Predicting human safety: Screening and computational approaches

Drug Discovery Today

Volumn 5, Issue 10, 2000, Pages 445-454

  Johnson, Dale E ^a   Wolfgang, Grushenka H I ^a  

^a ddplatform LLC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER; DATA BASE; DRUG DEVELOPMENT; DRUG SAFETY; DRUG SCREENING; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; SAFETY; SCREENING; SIMULATION; TOXICITY;

EID: 0034307909     PISSN: 13596446     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1359-6446(00)01559-2     Document Type: Review

References (61)

1. Johnson D. The discovery-development interface has become the new interfacial phenomenon. Drug Discovery Today. 4:1999;535-536.
2. Douglas, F.L. et al. (1998) How to spend a billion dollars. The two basic R&D archetypes: winning with internal critical mass vs. exploiting the best, wherever it may lie. In In Vivo: The Business and Medicine Report Windhover Information July/August, 19-26.
3. Kennedy T. Managing the discovery/development interface. Drug Discovery Today. 2:1997;436-444.
4. Caldwell G. Compound optimization in early- and late-phase drug discovery. Acceptable pharmacokinetic properties utilizing combined physicochemical, in vitro and in vivo screens Curr. Opin. Drug Discov. Dev. 3:2000;30-41.
5. Fuse E.et al. Prediction of the maximal tolerated dose (MTD) and therapeutic effect of anticancer drugs in humans. integration of pharmacokinetics with pharmacodynamics and toxicodynamics Cancer Treat. Rev. 21:1995;133-157.
6. Johnson D.E.et al. The chemistry of metabolic and toxicologic processes, Editorial Overview. Curr. Opin. Drug Discov. Dev. 3:2000;29.
7. DiMasi J.A. Success rates for new drugs entering clinical testing in the United States. Clin. Pharmacol. Ther. 58:1995;1-14.
8. An industrial revolution in R&D. 2000;PriceWaterhouseCoopers.
9. Rodrigues A. Preclinical drug metabolism in the age of high-throughput screening. An industrial perspective Pharm. Res. 14:1997;1504-1510.
10. Rodrigues A. Rational high-throughput screening in preclinical drug metabolism. Med. Chem. Res. 8:1998;422-433.
11. Silverman L.et al. New assay technologies for high-throughput screening. Curr. Opin. Chem. Biol. 2:1998;397-403.
12. Tarbit M., Berman J. High-throughput approaches for evaluating absorption, distribution, metabolism and excretion properties of lead compounds. Curr. Opin. Chem. Biol. 2:1998;411-416.
13. Bayliss M., Frick L. High-throughput pharmacokinetics. cassette dosing Curr. Opin. Drug Discov. Develop. 2:1999;20-25.
14. Crespi C.et al. Higher-throughput screening with human cytochromes P450. Curr. Opin. Drug Discov. Develop. 2:1999;15-19.
15. Cox K. Novel in vivo procedure for rapid pharmacokinetic screening of discovery compounds in rats. Drug Discovery Today. 4:1999;232-237.
16. Lipinski C.et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23:1997;3-25.
17. Ulrich R.et al. Cultured hepatocytes as investigational models for hepatic toxicity. practical application in drug discovery and development Toxicol. Lett. 1995;82-83. 107-115.
18. Houston J., Carlile D. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab. Rev. 29:1997;891-922.
19. Iwatsubo T.et al. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol. Ther. 73:1997;147-171.
20. Lee C.et al. selection of development candidates based on in vitro permeability measurements. Adv. Drug Deliv. Rev. 23:1997;47-62.
21. Obach R.et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 283:1997;46-58.
22. Stewart B. Discrimination between drug candidates using models for evaluation of intestinal absorption. Adv. Drug Deliv. Rev. 23:1997;27-45.
23. Crespi C.et al. High throughput screening for inhibition of cytochrome P450 metabolism. Med. Chem. Res. 8:1998;457-471.
24. Makhey V.et al. Characterization of the regional intestinal kinetics of drug efflux in rat and human intestine and in Caco-2 cells. Pharm. Res. 15:1998;1160-1167.
25. Cronin M. Computer-aided prediction of drug toxicity in high-throughput screening. Pharmacol. Commun. 4:1998;157-163.
26. Grass G. Simulation models to predict oral drug absorption from in vitro data. Adv. Drug Deliv. Rev. 23:1997;199-219.
27. Olson H.et al. The predictivity of the toxicity of pharmaceuticals in humans from animal data- an interim assessment. Toxicol. Lett. 1998;102-103. 535-538.
28. Olson H.et al. Concordance of pharmaceuticals toxicity in humans with animal data. Toxicol. Sci. 54:2000;355.
