在药物开发过程中，色谱法经常用于原料药和药品的纯度和稳定性测试。反相液相色谱（RPLC）因其广泛的应用范围而成为最广泛使用的方法之一。在药物开发的后期阶段，定义最终 API 关键质量属性的指定杂质和降解产物（也称为关键预测样品集 (KPSS)）通常得到明确定义和控制。此时，方法审查可以选择最合适的技术，该技术应该是提供最佳鲁棒性的技术 (ICH-Q14[1])，并在质量源于设计 (QbD) 方法的支持下。超临界流体色谱 (SFC) 因其经过验证的选择性多样性而成为首选技术。随着实验室努力减少碳足迹，采用对环境最有利的技术（例如但不限于 SFC）也变得越来越重要。重新开发一种方法需要大量的人员、材料和时间资源。该过程中任何可以自动化的步骤都可以促进这种方法，加快该方法的交付，同时保持稳健性。在本文中，我们描述了如何开发 SFC 方法来对晚期肿瘤候选药物进行纯度分析，利用 SFC 对结构相似分析物的卓越选择性，这归因于与 KPSS 的 R2 低至 0.014 的高正交性。我们描述了两种自动化方法开发的方法。首先是多因素实验设计（DoE），其次是通过贝叶斯算法进行优化，该算法在一夜之间完成，突出了潜力和局限性，并深入了解了鲁棒性。与传统优化方法相比，这两种方法都通过在流程中嵌入不同程度的自动化来实现基线分离，并且大大减少了资源需求。最后，我们描述了实施 SFC 方法可以产生的有益环境影响，与 RPLC 相比，计算出的绿色分数降低到 17% 到 30% 之间的值，具体取决于每个序列的运行次数。版权所有 © 2024 Elsevier B.V. 保留所有权利。

During drug development, chromatography is frequently used for purity and stability testing of both drug substance and drug product. Reversed phase liquid chromatography (RPLC) is one of the most widely used methodologies due to its wide scope of application. In the later stages of drug development, the specified impurities and degradation products that define the critical quality attribute of the final API, also known as Key Predictive Sample Set (KPSS), are usually well defined and controlled. At this point, a method review enables selecting the most appropriate technique which should be the one providing optimal robustness (ICH-Q14[1]), with the support of Quality by Design (QbD) approaches. Supercritical Fluid Chromatography (SFC) is a preferred technique for its proven diversity in selectivity. The adoption of a technique which presents the most favourable environmental impact, such as, but not limited to, SFC, is also becoming increasingly important as laboratories strive to reduce carbon footprint. Re-developing a method requires high resource-demands in terms of staff, materials, and time. Any step of the process that can be automated can facilitate this approach, speeding up the delivery of the method whilst preserving robustness. In this article we describe how an SFC method was developed for the purity profiling of a late-stage oncology candidate, taking advantage of the superior selectivity of SFC towards structurally similar analytes, owed to the high orthogonality with R2 as low as 0.014 towards the KPSS. We describe two approaches to automate the method development. Firstly, a multifactorial design of experiments (DoE) and secondly, an optimization via a Bayesian algorithm, which was completed in one night, highlighting the potential and limitations, with an insight into the robustness. Both methods achieved baseline separation with varying levels of automation embedded into the process and a large reduction of the resource demands when compared to traditional optimisation methods. Finally, we describe the beneficial environmental impact that implementing SFC methods can yield, with a calculated green score reduced to a value between 17 and 30 % compared to RPLC, depending on the number of runs per sequence.Copyright © 2024 Elsevier B.V. All rights reserved.