Abstract Background: Chronic kidney disease (CKD) is a progressive disorder characterized by irreversible loss of renal function and is strongly associated with diabetes mellitus. Drug repurposing offers a cost-effective strategy to identify new therapeutic applications for existing drugs. Objective: This study aimed to perform a comparative evaluation of the pharmacokinetic (ADMET) properties and drug-likeness profiles of Dapagliflozin and Sitagliptin using in silico approaches to assess their suitability for repurposing in CKD. Methods: The chemical structures of Dapagliflozin and Sitagliptin were retrieved from the PubChem database in SMILES format and analyzed using the DeepPK platform. Various pharmacokinetic parameters, including absorption, distribution, metabolism, excretion, and toxicity, were predicted using deep learning-based computational models. A comparative analysis was conducted to evaluate key parameters such as intestinal absorption, bioavailability, cytochrome P450 interactions, clearance, half-life, and toxicity endpoints. Results: Both drugs exhibited favorable absorption characteristics, including high intestinal absorption and good oral bioavailability. Dapagliflozin demonstrated superior distribution properties, including enhanced tissue penetration and higher fraction unbound. In contrast, Sitagliptin showed greater metabolic stability with minimal interaction with cytochrome P450 enzymes and a longer predicted half-life. Toxicity analysis revealed that Dapagliflozin exhibited potential risks such as mutagenicity and hERG inhibition, whereas Sitagliptin demonstrated a comparatively safer toxicity profile. Conclusion: While Dapagliflozin shows strong pharmacokinetic properties in terms of absorption and distribution, Sitagliptin appears to be a more suitable candidate for drug repurposing in CKD due to its balanced pharmacokinetic behavior and improved safety profile. This study highlights the importance of in silico ADMET analysis in early-stage drug evaluation and repurposing strategies.
Padvi et al. (2026) studied this question.