Modelling the pharmacodynamics of rituximab biosimilar in children with rheumatological conditions
Soumya Perinparajah (1,2), John Booth (3), Mohsin Shah (3), Persis Amrolia (4), S.Y. Amy Cheung (2), James W. T. Yates (5), Nigel Klein (1), Joseph F. Standing (1, 6)
(1) Infection, Immunity and Inflammation Research & Teaching Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom, (2) Integrated Drug Development, Certara, New Jersey, United States, (3) Digital Research and Informatics Unit, Great Ormond Street Hospital and Great Ormond Street Institute of Child Health and NIHR GOSH Biomedical Research Centre, London, United Kingdom, (4) Department of Bone Marrow Transplantation, Great Ormond Street Hospital for Children, London, United Kingdom, (5) DMPK Modelling, In-Vitro In-Vivo Translation, GlaxoSmithKline, Stevenage, United Kingdom, (6) Department of Pharmacy, Great Ormond Street Hospital for Children, London, United Kingdom.
Introduction: Following the expiration of rituximab’s patents in 2013 and 2016, rituximab biosimilars have emerged in clinical use in recent years [1]. Whilst their administration is now common in various indications in rheumatology and oncology, their pharmacodynamics (PD) have yet to be characterised using a modelling approach, especially in the paediatric population.
Objective: To develop a mechanistic non-linear mixed effects (NLME) model to quantify the PD of rituximab biosimilar on CD19+ B cell reconstitution in children with rheumatological conditions.
Methods: Retrospective electronic data from routine clinical practice at a tertiary paediatric hospital were collected from paediatric patients who had CD19+ B cell counts and drug administrations of Truxima rituximab biosimilar for rheumatology indications in the period 19/04/2019 to 14/01/2021. A single dose constituted 375 mg/m2 administered via intravenous infusion. A previously developed two compartment kinetic-pharmacodynamic (K-PD) model to quantify rituximab effect following paediatric haematopoietic stem cell transplantation (HSCT) (unpublished) was used as the starting point, which incorporated drug effect with an Emax model. HSCT-specific parameters were removed, and age scaling was applied using a B cell maturation function. Proportional and combined residual error models were tested, and both M5 and M3 methods were tested for handling CD19+ B cell counts below the lower limit of quantification (LLOQ). Data analyses were performed in R version 3.5.1 [2] and models were fitted in NONMEM® version 7.4.3 using the Laplacian conditional estimation with interaction algorithm [3].
Results: A two compartment turnover model was fitted to 139 measurements of CD19+ B cell counts from 25 children (median age, 12.8 years; range, 1.7 - 17.8 years). Over a third of patients had a diagnosis of systemic lupus erythematosus. The final model had a combined error model, employed the M3 method, and had the following parameter estimates (% residual standard error): CD19+ B cell production rate constant (λ), 2.99x106 cells/L (1.92); CD19+ B cell death rate constant (μ), 0.0145 cells day-1 (0.68); rituximab biosimilar elimination rate (Ke), 0.114 day-1 (2.84); maximum killing effect of rituximab biosimilar (Emax), 79.6 (0.89) and rituximab biosimilar dose producing 50% of maximum killing effect (ED50), 1.04 mg (4.13). Elimination half-lives for rituximab biosimilar and CD19+ B cells were 6.08 days and 47.8 days respectively.
Conclusions: A K-PD model adequately described the PD of Truxima rituximab biosimilar in a paediatric population. Future work aims to test clinically relevant covariates including diagnosis, gender, and concomitant medications, in line with previous work by Pan et al [4], as well as perform simulations to test various dosing regimens of the rituximab biosimilar to assess the impact on patients’ CD19+ B cell trajectories. This work has the potential to inform dose bioequivalence of Truxima rituximab biosimilar with reference rituximab for paediatric rheumatology indications.
References:
[1] Greenwald, M., Tesser, J. & Sewell, K. L. Biosimilars Have Arrived: Rituximab. Arthritis
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[2] Core Development Team R. A Language and Environment for Statistical Computing. 2020. http://www.r-project.org.
[3] Sheiner, L. B., & Beal, S. L. (1983). Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data. Journal of pharmacokinetics and biopharmaceutics, 11(3), 303-319.
[4] Pan, S. et al. Pharmacodynamics of rituximab on B lymphocytes in paediatric patients with autoimmune diseases. British Journal of Clinical Pharmacology (2019).doi:10.1111/bcp.13970