ArticleClinical and translational science2024
Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population.
Article in Clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Real-World Claims Data on Prescribed Drugs and the Associated Drug-Drug Interaction Risk in Sickle Cell Disease in the United States.Clinical and translational science · 2026Article
- Closing the loop: human-augmented, mechanistically enhanced AI for proactive management of drug-drug interactions.Frontiers in pharmacology · 2026Article
- Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors.Journal of clinical medicine · 2024Article
- Translating real-world evidence/real-world data.Clinical and translational science · 2024Article
- Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population.Clinical and translational science · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development teams must evaluate the risk/benefit profile of new drug candidates that perpetrate drug-drug interactions (DDIs). Real-world data (RWD) can inform this decision. The purpose of this study was to develop a predicted impact score for DDIs perpetrated by three hypothetical drug candidates via CYP3A, CYP2D6, or CYP2C9 in type 2 diabetes mellitus (T2DM), obesity, or migraine. Optum Market Clarity was analyzed to estimate use of CYP3A, CYP2D6, or CYP2C9 substrates classified in the University of Washington Drug Interaction Database as moderate sensitive, sensitive, narrow therapeutic index, or QT prolongation. Scoring was based on prevalence of exposure to victim substrates and characteristics (age, polypharmacy, duration of exposure, and number of prescribers) of those exposed. The study population of 14,163,271 adults included 1,579,054 with T2DM, 3,117,753 with obesity, and 410,436 with migraine. For T2DM, 71.3% used CYP3A substrates, 44.3% used CYP2D6 substrates, and 44.3% used CYP2C9 substrates. For obesity, 57.1% used CYP3A substrates, 34.6% used CYP2D6 substrates, and 31.0% used CYP2C9 substrates. For migraine, 64.1% used CYP3A substrates, 44.0% used CYP2D6 substrates, and 28.9% used CYP2C9 substrates. In our analyses, the predicted DDI impact scores were highest for DDIs involving CYP3A, followed by CYP2D6, and CYP2C9 substrates, and highest for T2DM, followed by migraine, and obesity. Insights from RWD can be used to estimate a predicted DDI impact score for pharmacokinetic DDIs perpetrated by new drug candidates currently in development. This score can inform the risk/benefit profile of new drug candidates in a target patient population.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.