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Updated: May 17, 2026
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Published on: February 17, 2022
Model-Informed Dosing Strategies for Epcoritamab Monotherapy in Relapsed or Refractory Large B-Cell Lymphoma
Xiaozhi Liao1, Tommy Li2, Steven Xu2
1University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract:
Dose selection is a central challenge in drug development, and direct evaluation of alternative regimens is often ethically difficult. We developed a joint model integrating pharmacokinetics, pharmacodynamics, and Deauville score to evaluate alternative dosing strategies for a CD3 × CD20 bispecific antibody, epcoritamab, as monotherapy in relapsed or refractory (R/R) large B-cell lymphoma (LBCL) after ≥2 lines of systemic therapy. The model was developed using clinical data from 165 patients and simultaneously described plasma epcoritamab concentrations, tumor burden dynamics, and longitudinal Deauville scores. Simulations were conducted in 1,000 virtual patients to evaluate three post-complete response (CR) dosing strategies: continuous, fixed-duration, and stop-at-CR. Responder subgroups were defined based on CR durability, and predictors of patients likely to require continuous treatment were explored. Simulations indicated that continued epcoritamab treatment after CR was associated with higher simulated CR rates over the long term, with simulated 2-year CR rates of 46.4% (continuous), 37.2% (fixed-duration), and 27.0% (stop-at-CR). Exploratory analyses suggested that indicators of residual disease, such as limited early tumor reduction and elevated circulating tumor DNA levels, help identify patients most likely to benefit from continuous treatment. This modeling framework provides a quantitative approach for evaluating alternative dosing strategies for epcoritamab monotherapy in R/R LBCL and supports the potential value of continuous therapy in maintaining long-term CR, as suggested by model-based simulations. However, this study focuses solely on efficacy outcomes; therefore, further validation and incorporation of safety outcomes are needed to fully support clinical decision-making and regimen optimization.
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