Operator-free HPLC automated method development guided by Bayesian optimization†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-06-14 DOI:10.1039/D4DD00062E
Thomas M. Dixon, Jeanine Williams, Maximilian Besenhard, Roger M. Howard, James MacGregor, Philip Peach, Adam D. Clayton, Nicholas J. Warren and Richard A. Bourne
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Abstract

The need to efficiently develop high performance liquid chromatography (HPLC) methods, whilst adhering to quality by design principles is of paramount importance when it comes to impurity detection in the synthesis of active pharmaceutical ingredients. This study highlights a novel approach that fully automates HPLC method development using black-box single and multi-objective Bayesian optimization algorithms. Three continuous variables including the initial isocratic hold time, initial organic modifier concentration and the gradient time were adjusted to simultaneously optimize the number of peaks detected, the resolution between peaks and the method length. Two mixtures of analytes, one with seven compounds and one with eleven compounds, were investigated. The system explored the design space to find a global optimum in chromatogram quality without human assistance, and methods that gave baseline resolution were identified. Optimal operating conditions were typically reached within just 13 experiments. The single and multi-objective Bayesian optimization algorithms were compared to show that multi-objective optimization was more suitable for HPLC method development. This allowed for multiple chromatogram acceptance criteria to be selected without having to repeat the entire optimization, making it a useful tool for robustness testing. Work in this paper presents a fully “operator-free” and closed loop HPLC method optimization process that can find optimal methods quickly when compared to other modern HPLC optimization techniques such as design of experiments, linear solvent strength models or quantitative structure retention relationships.

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贝叶斯优化指导下的免操作 HPLC 自动方法开发
高效开发高效液相色谱(HPLC)方法,同时遵守设计质量原则,对于活性药物成分合成过程中的杂质检测至关重要。本研究采用黑盒单目标和多目标贝叶斯优化算法,重点介绍了一种全自动 HPLC 方法开发的新方法。对初始等度保持时间、初始有机改性剂浓度和梯度时间等三个连续变量进行了调整,以同时优化检测到的峰数、峰间分辨率和方法长度。研究了两种分析物混合物,一种含有 7 种化合物,另一种含有 11 种化合物。系统探索了设计空间,以便在无需人工辅助的情况下找到色谱质量的总体最优值,并确定了可提供基线分辨率的方法。通常只需 13 次实验就能达到最佳操作条件。对单一目标和多目标贝叶斯优化算法进行了比较,结果表明多目标优化更适用于高效液相色谱方法的开发。这样就可以选择多个色谱接受标准,而无需重复整个优化过程,使其成为稳健性测试的有用工具。与实验设计、线性溶剂强度模型或定量结构保留关系等其他现代高效液相色谱优化技术相比,本文提出的完全 "免操作 "的闭环高效液相色谱方法优化流程能快速找到最佳方法。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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