Hologram QSAR Models of a Series of 6-Arylquinazolin-4-Amine Inhibitors of a New Alzheimer’s Disease Target: Dual Specificity Tyrosine-Phosphorylation-Regulated Kinase-1A Enzyme

Alzheimer’s disease (AD) is becoming one of the most disturbing health and socioeconomic problems nowadays, as it is a neurodegenerative pathology with no treatment, which is expected to grow further due to population ageing. Actual treatments for AD produce only a modest amelioration of symptoms, although there is a constant ongoing research of new therapeutic strategies oriented to improve the amelioration of the symptoms, and even to completely cure the disease. A principal feature of AD is the presence of neurofibrillary tangles (NFT) induced by the aberrant phosphorylation of the microtubule-associated protein tau in the brains of affected individuals. Glycogen synthetase kinase-3 beta (GSK3β), casein kinase 1 delta (CK1δ), dual-specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) and dual-specificity kinase cdc2-like kinase 1 (CLK1) have been identified as the principal proteins involved in this process. Due to this, the inhibition of these kinases has been proposed as a plausible therapeutic strategy to fight AD. In this study, we tested in silico the inhibitory activity of different marine natural compounds, as well as newly-designed molecules from some of them, over the mentioned protein kinases, finding some new possible inhibitors with potential therapeutic application.

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Activation of Tyrosine Phosphorylation Signaling in Erythrocytes of Patients with Alzheimer’s Disease

Neuroscience ◽

10.1016/j.neuroscience.2020.02.050 ◽

2020 ◽

Vol 433 ◽

pp. 36-41

Author(s):

Cinzia Mallozzi ◽

Alessio Crestini ◽

Carmen D'Amore ◽

Paola Piscopo ◽

Marisa Cappella ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Tyrosine Phosphorylation

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Understanding the quantitative structure–activity relationship of acetylcholinesterase inhibitors for the treatment of Alzheimer's disease

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633615500406 ◽

2015 ◽

Vol 14 (06) ◽

pp. 1550040 ◽

Cited By ~ 1

Author(s):

Anuradha Sharma ◽

Poonam Piplani

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Quantitative Structure Activity Relationship ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Structural Basis ◽

Quantitative Structure ◽

Structure Activity ◽

Qsar Models ◽

Relationship Of

Alzheimer's disease (AD) is the most common cause of dementia in old aged people and clinically used drugs for treatment are associated with side effects. Thus, there is a current demand for the discovery and development of new potential molecules. However, the recent advances in drug therapy have challenged the predominance of the disease. In this manuscript, an attempt has been made to develop the 2D and 3D quantitative structure–activity relationship (QSAR) models for a series of rutaecarpine, quinazolines and 7,8-dehydrorutaecarpine derivatives to obtain insights to Acetylcholinesterase (AChE) inhibition. Five different QSAR models have been generated and validated using a set of 52 compounds comprising of varying scaffolds with IC50 values ranging from 11,000 nM to 0.6 nM. These AChE-specific prediction models (M1–M5) adequately reflect the structure–activity relationship of the existing AChE inhibitors. Out of all developed models, QSAR model generated using ADME properties has been found to be the best with satisfactory statistical significance (regression (r2) of 0.9309 and regression adjusted coefficient of variation [Formula: see text] of 0.9194). The QSAR models highlight the importance of aromatic moiety as their presence in the structure influence the biological activity. Additional insights on the compounds show that acyclic amines attached to side chain have lower activity than cyclic amines. The QSAR models pinpointing structural basis for the AChEIs suggest new guidelines for the design of novel molecules.

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Dual-specificity tyrosine(Y)-phosphorylation regulated kinase 1A-mediated phosphorylation of amyloid precursor protein: evidence for a functional link between Down syndrome and Alzheimer’s disease

Journal of Neurochemistry ◽

10.1111/j.1471-4159.2007.05075.x ◽

2008 ◽

Vol 104 (5) ◽

pp. 1333-1344 ◽

Cited By ~ 116

Author(s):

Soo-Ryoon Ryoo ◽

Hyun-Jeong Cho ◽

Hye-Won Lee ◽

Hey Kyeong Jeong ◽

Chinzorig Radnaabazar ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Down Syndrome ◽

Amyloid Precursor Protein ◽

Precursor Protein ◽

Functional Link ◽

Dual Specificity

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In Silico ADME and QSAR Studies on a Set of Coumarin Derivatives As Acetylcholinesterase Inhibitors Against Alzheimer’s Disease: CoMFA, CoMSIA, Topomer CoMFA, and HQSAR

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190712095907 ◽

2020 ◽

Vol 17 (6) ◽

pp. 684-712

Author(s):

