University of Manchester
The “robot scientist” Eve Photo from Ross King,
The antimicrobial agent triclosan may be able to fight drug-resistant malaria, according to research published in Scientific Reports.
For this work, researchers used an artificially intelligent “robot scientist” named Eve to perform a high-throughput screen of potential antimalarial compounds.
The screen revealed that triclosan is effective against Plasmodium parasites that have grown resistant to the antimalarial drug pyrimethamine.
Researchers have known for some time that triclosan inhibits in vitro growth of Plasmodium parasites. They assumed this was because triclosan inhibits the enzyme enoyl reductase (ENR).
However, subsequent work showed that improving triclosan’s ability to target ENR had no effect on parasite growth in the blood.
With the current study, researchers discovered that triclosan also affects parasite growth by inhibiting an enzyme called dihydrofolate reductase (DHFR), which is the target of the antimalarial drug pyrimethamine.
The researchers conducted growth competition experiments with 3 yeast strains dependent on a DHFR enzyme from Plasmodium falciparum, a DHFR enzyme from Plasmodium vivax, and human DHFR.
The team used Eve to screen compounds and identify drugs that inhibit the parasite targets but not the human counterpart.
The researchers found that triclosan was able to target and act on DHFR in both wild-type and pyrimethamine-resistant P falciparum and P vivax parasites.
Because triclosan inhibits both ENR and DHFR, the researchers think it may be possible to target Plasmodium parasites at both the liver and blood stages.
“The discovery by our robot ‘colleague’ Eve that triclosan is effective against malaria targets offers hope that we may be able to use it to develop a new drug,” said study author Elizabeth Bilsland, PhD, of the University of Campinas in Brazil.
“We know it is a safe compound, and its ability to target 2 points in the malaria parasite’s life-cycle means the parasite will find it difficult to evolve resistance.”
Eve was developed to speed up the drug discovery process by automatically developing and testing hypotheses to explain observations, run experiments using laboratory robotics, interpret the results to amend hypotheses, and then repeat the cycle.
“Artificial intelligence and machine-learning enables us to create automated scientists that do not just take a ‘brute force’ approach but, rather, take an intelligent approach to science,” said Ross King, PhD, a professor at the University of Manchester in the UK who led the development of Eve.
“This could greatly speed up the drug discovery process and potentially reap huge rewards.”
