Phytomedicine : international journal of phytotherapy and phytopharmacology · 2026
AI-driven discovery of synergistic combinations from Psoralea corylifolia fruit for treating osteoporosis.
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Liu Chun-Lu, Li Ming-Rui, Long Jing, Qian Si-Tong, Zhang Cai, Li Ping, Jiang Yan, Li Hui-Jun
Аннотация
BACKGROUND: Estrogen-deficient osteoporosis demands safe and effective therapies. Psoralea corylifolia fruit (PCF), a phytoestrogenic herbal medicine for osteoporosis, lacks clarified key active components and synergistic combinations, which limits its application.
PURPOSE: This study established an AI-driven stepwise strategy to identify anti-osteoporotic synergistic combination from PCF, quantify their synergism, and validate pharmacodynamics, thus providing a reference for complex disease treatment.
METHODS: An AI-driven stepwise integrated strategy was proposed: Ⅰ) Artificial intelligence-assisted preliminary screening of PCF active components via dual estrogenic/anti-osteoporotic models; Ⅱ) molecular simulation (docking/dynamics) for secondary screening of candidate components; Ⅲ) multi-dimensional evaluation for precision targeting of lead components; Ⅳ) Chou-Talalay method used to determine synergism (combination index, CI<0.9), additivity (0.9≤CI≤1.1), or antagonism (CI>1.1). Ultimately, the pharmacodynamic activity of the screened synergistic combination was validated through systematic in vitro and in vivo pharmacodynamic evaluations, combined bioequivalence analysis based on 90% confidence interval.
RESULTS: Machine learning (ML) screening identified 20 active components, narrowed to 6 via molecular simulations. Three leads viz neobavaisoflavone (NBIF), bavachin (BA) and isobavachalcone (IB) were finalized. The NBIF+BA synergistic combination with significant synergism (CI<0.9) showed superior efficacy to individual components, and equivalence to PCF extract (90% confidence interval: 78.12%-132.15%).
CONCLUSION: This AI-driven strategy successfully identified a synergistic combination of NBIF+BA with potent anti-osteoporotic effects from PCF, presenting a promising approach for the treatment of complex diseases.
PURPOSE: This study established an AI-driven stepwise strategy to identify anti-osteoporotic synergistic combination from PCF, quantify their synergism, and validate pharmacodynamics, thus providing a reference for complex disease treatment.
METHODS: An AI-driven stepwise integrated strategy was proposed: Ⅰ) Artificial intelligence-assisted preliminary screening of PCF active components via dual estrogenic/anti-osteoporotic models; Ⅱ) molecular simulation (docking/dynamics) for secondary screening of candidate components; Ⅲ) multi-dimensional evaluation for precision targeting of lead components; Ⅳ) Chou-Talalay method used to determine synergism (combination index, CI<0.9), additivity (0.9≤CI≤1.1), or antagonism (CI>1.1). Ultimately, the pharmacodynamic activity of the screened synergistic combination was validated through systematic in vitro and in vivo pharmacodynamic evaluations, combined bioequivalence analysis based on 90% confidence interval.
RESULTS: Machine learning (ML) screening identified 20 active components, narrowed to 6 via molecular simulations. Three leads viz neobavaisoflavone (NBIF), bavachin (BA) and isobavachalcone (IB) were finalized. The NBIF+BA synergistic combination with significant synergism (CI<0.9) showed superior efficacy to individual components, and equivalence to PCF extract (90% confidence interval: 78.12%-132.15%).
CONCLUSION: This AI-driven strategy successfully identified a synergistic combination of NBIF+BA with potent anti-osteoporotic effects from PCF, presenting a promising approach for the treatment of complex diseases.