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Dapagliflozin was recently approved for use in adults with chronic heart failure with reduced ejection fraction (HFrEF) with/without type 2 diabetes mellitus (T2DM). The objectives of this analysis were to characterize dapagliflozin pharmacokinetics in patients with HFrEF and to compare dapagliflozin systemic exposure between adults with HFrEF with/without T2DM and adults with T2DM. A nonlinear mixed‐effects modelling approach was applied; the population‐pharmacokinetic model was developed using 9735 dapagliflozin plasma concentrations from 2744 patients. The final two‐compartmental model adequately described the observed dapagliflozin concentrations, with a similar estimated apparent clearance compared with a previous estimate in patients with T2DM without HF and in healthy subjects (23.0 [95% CI: 22.6–23.9] L/h vs. 22.9 [95% CI: 22.1–23.7] L/h). The model‐predicted median area under the dapagliflozin concentration–time profile was ≤1.2‐fold higher in patients with HFrEF vs. those with T2DM without HFrEF, which is not considered clinically relevant. Dapagliflozin exposure was similar between patients with HFrEF with/without T2DM and T2DM patients without HFrEF. [ABSTRACT FROM AUTHOR]

Potassium (K+) is the main intracellular cation in the body. Elevated K+ levels (hyperkalemia) increase the risk of life‐threatening arrhythmias and sudden cardiac death. However, the details of K+ homeostasis and the effects of orally administered K+ binders, such as sodium zirconium cyclosilicate (SZC), on K+ redistribution and excretion in patients remain incompletely understood. We built a fit‐for‐purpose systems pharmacology model to describe K+ homeostasis in hyperkalemic subjects and capture serum K+ (sK+) dynamics in response to acute and chronic administration of SZC. The resulting model describes K+ distribution in the gastrointestinal (GI) tract, blood, and extracellular and intracellular spaces of tissue, renal clearance of K+, and K+–SZC binding and excretion in the GI tract. The model, which was fit to time‐course sK+ data for individual patients from two clinical trials, accounts for bolus delivery of K+ in meals and oral doses of SZC. The virtual population of patients derived from fitting the model to these trials was then modified to predict the SZC dose–response and inform clinical trial design in two new applications: emergency lowering of sK+ in severe hyperkalemia and prevention of hyperkalemia between dialysis sessions in patients with end‐stage chronic kidney disease. In both cases, the model provided novel and useful insight that was borne out by the now completed clinical trials, providing a concrete case study of fit‐for‐purpose, model‐informed drug development after initial approval of a drug. [ABSTRACT FROM AUTHOR]

Introduction: Dapagliflozin-induced improvement of glycemic control in patients with inadequately controlled type 1 diabetes (T1D) is complicated by the delicate balance between blood glucose and exogenous insulin. In this work, we developed a semi-mechanistic population exposure-response model using pooled patient-level data to characterize the joint effect of dapagliflozin and insulin on average daily glucose concentrations and glycated hemoglobin (HbA1c) levels in patients with T1D. Methods: A non-linear mixed-effects model was developed in Monolix (Lixoft, France) and R software (R Project, www.r-project.org) using pooled patient-level data from phase 2 and phase 3 trials (NCT01498185, NCT02460978, NCT02268214). Results: Because of the apparent lack of association between bolus insulin dose and glucose concentrations measured by continuous glucosemonitoring the model was able to capture the quantitative link between basal, but not bolus, insulin dose and plasma glucose. Even so, this association remained flat, with a 50% decrease in the basal insulin dose from pretreatment level, resulting in ~5% increase in glucose exposure. Therefore, dapagliflozin efficacy was not significantly affected by the insulin dose adjustment, with 24-week HbA1c reduction on 10-mg dapagliflozin treatment changing from -0.5 [95%CI: -0.55, -0.45] to -0.42 [95%CI: -0.48, -0.36] after adjustment. At the same time, the analysis revealed ~2-fold steeper slope of glucose-HbA1c relationship in dapagliflozin-treated patients vs. control group, suggesting the presence of additional dapagliflozin treatment-related benefits, not explained by the dapagliflozin-mediated ~4% increase in plasma hemoglobin levels. Finally, the efficacy of 5 and 10-mg doses, represented by themean HbA1c reduction at week 24 of dapagliflozin treatment, was shown to be notably greater than the 1- and 2.5-mg doses. Discussion: This research is an attempt to deconvolute and reconstruct dapagliflozin-HbA1c dose-response relationship in T1D by accounting for the drug's action on both daily insulin dose and plasma glucose on a subject-level. While the model is able to adequately capture the observed data, it also revealed that the variability in CGM is poorly approximated by the variability in insulin dose alone. Furthermore, the slope of CGM/HbA1c relationship may differ depending on the population and treatment scenarios. As such, a deeper dive into the physiological mechanisms is required to better quantify the intricate network of glycemic response under dapagliflozin treatment. [ABSTRACT FROM AUTHOR]

