic esters. J Med Chem (1999) 42(25):5160-5168. • This study has a number of strengths. It analyzes a novel endpoint and develops QSARs from a purely mechanistic viewpoint. It involves the development of anew descriptor (If°°) that may be useful for other structure-metabolism studies. Finally, the authors have taken a sensibly pragmatic approach to using compilations of human data and have accepted that some compounds may be removed from the model, even if the exact reasons for so doing are not fully known.
22. Enslein K, Gombar VK, Blake BW, Maibach HI, Hostynek JJ, Sigman CC, Bagheri D: A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay. Food Chem Toxicol (1997) 35(1011):1091-1098.
23. Greene N, Judson PN, Langowski JJ, Marchant ÇA: Knowledge-based expert systems for toxicity and metabolism predictions: DEREK, StAR and METEOR. SAR QSAR Environ Res (1999) 10(2-3):299-314.
24. Klopman G: The MultiCASE program II: Baseline activity identification algorithm (BAIA). J Chem Inf Comput Sei (1998) 38(1 ):78-81. • This is one of the few advances in the methodology of expert systems for predicting toxicity in recent years. It means that underlying trends (particularly those associated with hydrophobicity) are found. It allows the CASE algorithm to be more 'mechanistic' and also means that unspecific effects (such as narcosis) will be captured and better described.
25. Klopman G, Saiakhov R, Rosenkranz HS, Hermans JLM: Multiple Computer-Automated Structure Evaluation program study of aquatic toxicity 1 : Guppy. Environ Toxicol C/7e/n(1999) 18(11):2497-2505.
26. Rosenkranz HS, Cunningham AR, Zhang YP, Claycamp HG, Macina OT, Sussman NB, Grant SG, Klopman G: Development, characterization and application of predictive toxicology models. SAR QSAR Environ Res (1999)10(2-3):277-298.
27. Matthews EJ, Contrera JF: A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul Toxicol Pharmacol (1998) 28(3):242-264. • This describes important progress in the development of the knowledge and predictive ability of expert systems. The MCASE model is being updated using knowledge from the PDA, ie, proprietary data. Some collaborations and the release of proprietary data in some form should be encouraged and will only serve to further the value and usefulness of these predictive systems.
28. Darvas F, Dormän G: Early integration of ADME/Tox parameters into the design process of combinatorial libraries. Chim Oggi(1999) 17(7/8):10-13.
29. Lewis DFV, Lake BG: Molecular modeling of the rat peroxisome proliferator-activated receptor-a (rPPARa) by homology with the human retinoic acid X receptor a (hRXRa) and investigation of ligand binding interactions I: QSARs. Toxicolin l///ro(1998) 12(6):619-632. • This is an interesting paper that crosses the divide between qualitative molecular and homology modeling and QSARs. It also provides a proposed structural template for peroxisome proliferation.
30. Sadler BR, Cho SJ, Ishaq KS, Chae K, Korach KS: Threedimensional quantitative structure-activity relationship study of non-steroidal estrogen receptor ligands using the comparative molecular field analysis/cross validated r2guided region selection approach. J Med Chem (1998) 48(13):2261-2267.
31. Tong W, Lewis DR, Perkins R, Chen Y, Welsh WJ, Goddette DW, Heritage TW, Sheehan DM: Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sc/(1998) 38(4):669-677.
32. Gao H, Williams C, Labute P, Bajorath J: Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands. J Chem Inf Comput Sei (1999) 39(1):164-168. •• This is an exciting paper in which a relatively large dataset is used and the so-called Binary QSAR theory is applied to predicting an endpoint. The Binary QSAR is based upon Bayes Theorem and is applicable to qualitative (active/non-active) data. The models that are revealed can be related back to mechanistically interprétable parameters. Such an approach may have a variety of uses, eg, in the modeling of toxicity data from HTS, and in the modeling of 'rough' data including those from toxicity databases.
33. Bradbury SP, Mekenyan OG, Ankley GT: The role of ligand flexibility in predicting biological activity: structure-activity relationships for aryl hydrocarbon, estrogen, and androgen receptor binding affinity. Environ Toxicol Chem (1998)17(1):15-25. • A good mechanistically-based review of QSARs for endocrine disruption and other endpoints.
34. Mekenyan OG, Nikolova N, Karabunarliev S, Bradbury SP, Ankley GT, Hansen B: New developments in a hazard identification algorithm for hormone receptor ligands. Quant Struct-Act Relat (1999) 18(2):139-153.
35. 2. 7bx/co/(1999) 139(1-2):53-79. ••An excellent paper that brings together many of the advances in this area, and gives the reader a taste of what could be achieved by the use of detailed molecular modeling of enzyme substrates.
36. Wang J, Ramnarayan K: Toward designing drug-like libraries: A novel computational approach for prediction of drug feasibility of compounds. J Combinatorial Chem (1999) 1(6):524-533.
37. Koehler RT, Dixon SL, Villar HO: LASSOO: A generalized directed diversity approach to the design and enrichment of chemical libraries. J Med Chem (1999) 42(22):4695-4704.
38. McKinnon MB, Kaiser KLE: The desk-top computer spreadsheet as a scientific database and powerful research tool. Chemosphere (1993) 27(7):1159-1169.
39. Todd MD, Lin XD, Stankowski LF, Desai M, Wolfgang GH: Toxicity screening of a combinatorial library: Correlation of cytotoxicity and gene induction to compound structure. J Biomol Screening (1999) 4(3):259-268. • This paper gives the reader an insight into the possibilities that may exist in the very near future for screening large numbers of compounds. HTS may provide a large quantity of lexicological information, techniques such as those described by Wang et al [16] and Gao et al [32] may be useful to obtain extra knowledge from the information.
40. Srinivasan A, King RD: Feature construction with Inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Mining Knowledge Discovery (1999) 3(1 ):37-57.
41. Gini G, Testaguzza V, Benfenati E, Todeschini R: Hybrid toxicology expert system: architecture and implementation of a multi-domain hybrid expert system for toxicology. Chemometrics Intell Lab Sys (1998) 43(1-2):135145.
42. Further details are available from Dr PN Judson, LHASA Ltd, School of Chemistry, University of Leeds, Leeds, LS2 9JT, UK. • The consortium approach is an excellent and long-overdue move to release proprietary toxicological information. Readers holding such data are urged to release it for use in the development of better computational toxicity prediction models.
43. Rockett JC, Dix DJ: Application of DNA arrays to toxicology. Environ Health Persp (1999) 107(8):681-685. • A good introduction (based upon a workshop entitled Application of Microarrays to Toxicology) to the use of microarrays. This will undoubtedly be a growth area in toxicology in the near future, and may provide information for development of computational toxicity prediction models.
44. Purdy R: A mechanism-mediated model for carcinogenicity: model content and prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 25 organic chemicals. Environ Health Persp (1996) 104(5):1085-1094. • This is an excellent approach, which describes a hierarchical system of qualitative rules and QSARs. It is based soundly on mechanistic principles.
45. Worth AP, Barratt MD, Houston JB: The validation of computational prediction techniques. Alt Lab Animals (1998)26:241-247.