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

Brahmachari, Goutam; Choo, Chee Yan; Ambure, Pravin; Roy, Kunal

doi:10.1016/j.bmc.2015.06.005

Cited by 48 publications

(17 citation statements)

References 32 publications

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“…For this purpose, we have chosen 6 published/previously used data sets for developing predictive in silico QSAR models towards various endpoints. The first data set represents toxicity of 306 ionic liquids towards Vibrio fischeri , the second set is on odor threshold of 86 wine components, the third set deals with sweetness potency of 240 organic molecules, the fourth set is on 224 cyclin‐dependent kinase 5/p25 (CDK5/p25) inhibitors, the fifth set represents acetylcholinesterase (AChE) inhibitory activity of 426 functionalized organic chemicals, and the final set is a data set comprising solubility of C 60 in 156 organic solvents . All 6 data sets were first rationally divided into respective training and test sets using 3 different techniques, namely, sorted response (Division 1), Kennard‐Stone algorithm (Division 2), and modified k ‐medoids clustering (Division 3) using 2 software tools, namely, Dataset Division version 1.2 (for sorted response and Kennard‐Stone algorithm‐based divisions) and Modified k‐Medoid version 1.2 developed by us .…”

Section: Methodsmentioning

confidence: 99%

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

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

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100

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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.QSAR models (based on OECD guidelines 4 ) can be used to "generate" data which can be used for regulatory decisions. The importance of QSAR has also greatly enhanced in recent years due to their potential application in challenging areas like modeling of responses for chemical mixtures, bioactive peptides, nanomaterials, and others. 5 The accuracy and reliability of predictions of QSAR models are very important in their application in regulatory decision support process. Validation is the method which checks reliability and precision of predictions of QSAR models. 6 Although, several techniques including cross-validation, Y-scrambling, and test set validation are commonly employed, in general, external validation is considered as the gold standard for checking predictive ability of QSAR models. 7 However, some group of scientists think cross-validation is better suited for checking predictive ability of QSAR models in order to avoid loss of information from splitting of the data set into training and test sets. 8 They have also argued that the test of predictive ability of QSAR models from a single training-test split is biased and insufficient. 8 Although a comparison of suitability of cross-validation vs external validation for judging predictive ability of QSAR models is a matter of debate, the importance of a test to check quality of predictions ...

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Section: Methodsmentioning

confidence: 99%

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

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

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100

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“…For the synthesis of ethyl 1,2,6‐triphenyl‐4‐(phenylamino)‐1,2,5,6‐tetrahydropyridine‐3‐carboxylate, catalytic activity of other catalysts such as FeCl 3 , AlCl 3 , CuCl 2 .2H 2 O, sodium acetate, magnesium acetate, HCl, HI, Et 3 N.HI, N‐ bromosuccinimide, [Hmim]HSO 4 etc were also tested and found not suitable for this reaction. In vitro study revealed that the synthesized compounds having chloro, fluoro, nitro‐substituted phenyl ring at the 2‐ and 6‐ positions of piperidine greatly enhance the acetylcholinesterase (AChE) inhibitory activity . The same synthesized compounds were also screened for their anti‐cancer and free radical scavenging activity .…”

Section: Metal‐catalyzed Synthesis Of 126‐triaryl‐4‐arylamino‐pipermentioning

confidence: 99%

“…Recently, a series of synthetic 1,2,6‐triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐carboxylates (Figure ) has also been evaluated to possess potent anti‐cancer, anti‐microbial, anti‐oxidant, anti‐fungal, anti‐inflammatory, acetylcholinesterase (AChE), anti‐malarial inhibitory activities ,. Structure activity studies revealed that incorporation of halogens, nitro or 3,4‐methylenedixoxy‐substitutions at the phenyl ring attached to the 2‐ and 6‐ positions of 1,2,6‐triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐carboxylates greatly enhanced the AChE inhibitory activity . Presence of methoxy substituent in the N ‐phenyl group enhanced the blood schizontocidal activity whereas incorporation of benzyloxy substituent in the same position reduced the antimalarial efficacy .…”