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by abnormally high blood glucose concentrations due to dysfunction of the insulin‐producing beta‐cells in the pancreas. Dapagliflozin, an inhibitor of renal glucose reabsorption, has the potential to improve often suboptimal glycemic control in patients with T1DM through insulin‐independent mechanisms and to partially mitigate the adverse effects associated with long‐term insulin administration. In this work, we have adapted a systems pharmacology model of type 2 diabetes mellitus to describe the T1DM condition and characterize the effect of dapagliflozin on short‐ and long‐term glycemic markers under various treatment scenarios. The developed platform serves as a quantitative tool for the in silico evaluation of the insulin‐glucose‐dapagliflozin crosstalk, optimization of the treatment regimens, and it can be further expanded to include additional therapies or other aspects of the disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dapagliflozin improves glycemic control in patients with type 2 diabetes mellitus (T2DM) and is approved in Japanese patients with type 1 diabetes mellitus (T1DM) with inadequate glycemic control. The objectives of this work were to characterize the dapagliflozin pharmacokinetics (PK) in patients with T1DM, assess the influence of covariates on dapagliflozin PK, and compare dapagliflozin systemic exposure between patients with T1DM and T2DM. Population PK analysis was performed using a nonlinear mixed‐effect modeling approach. The analysis included 5793 dapagliflozin plasma concentrations from 1150 adult patients with T1DM (global population), who were on routine insulin therapy, collected from 1 phase 2 (NCT01498185) and 2 phase 3 (DEPICT‐1, NCT02268214; DEPICT‐2, NCT02460978) studies. Covariate effects were investigated using stepwise covariate modeling. Model‐derived area under the concentration‐time curve (AUC) in patients with T1DM was compared to AUC in patients with T2DM (using data from historical dapagliflozin studies). The final 2‐compartmental model adequately described the dapagliflozin concentrations in patients with T1DM. The estimated apparent clearance was 20.5 L/h. Renal function (measured as estimated glomerular filtration rate), sex, and body weight were identified as covariates, where patients with better renal function, male patients, and heavier patients had lower dapagliflozin systemic exposure. Among the covariates studied, none of the covariates affected dapagliflozin systemic exposure >1.4‐fold compared to a reference individual and were therefore deemed to be not clinically relevant. Dapagliflozin systemic exposure was comparable between patients with T1DM and T2DM. [ABSTRACT FROM AUTHOR]

Sodium‐glucose cotransporter‐2 inhibitors (SGLT2is) have been shown to reduce the risk of worsening heart failure (HF) in subjects with HF and a reduced ejection fraction (HFrEF) in multiple clinical trials. The DAPACARD clinical trial was conducted to examine the effects of dapagliflozin on cardiac substrate uptake, myocardial efficiency, and myocardial contractile work in subjects with type 2 diabetes mellitus. As a complement to the clinical study, a mechanistic mathematical model of cardiorenal physiology was used to quantify the influence of established natriuretic/diuretic effects of SGLT2i on cardiac function (myocardial efficiency and global longitudinal strain). Virtual participants reflecting the participant‐level characteristics in the DAPACARD trial were produced by varying model parameters over physiologically plausible ranges. A second virtual population was generated by inducing a state of HFrEF in the DAPACARD virtual participants with type 2 diabetes mellitus for comparison. Cardiac responses to placebo and SGLT2i were simulated over 42 days. Cardiac hemodynamic improvements were predicted in DAPACARD‐HFrEF virtual participants but not in DAPACARD virtual participants. In particular, the natriuresis/diuresis induced by SGLT2i improved the global longitudinal strain and myocardial efficiency in DAPACARD‐HFrEF virtual participants within the first 14 days (change from baseline: global longitudinal strain, –0.95%; and myocardial efficiency, 0.34%), whereas the global longitudinal strain and myocardial efficiency in DAPACARD virtual participants were slightly worse (change from baseline: global longitudinal strain, 0.35%; and myocardial efficiency: –0.01%). The results of the DAPACARD virtual participants modeling were in line with the clinical data but do not preclude additional effects from other mechanisms of SGLT2i. [ABSTRACT FROM AUTHOR]

