Dept. of Chemical Engineering, Indian Inst. of Technology, Hauz Khas, New Delhi, India.
Biotechnol Prog. 2019 Mar;35(2):e2758. doi: 10.1002/btpr.2758. Epub 2018 Dec 19.
A major challenge in chromatography purification of therapeutic proteins is batch-to-batch variability with respect to impurity levels and product concentration in the feed. Mechanistic model can enable process analytical technology (PAT) implementation by predicting impact of such variations and thereby improving the robustness of the resulting process and controls. This article presents one such application of mechanistic model of hydrophobic interaction chromatography (HIC) as a PAT tool for making robust pooling decisions to enable clearance of aggregates for a monoclonal antibody (mAb) therapeutic. Model predictions were performed before the actual chromatography experiments to facilitate feedforward control. The approach has been successfully demonstrated for four different feeds with varying aggregate levels (3.84%-5.54%) and feed concentration (0.6 mg/mL-1 mg/mL). The resulting pool consistently yielded a product with 1.32 ± 0.03% aggregate vs. a target of 1.5%. A comparison of the traditional approach involving column fractionation with the proposed approach indicates that the proposed approach results in achievement of satisfactory product purity (98.68 ± 0.03% for mechanistic model based PAT controlled pooling vs. 98.64 ± 0.16% for offline column fractionation based pooling). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2758, 2019.
在治疗蛋白的层析纯化中,一个主要的挑战是批次间杂质水平和进料中产品浓度的变异性。机理模型可通过预测此类变化的影响来实现过程分析技术(PAT)的实施,从而提高所得工艺和控制的稳健性。本文介绍了疏水相互作用层析(HIC)的机理模型作为 PAT 工具的一种应用,用于进行稳健的混合决策,以实现单克隆抗体(mAb)治疗药物的聚集体清除。在进行实际的层析实验之前,进行了模型预测,以实现前馈控制。该方法已成功应用于四种不同的进料,其聚集体水平(3.84%-5.54%)和进料浓度(0.6mg/mL-1mg/mL)各不相同。所得的混合液始终产生一种产品,其聚集体含量为 1.32±0.03%,而目标值为 1.5%。传统的基于柱分离的方法与所提出的方法的比较表明,所提出的方法可实现令人满意的产品纯度(基于机理模型的 PAT 控制混合的 98.68±0.03%与基于离线柱分离的混合的 98.64±0.16%)。©2018 美国化学工程师协会生物技术进展,35:e2758,2019。