Kavoni Hossein, Savizi Iman Shahidi Pour, Lewis Nathan E, Shojaosadati Seyed Abbas
Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Bioengineering, University of California, San Diego, CA, USA; Department of Pediatrics, University of California, San Diego, CA, USA.
Biotechnol Adv. 2025 Jan-Feb;78:108480. doi: 10.1016/j.biotechadv.2024.108480. Epub 2024 Nov 19.
The production of monoclonal antibodies (mAbs) using Chinese Hamster Ovary (CHO) cells has revolutionized the treatment of numerous diseases, solidifying their position as a cornerstone of the biopharmaceutical industry. However, achieving maximum mAb production while upholding strict product quality standards remains a significant hurdle. Optimizing cell culture media emerges as a critical factor in this endeavor, requiring a nuanced understanding of the complex interplay of nutrients, growth factors, and other components that profoundly influence cellular growth, productivity, and product quality. Significant strides have been made in media optimization, including techniques such as media blending, one factor at a time, and statistical design of experiments approaches. The present review provides a comprehensive analysis of the recent advancements in culture media design strategies, focusing on the comparative application of systems biology (SB) and machine learning (ML) approaches. The applications of SB and ML in optimizing CHO cell culture medium and successful examples of their use are summarized. Finally, we highlight the immense potential of integrating SB and ML, emphasizing the development of hybrid models that leverage the strengths of both approaches for robust, efficient, and scalable optimization of mAb production in CHO cells. This review provides a roadmap for researchers and industry professionals to navigate the complex landscape of mAb production optimization, paving the way for developing next-generation CHO cell culture media that drive significant improvements in yield and productivity.
利用中国仓鼠卵巢（CHO）细胞生产单克隆抗体（mAb）彻底改变了多种疾病的治疗方式，巩固了其作为生物制药行业基石的地位。然而，在维持严格的产品质量标准的同时实现单克隆抗体的最大产量仍然是一个重大障碍。优化细胞培养基成为这一努力中的关键因素，这需要对营养物质、生长因子和其他对细胞生长、生产力和产品质量有深远影响的成分之间复杂的相互作用有细致入微的理解。在培养基优化方面已经取得了重大进展，包括培养基混合、一次一个因素以及实验设计统计方法等技术。本综述对培养基设计策略的最新进展进行了全面分析，重点关注系统生物学（SB）和机器学习（ML）方法的比较应用。总结了SB和ML在优化CHO细胞培养基中的应用及其成功案例。最后，我们强调了整合SB和ML的巨大潜力，强调开发利用两种方法的优势来对CHO细胞中mAb生产进行稳健、高效和可扩展优化的混合模型。本综述为研究人员和行业专业人士在单克隆抗体生产优化的复杂领域中导航提供了路线图，为开发能够显著提高产量和生产力的下一代CHO细胞培养基铺平了道路。
