Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists

Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for ther...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2022-06, Vol.27 (13), p.4026
Main Authors: Pal, Sourav, Ghosh Dastidar, Uddipta, Ghosh, Trisha, Ganguly, Dipyaman, Talukdar, Arindam
Format: Article
Language:English
Subjects:
Autoimmune diseases
Binding
Correlation coefficient
Correlation coefficients
Design
Diabetes
drug design
Drug development
Eigenvalues
Electrostatic properties
Hypotheses
Immune system
Kinases
Ligands
Microorganisms
Molecular docking
Molecular dynamics
Optimization
Pathogens
Pharmacology
pharmacophore model
QSAR
Structure-activity relationships
Therapeutic targets
TLR7 antagonists
TLR7 protein
Toll-like receptors
Toxicity
Two dimensional models
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Summary:Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic use. We conducted a ligand-based drug design of new TLR7 antagonists through a concerted effort encompassing 2D-QSAR, 3D-QSAR, and pharmacophore modelling of 54 reported TLR7 antagonists. The developed 2D-QSAR model depicted an excellent correlation coefficient (R2training: 0.86 and R2test: 0.78) between the experimental and estimated activities. The ligand-based drug design approach utilizing the 3D-QSAR model (R2training: 0.95 and R2test: 0.84) demonstrated a significant contribution of electrostatic potential and steric fields towards the TLR7 antagonism. This consolidated approach, along with a pharmacophore model with high correlation (Rtraining: 0.94 and Rtest: 0.92), was used to design quinazoline-core-based hTLR7 antagonists. Subsequently, the newly designed molecules were subjected to molecular docking onto the previously proposed binding model and a molecular dynamics study for a better understanding of their binding pattern. The toxicity profiles and drug-likeness characteristics of the designed compounds were evaluated with in silico ADMET predictions. This ligand-based study contributes towards a better understanding of lead optimization and the future development of potent TLR7 antagonists.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules27134026