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配方LAI:基于生理学的机器学习框架,用于加快长效注射配方的开发

Ping Xiong¹, Jinying Zhu¹, Hao Zhong¹

¹The State Key Laboratory of Mechanism and Quality of Chinese Medicine (MQCM), Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, 999078, China.

Journal of controlled release : official journal of the Controlled Release Society

|November 19, 2025

概括

开发用于慢性疾病的长效注射剂 (LAI) 是缓慢的. 使用机器学习和建模的新计算框架加速了LAI设计,将开发时间从几年缩短到几个月.

科学领域:

药物输送和配方科学药物输送和配方科学
计算机建模和模拟.
药理动力学和药理动力学

背景情况:

长效注射剂 (LAI) 为慢性疾病管理提供了潜力,但在发展时间,机制理解和体外-体内相关性方面面临挑战.
目前的LAI开发严重依赖于漫长的试错过程,阻碍了有效的转化到临床实践.

研究的目的:

开发和验证一个闭环计算框架,集成机器学习 (ML),生理学基础的药代动力学 (PBPK) 建模和分子动力学 (MD) 模拟,以加速LAI配方设计.
证明该框架能够预测最佳的体外释放特征,并指导开发高效的LAI配方.

主要方法:

整合ML,PBPK/PD建模和MD模拟以创建一个预测计算框架.
使用Zilretta® (基于PLGA的注射剂) 作为PBPK/PD建模的基准,以定义目标体外释放配置文件.
开发了一种基于ML预测的优化在位形成凝 (ISFG) 配方,用于氨酸的关节内输送.

主要成果:

计算框架成功指导了优化ISFG配方的开发.
开发的ISFG配方在老鼠的药理动力学研究中显示,与商业悬浮剂相比,药物暴露增加了34%.
MD模拟提供了对药物-聚合物相互作用的见解,这对于实现持续释放至关重要.

结论:

拟议的计算框架将LAI临床前开发时间从5-7年大幅缩短到大约1-2年.
这种预测性的"计算和验证"方法重新定义了LAI配方的开发,远离实证方法.
该框架提供了一个可扩展的战略,用于合理和有效地开发长效注射配方.

关键词:

在现场形成凝.长效注射剂长效注射剂机器学习机器学习分子动力学分子动力学基于生理学的药物动力学建模.

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