Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volumn 8, Issue 2, 2016, Pages 104-122

  Boland, Mary Regina ^a,b   Jacunski, Alexandra ^a   Lorberbaum, Tal ^a   Romano, Joseph D ^a   Moskovitch, Robert ^a   Tatonetti, Nicholas P ^a,b  

^a Columbia University ^*  (United States)

^b Observational Health Data Sciences and Informatics   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BETA ADRENERGIC RECEPTOR; CARBAMAZEPINE; CYCLOSPORIN; CYTOCHROME P450 2D6; EXENDIN 4; HYPERICUM PERFORATUM EXTRACT; NEW DRUG; PAROXETINE; PHENYTOIN; PRESCRIPTION DRUG; PSYCHOTROPIC AGENT; ROSIGLITAZONE; SEROTONIN RECEPTOR; TORCETRAPIB; TRIPTAN DERIVATIVE;

ADVERSE DRUG REACTION; ARTICLE; CLINICAL TRIAL (TOPIC); COMORBIDITY; DATA BASE; DRUG INDUSTRY; DRUG MECHANISM; DRUG PROTEIN BINDING; DRUG SAFETY; DRUG TARGETING; ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL FACTOR; GENE ONTOLOGY; GENETIC ASSOCIATION; HUMAN; LIFESTYLE; MEDICAL INFORMATION; MEDICAL SUBJECT HEADINGS; MEDICAL TERMINOLOGY; PERSONALIZED MEDICINE; PHARMACOGENOMICS; POLYPHARMACY; POPULATION; POSTMARKETING SURVEILLANCE; PREDICTION; PROTEIN DNA INTERACTION; PROTEIN INTERACTION; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; QT PROLONGATION; QUESTIONNAIRE; REGISTER; RISK ASSESSMENT; SAMPLE SIZE; SCREENING; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE; SYSTEMS BIOLOGY; SYSTEMS PHARMACOLOGY; TOXICOGENETICS; UNSPECIFIED SIDE EFFECT; ANIMAL; METABOLISM; PATHOLOGY; PATHOPHYSIOLOGY; PROCEDURES;

ANIMALS; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; SYSTEMS BIOLOGY;

EID: 84958680331     PISSN: 19395094     EISSN: 1939005X     Source Type: Journal    
DOI: 10.1002/wsbm.1323     Document Type: Article

References (114)

1. Budnitz DS, Shehab N, Kegler SR, Richards CL. Medication use leading to emergency department visits for adverse drug events in older adults. Ann Intern Med 2007, 147:755-765.
2. Barker KN, Flynn EA, Pepper GA, Bates DW, Mikeal RL. Medication errors observed in 36 health care facilities. Arch Intern Med 2002, 162:1897-1903.
3. Gandhi TK, Weingart SN, Borus J, Seger AC, Peterson J, Burdick E, Seger DL, Shu K, Federico F, Leape LL, et al. Adverse drug events in ambulatory care. N Engl J Med 2003, 348:1556-1564.
4. Southworth MR, Reichman ME, Unger EF. Dabigatran and postmarketing reports of bleeding. N Engl J Med 2013, 368:1272-1274.
5. Paul SM, Mytelka DS, Dunwiddie CT. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov 2010, 9:203-214.
6. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711-715.
7. Jacunski A, Tatonetti NP. Connecting the dots: applications of network medicine in pharmacology and disease. Clin Pharmacol Ther 2013, 94:659-669.
8. Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature 2008, 455:1054-1056.
9. Berger SI, Iyengar R. Role of systems pharmacology in understanding drug adverse events. WIREs Syst Biol Med 2010, 3:129-135.
10. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2012, 41:D808-D815.
11. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2013, 42:D1091-D1097.
12. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 2010, 6:343.
13. Tatonetti NP, Liu T, Altman RB. Predicting drug side effects by chemical systems biology. Genome Biol 2009, 10:238.
14. Shetty KD, Dalal SR. Using information mining of the medical literature to improve drug safety. J Am Med Inform Assoc 2011, 18:668-674.
15. Leaman, R, Wojtulewicz L, Sullivan R, Skariah A, Yang J, Gonzalez G. Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In: BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Association for Computational Linguistics, Uppsala, Sweden, 2010, pp. 117-125.