Aim: To provide evidence on the cardiovascular and renal safety of metformin in chronic kidney disease (CKD) stages 3 to 4. Materials and Methods: This post hoc analysis compared participants with an estimated glomerular filtration rate (eGFR) of 15 to 59 mL/min/1.73m2 in the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) and the Saxagliptin and Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus (SAVOR‐TIMI 53) trials taking metformin, with those not exposed to metformin during these trials, using a propensity‐matching approach. Adjusted Cox proportional hazards models were used to assess risk of major adverse cardiovascular events (MACE) and all‐cause mortality (ACM). Metformin effect on eGFR slope was calculated using a mixed‐model repeated measures analysis, and the number of lactic acidosis events was tabulated. Results: No strong trend for lower metformin doses with lower eGFR values was observed in either the EXSCEL or SAVOR‐TIMI 53 trials. In the 1745 metformin‐using participants matched to non‐metformin users, metformin had neutral effects on MACE (hazard ratio [HR] 0.91, 95% confidence interval [CI] 0.76–1.08; P = 0.28) and ACM (HR 0.86, 95% CI 0.70–1.07; P = 0.18), with no interaction by CKD stage, or with use of exenatide or saxagliptin. An improvement in eGFR slope was observed with metformin in the CKD stage 3B cohort in SAVOR‐TIMI 53, but not in other groups. Conclusions: This analysis of participants with CKD stages 3 to 4 from two cardiovascular outcomes trials supports the cardiorenal safety of metformin, but does not suggest a consistent benefit on MACE, ACM, or eGFR slope across this population. [ABSTRACT FROM AUTHOR]

The Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure (DAPA‐HF) study demonstrated that dapagliflozin, a sodium‐glucose cotransporter‐2 inhibitor (SGLT2i), reduced heart failure hospitalization and cardiovascular death in patients with heart failure with reduced ejection fraction (HF‐rEF), with and without type 2 diabetes mellitus. Multiple potential mechanisms have been proposed to explain this benefit, which may be multifactorial. This study aimed to quantify the contribution of the known natriuretic/diuretic effects of SGLT2is to changes in cardiac hemodynamics, remodeling, and fluid homeostasis in the setting of HF‐rEF. An integrated cardiorenal mathematical model was used to simulate inhibition of SGLT2 and its consequences on cardiac hemodynamics in a virtual population of HF‐rEF patients generated by varying model parameters over physiologically plausible ranges and matching to baseline characteristics of individual DAPA‐HF trial patients. Cardiovascular responses to placebo and SGLT2i over time were then simulated. The baseline characteristics of the HF‐rEF virtual population and DAPA‐HF were in good agreement. SGLT2i‐induced diuresis and natriuresis that reduced blood volume and interstitial fluid volume, relative to placebo within 14 days. This resulted in decreased left ventricular end‐diastolic volume and pressure, indicating reduced cardiac preload. Thereafter, blood volume and interstitial fluid volume again began to accumulate, but pressures and volumes remained shifted lower relative to placebo. After 1 year, left ventricle mass was lower and ejection fraction was higher than placebo. These simulations considered only hemodynamic consequences of the natriuretic/diuretic effects of SGLT2i, as other mechanisms may contribute additional benefits besides those predictions. [ABSTRACT FROM AUTHOR]

