ArticleInternational journal for parasitology. Drugs and drug resistance2023
Screening of the activity of sixty essential oils against plasmodium early mosquito stages in vitro and machine learning analysis reveals new putative inhibitors of malaria parasites.
Article in International journal for parasitology. Drugs and drug resistance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 2 citations in OpenAlex.
- Gene Expression, Docking and Machine Learning in Malaria Drug Discovery: A Systematic Review.Biochemistry research international · 2026Review
- First Evidence of Anti-Biomedicines · 2025Article
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11 authors at 4 institutions in 3 countries.
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Abstract
Malaria, an infectious disease with a tremendous impact on human health is caused by Plasmodium parasites, and transmitted by Anopheles mosquitoes. New approaches to control the disease involve transmission blocking strategies aiming to target the parasite in the mosquito. Here, we investigated the putative inhibitory activity of essential oils and their components on the early mosquito stages of the parasite. We employed an in vitro assay of gametocyte-to-ookinete development of the rodent model parasite Plasmodium berghei combined with high content screening. 60 essential oils with known composition were tested. The results revealed that fifteen EOs had inhibitory activity. Furthermore, a machine learning approach was used to identify the putative inhibitory components. Five of the most important chemical components indicated by the machine learning-based models were actually confirmed by the experimental approach. This combined approach was used for the first time to identify the potential transmission blocking activity of essential oils and single components at the zygote and ookinete stages.
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