Structure-based pharmacophore design and virtual screening for novel potential inhibitors of epidermal growth factor receptor as an approach to breast cancer chemotherapy

Investigation of the Anticancer Potential of 2-alkoxycarbonylallyl Esters Against Metastatic Murine Breast Cancer Line 4T1 Targeting the EGFR: A Combined Molecular Docking, QSAR, and Machine Learning Approach

Background:

The search for novel and potent anticancer drugs is imperative. This present study aims to unravel the mechanisms of action of 2-alkoxyecarbonyl esters using robust model(s) that can accurately predict the bioactivity of novel compounds. Twenty-four potential anticancer 2- alkoxycarbonylallyl ester compounds obtained from the literature were employed in building a 3D-QSAR model.

Objectives:

The objective of this study is to determine the predictive ability of the GFA-based QSAR models and extreme machine learning models and compare them. The lead compounds and newly designed compounds were docked at the active site of a human epidermal growth factor receptor (EGFR) kinase domain to determine their binding modes and affinity.

Methods:

QikProp program and Spartan packages were employed for screening compounds for druglikeness and toxicity. QSAR models were equally used to predict the bioactivities of these molecules using the Material Studio package. Molecular docking of the molecules at the active site of an EGFR receptor, 1M17, was done using Auto dock tools.

Results:

The model of choice, with r2pred (0.857), satisfied the recommended standard for a stable and reliable model. The low value of r2, Q2 for several trials and cRp2 (0.779 ≥ 0.5) and the high value of correlation coefficient r2 for the training set (0.918) and test set (0.849) provide credence to the predictability of the model. The superior inhibition of EGFR displayed by the lead compounds (20 and 21) with binding energies of 6.70 and 7.00 kcalmol-1, respectively, is likely due to the presence of double bonds and α-ester groups. ADMET screening showed that these compounds are highly druggable. The designed compounds (A and B) displayed better inhibition of EGFR.

Conclusion:

The QSAR model used here performed better than the Random Forest Regression model for predicting the bioactivity of these anticancer compounds, while the designed compounds (A and B) performed better with higher binding affinity than the lead compounds. Implementing the developed model would be helpful in the search for novel anticancer agents.

Structure-based pharmacophore mapping and virtual screening of natural products to identify polypharmacological inhibitor against c-MET/EGFR/VEGFR-2

Three receptor tyrosine kinases (RTKs), c-MET, EGFR, and VEGFR-2 have been identified as potential oncogenic targets involved in tumor development, metastasis, and invasion. Designing inhibitors that can simultaneously interact with multiple targets is a promising approach, therefore, inhibiting these three RTKs with a single chemical component might give an effective chemotherapeutic strategy for addressing the disease while limiting adverse effects. The in-silico methods have been developed to identify the polypharmacological inhibitors particularly for drug repurposing and multitarget drug design. Here, to find a viable inhibitor from natural source against these three RTKs, structure-based pharmacophore mapping and virtual screening of SN-II database were carried out. The filtered compound SN00020821, identified as Cedeodarin, from different computational approaches, demonstrated good interactions with all the three targets, c-MET/EGFR/VEGFR-2, with interaction energies of -42.35 kcal/mol, -49.32 kcal/mol and -44.83 kcal/mol, respectively. SN00020821displayed stable key interactions with critical amino acids of all the three receptors' kinase catalytic domains including "DFG motif" explored through the MD simulations. Furthermore, it also met the ADMET requirements and was determined to be drug-like as predicted from the Lipinski's rule of five and Veber's rule. Finally, SN00020821 provides a novel molecular scaffold that could be investigated further as a polypharmacological anticancer therapeutic candidate that targets the three RTKs.Communicated by Ramaswamy H. Sarma.

Molecular Docking and Biophysical Studies for Antiproliferative Assessment of Synthetic Pyrazolo-Pyrimidinones Tethered with Hydrazide-Hydrazones

Chemotherapy represents the most applied approach to cancer treatment. Owing to the frequent onset of chemoresistance and tumor relapses, there is an urgent need to discover novel and more effective anticancer drugs. In the search for therapeutic alternatives to treat the cancer disease, a series of hybrid pyrazolo[3,4-d]pyrimidin-4(5H)-ones tethered with hydrazide-hydrazones, 5a-h, was synthesized from condensation reaction of pyrazolopyrimidinone-hydrazide 4 with a series of arylaldehydes in ethanol, in acid catalysis. In vitro assessment of antiproliferative effects against MCF-7 breast cancer cells, unveiled that 5a, 5e, 5g, and 5h were the most effective compounds of the series and exerted their cytotoxic activity through apoptosis induction and G0/G1 phase cell-cycle arrest. To explore their mechanism at a molecular level, 5a, 5e, 5g, and 5h were evaluated for their binding interactions with two well-known anticancer targets, namely the epidermal growth factor receptor (EGFR) and the G-quadruplex DNA structures. Molecular docking simulations highlighted high binding affinity of 5a, 5e, 5g, and 5h towards EGFR. Circular dichroism (CD) experiments suggested 5a as a stabilizer agent of the G-quadruplex from the Kirsten ras (KRAS) oncogene promoter. In the light of these findings, we propose the pyrazolo-pyrimidinone scaffold bearing a hydrazide-hydrazone moiety as a lead skeleton for designing novel anticancer compounds.