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Process Mapping and Optimization Study of CHO Cell Cultures for mAb Production using Ambr® 250 High-throughput Parallel Bioreactors

生物反应器 吞吐量 中国仓鼠卵巢细胞 过程(计算) 生产(经济) 工艺工程 化学 工艺优化 计算机科学 化学工程 工程类 生物化学 受体 有机化学 宏观经济学 经济 操作系统 电信 无线
作者
Achinta Bordoloi,Farid Talebnia
标识
DOI:10.21203/rs.3.rs-6831589/v1
摘要

Abstract The demand to accelerate monoclonal antibody (mAbs) process development timelines using Chinese hamster ovary (CHO) host cells to bring therapies to patients sooner is gaining momentum. The applicability of single use high throughput (HTP) bioreactor system such as ambr250 facilitating precise and automated control is very promising. This entails optimizing process parameters through design of experiments (DoE) using less resources and time, compared to traditionally employed large scale bench top reactors (2-5L). It is imperative to improve mAb productivity through robust process development to mitigate current manufacturing challenges. In this study, a systematic mapping approach was employed to identify critical process parameters (CPP) and improve process efficacy. A central composite design (CCD) was used in ambr250 bioreactors to investigate the impact of initial seeding density (SD) and feeding rate (FR) on mAb production. Variance in the SD and FR impacted the cell performance and mAb titer profile based on which parameter optimization was done using response surface methodology. Significant impact of FR and SD was identified leading to improved mAb titer of up to 5 g/L. Bioreactors operated at SD > 1 x 106 cells/mL and FR of > 2 % were more productive, and respective optimal FR and SD were estimated at 2.68 % and 1.1 x106 cells/mL. The cell viability and productivity were well-maintained at optimal conditions allowing extended cultivation time for higher mAb titer. These findings optimizing operating range of CPPs to improve productivity by using HTP ambr250 scaled-down platform would provide a framework for quicker early phase process development, allowing reliable scalability to commercial manufacturing. Improving productivity and providing robust estimates for manufacturing scale would significantly cut costs and reduce timelines for biologics development and facilitate patient access.
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