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To evaluate current knowledge, we performed a comprehensive Google Scholar search (2003\u20132024) and selected studies that employed PBPK platforms, reported quantitative validation, and focused on chemotherapy-related interactions. From these reports, key modeling parameters, validation metrics, and clinically relevant outcomes were extracted, and then the information was synthesized to identify common trends. Collectively, the evidence indicates that unintended changes in drug exposure\u2014most often mediated by CYP3A4 inhibition or induction\u2014may modify efficacy, toxicity, and overall anticancer response; nevertheless, PBPK models reproduce these effects with high accuracy, and emerging AI-enhanced approaches promise even finer precision. Accordingly, our synthesis underscores how PBPK modeling can help clinicians forecast interaction risk, individualize dosing, and avert therapeutic failure, especially in polypharmacy settings. Integrating these models into routine oncology practice therefore offers a proactive path toward safer, more personalized chemotherapy and, ultimately, better patient outcomes within an increasingly complex therapeutic landscape.<\/jats:p>","DOI":"10.1177\/10915818251345116","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T17:09:30Z","timestamp":1749661770000},"page":"424-440","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Predictive Modeling of Pharmacokinetic Drug\u2013Drug and Herb\u2013Drug Interactions in Oncology: Insights From PBPK Studies"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4837-2263","authenticated-orcid":false,"given":"Enes Emre","family":"Ta\u015f","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, Bo\u011fazi\u00e7i University, \u0130stanbul, 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