Evaluation of the Pharmacokinetics of Dapagliflozin in Patients With Chronic Kidney Disease With or Without Type 2 Diabetes Mellitus.

Evidence shows that sodium-glucose cotransporter 2 inhibitors, such as dapagliflozin, can delay the progressive decline of kidney function in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). We used a population pharmacokinetics (popPK) model to characterize the pharmacokinetics of dapagliflozin in patients with CKD and compare dapagliflozin systemic exposure in different populations, such as CKD with or without T2DM and T2DM without CKD. A 2-compartmental popPK model was developed from a previous popPK model. The final popPK model was based on 9715 dapagliflozin plasma concentrations from 3055 patients included in clinical studies involving adults with CKD with or without T2DM, adults with T2DM, healthy subjects, and pediatric patients with T2DM. Overall, the apparent clearance for patients treated with dapagliflozin was 21.6 L/h, similar to previous estimates in adults with T2DM and healthy subjects (22.9 L/h). Model-derived area under the plasma concentration-time curve (AUC) was not meaningfully different between patients with CKD with and without T2DM. Median AUC was 1.6-fold higher in adult patients with CKD with T2DM compared with adult patients with T2DM without CKD. Compared with patients with normal kidney function (estimated glomerular filtration rate ≥90 mL/min/1.73 m2 ), median AUC was 2.4-fold higher in patients with CKD (with/without T2DM) with estimated glomerular filtration rate 15-29 mL/min/1.73 m2 owing to decreased renal clearance of dapagliflozin. A higher AUC was observed in patients with a higher age or lower body weight but was not considered clinically relevant. This popPK model adequately described dapagliflozin pharmacokinetics and found that systemic exposure in patients with CKD was consistent, irrespective of T2DM status.

Effects of exenatide and open-label SGLT2 inhibitor treatment, given in parallel or sequentially, on mortality and cardiovascular and renal outcomes in type 2 diabetes: insights from the EXSCEL trial.

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) improve cardiovascular and renal outcomes in patients with type 2 diabetes through distinct mechanisms. However, evidence on clinical outcomes in patients treated with both GLP-1 RA and SGLT2i is lacking. We aim to provide insight into the effects of open-label SGLT2i use in parallel with or shortly after once-weekly GLP-1 RA exenatide (EQW) on cardiorenal outcomes. METHODS: In the EXSCEL cardiovascular outcomes trial EQW arm, SGLT2i drop-in occurred in 8.7% of participants. These EQW+SGLT2i users were propensity-matched to: (1) placebo-arm participants not taking SGLT2i (n = 572 per group); and to (2) EQW-arm participants not taking SGLT2i (n = 575), based on their last measured characteristics before SGLT2i initiation, and equivalent study visit in comparator groups. Time-to-first major adverse cardiovascular event (MACE) and all-cause mortality (ACM) were compared using Cox regression analyses. eGFR slopes were quantified using mixed model repeated measurement analyses. RESULTS: In adjusted analyses, the risk for MACE with combination EQW+SGLT2i use was numerically lower compared with both placebo (adjusted hazard ratio 0.68, 95% CI 0.39-1.17) and EQW alone (0.85, 0.48-1.49). Risk of ACM was nominally significantly reduced compared with placebo (0.38, 0.16-0.90) and compared with EQW (0.41, 0.17-0.95). Combination EQW+SGLT2i use also nominally significantly improved estimated eGFR slope compared with placebo (+ 1.94, 95% CI 0.94-2.94 mL/min/1.73 m2/year) and EQW alone (+ 2.38, 1.40-3.35 mL/min/1.73 m2/year). CONCLUSIONS: This post hoc analysis supports the hypothesis that combinatorial EQW and SGLT2i therapy may provide benefit on cardiovascular outcomes and mortality. Trial registration Clinicaltrials.gov, Identifying number: NCT01144338, Date of registration: June 15, 2010.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development.

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium-glucose cotransporter-2 inhibitor drug class, and one enabling rational selection of immuno-oncology drug combinations.