Molecular modeling studies of quinazolinone derivatives as novel PI3Kδ selective inhibitors

Abstract

The forced expression of phosphoinositide 3-kinase d (PI3Kd) in B cells was found to be oncogenic, rendering PI3Kd an attractive drug target for chronic lymphocytic leukaemia. This study aimed to systemically explore the interaction mechanism of novel quinazolinone scaffold-based derivatives as PI3Kd inhibitors using 3D-QSAR, molecular docking, pharmacophore model and molecular dynamics (MD) simulations. The 3D-QSAR models CoMFA, CoMSIA and Topomer CoMFA were established to discover critical structural factors affecting PI3Kd inhibitory activity. The models showed suitable reliabilities (q 2 0.741, 0.712 and 0.711) and predictive abilities (r pred 2 0.851, 0.738 and 0.828, respectively).Contour maps indicated that the bioactivity of PI3Kd inhibitor was affected most by electrostatic and hydrophobic fields. The Surflex-Dock and pharmacophore model result showed that enhancing the Hbond interaction of the key substituents around the 2-and 4-positions of pyrimidine with Glu826, Val828 and Asp911, as well as the electrostatic interactions of substituents around the 3-position of benzene with Ser831, Asp832 and Asn836, significantly affected the improvement in the activity and stability of the inhibitor. Based on these results, 10 novel PI3Kd inhibitors with higher predicted activity and binding affinity were designed by introducing the heterocycles pyrrolopyridine or purine. 10 ns MD simulations further study the stable docking conformation of designed compounds, which showed strong hydrogen bond interactions with key residues Ser831 and Asp832 in a propeller-like fashion.These results provided strong guidance for the discovery and optimization of novel potent PI3Kd selective inhibitors.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

“…So far, a number of different ligand-based in silico methods and protocols have been employed by different researchers for the discovery of PI3K inhibitors, and these include 2D-quantitative structure–activity relationship (2D-QSAR) modelling, 3D-QSAR, 3D-pharmacophore mapping, etc. [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. However, all these methods suffer from at least one of the following shortcomings.…”

The quantitative pyrrole protection of l-phenylalanine/l-phenylalaninol in aqueous media and rationally updating the mechanisms of the Clauson-Kaas reaction through DFT study

“…Belonging to the family of hexacyclic quinazolinone alkaloids, bioactive chaetominines have received much attention among chemists and biochemists. 11 On the basis of 3D-QSAR, molecular docking, pharmacophore model and molecular dynamics simulations, 12 we designed chaetominine mimics in continuous pursuit of ASK1 and PI3K inhibitors. 13 Based on the structure virtual screening, the l -phenylalanine derivative 19 was chosen as a promising intermediate through rational dockings, in addition to the comparison with the PI3K pan-inhibitor copanlisib.…”

Drug Design, Molecular Docking and Molecular Dynamics Simulations of Indole Class HIV‐1 NNRTIs Explored with QSAR and Topomer Search

“…The predictive correlation coefficient values ( R 2 pred ), r 2 , k , k’ , r 0 2 , r 0 ’ 2 , r m 2 , r m ’2 , Δ r m 2 , $${\overline {r_m^2 } }$$ are the essential parameter to prove how capable the model is of making positive external predictions. And the ideal model needs to meet R 2 pre d greater than 6, both the value of r m 2 and r m ’2 are higher than 0.5, k and k’ are in the range of 0.85 and 1.15, Δ r m 2 is lower 0.2, $${\overline {r_m^2 } }$$ larger than 0.5 [27–29] . Furthermore, the most significant external prediction parameters R 2 pred of the model is calculated as follows: [20,30] $$$\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr r_{pred}^2 = {{\left( {SD - PRESS} \right)} \over {SD}}\hfill\cr}}$$$ …”

“…And the ideal model needs to meet R 2 pred greater than 6, both the value of r m 2 and r m '2 are higher than 0.5, k and k' are in the range of 0.85 and 1.15, Δr m 2 is lower 0.2, r 2 m larger than 0.5. [27][28][29] Furthermore, the most significant external prediction parameters R 2 pred of the model is calculated as follows: [20,30]…”