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AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin
Pradeep Kumar Yadalam1, Raghavendra Vamsi Anegundi1, Ramya Ramadoss2
1Department of Periodontics, Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Background:
Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS).
Objective:
In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials.
Methods:
To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA.
Results:
Maps that were generated were used to determine the screen's r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors.
Conclusion:
With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model's robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.
Insights
This study utilized three-dimensional quantitative structure-activity relationships (3D-QSAR) and Comparative molecular field analysis (CoMFA) to develop predictive models for sclerostin inhibitors. The findings can guide the design of novel sclerostin inhibitors using existing FDA-approved medications.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Pharmacology
Background:
- Wnt activation is crucial for bone formation and preventing bone loss.
- Sclerostin antagonists, like romosozumab, are developed to target this pathway but carry cardiac risks.
- Three-dimensional quantitative structure-activity relationships (3D-QSAR) predict biological activity based on molecular 3D features.
Purpose of the Study:
- To investigate ligand-receptor interactions using 3D-QSAR Comparative molecular field analysis (CoMFA).
- To identify key molecular features essential for the activity of sclerostin inhibitors.
- To develop a robust model for predicting the efficacy of potential sclerostin inhibitors.
Main Methods:
- Employed Comparative molecular field analysis (CoMFA) for 3D-QSAR analysis.
- Utilized Open 3D-QSAR software, including Py-MolEdit, Py-ConfSearch, and Py-CoMFA.
- Analyzed fifty Food and Drug Administration (FDA)-approved medications to establish structure-activity relationships.
Main Results:
- Developed a statistically significant CoMFA model with a cross-validated q2 of 0.532 and conventional r2 of 0.969.
- Determined that hydroxamic acid inhibitors are more sensitive to steric (70%) than electrostatic (30%) fields.
- Identified steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) properties as critical for sclerostin inhibitor design.
Conclusions:
- Successfully constructed statistically meaningful 3D-QSAR and CoMFA models to predict ligand inhibitory effects.
- Validated the model's robustness using a test set.
- This research can facilitate the development of more effective sclerostin inhibitors derived from FDA-approved drugs.
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