Geneva Health Forum 2024

Development of docking based QSAR model for the identification of novel mosquito repellents

Not scheduled

15m

Geneva

Scientific poster Towards the elimination of malaria

Description

1. Introduction – Objectives
   Vector-borne diseases caused by either parasites, viruses or bacteria, contribute nearly 17% of all infectious diseases resulting in more than 700,000 deaths annually. Anopheline mosquitoes are carriers of the parasitic infection known as malaria. The global upswing in malaria is highlighted in the 2023 World Malaria Report, indicating an estimated 249 million cases in 2022, exceeding pre-pandemic levels. Challenges such as disruptions caused by Covid-19, drug resistance, humanitarian crises, and the impacts of climate change pose threats to the overall global malaria response. Drugs, novel chemical entities, preventive measures and community mobilization play a crucial role in averting numerous vector-borne diseases. One of the most effective and non-intrusive preventive measure to control malaria is through the use of mosquito repellents. DEET (N,N-diethyl-meta-toluamide) has been a gold standard for a long time and is still in use but there have been reports of seizures, uncoordinated movements, agitation, aggressive behaviour, low blood pressure, and skin and gastric irritation on its continued exposure. The studies described here utilizes computer aided drug design (CADD) in designing novel mosquito repellents without undesirable effects on human and environment.
2. Methodology
   Computer aided drug design (CADD) has been a very effective tool in drug discovery. In Structure-Based Drug Design (SBDD), molecular docking emerges as a vital tool that offers optimal binding modes for the ligand with the target protein. This includes their positions, conformations, and the associated binding energies. Despite being a highly effective tool in drug design, molecular docking faces challenges with its scoring functions thereby struggling to accurately predict binding energies due to the algorithm's limitations in foreseeing interactions like entropy change and solvation effects. These limitations hinder the accurate prediction of binding energy changes, resulting in poor correlations between observed biological activity and docking scoring functions. In this context, we have developed a novel method by taking into account the specific interactions between a ligand and the amino acid residues present in the active site. Here, we report QSAR equations developed based on our novel method on some of the reported mosquito repellents from literature including nootkatone as well as standard DEET which can be used for the identification of novel chemical entities as mosquito repellents.
3. Results and Discussions
   Our SBDD based QSAR methodology resulted in a multi linear regression model with a good correlation coefficient “r” > 0.80. The equation predicted the observed activities of DEET and (+) nootkatone with encouraging results.
4. Conclusions
   The model developed through our SBDD based QSAR methodology well predicted the already known mosquito repellents such as DEET and (+) nootkatone thus may be utilized in predicting the compounds which can be developed as mosquito repellents and may be less toxic to human and environments.

Acknowledgement: The authors thank Global Institute of Pharmaceutical Education and Research (GIPER) for the financial support.