DOI: 10.5281/zenodo.10558577
title: Drug Repurposing for malaria therapy: An in silico study
title: In silico screening of drugs in the drug data bank for antimalarial actiivity

description: Drug Repurposing for malaria therapy: An in silico study

Malaria, an endemic disease in Africa, is caused by five protozoan parasite species, with Plasmodium falciparum causing the most severe infections. In the year 2022, over 233 million malaria cases were recorded in Africa, resulting in approximately 580,000 deaths. Resistance to the World Health Organization's endorsed Artemisinin-based combination therapies (ACT) for malaria treatment underscores the urgent need for innovative approaches to combat this debilitating disease.

Drug repurposing, also known as drug repositioning or reprofiling, involves the identification of novel applications or uses for pre-existing medications initially designed for distinct therapeutic purposes. Numerous drugs exhibit multiple biological effects, and their modes of action may be pertinent to ailments beyond their originally intended usage. Drug repurposing therefore entails investigating the prospective utility of existing drugs in addressing diverse diseases or medical conditions instead of creating an entirely new pharmaceutical agent.

This study aimed at carrying out in silico screening of the drugs in the drug data bank for their potential antimalarial activity.

In this study, drugs categorized into 14 drug classes, including Antibiotics (105), Antihypertensives (177), Anti-inflammatory (79), Anti-diuretics (11), Anticancer (25), Antidiabetics (16), Anti-asthmatics (80), Vitamins (118), Antiemetics (36), Antidepressants (72), Statins (14), Diuretics (115), Antifungals (88), and Antivirals (115), were downloaded from the Drug Bank (https://go.drugbank.com/). Chloroquine and dihydroartemisinin were used as the reference compounds. The drug structures, initially in SDF format, were transformed to PDBQT format utilizing OpenBabel 2.4.1 (https://openbabel.org/).

Seven plasmodium targets, namely Plasmodium falciparum lactate dehydrogenase in complex with chloroquine (1CET), Plasmodium falciparum transketolase (3QVI), Plasmodium falciparum dihydroorotate dehydrogenase (6GJG), Falcipain 2 (3BPF), Falcipain 3 (3BPM), Plasmepsin 11 (3F9Q), and Plasmepsin 1V (ILS5), were retrieved in PDB format from the Protein Data Bank (https://www.rcsb.org/) and were converted to PDBQT format using Autodock 4.2 (http://mgltools.scripps.edu/).

Molecular docking simulations of the drugs with each target were conducted employing Autodock Vina (http://vina.scripps.edu/) with grid parameters detailed in Table 1. The resulting binding affinities of each drug to individual targets are presented in the accompanying file.

Table 1. Molecular Docking Grid Parameters






S/N



Target



Center x



Center y



Center z



Size x



Size y



Size z





1



1CET



34.792



15.612



18.721



56



42



62





2



3QVI



11.39



-8.53



-1.98



66



63



54





3



6GJG



11.94



-2.72



6.81



52



55



52





4



3BPF



-47.49



-12.34



-12.62



73



59



59





5



3BPM



-47.49



-12.34



-12.62



73



59



59





6



3F9Q



16.36



1.72



25.65



52



53



71





7



1LS5



-27.37



31.31



45.59



58



50



59