Traditional Chinese medicine (TCM) possesses unique advantages in the management of blood glucose and lipids. However, there is still a significant gap in the exploration of its pharmacologically active components. Integrated strategies encompassing deep-learning prediction models and active validation based on absorbable ingredients can greatly improve the identification rate and screening efficiency in TCM. In this study, the affinity prediction of 11,549 compounds from the traditional Chinese medicine system’s pharmacology database (TCMSP) with dipeptidyl peptidase-IV (DPP-IV) based on a deep-learning model was firstly conducted. With the results, Gardenia jasminoides Ellis (GJE), a food medicine with homologous properties, was selected as a model drug. The absorbed components of GJE were subsequently identified through in vivo intestinal perfusion and oral administration. As a result, a total of 38 prototypical absorbed components of GJE were identified. These components were analyzed to determine their absorption patterns after intestinal, hepatic, and systemic metabolism. Virtual docking and DPP-IV enzyme activity experiments were further conducted to validate the inhibitory effects and potential binding sites of the common constituents of deep learning and sequential metabolism. The results showed a significant DPP-IV inhibitory activity (IC₅₀ 53 ± 0.63 μg/mL) of the iridoid glycosides’ potent fractions, which is a novel finding. Genipin 1-gentiobioside was screened as a promising new DPP-IV inhibitor in GJE. These findings highlight the potential of this innovative approach for the rapid screening of active ingredients in TCM and provide insights into the molecular mechanisms underlying the anti-diabetic activity of GJE.

The objectives of this study were to support dose selection of a novel FXR agonist XZP-5610 in first-in-human (FIH) trials and to predict its liver concentrations in Chinese healthy adults. Key parameters for extrapolation were measured using in vitro and in vivo models. Allometric scaling methods were employed to predict human pharmacokinetics (PK) parameters and doses for FIH clinical trials. To simulate the PK profiles, a physiologically based pharmacokinetic (PBPK) model was developed using animal data and subsequently validated with clinical data. The PBPK model was employed to simulate XZP-5610 concentrations in the human liver across different dose groups. XZP-5610 exhibited high permeability, poor solubility, and extensive binding to plasma proteins. After a single intravenous or oral administration of XZP-5610, the PK parameters obtained from rats and beagle dogs were used to extrapolate human parameters, resulting in a clearance of 138 mL/min and an apparent volume of distribution of 41.8 L. The predicted maximum recommended starting dose (MRSD), minimal anticipated biological effect level (MABEL), and maximum tolerated dose (MTD) were 0.15, 2, and 3 mg, respectively. The PK profiles and parameters of XZP-5610, predicted using the PBPK model, demonstrated good consistency with the clinical data. By using allometric scaling and PBPK models, the doses, PK profile, and especially the liver concentrations were successfully predicted in the FIH study.

Background: SYHA1805 is a potent farnesoid X receptor (FXR) agonist currently in development for Metabolic Dysfunction-Associated Steatohepatitis (MASH). Methods: To determine the first-in-human (FIH) dose and guide its clinical development, an integrated approach combining in vitro and in vivo ADME and toxicological characterizations, cross-species allometric scaling (AS), and physiologically based pharmacokinetic (PBPK) modeling was employed. Results: Using monkeys and rats as extrapolation species, AS predicted a human intravenous clearance of 20.7 L/h and a steady-state volume of distribution of 15.1 L. Based on body surface area and exposure-based modeling, an FIH dosing regimen for single-dose administration was proposed, ranging from a 30 mg starting dose to a 3000 mg maximum, with an effective dose of 1150 mg. These dosing strategies were further supported by PBPK models, which accurately estimated human systemic exposure. The model simulations were subsequently validated by clinical trial data from a single ascending dose (SAD) study (CTR20202354). Conclusions: These findings establish a robust pharmacokinetic foundation for the continued clinical advancement of SYHA1805.