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基于疏水参数补偿模型的串联柱液相色谱法拆分药物化合物。

Development of tandem-column liquid chromatographic methods for pharmaceutical compounds using simulations based on hydrophobic subtraction model parameters.

机构信息

Department of Chemistry, Drexel University, 32 South 32nd St., Philadelphia, PA 19104 USA.

Chemical Process Development, Bristol Myers Squibb, 1 Squibb Dr, New Brunswick, NJ 08903 USA.

出版信息

J Chromatogr A. 2023 Apr 26;1695:463925. doi: 10.1016/j.chroma.2023.463925. Epub 2023 Mar 12.

Abstract

The liquid chromatography (LC) analysis of small molecule pharmaceutical compounds and related impurities is crucial in the development of new drug substances, but developing these separations is usually challenging due to analyte structural similarities. Tandem-column LC (TC-LC) has emerged as a powerful approach to achieve alternative separation selectivity compared to conventional single column separations. However, one of the bottlenecks associated with use of tandem column approaches is time-consuming column pair screening and selection. Herein, we compared critical resolution (R) in single column vs. TC-LC separations for a given set of small molecule pharmaceutical compounds and developed a column selection workflow that uses separation simulations based on parameters from the hydrophobic subtraction model (HSM) of reversed-phase selectivity. In this study, HSM solute parameters were experimentally determined for a small molecule pharmaceutical (Linrodostat) and ten of its related impurities using multiple linear regression of their retentions on 16 selected RPLC columns against in-house determined HSM column parameters. R values were calculated based on HSM database column parameters for a pool of about 200 available stationary phases in both single-phase column (2.1 mm i.d. × 100 mm) or tandem column paired (two 2.1 mm i.d. × 50 mm) formats. Four column configurations (two single and two tandem) were predicted to achieve successful separations under isocratic HSM separation conditions, with a fifth tandem pair predicted to have a single co-elution. Of these five potential candidates, one tandem pair yielded compete baseline resolution of the 11-component mixture in an experimental separation. In this specific case, the tandem column pairs outperformed single-phase columns, with better predicted and experimental R values for the Linrodostat mixture under the HSM separation conditions. The results reported in this study demonstrated the enormous selectivity potential of TC-LC in pharmaceutical compound separations and are consistent with our previous study that examined the potential of tandem column approaches using purely computational means, though there is room for substantial improvement in the prediction accuracy. The proposed workflow can be used to prioritize a small number of column combinations by computational means before any experiments are conducted. This is highly attractive from the point of view of time and resource savings considering over 200,000 different tandem column pairings are possible using columns for which there are data in the HSM database.

摘要

小分子药物化合物和相关杂质的液相色谱(LC)分析在新药物物质的开发中至关重要,但由于分析物结构相似,开发这些分离通常具有挑战性。串联柱 LC(TC-LC)已成为一种强大的方法,可以与传统的单柱分离相比实现替代的分离选择性。然而,与串联柱方法相关的一个瓶颈之一是耗时的柱对筛选和选择。在此,我们比较了给定的小分子药物化合物单柱与 TC-LC 分离的关键分辨率(R),并开发了一种基于反相选择性疏水减除模型(HSM)参数的分离模拟的柱选择工作流程。在这项研究中,使用多元线性回归法,根据保留时间对 16 根选定的反相高效液相色谱柱上的小分子药物(Linrodostat)及其十种相关杂质的保留时间,从反相选择性的 HSM 柱参数中实验确定了小分子药物的 HSM 溶质参数。根据 HSM 数据库柱参数,在单相柱(2.1mm i.d.×100mm)或串联柱配对(两个 2.1mm i.d.×50mm)格式中对大约 200 种可用固定相的混合物计算 R 值。预测了四种柱配置(两种单柱和两种串联柱)在等度 HSM 分离条件下能够实现成功分离,其中一种串联对预测有单一共洗脱。在这五个潜在候选物中,一个串联对在实验分离中实现了 11 个成分混合物的完全基线分离。在这种特殊情况下,TC-LC 在药物化合物分离中具有巨大的选择性潜力,在 HSM 分离条件下,Linrodostat 混合物的预测和实验 R 值优于单相柱。本研究结果表明,TC-LC 在药物化合物分离中具有巨大的选择性潜力,与我们之前使用纯计算方法研究串联柱方法的潜力的研究结果一致,尽管在预测准确性方面仍有很大的改进空间。在所提出的工作流程中,可以在进行任何实验之前,通过计算手段对少数几种柱组合进行优先级排序。考虑到使用 HSM 数据库中有数据的柱子,可能有超过 200,000 种不同的串联柱组合,从节省时间和资源的角度来看,这是非常有吸引力的。

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