Compound identification of Shuangxinfang and its potential mechanisms in the treatment of myocardial infarction with depression: insights from LC-MS/MS and bioinformatic prediction.

Background: Patients with myocardial infarction (MI) have a high incidence of depression, which deteriorates the cardiac function and increases the risk of cardiovascular events. Shuangxinfang (Psycho-cardiology Formula, PCF) was proved to possess antidepressant and cardioprotective effects post MI. However, the compounds of PCF remain unidentified, and the pertinent mechanism is still not systematic. The purpose of this study is to determine the ingredients of PCF, further to probe the underlying mechanism for MI with depression. Methods: The compounds of PCF were qualitatively identified by LC-MS/MS. The optimal dosage for lavage with the PCF solution in rats was determined to be 1 mL/100 g/day for a duration of 5 days. We also detected the PCF components migrating to blood in the control and model rats. Then the targets of PCF compounds were searched on Swiss target database, and the targets of depression and MI were predicted on TTD, OMIM, GeneCards, DrugBank and PharmGkb database. All the targets were intersected to construct the Protein-Protein Interaction (PPI) network on Metascape platform and the herb-compound-target (HCT) network on Cytoscape, to identify the hub targets. GO and KEGG pathway enrichment analysis were conducted on DAVID platform. Molecular docking was modeled on AutoDock Vina software. Results: There were 142 bioactive compounds from PCF acting on 270 targets in a synergistic way. And a total of seven components migrating to blood were identified, including Miltionone I, Neocryptotanshinone, Danshenxinkun A, Ferulic acid, Valerophenone, Vanillic acid and Senkyunolide D. Then SRC and MAPK3 were obtained as the hub proteins by degree value in PPI network, and P2RY12 was picked out as seed proteins ranked by scores from MCODES. Further analysis of biological process and signaling pathways also revealed the significance of ERK/MAPK. Statistical analyses (e.g., GO and KEGG pathway enrichment, PPI network analysis) demonstrated the significance of the identified targets and pathways ( Conclusion: We identified PCF's bioactive compounds and predicted its therapeutic mechanism for MI with depression using LC-MS/MS and bioinformatics. Key targets SRC, MAPK3, and seed protein P2RY12 were crucial for PCF's cardio-neuroprotective effects. Neocryptotanshinone showed the strongest binding to MAPK3, suggesting it as a pivotal active ingredient. These findings offer new insights and targets for future research on PCF.