Hyunjung Lee1, Seongwon Park2, Prof. Soyoung Lee2, Prof. Hwi-yeol Yun1,2,3, Prof. Jung-woo CHAE1,2,3
1Department of Bio-AI convergence, Chungnam National University, 2College of Pharmacy, Chungnam National University, 3Senior Health Convergence Research Center, Chungnam National University
Objectives: Accurate prediction of drug concentrations in liver tissue is essential for understanding drug-induced liver injury (DILI) mechanisms, especially in cases of prolonged or repeated drug exposure that may exacerbate hepatic damage. Physiologically based pharmacokinetic (PBPK) modeling provides a robust approach to simulating drug distribution across multiple physiological compartments, aiding in assessing hepatotoxic risk. This study aimed to develop an advanced parent-metabolite network-based PBPK model for Nevirapine in Sprague-Dawley (SD) rats to investigate how parent compounds and metabolites influence hepatic drug disposition and potential toxicity. Methods: In vivo experiments were conducted on SD rats administered Nevirapine at doses of 18, 36, and 72 mg/kg (corresponding to human equivalent doses of 200, 400, and 800 mg) across different regimens: single-dose, 1-week, 2-week, and 4-week exposures. These regimens were designed to mimic acute and chronic exposure conditions relevant to clinical settings. Blood samples were collected at 0.5, 1, 1.5, 2, 4, 6, 8, and 12 hours post-final dose to assess systemic drug levels, while liver tissues were harvested at after the final time point to measure hepatic drug concentrations. Sample preparation involved homogenization of liver tissues followed by protein precipitation for analyte extraction. Quantification of Nevirapine and its metabolites in plasma and liver samples was performed using liquid chromatography-quadrupole mass spectrometry (LC-MS/MS). PBPK modeling, implemented in Berkeley Madonna, integrated plasma and liver compartments with a parent-metabolite network to characterize metabolic pathways and tissue distribution dynamics. Physiological parameters such as organ volumes, blood flows, and tissue-to-plasma partition coefficients for SD rats were incorporated. In this study, human NVP plasma concentrations known to cause toxicity were converted to SD rat equivalents using the body surface area-based allometric scaling approach to enable DILI risk assessment. Model performance was evaluated using statistical metrics including prediction error (PE), root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). Results: Pharmacokinetic analysis indicated significant hepatic accumulation of Nevirapine, with distinct distribution patterns between plasma and liver compartments depending on dosing regimen and duration. These patterns suggested preferential hepatic retention over systemic circulation. This accumulation was consistent with Nevirapine’s known autoinduction and enterohepatic recirculation properties, which enhance hepatic drug levels over time, particularly with repeated dosing. To assess DILI risk, human NVP plasma toxicity thresholds (6-8 µg/mL) were converted to SD rat equivalents (1.03-1.37 µg/mL) using the body surface area-based allometric scaling approach. Differences in lipophilicity between Nevirapine and its metabolite 12-hydroxynevirapine influenced their distinct tissue distribution profiles. The developed PBPK model accurately captured time-concentration profiles in both plasma and liver tissues. Statistical analysis demonstrated acceptable prediction errors and RMSE values, validating the model’s reliability in predicting pharmacokinetic behavior. Notably, the model indicated increased drug clearance with repeated dosing, as plasma concentration profiles exhibited progressively lower AUC values in the 2-week and 4-week dosing groups despite identical dosing regimens. Quantitative assessment of metabolite formation revealed more pronounced concentration changes than those observed for the parent drug, highlighting the critical role of metabolic pathways in shaping Nevirapine’s hepatic pharmacokinetics and its potential impact on liver function. Conclusions: This study successfully developed a parent-metabolite network-based PBPK model for Nevirapine that integrates plasma and hepatic compartments. The model demonstrated high predictive accuracy for both Nevirapine and its metabolite 12-hydroxynevirapine, providing a valuable tool for understanding the interaction between parent compounds and their metabolic derivatives in hepatic drug disposition. Our findings underscore the necessity of considering 12-hydroxynevirapine formation and distribution when assessing the hepatotoxic potential of drugs like Nevirapine, as these factors significantly influence liver exposure and potential injury risk. The mechanistic framework established in this study can be extended to other drugs with similar metabolic profiles and DILI risk, enhancing its applicability in pharmacokinetic and toxicological studies. Additionally, the validated model offers a reliable tool for predicting hepatic drug concentrations across different dosing regimens, contributing to preclinical drug safety assessments.
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Reference: PAGE 33 (2025) Abstr 11528 [www.page-meeting.org/?abstract=11528]
Poster: Drug/Disease Modelling - Absorption & PBPK