Population Pharmacodynamic Modelling of the CD19+ Suppression Effects of Rituximab in Paediatric Patients with Neurological and Autoimmune Diseases

Author:

Riva Natalia1234ORCID,Brstilo Lucas23ORCID,Sancho-Araiz Aymara12ORCID,Molina Manuel2,Savransky Andrea5,Roffé Georgina6,Sanz Marianela6,Tenembaum Silvia5ORCID,Katsicas Maria M.7,Trocóniz Iñaki F.148ORCID,Schaiquevich Paula23ORCID

Affiliation:

1. Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Sciences, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain

2. Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina

3. National Council of Scientific and Technical Research (CONICET), Buenos Aires C1425 FQB, Argentina

4. Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain

5. Neurology Service, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina

6. Laboratory of Cellular Immunology, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina

7. Immunology and Rheumatology Service, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina

8. Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, 31009 Pamplona, Spain

Abstract

Background: Limited pharmacotherapy and the failure of conventional treatments in complex pathologies in children lead to increased off-label use of rituximab. We aimed to characterize the time course of CD19+ B lymphocytes (CD19+) under treatment with intravenous rituximab in children with neurologic and autoimmune diseases and to evaluate the impact of covariates (i.e., demographics, diagnosis and substitution between innovator and biosimilar product) on rituximab pharmacodynamics and disease activity. Methods: Pre- and post-drug infusion CD19+ in peripheral blood were prospectively registered. A population pharmacodynamic model describing the time course of CD19+ was developed with NONMEM v7.4. Simulations of three different rituximab regimens were performed to assess the impact on CD19+. Logistic regression analysis was performed to identify predictors of clinical response recorded through disease activity scores. Results: 281 measurements of CD19+ lymphocyte counts obtained from 63 children with neurologic (n = 36) and autoimmune (n = 27) diseases were available. The time course of CD19+ was described with a turn-over model in which the balance between synthesis and degradation rates is disrupted by rituximab, increasing the latter process. The model predicts half-lives (percent coefficient of variation, CV(%)) of rituximab and CD19+ of 11.6 days (17%) and 173.3 days (22%), respectively. No statistically significant effect was found between any of the studied covariates and model parameters (p > 0.05). Simulations of different regimens showed no clinically significant differences in terms of CD19+ repopulation times. A trend towards a lack of clinical response was observed in patients with lower CD19+ repopulation times and higher areas under the CD19+ versus time curve. Conclusions: Rituximab pharmacodynamics was described in a real-world setting in children suffering from autoimmune and neurologic diseases. Diagnosis, substitution between innovator rituximab and its biosimilars or type of regimen did not affect rituximab-induced depletion of CD19+ nor the clinical response in this cohort of patients. According to this study, rituximab frequency and dosage may be chosen based on clinical convenience or safety reasons without affecting CD19+ repopulation times. Further studies in larger populations are required to confirm these results.

Funder

Fundación Garrahan, Argentina

Publisher

MDPI AG

Subject

Pharmaceutical Science

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