Pharmacokinetic modeling to optimize development and therapeutic potential of mAb-based therapies

Monoclonal antibody (mAb)-based therapies have revolutionized modern medicine, offering highly specific and effective treatments for a wide range of diseases, including cancer, autoimmune disorders, infectious diseases, and neurological conditions. With over 200 FDA-approved mAb-based drugs and nearly 1400 investigational candidates in development, their therapeutic versatility continues to expand. Beyond conventional mAbs, bispecific and multispecific antibodies, and antibody-drug conjugates exemplify modality diversification aimed at enhancing selectivity, potency, and tissue targeting. These complex architectures introduce distinctive pharmacokinetic (PK) behaviors such as target-mediated drug disposition (TMDD) and payload deconjugation that challenge traditional small-molecule paradigms. Advanced PK modeling has become central to model-informed drug discovery and development for these agents, enabling quantitative translation from preclinical systems, rational first-in-human dose selection, exposure-response integration, immunogenicity impact assessment, and regimen optimization to balance efficacy and safety. Mechanistic approaches-including full and reduced TMDD models, physiologically-based PK modeling, and hybrid systems pharmacology frameworks-now complemented by machine learning and artificial intelligence, can accelerate parameter inference, refine PK predictions, and expand therapeutic potential. This review explores the pivotal role of PK modeling in addressing the challenges posed by mAb-based therapies, highlighting its contribution to improving efficiency, precision, and success in drug development. By leveraging novel tools and methodologies, PK modeling continues to pave the way for future innovations in antibody therapeutics, ensuring optimized therapeutic outcomes across diverse patient populations.