[Phenotypic drug discovery promotes research and development of innovative drugs based on traditional Chinese medicine].

Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

Develop a bone mineral density T-score distribution nomograms based on osteoporosis risk factors for middle-aged and older adults.

PURPOSE: This study aimed to understand how age, health status, and lifestyle impact bone mineral density (BMD) in middle-aged and older adults, focusing on predicting osteoporosis risk. METHODS: This study included 2836 participants aged 50-88 from the Health Improvement Program of Bone (HOPE) conducted from 2021 to 2023. We used logistic regression to make a prediction tool. Then checked its accuracy and reliability using receiver operating characteristic (ROC) and calibration curves. RESULTS: Factors like age, body weight, prior fractures, and smoking were independently found to affect BMD T-score distribution in men. In women, age and body weight were identified as independent factors influencing BMD T-score distribution. A nomogram was created to visually illustrate these predictive relationships. CONCLUSIONS: The nomogram proved highly accurate in identifying men aged 50 and above and postmenopausal women based on their BMD T-score distribution, improving clinical decision-making and patient care in osteoporosis evaluation and treatment.