16. Moskovitch, R., Walsh, C., Hripsack, G. & Tatonetti, N. Prediction of biomedical events via time intervals mining. In: ACM KDD Workshop on Connected Health in Big Data Era, New York, NY, August 2014.
17. Moskovitch R, Shahar Y. Classification-driven temporal discretization of multivariate time series. Data Min Knowl Disc 2015, 29:871-913. doi:10.1007/s10618-014-0380-z.
18. Moskovitch R, Shahar Y. Fast time intervals mining using the transitivity of temporal relations. Knowl Inf Syst 2013, 42:21-48.
19. Sacchi L, Dagliati A, Bellazzi R. Analyzing complex patients' temporal histories: new frontiers in temporal data mining. Methods Mol Biol 2015, 1246:89-105.
20. Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics 2010, 11(Suppl 9):S7.
21. Rouane-Hacene M, Toussaint Y, Valtchev P. Mining safety signals in spontaneous reports database using concept analysis. Artif Intell 2009, 5651:285-294.
22. Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res 2013, 22:39-56.
23. Overby CL, Hripcsak G, Shen Y. Estimating heritability of drug-induced liver injury from common variants and implications for future study designs. Sci Rep 2014, 4:5762.
24. Haerian K, Varn D, Vaidya S, Ena L, Chase HS, Friedman C. Detection of pharmacovigilance-related adverse events using electronic health records and automated methods. Clin Pharmacol Ther 2012, 92:228-234.
25. Boland MR, Tatonetti NP. Are all vaccines created equal? Using electronic health records to discover vaccines associated with clinician-coded adverse events. AMIA Jt Summits Transl Sci Proc 2015:196-200.
26. Casey CG, Iskander JK, Roper MH, Mast EE, Wen XJ, Torok TJ, Chapman LE, Swerdlow DL, Morgan J, Heffelfinger JD, et al. Adverse events associated with smallpox vaccination in the United States, January-October 2003. JAMA 2005, 294:2734-2743.
27. LaRussa PS, Edwards KM, Dekker CL, Klein NP, Halsey NA, Marchant C, Baxter R, Engler RJM, Kissner J, Slade BA. Understanding the role of human variation in vaccine adverse events: the clinical immunization safety assessment network. Pediatrics 2011, 127:S65-S73.
28. Luo Z, Zhang G-Q, XU R. Mining patterns of adverse events using aggregated clinical trial results. AMIA Jt Summits Transl Sci Proc 2013, 2013:112.
29. Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009, 16:328-337.
30. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 2005, 12:448-457.
31. Eriksson R, Jensen PB, Frankild S, Jensen LJ, Brunak S. Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. J Am Med Inform Assoc 2013, 20:947-953.
32. Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther 2012, 91:1010-1021.
33. Motsinger-Reif AA et al. Genome-wide association studies in pharmacogenomics: successes and lessons. Pharmacogenet Genomics 2013, 23:383-394.
34. Hajjar ER, Cafiero AC, Hanlon JT. Polypharmacy in elderly patients. Am J Geriatr Pharmacother 2008, 5:345-351.
35. Sun X, Vilar S, Tatonetti NP. High-throughput methods for combinatorial drug discovery. Sci Transl Med 2013, 5:205rv1.
36. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science 2008, 321:263-266.
37. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007, 25:197-206.
38. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, et al. Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
39. Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010, 26:i246-i254.
40. Zhao S, Li S. PLOS ONE: network-based relating pharmacological and genomic spaces for drug target identification. PLoS One 2010, 5:e11764.
41. Jiang Y, Li Y, Kuang Q, Ye L, Wu Y, Yang L, Li M. Predicting putative adverse drug reaction related proteins based on network topological properties. Anal Methods 2014, 6:2692-2698.
42. Huang L-C, Wu X, Chen JY. Predicting adverse side effects of drugs. BMC Genomics 2011, 12:S11.
43. Kuhn M, Al Banchaabouchi M, Campillos M, Jensen LJ, Gross C, Gavin A-C, Bork P. Systematic identification of proteins that elicit drug side effects. Mol Syst Biol 2013, 9:663.
44. Huang J, Niu C, Green CD, Yang L, Mei H, Han J-D J. Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Comput Biol 2013, 9:e1002998.
