Title:Structure-Activity Relationship Studies on VEGFR2 Tyrosine Kinase
Inhibitors for Identification of Potential Natural Anticancer Compounds
Volume: 20
Issue: 6
Author(s): Meenakshi Verma, Aqib Sarfraz, Inamul Hasan, Prema Gauri Vasudev and Feroz Khan*
Affiliation:
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad,
201002, Uttar Pradesh, India;
- Technology Dissemination & Computational Biology Division, CSIR-Central
Institute of Medicinal and Aromatic Plants, P.O.- CIMAP, Kukrail Picnic Spot Road, Lucknow, 226015 Uttar Pradesh,
India
Keywords:
QSAR, molecular docking, dynamics simulation, ADMET, VEGFR2, tyrosine kinase, inhibitors.
Abstract:
Background: Over-expression of Vascular Endothelial Growth Factor Receptors (VEGFRs)
leads to the hyperactivation of oncogenes. For inhibition of this hyperactivation, the USA
Food Drug Administration (FDA) has approved many drugs that show adverse effects, such as
hypertension, hypothyroidism, etc. There is a need to discover potent natural compounds that show
minimal side effects. In the present study, we have taken structurally diverse known VEGFR2 inhibitors
to develop a Quantitative Structure-Activity Relationship (QSAR) model and used this
model to predict the inhibitory activity of natural compounds for VEGFR2.
Methods: The QSAR model was developed through the forward stepwise Multiple Linear Regression
(MLR) method. A developed QSAR model was used to predict the inhibitory activity of natural
compounds. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) assessment
and molecular docking studies were performed. The binding stability of the natural compounds
with VEGFR2 was elucidated through Molecular Dynamics (MD) simulation.
Results: The developed QSAR model against VEGFR2 showed the regression coefficient of the
training dataset (r2) as 0.81 and the external regression coefficient of the test dataset (r2
test) 0.71.
Descriptors, viz., electro-topological state of potential hydrogen bonds (maxHBint2, nHBint6),
atom types (minssNH), maximum topological distance matrix (SpMAD_Dt), and 2D autocorrelation
(ATSC7v), have been identified. Using this model, 14 natural compounds have been selected
that have shown inhibitory activity for VEGFR2, of which six natural compounds have been found
to possess a strong binding affinity with VEGFR2. In MD simulation, four complexes have shown
binding stability up to 50ns.
Conclusion: The developed QSAR model has identified 5 conserved activity-inducing physiochemical
properties, which have been found to be correlated with the anticancer activity of the nonidentical
ligand molecules bound with the VEGFR2 kinase. Lavendustin_A, 3’-O-acetylhamaudol,
and arctigenin have been obtained as possible lead natural compounds against the VEGFR2 kinase.