Aims: To develop a quantitative systems pharmacology model to describe the effect of dapagliflozin (a sodium‐glucose co‐transporter‐2 [SGLT2] inhibitor) on glucose‐insulin dynamics in type 2 diabetes mellitus (T2DM) patients, and to identify key determinants of treatment‐mediated glycated haemoglobin (HbA1c) reduction. Materials and methods: Glycaemic control during dapagliflozin treatment was mechanistically characterized by integrating components representing dapagliflozin pharmacokinetics (PK), glucose‐insulin homeostasis, renal glucose reabsorption, and HbA1c formation. The model was developed using PK variables, glucose, plasma insulin, and urinary glucose excretion (UGE) from a phase IIa dapagliflozin trial in patients with T2DM (NCT00162305). The model was used to predict dapagliflozin‐induced HbA1c reduction; model predictions were compared to actual data from phase III trials (NCT00528879, NCT00683878, NCT00680745 and NCT00673231). Results: The integrated glucose‐insulin‐dapagliflozin model successfully described plasma glucose and insulin levels, as well as UGE in response to oral glucose tolerance tests and meal intake. HbA1c reduction was also well predicted. The results show that dapagliflozin‐mediated glycaemic control is anticorrelated to steady‐state insulin concentration and insulin sensitivity. Conclusions: The developed model framework is the first to integrate SGLT2 inhibitor mechanism of action with both short‐term glucose‐insulin dynamics and long‐term glucose control (HbA1c). The results suggest that dapagliflozin treatment is beneficial in patients with inadequate glycaemic control from insulin alone and this benefit increases as insulin control diminishes. [ABSTRACT FROM AUTHOR]

The effects of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) on renal outcomes in patients with type 2 diabetes at high cardiovascular risk are modest or neutral. However, GLP‐1RAs may confer clinical benefits in those at high risk of progressive renal function loss. We examined the effects of once‐weekly exenatide (EQW) on estimated glomerular filtration rate (eGFR) slope and urinary albumin:creatinine ratio (UACR) as a function of baseline UACR in 3503 EXSCEL participants (23.7%) with eGFR data available and 2828 participants (19.2%) with UACR change data available. EQW improved eGFR slope assessed via mixed model repeated measures, compared with placebo, in participants with baseline UACR >100 mg/g (0.79 mL/min/1.73 m2/year [95% confidence interval {CI} 0.24–1.34]) and UACR >200 mg/g (1.32 mL/min/1.73 m2/year [95% CI 0.57–2.06]), but not at lower UACR thresholds. EQW reduced UACR, compared with placebo, assessed via analysis of covariance, consistently across subgroups with baseline UACR >30 mg/g (28.2% reduction), baseline UACR >100 mg (22.5% reduction) and baseline UACR >200 mg (34.5% reduction). This post hoc EXSCEL analysis suggests that EQW reduces UACR, with improvement in eGFR slope specifically in participants with elevated baseline UACR. [ABSTRACT FROM AUTHOR]

Aim: To assess whether the previously developed multivariable risk prediction framework (PRE score) could predict the renal effects observed in the EXSCEL cardiovascular outcomes trial using short‐term changes in cardio‐renal risk markers. Materials and Methods: Changes from baseline to 6 months in HbA1c, systolic blood pressure (SBP), body mass index (BMI), haemoglobin, total cholesterol, and new micro‐ or macroalbuminuria were evaluated. The renal outcomes were defined as a composite of a sustained 30% or 40% decline in estimated glomerular filtration rate (eGFR) or end‐stage renal disease (ESRD). Relationships between risk markers and long‐term renal outcomes were determined in patients with type 2 diabetes from the ALTITUDE study using multivariable Cox regression analysis, and then applied to short‐term changes in risk markers observed in EXSCEL to predict the exenatide‐induced impact on renal outcomes. Results: Compared with placebo, mean HbA1c, BMI, SBP and total cholesterol were lower at 6 months with exenatide, as was the incidence of new microalbuminuria. The PRE score predicted a relative risk reduction for the 30% eGFR decline + ESRD endpoint of 11.3% (HR 0.89; 95% CI 0.83–0.94), compared with 12.7% (HR 0.87; 0.77–0.99) observed risk reduction. For the 40% eGFR decline + ESRD endpoint, the predicted and observed risk reductions were 11.0% (HR 0.89; 0.82–0.97) and 13.7% (HR 0.86, 0.72–1.04), respectively. Conclusions: Integrating short‐term risk marker changes into a multivariable risk score predicted the magnitude of renal risk reduction observed in EXSCEL. [ABSTRACT FROM AUTHOR]

