Titer and charge-based heterogeneity multiattribute monitoring of mAbs in cell culture harvest using 2D ProA CEX MS

化学 效价 抗体 免疫学 生物
作者
Deepika Sarin,Sunil Kumar,Anurag S. Rathore
出处
期刊:Talanta [Elsevier]
卷期号:: 126232-126232
标识
DOI:10.1016/j.talanta.2024.126232
摘要

Robust monitoring of heterogeneity in biopharmaceutical development is crucial for producing safe and efficacious biotherapeutic products. Multiattribute monitoring (MAM) has emerged as an efficient tool for monitoring of mAb heterogeneities like deamidation, sialylation, glycosylation, and oxidation. Conventional biopharma analysis during mAb development relies on use of one-dimensional methods for monitoring titer and charge-based heterogeneity using non-volatile solvents without direct coupling with mass spectrometry (MS). This approach requires analysis of mAb harvest by ProA for titer estimation followed by separate cation exchange chromatography (CEX) analysis of the purified sample for estimating charge-based heterogeneity. This can take up to 60-90 minutes due to the required fraction collection and buffer exchange steps. In this work, a native two-dimensional liquid chromatography (2DLC) mass spectrometry method has been developed with Protein A chromatography in the first dimension for titer estimation and cation exchange chromatography (CEX) in the second dimension for charge variant analysis. The method uses volatile salts for both dimensions and enables easy coupling to MS. The proposed 2DLC method exhibits a charge variant profile that is similar to that observed via the traditional methods and takes only 15 minutes for mass identification of each variant. A total of six charge variants were separated by the CEX analysis after titer estimation, including linearity assessment from 5 ug to 160 ug of injected mAb sample. The proposed method successfully estimated charge variants for the mAb innovator and 4 of its biosimilars, showcasing its applicability for biosimilarity exercises. Hence, the 2D ProA CEX MS method allows direct titer and charge variant estimation of mAbs in a single workflow.
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