29. Clark D.L.et al. Predictive value of preclinical toxicology studies for platinum anticancer drugs. Clin. Cancer Res. 5:1999;1161-1167.
30. Newell D.R.et al. Evaluation of rodent-only toxicology for early clinical trials with novel cancer therapeutics. Br. J. Cancer. 81:1999;760-768.
31. McKenzie R.et al. Hepatic failure and lactic acidosis due to fialuridine (FIAU), an investigational nucleoside analogue for chronic hepatitis B. New Engl. J. Med. 333:1995;1099-1105.
32. Morton D.M. Importance of species in drug toxicity testing. Toxicol. Lett. 1998;102-103. 545-550.
33. Tennant B.C.et al. Antiviral activity and toxicity of Fialuridine in the woodchuck model of hepatitis B virus infection. Hepatology. 28:1998;179-191.
34. Honkoop P. Mitochondrial injury. Lessons from the fialuridine trial. Drug Safety. 17:1997;1-7.
35. Medical Economics Company (2000) Rezulin® monograph. In: Physician's Desk Reference (54th edn), pp. 2278-2282.
36. Anon (2000), Avandia, Actos to accelerate market growth with Rezulin's withdrawal. The Pink Sheet, 62, No.13.
37. Jo M.et al. Apoptosis induced in normal human hepatocytes by tumor necrosis factor-related apoptosis-inducing ligand. Nat. Med. 6:2000;564-567.
38. Walczak H.et al. Tumoricidal activity of tumor necrosis factor-related apoptosis-inducing ligand in vivo. Nat. Med. 5:1999;157-163.
39. Ashkenazi A.et al. Safety and antitumor activity of recombinant soluble Apo2 ligand. J. Clin. Invest. 104:1999;155-162.
40. Davila J.C.et al. Predictive value of in vitro model systems in toxicology. Annu. Rev. Pharmacol. Toxicol. 38:1998;63-96.
41. Brusick D. Genetic Toxicology. Hayes A.W. Principles and Methods of Toxicology. 1994;545-577 Raven Press.
42. Parkinson A. An overview of current cytochrome P450 technology for assessing the safety and efficacy of new materials. Toxicol. Pathol. 24:1996;45-57.
43. Obach R.S. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data. an examination of in vitro half-life approach and nonspecific binding to microsomes Drug Metab. Dis. 27:1999;1350-1359.
44. Smith D.A., van de Waterbeemd H. Pharmacokinetics and metabolism in early drug discovery. Curr. Opin. Chem. Biol. 3:1999;373-378.
45. Davit B.et al. FDA evaluations using in vitro metabolism to predict and interpret in vivo metabolic drug-drug interactions. Impact on labeling J. Clin. Pharmacol. 39:1999;899-910.
46. Benfenati E., Gini G. Computational predictive programs (expert systems) in toxicology. Toxicology. 119:1997;213-225.
47. Richard A.M. Application of SAR methods to non-congeneric databases associated with carcinogenicity and mutagenicity. issues and approaches Mutat. Res. 305:1994;73-97.
48. Richard A.M. Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet? Mutat. Res. 400:1998;493-507.
49. Dearden J.C.et al. The development and validation of expert systems for predicting toxicity. ATLA. 25:1997;223-252.
50. Greene N. Computer software for risk assessment. J. Chem. Inf. Comput. Sci. 37:1997;148-150.
51. Greene N.et al. Knowledge-based expert systems for toxicity and metabolism prediction. DEREK, StAR and METEOR SAR QSAR Environ. Res. 10:1999;299-314.
52. Ridings J.E.et al. Computer prediction of possible toxic action from chemical structure. an update on the DEREK system Toxicology. 106:1996;267-279.
53. Barratt M.D., Langowski J.J. Validation and subsequent development of the DEREK skin sensitization rulebase by analysis of the BgVV list of contact allergens. J. Chem. Inf. Comput. Sci. 39:1999;294-298.
54. Benigni R. The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity. definitive results Mutat. Res. 387:1997;35-45.
55. Zeiger E.et al. Prediction of Salmonella mutagenicity. Mutagenesis. 11:1996;471-484.
56. Rosenkranz H.S.et al. Development, characterization and application of predictive toxicology models. SAR QSAR Environ. Res. 10:1999;277-298.
57. Pollack N.et al. Chemical diversity approach for evaluating mechanistic relatedness among toxicological phenomena. SAR QSAR Environ. Res. 10:1999;533-543.
58. Matthews E.J., Contrera J.F. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES Software. Reg. Toxicol. Pharmacol. 28:1998;242-264.
59. Abstracts of the 39th Annual meeting of the Society of Toxicolog. Toxicol. Sci. 54(Suppl.):2000;. No. 1.
60. Pennie W.D.et al. The principles and practice of toxicogenomics. applications and opportunities Toxicol. Sci. 54:2000;277-283.
61. Steiner S., Anderson N.L. Expression profiling in toxicology- potentials and limitations. Toxicol. Lett. 2000;112-113. 467-471.