Ligand-based modeling followed by in vitro bioassay yielded new potent glucokinase activators

Taha, Mutasem O.; Habash, Maha; Hatmal, Ma’mon M.; Abdelazeem, Ahmed H.; Qandil, Amjad M.

doi:10.1016/j.jmgm.2014.12.003

Cited by 20 publications

(19 citation statements)

References 43 publications

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“…The resulting pharmacophores were validated using receiver operating characteristic (ROC) curve analysis, which evaluates the ability of tested pharmacophore to discriminate active compounds from a list composed of known 50 active ligands, i.e., against the targeted enzyme (i.e., PI3K-γ, Renin, or JAK1, IC 50 ≤ 1.0 μM), and a larger list of decoys (300–750 compounds, IC 50 range from 2 to 10 000 μM). − The testing sets were assembled from CHEMBL database . Details of the ROC analysis are shown in Supporting Information Section SM-2. − ,,,− ,− …”

Section: Methodsmentioning

confidence: 99%

“…A pharmacophore is an abstract description of steric and electronic features necessary to be present in a particular ligand to allow optimal recognition and binding to a specific receptor and to trigger subsequent biological function. − Pharmacophore models are derived either by structure-based or ligand-based approaches. − In structure-based methodologies, pharmacophore models are extracted from crystallographic complexes involving high affinity ligands cocrystallized within targeted proteins . However, despite ligand–receptor binding involving numerous attractive interactions, only a few can be defined as pharmacophoric, i.e., critical for complex stability. − …”

Section: Introductionmentioning

confidence: 99%

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Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

Hatmal

Taha

2018

J. Chem. Inf. Model.

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We previously combined molecular dynamics (classical or simulated annealing) with ligand-receptor contacts analysis as a means to extract valid pharmacophore model(s) from single ligand-receptor complexes. However, molecular dynamics methods are computationally expensive and time-consuming. Here we describe a novel method for extracting valid pharmacophore model(s) from a single crystallographic structure within a reasonable time scale. The new method is based on ligand-receptor contacts analysis following energy relaxation of a predetermined set of randomly deformed complexes generated from the targeted crystallographic structure. Ligand-receptor contacts maintained across many deformed/relaxed structures are assumed to be critical and used to guide pharmacophore development. This methodology was implemented to develop valid pharmacophore models for PI3K-γ, RENIN, and JAK1. The resulting pharmacophore models were validated by receiver operating characteristic (ROC) analysis against inhibitors extracted from the CHEMBL database. Additionally, we implemented pharmacophores extracted from PI3K-γ to search for new inhibitors from the National Cancer Institute list of compounds. The process culminated in new PI3K-γ/mTOR inhibitory leads of low micromolar ICs.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

Hatmal

Taha

2018

J. Chem. Inf. Model.

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“…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]. …”

Section: Case Studies Of Computer-aided Drug Design For Diabetesmentioning

confidence: 99%

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

Bibi¹,

Sakata

2016

CAD

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BackgroundDiabetes 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.ObjectiveMost 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.MethodsPharmaceutical 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.ResultsThe 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.ConclusionDrug 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.

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“…Human glucokinase (GK, EC 2.7.1.2) is the principal regulating sensor of plasma glucose levels . GK has a lower affinity for glucose than the other hexokinases; one active site, binding glucose and MgATP, and another site for a putative allosteric activator . A study by Kamata et al .…”

mentioning

confidence: 99%

“…Human glucokinase (GK, EC 2.7.1.2) is the principal regulating sensor of plasma glucose levels (3)(4)(5). GK has a lower affinity for glucose than the other hexokinases; one active site, binding glucose and MgATP, and another site for a putative allosteric activator (6). A study by Kamata et al (7) showed that the allosteric site is some 20 A from the glucose-binding site, and can bind small molecules called glucokinase activators (GKAs), which have indeed been discovered and hold great promise as new antidiabetic agents (8,9).…”

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confidence: 99%

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

et al. 2015

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In this study, quantitative structure-activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three-dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient (r(2)) between 0.674 and 0.743 and cross-validation (q(2)) between 0.509 and 0.610, indicating good predictive capacity. The 4D-QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.

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Ligand-based modeling followed by in vitro bioassay yielded new potent glucokinase activators

Cited by 20 publications

References 43 publications

Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

Combining Stochastic Deformation/Relaxation and Intermolecular Contacts Analysis for Extracting Pharmacophores from Ligand–Receptor Complexes

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

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators

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