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

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.

Keywords

• Malaria
• Ookinete
• Machine learning
• Essential oils