Shu-Hao Hsu

Background/Objectives: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. To address this limitation, an in silico-based model selection approach for optimizing hepatic clearance predictions was introduced in a previous study. The current study extends this strategy by verifying the accuracy of the selected models using ex situ experimental data, particularly for drugs whose model choices are influenced by protein binding. Methods: Commonly prescribed drugs were classified according to their hepatic extraction ratios and protein-binding properties. Building on previous studies that employed multinomial logistic regression analysis for model selection, a three-phase classification method was implemented to identify five representative drugs: diazepam, diclofenac, rosuvastatin, fluoxetine, and tolbutamide. Subsequently, an isolated perfused rat liver (IPRL) system was used to evaluate the accuracy of the in silico method. Results: As the unbound fraction increased for diazepam and diclofenac, the most suitable predictive model shifted from the initially preferred well-stirred model (WSM) to the modified well-stirred model (MWSM). For rosuvastatin, the MWSM provided a more accurate prediction. These three capacity-limited, binding-sensitive drugs conformed to the outcomes predicted by the multinomial logistic regression analysis. Fluoxetine was best described by the WSM, which is consistent with its flow-limited classification. For tolbutamide, a representative capacity-limited, binding-insensitive drug, no significant differences were observed among the various models. Conclusions: These findings demonstrate the accuracy of an in silico-based model selection approach for predicting liver metabolism and highlight its potential for guiding dosage adjustments. Furthermore, the IPRL system serves as a practical tool for validating the accuracy of the results derived from this approach.

Herbal medicines have been widely used for the past millennia. Traditional Chinese medicine (TCM) is a major modality in Chinese medical care and has garnered global attention owing to its pharmacological effects and multi-targeted actions. The increased incidence of sequential or concurrent use of herbs and drugs in patients forces us to consider herb-drug interactions (HDIs) in this modern era. One of the main causes of HDIs is modulation of drug metabolism, in which cytochrome P450 (CYP), UDP-glucuronosyltransferase (UGT), and transporters play primary roles. In this review, we focus on in vivo studies of HDIs, particularly in the treatment of cardiovascular disease (CVD), which is currently the leading cause of disease-related mortality worldwide. A total of 55 HDIs are summarized, and their potential underlying mechanisms are examined. The pharmacokinetic (PK) and pharmacodynamic (PD) effects of three single herbs (Danshen, Ginseng, and Ginkgo) and four compound prescriptions (Shenmai injection, Shengmai-San, Shu-Jing-Hwo-Shiee-Tang, and Wu-Chu-Yu-Tang) are discussed. Due to the complex compositions and PK/PD profiles of TCMs, the determinants of significant HDIs have been listed to further define the pros and cons of HDIs in medical care.