RESUMO
BACKGROUND: As a "multi-components and multi-efficacy" complex system, traditional Chinese herbs are universally distributed and applied in treating clinical diseases. However, the efficacy deviation and ambiguous clinical location are affected by different effects and content of components caused by uncertain factors in the production process. It further restricts resource allocation and clinical medication and hinders modernization and globalization. In this study, a precise efficacy determination strategy was innovatively proposed, aiming to quantitatively predict the efficacy of herbs and obtain precise medicinal materials. Quality-markers (Q-markers) characterizing the efficacy are conducive to achieving precise efficacy determination. PURPOSE: With the anticancer efficacy of Astragali radix (AR) as a case, the present study was designed to establish a methodology for precise efficacy determination based on Q-markers characterizing specific efficacy. METHODS: Guided by the basic principles of Q-markers, the potential Q-markers characterizing the anticancer efficacy of AR were screened through molecular simulation and network pharmacology. The activity of Q-markers was evaluated on MDA-MB-231 cells, and the content of Q-markers was determined by HPLC. A quantitative efficacy prediction model of the relationship between the influencing factors and anticancer efficacy was further constructed through the effect-constituents index (ECI) and machine learning and verified by biotechnology, which can be directly applied to predict the efficacy in numerous samples. RESULTS: Astragaloside I, astragaloside II, and astragaloside III inhibited the proliferation of MDA-MB-231 cells and were successfully quantified in AR samples, reflecting the effectiveness and measurability of Q-markers. Gradient Boost Regression showed the best performance in the quantitative efficacy prediction model with EVtest= 0.815, R2test= 0.802. The results of precise efficacy determination indicated that 1-2-3 (Wuzhai, Shanxi, two years, C segment) sample performed best in 54 batches of AR samples with biased anticancer efficacy. Furthermore, AR samples with higher ECI had higher anticancer efficacy and vice versa. CONCLUSION: The precise efficacy determination strategy established in the present study is reliable and proved in the AR case, which is expected to support resource allocation optimization, efficacy stability improvement, and precise clinical medication achievement.