Strategic applications of toxicogenomics in early drug discovery

Current Opinion in Pharmacology

Volumn 8, Issue 5, 2008, Pages 654-660

  Ryan, Timothy P ^a   Stevens, James L ^a   Thomas, Craig E ^a  

^a ELI LILLY AND COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NEW DRUG;

AGING; DRUG IDENTIFICATION; DRUG INDUSTRY; DRUG RESEARCH; DRUG SAFETY; DRUG TARGETING; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENOMICS; HUMAN; IN VITRO STUDY; IN VIVO STUDY; LIVER TOXICITY; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PRODUCTIVITY; REVIEW; STRUCTURE ACTIVITY RELATION; TOXICOGENETICS; TREATMENT OUTCOME;

ANIMALS; DRUG DESIGN; DRUG EVALUATION, PRECLINICAL; FORECASTING; HUMANS; PHARMACEUTICAL PREPARATIONS; RESEARCH; TOXICOGENETICS;

EID: 54849442447     PISSN: 14714892     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.coph.2008.07.011     Document Type: Review

References (48)

1. Caskey C.T. The drug development crisis: efficiency and safety. Annu Rev Med 58 (2007) 1-16. This review begins with a summary of the poor productivity in the pharmaceutical industry. It expands into an analysis of opportunities to improve the way safety and efficacy is determined in a phase-by-phase approach, beginning with target selection. An emphasis was placed on implementing non-traditional technologies such as knockout animals, RNAi, and imaging.
2. Fielden M.R., and Kolaja K.L. The role of early in vivo toxicity testing in drug discovery toxicology. Expert Opin Drug Saf 7 (2008) 107-110
3. Woodcock J., and Woosley R. The FDA critical path initiative and its influence on new drug development. Annu Rev Med 59 (2008) 1-12. A very recent treatise from authors who are heavily involved in Critical Path activities. This review summarizes the challenges in the majority of pharmaceutical pipelines, describes the major opportunities for improvement outlined in the FDA's 2004 Critical Path White Paper, and highlights the various efforts underway throughout government and industry to address the identified gaps.
4. Committee on the Assessment of the US Drug Safety System: The Future of Drug Safety: Promoting and Protecting the Health of the Public. 2007, National Academy Press.
5. Kola I., and Landis J. Can the pharmaceutical industry reduce attrition rates?. Nat Rev Drug Discov 3 (2004) 711-715
6. Foster W.R., Chen S.J., He A., Truong A., Bhaskaran V., Nelson D.M., Dambach D.M., Lehman-McKeeman L.D., and Car B.D. A retrospective analysis of toxicogenomics in the safety assessment of drug candidates. Toxicol Pathol 35 (2007) 621-635. This article summarizes a very large retrospective, toxicogenomic analysis of 33 compounds that were in safety assessment studies at Bristol Myers Squibb. The data demonstrated an inconsistent correlation between transcription and histopathology, although gene expression was able to detect a significant effect after fewer doses than pathology in 60% of the cases. The overall conclusion was that toxicogenomics can be used in conjunction with traditional pathology assessment to better understand mechanism of toxicity and to make more informed risk management decisions.
7. Wilke R.A., Lin D.W., Roden D.M., Watkins P.B., Flockhart D., Zineh I., Giacomini K.M., and Krauss R.M. Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nat Rev Drug Discov 6 (2007) 904-916
8. Weinshilboum R.M., and Wang L. Pharmacogenetics and pharmacogenomics: development, science, and translation. Annu Rev Genomics Hum Genet 7 (2006) 223-245
9. Grossman I. Routine pharmacogenetic testing in clinical practice: dream or reality?. Pharmacogenomics 8 (2007) 1449-1459
10. Kramer J.A., Sagartz J.E., and Morris D.L. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Nat Rev Drug Discov 6 (2007) 636-649
11. Stevens J.L. Future of toxicology-mechanisms of toxicity and drug safety: where do we go from here?. Chem Res Toxicol 19 (2006) 1393-1401
12. Mayne J.T., Ku W.W., and Kennedy S.P. Informed toxicity assessment in drug discovery: systems-based toxicology. Curr Opin Drug Discov Dev 9 (2006) 75-83. A current opinion article that details nicely the paradigm for conducting what is termed a contemporary safety-discovery program. The authors propose an intelligent assessment of safety that is 'fit-for-purpose' as opposed to large scale, non-targeted safety screening on all programs. This article serves as an adjunct for some of the viewpoints expressed in our article.
