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一种多步骤的机器学习方法,以加速基于QbD的蛋白质喷雾干燥工艺开发.

Daniela Fiedler1, Elisabeth Fink2, Isabella Aigner2

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概括

使用替代材料的新型机器学习 (ML) 方法显著减少了开发蛋白质喷雾干燥设计空间 (DS) 所需的实验. 这种方法有效地优化了复杂的生物过程,最大限度地减少了用于喷雾干燥优化的昂贵试错.

关键词:
人工神经网络的人工神经网络生物制剂 生物制剂 生物制剂设计实验的设计.机器学习是机器学习.蛋白质蛋白质是一种蛋白质.喷雾干燥 喷雾干燥

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 材料科学 材料科学 材料科学
  • 生物技术是生物技术.

背景情况:

  • 开发喷雾干燥蛋白质的设计空间 (DS) 通常需要广泛的实验设计 (DoE),由于昂贵的生物制剂,这很昂贵.
  • 优化生物制剂的喷雾干燥过程需要尽量减少实验运行,同时保持准确性.

研究的目的:

  • 研究一种材料效率高,多步机器学习 (ML) 方法的有效性,用于开发用于蛋白质喷雾干燥的DS.
  • 评估使用替代物质,特别是乳糖,与ML一起用于DS发展的适宜性.
  • 将ML模型的预测性能与传统DoE模型进行比较.

主要方法:

  • 实验设计 (DoE) 使用代用材料 (乳糖) 来生成机器学习 (ML) 模型的训练数据.
  • 开发了一种多步骤的ML方法,并与使用多变量回归的基准DoE方法进行比较.
  • 来自ML和DoE方法的模型预测与使用实际蛋白质配方的实验运行进行了验证.

主要成果:

  • 使用替代材料的ML方法证明了其适合开发蛋白质喷雾干燥DS,从而减少了实验负担.
  • 乳糖被发现是一个合适的替代材料,而拟议的ML方法显示出相对于传统的DoE的优势.
  • 蛋白质度超过35毫克/毫升和颗粒大小大于6微米的限制被观察到.
  • 在研究的DS中,蛋白质的二次结构被保留,产量通常高于75%和残留水分低于10%的重量.

结论:

  • 拟议的材料效率高的ML方法为开发蛋白质喷雾干燥设计空间提供了可行且具有成本效益的替代方案.
  • 这种方法对昂贵的生物药物特别有益,因为尽量减少实验运行至关重要.
  • 需要进一步研究以解决较高蛋白质度和颗粒大小的限制.