Evidence map›Paper›PMID 40108739›Full record

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.

Javier Sanchez, Philippe B Pierrillas, Nicolas Frey, Gregor P Lotz, Siv Jönsson, Lena E Friberg, Nicolas Frances

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Javier SanchezRoche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
Philippe B PierrillasRoche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
Nicolas FreyRoche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
Gregor P LotzRoche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Munich, Germany.
Siv JönssonDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0001-8240-0865
Lena E FribergDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0002-2979-679X
Nicolas FrancesRoche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Antibodies, BispecificBiological ProductsModels, BiologicalAlgorithmsAntibodies, Monoclonal, HumanizedCD3 ComplexFemaleHumansMaleMiddle AgedT-LymphocytesAntibodies, BispecificAntibodies, Monoclonal, HumanizedBiological ProductsCD3 Complexanti‐drug antibodiesbispecific antibodymodelingpopulation pharmacokinetics

Identifiers

PMID40108739
PMCPMC12167918

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.