Section: Introductionmentioning

confidence: 99%

One‐Pot Pseudo Five Component Synthesis of Biologically Relevant 1,2,6‐Triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐ carboxylates: A Decade Update

Kaur¹,

Devi²,

Kumari³

et al. 2018

ChemistrySelect

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Piperidines, an important class of N‐heterocycles, are very common in naturally occurring bioactive compounds. Recently, functionalized piperidines, specifically, 1,2,6‐triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐carboxylates have drawn considerable attention due to its diverse pharmacological efficacies that include anti‐cancer, anti‐microbial, anti‐oxidant, anti‐fungal, anti‐inflammatory and acetylcholinesterase (AChE) inhibitory activities. As a result, during the last decade, a number of methods reported for the synthesis of 1,2,6‐triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐carboxylates via one‐pot pseudo five‐component reactions of aromatic aldehydes, anilines and β‐keto esters under various reaction conditions. The present review deals with the ‘‘up to date” developments on the synthesis of 1,2,6‐triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐carboxylates reported so far.

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“…We also evaluated these synthetic functionalized piperidines ( 41 ) for their in vitro acetylcholinesterase (AChE) inhibitory activities, 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 . We observed that halogenation, nitration or 3,4‐methylenedioxy substitution at the phenyl rings attached to the 2‐ and 6‐positions of the 1,2,5,6‐tetrahydropyridine nucleus greatly enhanced the AChE inhibitory activity.…”

Section: Designing Organic Transformations At Ambient Conditionsmentioning

confidence: 99%

“…Therefore, we suggest that these compounds might show dual action against AD by blocking the cholinergic effects as well as inhibiting the formation of Aβ deposits. The intermolecular interactions (molecular docking studies) of two of the active compounds studied, methyl 2,6‐bis(4‐chlorophenyl)‐1‐phenyl‐4‐(phenylamino)‐1,2,5,6‐tetrahydropyridine‐3‐carboxylate ( 41c ) and methyl 2,6‐bis(3‐nitrophenyl)‐1‐phenyl‐4‐(phenylamino)−1,2,5,6‐tetrahydropyridine‐3‐carboxylate ( 41d ), are presented herein in Scheme …”

Section: Designing Organic Transformations At Ambient Conditionsmentioning

confidence: 99%

Design of Organic Transformations at Ambient Conditions: Our Sincere Efforts to the Cause of Green Chemistry Practice

Brahmachari

2015

The Chemical Record

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This account summarizes our recent efforts in designing a good number of important organic transformations leading to the synthesis of biologically relevant compounds at room temperature and pressure. Currently, the concept of green chemistry is globally acclaimed and has already advanced quite significantly to emerge as a distinct branch of chemical sciences. Among the principles of green chemistry, one principle is dedicated to the "design of energy efficiency" - that is, to develop synthetic strategies that require less or the minimum amount of energy to carry out a specific reaction with optimum productivity - and the most effective way to save energy is to develop strategies/protocols that are capable enough to carry out the transformations at ambient temperature! As part of on-going developments in green synthetic strategies, the design of reactions under ambient conditions coupled with other green aspects is, thus, an area of current interest. The concept of developing reaction strategies at room temperature and pressure is now an emerging field of research in organic chemistry and is progressing steadily. This account is aimed to offer an overview of our recent research works directly related to this particular field of interest, and highlights the green chemistry practice leading to carbon-carbon and carbon-heteroatom bond-forming reactions of topical significance. Green synthetic routes to a variety of biologically relevant organic molecules (heterocyclic, heteroaromatic, alicyclic, acyclic, etc.) at room temperature and pressure are discussed.

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

Cited by 48 publications

References 32 publications

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

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

One‐Pot Pseudo Five Component Synthesis of Biologically Relevant 1,2,6‐Triaryl‐4‐arylamino‐piperidine‐3‐ene‐3‐ carboxylates: A Decade Update

Design of Organic Transformations at Ambient Conditions: Our Sincere Efforts to the Cause of Green Chemistry Practice

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