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使用基于同源建模的蛋白质电荷的 Donnan 模型预测超滤和稀释过程中药物物质的配方条件。

Predicting Formulation Conditions During Ultrafiltration and Dilution to Drug Substance Using a Donnan Model with Homology-Model Based Protein Charge.

机构信息

Department of Pharmaceutical Sciences, 1 DNA Way, South San Francisco, CA 94080, United States.

Department of Purification Development, 1 DNA Way, South San Francisco, CA 94080, United States.

出版信息

J Pharm Sci. 2023 Mar;112(3):820-829. doi: 10.1016/j.xphs.2022.10.028. Epub 2022 Nov 4.

Abstract

In the manufacturing of therapeutic monoclonal antibodies (mAbs), the final steps of the purification process are typically ultrafiltration/diafiltration (UF/DF), dilution, and conditioning. These steps are developed such that the final drug substance (DS) is formulated to the desired mAb, buffer, and excipient concentrations. To develop these processes, process and formulation development scientists often perform experiments to account for the Gibbs-Donnan and volume-exclusion effects during UF/DF, which affect the output pH and buffer concentration of the UF/DF process. This work describes the development of an in silico model for predicting the DS pH and buffer concentration after accounting for the Gibbs-Donnan and volume-exclusion effects during the UF/DF operation and the subsequent dilution and conditioning steps. The model was validated using statistical analysis to compare model predictions against experimental results for nine molecules of varying protein concentrations and formulations. In addition, our results showed that the structure-based in silico approach used to calculate the protein charge was more accurate than a sequence-based approach. Finally, we used the model to gain fundamental insights about the Gibbs-Donnan effect by highlighting the role of the protein charge concentration (the protein concentration multiplied with protein charge at the formulation pH) on the Gibbs-Donnan effect. Overall, this work demonstrates that the Gibbs-Donnan and volume-exclusions effects can be predicted using an in silico model, potentially alleviating the need for experiments.

摘要

在治疗性单克隆抗体 (mAb) 的制造中,纯化过程的最后步骤通常是超滤/渗滤 (UF/DF)、稀释和调节。这些步骤的开发使得最终药物物质 (DS) 被配制为所需的 mAb、缓冲液和赋形剂浓度。为了开发这些工艺,工艺和配方开发科学家经常进行实验,以考虑 UF/DF 过程中的吉布斯-唐南和体积排除效应,这会影响 UF/DF 过程的输出 pH 和缓冲液浓度。这项工作描述了一种用于预测 DS pH 和缓冲液浓度的计算模型的开发,该模型考虑了 UF/DF 操作以及随后的稀释和调节步骤中的吉布斯-唐南和体积排除效应。该模型通过统计分析进行了验证,将模型预测与九种不同蛋白质浓度和配方的实验结果进行了比较。此外,我们的结果表明,用于计算蛋白质电荷的基于结构的计算方法比基于序列的方法更准确。最后,我们使用该模型通过强调蛋白质电荷浓度(制剂 pH 下的蛋白质浓度乘以蛋白质电荷)对吉布斯-唐南效应的作用,来深入了解吉布斯-唐南效应。总的来说,这项工作表明可以使用计算模型来预测吉布斯-唐南和体积排除效应,从而可能减轻对实验的需求。

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