Document Type : Original Article
Authors
1
Department of Pharmacy, Faculty of Medicine and Health Science, Maulana Malik Ibrahim State Islamic University, Malang, Indonesia
2
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Widya Mandira Catholic University, Kupang, Indonesia
3
Faculty of Pharmacy, Hang Tuah University, Surabaya, Indonesia
4
Department of Pharmacy, Faculty of Matemathics dan Natural Science, Tadulako University, Palu, Indonesia
5
Department of Pharmacy, Faculty of Medicine and Health Science, Maulana Malik Ibrahim State Islamic University, Malang, East Java, Indonesia
6
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia
Abstract
Malaria, which is prevalent in 85 countries worldwide, poses a significant threat to global mortality rates, particularly in regions like East Nusa Tenggara, Indonesia. Conventional anti-malarial drugs like chloroquine and artemisinin face diminishing effectiveness due to Plasmodium sp. resistance. Historically, among the Tetun people in East Nusa Tenggara, Alstonia spectabilis served as an anti-malarial remedy. The purpose of this research was to predict A. spectabilis's potential as an antimalaria treatment through network pharmacology. By analyzing SMILES metabolite codes from UPLC-QToF-MS/MS and GC-MS via BindingDB, TargetNet, Ensemble Similarity Approach, and SwissTargetPrediction, potential protein targets implicated in malaria pathogenesis were identified. Leveraging databases like GeneCards®, The Human Gene Database, DrugBank Online Database, and OMIM, numerous protein targets associated with malaria and Plasmodium sp. were revealed. Interactions between active compounds and protein targets were forecasted using GeneCard, Drugbank, OMIM, and DisGeNET. 2,707 genes from pharmacological activity databases and 6,802 therapy targets for malaria were identified. The Venn diagram analysis refined the selection to 657 target genes. Protein-protein interaction networks were constructed using the STRING database and Cytoscape software, with Cluster 6 spotlighted for its association with malaria pathogenesis. Top-ranking genes, including ITGB2, ITGB1, ITGAL, ITGA4, and ITGB3, were identified based on degree parameters. While ITGB2 remains in the preliminary stage, its potential correlation with malaria is hypothesized, given its association with immune responses like inflammation and adaptive immunity. Finally, A. spectabilis shows promise as a potential antimalaria drug because it changes the immune system by increasing ITGB2 expression. This research sheds light on novel avenues for combating malaria.
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