Affiliation:
1. Center for Translational Medicine University of Maryland School of Pharmacy Baltimore MD USA
Abstract
AbstractObesity is a growing global health concern associated with high comorbidity rates, leading to an increasing number of patients who are obese requiring medication. However, clinical trials often exclude or under‐represent individuals who are obese, creating the need for a methodology to adjust labeling to ensure safe and effective dosing for all patients. To address this, we developed a 2‐part decision tree framework to prioritize drugs for dedicated pharmacokinetic studies in obese subjects. Leveraging current drug knowledge and modeling techniques, the decision tree system predicts expected exposure changes and recommends labeling strategies, allowing stakeholders to prioritize resources toward the drugs most in need. In a case study evaluating 30 drugs from literature across different therapeutic areas, our first decision tree predicted the expected direction of exposure change accurately in 73% of cases. We conclude that this decision tree system offers a valuable tool to advance research in obesity pharmacology and personalize drug development for patients who are obese, ensuring safe and effective medication.
Subject
Pharmacology (medical),Pharmacology
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