In silico identification of novel lead compounds with AT1 receptor antagonist activity: successful application of chemical database screening protocol

Pal, Mahima; Paliwal, Sarvesh

doi:10.1186/2191-2858-2-7

Cited by 7 publications

(8 citation statements)

References 18 publications

(21 reference statements)

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“…These random spreadsheets were used to generate hypotheses employing exactly the same features as used in generating the initial hypothesis. The experimental activities in the training set were scrambled randomly using Cat Scramble (Pal and Paliwal, 2012) program, and the resulting training set was used for a HypoGen run. In this manner, all parameters were taken from the initial HypoGen calculation.…”

Section: (I) Cat Scramble Testmentioning

confidence: 99%

Identification of novel and structurally diverse N-Methyl-D-Aspartate Receptor Antagonists: Successful Application of Pharmacophore Modeling, Virtual Screening and Molecular Docking

Sharma

Mittal

Singh

et al. 2018

Preprint

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In view of "excitotoxic" effects of glutamate, wherein excessive excitatory input causes increase in intracellular Ca 2+ and ultimately cell death, NMDA receptor has emerged as an important target for treatment and prevention of several neurological disorders, like Alzheimer disease.Prompted by the successful application of in-silico pharmacophore-based virtual screening in lead identification, we have made an effort to implement in-silico protocols to identify novel NMDA receptor antagonist. A series of novel benzo[b]quinolizinium cations as NMDA receptor antagonists have been used as a starting point to develop prognostic pharmacophore models. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, one hydrogen bond acceptor, one hydrophobic and two ring aromatic, showed a correlation (r) of 0.89, root mean square of 0.259, and the cost difference of 43.01 bits between null and fixed cost. The model was thoroughly validated and subjected to a chemical database search, which lead to the identification of 400 hits from NCI and Maybridge databases which were checked for Lipinski's violation and predictive potency. This reduced the list to 10 compounds, out of which, two most potent compounds were subjected to molecular docking using Libdock software and interestingly, all the docked conformations showed hydrogen bond interactions with important amino acids Tyr214, His88, Thr174, Val169 and Arg121. In summary, through our validated pharmacophore-based virtual screening protocol, we have identified two potent, structurally diverse, druggable and novel NMDA receptor antagonist which might be of great help to address the unmet medical need of Alzheimer disease.

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Section: (I) Cat Scramble Testmentioning

confidence: 99%

Identification of novel and structurally diverse N-Methyl-D-Aspartate Receptor Antagonists: Successful Application of Pharmacophore Modeling, Virtual Screening and Molecular Docking

Sharma

Mittal

Singh

et al. 2018

Preprint

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show abstract

“…Moreover, the cost difference between total and fixed costs for the best hypothesis was only 14.833 bits, indicating the high probability of the true correlation of the data. The configuration cost (17.286) of the hypothesis slightly exceeded the limit of 17 bits but can be accepted as the model meets the other criteria of validation [27]. Highly validated and statistically perfect pharmacophore model (Hypo1) was used to predict the activity of training set compounds.…”

Section: Resultsmentioning

confidence: 99%

Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery

Pandey

Paliwal

2014

Journal of Chemistry

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For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.

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“…Afterward, pharmacophore models were computed using 3D QSAR Pharmacophore module and high scoring hypotheses were collected 14 . The top scoring hypotheses were selected on the basis of correl, rms, weight, configuration, fixed cost, null cost, and total cost values 15 , 16 .…”

Section: Methodsmentioning

confidence: 99%

Pharmacophore-driven Identification of N-Methyl-D-Receptor Antagonists as Potent Neuroprotective Agents Validated UsingIn-VivoStudies

Sharma

Mittal

Singh

et al. 2018

Preprint

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Alzheimer's disease (AD), the most widespread cause of dementia is delineated by progressive cognitive impairment in the elderly people. During its progression, N-Methyl-D-Aspartate receptor antagonists are known to play a key role in the mechanisms of learning and memory.Extensive side effects alongside other effects on learning and memory have limited the therapeutic significance of various blockers and antagonists of the NMDA receptor. In this study, we identify potential compounds targeted against NMDA. In order to reveal the essential structural features for NMDA receptor, three-dimensional pharmacophore models are constructed based on a set of known NMDA inhibitors. This is followed by virtual screening which results in novel chemical compounds having the potential to inhibit NMDA. The lead compounds are then subjected to molecular docking and assessed by a scoring function, which results in two compounds with high Libdock scores. These compounds also show interactions with important residues at the active site. The compounds are shortlisted on the basis of high estimated activity, fit values, LibDock score, no violation to Lipinski's and availability for procuring.Of the shortlisted compounds, one compound satisfying the entire aforementioned criterion is further tested using in-vivo studies on mice with the help of an eight-arm radial maze. The pharmacophore-based virtual screening protocol presented in this study pave the way forward to address the unmet medical need of Alzheimer disease.

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In silico identification of novel lead compounds with AT1 receptor antagonist activity: successful application of chemical database screening protocol

Cited by 7 publications

References 18 publications

Identification of novel and structurally diverse N-Methyl-D-Aspartate Receptor Antagonists: Successful Application of Pharmacophore Modeling, Virtual Screening and Molecular Docking

Identification of novel and structurally diverse N-Methyl-D-Aspartate Receptor Antagonists: Successful Application of Pharmacophore Modeling, Virtual Screening and Molecular Docking

Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery

Pharmacophore-driven Identification of N-Methyl-D-Receptor Antagonists as Potent Neuroprotective Agents Validated UsingIn-VivoStudies

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