Publication Cover
Expert Review of Precision Medicine and Drug Development
Personalized medicine in drug development and clinical practice
Volume 4, 2019 - Issue 3
808
Views
46
CrossRef citations to date
0
Altmetric
Review

Use of big data in drug development for precision medicine: an update

, &
Pages 189-200 | Received 14 Feb 2019, Accepted 08 May 2019, Published online: 20 May 2019

References

  • Altevogt BM, Davis M, Pankevich DE, et al. Improving and accelerating therapeutic development for nervous system disorders: workshop summary. Washington, DC: National Academies Press; 2014.
  • Force UPST. Aspirin use to prevent cardiovascular disease and colorectal cancer: preventive medication. (Ed.^Eds); 2016.
  • Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41.
  • Cha Y, Erez T, Reynolds I, et al. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol. 2018;175(2):168–180.
  • Kim RS, Goossens N, Hoshida Y. Use of big data in drug development for precision medicine. Expert Rev Precis Med Drug Dev. 2016;1(3):245–253.
  • Dimitrakopoulou K, Dimitrakopoulos GN, Sgarbas KN, et al. Tamoxifen integromics and personalized medicine: dynamic modular transformations underpinning response to tamoxifen in breast cancer treatment. Omics. 2014;18(1):15–33.
  • Jain A, Rakhi N, Bagler G. Analysis of food pairing in regional cuisines of India. PloS One. 2015;10(10):e0139539.
  • Higdon R, Earl RK, Stanberry L, et al. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. Omics. 2015;19(4):197–208.
  • Özdemir V, Faris J, Srivastava S. Crowdfunding 2.0: the next‐generation philanthropy: A new approach for philanthropists and citizens to co‐fund disruptive innovation in global health. EMBO Rep. 2015;16(3):267–271.
  • Calimlioglu B, Karagoz K, Sevimoglu T, et al. Tissue-specific molecular biomarker signatures of type 2 diabetes: an integrative analysis of transcriptomics and protein–protein interaction data. Omics. 2015;19(9):563–573.
  • Network CGAR. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059–2074.
  • Network CGAR. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372(26):2481–2498.
  • Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57.
  • Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science. 2004;306(5696):636–640.
  • Sanyal A, Lajoie BR, Jain G, et al. The long-range interaction landscape of gene promoters. Nature. 2012;489(7414):109.
  • Djebali S, Davis CA, Merkel A, et al. Landscape of transcription in human cells. Nature. 2012;489(7414):101.
  • Liu CH, Abrams ND, Carrick DM, et al.  Biomarkers of chronic inflammation in disease development and prevention: challenges and opportunities. Nat Immunol  2017;18:1175–80.
  • Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2018;1–14. Available from: https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxx069/4817524.
  • Borrebaeck CAJNRC. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat Rev Cancer. 2017;17(3):199.
  • Van’t Veer LJ, Dai H, Van De Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530.
  • Van De Vijver MJ, He YD, Van’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999–2009.
  • Schully SD, Carrick DM, Mechanic LE, et al. Leveraging biospecimen resources for discovery or validation of markers for early cancer detection. JNCI. 2015;107:4.
  • Ransohoff DF. Proteomics aetflix®? Clin Chem. 2010;56(2):172–176.
  • Füzéry AK, Levin J, Chan MM, et al. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteomics. 2013;10(1):13.
  • Sanseau P, Agarwal P, Barnes MR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30(4):317.
  • Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Nat Acad Sci. 2009;106(23):9362–9367.
  • Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov. 2013;12(8):581.
  • Wang Z-Y, Zhang H-Y. Rational drug repositioning by medical genetics. Nat Biotechnol. 2013;31(12):1080.
  • Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47(8):856.
  • Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376.
  • Florez JC. Mining the genome for therapeutic targets. Diabetes 2017. dbi160069.
  • Kathiresan S, Melander O, Guiducci C, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40(2):189.
  • Denny JC, Driest SL, Wei WQ, et al. The influence of big (clinical) data and genomics on precision medicine and drug development. Clin Pharmacol Ther. 2018;103(3):409–418.