The aim of this research was to differentiate dapagliflozin, empagliflozin, and canagliflozin based on their capacity to inhibit sodium‐glucose cotransporter (SGLT) 1 and 2 in patients with type 2 diabetes using a previously developed quantitative systems pharmacology model of renal glucose filtration, reabsorption, and excretion. The analysis was based on pooled, mean study‐level data on 24‐hour urinary glucose excretion, average daily plasma glucose, and estimated glomerular filtration rate collected from phase I and II clinical trials of SGLT2 inhibitors. Variations in filtered glucose across clinical studies were shown to drive the apparent differences in the glucosuria dose–response relationships among the gliflozins. A normalized dose–response analysis demonstrated similarity of dapagliflozin and empagliflozin, but not canagliflozin. At approved doses, SGLT1 inhibition by canagliflozin but not dapagliflozin or empagliflozin contributed to ~ 10% of daily urinary glucose excretion. [ABSTRACT FROM AUTHOR]

Aim: To develop a quantitative drug‐disease systems model to investigate the paradox that sodium‐glucose co‐transporter (SGLT)2 is responsible for >80% of proximal tubule glucose reabsorption, yet SGLT2 inhibitor treatment results in only 30% to 50% less reabsorption in patients with type 2 diabetes mellitus (T2DM). Materials and methods: A physiologically based four‐compartment model of renal glucose filtration, reabsorption and excretion via SGLT1 and SGLT2 was developed as a system of ordinary differential equations using R/IQRtools. SGLT2 inhibitor pharmacokinetics and pharmacodynamics were estimated from published concentration‐time profiles in plasma and urine and from urinary glucose excretion (UGE) in healthy people and people with T2DM. Results: The final model showed that higher renal glucose reabsorption in people with T2DM versus healthy people was associated with 54% and 28% greater transporter capacity for SGLT1 and SGLT2, respectively. Additionally, the analysis showed that UGE is highly dependent on mean plasma glucose and estimated glomerular filtration rate (eGFR) and that their consideration is critical for interpreting clinical UGE findings. Conclusions: Quantitative drug‐disease system modelling revealed mechanistic differences in renal glucose reabsorption and UGE between healthy people and those with T2DM, and clearly showed that SGLT2 inhibition significantly increased glucose available to SGLT1 downstream in the tubule. Importantly, we found that the findings of lower than expected UGE with SGLT2 inhibition are explained by the shift to SGLT1, which recovered additional glucose (~30% of total). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: To assess the dapagliflozin exposure‐response relationship in Japanese and non‐Japanese patients with type 1 diabetes mellitus (T1DM) and investigate if a dose adjustment is required in Japanese patients. Materials and Methods: Data from two clinical studies were used to develop a non‐linear mixed effects model describing the relationship between dapagliflozin exposure (area under the concentration curve) and response (24‐hour urinary glucose excretion [UGE]) in Japanese and non‐Japanese patients with T1DM. The effects of patient‐level characteristics (covariates; identified using a stepwise procedure) on response was also assessed. Simulations were performed using median‐normalized covariate values. Results: Data from 84 patients were included. Average self‐monitored blood glucose (SMBG) at day 7, change from baseline in total insulin dose at day 7, and baseline estimated glomerular filtration rate (eGFR) all had a significant effect on 24‐hours UGE, with SMBG being the most influential. Dapagliflozin systemic exposure for matching doses and baseline eGFR was similar between Japanese and non‐Japanese patients; however, higher SMBG and a greater reduction in total insulin dose was observed in the Japanese population. When the significant covariates were included, the model fit the data well for both populations, and accurately predicted exposure‐response in the Japanese and non‐Japanese populations, in agreement with the observed data. Conclusions: There was no difference in dapagliflozin exposure‐response in Japanese and non‐Japanese patients with T1DM once differences in renal function, glycaemic control and insulin dose reductions between studies were considered. Therefore, no dose adjustment is recommended in Japanese patients with T1DM. [ABSTRACT FROM AUTHOR]

