机器学习用于预测PLGA微球的药物释放行为
Andrew F Catapano1, Ling Zheng1, Xudong Yuan2
1Monmouth University, West Long Branch, NJ, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
机器学习准确地预测了从聚乳酸-同-甘油酸 (PLGA) 微球中释放的药物. 这种方法优化了长效药物配方,减少了低效的试错开发.
科学领域:
- 生物材料科学 生物材料科学
- 制药技术 制药技术 制药技术
- 计算化学的计算化学
背景情况:
- 聚乳糖糖酸 (PLGA) 微球对于持续的药物输送至关重要,提高了患者的遵守性.
- 目前的开发依赖于低效的试错方法,因为复杂的配方因素影响药物释放.
研究的目的:
- 开发和验证机器学习模型,用于从PLGA微球中预测体外药物释放概况.
- 确定影响药物释放动态的关键配方参数.
主要方法:
- 从科学文献中编制了113种PLGA配方的综合数据集.
- 训练了多个机器学习算法,并对预测性能进行了评估.
- 进行了特征重要性分析,以了解影响释放的关键因素.
主要成果:
- 最优的机器学习模型实现了高精度,R2=0.9415,RMSE=6.99%,MAE=4.35%. 机器学习模型实现了高精度,R2=0.9415,RMSE=6.99%,MAE=4.35%.
- 该模型展示了强大的预测能力,用于在体外释放药物从PLGA微球.
- 通过特征重要性分析确定了影响药物释放的关键因素.
结论:
- 机器学习为基于PLGA的药物输送系统的合理设计和优化提供了一个强大的工具.
- 准确预测药物释放概况可以加速配方开发并改善治疗结果.
- 这种数据驱动的方法促进了长效注射配方的高效开发.
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