Monoclonal Antibody Dose Response Modeling Case Study

Monoclonal Antibody Dose Response: Dupilumab Case Study Using mPBPK Modeling

Monoclonal Antibody Dose Response Modeling

This presentation, showcased as a poster at ASCPT 2025, delves into the transformative potential of monoclonal antibodies (mAbs) in modern medicine. While mAbs have revolutionized the treatment of diseases with their high potency, specificity, and convenient dosing, there remains a gap in demonstrating the predictive value of pharmacokinetic/pharmacodynamic (PK/PD) models in clinical dose-response predictions.  

Using dupilumab, a groundbreaking mAb approved for atopic dermatitis, asthma, and other type 2 inflammatory diseases, as a case study, this work explores how a minimal physiologically based PK/PD (mPBPK) model was developed to predict receptor occupancy and correlate it with clinical efficacy. The findings underscore the power of innovative modeling techniques to optimize dose selection and improve therapeutic outcomes.  

This cutting-edge research opens doors to advancements across diverse diseases and treatments. Don’t miss the opportunity to explore this groundbreaking work, watch the full presentation below. 

About the Author

Kai Bartlette, PhD, Pharmacometrician II at Allucent

Kai Bartlette, PhD, brings over eight years of combined research and professional experience, including more than four years in the pharmaceutical industry. Dr. Bartlette supports model-informed drug development (MIDD) through population pharmacokinetic/pharmacodynamic (PK/PD) analysis, physiologically based pharmacokinetic (PBPK) modeling, and quantitative systems pharmacology (QSP). Her work focuses on leveraging computational and mathematical modeling to inform dose selection, optimize clinical trial design, and improve therapeutic outcomes. Dr. Bartlette’s expertise spans translational modeling, model uncertainty quantification, and the development of deterministic models of physiological systems, including glucose homeostasis in pediatric populations.  

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