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新技术方法在新药研发中的应用

Applications of new approach methodologies in new drug research and development
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摘要 新药研发是一个漫长且复杂的过程,需要高投入并伴有高风险和较低的成功率,因此提高新药研发效率,以最快的速度获得高质量的药物分子是所有新药研发企业梦寐以求的结果。为了克服传统二维细胞和动物实验模型简单、与人相关性差、低预测性和/或高成本的弊端,开发和使用人体相关性更好、预测性更高、耗时更少、花费更低、动物福利更好的新技术方法已经迫在眉睫。高通量组学、类器官、器官芯片、模型引导的药物开发、人工智能等新型的技术和方法经过几十年的发展已经越来越成熟,并已经在新药研发领域展现出潜在的应用价值和应用前景。本文将对上述5种新技术方法在新药研发中的应用进行综述,使读者能够对这些新技术方法有一定的了解,以促进这些新技术方法在新药研发中的应用。 New drug research and development process has been affected adversely by extensive timelines and complexity,high expense,high risk and low success rate,which indicates that there is an urgent need to improve new drug research and development efficiency to get high quality drug candidates as fast as possible,which also is the dream of all new drug research and development companies.To overcome the shortages of traditional twodimensional cell culture models and animal models including simplicity,less relevance to human,low predictability and/or high cost,developing and adopting new approach methodologies(NAMs)with high predictability and human relevance,reduced timeline and cost,and recognized animal welfare,are highly desirable.After advances in past few decades,the high-throughput omics,organoids,organ-on-chip,model-informed drug development,and artificial intelligence have become mature and demonstrated potential application value and perspective in new drug research and development.This review covers the applications of these NAMs in new drug research and development and intents to make the readers understand these NAMs and to promote the applications of these NAMs in new drug research and development.
作者 王春明 牛张明 张龙 WANG Chun-ming;NIU Zhang-ming;ZHANG Long(MindRank Therapeutics(Suzhou)New Drug Research and Development Co.,Ltd.,Suzhou 215100,China;MindRank AI Ltd.,Hangzhou 310000,China)
出处 《中国新药杂志》 CAS CSCD 北大核心 2024年第14期1458-1465,共8页 Chinese Journal of New Drugs
关键词 新药研发 新技术方法 组学 类器官 器官芯片 模型引导的药物开发 人工智能 new drug research and development new approach methodologies omics organoids organon-chip model-informed drug development artificial intelligence
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