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Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration
Biotechnology and Bioengineering ( IF 3.5 ) Pub Date : 2023-04-22 , DOI: 10.1002/bit.28406
Abhijeet Satwekar 1 , Anubhab Panda 2 , Phani Nandula 2 , Sriharsha Sripada 2 , Ramachandiran Govindaraj 2 , Mara Rossi 1
Affiliation  

Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly benefit from the adoption of digital technologies, but the process of implementing these technologies can be slow and complex. This article presents a “Digital by Design” managerial approach, adapted from pharmaceutical quality by design principles, for designing and implementing an artificial intelligence (AI)-based solution for chromatography peak integration process in the healthcare industry. We report the use of a convolutional neural network model to predict analytical variability for integrating chromatography peaks and propose a potential GxP framework for using AI in the healthcare industry that includes elements on data management, model management, and human-in-the-loop processes. The component on analytical variability prediction has a great potential to enable Industry 4.0 objectives on real-time release testing, automated quality control, and continuous manufacturing.

中文翻译:

数字化设计方法,用于开发用于自动色谱峰集成的通用深度学习 AI 架构

色谱数据处理因多次被美国食品药品监督管理局 483 引用和警告信而受到关注,凸显了对稳健技术解决方案的需求。医疗保健行业有可能从采用数字技术中获益匪浅,但实施这些技术的过程可能缓慢而复杂。本文介绍了一种“数字化设计”管理方法,该方法改编自制药质量源于设计原则,用于设计和实施基于人工智能 (AI) 的医疗保健行业色谱峰集成过程解决方案。我们报告了使用卷积神经网络模型来预测积分色谱峰的分析变异性,并提出了一个潜在的 GxP 框架,用于在医疗保健行业中使用 AI,其中包括数据管理、模型管理和人在环过程中的元素. 分析变异性预测的组成部分具有巨大的潜力,可以实现实时发布测试、自动化质量控制和连续制造的工业 4.0 目标。
更新日期:2023-04-22
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