Decoding herbal combination models through systematic strategies: insights from target information and traditional Chinese medicine clinical theory

Traditional Chinese medicine (TCM) utilizes intricate herbal formulations that exemplify the principles of compatibility and synergy. However, the rapid proliferation of herbal data has resulted in redundant information, complicating the understanding of their potential mechanisms. To address this issue, we first established a comprehensive database that encompasses 992 herbs, 18 681 molecules, and 2168 targets. Consequently, we implemented a multi-network strategy based on a core information screening method to elucidate the highly intertwined relationships among the targets of various herbs and to refine herbal target information. Within a non-redundant network framework, separation and overlap analysis demonstrated that the networking of herbs preserves essential clinical information, including their properties, meridians, and therapeutic classifications. Furthermore, two notable trends emerged from the statistical analyses of classical TCM formulas: the separation of herbs and the overlap between herbs and diseases. This phenomenon is termed the herbal combination model (HCM), validated through statistical analyses of two representative case studies: the common cold and rheumatoid arthritis. Additionally, in vivo and in vitro experiments with the new formula YanChuanQin (YanHuSuo—Corydalis Rhizoma, ChuanWu—Aconiti Radix, and QinJiao—Gentianae Macrophyllae Radix) for acute gouty arthritis further support the HCM. Overall, this computational method provides a systematic network strategy for exploring herbal combinations in complex and poorly understood diseases from a non-redundant perspective.