Application of Model‐Informed Drug Development in Dose Selection and Optimization for siRNA Therapies

选择(遗传算法) 药物开发 药品 药理学 医学 重症监护医学 计算生物学 计算机科学 生物 人工智能
作者
Y. Yuan,Liang Li,Justin Earp,Lian Ma,Venkatesh Atul Bhattaram,Vishnu L. Sharma,Alexander B. Tong,Yaning Wang,Jiang Liu,Hao Zhu
出处
期刊:The Journal of Clinical Pharmacology [Wiley]
标识
DOI:10.1002/jcph.2418
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
李健的小迷弟应助penghaha采纳,获得10
1秒前
Henry应助土豪的成威采纳,获得10
2秒前
杜熙凤完成签到,获得积分10
3秒前
小黎完成签到,获得积分10
3秒前
3秒前
秋海棠发布了新的文献求助10
4秒前
Ha La La La发布了新的文献求助10
6秒前
Yesir完成签到,获得积分10
6秒前
赘婿应助Two-Capitals采纳,获得10
7秒前
mayimo完成签到,获得积分10
7秒前
cao发布了新的文献求助10
8秒前
8秒前
9秒前
vivian发布了新的文献求助20
10秒前
10秒前
酷波er应助金属喵酱采纳,获得10
11秒前
太阳完成签到 ,获得积分10
11秒前
Lychee完成签到 ,获得积分10
11秒前
呼昂黄发布了新的文献求助30
12秒前
12秒前
肥陈完成签到,获得积分10
13秒前
Ha La La La完成签到,获得积分10
14秒前
nemo完成签到,获得积分10
15秒前
完美世界应助GuanYZ采纳,获得10
16秒前
17秒前
18秒前
20秒前
nemo发布了新的文献求助20
20秒前
ezio完成签到 ,获得积分10
20秒前
cao完成签到,获得积分20
21秒前
yynfyy发布了新的文献求助10
23秒前
23秒前
23秒前
呼昂黄完成签到,获得积分10
23秒前
wsy发布了新的文献求助10
24秒前
24秒前
852应助科研通管家采纳,获得10
26秒前
NexusExplorer应助科研通管家采纳,获得10
26秒前
26秒前
高分求助中
Manual of Clinical Microbiology, 4 Volume Set (ASM Books) 13th Edition 1000
We shall sing for the fatherland 500
Chinese-English Translation Lexicon Version 3.0 500
Electronic Structure Calculations and Structure-Property Relationships on Aromatic Nitro Compounds 500
マンネンタケ科植物由来メロテルペノイド類の網羅的全合成/Collective Synthesis of Meroterpenoids Derived from Ganoderma Family 500
Emerging Pollutants 410
[Lambert-Eaton syndrome without calcium channel autoantibodies] 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2378586
求助须知:如何正确求助?哪些是违规求助? 2085952
关于积分的说明 5234775
捐赠科研通 1812978
什么是DOI,文献DOI怎么找? 904702
版权声明 558574
科研通“疑难数据库(出版商)”最低求助积分说明 482979