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

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. [ABSTRACT FROM AUTHOR]