机器学习在药物制药过程建模中的实施现状,用于口服固体剂型
Maryam Rezaeizadeh1, Sonia M Razavi2, Fernando J Muzzio3
1Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), East Windsor, NJ, USA; Rutgers University, Ernest Mario School of Pharmacy, Pharmaceutical Science, Piscataway, NJ, USA.
人工智能 (AI) 和机器学习 (ML) 正在改变口服固体剂型的制药制造业. 这些技术使预测建模和实时优化成为可能,尽管数据和集成方面的挑战仍然存在.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 工业化学 工业化学 工业化学
- 数据科学数据科学数据科学
背景情况:
- 制药行业正在转向先进制造业,这是由监管举措和工业4.0推动的.
- 这种转变需要预测建模,实时优化和质量控制.
研究的目的:
- 审查机器学习 (ML) 在口服固体剂型制造中的应用.
- 专注于单元操作和过程分析技术 (PAT).
主要方法:
- 在制药生产中对ML应用的审查.
- 专注于湿颗粒,挤出和PAT框架.
- 对ML进行分析,以预测关键质量属性和优化过程参数.
主要成果:
- ML的应用包括预测颗粒大小分布和水分含量.
- ML优化了挤出工艺参数,以达到所需的产品品质.
- 使用 ML 开发可适应的实时 PAT 框架.
结论:
- 对于先进的制药制造要求,ML显示出显著的前景.
- 挑战包括数据可用性,模型可解释性和整合性.
- 未来的工作涉及数字双胞胎,可扩展性和不确定性量化.
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