稳定性建模方法,使患者能够更早地访问
Andrew Lennard1, Boris Zimmermann2, Didier Clenet3
1Amgen Limited, 4 Uxbridge Business Park, Sanderson Road, Uxbridge UB8 1DH, UK.
Journal of pharmaceutical sciences
|September 29, 2024
概括
使用人工智能和统计数据的预测稳定性建模提供了对药物的可靠保质期预测. 这种方法通过补充实时数据来加快新药的可用性,确保质量和安全.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 监管科学 监管科学
背景情况:
- 预测稳定性建模越来越被接受,用于确定药品的保质期.
- 科学和基于风险的方法可以克服监管提交不完整实时稳定性数据的局限性.
- 加快药物可用性对于患者获得新疗法至关重要.
研究的目的:
- 突出预测稳定性建模在保质期确定中的能力.
- 强调这些模型在加快监管提交和药物可用性方面的作用.
- 讨论各种统计和人工智能工具在稳定性评估中的整合.
主要方法:
- 利用统计工具,先验知识和行业经验进行预测建模.
- 使用诸如加速稳定性评估程序 (ASAP) 和高级运动建模 (AKM) 等方法.
- 纳入贝叶斯统计和人工智能 (AI) 应用程序,如机器学习 (ML),用于合成和生物分子.
主要成果:
- 预测模型提供了强大而可靠的保质期/过期或重新测试期预测.
- 这些模型有助于在没有完整的实时稳定性数据的情况下进行监管提交.
- 对于解决生物稳定性建模中的局限性而言,先前的知识特别有价值.
结论:
- 预测稳定性建模,包括AI/ML,为保质期预测提供了可靠的替代方案.
- 通过实时数据进行持续的验证,使监管机构对这些方法产生了信心.
- 稳定性建模的监管接受可以加快患者获得必需药物的速度,而不会影响安全性或疗效.
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