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

View Article: PubMed Central

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-in-development 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.

No MeSH data available.


The structure of the potent hit (NSC32458) identified by using the QSAR-based pharmacophore modeling and virtual screening approaches shows highest GK activation of ≈6 folds at 10μM.
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Figure 2: The structure of the potent hit (NSC32458) identified by using the QSAR-based pharmacophore modeling and virtual screening approaches shows highest GK activation of ≈6 folds at 10μM.

Mentions: Glucokinase activator (GKA) has been recently shown to be a valid anti-diabetic target. GKA serves as a glucose sensor in the insulin-producing pancreatic beta-cells, and determines the rate of glucose metabolism by regulating the amount of insulin produced and released from pancreatic beta-cells in response to the amount of glucose in the blood. Elevated levels of glucose will increase GKA levels in the pancreas, thereby increasing the release of insulin. In addition, GKA influences hepatic lipid metabolism and gluconeogenesis in the liver [49]. Target protein structure having PDB id: 3VF6 use in this study for new GKAs identification with good bioactivity is shown in Fig. 6 (A). Taha et al. identified a series of novel GKAs using virtual screening. They used a multistage approach, integrating pharmacophore modeling and QSAR analysis followed by virtual screening of the NCI library of 238,819 compounds. Pharmacophore exploration of GKA was performed using Catalyst software. QSAR-based pharmacophore analysis was conducted to select the best combination of molecular descriptors and pharmacophore models that are capable of explaining bioactivity variation across a list of training dataset. The authors concluded that a novel GKA activation pharmacophore model appeared in the optimal QSAR equation. The resulting pharmacophore model yielded a good receiver operating characteristic on validation, and therefore this information was used as three-dimensional search query to screen the NCI database for new GKA activators. Virtual screening identified 10 promising bio-activators from the NCI library of compounds. The most potent NCI hit illustrated 6.3-fold GKA activation at 10 μM shown in Fig. 2. These results illustrated that the ligand-based virtual screening protocol was helpful to identify novel GKA leads for subsequent development into potential anti-diabetic agents [50].


Current Status of Computer-Aided Drug Design for Type 2 Diabetes
The structure of the potent hit (NSC32458) identified by using the QSAR-based pharmacophore modeling and virtual screening approaches shows highest GK activation of ≈6 folds at 10μM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4997964&req=5

Figure 2: The structure of the potent hit (NSC32458) identified by using the QSAR-based pharmacophore modeling and virtual screening approaches shows highest GK activation of ≈6 folds at 10μM.
Mentions: Glucokinase activator (GKA) has been recently shown to be a valid anti-diabetic target. GKA serves as a glucose sensor in the insulin-producing pancreatic beta-cells, and determines the rate of glucose metabolism by regulating the amount of insulin produced and released from pancreatic beta-cells in response to the amount of glucose in the blood. Elevated levels of glucose will increase GKA levels in the pancreas, thereby increasing the release of insulin. In addition, GKA influences hepatic lipid metabolism and gluconeogenesis in the liver [49]. Target protein structure having PDB id: 3VF6 use in this study for new GKAs identification with good bioactivity is shown in Fig. 6 (A). Taha et al. identified a series of novel GKAs using virtual screening. They used a multistage approach, integrating pharmacophore modeling and QSAR analysis followed by virtual screening of the NCI library of 238,819 compounds. Pharmacophore exploration of GKA was performed using Catalyst software. QSAR-based pharmacophore analysis was conducted to select the best combination of molecular descriptors and pharmacophore models that are capable of explaining bioactivity variation across a list of training dataset. The authors concluded that a novel GKA activation pharmacophore model appeared in the optimal QSAR equation. The resulting pharmacophore model yielded a good receiver operating characteristic on validation, and therefore this information was used as three-dimensional search query to screen the NCI database for new GKA activators. Virtual screening identified 10 promising bio-activators from the NCI library of compounds. The most potent NCI hit illustrated 6.3-fold GKA activation at 10 μM shown in Fig. 2. These results illustrated that the ligand-based virtual screening protocol was helpful to identify novel GKA leads for subsequent development into potential anti-diabetic agents [50].

View Article: PubMed Central

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-in-development 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.

No MeSH data available.