神经网络预测药品和医疗器械中可提取和可漏的响应因子
Yuanlin Deng1, Anthony Grice1, Michael Louis1
1Jordi Labs, an RQM+ company, 300 Gilbert Street, Mansfield MA 02048.
PDA journal of pharmaceutical science and technology
|January 30, 2026
概括
使用先进的神经网络模型准确预测相对响应因子,可以显著提高药品可提取和可漏的量化,确保患者的安全.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 患者安全依赖于药品和医疗器械的化学表征.
- 精确量化可提取物和可浸出物对于管理化学物质暴露的风险至关重要.
- 质谱法方法的变化使准确的化学反应估计变得复杂.
研究的目的:
- 在提取和漏 (E&L) 分析中开发相对响应因子 (RRF) 的in-silico预测模型.
- 通过解决响应因子变异性来提高E&L量化的准确性和速度.
主要方法:
- 开发了三种神经网络模型和RRF预测的子模型.
- 适用于液体染色体质谱 (LC-MS) 和气体染色体质谱 (GC-MS) 的正负电离模式的模型.
- 选择多种化学化合物,使用物理化学性质覆盖方法进行模型培训和验证.
主要成果:
- 训练数据的平均绝对误差为0.47,样本外数据的平均误差为0.65,表明可预测性高,过拟合性低.
- 在训练组之外的90% (44/49) 的测试化学品中,已证明预测准确度超过已知值的60%.
- 验证了模型对各种化合物的有效性.
结论:
- 复杂的神经网络建模提供了一个可行的解决方案,以克服E&L分析中的响应因子变化.
- 这种方法有可能加快并提高E&L量化的准确性.
- 该研究提供了使用计算方法来改进药品安全评估的概念验证.
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