Caspase-3 Inhibition Prediction of Pyrrolo[3,4-c] Quinoline-1,3-Diones Derivatives using Computational Tools
By: PANDEY, VANDANA
.
Publisher: Mumbai Indian Journal of Pharmaceutical Science 2021Edition: Vol.83(3), May-June.Description: 504-514p.Subject(s): PHARMACEUTICSOnline resources: Click here
In:
Indian journal of pharmaceutical sciencesSummary: In the present work, two dimensional quantitative structure activity relationship, molecular docking and
absorption, distribution, metabolism, excretion and toxicity analyses were performed to pyrrolo[3,4-c]
quinoline-1,3-diones derivatives, previously reported as caspase-3 inhibitors. A total of one hundred
fifteen compounds were used to build linear multiple linear regression (multiple linear regression) and
non-linear (artificial neural networks) quantitative structure activity relationship models, using genetic
algorithm as a feature selection method. Both models were thoroughly validated following Organization
for economic cooperation and development principles by internal and external validation as well as
the domain of application (antiphase domain). Both Genetic algorithm-multiple linear regression
(Rtrain=0.88, Rtest=0.94, mape test=5.3 and rmse test=0.41) and Genetic algorithm-artificial neural network
(Rtrain=0.9, Rtest=0.93, mape test=4.5 and rmse test=0.4) models are statistically robust with high external
predictive ability. Molecular docking simulations were performed on selected inhibitors revealed that
binding energy values are in accordance with inhibitory activity values against caspase-3, which is
modulated by hydrogen bondings, Pi stacking and hydrophobic interactions. The docking studies suggest
that the inhibitors bind with an allosteric site of the enzyme formed by ARG207B, SER251B, PHE250
and PHE256 of the B chain. Besides, in silico, absorption, distribution, metabolism, excretion and toxicity
profiles of selected inhibitors were checked to evaluate the key pharmacokinetic, physiochemical and
druglikeness features.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Pharmacy Archieval Section | Not for loan | 2021-2022558 |
In the present work, two dimensional quantitative structure activity relationship, molecular docking and
absorption, distribution, metabolism, excretion and toxicity analyses were performed to pyrrolo[3,4-c]
quinoline-1,3-diones derivatives, previously reported as caspase-3 inhibitors. A total of one hundred
fifteen compounds were used to build linear multiple linear regression (multiple linear regression) and
non-linear (artificial neural networks) quantitative structure activity relationship models, using genetic
algorithm as a feature selection method. Both models were thoroughly validated following Organization
for economic cooperation and development principles by internal and external validation as well as
the domain of application (antiphase domain). Both Genetic algorithm-multiple linear regression
(Rtrain=0.88, Rtest=0.94, mape test=5.3 and rmse test=0.41) and Genetic algorithm-artificial neural network
(Rtrain=0.9, Rtest=0.93, mape test=4.5 and rmse test=0.4) models are statistically robust with high external
predictive ability. Molecular docking simulations were performed on selected inhibitors revealed that
binding energy values are in accordance with inhibitory activity values against caspase-3, which is
modulated by hydrogen bondings, Pi stacking and hydrophobic interactions. The docking studies suggest
that the inhibitors bind with an allosteric site of the enzyme formed by ARG207B, SER251B, PHE250
and PHE256 of the B chain. Besides, in silico, absorption, distribution, metabolism, excretion and toxicity
profiles of selected inhibitors were checked to evaluate the key pharmacokinetic, physiochemical and
druglikeness features.
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