Objective: The sodium-glucose cotransporter 2 inhibitors (SGLT2i) empagliflozin and canagliflozin reduce the incidence of major adverse cardiovascular events (MACE), all-cause mortality (ACM), and renal events in cardiovascular outcomes trials, with observational real-world evidence suggesting class effect benefits that include dapagliflozin. We examined the placebo arm of the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) to determine whether the effects of drop-in open-label dapagliflozin on MACE, ACM, and estimated glomerular filtration rate (eGFR) were consistent with the SGLT2i class as a whole.Research Design and Methods: SGLT2i drop-in therapy occurred in 10.6% of EXSCEL participants, with 5.2% taking dapagliflozin. Propensity-matched cohorts of SGLT2i users and nonusers (n = 709 per group) were generated on the basis of their characteristics before open-label SGLT2i drop-in or at baseline for participants taking SGLT2i at enrollment and an equivalent study visit for non-SGLT2i users. Time to first adjudicated MACE and ACM was analyzed using Cox regression. eGFR slopes were compared between matched cohorts using a mixed-model repeated-measures analysis.Results: In adjusted analyses, SGLT2i users (compared with nonusers) had a numerically lower risk of MACE (adjusted hazard ratio 0.79 [95% CI 0.49-1.28]), as did dapagliflozin users (0.55 [0.26-1.15]). SGLT2i users had a significantly lower ACM risk (0.51 [0.27-0.95]; dapagliflozin: 0.66 [0.25-1.72]). Compared with nonusers, eGFR slope was significantly better for SGLT2i users overall (+1.78 [95% CI 0.87-2.69] mL/min/1.73 m2 per year) and for dapagliflozin users (+2.28 [1.01-3.54] mL/min/1.73 m2 per year).Conclusions: This post hoc analysis of the placebo arm of EXSCEL supports a beneficial class effect for all SGLT2i, including dapagliflozin, for reduced ACM and less eGFR decline. [ABSTRACT FROM AUTHOR]

This study aimed to quantify the effect of the immediate release (IR) of exenatide, a short‐acting glucagon‐like peptide‐1 (GLP‐1) receptor agonist (GLP‐1RA), on gastric emptying rate (GER) and the glucose rate of appearance (GluRA), and evaluate the influence of drug characteristics and food‐related factors on postprandial plasma glucose (PPG) stabilization under GLP‐1RA treatment. A quantitative systems pharmacology (QSP) approach was used, and the proposed model was based on data from published sources including: (1) GLP‐1 and exenatide plasma concentration‐time profiles; (2) GER estimates under placebo, GLP‐1 or exenatide IR dosing; and (3) GluRA measurements upon food intake. According to the model's predictions, the recommended twice‐daily 5‐ and 10‐μg exenatide IR treatment is associated with GluRA flattening after morning and evening meals (48%‐49%), whereas the midday GluRA peak is affected to a lesser degree (5%‐30%) due to lower plasma drug concentrations. This effect was dose‐dependent and influenced by food carbohydrate content, but not by the lag time between exenatide injection and meal ingestion. Hence, GER inhibition by exenatide IR represents an important additional mechanism of its effect on PPG. [ABSTRACT FROM AUTHOR]

