Physiologically based pharmacokinetic modeling supports investigation of potential drug-drug interactions in the pre- and early post-hematopoietic stem cell transplantation stages
IntroductionDrug-drug interactions (DDIs) are an important issue in medication safety and a potential cause of adverse drug events in the pre- and early post-hematopoietic stem cell transplantation (HSCT). This study introduced a physiologically based pharmacokinetic (PBPK) modeling platform to eval...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1578643/full |
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| Summary: | IntroductionDrug-drug interactions (DDIs) are an important issue in medication safety and a potential cause of adverse drug events in the pre- and early post-hematopoietic stem cell transplantation (HSCT). This study introduced a physiologically based pharmacokinetic (PBPK) modeling platform to evaluate complex DDIs in these critical stages and to optimize dosing for personalized treatment.MethodsPBPK models were developed using a bottom-up with middle-out approach and executed with PK-Sim® software. Model validation required that predicted PK values fall within a twofold range of observed data. Then, the validated model was used to simulate alternative dosing regimens to achieve target therapeutic levels.ResultsPBPK models were developed and evaluated for 13 drugs commonly used in HSCT, including cyclosporine, tacrolimus, sirolimus, busulfan, phenytoin, voriconazole, posaconazole, itraconazole, fluconazole, letermovir, fosaprepitant, aprepitant, and omeprazole. Simulation results indicated marked DDIs in the pre- and early post-HSCT phases, particularly involving cyclosporine and phenytoin. Several drugs notably increased cyclosporine concentrations, while phenytoin substantially reduced the exposure to other medications.ConclusionThis PBPK modeling platform provides a robust tool for identifying and mitigating DDIs in the pre- and early post-HSCT phases. By enabling the optimization of treatment regimens, this model serves as a valuable tool for improving drug safety and therapeutic outcomes for patients with HSCT. |
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| ISSN: | 1663-9812 |