Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines.

Abstract

Multiherbal medicines are traditionally used as personalized medicines with custom combinations of crude drugs; however, the mechanisms of multiherbal medicines are unclear. In this study, we developed a novel pathway-based method to predict therapeutic effects and the mode of action of custom-made multiherbal medicines using machine learning. This method considers disease-related pathways as therapeutic targets and evaluates the comprehensive influence of constituent compounds on their potential target proteins in the disease-related pathways. Our proposed method enabled us to comprehensively predict new indications of 194 Kampo medicines for 87 diseases. Using Kampo-induced transcriptomic data, we demonstrated that Kampo constituent compounds stimulated the disease-related proteins and a customized Kampo formula enhanced the efficacy compared with an existing Kampo formula. The proposed method will be useful for discovering effective Kampo medicines and optimizing custom-made multiherbal medicines in practice.