Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors (original) (raw)
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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 e®ects. 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 di®erent QSAR models have been generated and validated using a set of 52 compounds comprising of varying sca®olds with IC 50 values ranging from 11,000 nM to 0.6 nM. These AChE-speci¯c prediction models (M1– M5) adequately re°ect 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 signi¯cance (regression (r 2) of 0.9309 and regression adjusted coe±cient of variation (r 2 adj) of 0.9194). The QSAR models highlight the importance of aromatic moiety as their presence in the structure in°uence 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.
Journal of Molecular Graphics and Modelling, 2012
The 3D-QSAR analysis based on alignment independent descriptors (GRIND-2) was performed on the set of 110 structurally diverse, dual binding AChE reversible inhibitors. Three separate models were built, based on different conformations, generated following next criteria: (i) minimum energy conformations, (ii) conformation most similar to the co-crystalized ligand conformation, and (iii) docked conformation. We found that regardless on conformation used, all the three models had good statistic and predictivity. The models revealed the importance of protonated pyridine nitrogen of tacrine moiety for anti AChE activity, and recognized HBA and HBD interactions as highly important for the potency. This was revealed by the variables associated with protonated pyridinium nitrogen, and the two amino groups of the linker. MIFs calculated with the N1 (pyridinium nitrogen) and the DRY GRID probes in the AChE active site enabled us to establish the relationship between amino acid residues within AChE active site and the variables having high impact on models. External predictive power of the models was tested on the set of 40 AChE reversible inhibitors, most of them structurally different from the training set. Some of those compounds were tested on the different enzyme source. We found that external predictivity was highly sensitive on conformations used. Model based on docked conformations had superior predictive ability, emphasizing the need for the employment of conformations built by taking into account geometrical restrictions of AChE active site gorge. (M.D. Vitorović-Todorović).
QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease
Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment
Alzheimer's Disease (AD) is a multifactorial neurological syndrome with the combination of aging, genetic, and environmental factors triggering the pathological decline. Interestingly, the importance of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the ß-amyloid peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method advanced the scope of rational drug design and the search for the mechanism of drug action. It is a well-established fact that the chemical and pharmaceutical effects of a compound are closely related to its physico-chemical properties, which can be calculated by various methods from the compound structure. This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried out for a variety of cholinesterase inhibitors for the treatment of AD. These predi...
Molecules (Basel, Switzerland), 2012
Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer's disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, q 2 = 0.787; BChE, q 2 = 0. 904) and non-cross-validated (AChE, r 2 = 0.965; BChE, r 2 = 0.952) correlation coefficients. The models were used to predict the inhibitory potencies of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability. Contribution maps show that structural fragments containing aromatic moieties and long side chains increase potency. Both the HQSAR models and the contribution maps should be useful for the further design of novel, structurally related cholinesterase inhibitors.
Ligand-based 3D-QSAR Studies of Physostigmine Analogues as Acetylcholinesterase Inhibitors
Chemical Biology & Drug Design, 2009
Natural alkaloid Physostigmine is one of the most potent pseudo-irreversible inhibitor of Acetylcholinesterase. It was found to accelerate long-term memory process, but due to its short half life and variable bioavailability, has inconsistent clinical efficacy. 3D-QSAR studies based on the comparative molecular field analysis and comparative molecular similarity indices analysis were applied to a set of 40 Physostigmine derivatives which are divided into two classes: A and B. The study was conducted to obtain a highly reliable and extensive dynamic QSAR model based on alignment procedure with co-crystallized Ganstigmine as template. The strategy yielded significant 3D-QSAR models with the cross-validated q 2 values 0.762 and 0.754 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Resulted models were validated by external set of eight compounds yielding high correlation coefficient r 2 values of 0.730 and 0.720 for comparative molecular field analysis and comparative molecular similarity indices analysis, respectively. Furthermore, the analysis of comparative molecular field analysis and comparative molecular similarity indices analysis contour maps within the active site of AChE were conducted in order to understand the interactions between the receptor and the Physostigmine derivatives. This study will facilitate the rational design of more potent Physostigmine compounds which might have better activity and reduce toxicity for the treatment of Alzheimer disease.
Toward a general predictive QSAR model for gamma-secretase inhibitors
Molecular Diversity, 2013
Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neuralnetwork (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors.
Journal of Pharmacy and Pharmacology 3 (2015) 489-501, 2015
To find promising new multitargeted AD (Alzheimer's disease) inhibitors, the 3D-QSAR (three-dimensional quantitative structure-activity relationship) model for 32 AD inhibitors was established by using the CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity index analysis) methods. Results showed that the CoMFA and CoMSIA models were constructed successfully with a good cross-validated coefficient (q 2) and a non-cross-validated coefficient (R 2), and the binding modes obtained by molecular docking were in agreement with the 3D-QSAR results, which suggests that the present 3D-QSAR model has good predictive capability to guide the design and structural modification of novel multitargeted AD inhibitors. Meanwhile, we found that one side of inhibitory molecule should be small group so that it would be conductive to enter the gorge to interact with the catalytic active sites of AChE (acetylcholinesterase), and the other side of inhibitory molecule should be large group so that it would be favorable for interaction with the peripheral anionic site of AChE. Furthermore, based on the 3D-QSAR model and the binding modes of AChE and β-secretase (BACE-1), the designed molecules could both act on dual binding sites of AChE (catalytic and peripheral sites) and dual targets (AChE and BACE-1). We hope that our results could provide hints for the design of new multitargeted AD derivatives with more potency and selective activity.
Current neuropharmacology, 2016
Two different sets of AChE inhibitors, dataset-I (30 compounds) and dataset-II (20 compounds) were investigated through MLR aided linear and SVM aided non-linear QSAR models. The obtained QSAR models were found statistically fit, stable and predictive on validation scales. These QSAR models were further investigated for their common structure-activity relationship in terms of overlapping molecular descriptors selection. Atomic mass weighted 3D Morse descriptors (MATS5m) and Radial Distribution Function (RDF045m) descriptors were found in common SAR for both the datasets. Electronegativity weighted (MATS5e, HATSe, and Mor17e) descriptors have also been identified in regulative roles towards endpoint values of dataset-I and dataset-II. The common SAR identified in these linear and non-linear QSAR models could be utilized to design novel inhibitors of AChE with improved biological activity.
QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme
Current Research in Bioinformatics
This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.