制药制造中的人工智能:应用,案例研究和GxP实施考虑
Gowtham Nakka1, Sakshi Gupta2, Katti Kartik Reddy1
1University of Louisiana at Lafayette, College of Engineering, 104 E University Ave, Lafayette, LA 70504.
Journal of pharmaceutical sciences
|March 9, 2026
概括
人工智能 (AI) 和机器学习 (ML) 通过先进的过程控制和实时监控来增强制药制造. 成功实施需要将AI / ML与设计质量 (QbD) 和受监管环境的良好制造实践 (GMP) 整合起来.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 过程分析技术 (PAT) 是一种分析技术.
- 通过设计的质量 (QbD)
背景情况:
- 人工智能和机器学习在制药生产中越来越多地被用于改进工艺控制和监控.
- 整合PAT和QbD原则使AI/ML应用程序,如软传感器和先进的控制系统.
- 了解良好制造实践 (GMP) 对于在受监管环境中成功实施至关重要.
研究的目的:
- 在制药制造中绘制常见的AI用例原型.
- 定义AI/ML应用的数据要求,预期使用和验证需求.
- 将监管方面的考虑纳入制药质量体系 (PQS).
主要方法:
- 整合同行评审研究和监管文件.
- 为人工智能使用案例原型开发映射系统.
- 分析了连续制造和生物加工领域的八个案例研究.
主要成果:
- 人工智能/ML应用已被证明用于多变量统计过程控制,强度软传感器和过程中控制.
- 案例研究包括模型预测控制,色谱异常检测和自动视觉检查.
- 成功部署需要具有完整可追溯性的强大数据监控系统.
结论:
- 人工智能/ML为优化制药制造流程提供了巨大的潜力.
- 可持续的AI/ML实施需要遵守GMP和强大的数据管理.
- 在整个模型生命周期中遵守法规对于将AI/ML整合到PQS至关重要.
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