45. Guimerà R, Sales-Pardo M. A network inference method for large-scale unsupervised identification of novel drug-drug interactions. PLoS Comput Biol 2013, 9:e1003374.
46. Cobanoglu MC, Liu C, Hu F, Oltvai ZN, Bahar I. Predicting drug-target interactions using probabilistic matrix factorization. J Chem Inf Model 2013, 53:3399-3409.
47. Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkin JL, Lavan P, Weber E, Doak AK, Cote S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 2012, 486:361-367.
48. Scheiber J, Jenkin JL, Sukuru SCK, Bender A, Mikhailov D, Milik M, Azzaoui K, Whitebread S, Hamon J, Urban L, et al. Mapping adverse drug reactions in chemical space. J Med Chem 2009, 52:3103-3107.
49. Duran-Frigola M, Rossell D, Aloy P. A chemo-centric view of human health and disease. Nat Commun 2014, 5:5676.
50. Atias N, Sharan R. An algorithmic framework for predicting side effects of drugs. J Comput Biol 2011, 18, 18:207-218.
51. Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen X-W, Matheny ME, Xu H. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J Am Med Inform Assoc 2012, 19:e28-e35.
52. Huang L-C, Wu X, Chen JY. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics 2013, 13:313-324.
53. LaBute MX, LaBute MX, Zhang X, Lenderman J, Bennion BJ, Wong SE, Lightstone FC. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines. PLoS One 2014, 9:e106298.
54. Smith RD, Dunbar JB, Jr., Ung P M-U, Esposito EX, Yang C-Y, Wang S, Carlson HA. CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 2011, 51:2115-2131.
55. Sousa SF, Ribeiro AJM, Coimbra JTS, Neves RPP, Martins SA, Moorthy NSHN, Fernandes PA, Ramos MJ. Protein-ligand docking in the new millennium-a retrospective of 10 years in the field. Curr Med Chem 2013, 20:2296-2314.
56. Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Belov D, Altman RB, Pande VS. Cloud-based simulations on Google exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 2014, 6:15-21.
57. Stjernschantz E, Oostenbrink C. Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 2010, 98:2682-2691.
58. Campbell AJ, Lamb ML, Joseph-McCarthy D. Ensemble-based docking using biased molecular dynamics. J Chem Inf Model 2014, 54:2127-2138.
59. Scheiber J, Chen B, Milik M, Sukuru SCK, Bender A, Mikhailov D, Whitebread S, Hamon J, Azzaoui K, Urban L, et al. Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis. J Chem Inf Model 2009, 49:308-317.
60. Wallach I, Jaitly N, Lilien R. A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways. PLoS One 2010, 5:e12063.
61. Xie L, Li J, Xie L, Bourne PE. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol 2009, 5:e1000387.
62. Chen X, Xu J, Huang B, Li J, Wu X, Ma L, Jia X, Bian X, Tan F, Liu L, et al. A sub-pathway-based approach for identifying drug response principal network. Bioinformatics 2011, 27:649-654.
63. Lee S, Lee KH, Song M, Lee D. Building the process-drug-side effect network to discover the relationship between biological processes and side effects. BMC Bioinformatics 2011, 12:S2.
64. Duran-Frigola M, Aloy P. Analysis of chemical and biological features yields mechanistic insights into drug side effects. Chem Biol 2013, 20:594-603.
65. Gottlieb A, Altman RB. Integrating systems biology sources illuminates drug action. Clin Pharmacol Ther 2014, 95:663-669.
66. Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of drug-induced myocardial infarction-related protein targets through the prediction of drug-target interactions and analysis of biological processes. Chem Res Toxicol 2014, 27:1263-1281.
67. Silberberg Y, Gottlieb A, Kupiec M, Ruppin E, Sharan R. Large-scale elucidation of drug response pathways in humans. J Comput Biol 2012, 19:163-174.
68. Song M, Yan Y, Jiang Z. Drug-pathway interaction prediction via multiple feature fusion. Mol Biosyst 2014, 10:2907-2913.
69. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics 2012, 28:i522-i528.
70. Shear NH, Spielberg SP. Anticonvulsant hypersensitivity syndrome. In vitro assessment of risk. J Clin Invest 1988, 82:1826-1832.
