Addressing toxicity risk when designing and selecting compounds in early drug discovery

Drug Discovery Today

Volumn 19, Issue 5, 2014, Pages 688-693

  Segall, Matthew D ^a   Barber, Chris ^b  

^a OPTIBRIUM LTD   (United Kingdom)

^b LHASA LIMITED   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

NEW DRUG; DRUG;

ADVERSE DRUG REACTION; DRUG APPROVAL; DRUG DESIGN; DRUG RESEARCH; PREDICTION; REVIEW; RISK ASSESSMENT; ANIMAL; CHEMISTRY; DRUG DEVELOPMENT; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMAN; PROCEDURES; RISK FACTOR; TRENDS;

ANIMALS; DRUG DESIGN; DRUG DISCOVERY; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; PHARMACEUTICAL PREPARATIONS; RISK FACTORS;

EID: 84901488213     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2014.01.006     Document Type: Review

References (30)

1. CMR International Global R&D Performance Metrics programme, CMR International, a Thomson Reuters business.
2. S.M. Paul et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge Nat. Rev. Drug Discov. 9 2010 203 214
3. K.E. Lasser et al. Timing of new black box warnings and withdrawals for prescription medications JAMA 287 2002 2215 2220
4. T. Kennedy Managing the drug discovery/development interface Drug Discov. Today 2 1997 436 444
5. M.H. Tarbit, and J. Berman High-throughput approaches for evaluating absorption, distribution, metabolism and excretion properties of lead compounds Curr. Opin. Chem. Biol. 2 1998 411 416
6. H. Van de Waterbeemd, and E. Gifford ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2 2003 192 204
7. S. Ekins et al. Towards a new age of virtual ADME/TOX and multidimensional drug discovery J. Comput. Aided Mol. Des. 16 2001 381 401
8. I. Kola, and J. Landis Can the pharmaceutical industry reduce attrition rates Nat. Rev. Drug Discov. 3 2004 711 716
9. J.C. Dearden et al. The development and validation of expert systems for predicting toxicity: the report and recommendations of an ECVAM/ECB workshop (ECVAM Workshop 24) ATLA 25 1997 223 252
10. A. Roncaglioni et al. In silico methods to predict drug toxicity Curr. Opin. Pharmacol. 13 2013 802 806
11. T. Arodz et al. Ensemble of linear models for predicting drug properties J. Chem. Inf. Model. 46 2006 416 423
12. F. Nigsch, and J.B.O. Mitchell How to winnow actives from inactives: introducing molecular orthogonal sparse bigrams (MOSBs) and multiclass Winnow J. Chem. Inf. Model. 48 2008 306 318
13. F. Nigsch et al. Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics J. Chem. Inf. Model. 48 2008 2313 2325
14. A. Tropsha Recent trends in statistical QSAR modeling of environmental chemical toxicity A. Luch, Molecular, Clinical and Environmental Toxicology Vol. 3 2012 Springer
15. N. Greene et al. Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR SAR QSAR Environ. Res. 10 1999 299 314
16. J.C. Dearden In silico prediction of drug toxicity J. Comput. Aided Mol. Des. 17 2003 119 127
17. M. Cronin, and J. Madden In Silico Toxicology: Principles and Application 2010 RSC Publishing
18. N. Greene Computer systems for the prediction of toxicity: an update Adv. Drug Deliv. Rev. 54 2002 417 431
19. G.T. Ankley et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment Environ. Toxicol. Chem. 29 2010 730 741
20. C.A. Marchant et al. In silico tools for sharing data and knowledge on toxicity and metabolism: Derek for windows, Meteor, and Vitic Toxicol. Mech. Methods 18 2008 177 187
21. P.N. Judson et al. Assessing confidence in predictions made by knowledge-based systems Toxicol. Res. 2 2013 70 79
22. A. Sutter et al. Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities Regul. Toxicol. Pharmacol. 67 2013 39 52
23. K.L. Dobo et al. In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey Regul. Toxicol. Pharmacol. 62 2012 449 455
24. M.D. Segall Multi-parameter optimization: identifying high quality compounds with a balance of properties Curr. Pharm. Des. 18 2012 1292 1310
25. M.D. Segall et al. Focus on success: using in silico optimisation to achieve an optimal balance of properties Expert Opin. Drug Metab. Toxicol. 2 2006 325 337
26. K. Lasseter et al. Safety and pharmacokinetics of multiple doses of aclidinium bromide administered twice daily in healthy volunteers Pulm. Pharmacol. Ther. 25 2012 192 199
27. K. Wang et al. Circulating microRNAs, potential biomarkers for drug-induced liver injury Proc. Natl. Acad. Sci. U. S. A. 106 2009 4402 4407
28. A.J. Olaharski et al. Identification of a kinase profile that predicts chromosome damage induced by small molecule kinase inhibitors Toxicol. Sci. 118 2010 266 275
29. R.A. Moore et al. Tolerability and adverse events in clinical trials of celecoxib in osteoarthritis and rheumatoid arthritis: systematic review and meta-analysis of information from company clinical trial reports Arthritis Res. Ther. 7 2005 R644 R665
30. D.J. Rogers, and T.T. Tanimoto A computer program for classifying plants Science 132 1960 1115 1118