积极学习和高斯过程用于溶解模型的开发:基于人工智能的数据高效方法
Roshan A Patel1, Siddharth S Kesharwani1, Fady Ibrahim1
1Drug Product Development, Synthetics Platform, Sanofi, 350 Water St., Cambridge, MA 02141, USA.
概括
高斯过程回归 (GPR) 和主动学习显著减少了对制药溶解建模的数据需求. 这些方法提高了预测准确性,并比传统方法更有效地识别关键处理参数.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 在体外溶解测试对于药品质量控制和配方优化至关重要.
- 数据驱动的溶解模型通过预测结果,减少物理实验和识别关键影响因素来提供效率.
- 尽量减少模型开发的数据要求,提高了这些预测工具的实际实用性.
研究的目的:
- 调查高斯过程回归 (GPR) 和主动学习在减少预测溶解建模数据需求方面的有效性.
- 将GPR的性能与传统的多项式模型进行比较,用于溶解预测.
- 评估这些方法识别影响药物溶解的关键处理参数的能力.
主要方法:
- 实验设计 (DOE) 研究在五个处理参数中进行,以生成B化合物的溶解数据.
- 用高斯过程回归 (GPR) 和多项式模型进行训练并使用生成的数据集进行比较.
- 沙普利添加式解释 (SHAP) 用于GPR模型解释和参数重要性评估.
- 积极学习策略被追溯分析,以评估它们在选择最佳实验子集中的潜力.
主要成果:
- 与多项式模型相比,在相同的数据集上训练时,GPR表现出更高保真度的溶解预测.
- 沙普利的添加式解释有效地识别和排名了影响溶解的各种加工参数的重要性.
- 追溯分析表明,积极学习可以识别比完全的DOE更小,更具信息性的实验集,用于模型开发和参数识别.
结论:
- 与传统的多项式模型相比,高斯过程回归为药物溶解提供了优越的预测性能.
- 积极学习策略可以大幅减少开发准确溶解模型和理解过程参数关系所需的实验负担.
- 将GPR和主动学习结合使用,为制药产品开发和质量控制提供了一个强大的,数据效率高的方法.
相关概念视频
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