71. Gennis MA, Vemuri R, Burns EA, Hill JV, Miller MA, Spielberg SP. Familial occurrence of hypersensitivity to phenytoin. Am J Med 1991, 91:631-634.
72. Knowles SR, Uetrecht J, Shear NH. Idiosyncratic drug reactions: the reactive metabolite syndromes. Lancet 2000, 356:1587-1591.
73. Tassaneeyakul W, Tiamkao S, Jantararoungtong T. Association between HLA-B* 1502 and carbamazepine-induced severe cutaneous adverse drug reactions in a Thai population. Epilepsia 2010, 51:926-930.
74. Hung S-I, Chung W-H, Jee S-H, Chen W-C, Chang Y-T, Lee W-R, Hu S-L, Wu M-T, Chen G-S, Wong T-W, et al. Genetic susceptibility to carbamazepine-induced cutaneous adverse drug reactions. Pharmacogenet Genomics 2006, 16:297-306.
75. Ozeki T, Mushiroda T, Yowang A, Takahashi A, Kubo M, Shirakata Y, Ikezawa Z, Iijima M, Shiohara T, Hashimoto K, et al. Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum Mol Genet 2011, 20:1034-1041.
76. Lonjou C, Borot N, Sekula P, Ledger N, Thomas L, Halevy S, Naldi L, Bouwes-Bavinck J-N, Sidoroff A, de Toma C, et al. A European study of HLA-B in Stevens-Johnson syndrome and toxic epidermal necrolysis related to five high-risk drugs. Pharmacogenet Genomics 2008, 18:99-107.
77. Lonjou C, Thomas L, Borot N, Ledger N, de Toma C, LeLouet H, Graf E, Schumacher M, Hovnanian A, Mockenhaupt M, et al. A marker for Stevens-Johnson syndrome...: ethnicity matters. Pharmacogenomics J 2006, 6:265-268.
78. Berger SI, Ma'ayan A, Iyengar R. Systems pharmacology of arrhythmias. Sci Signal 2010, 3:ra30.
79. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B. Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr 2005, 82:497-503.
80. Winnike JH, Li Z, Wright FA, Macdonald JM, O'Connell TM, Watkins PB. Use of pharmacometabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans. Clin Pharmacol Ther 2010, 88:45-51.
81. Cunningham K, Claus SP, Lindon JC, Holmes E, Everett JR, Nicholson JK, Coen M. Pharmacometabonomic characterization of xenobiotic and endogenous metabolic phenotypes that account for inter-individual variation in isoniazid-induced toxicological response. J Proteome Res 2012, 11:4630-4642.
82. Lenz EM, Bright J, Wilson ID, Hughes A, Morrisson J, Lindberg H, Lockton A. Metabonomics, dietary influences and cultural differences: a 1H NMR-based study of urine samples obtained from healthy British and Swedish subjects. J Pharm Biomed Anal 2004, 36:841-849.
83. Sun J, Beger RD, Schnackenberg LK. Metabolomics as a tool for personalizing medicine: 2012 update. Pers Med 2013, 10:149-161.
84. Nicholson JK, Wilson ID. Understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2003, 2:668-676.
85. Li H, Jia W. Cometabolism of microbes and host: implications for drug metabolism and drug-induced toxicity. Clin Pharmacol Ther 2013, 94:574-581.
86. Pleil JD, Sheldon LS. Adapting concepts from systems biology to develop systems exposure event networks for exposure science research. Biomarkers 2011, 16:99-105.
87. Gourley M, Miller FW. Mechanisms of disease: environmental factors in the pathogenesis of rheumatic disease. Nat Clin Pract Rheumatol 2007, 3:172-180.
88. Kasarskis A, Yang X, Schadt E. Integrative genomics strategies to elucidate the complexity of drug response. Pharmacogenomics 2011, 12:1695-1715.
89. Zhang M, Holman CDAJ, Price SD, Sanfilippo FM, Preen DB, Bulsara MK. Comorbidity and repeat admission to hospital for adverse drug reactions in older adults: retrospective cohort study. BMJ 2009, 338:a2752.
90. Onder G, Pedone C, Landi F, Cesari M, Vedova CD, Bernabei R, Gambassi G. Adverse drug reactions as cause of hospital admissions: results from the Italian group of pharmacoepidemiology in the elderly (GIFA). J Am Geriatr Soc 2002, 50:1962-1968.
