In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

References

• Wise L, Parkinson J, Raine J, et al. New approaches to drug safety: a pharmacovigilance tool kit. Nat Rev Drug Discov. 2009;8(10):779–782. doi: 10.1038/nrd3002
   PubMed Web of Science ®Google Scholar
• Hsu PL, Robbins H. Complete convergence and the law of large numbers. Proc Natl Acad Sci USA. 1947;33:25–31.
   Google Scholar
• Pharmacovigilance [Internet]. [cited 2023 Jul 11]. Available from: https://www.who.int/teams/regulation-prequalification/regulation-and-safety/pharmacovigilance
   Google Scholar
• Santoro A, Genov G, Spooner A, et al. Promoting and protecting public health: how the European union pharmacovigilance system works. Drug Saf. 2017;40(10):855–869. doi: 10.1007/s40264-017-0572-8
   PubMed Web of Science ®Google Scholar
• Pitts PJ, Louet HL, Moride Y, et al. 21st century pharmacovigilance: efforts, roles, and responsibilities. Lancet Oncol. 2016;17(11):e486–e492. doi: 10.1016/S1470-2045(16)30312-6
   PubMed Web of Science ®Google Scholar
• U.S. Food & Drug Administration. Preventable adverse drug reactions: a focus on drug interactions [internet]. FDA; 2021 [cited 2023 Jul 25]. Available from: https://www.fda.gov/drugs/drug-interactions-labeling/preventable-adverse-drug-reactions-focus-drug-interactions
   Google Scholar
• Wilke RA, Lin DW, Roden DM, et al. Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nat Rev Drug Discov. 2007;6(11):904–916. doi: 10.1038/nrd2423
   PubMed Web of Science ®Google Scholar
• Pontes H, Clément M, Rollason V. Safety signal detection: the relevance of literature review. Drug Saf. 2014;37(7):471–479. doi: 10.1007/s40264-014-0180-9
   PubMed Web of Science ®Google Scholar
• Banda JM, Evans L, Vanguri RS, et al. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data. 2016;3(1):160026. doi: 10.1038/sdata.2016.26
   PubMedGoogle Scholar
• Guo M, Shu Y, Chen G, et al. A real-world pharmacovigilance study of FDA adverse event reporting system (FAERS) events for niraparib. Sci Rep. 2022;12(1):20601. doi: 10.1038/s41598-022-23726-4
   PubMed Web of Science ®Google Scholar
• Dolladille C, Ederhy S, Sassier M, et al. Immune Checkpoint Inhibitor Rechallenge After Immune-Related Adverse Events in Patients With Cancer. JAMA Oncol. 2020;6(6):865–871. doi: 10.1001/jamaoncol.2020.0726
   PubMed Web of Science ®Google Scholar
• Reese JA, Li X, Hauben M, et al. Identifying drugs that cause acute thrombocytopenia: an analysis using 3 distinct methods. Blood. 2010;116(12):2127–2133. doi: 10.1182/blood-2010-03-276691
   PubMed Web of Science ®Google Scholar
• Research C for DE and. Questions and answers on FDA’s adverse event reporting system (FAERS) [internet]. FDA; 2019 [cited 2023 Jul 14]. Available from: https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers.
   Google Scholar
• Uppsala Monitoring Centre. VigiBase Services [Internet]. [cited 2023 Jul 14]. Available from: https://who-umc.org/vigibase/vigibase-services/
   Google Scholar
• European Medicines Agency. EudraVigilance [Internet]. European Medicines Agency. 2018 [cited 2023 Jul 14]. Available from: https://www.ema.europa.eu/en/human-regulatory/research-development/pharmacovigilance/eudravigilance
   Google Scholar
• Canada H About MedEffect Canada [Internet]. 2003 [cited 2023 Aug 16]. Available from: https://www.canada.ca/en/health-canada/services/drugs-health-products/medeffect-canada/medeffect-canada.html
   Google Scholar
• Vaccine adverse event reporting system (VAERS) [internet]. [cited 2023 Jul 14]. Available from: https://vaers.hhs.gov/index.html
   Google Scholar
• Medical dictionary for regulatory activities terminology (MedDRA). MedDRA hierarchy | MedDRA [Internet]. [cited 2023 Mar 21]. Available from: https://www.meddra.org/how-to-use/basics/hierarchy
   Google Scholar
• WHO collaborating centre for drug statistics methodology - structure and principles for the anatomical therapeutic chemical (ATC) classification system [Internet]. [cited 2023 Jul 14]. Available from: https://www.whocc.no/atc/structure_and_principles/
   Google Scholar
• Harpaz R, DuMouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012;91(6):1010–1021. doi: 10.1038/clpt.2012.50
   PubMed Web of Science ®Google Scholar
• Lavertu A, Vora B, Giacomini KM, et al. A new era in pharmacovigilance: toward real-world data and digital monitoring. Clin Pharmacol Ther. 2021;109(5):1197–1202. doi: 10.1002/cpt.2172
   PubMed Web of Science ®Google Scholar
• Huang L, Guo T, Zalkikar JN, et al. A review of statistical methods for safety surveillance. Ther Innov Regul Sci. 2014;48(1):98–108. doi: 10.1177/2168479013514236
   PubMed Web of Science ®Google Scholar
• Bate A, Evans SJW. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol Drug Saf. 2009;18(6):427–436. doi: 10.1002/pds.1742
   PubMed Web of Science ®Google Scholar
• Cone EB, Reese S, Marchese M, et al. Cardiovascular toxicities associated with abiraterone compared to enzalutamide–A pharmacovigilance study. EClinicalMedicine. 2021;36:100887.
   PubMedGoogle Scholar
• Sarker A, Ginn R, Nikfarjam A, et al. Utilizing social media data for pharmacovigilance: A review. J Biomed Informat. 2015;54:202–212. doi: 10.1016/j.jbi.2015.02.004
   PubMed Web of Science ®Google Scholar
• Menni C, Klaser K, May A, et al. Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom study app in the UK: a prospective observational study. Lancet Infect Dis. 2021;21(7):939–949. doi: 10.1016/S1473-3099(21)00224-3
   PubMed Web of Science ®Google Scholar
• Madigan D, Ryan P, Simpson S, et al. Bayesian methods in pharmacovigilance. Bayesian Stat. 2010;9:421–438.
   Google Scholar
• Tiwari R, Zalkikar J, Huang L. Signal Detection for medical scientists: likelihood ratio test-based methodology. Boca Raton, FL, USA: CRC Press; 2021.
   Google Scholar
• Suling M, Pigeot I. Signal detection and monitoring based on longitudinal healthcare data. Pharmaceutics. 2012;4(4):607–640.
   PubMedGoogle Scholar
• van Puijenbroek EP, Bate A, Leufkens HGM, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11(1):3–10. doi: 10.1002/pds.668
   PubMed Web of Science ®Google Scholar
• Evans SJW, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483–486.
   PubMed Web of Science ®Google Scholar
• Yates F. Contingency tables involving small numbers and the χ2 test. Suppl J R Stat Soc. 1934;1(2):217–235.
   Google Scholar
• Huang L, Zalkikar J, Tiwari RC. A likelihood ratio test based method for signal detection with application to FDA’s drug safety data. J Am Stat Assoc. 2011;106(496):1230–1241.
   Web of Science ®Google Scholar
• MacKay DJ. Information theory, inference and learning algorithms. Cambridge, UK: Cambridge university press; 2003.
   Google Scholar
• Rodrigues PP, Ferreira-Santos D, Silva A, et al. Causality assessment of adverse drug reaction reports using an expert-defined Bayesian network. Artif Intell Med. 2018;91:12–22. doi: 10.1016/j.artmed.2018.07.005
   PubMed Web of Science ®Google Scholar
• Bate A, Lindquist M, Edwards IR, et al. A bayesian neural network method for adverse drug reaction signal generation. E J Clin Pharmacol. 1998;54(4):315–321. doi: 10.1007/s002280050466
   PubMed Web of Science ®Google Scholar
• Dumouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. null. Am Stat. 1999;53(3):177–190. doi: 10.1080/00031305.1999.10474456
   Web of Science ®Google Scholar
• Heckerman D. Bayesian networks for data mining. Data Mining Knowledge Discovery. 1997;1(1):79–119. doi: 10.1023/A:1009730122752
   Web of Science ®Google Scholar
• Khouri C, Revol B, Lepelley M, et al. A meta-epidemiological study found lack of transparency and poor reporting of disproportionality analyses for signal detection in pharmacovigilance databases. J Clinical Epidemiol. 2021;139:191–198. doi: 10.1016/j.jclinepi.2021.07.014
   PubMed Web of Science ®Google Scholar
• Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug-Safety. 2002;25(6):381–392. doi: 10.2165/00002018-200225060-00001
   PubMed Web of Science ®Google Scholar
• Ang PS, Chen Z, Chan CL, et al. Data mining spontaneous adverse drug event reports for safety signals in Singapore–a comparison of three different disproportionality measures. Expert Opin Drug Saf. 2016;15(5):583–590. doi: 10.1517/14740338.2016.1167184
   PubMed Web of Science ®Google Scholar
• Xu X, Kawakami J, Gedara NIM, et al. Data mining methodology for response to hypertension symptomology—application to COVID-19-related pharmacovigilance. Elife. 2021;10:e70734. doi: 10.7554/eLife.70734
   PubMed Web of Science ®Google Scholar
• Nguyen LS, Cooper LT, Kerneis M, et al. Systematic analysis of drug-associated myocarditis reported in the World Health Organization pharmacovigilance database. Nat Commun. 2022;13(1):25. doi: 10.1038/s41467-021-27631-8
   PubMed Web of Science ®Google Scholar
• McBride WG. Thalidomide and congenital abnormalities. Lancet. 1961;2:90927–90928.
   Web of Science ®Google Scholar
• Finney DJ. From thalidomide to pharmacovigilance: a personal account. Side Effects Of Drugs Annual Elsevier. 2003;26:xxv–xxxii.
   Google Scholar
• Fornasier G, Francescon S, Leone R, et al. An historical overview over pharmacovigilance. Int J Clin Pharm. 2018;40(4):744–747. doi: 10.1007/s11096-018-0657-1
   PubMed Web of Science ®Google Scholar
• Lu M, Yin J, Zhu Q, et al. Artificial intelligence in pharmaceutical sciences. Eng. 2023. doi: 10.1016/j.eng.2023.01.014
   Google Scholar
• Wang DY, Salem J-E, Cohen JV, et al. Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. 2018;4(12):1721–1728. doi: 10.1001/jamaoncol.2018.3923
   PubMed Web of Science ®Google Scholar
• Salem J-E, Manouchehri A, Moey M, et al. Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study. Lancet Oncol. 2018;19(12):1579–1589. doi: 10.1016/S1470-2045(18)30608-9
   PubMed Web of Science ®Google Scholar
• Beers E, Van Puijenbroek EP, Bartelink IH, et al. Syndrome of inappropriate antidiuretic hormone secretion (SIADH) or hyponatraemia associated with valproic acid: four case reports from the Netherlands and a case/non-case analysis of vigibase. Drug Saf. 2010;33(1):47–55. doi: 10.2165/11318950-000000000-00000
   PubMed Web of Science ®Google Scholar
• Tatonetti NP, Ye PP, Daneshjou R, et al. Data-driven prediction of drug effects and interactions. Science translational medicine. Sci, trans med. 2012;4(125):ra12531–ra12531. doi: 10.1126/scitranslmed.3003377
   Web of Science ®Google Scholar
• Mazhar F, Battini V, Gringeri M, et al. The impact of anti-TNFα agents on weight-related changes: new insights from a real-world pharmacovigilance study using the FDA adverse event reporting system (FAERS) database. Expert Opin Biol Ther. 2021;21(9):1281–1290. doi: 10.1080/14712598.2021.1948529
   PubMed Web of Science ®Google Scholar
• Poleksic A, Xie L. Database of adverse events associated with drugs and drug combinations. Sci Rep. 2019;9(1):20025. doi: 10.1038/s41598-019-56525-5
   PubMedGoogle Scholar
• Zhou C, Peng S, Lin A, et al. Psychiatric disorders associated with immune checkpoint inhibitors: a pharmacovigilance analysis of the FDA adverse event reporting system (FAERS) database. Eclinicalmedicine. 2023;59:101967. doi: 10.1016/j.eclinm.2023.101967
   PubMedGoogle Scholar
• Ali TB, Schleret TR, Reilly BM, et al. Adverse effects of cholinesterase inhibitors in dementia, according to the pharmacovigilance databases of the United-States and Canada. PLoS One. 2015;10(12):e0144337. doi: 10.1371/journal.pone.0144337
   PubMed Web of Science ®Google Scholar
• Zhao S, Nishimura T, Chen Y, et al. Systems pharmacology of adverse event mitigation by drug combinations. Sci Trans Med. 2013;5(206):ra206140–ra206140. doi: 10.1126/scitranslmed.3006548
   Web of Science ®Google Scholar
• Wilson JL, Steinberg E, Racz R, et al. A network paradigm predicts drug synergistic effects using downstream protein–protein interactions. CPT Pharmacom Syst Pharma. 2022;11(11):1527–1538. doi: 10.1002/psp4.12861
   PubMedGoogle Scholar
• Berger SI, Ma’ayan A, Iyengar R. Systems pharmacology of arrhythmias. Sci Signaling. 2010;3(118):ra30–ra30.
