Differentiating the Sodium‐Glucose Cotransporter 1 Inhibition Capacity of Canagliflozin vs. Dapagliflozin and Empagliflozin Using Quantitative Systems Pharmacology Modeling

Abstract

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.

Cardiovascular effects and mechanisms of sodium‐glucose cotransporter‐2 inhibitors

“…It was found that SGLT2 inhibitors increased the risk of diabetic ketoacidosis, characterized by euglycemia (blood glucose less than 250 mg/dL) in the presence of severe metabolic acidosis and ketonemia, called euglycemic diabetic ketoacidosis DKA. 76 , 77 , 78 , 79 In addition, canagliflozin was found to increase the risk of amputation. 80 The reason was thought to be related to the circulating blood volume.…”

Do sodium‐glucose co‐transporter‐2 inhibitors increase plasma glucagon by direct actions on the alpha cell? And does the increase matter for the associated increase in endogenous glucose production?

“…As can be extracted from Table 3, the maximal concentration (C max ) of canagliflozin is 11‐fold higher than its IC 50 for SGLT1, whereas for dapagliflozin and empagliflozin, the C max is only a third and less than a 10th, respectively, of the IC 50 . Consistent with these relationships, it has recently been reported that approximately 10% of the daily urinary glucose excretion in response to approved doses of canagliflozin results from renal SGLT1 inhibition, whereas glucose excretion mediated by dapagliflozin and empagliflozin exclusively results from SGLT2 inhibition 65 . However, the potential clinical implication of these differences in terms of efficacy on blood glucose lowering remains to be investigated and would require head‐to‐head trials including canagliflozin versus dapagliflozin and/or empagliflozin, which are currently lacking.…”

“…Consistent with these relationships, it has recently been reported that approximately 10% of the daily urinary glucose excretion in response to approved doses of canagliflozin results from renal SGLT1 inhibition, whereas glucose excretion mediated by dapagliflozin and empagliflozin exclusively results from SGLT2 inhibition. 65 However, the potential clinical implication of these differences in terms of efficacy on blood glucose lowering remains to be investigated and would require head-to-head trials including canagliflozin versus dapagliflozin and/or empagliflozin, which are currently lacking. Cross-study comparisons do, however, not give reason to suspect that the differences in SGLT2 selectivity would affect maximal efficacy on blood glucose lowering, as comprehensively reviewed elsewhere.…”

Recent applications of quantitative systems pharmacology and machine learning models across diseases

“…Type 2 diabetes mellitus (T2DM), commonly known as type 2 diabetes, is a metabolic disorder translated by an aberrant accumulation of glucose in the blood due to a defect of insulin function and expression [ 73 ]. Different T2DM QSP models described below focused on drugs that could lower plasma glucose and filter it through other organs.…”