Systemic Exposure of Rituximab Increased by Ibrutinib: Pharmacokinetic Results and Modeling Based on the HELIOS Trial.

INTRODUCTION: In the HELIOS trial, bendamustine/rituximab (BR) plus ibrutinib (BR-I) improved disease outcomes versus BR plus placebo in previously treated chronic lymphocytic leukemia/small lymphocytic lymphoma. Here, we describe the pharmacokinetic (PK) observations, along with modeling to further explore the interaction between ibrutinib and rituximab. METHODS: 578 subjects were randomized to ibrutinib or placebo with BR (6 cycles). Ibrutinib PK samples and tumor measurements were obtained from all subjects; a subset was evaluated for bendamustine and rituximab PK. Population rituximab PK was assessed using nonlinear mixed-effects modeling. RESULTS: Dose-normalized plasma concentration-time bendamustine data were comparable between the arms. Systemic rituximab exposure was higher with BR-I versus BR; mean trough serum concentrations were 2- to 3-fold higher in the first three cycles and 1.2- to 1.7-fold higher subsequently. No relevant safety differences were observed. In the modeling, including treatment arm as a categorical covariate and tumor burden as a continuous time-varying covariate on overall rituximab clearance significantly improved fitting of the data. CONCLUSIONS: BR-I led to higher dose-normalized systemic rituximab exposure versus BR and more rapid steady-state achievement. The modeling data suggest that rituximab disposition is, at least in part, target mediated. Determining the clinical significance of these findings requires further assessments. TRIAL REGISTRATION: This study is registered at https://clinicaltrials.gov/ct2/show/NCT01611090 .

Population Pharmacokinetic Modeling of Canagliflozin in Healthy Volunteers and Patients with Type 2 Diabetes Mellitus.

BACKGROUND AND OBJECTIVES: Canagliflozin is an orally active, reversible, selective sodium-glucose co-transporter-2 inhibitor. A population pharmacokinetic (popPK) model of canagliflozin, including relevant covariates as sources of inter-individual variability, was developed to describe phase I, II, and III data in healthy volunteers and in patients with type 2 diabetes mellitus (T2DM). METHODS: The final analysis included 9061 pharmacokinetic (PK) samples from 1616 volunteers enrolled in nine phase I, two phase II, and three phase III studies and was performed using NONMEM(®) 7.1. Inter-individual variability was evaluated using an exponential model and the residual error model was additive in the log domain. The first-order conditional estimation method with interaction was applied and the model was parameterized in terms of rate constants. Covariate effects were explored graphically on empirical Bayes estimates of PK parameters, as shrinkage was low. Clinical relevance of statistically significant covariates was evaluated. The predictive properties of the model were illustrated by prediction-corrected visual predictive checks. RESULTS: A two-compartment PK model with lag-time and sequential zero- and first-order absorption and first-order elimination best described the observed data. Sex, age, and weight on apparent volume of distribution of the central compartment, body mass index on first-order absorption rate constant, and body mass index and over-encapsulation on lag-time, and estimated glomerular filtration rate (eGFR, by MDRD equation), dose, and genetic polymorphism (carriers of UGT1A9*3 allele) on elimination rate constant were identified as statistically significant covariates. The prediction-corrected visual predictive checks revealed acceptable predictive performance of the model. CONCLUSION: The popPK model adequately described canagliflozin PK in healthy volunteers and in patients with T2DM. Because of the small magnitude of statistically significant covariates, they were not considered clinically relevant. However, dosage adjustments are recommended for T2DM patients with renal impairment (eGFR ≥60 mL/min/1.73 m(2): 100 or 300 mg/day; eGFR of 45 to <60 mL/min/1.73 m(2): 100 mg/day).