离子交换层析分离单抗电荷异质体的模型辅助过程优化
Model-assisted process optimization of ion-exchange chromatography for monoclonal antibody charge variant separation
许茹枫 1陈煜成 1高丹 2焦静雨 2高栋 2王海彬 2姚善泾 1林东强1
作者信息
- 1. 浙江大学化学工程与生物工程学院,生物质化工教育部重点实验室,浙江 杭州 310058
- 2. 海正生物制药有限公司,浙江 杭州 311404
- 折叠
摘要
针对单抗电荷异质体分离,采用离子交换层析机理模型,预测洗脱分离行为,辅助工艺条件优化.设计了校准实验,拟合得到模型参数,模型计算与实验吻合良好,具有良好的预测能力.利用模型分析比较了不同洗脱方式,得到最优的两步阶跃洗脱方案,具有较高的收率,但发现该分离过程对盐浓度极为敏感.进一步针对第一步洗脱盐浓度进行过程稳健性约束的过程优化,发现盐浓度为108.5 mmol/L时过程稳健性增强.经实验验证,两步阶跃洗脱收率最高可达到85.3%,稳健约束优化后第一步等度洗脱盐浓度操作区间增大为98.9～117.5 mmol/L.结果表明,模型辅助的工艺优化可以进行复杂条件分析,促进难分离体系的分离过程优化,并能够针对过程稳健性给出合理解决方案.
Abstract
For the separation of monoclonal antibody charge heterogeneous species,an ion-exchange chromatography mechanism model is used to predict the elution and separation behavior and assist in the optimization of process conditions.The calibration experiments were designed to estimate the model parameters and the model simulation matched well with the experiments,demonstrating the model has good predictive ability.The model was used to compare and evaluate the influences of different elution modes and conditions.The optimal two-step elution was obtained to achieve high yield.However,it was found that the separation efficiency was highly sensitive to the salt concentration of first-step elution solution.Therefore,the process robustness was considered further to optimize the salt concentration,and it was found the salt concentration of 108.5 mmol/L could greatly enhance the process robustness.The results of validation experiments showed that the highest yield of two-step elution process reached 85.3%,and the operating range of the salt concentration of first-step elution was widened to 98.9-117.5 mmol/L.The results demonstrated that model-assisted process optimization could facilitate the complex condition analysis,promote the optimization and provide reasonable solutions for process robustness.
关键词
单克隆抗体/电荷异质体/分离/离子交换层析/层析模型/过程优化Key words
monoclonal antibody/charge variant/separation/ion-exchange chromatography/chromatography model/process optimization引用本文复制引用
基金项目
浙江省重点研发计划(2023C03116)
浙江省重点研发计划(2021C03089)
国家自然科学基金(22078286)
国家重点研发计划(2021YFE0113300)
出版年
2024
国家科技期刊平台
NSTL
万方数据