Eef Hoeben1, Willem De Winter2, Martine Neyens2, Damayanthi Devineni3, An Vermeulen2, Adrian Dunne2. 1. Model Based Drug Development, Quantitative Sciences, Janssen Research and Development, A Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, Beerse, 2340, Belgium. ehoeben@its.jnj.com. 2. Model Based Drug Development, Quantitative Sciences, Janssen Research and Development, A Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, Beerse, 2340, Belgium. 3. Clinical Pharmacology, Quantitative Sciences, Janssen Research and Development LLC, Raritan, NJ, USA.
Abstract
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).
RCT Entities:
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).
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