Use of big data in drug development for precision medicine

Expert Review of Precision Medicine and Drug Development

Volumn 1, Issue 3, 2016, Pages 245-253

  Kim, Rosa S ^a   Goossens, Nicolas ^a,b   Hoshida, Yujin ^a  

^a MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^b GENEVA UNIVERSITY HOSPITALS   (Switzerland)

Author keywords

Big data; drug development; high throughput screen; in silico drug discovery; precision medicine

Indexed keywords

EID: 85013176031     PISSN: None     EISSN: 23808993     Source Type: Journal    
DOI: 10.1080/23808993.2016.1174062     Document Type: Article

References (91)

1. Dickson M, Gagnon JP., Key factors in the rising cost of new drug discovery and development. Nature Rev Drug Discov. 2004;3:417–429.
2. Pammolli F, Magazzini L, Riccaboni M. The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov. 2011;10:428–438.
3. DiMasi JA, Feldman L, Seckler A, et al. Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther. 2010;87:272–277.
4. Hay M, Thomas DW, Craighead JL, et al. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32:40–51.
5. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–795.
6. Golub T. Counterpoint: data first. Nature. 2010;464:679.
7. Bender E. Big data in biomedicine. Nature. 2015;527:S1.
8. Reshef DN, Reshef YA, Finucane HK, et al. Detecting novel associations in large data sets. Science. 2011;334:1518–1524.
9. Good BM, Su AI. Crowdsourcing for bioinformatics. Bioinformatics. 2013;29:1925–1933.
10. Omberg L, Ellrott K, Yuan Y, et al. Enabling transparent and collaborative computational analysis of 12 tumor types within the cancer genome atlas. Nat Genet. 2013;45:1121–1126.
11. Farcomeni A. A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Stat Methods Med Res. 2008;17:347–388.
12. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401:788–791.
13. Tamayo P, Scanfeld D, Ebert BL, et al. Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc Natl Acad Sci U S A. 2007;104:5959–5964.
14. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321–332.
15. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368:2059–2074.
16. Brat DJ, Verhaak RG, et al.; Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372:2481–2498.
17. Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.
18. De Souza N. Genomics: the ENCODE project. Nat Methods. 2012;9:1046.
19. Thurman RE, Rynes E, Humbert R, et al. The accessible chromatin landscape of the human genome. Nature. 2012;489:75–82.
20. Sanyal A, Lajoie BR, Jain G, et al. The long-range interaction landscape of gene promoters. Nature. 2012;489:109–113.
21. Djebali S, Davis CA, Merkel A, et al. Landscape of transcription in human cells. Nature. 2012;489:101–108.
22. Soda M, Choi YL, Enomoto M, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007;448:561–566.
23. Solomon BJ, Mok T, Kim D-W, et al. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N Engl J Med. 2014;371:2167–2177.
24. Shaw AT, Kim D-W, Nakagawa K, et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med. 2013;368:2385–2394.
25. Gerber DE, Minna JD. ALK inhibition for non-small cell lung cancer: from discovery to therapy in record time. Cancer Cell. 2010;18:548–551.
26. Killock D. Lung cancer: a new generation of EGFR inhibition. Nat Rev Clin Oncol. 2015;12:373.
27. Shaw AT, Friboulet L, Leshchiner I, et al. Resensitization to crizotinib by the lorlatinib ALK resistance mutation L1198F. N Engl J Med. 2016;374:54–61.
28. Renfro LA, Mallick H, An M-W, et al. Clinical trial designs incorporating predictive biomarkers. Cancer Treat Rev. 2016;43:74–82.
29. Zang Y, Guo B. Optimal two-stage enrichment design correcting for biomarker misclassification. Stat Methods Med Res. 2015;0(0):1–16.
30. Mehta C, Schäfer H, Daniel H, et al. Biomarker driven population enrichment for adaptive oncology trials with time to event endpoints. Stat Med. 2014;33:4515–4531.
31. Redig AJ, Jänne PA. Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol. 2015;33:975–977.
32. McNeil C. NCI-MATCH launch highlights new trial design in precision-medicine era. J Natl Cancer Inst. 2015;107:djv193.