  • Jin G, Wong ST. Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today. 2014;19(5):637–644.
  • Disease pathways: A key to new drug discovery. (Ed.^Eds) NOVARTIS.com; 2013
  • Goh K-I, Cusick ME, Valle D, et al. The human disease network. Proc Nat Acad Sci. 2007;104(21):8685–8690.
  • Li Y, Agarwal P. A pathway-based view of human diseases and disease relationships. PloS One. 2009;4(2):e4346.
  • Delavan B, Roberts R, Huang R, et al. Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today. 2018;23(2):382–394.
  • LePendu P, Iyer SV, Bauer‐Mehren A, et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther. 2013;93(6):547–555.
  • Leeper NJ, Bauer-Mehren A, Iyer SV, et al. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes. PloS One. 2013;8(5):e63499.
  • Bowton E, Field JR, Wang S, et al. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med. 2014;6(234):234cm233–234cm233.
  • Roden DM, Denny JC. Integrating electronic health record genotype and phenotype datasets to transform patient care. Clin Pharmacol Ther. 2016;99(3):298–305.
  • Denny JC, Bastarache L, Ritchie MD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31(12):1102.
  • Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2013;42(D1):D1091–D1097.
  • Rastegar-Mojarad M, Ye Z, Kolesar JM, et al. Opportunities for drug repositioning from phenome-wide association studies. Nat Biotechnol. 2015;33(4):342.
  • Devillers J. Methods for building QSARs. In: Computational Toxicology. Totowa, NJ: Humana Press. 2013. p. 3–27.
  • Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. New York (NY): Academic press; 2015.
  • Munos BH, Chin WW. How to revive breakthrough innovation in the pharmaceutical industry. Sci Transl Med. 2011;3(89):89cm16–89cm16.
  • Mignani S, Huber S, Tomas H, et al. Why and how have drug discovery strategies in pharma changed? What are the new mindsets? Drug Discov Today. 2016;21(2):239–249.
  • Novac N. Challenges and opportunities of drug repositioning. Trends Pharmacol Sci. 2013;34(5):267–272.
  • Deotarse PPJAS, Baile MB, Kolhe NS, et al. Drug repositioning: a review. Int J Pharma Res Rev. 2015; 4(8):51–58.
  • Shaw AT, Kim D-W, Nakagawa K, et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med. 2013;368(25):2385–2394.
  • Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462(7270):175.
  • 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(4):321–330.
  • Dudley JT, Deshpande T, Butte AJ. Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform. 2011;12(4):303–311.
  • Wagner A, Cohen N, Kelder T, et al. Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia. Mol Syst Biol. 2015;11(3):791.
  • Hsieh Y-Y, Chou C, Lo H, et al. Repositioning of a cyclin-dependent kinase inhibitor GW8510 as a ribonucleotide reductase M2 inhibitor to treat human colorectal cancer. Cell Death Discov. 2016;2:16027.
  • Kunkel SD, Suneja M, Ebert SM, et al. mRNA expression signatures of human skeletal muscle atrophy identify a natural compound that increases muscle mass. Cell Metab. 2011;13(6):627–638.
  • Malcomson B, Wilson H, Veglia E, et al. Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosis. Proc Nat Acad Sci. 2016;113(26):E3725–E3734.
  • 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(5795):1929–1935.
  • Subramanian A, Narayan R, Corsello SM, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437–1452. e1417.
  • Wang Z, Monteiro CD, Jagodnik KM, et al. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat Commun. 2016;7:12846.
  • Pacini C, Iorio F, Gonçalves E, et al. DvD: an R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data. Bioinformatics. 2012;29(1):132–134.
  • Zhang S-D, Gant TW. sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics. 2009;10(1):236.
  • Hay M, Thomas DW, Craighead JL, et al. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32(1):40.
  • Patlewicz G, Fitzpatrick JM. Current and future perspectives on the development, evaluation, and application of in silico approaches for predicting toxicity. Chem Res Toxicol. 2016;29(4):438–451.
  • Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016;6(2):147–172.
  • Yang H, Sun L, Li W, et al. In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem. 2018;6:30.
