Optimization strategies for monoclonal antibody production: advances in simulation and artificial intelligence in bioprocessing

Clinical Snapshot

Key Findings

• P Value: Not applicable to this review format

• Effect Size: Not quantified; review reports qualitative improvements in productivity, CQA maintenance, and cost reduction from cited primary studies without pooled effect estimates

• Primary Outcome: Narrative synthesis of optimisation strategies for mAb manufacturing using process simulation and AI/ML across USP, DSP, and plant-level operations

• Nnt Or Sensitivity: Not applicable; no diagnostic, therapeutic, or prognostic NNT/sensitivity/specificity data reported. Manufacturing cost and cycle time improvements are referenced qualitatively from primary sources but not aggregated

• Confidence Interval: Not reported — no meta-analytic pooling performed

Clinical Application

Implementation of AI/ML and simulation-based optimisation in mAb manufacturing is feasible at large-scale facilities with existing data infrastructure, but significant barriers remain for smaller manufacturers including data standardisation, validated model development, and regulatory acceptance pathways. The review appropriately notes that benefits are contingent on robust process-specific validation Australia's mAb therapeutic landscape is substantial, with numerous agents listed on the Pharmaceutical Benefits Scheme (PBS) across oncology, immunology, and rare diseases. The TGA regulates biologics under the Biologicals framework, and any AI/ML-assisted manufacturing changes would require TGA notification or variation depending on the nature of the process change. The Australian Government's Medical Research Future Fund (MRFF) and the emerging domestic biomanufacturing strategy (including the mRNA and biologics manufacturing initiatives post-COVID-19) make this review relevant to Australian bioprocessing policy. RACGP and specialist college guidelines do not directly address manufacturing optimisation, but improved manufacturing efficiency could enhance PBS listing viability for high-cost mAb therapies and support biosimilar market entry, which is a priority for the Australian healthcare system Biopharmaceutical manufacturers, bioprocess engineers, and clinical pharmacologists involved in mAb development and production. Indirectly relevant to clinicians and health economists assessing mAb therapy availability, biosimilar access, and cost-effectiveness in therapeutic decision-making

Abstract

The rapid growth of monoclonal antibody (mAb) therapies has increased the need for efficient, scalable, and affordable manufacturing processes. However, mAb production remains complex because of nonlinear upstream cell culture behavior, expensive downstream purification, especially Protein-A chromatography, and plant-level bottlenecks that can increase cost, cycle time, and manuacturing uncertainty. This review examines recent developments in mAb manufacturing with focus on process simulation, mathematical optimization, and artificial intelligence/machine learning (AI/ML) across upstream processing (USP), downstream processing (DSP), and integrated plant-level operation. In USP, media optimization, dynamic feeding, high-density cultures, and continuous perfusion bioreactors are discussed in relation to productivity and critical quality attributes (CQAs). In DSP, alternative and intensified purification strategies are reviewed with a focus on recovery, impurity clearance, scalability, cost, and technology maturity. AI/ML applications are also discussed from early-stage development and cell-line screening to upstream control, CQA prediction, chromatography optimization, and downstream decision support. Despite these advancements, challenges such as data heterogeneity, limited standardized datasets, model transferability, and regulatory constraints remain important barriers to implementation. Overall, this review uniquely connects simulation and AI/ML approaches to practical optimization across the full mAb manufacturing workflow, including design, scheduling, debottlenecking, purification, monitoring, and quality prediction. The combination of process simulation, continuous bioprocessing, and AI/ML-based decision support may enable more flexible, reliable, and cost-effective mAb manufacturing. However, these benefits depend on validation through robust models, process-specific case studies, and technoeconomic analysis.

References

1. 1.Kubra, K., & Shaik, M. A. (2026). Optimization strategies for monoclonal antibody production: advances in simulation and artificial intelligence in bioprocessing. mAbs. https://doi.org/10.1080/19420862.2026.2706886