Abstract
Experimental activity of a compound on cancer cell line/target is mostly analyzed in the form of percentage inhibition at different concentration gradient and time of incubation. In this study a statistical model has been developed referred as in silico assay using support vector regression model, which can act with change in concentration gradient and time of incubation. This model is a function of concentration gradient, treatment hour and independent components; which calculate the percentage inhibition in combination of above three components. This model is designed to screen tetracyclic triterpenoids active against human breast cancer cell line MCF7. The model has been statistically validated, checked for applicability domain and predicted results were reconfirmed by MTT assay, for example Oenotheranstrol derivatives, OenA & B. Computational SAR, target and docking studies were performed to understand the cytotoxic mechanism of action of Oenotheranstrol compounds. The proposed in silico assay model will work for specific chemical family for which it will be optimized. This model can be used to analyze growth kinetics pattern on different human cancer cell lines for designed compounds.
Highlights
In vitro experiment is essential for screening of small molecules active against specific target/cell line
The aim of the present study was to develop in silico assay model for tetracyclic triterpenoids for screening of anticancer compounds against human breast cancer cell line MCF7
Compound OenB showed structural similarity of 81.61% with ‘Dehydroepiandrosterone’ bound with CYP1A1 and ‘Pregnenolone’ bound with CYP2C19 [13,14]. These results suggest that OenA and B have interaction ability with anticancer target enzyme (Aromatase: cytochrome P450 19A1)
Summary
In vitro experiment is essential for screening of small molecules active against specific target/cell line. Different human cancer cell lines are used in in vitro experiment for the screening of anticancer compounds [1]. In virtual screening, screening of compounds is performed by quantitative structureactivity relationship (QSAR) approach to predict the possible activity. QSAR models do not predict the compound’s percentage inhibition, in accordance to concentration gradient and time of incubation. These limitations of QSAR model mostly restrict the prediction of low activity compounds. To overcome these barriers, support vector regression (SVR) model (in silico assay) has been proposed
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