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Acton EK, Hennessy S, Gelfand MA, Leonard CE, Bilker WB, Shu D, Willis AW, Kasner SE. Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations. Clin Pharmacol Ther 2024; 116:448-459. [PMID: 38860403 PMCID: PMC11262479 DOI: 10.1002/cpt.3310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/06/2024] [Indexed: 06/12/2024]
Abstract
The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.
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Affiliation(s)
- Emily K. Acton
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Sean Hennessy
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Michael A. Gelfand
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
| | - Charles E. Leonard
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
| | - Warren B. Bilker
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Di Shu
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Allison W. Willis
- Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Translational Center of Excellence for Neuroepidemiology and Neurology Outcomes Research, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, US
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
| | - Scott E. Kasner
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, US
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Chen C, Hennessy S, Brensinger CM, Bilker WB, Dublin S, Chung SP, Horn JR, Bogar KF, Leonard CE. Antidepressant drug-drug-drug interactions associated with unintentional traumatic injury: Screening for signals in real-world data. Clin Transl Sci 2023; 16:326-337. [PMID: 36415144 PMCID: PMC9926061 DOI: 10.1111/cts.13452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/23/2022] [Accepted: 10/27/2022] [Indexed: 11/24/2022] Open
Abstract
Antidepressants are associated with traumatic injury and are widely used with other medications. It remains unknown how drug-drug-drug interactions (3DIs) between antidepressants and two other drugs may impact potential injury risks associated with antidepressants. We aimed to generate hypotheses regarding antidepressant 3DI signals associated with elevated injury rates. Using 2000-2020 Optum's de-identified Clinformatics Data Mart, we performed a self-controlled case series study for each drug triad consisting of an antidepressant + codispensed drug (base-pair) with a candidate interacting medication (precipitant). We included persons aged greater than or equal to 16 years who (1) experienced an injury and (2) used a candidate precipitant, during base-pair therapy. We compared injury rates during observation time exposed to the drug triad versus the base-pair only, adjusting for time-varying covariates. We calculated adjusted rate ratios (RRs) using conditional Poisson regression and accounted for multiple comparisons via semi-Bayes shrinkage. Among 147,747 eligible antidepressant users with an injury, we studied 120,714 antidepressant triads, of which 334 (0.3%) were positively associated with elevated injury rates and thus considered potential 3DI signals. Adjusted RRs for signals ranged from 1.31 (1.04-1.65) for sertraline + levothyroxine with tramadol (vs. without tramadol) to 6.60 (3.23-13.46) for escitalopram + simvastatin with aripiprazole (vs. without aripiprazole). Nearly half of the signals (137, 41.0%) had adjusted RRs greater than or equal to 2, suggesting strong associations with injury. The identified signals may represent antidepressant 3DIs of potential clinical concern and warrant future etiologic studies to test these hypotheses.
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Affiliation(s)
- Cheng Chen
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sean Hennessy
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Leonard Davis Institute of Health EconomicsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Colleen M. Brensinger
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Warren B. Bilker
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sascha Dublin
- Kaiser Permanente Washington Health Research InstituteSeattleWashingtonUSA
- Department of Epidemiology, School of Public HealthUniversity of WashingtonSeattleWashingtonUSA
| | - Sophie P. Chung
- Epocrates Medical InformationAthenaHealth, Inc.WatertownMassachusettsUSA
| | - John R. Horn
- Department of Pharmacy, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Kacie F. Bogar
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Charles E. Leonard
- Center for Real‐World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Leonard Davis Institute of Health EconomicsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions. Clin Ther 2023; 45:117-133. [PMID: 36732152 DOI: 10.1016/j.clinthera.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/15/2022] [Accepted: 01/09/2023] [Indexed: 02/01/2023]
Abstract
Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.
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