摘要
中医药在新型冠状病毒疫情防控中的贡献彰显了其在维护人民生命健康中不可或缺的地位.“有毒”中药占常用中药材和饮片的13.47%,市场规模超过168亿元,临床一定范围内具有确切疗效及重要价值[1,2].然而,随着中医药疗效在世界范围内被逐渐认可,中药不良反应事件的数量及关注度近年均有所上升,例如,2017年Nature发表的“China rolls back regulations for traditional medicine despite safety concerns”指出,中药的安全隐患是其临床应用时需要解决的一个至关重要的问题[1].同年,Science Translational Medicine发表的封面论文[2]指出,马兜铃酸与肝癌的发生发展显著相关,导致含有马兜铃酸的植物被世界卫生组织国际癌症研究机构列入一类致癌物清单.随着国际上对中药安全使用的需求日趋严格,要求“说清楚、讲明白”的呼声日益增高,符合中医药特点的中药安全性评价体系的不完善不仅会导致“有毒”中药国际化进程陷入困境,也将严重影响“有毒”中药及其上市品种在临床更广泛的应用,制约中医药现代化、国际化进程.大数据科技创新为中医药行业发展带来了新的机遇与挑战.2020年,《中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见》首次将“数据”列为一种新型生产要素,明确了数据资源的战略意义,为中医药科研界及产业界如何促进数据开发利用、实现数据赋能带来了重要命题;依托北斗地基增强系统、高精度位置服务终端、时空智能解决方案,数据学和数据科学领域的蓬勃发展将为解决“有毒”中药安全使用的临床需求及成果转化过程中存在的瓶颈问题带来新的契机.
The safety issues of Chinese medicine have always been regarded as a vital issue, which has been restricting the development of the Chinese medicine industry and has long had implications for public health. This is especially true for the clinical drug application of “toxic” Chinese medicines which are generally acknowledged as being a fairly complicated and hazard-prone area. These distinguishing features of “toxic” Chinese medicines noted above have limited the safe and rational clinical drug application of traditional Chinese medicine. However, previous research approaches have been subject to many limitations such as insufficient integration of basic research with clinical research, the lack of leadership and guidance for the system view and overall view. These trends of current academic studies show that there are plenty of serious difficulties in transforming the research data into effective evidence, to support the present demand for the rational clinical drug application for “toxic” Chinese medicines. In the wake of the inclusion of data as one of the elements in the scope of market-oriented reform, the scientific and technological innovation in the area of big data has occasioned the birth of data science. This will generate new opportunities for the development of the traditional Chinese medicine industry. Our team is in the early stages of developing a methodological system for traditional Chinese medicine evidence-based research which is based on the “four syndromes”, with characteristics of “syndrome production-syndrome differentiation-syndrome and application-syndrome verification”. This paper is guided by traditional Chinese medicine theory, drawing on the experience from the concept of evidence-based medicine, and introducing multidisciplinary and interdisciplinary technologies, such as digital twins, and artificial intelligence. Based on these parameters, our team further proposes the research concept of evidence-based Chinese medicine toxicology, and plans to build up one new mode of data-intelligence fusion research on clinical “toxic” Chinese medicines. To be specific, multi-disciplinary research methods will be applied,such as conventional toxicology, translational toxicology, toxicological genomics and clinical medicine. The research data of “toxic” Chinese medicines will subsequently be analyzed, which could include studies on clinical characteristics, toxic substance basis, toxicokinetics, toxic molecular mechanisms and toxicity-effect conversion regulation. Further, the integrative transformation between data and evidence will be carried out with the help of artificial intelligence, a diseasesyndrome digital twin modeling system and other evidence-based toxicological technologies. Thus, the evaluation and prediction method using multiple and integrated evidence for “toxic” Chinese medicines will be set up, and then, the research method and technical path of evidence-based Chinese medicine toxicology will be refined, by being built around the core of data-intelligence fusion. In conclusion, this article designates data as the foundation and intelligence as the navigation, to provide new ideas and a reference base for the safety research into Chinese medicine.
作者
商洪才
Hongcai Shang(Dongzhimen Hospital,Beijing University of Chinese Medicine,Beijing 100700,China)
出处
《科学通报》
EI
CAS
CSCD
北大核心
2022年第2期118-124,共7页
Chinese Science Bulletin
基金
国家杰出青年科学基金(81725024)资助。
