Quantitative Clinical Pharmacology Supports the Bridging From i.v. Dosing and Approval of s.c. Rituximab in B-Cell Hematological Malignancies

Abstract

A fixed-dose subcutaneous (s.c.) formulation of the anti-CD20 antibody, rituximab, has been developed to address safety, infusion time, and patient comfort concerns relating to intravenous (i.v.) dosing, and has been approved based upon a pharmacokinetic (PK)-clinical bridging strategy, which demonstrated noninferiority of s.c. vs. i.v. dosing in malignancies, including follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). A clinical development plan was undertaken to identify rituximab s.c. doses achieving noninferior exposure to rituximab i.v., and to confirm PK-clinical bridging, with the same efficacy and similar safety. This drew upon data from 1,579 patients with FL, CLL, or diffuse large B-cell lymphoma in 5 clinical studies, and showed minimum steady-state serum concentration (C_trough ) as the most appropriate exposure bridging measure. Population PK models were developed, simulations were run using covariates and PK parameters from clinical studies, and exposure-efficacy and -safety analyses performed. Population PKs showed a two-compartment model with time-dependent and -independent clearances. Clearance and volume were predominantly influenced by body surface area; disposition and elimination were similar for the s.c. and i.v. formulations. After s.c. administration, patients with FL and CLL achieved noninferior exposures to i.v. dosing. Overall, rituximab exposure and route of administration did not influence clinical responses in patients with FL or CLL, and there was no association between exposure and safety events. C_trough was shown to be an effective pharmacologic-clinical bridging parameter for rituximab in patients with FL or CLL. Clinically effective exposures are achieved with either s.c. or i.v. dosing.

Figure 1

Scheme of the rituximab PopPK model (a)^a and characterization of the contribution of each PK model component to the elimination of rituximab following i.v. (b)^b and s.c. (c)^b dosing in patients with FL using model‐based conditional simulations. ^aArrows pointing to the compartment indicate drug input (doses). Arrows pointing from the compartment indicate drug output (clearance). Ordinary differential equation is provided in the Supplementary Methods . ^bConcentration–time courses were simulated following i.v. (375 mg/m² C1–6) or s.c. dosing (375 mg/m² i.v. in C1, followed by 1,400 mg s.c. in C2–6). CL, clearance ($\text{CL}={\text{CL}}_T\times \exp (‐k_{\text{des}}\times t)+C{L}_{inf}$); Cl_inf, time‐independent clearance; CL _T , time‐dependent clearance; FL, follicular lymphoma; F_SC, subcutaneous bioavailability; i.v., intravenous; K_a, subcutaneous absorption rate constant; k_des, rate constant of decay of CL _T with time; PopPK, population pharmacokinetics; Q, inter‐compartmental clearance; RTX, rituximab; s.c., subcutaneous; t, time; V_C, central volume; V_P, peripheral volume; V_max, K_Vmax, target‐mediated elimination from the drug and the target compartments.

Figure 2

Covariate effects on the hazard ratio for final PFS CPH model in FL. The following covariates were tested in the CPH model: demographic variables (bodyweight, BSA, BMI, age, sex, and race); disease characteristics at baseline (ECOG performance status, time from diagnosis, BSIZ, BM involvement at baseline, symptomatic splenic enlargement, symptomatic hepatic enlargement, Ann Arbor stage at diagnosis, FLIPI); other measures at baseline (WBC, BBCE, and lymphocyte count), route of administration, and concomitant chemotherapy. BBCE, B‐cell count at baseline; BM, bone marrow; BMI, body mass index; BSA, body surface area; BSIZ, baseline tumor size; C, cycle; CI, confidence interval; CPH, Cox proportional hazards; ECOG, Eastern Cooperative Oncology Group; FL, follicular lymphoma; FLIPI, Follicular Lymphoma International Prognostic Index; PFS, progression‐free survival; WBC, white blood cell count.

Figure 3

Relationships between probability of any AE grade ≥ 3 and C_trough at EOI (FL) or EOT (CLL) (C_trLD) and average concentrations over the induction period (C_meanLD). (a) Entire induction period. (b) From C2 until EOI. Squares and vertical lines show observed fraction of subjects with events in each exposure tertile and 95% CI for these fractions. As all patients received R‐i.v. during C1, safety relationships were also examined from C2 to EOI to strictly compare the R‐i.v. and R‐s.c. formulations. Circles illustrate the observed response (vertically jittered for better visualization). Lines show the logistic regression lines. Shaded regions are the 90% CIs for the regression lines. P values are obtained from the glm function of R for the slope of the logistic regression models. Red and green circles represent R‐i.v. and R‐s.c., respectively. AE, adverse event; C, cycle; CI, confidence interval; C_mean, average serum concentration; C_trough, minimum serum concentration; EOI, end of induction; EOT, end of treatment; FL, follicular lymphoma; R‐i.v., intravenous rituximab; R‐s.c., subcutaneous rituximab.

Figure 4

Relationships between probability of ARRs and C_trough at EOI (C_trLD) and C_mean over induction period (C_meanLD). (a) For the entire induction period. (b) From C2 until EOI. Squares and vertical lines show observed fraction of subjects with events in each exposure tertile and 95% CI for these fractions. Circles illustrate the observed response (vertically jittered for better visualization). Lines show the logistic regression lines. Shaded regions are the 90% CIs for the regression lines. P values are obtained from the glm function of R for the slope of the logistic regression models. Red and green circles represent R‐i.v. and R‐s.c., respectively. ARR, administration‐related reaction; C, cycle; CI, confidence interval; C_mean, average serum concentration; C_trough, minimum serum concentration; EOI, end of induction; R‐i.v., intravenous rituximab; R‐s.c., subcutaneous rituximab.