[Machine Learning-Assisted Efficacy Evaluation of Resveratrol Therapy in a Mouse Model of Acute Pancreatitis].

Objective: To develop a machine learning (ML)-based prediction model for assessing the therapeutic effects of resveratrol (RES) on the pathological damage of acute pancreatitis (AP), and to optimize RES administration strategies for AP through validation using an animal model. Methods: AAn ML-based prediction model was constructed using published data. Interpretability analysis was applied to identify high-efficacy zones within the parameter space of administration dose and frequency, which was followed by rigorous screening to select the optimal dosing strategy that balanced therapeutic efficacy and experimental feasibility. A total of 32 C57BL/6 mice were randomly assigned to 4 groups ( Results: The gradient boosting decision tree model, optimized via Hyperopt, yielded the best performance, predicting that the optimal dose and administration frequency were 19.992 mg/kg and 3.828 times, respectively. Accordingly, a regimen of 20 mg/kg RES, administered four times, was used in the animal experiments. Compared with the Ctrl group, the CER-AP group exhibited higher pancreatic pathology scores and elevated levels of serum amylase, lipase, pancreatic myeloperoxidase, and trypsin, with all differences reaching statistical significance (all Conclusion: A regimen of 20 mg/kg RES administered four times effectively alleviates the severity of CER-induced AP. The therapeutic benefits appear to arise from a multi-target regulatory network that simultaneously suppresses inflammatory cascades, mitigates oxidative stress, and reduces apoptosis, thereby reducing pancreatic tissue damage and systemic inflammatory responses.