开发可预测的统计模型,以获取药物产品的有价值洞察力回忆
Jayshil A Bhatt1,2,3, Kenneth R Morris4, Rahul V Haware5,4,6
1Arnold and Marie Schwartz College of Pharmacy, Long Island University, 75 Dekalb Ave L130, Brooklyn, New York, 11201, USA. bhattjayshil@gmail.com.
AAPS PharmSciTech
|October 23, 2024
概括
人工智能 (AI) 和机器学习 (ML) 可以通过分析配方和制造复杂性来预测药品召回. 药物半衰期和BCS I类等关键因素影响召回风险,提高药物开发质量.
科学领域:
- 制药科学 制药科学
- 人工智能的人工智能
- 药物开发 药物开发
背景情况:
- 制药产品召回是一个重大的全球挑战,需要先进的工具来降低风险.
- 优化药物开发流程对于防止召回和确保产品质量至关重要.
研究的目的:
- 利用人工智能和机器学习 (ML) 来分析影响制药产品召回的因素.
- 开发预测模型来评估产品的复杂性和预测召回的可能性.
主要方法:
- 利用FDAZilla和SafeRX工具构建一个开放的数据库模型.
- 开发了使用多变量分析和最小绝对缩小和选择运算符 (LASSO) 方法的预测统计模型.
- 分析了包括输送途径,剂量形式,剂量,BCS分类,物理化学性质,释放类型,半衰期和制造复杂性在内的关键描述因素.
主要成果:
- 确定了影响产品召回风险的关键描述因素:BCS类I,剂量数,释放概况和药物半衰期.
- 分配的风险数字和计算的累积风险数字,以评估产品的复杂性和召回可能性.
- 拉索模型在确认召回风险预测的关键描述符方面达到71%的准确性.
结论:
- 介绍了一种整体的AI和ML方法,用于评估和预测制药产品召回.
- 强调配方复杂性和制造工艺在减轻产品质量风险方面的重要性.
- 强调关键描述符在预测和防止制药产品召回中的作用.
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