13. Lipinski C., and Hopkins A. Navigating chemical space for biology and medicine. Nature 432 (2004) 855-861
14. ®, the Introduction also summarizes other available commercially pathway analysis tools. Both in vivo and in vitro data sets analyzed with other expression analysis tools such as IPA-Tox from Ingenuity Systems are also presented.
15. Zidek N., Hellmann J., Kramer P.J., and Hewitt P.G. Acute hepatotoxicity: a predictive model based on focused illumina microarrays. Toxicol Sci 99 (2007) 289-302
16. Lord P.G., Nie A., and McMillian M. Application of genomics in preclinical drug safety evaluation. Basic Clin Pharmacol Toxicol 98 (2006) 537-546
17. Waring J.F., Yang Y., Healan-Greenberg C.H., Adler A.L., Dickinson R., McNally T., Wang X., Weitzberg M., Xu X., Lisowski A., et al. Gene expression analysis in rats treated with experimental acetyl-coenzyme A carboxylase inhibitors suggests interactions with the peroxisome proliferator-activated receptor alpha pathway. J Pharmacol Exp Ther 324 (2008) 507-516
18. Pognan F. Toxicogenomics applied to predictive and exploratory toxicology for the safety assessment of new chemical entities: a long road with deep potholes. Prog Drug Res 64 (2007) 217-219 238
19. Iorns E., Lord C.J., Turner N., and Ashworth A. Utilizing RNA interference to enhance cancer drug discovery. Nat Rev Drug Discov 6 (2007) 556-568
20. Thomsen W., Frazer J., and Unett D. Functional assays for screening GPCR targets. Curr Opin Biotechnol 16 (2005) 655-665
21. Hamamichi S., Rivas R.N., Knight A.L., Cao S., Caldwell K.A., and Caldwell G.A. Hypothesis-based RNAi screening identifies neuroprotective genes in a Parkinson's disease model. Proc Natl Acad Sci U S A 105 (2008) 728-733
22. Kathiresan S., Melander O., Guiducci C., Surti A., Burtt N.P., Rieder M.J., Cooper G.M., Roos C., Voight B.F., Havulinna A.S., et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40 (2008) 189-197. The authors used genome wide association studies to identify 18 loci associated with blood lipoprotein levels. Of the six novel genes identified, several are probably drugable. Validation of this approach for discovering novel drug targets is demonstrated by current efforts within the pharmaceutical industry on several of the remaining 12 genes demonstrating prior evidence of association with disease susceptibility.
23. Gutierrez J.A., Solenberg P.J., Perkins D.R., Willency J.A., Knierman M.D., Jin Z., Witcher D.R., Luo S., Onyia J.E., and Hale J.E. Ghrelin octanoylation mediated by an orphan lipid transferase. Proc Natl Acad Sci U S A 105 (2008) 6320-6325
24. Yang J., Brown M.S., Liang G., Grishin N.V., and Goldstein J.L. Identification of the acyltransferase that octanoylates ghrelin, an appetite-stimulating peptide hormone. Cell 132 (2008) 387-396
25. Hishikawa D., Shindou H., Kobayashi S., Nakanishi H., Taguchi R., and Shimizu T. Discovery of a lysophospholipid acyltransferase family essential for membrane asymmetry and diversity. Proc Nat Acad Sci U S A 105 (2008) 2830-2835. This and the two prior references used siRNA and other genomic techniques to identify enzymes of an exciting class of proteins (mBOATs) long sought after on the basis hypothesized enzymatic functions important in metabolic diseases. Developing drugs to this unprecedented family of proteins presents challenges for toxicologists because of the poor state of biological characterization of this protein family.
26. Dina C., Meyre D., Gallina S., Durand E., Korner A., Jacobson P., Carlsson L.M., Kiess W., Vatin V., Lecoeur C., et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Gene 39 (2007) 724-726
27. Walker J.R., Su A.I., Self D.W., Hogenesch J.B., Lapp H., Maier R., Hoyer D., and Bilbe G. Applications of a rat multiple tissue gene expression data set. Genome Res 14 (2004) 742-749
28. Gene Expression Omnibus (2008), National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/geo/ http://www.ncbi.nlm.nih.gov/geo/
29. Jackson A.L., Bartz S.R., Schelter J., Kobayashi S.V., Burchard J., Mao M., Li B., Cavet G., and Linsley P.S. Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol 21 (2003) 635-637
30. Dai X., De Souza A.T., Dai H., Lewis D.L., Lee C.K., Spencer A.G., Herweijer H., Hagstrom J.E., Linsley P.S., Bassett D.E., et al. PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy. PLoS Comput Biol 3 (2007) e30. A sophisticated bioinformatics approach to extracting mechanistic information from gene expression data. These investigators treated mice with siRNA directed against the gene for PPARα to deduce the transcriptional network underlying liver hypertrophy.
