Trelagliptin Succinate

This study investigated the effects of trelagliptin and remogliflozin, alone and in combination with alpha lipoic acid (ALA), on cardiac biomarkers in diabetic cardiomyopathy (DCM). We aimed to assess the management of glucotoxicity consequences in streptozotocin-induced diabetic rats by measuring serum levels of pharmacologically active endogenous ligands. Forty-eight male rats were divided into different treatment groups, including negative control, positive control, and four experimental groups. After inducing diabetes, the rats were treated for 28 days, and serum levels of biomarkers associated with oxidative stress (malondialdehyde and thioredoxin-interacting protein), inflammation (nuclear factor NF-kappa-B p105 and lipoprotein-associated phospholipase A2), and myopathy (neprilysin and high selective cardiac troponin T) were measured. Immunohistochemical analysis of heart cells was also performed. The results showed that inducing hyperglycemia increased serum glucose levels and biomarkers associated with DCM. However, all treatment groups exhibited a significant decrease in these biomarkers and an increase in insulin levels compared to the diabetic control group. The groups receiving combination therapy with ALA showed greater improvements in cardiac biomarkers compared to the individual treatments. The immunohistochemical analysis supported these findings by demonstrating a reduction in the percentage area of cathepsin B, a protein involved in DCM pathophysiology. In conclusion, supplementing the base treatments with ALA showed promise in enhancing cardiac biomarkers associated with DCM. The combination of trelagliptin, remogliflozin, and ALA may have additional clinical value in managing DCM by targeting oxidative stress, inflammation, and glucotoxicity. However, further research is needed to validate these findings and explore their potential clinical applications.

This study aimed to build a mathematical model of physiologically based pharmacokinetic combined DPP-4 occupancy (PBPK-DO) in humans to provide some recommendations for dosing adjustment in patients with renal impairment.

The PBPK-DO model was built using physicochemical and biochemical properties and binding kinetics data of TRE and OMA, and then validated by the clinically observed pharmacokinetics (PK) and pharmacodynamics (PD). Finally, the model was applied to determine dose adjustment in patients with renal impairment.

The predicted PK and DPP-4 occupancy matched well with the clinically observed data, and all absolute average-folding errors (AAFEs) were within 2. The simulations showed that TRE and OMA were both suggested to only support dose reduction by half in patients with severe renal impairment based on this PBPK-DO model, which is different from the commendations only in terms of their AUC_0-336 changes. These simulation results were in good agreement with clinical recommendations about dosage adjustment in patients.

The present PBPK-DO model can simultaneously predict PK and PD of TRE and OMA in humans and also provide valuable recommendations for dosing adjustment in renal impairment patients, which cannot be achieved by alone depending on PK change.