Toothpaste Ingredient could Fight Drug-Resistant Strains of Malaria

Common ingredient of toothpaste, triclosan, has potential to interrupt malaria infections at two critical stages in the liver and blood. 

Around half a million people suffer from malaria annually and majority of them are children in Africa. Although the disease can be treated with a number of drugs, resistance to these medicines is increasing, raising the risk that some strains may become untreatable in near future. Scientists from Britain’s Cambridge University used AI robot to conduct high-throughput screening. The study was published in the journal Scientific Reports on January 19, 2018.

Malaria parasites proliferate into red blood cells and spread around the body, causing fever and potentially life-threatening complications. Triclosan was previously known to halt malaria parasites’ growth at the blood stage of the infection by inhibiting the action of an enzyme known as enoyl reductase (ENR), which is involved in production of fatty acids. Bilsland’s team found that triclosan also inhibits an entirely different enzyme of the malaria parasite, called DHFR. It is the target of the antimalarial pyrimethamine, a drug to which malaria parasites are increasingly developing resistance in Africa.

The Cambridge team’s work showed that triclosan was able to target and act on this enzyme even in pyrimethamine-resistant parasites. “The discovery by our robot colleague that triclosan is effective against malaria targets offers hope that we may be able to use it to develop a new drug,” said Elizabeth Bilsland, co-author of work. “We know it is a safe compound, and its ability to target two points in the malaria parasite’s lifecycle means the parasite will find it difficult to evolve resistance.”

The AI robot scientist used in the study nicknamed Eve was designed to automate and speed up the drug discovery process. According to Self-Reconfiguring Modular Robot Market report published by Coherent Market Insights, AI robotic systems are responsible for designing, controlling, fabricating, and motion planning of kinematic machines by using variable morphology. Eve does this by automatically developing and testing hypotheses to explain observations, running experiments using laboratory robotics, interpreting the results, altering the hypotheses, and repeating the cycle.

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