Data Mining and Network Pharmacology Characterize Medication Patterns of Chinese Herbal Medicine for Bovine Viral Diarrhea.

The purpose of this study was to use data mining and network pharmacology to determine drug patterns for bovine viral diarrhea (BVD). The frequency, properties, tastes, meridian tropism, and functions of prescription data were gathered and examined from four literature databases (2004-2024). Cluster analysis was used to find prescription patterns, and the Apriori algorithm (SPSS Modeler 18) was used to find associations. A total of 391 literature-derived prescription records were included in the analysis, involving 189 distinct herbal medicines and a cumulative herb-use frequency of 2031 occurrences. These herbs primarily enter the liver meridian and were categorized as cold, warm, or neutral. The predominant tastes were bitter, pungent, and sweet. Five frequently recorded herbs and 14 co-occurrence patterns among herbs were extracted. According to traditional Chinese medicine (TCM), spleen-stomach deficiencies and damp-heat pathogens are linked to BVD. These medication patterns were mainly associated with heat-clearing, detoxification, spleen-strengthening, and Qi-regulating strategies in TCVM theory. Targets were screened, PPI networks were constructed, and enrichment studies for core herbs (Baitouweng, Huangbo, Huangqin, Qinpi, and Zhizi) were performed using network pharmacology. The binding affinities between disease targets and active components were further assessed using molecular docking. The findings provide a descriptive summary of medication patterns and generate preliminary hypotheses regarding potential compound-target-pathway associations involved in the symptomatic and supportive use of TCM for BVD.