Pravin Ambure

Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents.

Alzheimers disease (AD) is turning out to be one of the lethal diseases in older people. Acetylcholinesterase (AChE) is a crucial target in designing of drugs against AD. The present in silico study was carried out to explore natural compounds as potential AChE inhibitors. Virtual screening, via drug-like ADMET filter, best pharmacophore model and molecular docking analyses, has been utilized to identify putative novel AChE inhibitors. The InterBioScreens Natural Compound (NC) database was first filtered by applying drug-like ADMET properties and then with the pharmacophore-based virtual screening followed by molecular docking analyses. Based on docking score, interaction patterns and calculated activity, the final hits were selected and these consist of coumarin and non-coumarin classes of compounds. Few hits were found to have been already reported for their AChE inhibitory activity in different literatures confirming reliability of our pharmacophore model. The remaining hits are suggested to be potential AChE inhibitors for AD.

In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores

A series of densely functionalized piperidine (FP) scaffolds was synthesized following a diastereoselective one-pot multicomponent protocol under eco-friendly conditions. The FPs were evaluated in vitro for their acetylcholinesterase (AChE) inhibitory activity, and in silico studies for all the target compounds were carried out using pharmacophore mapping, molecular docking and quantitative structure-activity relationship (QSAR) analysis in order to understand the structural features required for interaction with the AChE enzyme and the key active site residues involved in the intermolecular interactions. Halogenation, nitration or 3,4-methylenedixoxy-substitution at the phenyl ring attached to the 2- and 6-positions of 1,2,5,6-tetrahydropyridine nucleus in compounds 14-17, 19, 20, 24 and 26 greatly enhanced the AChE inhibitory activity. The docking analysis demonstrated that the inhibitors are well-fitted in the active sites. The in silico studies enlighten the future course of studies in modifying the scaffolds for better therapeutic efficacy against the deadly Alzheimers disease.

Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer's disease: an in silico approach

A congeneric series of 224 cyclin-dependant kinase 5/p25 (CDK5/p25) inhibitors was exploited to understand the structural requirements for improving activity against CDK5/p25 and selectivity over CDK2. The CDK5/p25 enzyme complex plays a significant role in the formation of neurofibrillary tangles in Alzheimers disease. In the present study, 2D-quantitative structure–activity relationship (2D-QSAR), group or fragment based QSAR (G-QSAR), and quantitative activity–activity relationship (QAAR) models were developed and validated with satisfactory performance as evidenced from statistical metrics, indicating the reliability and robustness of the models. The 2D-QSAR and G-QSAR models explore the structural requirements for improving activity, while the QAAR model facilitates the better understanding of features required for selectivity of the inhibitors. The docking study further provides information regarding the key active site residues and structural features important for proper binding in the active site of the CDK5/p25 complex.

Advances in quantitative structure--activity relationship models of anti-Alzheimer's agents

Introduction: Alzheimer’s disease (AD) is one of the lethal diseases, mainly affecting older people. The unclear root cause and involvement of various enzymes in the pathological conditions confirm the complexity of the disease. Quantitative structure–activity relationship (QSAR) techniques are of great significance in the design of drugs against AD. Areas covered: In the present review, the authors provide a basic background about AD and QSAR techniques. Furthermore, they review the various QSAR studies reported against various targets of AD. The information provided for each QSAR study includes chemical scaffold and target enzyme under study, applied QSAR technique and outcomes of the respective study. Expert opinion: In silico techniques like QSAR hold great potential in designing leads against a complex disease like AD. In combination with other in silico techniques, QSAR can provide more useful and rational insight to facilitate the discovery of novel compounds. Only few QSAR studies on imaging agents have been reported; hence, more QSAR studies are recommended to explore the biomarker or imaging agents for improving diagnosis. Again, for proper symptomatic treatment, multi-target drugs acting on more than one target are required. Hence, more multi-target QSAR studies are recommended in future to achieve this goal.

Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against Aβ plaques in Alzheimer's disease: a predictive QSAR approach

Beta (β)-site amyloid precursor protein cleaving enzyme 1 (BACE1) is one of the most important targets in Alzheimers disease (AD), which is responsible for production and accumulation of beta amyloid (Aβ). The Aβ plaques are one of the major hallmarks, which are believed to be the fundamental cause of AD. Thus, a rational treatment strategy is to find efficient BACE1 inhibitors against AD. In the present study, we have developed two quantitative structure–activity relationship (QSAR) models (viz. two dimensional-QSAR and group based-QSAR), employing 91 cyclic sulfone (or sulfoxide) hydroxyethylamines as human BACE1 (hBACE1) inhibitors to identify the important structural features that are responsible for their activity. To our knowledge, this is the first QSAR report for this chemical category of BACE1 inhibitors. The developed models were stringently validated utilizing various validation metrics including the recently proposed mean absolute error (MAE)-based criteria. Based on this study, we observed that MAE-based criteria are the most appropriate way to determine the true prediction quality of the models. Unlike R2-based metrics, the MAE-based criteria were found to provide an unbiased judgment irrespective of the response range and/or distribution of response values around the training/test mean. The mechanistic interpretation of each model was performed in detail, while focusing on the important substructures that were found favorable or unfavorable for hBACE1 activity.

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?: Quality of predictions from an “intelligent” use of multiple models

Quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an “intelligent” selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software‐tools.

Exploring Structural Requirements of Imaging Agents Against Aβ Plaques in Alzheimer’s Disease: A QSAR Approach

Exploring molecular imaging agents against the beta amyloid (Aβ) plaques for an early detection of Alzheimers disease (AD) is one of the emerging research areas in medicinal chemistry. In the present in-silico study, a congeneric series of 44 imaging agents, including 17 positron emission tomography (PET) and 27 single photon emission computed tomography (SPECT) imaging agents, was utilized to understand the structural features required for having essential binding affinity against Aβ plaques. Here, 2D-quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (G-QSAR) models have been developed using genetic function approximation (GFA) and validated using various statistical metrics. Both the models showed satisfactory performance signifying the reliability and robustness of the developed QSAR models. The vital information gained from both the QSAR models will be useful in developing new PET and SPECT imaging agents and also in predicting their binding affinity against Aβ plaques. The results of this study would be important in view of the widespread clinical applicability of the SPECT imaging agents, especially in the developing countries. In this study, we have also designed some imaging agents based on the information provided by the models. Some of these designed compounds were predicted to be similar to or more active than the most active imaging agents present in the original dataset.

Identifying natural compounds as multi-target directed ligands against Alzheimer’s disease: an in silico approach

Alzheimer’s disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/. The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database (https://www.ibscreen.com/natural-compounds). Further, the natural compounds that were categorized as ‘actives’ in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

CADD Modeling of Multi-Target Drugs Against Alzheimer's Disease

Alzheimers disease (AD) is a neurodegenerative disorder that is described by multiple factors linked with the progression of the disease. The currently approved drugs in the market are not capable of curing AD; instead, they merely provide symptomatic relief. Development of multi-target directed ligands (MTDLs) is an emerging strategy for improving the quality of the treatment against complex diseases like AD. Polypharmacology is a branch of pharmaceutical sciences that deals with the MTDL development. In this mini-review, we have summarized and discussed different strategies that are reported in the literature to design MTDLs for AD. Further, we have discussed the role of different in silico techniques and online resources in computer-aided drug discovery (CADD), for designing or identifying MTDLs against AD.