Discovery of potential inhibitors for stat3: ligand based 3D pharmacophore, virtual screening, molecular docking,
Kaviarasan Lakshmanan1, Praveen T K2, Sreedhara Ranganath K Pai3
1Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India.
Abstract:
A large analysis of the signal transducer and activator of transcription (STAT3) in cancer is currently being carried out. It regulates gene expression, which is required for normal cellular functions such as differentiation, cell growth, proliferation, survival, maturation, and immunity. A ligand-based pharmacophore model was created using 3 D QSAR pharmacophore generation methodology in Discovery studio 4.1 clients to imagine structurally diverse novel chemical entities as STAT3 inhibitors with improved efficacy. Chemical properties of 48 different derivatives were included in the training package. Hypo1 was chosen as the query model for screening 1,45,000 drug-like molecules from the SPECS database, with these molecules subjected to the Lipinski rule of 5, Verber's rule, and SMART filtration. After filtration, the molecule was examined further using molecular docking analysis on the active site of STAT3. The binding interaction(s) and pharmacophore mapping were used to select the 19 possible inhibitory molecules. These 19 hits were then tested for toxicity using the TOPKAT software. In MD simulations and MM-PBSA calculations, the tested compound specs 28 provided the best results, suggesting that this ligand has the ability to inhibit more effectively. Based in-silico finding 19 compounds are subjected to in vitro anticancer activity against MDA-MB-231 and MCF-7 cell lines. Based on results compounds specs 11 and specs 13 shows significant activity compared to other compounds and these compounds were subjected to apoptosis assay. The tested compounds induced morphologic changes were dose and time dependent by which all the tested compound exhibits stronger anti-tumor effects.Communicated by Ramaswamy H. Sarma.
Insights
Researchers developed a novel computational method to identify potential STAT3 inhibitors for cancer therapy. This approach identified promising compounds, specs 11 and specs 13, demonstrating significant anticancer activity in cell line studies.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Cancer biology
Background:
- Signal transducer and activator of transcription 3 (STAT3) plays a crucial role in regulating gene expression essential for cellular functions.
- Dysregulation of STAT3 is implicated in various cancers, making it a significant therapeutic target.
- Developing novel STAT3 inhibitors is critical for effective cancer treatment strategies.
Purpose of the Study:
- To design and identify novel chemical entities as potent STAT3 inhibitors using computational approaches.
- To screen a large database of molecules for potential STAT3 inhibitory activity.
- To evaluate the anticancer efficacy of identified compounds against human cancer cell lines.
Main Methods:
- A 3D Quantitative Structure-Activity Relationship (QSAR) pharmacophore model was generated using Discovery Studio 4.1.
- High-throughput virtual screening of 145,000 drug-like molecules from the SPECS database.
- Inclusion of Lipinski's Rule of 5, Verber's rule, and SMART filtration for molecule selection.
- Molecular docking analysis, molecular dynamics (MD) simulations, and MM-PBSA calculations.
- In vitro anticancer activity assays against MDA-MB-231 and MCF-7 cell lines.
- Apoptosis assays to confirm the mechanism of action.
Main Results:
- A pharmacophore model (Hypo1) was developed and used for virtual screening.
- 19 potential STAT3 inhibitory molecules were identified after rigorous filtration and docking.
- Compound specs 28 showed the best binding affinity and stability in MD simulations.
- Compounds specs 11 and specs 13 exhibited significant in vitro anticancer activity against tested cell lines.
- Tested compounds demonstrated dose- and time-dependent induction of morphologic changes indicative of apoptosis.
Conclusions:
- The computational strategy successfully identified novel STAT3 inhibitors with promising anticancer potential.
- Compounds specs 11 and specs 13 represent lead candidates for further development in cancer therapy.
- The study highlights the effectiveness of integrated in silico and in vitro approaches for drug discovery targeting STAT3.
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