91. Boran ADW, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel 2010, 13:297-309.
92. Perez EA. Cardiac toxicity of ErbB2-targeted therapies: what do we know? Clin Breast Cancer 2008, 8(Suppl 3):S114-S120.
93. Tan-Chiu E, Yothers G, Romond E. Assessment of cardiac dysfunction in a randomized trial comparing doxorubicin and cyclophosphamide followed by paclitaxel, with or without trastuzumab as adjuvant therapy in node-positive, human epidermal growth factor receptor 2-overexpressing breast cancer: NSABP B-31. J Clin Oncol 2005, 23:7811-7819.
94. Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med 2012, 4:125ra31.
95. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008, 4:682-690.
96. He Z, Zhang J, Shi X-H, Hu L-L, Kong X, Cai Y-D, Chou K-C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One 2010, 5:e9603.
97. Turner RM, Park BK, Pirmohamed M. Parsing interindividual drug variability: an emerging role for systems pharmacology. WIREs Syst Biol Med 2015, 7:221-241. doi:10.1002/wsbm.1302.
98. Sharma V, Sarkar IN. Leveraging concept-based approaches to identify potential phytotherapies. J Biomed Inform 2013, 46:602-614.
99. Staniek A, Bouwmeester H, Fraser PD, Kayser O, Martens S, Tissier A, van der Krol S, Wessjohann L, Warzecha H. Natural products-modifying metabolite pathways in plants. Biotechnol J 2013, 8:1159-1171.
100. Vargas HM, Amouzadeh HR, Engwall MJ. Nonclinical strategy considerations for safety pharmacology: evaluation of biopharmaceuticals. Expert Opin Drug Saf 2013, 12:91-102.
101. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 2012, 52:1757-1768.
102. Kaiser M, Wetzel S, Kumar K, Waldmann H. Biology-inspired synthesis of compound libraries. Cell Mol Life Sci 2008, 65:1186-1201.
103. Yue Y, Chu G-X, Liu X-S, Tang X, Wang W, Liu G-J, Yang T, Ling T-J, Wang X-G, Zhang Z-Z, et al. TMDB: a literature-curated database for small molecular compounds found from tea. BMC Plant Biol 2014, 14:243.
104. Howland RH. Update on St. John's wort. J Psychosoc Nurs Ment Health Serv 2010, 48:20-24.
105. Huang S-M, Lesko LJ. Drug-drug, drug-dietary supplement, and drug-citrus fruit and other food interactions: what have we learned? J Clin Pharmacol 2004, 44:559-569.
106. Zhou X, Li Y, Chen X. Computational identification of bioactive natural products by structure activity relationship. J Mol Graph Model 2010, 29:38-45.
107. Cami A, Arnold A, Manzi S, Reis B. Predicting adverse drug events using pharmacological network models. Sci Transl Med 2011, 3:114ra127.
108. Zhao S, Nishimura T, Chen Y, Azeloglu EU, Gottesman O, Giannarelli C, Zafar MU, Benard L, Badimon JJ, Hajjar RJ, et al. Systems pharmacology of adverse event mitigation by drug combinations. Sci Transl Med 2013, 5:206ra140.
109. Lorberbaum T, Nasir M, Keiser MJ, Vilar S, Hripcsak G, Tatonetti NP. Systems pharmacology augments drug safety surveillance. Clin Pharmacol Ther 2014, 97:151-158.
110. Ryan PB, Madigan D, Stang PE, Schuemie MJ. Medication-wide association studies. CPT Pharmacometrics Syst Pharmacol 2013, 2:1-12.
111. Perkins JR, Sanak M, Canto G, Blanca M, Cornejo-García JA. Unravelling adverse reactions to NSAIDs using systems biology. Trends Pharmacol Sci 2015, 36:172-180.
112. Kuang Q, Wang MQ, Li R, Dong YC, Li Y, Li M. A systematic investigation of computation models for predicting adverse drug reactions (ADRs). PLoS One 2014, 9:e105889.
113. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, Richards DR, McDonald-Smith GP, Gao H, Hennessy L, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci 2013, 110:3507-3512.
114. Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the observational medical outcomes partnership. Stat Med 2012, 31:4401-4415.