Antigenic burden and serum IgG concentrations influence rituximab pharmacokinetics in rheumatoid arthritis patients

Abstract

Aims: Rituximab is a monoclonal antibody directed against CD20, which is approved in rheumatoid arthritis (RA). This study aimed at assessing the influence of CD19+ cell counts as target-antigen amount, and of immunoglobulin G (IgG) serum concentrations on rituximab pharmacokinetics in RA patients.

Methods: In a cohort of 64 RA patients who had received repetitive courses of rituximab, the influence of CD19+ cell count, IgG serum concentration, body surface area, sex and disease activity score in 28 joints on rituximab pharmacokinetic parameters was assessed using a population pharmacokinetic analysis.

Results: A two-compartment model, with first-order distribution and elimination best described the data. The volume of distribution of central compartment and clearance of rituximab were estimated at 4.7 l and 0.56 l day^-1 , respectively. Distribution and elimination half-lives were 0.9 days and 17.3 days, respectively. As expected, the central volume of distribution increased with body surface area (P = 0.012) and was higher in male than in female (P = 0.004). We found that the elimination rate constant (k₁₀ ) increased with CD19+ count (P = 0.00022) and IgG concentration (P = 7.4 × 10^-8 ), and that k₁₀ decreased with time (P = 0.00015), partly explained by a change in target-antigen amount.

Conclusions: The association between CD19+ count and k₁₀ may be explained by target-mediated drug disposition, while the association between IgG serum concentration and k₁₀ may be explained by a saturation of the neonatal Fc receptor at high IgG concentrations, resulting in decreased recycling of rituximab.

Keywords: elimination rate constant; immunoglobulin; pharmacokinetics; rheumatoid arthritis; rituximab.

Figure 1

Diagnostic plots of the pharmacokinetic model: (A) observed vs. population model‐predicted rituximab concentrations; (B) observed vs. individual model‐predicted rituximab concentrations; (C) normalized prediction distribution errors (NPDE) vs. gaussian law; (D) population weighted residuals vs. population predicted rituximab concentrations; (E) individual weighted residuals vs. individual predicted rituximab concentrations; (F) prediction‐corrected visual predictive check; observed concentrations (black circles), theoretical (dashed bold lines) and empirical (continuous thin lines) percentiles (from bottom to top: 10%, 50% and 90% percentiles) and prediction interval (from bottom to top: 10%, 50% and 90% prediction intervals)

Figure 2

Individual volume of distribution (V₁, top) and elimination rate constant (k₁₀, bottom) estimates vs. covariates: (A) sex, (B) body surface area and (C) rituximab treatment course on V₁, and (D) serum IgG concentrations and (E) CD19 counts on k₁₀. Open circles are observed values, lines are correlation lines, Horizontal lines of boxplots represent, from bottom to top, , 5^th, 25^th, 50^th, 75^th and 95^th percentiles of pharmacokinetics parameters

Figure 3

Simulations of five rituximab pharmacokinetic profiles using typical pharmacokinetics parameters, (A) increasing CD19 counts (10 to 500 mm⁻³), (B) increasing serum IgG concentrations (5 to 25 g l^–1), and (C) for rituximab courses 1–5 showing time‐dependence of rituximab pharmacokinetics