Machine learning-driven optimization of culture conditions and media components to mitigate charge heterogeneity in monoclonal antibody production: current advances and future perspectives

Abstract

Charge heterogeneity in monoclonal antibodies (mAbs), caused by post-translational modifications, remains a substantial obstacle to ensuring consistent, stable, and effective therapeutics. Conventional optimization techniques, such as one-factor-at-a-time and design of experiments, often fail to capture the complex, nonlinear interactions between culture parameters (e.g. pH, temperature, duration) and medium components (e.g. glucose, metal ions, amino acids). This review highlights machine learning (ML) as a powerful approach for modeling these relationships and forecasting charge variant profiles in CHO cell-based mAb process development. We summarize supervised learning and regression methods used to link process conditions with charge heterogeneity and present case studies showing ML's role in reducing acidic and basic variants. We also discuss challenges related to data quality, model interpretability, scalability, and regulatory compliance. Finally, we propose a roadmap for adaptive, ML-driven optimization strategies for bioprocess development, aligned with Quality-by-Design principles.

Keywords: Monoclonal antibody; bioprocessing; charge heterogeneity; machine learning; medium optimization; quality by design.

Figure 1.

Comparative evaluation of OFAT, DoE, MVDA, and ML capabilities for bioprocess optimization. This radar chart compares four approaches, OFAT, DoE, MVDA, and ML, across six key dimensions relevant to CHO cell culture optimization for charge heterogeneity: process integration, predictive accuracy, result interpretability, industrial scalability (higher values preferred), and modeling complexity, data requirements (lower values preferred). Process integration refers to how well a method fits into the full development pipeline, including monitoring and control, while industrial scalability reflects its applicability from lab to production scale. ML (yellow) shows strong performance in accuracy and integration, though it demands more data and modeling complexity. SB offers deep biological insights but has limited direct use in charge variant prediction. The assessment is qualitative, based on published experimental and industrial studies.

Figure 2.

Machine learning-driven workflow for monoclonal antibody charge heterogeneity mitigation.