在药物开发过程中,色谱法通常用于原料药和药物产品的纯度和稳定性测试。反相液相色谱法(RPLC)由于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,定义最终API关键质量属性的指定杂质和降解产物,也称为关键预测样本集(KPSS),通常是很好的定义和控制。在这一点上,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]),在质量设计(QBD)方法的支持下。超临界流体色谱法(SFC)是一种优选的技术,因为其在选择性方面证明了多样性。采用具有最有利环境影响的技术,例如,但不限于,SFC,随着实验室努力减少碳足迹,也变得越来越重要。重新开发一种方法需要对员工的资源要求很高,材料,和时间。可以自动化的过程的任何步骤都可以促进这种方法,加快方法的交付,同时保持鲁棒性。在本文中,我们描述了如何开发SFC方法用于晚期肿瘤学候选物的纯度分析。利用SFC对结构相似分析物的优越选择性,归因于高正交性,R2对KPSS低至0.014。我们描述了两种自动化方法开发的方法。首先,多因素实验设计(DoE),其次,通过贝叶斯算法进行优化,它在一个晚上完成,强调潜力和局限性,深入了解稳健性。与传统的优化方法相比,两种方法都实现了基线分离,并实现了不同程度的自动化,并大大降低了资源需求。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%至30%之间的值,取决于每个序列的运行次数。
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.