Work

In Silico Analysis of Phytochemicals of Native Pakistani Plants Having Antidiabetic Potential Targeting GLP ‐ 1R and SGLT2 Proteins

Year: 2026

Type: article

Abstract: ABSTRACT This study examines the potential of phytochemicals from native Pakistani plants to mitigate the effects of diabetes by interacting with GLP‐1R and SGLT2 proteins using bioinformatics methods, thereby highlighting the application of bioinformatics in this context. Diabetes Mellitus (DM) is one of the serious global health issues and is mostly treated by traditional methods that often lead to severe or fatal reactions. Consequently, other effective treatments with low risk of side effects and high impact can be pursued. This research compiles 28 plant species known for their antidiabetic properties and assesses the therapeutic effects of their phytochemical classes, including saponins and flavonoids. The methodology involves several key steps: for example, the selection and categorization of phytochemicals, the acquisition of targeted proteins (in this case study, GLP‐1R and SGLT2), the identification of binding sites, and, finally, molecular docking to predict interactions between the target proteins and phytochemicals. Moreover, ADMET analysis is performed to ensure the phytochemicals are safe and efficient as therapeutic. It is found that phytochemicals such as luteolin and calotropagenin have a high affinity for the target proteins in the final results. This indicates that they can increase insulin production while inhibiting glucose reabsorption. ADMET analysis confirmed that these phytochemicals possess favorable pharmacokinetic properties, supporting their safety profile. Furthermore, clinical trials and in vivo studies are necessary to ensure the efficacy and safety of these compounds in the human population. The use of bioinformatics techniques in drug research highlights the role of phytochemicals in developing safer and effective antidiabetic treatments. Trial Registration Not applicable

Source: Food Science & Nutrition

DOI: 10.1002/fsn3.71688