加药
耐受性
选择(遗传算法)
药物开发
药品
药理学
医学
药效学
精密医学
药代动力学
临床试验
小干扰RNA
毒品类别
人口
重症监护医学
生物信息学
计算生物学
计算机科学
生物
不利影响
内科学
病理
机器学习
核糖核酸
基因
环境卫生
生物化学
作者
Ye Yuan,Liang Li,Justin Earp,Lian Ma,Venkatesh Atul Bhattaram,Vishnu Sharma,Alex Tong,Yaning Wang,Jiang Liu,Hao Zhu
摘要
Abstract The application of model‐informed drug development (MIDD) has revolutionized drug development and regulatory decision making, transforming the process into one that is more efficient, effective, and patient centered. A critical application of MIDD is to facilitate dose selection and optimization, which play a pivotal role in improving efficacy, safety, and tolerability profiles of a candidate drug. With the surge of interest in small interfering RNA (siRNA) drugs as a promising class of therapeutics, their applications in various disease areas have been extensively studied preclinically. However, dosing selection and optimization experience for siRNA in humans is limited. Unique challenges exist for the dose evaluation of siRNA due to the temporal discordance between pharmacokinetic and pharmacodynamic profiles, as well as limited available clinical experience and considerable interindividual variability. This review highlights the pivotal role of MIDD in facilitating dose selection and optimization for siRNA therapeutics. Based on past experiences with approved siRNA products, MIDD has demonstrated its ability to aid in dose selection for clinical trials and enabling optimal dosing for the general patient population. In addition, MIDD presents an opportunity for dose individualization based on patient characteristics, enhancing the precision and effectiveness of siRNA therapeutics. In conclusion, the integration of MIDD offers substantial advantages in navigating the complex challenges of dose selection and optimization in siRNA drug development, which in turn accelerates the development process, supports regulatory decision making, and ultimately improves the clinical outcomes of siRNA‐based therapies, fostering advancements in precision medicine across a diverse range of diseases.
科研通智能强力驱动
Strongly Powered by AbleSci AI