Prediction of Activity Spectra of Substances Assisted Prediction of Biological Activity Spectra of Potential Anti-Alzheimer’s Phytoconstituents

Anand, Abhinav; Sharma, Neha; Khurana, Navneet

doi:10.22159/ajpcr.2017.v10s4.21330

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…If Pa value is <0.5, then probability of observing that activity in pharmacological experiment is less. [11] In biological and biomedical sciences, animals are used for research to enhance the knowledge of human. The experimental research on animal models also enhance concern, awareness of animals used ethically, the use of animals in research and education as per an application of ethics, the role of National Council for the Control of Animal Experimentation.…”

Section: Original Articlementioning

confidence: 99%

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Khurana¹

2018

IJGP

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Context: Alzheimer's disease (AD) is a progressive neurodegenerative disorder having important concern in today's society. The prediction of activity spectra of substances (PASS) software allows prediction of the pharmacological activity on behalf of canonical simplified molecular-input line-entry system (SMILES) of the substance. PASS predicted the activity of substances in two manners, i.e. probable activity (Pa) and probable inactivity. Objective: In this study, we elaborated the benefit of PASS to define 3Rs ethics (replacement, reduction, and refinement) in animal study for anti-Alzheimer's activities of phytochemicals. Materials and Methods: Pa values of different phytochemicals important for AD predicted using PASS. For the prediction of activity in the different targets of AD, canonical SMILES of phytochemicals was obtained from PubChem website. Results and Discussion: Based on these Pa values predicted for different target sites of AD, screening of pharmacologically important phytochemicals was performed so that those phytochemicals can be further explored for in vivo pharmacological activity against AD to justify the 3Rs ethics. Conclusion: PASS online software provides an informative data to support the reported activities of the phytochemicals.

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Section: Original Articlementioning

confidence: 99%

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Khurana¹

2018

IJGP

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“…If Pa value is more than 0.5 but <0.7 for any activity, then the probability of that action is less in pharmacological experiment. If Pa value is <0.5, then the probability of observing that activity in pharmacological experiment is less [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Anti-Parkinson Potential of Phytoconstituents Using Prediction of Activity Spectra of Substances Software

Kumar

Anand

et al. 2018

Asian J Pharm Clin Res

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Objective: Neurodegenerative disorders are group of diseased conditions in which there is loss of neuron cells occur. The main objective of this study to find/search out the phytochemical with the help of prediction of activity spectra of substances (PASS), those show maximum activity over the selected targets of the Parkinson's disease (PD).Methods: PASS is a valuable software which is used in this study, to predict the anti-Parkinson activity of different compounds. Canonical simplified molecular-input line-entry system is used for the prediction of anti-Parkinson activity which is obtained from PubChem website. The predicted activity also compared with marketed compound like levodopa.Results: From the study, it was found that resveratrol was the only compound which has the activity on all the selected targets. On the other hand, stemazole and celastrol were found to have the least active compounds as both have the activity only on a single target. Conclusion:In this research work, we tried to compile the information regarding the PASS predicted anti-Parkinson activity of some important phytoconstituents. We found that resveratrol can be a target for further investigation in the development of drug therapy for PD.

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“…D iabetes is the issue in which human body cannot make its own particular vitality. [1][2][3][4][5][6][7][8][9][10][11][12] Rather than getting transform into vitality, it gets changed over into sugar or supposed glucose. Pancreas is the organ which secretes the hormones called insulin which takes glucose into the phone into for vitality generation.…”

Section: Introductionmentioning

confidence: 99%

“…These more up to date exercises of the compound give understanding to helpful applications. [3] Atomic docking is an entrenched computational system which predicts the cooperation vitality between two particles. This strategy principally joins calculations such as sub-atomic progression, Monte Carlo reproduction, and piece based hunt strategies which are said in points of interest in later part.…”

Section: Introductionmentioning

confidence: 99%

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Sharma¹

2018

IJGP

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Context: Diabetes mellitus is not a solitary sickness yet is a gathering of metabolic issue influencing countless on the planet. It is essentially described by incessant hyperglycemia because of deformities in insulin discharge or insulin activity. It is predicated that the quantity of diabetes individual on the planet could reach up to 366 million by the year 2030. Even though the instances of diabetes are expanding step by step, aside from insulin and oral hypoglycemic medications, no other method for treatment has been effectively grown up until now. Objective: In the present study, an initiative is tried to delineate the usefulness of prediction of activity spectra for substances (PASS) online software and molecular docking technique for providing new molecular ways of predicting new antidiabetic drug targets of potential phytoconstituents. Materials and Methods: In the study, important phytoconstituents having reported in vitro and in vivo antidiabetic activities have been reviewed. Among them, few phytoconstituents were selected for presenting to PASS online software. Pa and Pi value was predicted for these phytoconstituents on different antidiabetic target sites. Based on PASS prediction, five phytoconstituents were selected for molecular docking study using AutoDock Vina 4.0. Three target sites which were dipeptidyl peptidase-4 (DPP-4), glucagon-like peptide-1 (GLP-1), and α glucosidase were selected for prediction of probable affinities of these 5 selected phytoconstituents. Result and Discussion: Among these five constituents, diosmin showed best binding affinity with DPP-4, GLP-1, and α glucosidase that was −10.2 kcal/mol, −8.3 kcal/mol, and −9.7kcal/mol, followed by kaempferol. Results of the present study can be utilized for designing of further in vitro and in vivo antidiabetic studies for these phytoconstituents. Conclusion: This study suggested the usefulness of these software in predicting the probable antidiabetic targets sites of potential antidiabetic phytoconstituents.

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Prediction of Activity Spectra of Substances Assisted Prediction of Biological Activity Spectra of Potential Anti-Alzheimer’s Phytoconstituents

Cited by 11 publications

References 27 publications

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Prediction of Anti-Parkinson Potential of Phytoconstituents Using Prediction of Activity Spectra of Substances Software

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