  • Fowler S, Schnall JG. TOXNET: information on toxicology and environmental health. AJN Am J Nurs. 2014;114(2):61–63.
  • Judson R, Richard A, Dix D, et al. ACToR—aggregated computational toxicology resource. Toxicol Appl Pharmacol. 2008;233(1):7–13.
  • Williams-DeVane CR, Wolf MA, Richard AM. DSSTox chemical-index files for exposure-related experiments in ArrayExpress and Gene Expression Omnibus: enabling toxico-chemogenomics data linkages. Bioinformatics. 2009;25(5):692–694.
  • Martin M, Judson R. ToxRefDB-release user-friendly web-based tool for mining ToxRefDB. Washington, DC: US Environmental Protection Agency; 2010.
  • Schmidt U, Struck S, Gruening B, et al. SuperToxic: a comprehensive database of toxic compounds. Nucleic Acids Res. 2008;37(suppl_1):D295–D299.
  • Wishart D, Arndt D, Pon A, et al. T3DB: the toxic exposome database. Nucleic Acids Res. 2014;43(D1):D928–D934.
  • Cheng F, Li W, Zhou Y et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012;52:3099–105.
  • Wang Y, Xiao J, Suzek TO, et al. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(suppl_2):W623–W633.
  • Gaulton A, Hersey A, Nowotka M, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2016;45(D1):D945–D954.
  • Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2015;44(D1):D1045–D1053.
  • Jiang P, Sellers WR, Liu XS. Big data approaches for modeling response and resistance to cancer drugs. Annu Rev Biomed Data Sci. 2018;1:1–27
  • Sanz F, Pognan F, Steger-Hartmann T, et al. Legacy data sharing to improve drug safety assessment: the eTOX project. Nat Rev Drug Discov. 2017;16(12):811.
  • Yang JJ, Landier W, Yang W, et al. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia. J Clin Oncol. 2015;33(11):1235.
  • Pratt V, McLeod H, Dean L, et al. Mercaptopurine Therapy and TPMT Genotype. Medical Genetics Summaries; 2012.
  • de Beaumais TA, Fakhoury M, Medard Y, et al. Determinants of mercaptopurine toxicity in paediatric acute lymphoblastic leukemia maintenance therapy. Br J Clin Pharmacol. 2011;71(4):575–584.
  • Eduati F, Mangravite LM, Wang T, et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol. 2015;33(9):933.
  • Abdo N, Xia M, Brown CC, et al. Population-based in vitro hazard and concentration–response assessment of chemicals: the 1000 genomes high-throughput screening study. Environ Health Perspect. 2015;123(5):458.
  • Low SK, Zembutsu H, Nakamura Y. Breast cancer: the translation of big genomic data to cancer precision medicine. Cancer Sci. 2018;109(3):497–506.
  • Ingle JN, Schaid DJ, Goss PE, et al. Genome-wide associations and functional genomic studies of musculoskeletal adverse events in women receiving aromatase inhibitors. J Clin Oncol. 2010;28(31):4674.
  • Chung S, Low S-K, Zembutsu H, et al. A genome-wide association study of chemotherapy-induced alopecia in breast cancer patients. Breast Cancer Res. 2013;15(5):R81.
  • Godman B, Finlayson AE, Cheema PK, et al. Personalizing health care: feasibility and future implications. BMC Med. 2013;11(1):179
  • Keegan BMJTLN. Natalizumab for multiple sclerosis: a complicated treatment. Lancet Neurol. 2011;10(8):677–678.
  • Kappos L, Bates D, Edan G, et al. Natalizumab treatment for multiple sclerosis: updated recommendations for patient selection and monitoring. Lancet Neurol. 2011;10(8):745–758.
  • Mok TS, Wu Y-L, Thongprasert S, et al. Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361(10):947–957.
  • Shah RR, Shah D. Personalized medicine: is it a pharmacogenetic mirage? Br J Clin Pharmacol. 2012;74(4):698–721.
  • Ma J, Sheridan RP, Liaw A, et al. Deep neural nets as a method for quantitative structure–activity relationships. J Chem Inf Model. 2015;55(2):263–274.
