Physiologically Based Pharmacokinetic and Drug–Drug Interaction Modeling of Efavirenz, Etravirine, and Saquinavir in Prostate Cancer

Background: Prostate cancer remains one of the most prevalent malignancies worldwide, with high mortality in advanced and metastatic stages. Drug repurposing offers a cost-effective and time-efficient strategy to identify new therapeutic options. Objectives: This study aimed to apply physiologically based pharmacokinetic (PBPK) modeling to evaluate repurposed antiretroviral drugs efavirenz (EFV), etravirine (ETV), and saquinavir (SAQ) in prostate cancer, and to assess potential drug–drug interactions (DDIs) between EFV and ETV. Methods: PBPK models for EFV and SAQ were obtained and an ETV was developed and validated using literature and ADMET Predictor® data. Prostate tissue models were modified to simulate malignant conditions, and population-based simulations examined the influence of age and obesity. The GastroPlus® DDI module was applied to explore mechanistic interactions between EFV and ETV under different physiological scenarios. Results: Tumor-specific prostate tissue alterations produced minimal systemic pharmacokinetic changes but increased total drug accumulated in simulated tissue, with differences in unbound concentrations, while demographic variables such as age and weight significantly affected drug exposure, which are comorbidities in prostate cancer. Lighter individuals exhibited higher plasma concentrations across all drugs, consistent with known previously reported pharmacokinetic trends in obese individuals. DDI simulations indicated only minor changes in ETV pharmacokinetics when combined with EFV, with no clinically significant interaction detected. Conclusions: The integration of PBPK modeling, population variability, and DDI analysis highlights the potential of SAQ, EFV, and ETV as viable drugs for prostate cancer repurposing, but with a heavy focus on dosing personalization. In silico approaches provide a useful framework for early preclinical evaluation and the optimization of repurposed drugs, supporting the early evaluation of repurposed drug candidates in oncology.