   PubMed Web of Science ®Google Scholar
• Candore G, Juhlin K, Manlik K, et al. Comparison of statistical signal detection methods within and across spontaneous reporting databases. Drug Saf. 2015;38(6):577–587. doi: 10.1007/s40264-015-0289-5
   PubMed Web of Science ®Google Scholar
• Seabroke S, Candore G, Juhlin K, et al. Performance of stratified and subgrouped disproportionality analyses in spontaneous databases. Drug Saf. 2016;39(4):355–364. doi: 10.1007/s40264-015-0388-3
   PubMed Web of Science ®Google Scholar
• Slattery J, Alvarez Y, Hidalgo A. Choosing thresholds for statistical signal detection with the proportional reporting ratio. Drug Saf. 2013;36(8):687–692.
   PubMed Web of Science ®Google Scholar
• Stamatellos V-P, Siafis S, Papazisis G. Disease-modifying agents for multiple sclerosis and the risk for reporting cancer: a disproportionality analysis using the US food and drug administration adverse event reporting system database. Brit J Clin Pharma. 2021;87(12):4769–4779.
   PubMed Web of Science ®Google Scholar
• Noguchi Y, Tachi T, Teramachi H. Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source. Brief Bioinform. 2021;22(6):bbab347.
   PubMed Web of Science ®Google Scholar
• Ding Y, Markatou M, Ball R. An evaluation of statistical approaches to postmarketing surveillance. Stat Med. 2020;39(7):845–874. doi: 10.1002/sim.8447
   PubMed Web of Science ®Google Scholar
• Moulis G, Palmaro A, Montastruc J-L, et al. Epidemiology of incident immune thrombocytopenia: a nationwide population-based study in France. Blood. 2014;124(22):3308–3315. doi: 10.1182/blood-2014-05-578336
   PubMed Web of Science ®Google Scholar
• Gagne JJ. Finding meaningful patterns in adverse drug event reports. JAMA Intern Med. 2014;174(12):1934–1935.
   PubMed Web of Science ®Google Scholar
• Mouffak A, Lepelley M, Revol B, et al. High prevalence of spin was found in pharmacovigilance studies using disproportionality analyses to detect safety signals: a meta-epidemiological study. J Clinical Epidemiol. 2021;138:73–79. doi: 10.1016/j.jclinepi.2021.06.022
   PubMed Web of Science ®Google Scholar
• Raschi E, Poluzzi E, Salvo F, et al. Pharmacovigilance of sodium-glucose co-transporter-2 inhibitors: what a clinician should know on disproportionality analysis of spontaneous reporting systems. Nutr Metab Cardiovasc Dis. 2018;28(6):533–542. doi: 10.1016/j.numecd.2018.02.014
   PubMed Web of Science ®Google Scholar
• Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58(5):295–300. doi: 10.1177/003591576505800503
   PubMedGoogle Scholar
• Arku D, Yousef C, Abraham I. Changing paradigms in detecting rare adverse drug reactions: from disproportionality analysis, old and new, to machine learning. Expert Opin Drug Saf. 2022;21(10):1235–1238.
