Study Design and Patient Population
Data were used from the “Effects of the SGLT2 inhibitor dapagliflozin on proteinuria in non-diabetic patients with chronic kidney disease” (DIAMOND) trial (NCT03190694), a randomised, placebo-controlled, double-blind, cross-over trial that assessed the kidney protective effects of dapagliflozin in non-diabetic patients with albuminuria. The study design and primary results have been reported elsewhere . In short, the DIAMOND trial enrolled 53 participants with non-diabetic kidney disease, characterised by 24-h urinary protein excretion > 500 mg/day and ≤ 3500 mg/day, and an estimated glomerular filtration rate (eGFR) ≥ 25 mL/min/1.73 m2. Participants had to be treated with a stable dose of an angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker for at least 4 weeks prior to enrolment. Participants were randomly assigned, in a cross-over design, to placebo followed by dapagliflozin 10 mg once daily, or dapagliflozin 10 mg once daily followed by placebo. Each treatment period lasted 6 weeks, followed by a 6-week wash-out period to avoid carry-over effects. The primary endpoint of the trial was 24-h proteinuria and secondary endpoints included body weight, measured glomerular filtration rate (mGFR), systolic blood pressure and urinary albumin-to-creatinine ratio (UACR). The study was performed in accordance with the Declaration of Helsinki and good clinical practice guidelines and participants gave their written informed consent before any study-specific procedure commenced.
Twenty-four-hour urine was collected to monitor proteinuria at the start and end of each treatment period. Body weight and systolic blood pressure were recorded at every visit to the clinic. Measured glomerular filtration rate was estimated by determining the plasma clearance of non-radioactive iohexol at the beginning and end of each treatment period. At the end of the treatment period, during GFR measurement, plasma samples of dapagliflozin were collected pre-dose, and every 30 min for 4 h after administration of dapagliflozin or placebo. Actual sampling and dosing times were recorded. The plasma concentration of dapagliflozin was measured using a liquid chromatography–tandem mass spectrometry method, which has been described elsewhere . This bioanalytical method was validated for selectivity, linearity, accuracy and precision, dilution integrity, stability and recovery. The accuracy was between 94.6 and 101.0% and precision (coefficient of variation) was between 0.0 and 13.7%.
Estimation of Individual Exposure to Dapagliflozin
A population pharmacokinetic model was used to estimate individual plasma exposure to dapagliflozin. Non-linear mixed-effects models were used to develop this population pharmacokinetic model. Model development was conducted using NONMEM version 7.3.0 (ICON Development Solutions, Ellicott City, MD, USA).
Different structural models with linear absorption and elimination processes were evaluated, including one- and two-compartment models with and without a lag time. Furthermore, the inclusion of transit compartments in the model to describe the absorption phase was also explored. A log-normal distribution was assumed for the inclusion of random effects in the stochastic model. Covariance between random effects was also evaluated. Additive, proportional and combined error models were explored to describe the residual variability. Covariate screening was performed for age, sex, race, ethnicity, eGFR, mGFR, body weight and region. We used correlation matrices of the empirical Bayes estimates of the parameters vs covariates to evaluate potential relationships. For discrete covariates, separate population parameters were estimated. For body weight, allometric scaling normalised by 70 kg was explored and, for other continuous covariates, the covariate was median normalised and a power coefficient was estimated.
First-order conditional estimation with interaction was used to obtain model parameters. Model selection and evaluation were based on the minimum objective function value (MOFV), standard goodness-of-fit plots, condition number, residual standard error of parameter estimates, and coefficient of variation of the random effects representing residual and random variability . The predictive performance of the model was evaluated using a visual predictive check.
Evaluation of the Association between Exposure and Kidney Response
Risk markers of interest were proteinuria, UACR, mGFR, systolic blood pressure and body weight, which are well-known risk markers for progression of kidney disease. The individual change from baseline was estimated for all risk markers in both the placebo as well as the active treatment period. For proteinuria and UACR, the change from baseline was log-transformed to approximate a normal distribution.
Using the population pharmacokinetic model, the plasma exposure, defined as the area under the plasma concentration–time curve (AUC0–inf), was estimated by dividing the 10-mg dose by the individual apparent clearance parameter. The association between exposure to dapagliflozin, in terms of AUC0–inf, and response was investigated using linear mixed-effects models. A random intercept model was fitted to the data to estimate the placebo response of each individual patient, which was compared to a random intercept model including AUC0–inf as fixed effect. A model comparison was performed using a likelihood ratio test, which assumes a chi-square distribution. A significant increase in the maximum likelihood indicates that the addition of AUC0–inf to the model explains residual and/or between-patient variability. Furthermore, a t test was performed to evaluate whether the fixed regression coefficient of AUC0–inf was significantly different from zero. All linear mixed-effects models were fitted using full maximum-likelihood estimation. The linear mixed-effects models were fitted using the lme function of the nlme package (version nlme_3.1-131) in R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria).