Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA.
Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA; Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
J Chromatogr A. 2024 May 10;1722:464830. doi: 10.1016/j.chroma.2024.464830. Epub 2024 Mar 24.
Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.
在（生物）制药行业开发有意义且可靠的分析方法通常具有挑战性，涉及繁琐的反复试验。在这项工作中，提出了一种使用基于人工智能的算法的自动化分析工作流程，用于简化方法开发和优化。通过使用反馈控制建模方法，从一开始就使用现成的 LC 仪器和软件技术开发和优化色谱方法，避免了手动用户干预。使用这些工具可以大大减少分析师在开发方法时的时间要求。本文介绍了有关色谱系统控制、流动相条件自动优化以及挑战性多组分混合物（例如小分子药物、肽、蛋白质和疫苗产品）最终分离情况的关键见解，通过详细比较手性方法开发过程展示了这一点。本文介绍了现代色谱仪器和基于人工智能的软件的强大功能，可加速跨（生物）制药模式的新分离分析的开发和部署，同时节省大量成本、提高方法稳健性和快速分析周转时间。