   PubMed Web of Science ®Google Scholar
• Cami A, Arnold A, Manzi S, et al. Predicting adverse drug events using pharmacological network models. Sci Trans Med. 2011;3(114):ra114127–ra114127. doi: 10.1126/scitranslmed.3002774
   Web of Science ®Google Scholar
• Lange JH, Reinders J-H, Tolboom JT, et al. Principal component analysis differentiates the receptor binding profiles of three antipsychotic drug candidates from current antipsychotic drugs. J Med Chem. 2007;50(21):5103–5108. doi: 10.1021/jm070516u
   PubMed Web of Science ®Google Scholar
• Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9(3):432–441.
   PubMed Web of Science ®Google Scholar
• Liao G, Yang Y, Xie A, et al. Applicability of anticancer drugs for the triple-negative breast cancer based on homologous recombination repair deficiency. Front Cell Dev Biol. 2022;10:845950. doi: 10.3389/fcell.2022.845950
   PubMed Web of Science ®Google Scholar
• Gibbs NE, Poole J, William G, et al. An algorithm for reducing the bandwidth and profile of a sparse matrix. SIAM J Numer Anal. 1976;13(2):236–250.
   Web of Science ®Google Scholar
• Stafford EG, Riviere JE, Xu X, et al. Pharmacovigilance in patients with diabetes: a data-driven analysis identifying specific RAS antagonists with adverse pulmonary safety profiles that have implications for COVID-19 morbidity and mortality. J Am Pharm Assoc. 2020;60(6):e145–e152. doi: 10.1016/j.japh.2020.05.018
   Google Scholar
• Jaberi-Douraki M, Meyer E, Riviere J, et al. Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity. Sci Rep. 2021;11(1):1–9. doi: 10.1038/s41598-021-92734-7
   PubMed Web of Science ®Google Scholar
• Xu X, Mazloom R, Goligerdian A, et al. Making sense of pharmacovigilance and drug adverse event reporting: comparative similarity association analysis using ai machine learning algorithms in dogs and cats. Top Companion Anim Med. 2019;37:100366. doi: 10.1016/j.tcam.2019.100366
   PubMed Web of Science ®Google Scholar
• Cai C, Fang J, Guo P, et al. In silico pharmacoepidemiologic evaluation of drug-induced cardiovascular complications using combined classifiers. J Chem Inf Model. 2018;58(5):943–956. doi: 10.1021/acs.jcim.7b00641
   PubMed Web of Science ®Google Scholar
• Fang J, Zhang P, Zhou Y, et al. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease. Nat Aging. 2021;1(12):1175–1188. doi: 10.1038/s43587-021-00138-z
   PubMedGoogle Scholar
• Nguyen PA, Born DA, Deaton AM, et al. Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat Commun. 2019;10(1):1579. doi: 10.1038/s41467-019-09407-3
   PubMedGoogle Scholar
• Xu X, Jaberi-Douraki M, Anwer F, et al. A novel risk assessment metric for antimyeloma therapies and drug interactions. J Clin Oncol. 2023;41(16_suppl):e24082–e24082. doi: 10.1200/JCO.2023.41.16_suppl.e24082
   Web of Science ®Google Scholar
• Xu X, Raza S, Gadara NM, et al. Identification of genes encoding targets associated with adverse events in multiple myeloma. J Clin Oncol. 2023;41(16_suppl):1556–1556. doi: 10.1200/JCO.2023.41.16_suppl.1556
   Web of Science ®Google Scholar
• Jaberi-Douraki M, Xu X, Faiman B, et al. Disparities in multiple myeloma: A global perspective on drug toxicity trends. J Clin Oncol. 2023;41(16_suppl):8015–8015. doi: 10.1200/JCO.2023.41.16suppl.8015
   Web of Science ®Google Scholar
• Drögemöller BI, Monzon JG, Bhavsar AP, et al. Association between SLC16A5 genetic variation and cisplatin-induced ototoxic effects in adult patients with testicular cancer. JAMA Oncol. 2017;3(11):1558–1562. doi: 10.1001/jamaoncol.2017.0502
   PubMed Web of Science ®Google Scholar
• Wolff J, Hefner G, Normann C, et al. Predicting the risk of drug–drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study. BMJ Open. 2021;11(4):e045276. doi: 10.1136/bmjopen-2020-045276
   PubMed Web of Science ®Google Scholar