Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling.

The risk of drug-induced liver injury (DILI) poses a major challenge for development of natural products derived from traditional Chinese medicines (NP-TCMs). It is urgent to find a new method for the safety assessment of the NP-TCMs. Recent study has reported an in vitro/in silico method to estimate the acceptable daily intake of hepatotoxic compounds using support vector machine (SVM) classifier and physiologically based pharmacokinetic (PBPK) modeling. However, this method is not suitable for estimating the dosing schedule of compounds which are administered in multiple daily doses. Thus, in this study, the method mentioned above was in particular optimized, and used to estimate the hepatotoxic plasma concentrations of 17 NP-TCMs. Additionally, the oral dosing schedules of the triptolide, emodin, matrine and oxymatrine were also predicted by the SVM classifier and PBPK modeling. The optimization included that: (1) in vitro cytotoxicity data of 28 training set compounds was optimized using benchmark concentrations (BMC) modeling; (2) AUC of the training set compound was used as the in vivo metric instead of C to better reflect the total daily exposure of compounds which are administered in multiple daily doses; (3) using the mean AUC in plasma as in vivo metric and BMC value as in vitro metric could achieve the better toxicity separation index (0.962 vs. 0.938); (4) The TSI for C and BMC values was 0.985 calculated in this study, and the results indicated that BMC modeling improved the separation performance. This optimized in vitro-in vivo extrapolation (IVIVE) workflow could extrapolate in vitro BMC to blood concentrations and the oral dosing schedule which are corresponding to certain risk of hepatotoxicity. The estimated safe dosing schedule of oxymatrine by this optimized method was close to the clinical recommended dosing regimen. The results indicate that the optimized method could be used to predict the dosing schedule of compounds administered in multiple daily doses, and our optimized workflow could be helpful for the safety assessment as well as the research and development on NP-TCMs.