31. Segalat L. Invertebrate animal models of diseases as screening tools in drug discovery. ACS Chem Biol 2 (2007) 231-236
32. Alestrom P., Holter J.L., and Nourizadeh-Lillabadi R. Zebrafish in functional genomics and aquatic biomedicine. Trends Biotechnol 24 (2006) 15-21
33. Zon L.I., and Peterson R.T. In vivo drug discovery in the zebrafish. Nat Rev Drug Discov 4 (2005) 35-44
34. Trans-NIH Mouse Initiative (2008), National Institutes of Health. http://www.nih.gov/science/models/mouse/index.html http://www.nih.gov/science/models/mouse/index.html
35. Francis R., McGrath G., Zhang J., Ruddy D.A., Sym M., Apfeld J., Nicoll M., Maxwell M., Hai B., Ellis M.C., et al. aph-1 and pen-2 are required for Notch pathway signaling, gamma-secretase cleavage of betaAPP, and presenilin protein accumulation. Dev Cell 3 (2002) 85-97
36. Searfoss G.H., Jordan W.H., Calligaro D.O., Galbreath E.J., Schirtzinger L.M., Berridge B.R., Gao H., Higgins M.A., May P.C., and Ryan T.P. Adipsin, a biomarker of gastrointestinal toxicity mediated by a functional gamma-secretase inhibitor. J Biol Chem 278 (2003) 46107-46116
37. Carere A., Stammati A., and Zucco F. In vitro toxicology methods: impact on regulation from technical and scientific advancements. Toxicol Lett 127 (2002) 153-160
38. Waring J.F., Jolly R.A., Ciurlionis R., Lum P.Y., Praestgaard J.T., Morfitt D.C., Buratto B., Roberts C., Schadt E., and Ulrich R.G. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 175 (2001) 28-42
39. Waring J.F., Ciurlionis R., Jolly R.A., Heindel M., and Ulrich R.G. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol Lett 120 (2001) 359-368
40. Hamadeh H.K., Bushel P.R., Jayadev S., DiSorbo O., Bennett L., Li L., Tennant R., Stoll R., Barrett J.C., Paules R.S., et al. Prediction of compound signature using high density gene expression profiling. Toxicol Sci 67 (2002) 232-240
41. Hamadeh H.K., Bushel P.R., Jayadev S., Martin K., DiSorbo O., Sieber S., Bennett L., Tennant R., Stoll R., Barrett J.C., et al. Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 67 (2002) 219-231
42. de L.F., Atienzar F.A., Marcq L., Dufrane S., Evrard S., Wouters L., Leroux F., Bertholet V., Gerin B., Whomsley R., et al. Use of a low-density microarray for studying gene expression patterns induced by hepatotoxicants on primary cultures of rat hepatocytes. Toxicol Sci 75 (2003) 378-392
43. Lamb J., Crawford E.D., Peck D., Modell J.W., Blat I.C., Wrobel M.J., Lerner J., Brunet J.P., Subramanian A., Ross K.N., et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 (2006) 1929-1935
44. Baker T.K., Carfagna M.A., Gao H., Dow E.R., Li Q., Searfoss G.H., and Ryan T.P. Temporal gene expression analysis of monolayer cultured rat hepatocytes. Chem Res Toxicol 14 (2001) 1218-1231
45. ® that highlight the value of the tool for predictive gene signatures and mechanism of action studies.
46. Kier L.D., Neft R., Tang L., Suizu R., Cook T., Onsurez K., Tiegler K., Sakai Y., Ortiz M., Nolan T., et al. Applications of microarrays with toxicologically relevant genes (tox genes) for the evaluation of chemical toxicants in Sprague Dawley rats in vivo and human hepatocytes in vitro. Mutat Res 549 (2004) 101-113
47. Heinloth A.N., Irwin R.D., Boorman G.A., Nettesheim P., Fannin R.D., Sieber S.O., Snell M.L., Tucker C.J., Li L., Travlos G.S., et al. Gene expression profiling of rat livers reveals indicators of potential adverse effects. Toxicol Sci 80 (2004) 193-202
48. Fielden M.R., Eynon B.P., Natsoulis G., Jarnagin K., Banas D., and Kolaja K.L. A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol Pathol 33 (2005) 675-683. This article describes a detailed set of studies in which 64 compounds (comprising both nephrotoxic and negative controls) were tested in rat toxicology studies and gene expression on microarrays was employed to extract a gene signature predictive of renal toxicity. The signature was forward validated using 21 independent compounds wherein it was demonstrated that when rats were dosed for five days and gene expression determined in kidney tissue, the gene signature correctly predicted the longer term renal toxicity 76% of the time. This report clearly demonstrates the potential for short-term studies to predict outcome in longer term studies.