模拟和预测平板电脑溶解减速,使用加速因子方法和受约束的神经网络
Yi Li1, Shalini Raj Unnikandam Veettil1, Tiffany Pham1
1Gilead Sciences, Foster City, CA 94404, USA.
Journal of pharmaceutical sciences
|September 28, 2025
概括
预测模型可以预测储存期间平板电脑溶解的减速. 加速度因子 (AF) 方法和受约束的神经网络有效预测了变化,帮助制定和包装决策.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 在储存期间平板电脑溶解的减缓可能会影响药物释放和生物可用性.
- 使用加速数据的预测稳定性模型对于评估长期存储风险至关重要.
- 了解和减轻溶解变化对于药物开发至关重要.
研究的目的:
- 为了比较基于科学和机器学习的模型来预测平板电脑溶解减速.
- 评估加速因子 (AF) 模型和受约束的神经网络的有效性.
- 确定控制储存条件的策略,以防止溶解变化.
主要方法:
- 应用经验加速因子 (AF) 模型来打开盘稳定性数据.
- 用AF模型约束神经网络,利用阿雷尼乌斯关系和第一阶段衰变.
- 与不受约束的神经网络和评估的包装存储数据进行了预测性能比较.
主要成果:
- 无论是AF方法还是受约束的神经网络都准确地预测了包装片中的溶解概况.
- 该AF模型确定了关键的湿度边界条件,以防止溶解减速.
- 用AF模型限制神经网络提高了预测性能.
结论:
- 加速稳定模型,特别是AF方法和受约束的神经网络,对预测溶解变化充满希望.
- 这些方法可以作为药物开发中的有价值的建模方法来重新想象稳定性 (MARS) 工具.
- 获得的洞察力有助于配方,包装选择和稳定性评估,以减轻溶解挑战.
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