In Vitro Testing of Potential Entamoeba histolytica Pyruvate Phosphate Dikinase Inhibitors

Saidin, Syazwan; Othman, Nurulhasanah; Noordin, Rahmah

doi:10.4269/ajtmh.17-0132

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…Within this pathway section, PGAM is the enzyme with the lowest activity in the cell 7 , which may contribute to the better control observed. Additionally, novel enzyme inhibitors were recently identified and tested in vitro 63 , 64 . Therefore, these models may be an interesting subject of future research in which the inhibitor effect on the flux can be assessed.…”

Section: Discussionmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

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Materials and methods Second part of glycolysis experimental data. Experimental data of PGAM, ENO and PPDK activities and pathway flux (J obs) are obtained from plots of a previous study 8. The free online software WebPlotDigitizer (Version 4.1, https ://autom eris.io/WebPl otDig itize r/) is used to extract data from plots. These data are available in Tables S1 and S2. Artificial neural networks (ANNs). ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems 18. To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1.1.456), an open-source integrated development environment for R 19 and two packages: NeuralNet (Version 1.44.2) and Nnet (Version 7.3-12) 20,21. Complex pathway SImulator (COPASI) metabolic networks. A first metabolic network of the studied pathway was kindly provided by the authors of a previous study 8. This model is developed on GEPASI 22 , an old software for metabolic pathway modeling, replaced by COPASI since 2002. The second part of the glycolysis is also modeled by using the open source software called COPASI (Version 4.24) 23. This software is used for metabolic network design, analysis and optimization. The resulting metabolic networks are based on the use of enzyme properties (kinetic parameters and mechanism-based rate equations). Ethics approval and consent to participate. Not applicable. Methodology Black-white-and grey-box approach procedure. To conduct the present study, a specific methodol

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Section: Discussionmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

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“…As well as appropriate targets, providing desirable leads is essential for the development of new drugs; therefore, a variety of methods for screening large numbers of compounds have been undertaken as the prerequisite step. For instance, high through-put in vitro assays using recombinant enzymes [16, 17], wild-type cells [18–20], or an engineered cell [20] have been used. However, screening a huge number of compounds, e .…”

Section: Introductionmentioning

confidence: 99%

“…g ., more than a million, is technically demanding. To compensate for this limitation of in vitro screening, in silico docking simulation is effective; the docking simulation rapidly predicts the binding free energy between a small molecule and a target protein using empirical energy functions, from which we can select potential leads for drug development from a large chemical library [16, 21]. Hence, in silico docking simulation has been performed as the primary screen in many drug discovery projects [22, 23].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Entamoeba histolytica adenosine 5′-phosphosulfate (APS) kinase; validation as a target and provision of leads for the development of new drugs against amoebiasis

et al. 2019

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Background Amoebiasis, caused by Entamoeba histolytica infection, is a global public health problem. However, available drugs to treat amoebiasis are currently limited, and no effective vaccine exists. Therefore, development of new preventive measures against amoebiasis is urgently needed. Methodology/Principal findings Here, to develop new drugs against amoebiasis, we focused on E . histolytica adenosine 5′-phosphosulfate kinase (EhAPSK), an essential enzyme in Entamoeba sulfolipid metabolism. Fatty alcohol disulfates and cholesteryl sulfate, sulfolipids synthesized in Entamoeba , play important roles in trophozoite proliferation and cyst formation. These processes are closely associated with clinical manifestation and severe pathogenesis of amoebiasis and with disease transmission, respectively. We validated a combination approach of in silico molecular docking analysis and an in vitro enzyme activity assay for large scale screening. Docking simulation ranked the binding free energy between a homology modeling structure of EhAPSK and 400 compounds. The 400 compounds were also screened by a 96-well plate-based in vitro APSK activity assay. Among fifteen compounds identified as EhAPSK inhibitors by the in vitro system, six were ranked by the in silico analysis as having high affinity toward EhAPSK. Furthermore, 2-(3-fluorophenoxy)-N-[4-(2-pyridyl)thiazol-2-yl]-acetamide, 3-phenyl-N-[4-(2-pyridyl)thiazol-2-yl]-imidazole-4-carboxamide, and auranofin, which were identified as EhAPSK inhibitors by both in silico and in vitro analyses, halted not only Entamoeba trophozoite proliferation but also cyst formation. These three compounds also dose-dependently impaired the synthesis of sulfolipids in E . histolytica . Conclusions/Significance Hence, the combined approach of in silico and in vitro -based EhAPSK analyses identified compounds that can be evaluated for their effects on Entamoeba . This can provide leads for the development of new anti-amoebic and amoebiasis transmission-blocking drugs. This strategy can also be applied to identify specific APSK inhibitors, which will benefit research into sulfur metabolism and the ubiquitous pathway terminally synthesizing essential sulfur-containing biomolecules.

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Antiamoebic drugs

Majeed¹,

Reang²,

Sharma³

et al. 2023

Medicinal Chemistry of Chemotherapeutic Agents

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In Vitro Testing of Potential Entamoeba histolytica Pyruvate Phosphate Dikinase Inhibitors

Cited by 4 publications

References 46 publications

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Characterization of Entamoeba histolytica adenosine 5′-phosphosulfate (APS) kinase; validation as a target and provision of leads for the development of new drugs against amoebiasis

Antiamoebic drugs

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