Bosten Emery, Pardon Marie, Chen Kai, Koppen Valerie, Van Herck Gerd, Hellings Mario, Cabooter Deirdre
Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium.
Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium.
Anal Chem. 2024 Aug 20;96(33):13699-13709. doi: 10.1021/acs.analchem.4c02700. Epub 2024 Jul 9.
In recent decades, there has been a growing interest in fully automated methods for tackling complex optimization problems across various fields. Active learning (AL) and its variant, assisted active learning (AAL), incorporating guidance or assistance from external sources into the learning process, play key roles in this automation by enabling the autonomous selection of optimal experimental conditions to efficiently explore the problem space. These approaches are particularly valuable in situations wherein experimentation is costly or time-consuming. This study explores the application of AAL in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved. The effectiveness of this approach is demonstrated via two practical examples, resulting in optimized separations in a limited number of experiments by optimizing the gradient slope. It is shown that the ability of AAL to leverage past knowledge and compound information to improve accuracy and reduce experimental runs offers a flexible alternative approach to fixed design methods.
近几十年来，人们对跨领域解决复杂优化问题的全自动化方法越来越感兴趣。主动学习（AL）及其变体辅助主动学习（AAL），将外部来源的指导或协助纳入学习过程，通过自主选择最优实验条件以有效探索问题空间，在这种自动化过程中发挥着关键作用。这些方法在实验成本高或耗时的情况下尤其有价值。本研究通过使用贝叶斯统计纳入历史数据和分析物信息以生成初始保留模型，探索了AAL在基于模型的液相色谱（LC）方法开发（MD）中的应用。该过程包括基于新实验更新模型参数，以及采用主动数据选择方法来选择后续步骤中最具信息价值的实验来运行。这种迭代过程平衡了模型利用和实验探索，直到实现令人满意的分离。通过两个实际例子证明了该方法的有效性，通过优化梯度斜率，在有限数量的实验中实现了优化分离。结果表明，AAL利用过去知识和复合信息提高准确性并减少实验次数的能力，为固定设计方法提供了一种灵活的替代方法。
