When a mosquito infected with malaria parasites bites someone, it transfers the parasites into their bloodstream via its saliva. These parasites work their way into the liver, where they mature and reproduce. After a few days, the parasites leave the liver and hijack red blood cells, where they continue to multiply, spreading around the body and causing symptoms, including potentially life-threatening complications.
Malaria kills over half a million people each year, predominantly in Africa and south-east Asia. While a number of medicines are used to treat the disease, malaria parasites are growing increasingly resistant to these drugs, raising the spectre of untreatable malaria in the future.
Now, in a study published today in the journal Scientific Reports, a team of researchers at the University of Cambridge employed the Robot Scientist ‘Eve’ in a high-throughput screen and discovered that triclosan, an ingredient found in many toothpastes, may help the fight against drug-resistance.
Scientists have known for some time that triclosan also inhibits the growth of the blood-stage of the malaria parasite, Plasmodium, in culture, and assumed that this was because it was targeting ENR, which is essential for the growth of the parasite in the liver. However, subsequent work showed that improving triclosan’s ability to target ENR had no effect on parasite growth in the blood.
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.
Professor Ross D King
Working with ‘Eve’, the research team discovered that in fact, triclosan affects parasite growth by specifically inhibiting an entirely different enzyme of the malaria parasite, called DHFR. This means that triclosan may be able to target the parasite at both the liver stage and the later blood stage.
Robot scientist Eve was developed by a team of scientists at the Universities of Manchester, Aberystwyth, and Cambridge to automate – and hence 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 their hypotheses, and then repeat the cycle, automating high-throughput hypothesis-led research.
Professor Ross King, from the Manchester Institute of Biotechnology, who led the development of Eve, says: “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. This could greatly speed up the drug discovery progress and potentially reap huge rewards.”
DHFR is the target of a well-established antimalarial drug, pyrimethamine; however, resistance to the drug among malaria parasites is common, particularly in Africa. The Cambridge team showed that triclosan was able to target and act on this enzyme even in pyrimethamine-resistant parasites.
Lead author Dr Elizabeth Bilsland, now an assistant professor at the University of Campinas (UNICAMP, Brazil), adds: “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. 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.”
Source : The University of Manchester