33. Gannon HS, Kaplan N, Tsherniak A, et al. Identification of an “exceptional responder” cell line to MEK1 inhibition: clinical implications for MEK-targeted therapy. Mol Cancer Res. 2015;14(2):207–215.
34. Chen Z, Cheng K, Walton Z, et al. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature. 2012;483:613–617.
35. Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520:609–611.
36. Mills JJ, Falls JG, De Souza AT, et al. Imprinted M6p/Igf2 receptor is mutated in rat liver tumors. Oncogene. 1998;16:2797–2802.
37. Sudmant PH, Rausch T, Gardner EJ, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526:75–81.
38. Robinson D, Van Allen EM, Wu YM, et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–1228.
39. Li L, Cheng WY, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7:311ra174.
40. Goossens N, Nakagawa S, Sun X, et al. Cancer biomarker discovery and validation. Transl Cancer Res. 2015;4:256–269.
41. Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148:1293–1307.
42. Iwayanagi T, Miyamoto S, Konno T, et al. TP atlas: integration and dissemination of advances in Targeted Proteins Research Program (TPRP)-structural biology project phase II in Japan. J Struct Funct Genomics. 2012;13:145–154.
43. Watkins AM, Arora PS. Structure-based inhibition of protein-protein interactions. Eur J Med Chem. 2015;94:480–488.
44. Caffrey M. A comprehensive review of the lipid cubic phase or in meso method for crystallizing membrane and soluble proteins and complexes. Acta Crystallogr Sect F: Struct Biol Commun. 2015;71:3–18.
45. Carroni M, Saibil HR. Cryo electron microscopy to determine the structure of macromolecular complexes. Methods. 2016;95:78–85.
46. Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462:175–181.
47. Lounkine E, Keiser MJ, Whitebread S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486:361–367.
48. Lamb J. The connectivity map: a new tool for biomedical research. Nat Rev Cancer. 2007;7:54–60.
49. Roti G, Stegmaier K. Genetic and proteomic approaches to identify cancer drug targets. Br J Cancer. 2012;106:254–261.
50. Stegmaier K, Ross KN, Colavito SA, et al. Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat Genet. 2004;36:257–263.
51. Dudley JT, Sirota M, Shenoy M, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3:96ra76.
52. Hieronymus H, Lamb J, Ross KN, et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell. 2006;10:321–330.
53. Hahn CK, Ross KN, Warrington IM, et al. Expression-based screening identifies the combination of histone deacetylase inhibitors and retinoids for neuroblastoma differentiation. Proc Natl Acad Sci U S A. 2008;105:9751–9756.
54. Li J, Zheng S, Chen B, et al. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17:2–12.
55. Power A, Berger AC, Ginsburg GS. Genomics-enabled drug repositioning and repurposing: insights from an IOM roundtable activity. JAMA. 2014;311:2063–2064.
56. Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–1935.
57. Comer E, Beaudoin JA, Kato N, et al. Diversity-oriented synthesis-facilitated medicinal chemistry: toward the development of novel antimalarial agents. J Med Chem. 2014;57:8496–8502.
58. Campillos M, Kuhn M, Gavin A-C, et al. Drug target identification using side-effect similarity. Science. 2008;321:263–266.
59. Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–607.
60. Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–575.
61. Bachovchin DA, Koblan LW, Wu W, et al. A high-throughput, multiplexed assay for superfamily-wide profiling of enzyme activity. Nat Chem Biol. 2014;10:656–663.
62. Boehm JS, Golub TR. An ecosystem of cancer cell line factories to support a cancer dependency map. Nat Rev Genet. 2015;16:373–374.
63. Devillers J. Methods for building QSARs. Comput Toxicol. 2013;II:3–27.
64. Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. 1st ed. Amsterdam: Academic Press; 2015.
65. Woo Y, Lai DY. OncoLogic: a mechanism-based expert system for predicting the carcinogenic potential of chemicals. In: Helma C, editor. Predictive Toxicology. 2005.Boca Raton (FL): Taylor & Francis; p. 385–431.
