The Adaptive Renal Response for Volume Homeostasis During 2 Weeks of Dapagliflozin Treatment in People With Type 2 Diabetes and Preserved Renal Function on a Sodium-Controlled Diet.

Introduction: Proximal tubule sodium uptake is diminished following sodium glucose cotransporter 2 (SGLT2) inhibition. We previously showed that during SGLT2 inhibition, the kidneys adapt by increasing sodium uptake at distal tubular segments, thereby maintaining body sodium balance. Despite continuous glycosuria, we detected no increased urine volumes. We therefore assessed the adaptive renal responses to prevent excessive fluid loss. Methods: We conducted a mechanistic open-label study in people with type 2 diabetes mellitus with preserved kidney function, who received a standardized sodium intake (150 mmol/d) to evaluate the effects of dapagliflozin on renin-angiotensin-aldosterone system (RAAS) hormones, volume-related biomarkers, urinary albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR), at start of treatment (day 4), end of treatment (day 14), and follow-up (day 18). Results: A total of 14 people were enrolled. Plasma renin and angiotensin II and urinary aldosterone and angiotensinogen were acutely and persistently increased during treatment with dapagliflozin. Plasma copeptin level was numerically increased after 4 days (21%). Similarly, fractional urea excretion was significantly decreased at start of treatment (-17%). Free water clearance was significantly decreased after 4 days (-74%) and 14 days (-41%). All changes reversed after dapagliflozin discontinuation. Conclusion: Dapagliflozin-induced osmotic diuresis triggers kidney adaptive mechanisms to maintain volume and sodium balance in people with type 2 diabetes and preserved kidney function. ClinicalTrials.gov (identification: NCT03152084).

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.

Why do SGLT2 inhibitors reduce heart failure hospitalization? A differential volume regulation hypothesis.

The effect of a sodium glucose cotransporter 2 inhibitor (SGLT2i) in reducing heart failure hospitalization in the EMPA-REG OUTCOMES trial has raised the possibility of using these agents to treat established heart failure. We hypothesize that osmotic diuresis induced by SGLT2 inhibition, a distinctly different diuretic mechanism than that of other diuretic classes, results in greater electrolyte-free water clearance and, ultimately, in greater fluid clearance from the interstitial fluid (IF) space than from the circulation, potentially resulting in congestion relief with minimal impact on blood volume, arterial filling and organ perfusion. We utilize a mathematical model to illustrate that electrolyte-free water clearance results in a greater reduction in IF volume compared to blood volume, and that this difference may be mediated by peripheral sequestration of osmotically inactive sodium. By coupling the model with data on plasma and urinary sodium and water in healthy subjects who received either the SGLT2i dapagliflozin or loop diuretic bumetanide, we predict that dapagliflozin produces a 2-fold greater reduction in IF volume compared to blood volume, while the reduction in IF volume with bumetanide is only 78% of the reduction in blood volume. Heart failure is characterized by excess fluid accumulation, in both the vascular compartment and interstitial space, yet many heart failure patients have arterial underfilling because of low cardiac output, which may be aggravated by conventional diuretic treatment. Thus, we hypothesize that, by reducing IF volume to a greater extent than blood volume, SGLT2 inhibitors might provide better control of congestion without reducing arterial filling and perfusion.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet.

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.