GLP-1 RA effects on major kidney outcome in unselected T2D patients at high cardiovascular (CV) risk are modest or neutral. However, GLP-1 RA may provide renal benefit in those at high risk of worsening kidney disease. We examined once-weekly exenatide (EQW) effects on eGFR slope and UACR change, as a function of baseline UACR, in a subset of EXSCEL participants. Of 14752 EXSCEL participants, eGFR slope was assessed in those with baseline UACR and ≥1 post-baseline eGFR (n=3503 [23.7%]) via mixed model repeated measures (MMRM) analysis (median follow-up 3.3 years). UACR percent change from baseline to first post-baseline measurement (median time 8.9 months) was assessed in those with baseline and ≥1 follow-up UACR (n=2828 [19.2%]) via ANCOVA of log-transformed UACR, with baseline UACR as a covariate. Participants with baseline UACR measurements were generally similar to the overall EXSCEL population, and balanced across treatment arms. EQW improved eGFR slope in those with baseline UACR>100mg/g and UACR>200mg/g, but not at lower UACR thresholds. No difference in EQW effect on eGFR was observed as a function of baseline eGFR, CV disease history, RAAS inhibitor use, or SBP. UACR improvement was similar across UACR categories. This EXSCEL post-hoc analysis suggests that EQW reduces UACR, with improvement in eGFR slope specifically in participants with elevated baseline UACR. Disclosure: L.E. Clegg: Employee; Self; AstraZeneca. A. van der Aart: None. R.C. Penland: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca. D.W. Boulton: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca, Bristol-Myers Squibb. C. Sjöström: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca. R.J. Mentz: Consultant; Self; Amgen, AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Sanofi. Research Support; Self; GlaxoSmithKline plc. R.R. Holman: Advisory Panel; Self; Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S. Research Support; Self; AstraZeneca, Bayer AG, Merck Sharp & Dohme Corp. H.L. Heerspink: Consultant; Self; AbbVie Inc., AstraZeneca, Boehringer Ingelheim International GmbH, CSL Behring, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Mitsubishi Tanabe Pharma Corporation, Mundipharma International, Retrophin, Inc. Funding: AstraZeneca [ABSTRACT FROM AUTHOR]

SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP-1 RA) improve cardiovascular (CV) and renal outcomes through distinct mechanisms. However, evidence is lacking on clinical outcomes in patients treated with both classes. EXSCEL, the cardiovascular outcomes trial for the GLP-1 RA exenatide, provides an opportunity to explore CV and renal outcomes with dual GLP-1RA and SGLT2i treatment, as ∼10% of participants had an SGLT2i added to their glycemic management during follow-up. Cohorts of SGLT2i users in the exenatide arm were propensity-matched to: 1) users of exenatide but not SGLT2i; 2) placebo arm participants that did not take SGLT2i, based on last measured characteristics before SGLT2i initiation. Subsequent time-to-first adjudicated major adverse cardiovascular event (MACE) and all-cause mortality (ACM) were compared using Cox regression analyses. eGFR decline was quantified in the matched cohorts using a mixed model repeated measurement (MMRM) analysis. Exenatide plus SGLT2i use numerically decreased the MACE hazard ratio and significantly reduced ACM risk, compared with exenatide alone and with placebo. The combination also significantly improved eGFR slope in both comparisons. This post hoc analysis offers support for the hypothesis that combinatorial exenatide and SGLT2i use may provide benefit on renal function and all-cause mortality. Disclosure: L.E. Clegg: Employee; Self; AstraZeneca. R.C. Penland: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca, Novartis AG. D.W. Boulton: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca, Bristol-Myers Squibb Company. S. Bachina: Employee; Self; AstraZeneca. M. Thuresson: Consultant; Self; AstraZeneca. H.L. Heerspink: Consultant; Self; AbbVie Inc., Astellas Pharma Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Fresenius Medical Care, Gilead Sciences, Inc., Janssen Research & Development, Merck & Co., Inc., Mitsubishi Tanabe Pharma Corporation. S. Gustavson: Employee; Self; AstraZeneca. Employee; Spouse/Partner; AstraZeneca. C. Sjöström: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca. J.A. Ruggles: Employee; Self; AstraZeneca. Stock/Shareholder; Self; Apple, AstraZeneca. A.F. Hernandez: Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation. Research Support; Self; AstraZeneca, GlaxoSmithKline plc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation. J.B. Buse: Consultant; Self; Neurimmune AG. Research Support; Self; AstraZeneca, National Center for Advancing Translational Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk A/S, Sanofi, vTv Therapeutics. Stock/Shareholder; Self; Mellitus Health, PhaseBio Pharmaceuticals, Inc., Stability Health. Other Relationship; Self; ADOCIA, AstraZeneca, Dance Biopharm Holdings Inc., Eli Lilly and Company, MannKind Corporation, NovaTarg, Novo Nordisk A/S, Senseonics, vTv Therapeutics, Zafgen, Inc. R.J. Mentz: Advisory Panel; Self; Boehringer Ingelheim International GmbH. Research Support; Self; GlaxoSmithKline plc. Other Relationship; Self; Amgen Inc., AstraZeneca, Bayer AG, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation. R.R. Holman: Advisory Panel; Self; Bayer AG, Novartis AG, Novo Nordisk A/S. Research Support; Self; AstraZeneca, Bayer AG, Merck Sharp & Dohme Corp. Other Relationship; Self; AstraZeneca, Bayer AG, GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc. [ABSTRACT FROM AUTHOR]

