Literature DB >> 27113465

Current Status of Computer-Aided Drug Design for Type 2 Diabetes.

Shabana Bibi, Katsumi Sakata1.   

Abstract

BACKGROUND: Diabetes is a metabolic disorder that requires multiple therapeutic approaches. The pancreas loses its functionality to properly produce the insulin hormone in patients with diabetes mellitus. In 2012, more than one million people worldwide died as a result of diabetes, which was the eighth leading cause of death.
OBJECTIVE: Most drugs currently available and approved by the U.S. Food and Drug Administration cannot reach an adequate level of glycemic control in diabetic patients, and have many side effects; thus, new classes of compounds are required. Efforts based on computer-aided drug design (CADD) can mine a large number of databases to produce new and potent hits and minimize the requirement of time and dollars for new discoveries.
METHODS: Pharmaceutical sciences have made progress with advances in drug design concepts. Virtual screening of large databases is most compatible with different computational methods such as molecular docking, pharmacophore, quantitative structure-activity relationship, and molecular dynamic simulation. Contribution of these methods in selection of antidiabetic compounds has been discussed.
RESULTS: The computer-aided drug design (CADD) approach has contributed to successful discovery of novel anti-diabetic agents. This mini-review focuses on CADD approach on currently approved drugs and new therapeutic agents-indevelopment that may achieve suitable glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels.
CONCLUSION: Drug design and development for type 2 diabetes have been actively studied. However, a large number of antidiabetic drugs are still in early stages of development. The conventional target- and structure-based approaches can be regarded as part of the efforts toward therapeutic mechanism-based drug design for treatment of type 2 diabetes. It is expected that further improvement in CADD approach will enhance the new discoveries.

Entities:  

Year:  2016        PMID: 27113465

Source DB:  PubMed          Journal:  Curr Comput Aided Drug Des        ISSN: 1573-4099            Impact factor:   1.606


  4 in total

1.  Probing the influence of carboxyalkyl groups on the molecular flexibility and the charge density of apigenin derivatives.

Authors:  Y J Qi; H N Lu; Y M Zhao; N Z Jin
Journal:  J Mol Model       Date:  2017-02-15       Impact factor: 1.810

2.  Identification of Effective and Nonpromiscuous Antidiabetic Drug Molecules from Penicillium Species.

Authors:  Shahzad Saleem; Shabana Bibi; Qudsia Yousafi; Tehzeem Hassan; Muhammad Saad Khan; Mohammad Mehedi Hasan; Hitesh Chopra; Mahmoud Moustafa; Mohammed Al-Shehri; Mohammad Khalid; Atul Kabra
Journal:  Evid Based Complement Alternat Med       Date:  2022-06-08       Impact factor: 2.650

3.  Preparation and Evaluation of Chitosan/PVA Based Hydrogel Films Loaded with Honey for Wound Healing Application.

Authors:  Hitesh Chopra; Shabana Bibi; Sandeep Kumar; Muhammad Saad Khan; Pradeep Kumar; Inderbir Singh
Journal:  Gels       Date:  2022-02-11

4.  Identification of α-Glucosidase Inhibitors from Scutellaria edelbergii: ESI-LC-MS and Computational Approach.

Authors:  Muddaser Shah; Hazir Rahman; Ajmal Khan; Shabana Bibi; Obaid Ullah; Saeed Ullah; Najeeb Ur Rehman; Waheed Murad; Ahmed Al-Harrasi
Journal:  Molecules       Date:  2022-02-16       Impact factor: 4.411

  4 in total

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