QSAR and Molecular Modeling Studies on a Series of Pyrrolidine Analogs Acting as BACE-1 Inhibitors

Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms

A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activities was used to build a QSAR model using mol2vec descriptors and support vector regression. The obtained model demonstrated strong predictive performance (training set: r2 = 0.790, RMSE = 0.540, MAE = 0.362; test set: r2 = 0.705, RMSE = 0.641, MAE = 0.495), indicating its reliability in identifying potent BACE-1 inhibitors. By applying this QSAR model together with molecular docking, seven compounds (ZINC8790287, ZINC20464117, ZINC8878274, ZINC96116481, ZINC217682404, ZINC217786309 and ZINC96113994) were identified as promising candidates, exhibiting predicted log IC50 values ranging from 0.361 to 1.993 and binding energies ranging from -10.8 to -10.7 kcal/mol. Further analysis using ADMET studies and molecular dynamics simulations provided further support for the potential of compound 279 (ZINC96116481) and compound 945 (ZINC96113994) as drug candidates. However, since our study is purely theoretical, further experimental validation through in vitro and in vivo studies is essential to confirm these promising findings.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to Treat Metabolic Syndrome

BACKGROUND

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism.

OBJECTIVE

To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome.

METHODS

In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor.

RESULTS

The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 μM and 0.0027 μM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed.

CONCLUSION

The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors

BACKGROUND

Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP).

OBJECTIVE

The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease.

METHODS

A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds.

RESULTS

In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions.

CONCLUSION

Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.

Recent Studies on Design and Development of Drugs Against Alzheimer's Disease (AD) Based on Inhibition of BACE-1 and Other AD-causative Agents

BACKGROUND

Alzheimer's disease (AD) is one of the neurodegenerative diseases and has been hypothesized to be a protein misfolding disease. In the generation of AD, β-secretase, γ-secretase, and tau protein play an important role. A literature search reflects ever increasing interest in the design and development of anti-AD drugs targeting β-secretase, γ-secretase, and tau protein.

OBJECTIVE

The objective is to explore the structural aspects and role of β-secretase, γ-secretase, and tau protein in AD and the efforts made to exploit them for the design of effective anti-AD drugs.

METHODS

The manuscript covers the recent studies on design and development of anti-AD drugs exploiting amyloid and cholinergic hypotheses.

RESULTS

Based on amyloid and cholinergic hypotheses, effective anti-AD drugs have been searched out in which non-peptidic BACE1 inhibitors have been most prominent.

CONCLUSION

Further exploitation of the structural aspects and the inhibition mechanism for β-secretase, γ-secretase, and tau protein and the use of cholinergic hypothesis may lead still more potent anti-AD drugs.