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Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
141

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机器学习框架用于提取压缩口服固体剂型的微粘弹性和微结构性质.

Tipu Sultan1, Enamul Hasan Rozin1, Shubhajit Paul2

  • 1Department of Mechanical and Aerospace Engineering, Photo-Acoustics Research Laboratory, Clarkson University, Potsdam, NY 13699-5725, USA.

International journal of pharmaceutics
|October 5, 2023
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概括

这项研究引入了一种新的机器学习方法,直接从超声波波形中提取口服固体剂型的微粘弹性和微结构性质. 该方法提供了一种快速,非破坏性的方法来评估制药制造业的关键质量属性.

关键词:
压缩口服固体剂量形式 (OSD)连续制造 (CM) 是指连续制造的过程.机器学习 (ML) 是指机器学习.微观结构的特征描述.微粘性弹性 微粘性弹性多个输出回归 (MOR)神经网络 (NN) 神经网络 (NN) 是一个神经网络.通过设计的质量 (QbD)实时释放 (RTR) 测试实时释放测试超声波非破坏性评估和测试 (NDE/NDT)超声波的分散是超声波的分散.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 制药技术 制药技术 制药技术
  • 应用物理 应用物理
  • 机器学习 机器学习

背景情况:

  • 口服固体剂量 (OSD) 形式是微粘弹性复合材料,其关键质量属性取决于微尺度的特性.
  • 超声波评估为OSD分析提供了一种非破坏性,快速的方法,但从波形中提取微特性是具有挑战性的.
  • 准确地描述OSD微特性对于确保产品质量至关重要,包括分解,药物释放和结构强度.

研究的目的:

  • 开发和演示一种基于机器学习 (ML) 的新技术,用于从OSD的超声波形式中提取微粘弹性和散射特性.
  • 为了解决直接从实验性超声波反应中确定OSD微性质的反向问题.
  • 用合成和物理OSD平板数据验证ML模型的有效性.

主要方法:

  • 开发用于波形分析的多输出回归模型和神经网络.
  • 产生具有已知的微特性的合成超声波形式,用于ML模型的训练和验证.
  • 从物理OSD平板进行超声波形式的实验采集,用于测试ML模型.

主要成果:

  • 开发的ML模型成功地从合成OSD波形中提取了微粘弹性和微结构性质.
  • 基于ML的技术在恢复虚拟平板电脑的特定微量级属性方面表现出有效性.
  • 使用物理OSD片的验证显示了ML衍生性质和已知的材料特性之间的一致性.

结论:

  • 一种基于ML的新方法可以从超声波形式中直接提取OSD微特性,克服反向问题的挑战.
  • 该技术为制药制造业的工艺控制和质量评估提供了强大的,非破坏性的工具.
  • 该研究证实了ML在推进复杂制药材料的表征方面的潜力.