Sodium glucose cotransporter 2 inhibitors (SGLT2i) reduce cardiovascular events and onset and progression of renal disease by mechanisms that remain incompletely understood but may include clearance of interstitial congestion and reduced glomerular hydrostatic pressure. The ongoing DAPASALT mechanistic clinical study will evaluate natriuretic, diuretic, plasma/extracellular volume, and blood pressure responses to dapagliflozin in people with type 2 diabetes with normal or impaired renal function (D-PRF and D-IRF, respectively) and in normoglycemic individuals with renal impairment (N-IRF). In this study, a mathematical model of renal physiology, pathophysiology, and pharmacology was used to prospectively predict changes in sodium excretion, blood and interstitial fluid volume (IFV), blood pressure, glomerular filtration rate, and albuminuria in DAPASALT. After validating the model with previous diabetic nephropathy trials, virtual patients were matched to DAPASALT inclusion/exclusion criteria, and the DAPASALT protocol was simulated. Predicted changes in glycosuria, blood pressure, glomerular filtration rate, and albuminuria were consistent with other recent studies in similar populations. Predicted albuminuria reductions were 46% in D-PRF, 34.8% in D-IRF, and 14.2% in N-IRF. The model predicts a similarly large IFV reduction between D-PRF and D-IRF and less, but still substantial, IFV reduction in N-IRF, even though glycosuria is attenuated in groups with impaired renal function. When DAPASALT results become available, comparison with these simulations will provide a basis for evaluating how well we understand the cardiorenal mechanism(s) of SGLT2i. Meanwhile, these simulations link dapagliflozin's renal mechanisms to changes in IFV and renal biomarkers, suggesting that these benefits may extend to those with impaired renal function and individuals without diabetes. SIGNIFICANCE STATEMENT: Mechanisms of SGLT2 inhibitors' cardiorenal benefits remain incompletely understood. We used a mathematical model of renal physiology/pharmacology to prospectively predict responses to dapagliflozin in the ongoing DAPASALT study. Key predictions include similarly large interstitial fluid volume (IFV) reductions between subjects with normal and impaired renal function and less, but still substantial, IFV reduction in those without diabetes, even though glycosuria is attenuated in these groups. Comparing prospective simulations and study results will assess how well we understand the cardiorenal mechanism(s) of SGLT2 inhibitors., (Copyright © 2020 by The American Society for Pharmacology and Experimental Therapeutics.)

The human cardiac sodium channel (hNav1.5, encoded by the SCN5A gene) is critical for action potential generation and propagation in the heart. Drug-induced sodium channel inhibition decreases the rate of cardiomyocyte depolarization and consequently conduction velocity and can have serious implications for cardiac safety. Genetic mutations in hNav1.5 have also been linked to a number of cardiac diseases. Therefore, off-target hNav1.5 inhibition may be considered a risk marker for a drug candidate. Given the potential safety implications for patients and the costs of late stage drug development, detection, and mitigation of hNav1.5 liabilities early in drug discovery and development becomes important. In this review, we describe a pre-clinical strategy to identify hNav1.5 liabilities that incorporates in vitro, in vivo, and in silico techniques and the application of this information in the integrated risk assessment at different stages of drug discovery and development.