Uttam Ashok More ◽

Sameera Patel ◽

Vidhi Rahevar ◽

Malleshappa Ningappa Noolvi ◽

Tejraj M. Aminabhavi ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

In Silico ◽

Acetylcholinesterase Inhibitors ◽

Field Analysis ◽

Molecular Field ◽

Comparative Molecular Field Analysis ◽

Topomer Comfa ◽

Molecular Field Analysis ◽

Qsar Models

Background: Alzheimer’s disease (AD) is increasingly being recognized as one of the lethal diseases in older people. Acetylcholinesterase (AChE) has proven to be the most promising target in AD, used for designing drugs against AD. Methods: In silico studies, 2D- or 3D-QSAR like hologram QSAR (HQSAR), Topomer comparative molecular field analysis (Topomer CoMFA), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to generate QSAR models for acetylcholinesterase inhibitors. Results: Acetylcholinesterase inhibitors used for the present study contain a series of 7- hydroxycoumarin derivatives connected by piperidine, piperazine, tacrine, triazole, or benzyl fragments through alkyl or amide spacer training set compounds were used to generate best model with a HQSAR q2 value of 0.916 and r2 value of 0.940; a Topomer CoMFA q2 value of 0.907 and r2 value of 0.959, CoMFA q2 value of 0.880 and r2 value of 0.960; and a CoMSIA q2 value of 0.865 and r2 value of 0.941. In addition, contour plots obtained from QSAR models suggested the significant regions that influenced the AChE inhibitory activity. Conclusion: In light of these results, this study provides knowledge about the structural requirements for the development of more active acetylcholinesterase inhibitors. In addition, the predicted ADME profile helps us to find CNS active molecules, the obtained prediction compared with well-known AChE inhibitors viz., ensaculin, tacrine, galantamine, rivastigmine, and donepezil. Based on the knowledge obtained from these studies, the hybridization approach is one of the best ways to find lead compounds and these findings can be useful in the treatment of Alzheimer's disease.

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2D- and 3D-QSAR Modeling of Imidazole-Based Glutaminyl Cyclase Inhibitors

Current Computer - Aided Drug Design ◽

10.2174/1573409915666190918150136 ◽

2019 ◽

Vol 15 ◽

Author(s):

Omar Husham Ahmed Al-Attraqchi ◽

Katharigatta N. Venugopala

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Virtual Screening ◽

Neurodegenerative Disorder ◽

3D Qsar ◽

Biological Evaluation ◽

Qsar Modeling ◽

Glutaminyl Cyclase ◽

Validation Parameters ◽

Qsar Models

Background: Glutaminyl cyclase (QC) is a novel target in the battle against Alzheimer’s disease, a highly prevalent neurodegenerative disorder. QC inhibitors have the potential to be developed as therapeutically useful anti-Alzheimer’s disease agents. Methods: Linear and non-linear 2D-quantitative structure–activity relationship (QSAR) models were developed using stepwise-multiple linear regression (S-MLR) and neural networks. Partial least squares (PLS) method was used to develop a 3D-QSAR model. Also, the developed models were used in a virtual screening of the ZINC database to identify potential QC inhibitors. Results: The 2D neural network model showed superior predictive ability, as demonstrated by the validation parameters R2 = 0.933, Q2 = 0.886 and R2pred = 0.911. The 3D-QSAR model’s steric and electrostatic fields’ isocontour maps were visualized and revealed important structural requirements associated with good activity. The virtual screening identified six compounds as potentially active QC inhibitors with improved pharmacokinetic profiles. Conclusion: The developed QSAR models can be used to predict the activity of compounds not yet synthesized and prioritize their synthesis and biological evaluation. Also, potentially active QC inhibitors have been identified with attractive lead-like properties that can be used to develop anti-Alzheimer’s disease agents.

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QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease

Journal of Computational Medicine ◽

10.1155/2013/312728 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Ghasem Ghasemi ◽

Sattar Arshadi ◽

Alireza Nemati Rashtehroodi ◽

Mahyar Nirouei ◽

Shahab Shariati ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Geometry Optimization ◽

Basis Set ◽

Mean Square ◽

Qsar Models ◽

Mlr Model

Sets of quinolizidinyl derivatives of bi- and tri-cyclic (hetero) aromatic systems were studied as selective inhibitors. On the pattern, quantitative structure-activity relationship (QSAR) study has been done on quinolizidinyl derivatives as potent inhibitors of acetylcholinesterase in alzheimer’s disease (AD). Multiple linear regression (MLR), partial least squares (PLSs), principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) were used to create QSAR models. Geometry optimization of compounds was carried out by B3LYP method employing 6–31 G basis set. HyperChem, Gaussian 98 W, and Dragon software programs were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, Unscrambler program was used for the analysis of data. In the present study, the root mean square error of the calibration and R2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R2 and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration and R2 using PLS method were obtained as 0.3726 and 0.62, respectively. According to the obtained results, it was found that MLR model is the most favorable method in comparison with other statistical methods and is suitable for use in QSAR models.

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DUAL SPECIFICITY PROTEIN PHOSPHATASES: Therapeutic Targets for Cancer and Alzheimer's Disease

The Annual Review of Pharmacology and Toxicology ◽

10.1146/annurev.pharmtox.45.120403.100040 ◽

2005 ◽

Vol 45 (1) ◽

pp. 725-750 ◽

Cited By ~ 64

Author(s):

Alexander P. Ducruet ◽

Andreas Vogt ◽

Peter Wipf ◽

John S. Lazo

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Discovery ◽

Therapeutic Targets ◽

Dual Specificity ◽

Complete Sequencing ◽

Dual Specificity Phosphatases ◽

Molecular Therapeutic ◽

Specificity Protein ◽

Molecular Therapeutic Targets

The complete sequencing of the human genome is generating many novel targets for drug discovery. Understanding the pathophysiological roles of these putative targets and assessing their suitability for therapeutic intervention has become the major hurdle for drug discovery efforts. The dual-specificity phosphatases (DSPases), which dephosphorylate serine, threonine, and tyrosine residues in the same protein substrate, have important roles in multiple signaling pathways and appear to be deregulated in cancer and Alzheimer's disease. We examine the potential of DSPases as new molecular therapeutic targets for the treatment of human disease.

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