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Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah

Dengue virus (DENV) infection is a rising health concern worldwide. Despite the alarming situation, there is no effective antiviral for DENV infections. With the increasing number of NS3 viral inhibitors developed for other diseases, the development of drugs targeting dengue NS3 protein is an intere...

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Main Author: Abdullah, Zafirah Liyana
Format: Thesis
Language:English
Published: 2024
Subjects:
Dengue
Online Access:https://ir.uitm.edu.my/id/eprint/102179/1/102179.pdf
https://ir.uitm.edu.my/id/eprint/102179/
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spelling my.uitm.ir.1021792024-12-09T09:25:15Z https://ir.uitm.edu.my/id/eprint/102179/ Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah Abdullah, Zafirah Liyana Dengue Dengue virus (DENV) infection is a rising health concern worldwide. Despite the alarming situation, there is no effective antiviral for DENV infections. With the increasing number of NS3 viral inhibitors developed for other diseases, the development of drugs targeting dengue NS3 protein is an interesting venture. Thus, this study is set forth to identify potential DENV inhibitors by building a prediction model of NS3 dengue antiviral. Initially, the models were built using bioactivity data of 62,354 compounds using ligand-based (L-B), and proteochemometric (PCM) modelling approaches. For the L-B approach, a Random Forest (RF) one-vs-one classification model was utilized while the PCM model employed the Parzen-Rosenblatt Windows (PRW) algorithm. Subsequently, the validated predictive models were used to screen marketed drugs, and in vitro assays were conducted to validate the drug’s viral inhibitory potential. Finally, the interactions that are responsible for the observed in vitro results were validated using molecular docking. The in silico studies revealed that both L-B and PCM models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation, in terms of its sensitivity (0.671). 2024 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/102179/1/102179.pdf Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah. (2024) PhD thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/102179.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Dengue
spellingShingle Dengue
Abdullah, Zafirah Liyana
Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
description Dengue virus (DENV) infection is a rising health concern worldwide. Despite the alarming situation, there is no effective antiviral for DENV infections. With the increasing number of NS3 viral inhibitors developed for other diseases, the development of drugs targeting dengue NS3 protein is an interesting venture. Thus, this study is set forth to identify potential DENV inhibitors by building a prediction model of NS3 dengue antiviral. Initially, the models were built using bioactivity data of 62,354 compounds using ligand-based (L-B), and proteochemometric (PCM) modelling approaches. For the L-B approach, a Random Forest (RF) one-vs-one classification model was utilized while the PCM model employed the Parzen-Rosenblatt Windows (PRW) algorithm. Subsequently, the validated predictive models were used to screen marketed drugs, and in vitro assays were conducted to validate the drug’s viral inhibitory potential. Finally, the interactions that are responsible for the observed in vitro results were validated using molecular docking. The in silico studies revealed that both L-B and PCM models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation, in terms of its sensitivity (0.671).
format Thesis
author Abdullah, Zafirah Liyana
author_facet Abdullah, Zafirah Liyana
author_sort Abdullah, Zafirah Liyana
title Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
title_short Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
title_full Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
title_fullStr Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
title_full_unstemmed Revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / Zafirah Liyana Abdullah
title_sort revitalizing old drugs: uncovering potential dengue antivirals through in silico target prediction and in vitro validation / zafirah liyana abdullah
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/102179/1/102179.pdf
https://ir.uitm.edu.my/id/eprint/102179/
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score 13.222552

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