  • Medina Marrero R, Marrero-Ponce Y, Barigye S, et al. QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents. SAR QSAR Environ Res. 2015;26(11):943–958.
  • Weidlich IE, Filippov IV, Brown J, et al. Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods. Bioorg Med Chem. 2013;21(11):3127–3137.
  • Newby D, Freitas AA, Ghafourian T. Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption. Eur J Med Chem. 2015;90:751–765.
  • Jain N, Gupta S, Sapre N, et al. In silico de novo design of novel NNRTIs: a bio-molecular modelling approach. RSC Adv. 2015;5(19):14814–14827.
  • Svetnik V, Liaw A, Tong C, et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947–1958.
  • Fernández-Delgado M, Cernadas E, Barro S, et al. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15(1):3133–3181.
  • Singh H, Singh S, Singla D, et al. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol Direct. 2015;10(1):10.
  • Mistry P, Neagu D, Trundle PR, et al. Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology. Soft Comput. 2016;20(8):2967–2979.
  • Kumari P, Nath A, Chaube R. Identification of human drug targets using machine-learning algorithms. Comput Biol Med. 2015;56:175–181.
  • Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–1554.
  • Mamoshina P, Vieira A, Putin E, et al. Applications of deep learning in biomedicine. Mol Pharm. 2016;13(5):1445–1454.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.
  • Zhang L, Tan J, Han D, et al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017;22(11):1680–1685
  • Xue H, Li J, Xie H, et al. Review of drug repositioning approaches and resources. Int J Biol Sci. 2018;14(10):1232
  • Sander J, Ester M, Kriegel H-P XX. Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Mining Knowledge Discovery. 1998;2(2):169–194.
  • Agrawal R, Gehrke J, Gunopulos D, et al. Automatic subspace clustering of high dimensional data for data mining applications. Seattle, WA: ACM; 1998.
  • Wang W, Yang J, Muntz R. STING: A statistical information grid approach to spatial data mining. In: VLDB. (Ed.^Eds); San Francisco, CA: Morgan Kaufmann Publishers Inc. 1997. P. 186–195.
  • Ankerst M, Breunig MM, Kriegel H-P, et al. OPTICS: ordering points to identify the clustering structure. In: ACM Sigmod record. (Ed.^Eds), Philadelphia, PA: ACM; 1999. P. 49–60.
  • Chen B, Ding Y, DJJPcb W. Assessing drug target association using semantic linked data. PLoS Comput Biol. 2012;8(7):e1002574.
  • Zhu Q, Tao C, Shen F, et al. Exploring the pharmacogenomics knowledge base (pharmgkb) for repositioning breast cancer drugs by leveraging web ontology language (OWL) and cheminformatics approaches. In: Biocomputing 2014. (World Scientific). Hawaii, USA: Pac Symp Biocomput. 2014. p. 172–182.
  • Consortium ICG. International network of cancer genome projects. Nature. 2010;464(7291):993.
  • Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–795.
  • Gaziano JM, Concato J, Brophy M, et al. Million Veteran program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214–223.
  • Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nat Rev Genet. 2018;19(4):208.
  • Photopoulos J. 13 countries to share 1 million genomes for research. (Ed.^Eds) bionews.org.uk; 2018
  • Rj R. The Genome War, Round Two. (Ed.^Eds) medium.com; 2015
  • Sardana D, Zhu C, Zhang M, et al. Drug repositioning for orphan diseases. Brief Bioinform. 2011;12(4):346–356.
  • Wang Y, Kung L, Byrd TAJTF, et al. Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol Forecasting Social Change. 2018;126:3–13.
  • Jensen MA, Ferretti V, Grossman RL, et al. The NCI genomic data commons as an engine for precision medicine. Blood. 2017;130(4):453–459.
  • Bhise NS, Ribas J, Manoharan V, et al. Organ-on-a-chip platforms for studying drug delivery systems. J Control Release. 2014;190:82–93.
  • Hotta A, Yamanaka S. From genomics to gene therapy: induced pluripotent stem cells meet genome editing. Annu Rev Genet. 2015;49:47–70.
  • Dilsizian SE, Siegel E. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2014;16(1):441.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.