[Evaluation for druggability of traditional Chinese medicine preparations in medical institutions based on human use experience].

Traditional Chinese medicine(TCM) preparations in medical institutions are an important source of research and development(R&D) of TCM new drug. With years of usage in therapy, these preparations' safety and effectiveness have generally been validated in clinic. However, there are still a few disadvantages in TCM new medicine development, such as similar prescriptions, excessive prescription ingredients, too broad clinical orientation, lack of solid clinical data, issue in pharmaceutical quality control, and intellectual property disputes. Nowadays, the Three-Combined Evaluation System has strengthened policy support for the new TCM R&D. In order to improve the success rate of TCM R&D, due to the difficulties within, this paper proposes the process of transforming TCM preparations in medical institutions into new TCM and advocates the evaluation for druggability based on Human Use Experience(HUE). The potencial preparations ought to follow traditional Chinese Medical theory, sufficient HUE data in indication, syndrome type of TCM, target population, usage, dosage, and course of treatment are required. Particular attention should be paid to the source, evolution, and improvement process of prescription, and evaluate the dosage, ingredients, and herb resources of prescription. To assess the feasibility of mass production, it is necessary to determine whether the pharmaceutical process is mostly consistent with the new drug and whether the dosage form is reasonable. By summarizing the clinical application of the preparations, the whole picture of its clinical application would be reveal as much as possible. It is beneficial to evaluate its clinical value and R&D prospect. In consideration of the lack of clinical safety data of preparations, safety profile needs to be collected according to the prescription. The quality of clinical data needs to be evaluated by focusing on the integrity and accuracy of data to reduce bias and confusion. Significant care should be paid to intellectual property protection to avoid legal disputes.