Do We Really Need End‐To‐End Continuous Processing for Biomanufacturing of Monoclonal Antibodies?

生物制造 连续生产 过程(计算) 生物制药 计算机科学 风险分析(工程) 生产(经济) 批处理 制造工程 产品(数学) 业务 过程分析技术 吞吐量 生物过程 生产成本 生化工程 制造业 过程开发 制造工艺 过程控制 工艺工程 单克隆抗体 自动化 在制品 下游加工
作者
Anurag S. Rathore,Subhankar Metya,Nitika Nitika
出处
期刊:Biotechnology and Bioengineering [Wiley]
卷期号:123 (3): 792-800
标识
DOI:10.1002/bit.70147
摘要

Patients around the world, especially in low- or medium-income countries (LMICs), are unable to afford the prohibitively high cost of monoclonal antibodies (mAbs). One promising approach to this issue of accessibility and affordability is intensifying the manufacturing process by employing continuous processing technology, which improves sustainability, efficiency, and process compactness, while reducings the operational expenses without sacrificing product safety or quality. Even though the biomanufacturing sector has been discussing continuous processing for more than 15 years, its adoption has not been as rapid as expected, with most companies still relying on traditional batch-based production systems. This brings up important inquiries: is it necessary to have a completely end-to-end continuous processing, or are hybrid and selectively intensified methods enough? In this article, we offer a perspective on the current status of continuous biomanufacturing, challenges associated with it and the production costs of five different intensification scenarios using a process simulation tool, incorporating both traditional batch processing and fully integrated continuous processing, and identifying the manufacturing hot-spots that result in the significant cost savings. Our findings suggest that instead of completely replacing the batch-process equipment, mAb manufacturing should strategically engage continuous technologies when they deliver clear value. Sustainable and efficient biopharmaceutical production that enables broad access and affordabilty is achievable through an evolutionary strategy based on planned intensification and risk-managed implementation. So, the question is not whether end-to-end continuous processing is required, but rather how to maximize its advantages while efficiently handling its complexity.
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