通过机器学习模型进行药物安全评估.
Nan Miles Xi1, Dalong Patrick Huang2
1Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA.
机器学习使用临床前数据准确预测药物诱导的Torsades de pointes (TdP) 风险. 这种方法通过在发育早期确定潜在的心脏风险来加强药物安全性评估.
科学领域:
- 心血管药理学心血管药理学
- 计算毒理学计算毒理学
- 药物安全科学 药物安全科学
背景情况:
- 药物诱导的Torsades de pointes (TdP) 在药物开发中构成重大风险.
- 准确预测TdP对于患者安全和监管部门的批准至关重要.
- 临床前测试为评估心脏责任提供了有价值的数据.
研究的目的:
- 评估用于预测药物诱导的TdP风险的机器学习模型.
- 为了利用临床前数据,特别是子心室形试验的数据,用于TdP风险预测.
- 在药物安全性评估中验证计算方法的实用性.
主要方法:
- 使用临床前数据开发和训练一个随机森林模型.
- 数据集是从子心室形测定中生成的.
- 绩效评价使用 leave-one-drug-out 对28种药物的交叉验证,这些药物来自综合性体外前节律失常试验倡议.
- 不确定性评估通过分层启动.
主要成果:
- 随机森林模型在预测药物诱导的TdP风险方面表现出有用性.
- 交叉验证提供了对模型性能的公正估计.
- 分层引导量化了模型预测中的不确定性.
结论:
- 机器学习方法是有效的预测药物诱导的TdP风险从临床前数据.
- 开发的方法可以适应其他临床前方案.
- 这种方法在全面的药物安全性评估中是有价值的补充工具.
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