Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation of the joints, leading to pain, swelling, and joint deformity. Effective management of RA involves the use of disease-modifying drugs that can slow down disease progression and alleviate symptoms. Among the potential targets for RA treatment is Bruton's tyrosine kinase (BTK), which plays a crucial role in B-cell signalling and contributes to the pathogenesis of RA.
Aims: QSARINS (QSAR-INSUBRIA) is software used for the development and validation of Quantitative Structure-Activity Relationship (QSAR) analysis. In the present work, this software was explored for pharmacophore optimization of the pyrrolo-pyrimidine nucleus for anti-rheumatoid activity.
Methods: A series of pyrrolo-pyrimidine derivatives were used to build the QSAR models. These models were generated to identify structural features that correlate significantly with the activity. We followed the assessment of statistical parameters to ensure thorough validation of all the QSAR models. The QSAR models demonstrating better statistical performance were selected, and descriptors of these models were analysed.
Results: The results showed that the QSAR models were highly statistically robust and exhibited a strong external predictive ability. Their structural features were also deduced.
Conclusion: This QSAR study provided crucial information about the specific molecular features that can be used for the optimization of the pharmacophores. This research provides valuable insights into the structural features essential for BTK inhibition and paves the way for the design and development of novel anti-rheumatic agents targeting BTK in RA.
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http://dx.doi.org/10.2174/0118715230272263231103094710 | DOI Listing |
In Silico Pharmacol
January 2025
Phyto-medicine and Computational Biology Laboratory, Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State Nigeria.
Unlabelled: Lead optimization is vital for turning hit compounds into therapeutic drugs. This study builds upon a prior in silico research, where the hit compounds had better binding affinity and stability compared to a reference drug. Using a genetic algorithm, 12,500 analogs of the top compounds from the prior study were generated.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematics, Wollega University, 395, Nekemte, Ethiopia.
Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, and macular degeneration. By combining modeling and decision-makings approaches, this study presents a cost-effective way to comprehend the behavior of molecules.
View Article and Find Full Text PDFAAPS PharmSciTech
January 2025
Unidad de Investigación y Desarrollo, Probiomed S.A. de C.V, C. P. 52400, Tenancingo, Estado de México, México.
The available literature indicates that amino acids can stabilize proteins. Our experimental data demonstrated that lysine and glutamic acid can stabilize recombinant human erythropoietin (rhEPO) at 40°C for at least 1 month, as measured by RP-UPLC. Studies with different excipient concentrations demonstrated optimal concentrations of these amino acids within 10-12 mM.
View Article and Find Full Text PDFSAR QSAR Environ Res
December 2024
Department of Biotechnology, RV College of Engineering, Bengaluru, India.
The Nipah virus (NiV) is an emerging pathogenic paramyxovirus that causes severe viral infection with a high mortality rate. This study aimed to model the effectual binding of marine sponge-derived natural compounds (MSdNCs) towards RNA-directed RNA polymerase (RdRp) of NiV. Based on the functional relevance, RdRp of NiV was selected as the prospective molecular target and 3D-structure, not available in its native form, was modelled.
View Article and Find Full Text PDFSAR QSAR Environ Res
December 2024
School of Computing and Data Sciences, FLAME University, Pune, India.
This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blood Brain Barrier Database (B3DB) dataset for BBB permeability prediction, we extracted nine different fingerprints. Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms were used to develop models for permeability prediction.
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