66. Marchant CA, Briggs KA, Long A. In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol Mech Methods. 2008;18:177–187.
67. Patlewicz G, Jeliazkova N, Safford R, et al. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res. 2008;19:495–524.
68. Kirchmair J, Göller AH, Lang D, et al. Predicting drug metabolism: experiment and/or computation? Nature Rev Drug Discov. 2015;14:387–404.
69. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol. 2007;152:9–20.
70. Smithing MP, Darvas F. HazardExpert: an expert system for predicting chemical toxicity. In: Finley JW, Robinson SF, Armstrong DJ, editors. Food safety assessment, ACS Symposium Series. Vol. 484. Washington (DC): American Chemical Society, 1992; p. 191–200.
71. Sung JH, Srinivasan B, Esch MB, et al. Using physiologically-based pharmacokinetic-guided “body-on-a-chip” systems to predict mammalian response to drug and chemical exposure. Exp Biol Med. (Maywood). 2014 Sep;239(9):1225–1239. doi:10.1177/1535370214529397.
72. Roncaglioni A, Toropov AA, Toropova AP, et al. In silico methods to predict drug toxicity. Curr Opin Pharmacol. 2013;13:802–806.
73. Kuster A, Adler N. Pharmaceuticals in the environment: scientific evidence of risks and its regulation. Philosophical Trans R Soc B: Biol Sci. 2014;369:20130587.
74. Jobling S, Williams R, Johnson A, et al. Predicted exposures to steroid estrogens in U.K. rivers correlate with widespread sexual disruption in wild fish populations. Environ Health Perspect. 2006;114 Suppl 1:32–39.
75. Brodin T, Fick J, Jonsson M, et al. Dilute concentrations of a psychiatric drug alter behavior of fish from natural populations. Science. 2013;339:814–815.
76. Speck-Planche A, Kleandrova VV, Luan F, et al. Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. Ecotoxicol Environ Saf. 2012;80:308–313.
77. Kar S, Roy K. QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. J Hazard Mater. 2010;177:344–351.
78. Dix DJ, Houck KA, Martin MT, et al. The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci. 2007;95:5–12.
79. Judson RS, Magpantay FM, Chickarmane V, et al. Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor. Toxicol Sci. 2015;148:137–154.
80. Law LW. Effects of combinations of antileukemic agents on an acute lymphocytic leukemia of mice. Cancer Res. 1952;12:871–878.
81. Law LW. Origin of the resistance of leukaemic cells to folic acid antagonists. Nature. 1952;169:628–629.
82. Sun X, Vilar S, Tatonetti NP. High-throughput methods for combinatorial drug discovery. Sci Transl Med. 2013;5:205rv201.
83. Pritchard JR, Bruno PM, Gilbert LA, et al. Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci U S A. 2013;110:E170–E179.
84. Postow MA, Chesney J, Pavlick AC, et al. Nivolumab and ipilimumab versus ipilimumab in untreated melanoma. N Engl J Med. 2015;372:2006–2017.
85. Swain SM, Baselga J, Kim S-B, et al. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med. 2015;372:724–734.
86. Robert C, Karaszewska B, Schachter J, et al. Improved overall survival in melanoma with combined dabrafenib and trametinib. New England J Med. 2015;372:30–39.
87. Skirton H, Goldsmith L, Jackson L, et al. Direct to consumer genetic testing: a systematic review of position statements, policies and recommendations. Clin Genet. 2012;82:210–218.
88. Eisenstein M. GSK collaborates with Apple on ResearchKit. Nat Biotechnol. 2015;33:1013–1014.
89. Egbring M, Kullak-Ublick GA, Russmann S. Phynx: an open source software solution supporting data management and web-based patient-level data review for drug safety studies in the general practice research database and other health care databases. Pharmacoepidemiol Drug Saf. 2010;19:38–44.
90. Byrd JC, Devi GR, De Souza AT, et al. Disruption of ligand binding to the insulin-like growth factor II/mannose 6-phosphate receptor by cancer-associated missense mutations. J Biol Chem. 1999;274:24408–24416.
91. Devi GR, De Souza AT, Byrd JC, et al. Altered ligand binding by insulin-like growth factor II/mannose 6-phosphate receptors bearing missense mutations in human cancers. Cancer Res. 1999;59:4314–4319.