Type 2 diabetes (T2D) is one of the most important and challenging chronic metabolic diseases worldwide. According to a WHO report, T2D-related complications cause thousands of deaths yearly. Various synthetic drugs have been developed to treat T2D, but their side effects are significant drawbacks. Similarly, natural product libraries have been screened to find potential anti-diabetic drug-like compounds. Some of the presently approved anti-diabetic medicines are based on the backbone of natural products. Despite considerable progress in drug design efforts, estimates predict that over the coming years the number of T2D-affected people will increase remarkably. Blindness, cardiovascular failure, stroke, heart attacks, and lower limb amputation are major clinical complications of diabetes. Various strategies for improving lifestyle, consuming a healthy diet, regular physical activity, and avoiding tobacco and alcohol have been considered non-drug treatments for T2D. Clinical reports predicted that T2D is responsible for more than 95% of diabetic cases, and the disease will be diagnosed years after onset.
The current strategies for developing anti-diabetic drugs mainly focus on the inhibitory profile of chemical ligands against specific enzymes/receptors in the human body. Due to the complications of this metabolic disease, such strategies cannot alone ultimately reduce the clinical challenges of T2D; integrated approaches should be applied to identify critical signaling pathways and target proteins involved in the onset of T2D. Recent progress in omics-based technologies enabled researchers to access various dimensions of big biological data related to T2D. In this regard, the combination of multi-facet high-throughput computational methods and experimental assays may open new perspectives to alleviate the health complications of T2D. Urgent efforts should be made to minimize the medical and financial burden of this disease for both affected individuals and the healthcare systems of countries affected. This Research Topic aims to cover the following areas in the treatment of T2D:
- Effective strategies using computational methods to repurpose FDA-approved drugs for T2D
- Integrated computational methods for developing anti-diabetic compounds
- Combination of experimental assays and omics-based technologies to identify new targets in T2D
- Developing synthetic/natural products with low or no side effects for T2D
- High-throughput reverse docking strategies using big data to screen potential drug-like compounds for T2D (these studies must use a combination of an experimental and an in silico approach
- High-quality transcriptomics analysis to identify up/down-regulated genes and proteins involved in T2D.
- Combinatorial experimental assays for developing anti-diabetic compounds.
- Studies that report on the gaps between computational calculations and experimental assays in relation to T2D
- Structural bases of drug design for T2D
- Systems biology of chemical ligands interaction with the human body receptors involved in T2D.
All the manuscripts submitted to the collection will need to fully comply with the Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version
here).
Specifically, please note: Pillar 1a (and 3d) Traditional context - The traditional context must be described in the introduction and supported with bibliographical primary references. This may be based on modern uses of a plant in general healthcare. It is essential that the material under study is described in sufficient detail (Pillar 2 a,b).
