While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions.

虽然机器学习（ML）为生物制药领域做出了重大贡献，但其应用仍处于早期阶段，无法为基于设计的生物制剂开发和制造提供直接支持，从而阻碍了生物工艺自动化的巨大潜力。他们的发展到制造业。然而，由于大规模生产数据的积累，基于机器学习的模型代替传统多元数据分析方法的采用显着增加。这一趋势主要是通过实施先进的过程分析技术来实时监控生物制药产品的过程变量和质量属性所推动的。鉴于生物产品设计、生物工艺开发和产品制造数据的复杂性和多维性，基于机器学习的方法越来越多地被用来实现准确、灵活和高性能的预测模型，以解决内部的分析、监控和控制问题。生物制药领域。本文旨在全面回顾目前机器学习解决方案在单克隆抗体上游、下游和产品配方过程的设计、监测、控制和优化中的应用。最后，本文深入讨论了与生物工艺本身、工艺数据以及机器学习模型在单克隆抗体工艺开发和制造中的使用相关的主要挑战。此外，它还提供了对创新机器学习方法的采用和新数字生物制药解决方案开发的新趋势的进一步见解。