ArticleCPT: pharmacometrics & systems pharmacology2025
A Model-Based Approach to Evaluate Anti-Drug Antibody Impact on Drug Exposure With Biologics: A Case Example With the CD3 T-Cell Bispecific Cibisatamab.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advanced assay design for characterizing anti-drug antibody responses in clinical serum samples.Bioanalysis · 2026Review
- From permeation enhancer to therapeutic enabler: Advances, applications, and translational perspectives in hyaluronidase-based drug delivery.Materials today. Bio · 2026Review
- Application of Allometric Scaling and Translational Modeling to Predict Human Pharmacokinetics of mRNA-Encoded Antibodies.Molecular pharmaceutics · 2026Article
- Therapeutic Alternatives to Recombinant Biologics: Mechanistic Framework, Clinical Evidence, and Selection Guidance.Drug design, development and therapy · 2026Review
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Authors and funding
7 authors.
Funding
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Abstract
The administration of biologics can lead to immunogenic responses that trigger anti-drug antibody (ADA) formation. ADAs can decrease drug exposure. A population pharmacokinetic (popPK) model was developed to describe clinical PK data with and without ADA-driven exposure loss with CEA-directed T-cell bispecific antibody cibisatamab. The PK of cibisatamab was evaluated in two clinical studies (as a single agent and in combination with the checkpoint inhibitor atezolizumab) in patients. The popPK model was developed on cibisatamab clinical PK data using the Stochastic Approximation -Expectation Maximization (SAEM) algorithm implemented in Monolix. Cibisatamab's PK followed a two-compartment model with linear clearance decreasing over time and ADA-associated exposure loss. ADA-driven exposure loss was implemented in the model by accounting for ADA formation, reversible binding to cibisatamab, and elimination of both free ADA and the ADA-cibisatamab complex from the central compartment. The impact of ADAs on PK exposure was time-dependent in the model, with the ADA formation described as a function of time (increasing from zero, reaching its estimated maximum value, and possibly decreasing down to 94% of this maximum value in some patients). The final model included a mixture component differentiating patients with and without exposure loss due to ADA formation (75% and 25% of patients, respectively). The investigated patient demographics, dose or dosing schedule, or atezolizumab coadministration were not identified as factors influencing exposure loss due to ADAs. The developed model can be used to differentiate patients with and without ADA-driven exposure loss, as well as for a precise PK characterization in patients even with ADA formation.
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