Observational studyClinical pharmacokinetics2026
Pharmacokinetic-Pharmacodynamic Modelling of Repeated Ocrelizumab Dosing in Relapsing-Remitting Multiple Sclerosis.
Observational study in Clinical pharmacokinetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Not yet cited in PubMed.
What it found
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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.
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.
Ocrelizumab VErsus Rituximab Off-Label at the Onset of Relapsing
Rituximab and Ocrelizumab in Serum With Multiple Sclerosis (ROS-MS)
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0 citing papers in PubMed.
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Authors and funding
9 authors.
Funding
Abstract
BACKGROUND AND
objectiveOcrelizumab (OCR) is an anti-CD20 monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS). Although the standard regimen consists of fixed 6-monthly infusions, therapeutic duration may vary between patients, highlighting the need for individualized dosing strategies. The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model able to predict patient-specific treatment responses, as a step towards a clinically applicable tool for optimizing OCR dosing precision in RRMS.
methodsSerum OCR concentrations and CD19⁺ lymphocyte counts from 11 treatment-naïve patients with newly diagnosed RRMS were analyzed. Samples were collected over 24 weeks following the first and second OCR infusions. Data were analyzed using a nonlinear mixed-effects population modelling approach in Monolix Suite 2023R1, with parameter estimation performed via the stochastic approximation expectation-maximization algorithm. Several structural models were evaluated, and model performance was assessed by fit statistics and visual predictive checks.
resultsThe resultant two-compartment PKPD model successfully described OCR disposition and CD19⁺ lymphocyte depletion/repopulation. The final model included clearance of OCR without target-mediated elimination and described the effect of OCR on the dynamics of CD19⁺ lymphocyte counts. Simulations demonstrated the ability of the model to estimate the time to CD19⁺ lymphocyte repopulation and to explore optimal time for follow-up measurements.
conclusionA novel PKPD model for OCR in RRMS that integrates drug exposure and CD19⁺ lymphocyte kinetics is presented. Although a small number of patients is included in the current study, this framework represents an important step toward clinically applicable, model-informed dosing strategies, with the potential to enhance treatment precision and support personalized therapy in RRMS. CLINICAL TRIALS REGISTRATION NUMBER: OVERLORD-MS: NCT04578639, ROS-MS: NCT06663111.
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Registered trials
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.