̽��ֱ�� of Cambridge - Steve Oliver

AI 'scientist' finds that toothpaste ingredient may help fight drug-resistant malaria

cjb250 — Thu, 18 Jan 2018 10:00:15 +0000

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 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.

When used in toothpaste, triclosan prevents the build-up of plaque bacteria by inhibiting the action of an enzyme known as enoyl reductase (ENR), which is involved in the production of fatty acids.

Scientists have known for some time that triclosan also inhibits the growth in culture of the malaria parasite Plasmodium during the blood-stage, and assumed that this was because it was targeting ENR, which is found in the liver. However, subsequent work showed that improving triclosan’s ability to target ENR had no effect on parasite growth in the blood.

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. DHFR is the target of a well-established antimalarial drug, pyrimethamine; however, resistance to the drug among malaria parasites is common, particularly in Africa. ̽��ֱ��Cambridge team showed that triclosan was able to target and act on this enzyme even in pyrimethamine-resistant parasites.

“Drug-resistant malaria is becoming an increasingly significant threat in Africa and south-east Asia, and our medicine chest of effective treatments is slowly depleting,” says Professor Steve Oliver from the Cambridge Systems Biology Centre and the Department of Biochemistry at the ̽��ֱ�� of Cambridge. “ ̽��ֱ��search for new medicines is becoming increasingly urgent.”

Because triclosan inhibits both ENR and DHFR, the researchers say it may be possible to target the parasite at both the liver stage and the later blood stage.

Lead author Dr Elizabeth Bilsland, now an assistant professor at the ̽��ֱ�� of Campinas, Brazil, adds: “ ̽��ֱ��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.”

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 at the ̽��ֱ�� of Manchester, 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.”

̽��ֱ��research was supported by the Biotechnology & Biological Sciences Research Council, the European Commission, the Gates Foundation and FAPESP (São Paulo Research Foundation).

Reference
Bilsland, E et al. Plasmodium dihydrofolate reductase is a second enzyme target for the antimalarial action of triclosan. Scientific Reports; 18 Jan 2018; DOI: 10.1038/s41598-018-19549-x

An ingredient commonly found in toothpaste could be employed as an anti-malarial drug against strains of malaria parasite that have grown resistant to one of the currently-used drugs. This discovery, led by researchers at the ̽��ֱ�� of Cambridge, was aided by Eve, an artificially-intelligent ‘robot scientist’.

Drug-resistant malaria is becoming an increasingly significant threat in Africa and south-east Asia, and our medicine chest of effective treatments is slowly depleting

Steve Oliver

Photo-Mix (Pixabay)

Toothpaste

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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Artificially-intelligent Robot Scientist ‘Eve’ could boost search for new drugs

cjb250 — Wed, 04 Feb 2015 00:00:01 +0000

Robot scientists are a natural extension of the trend of increased involvement of automation in science. They can automatically develop and test 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. Robot scientists are also well suited to recording scientific knowledge: as the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process.

In 2009, Adam, a robot scientist developed by researchers at the Universities of Aberystwyth and Cambridge, became the first machine to independently discover new scientific knowledge. ̽��ֱ��same team has now developed Eve, based at the ̽��ֱ�� of Manchester, whose purpose is to speed up the drug discovery process and make it more economical. In the study published today, they describe how the robot can help identify promising new drug candidates for malaria and neglected tropical diseases such as African sleeping sickness and Chagas’ disease.

“Neglected tropical diseases are a scourge of humanity, infecting hundreds of millions of people, and killing millions of people every year,” says Professor Steve Oliver from the Cambridge Systems Biology Centre and the Department of Biochemistry at the ̽��ֱ�� of Cambridge. “We know what causes these diseases and that we can, in theory, attack the parasites that cause them using small molecule drugs. But the cost and speed of drug discovery and the economic return make them unattractive to the pharmaceutical industry.

“Eve exploits its artificial intelligence to learn from early successes in her screens and select compounds that have a high probability of being active against the chosen drug target. A smart screening system, based on genetically engineered yeast, is used. This allows Eve to exclude compounds that are toxic to cells and select those that block the action of the parasite protein while leaving any equivalent human protein unscathed. This reduces the costs, uncertainty, and time involved in drug screening, and has the potential to improve the lives of millions of people worldwide.”

Eve is designed to automate early-stage drug design. First, she systematically tests each member from a large set of compounds in the standard brute-force way of conventional mass screening. ̽��ֱ��compounds are screened against assays (tests) designed to be automatically engineered, and can be generated much faster and more cheaply than the bespoke assays that are currently standard. This enables more types of assay to be applied, more efficient use of screening facilities to be made, and thereby increases the probability of a discovery within a given budget.

Eve’s robotic system is capable of screening over 10,000 compounds per day. However, while simple to automate, mass screening is still relatively slow and wasteful of resources as every compound in the library is tested. It is also unintelligent, as it makes no use of what is learnt during screening.

To improve this process, Eve selects at random a subset of the library to find compounds that pass the first assay; any ‘hits’ are re-tested multiple times to reduce the probability of false positives. Taking this set of confirmed hits, Eve uses statistics and machine learning to predict new structures that might score better against the assays. Although she currently does not have the ability to synthesise such compounds, future versions of the robot could potentially incorporate this feature.

Professor Ross King, from the Manchester Institute of Biotechnology at the ̽��ֱ�� of Manchester, says: “Every industry now benefits from automation and science is no exception. Bringing in machine learning to make this process intelligent – rather than just a ‘brute force’ approach – could greatly speed up scientific progress and potentially reap huge rewards.”

To test the viability of the approach, the researchers developed assays targeting key molecules from parasites responsible for diseases such as malaria, Chagas’ disease and schistosomiasis and tested against these a library of approximately 1,500 clinically approved compounds. Through this, Eve showed that a compound that has previously been investigated as an anti-cancer drug inhibits a key molecule known as DHFR in the malaria parasite. Drugs that inhibit this molecule are currently routinely used to protect against malaria, and are given to over a million children; however, the emergence of strains of parasites resistant to existing drugs means that the search for new drugs is becoming increasingly more urgent.

“Despite extensive efforts, no one has been able to find a new antimalarial that targets DHFR and is able to pass clinical trials,” adds Professor King. “Eve’s discovery could be even more significant than just demonstrating a new approach to drug discovery.”

̽��ֱ��research was supported by the Biotechnology & Biological Sciences Research Council and the European Commission.

Reference
Williams, K. and Bilsland, E. et al. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. Interface; 4 Feb 2015.

Eve, an artificially-intelligent ‘robot scientist’ could make drug discovery faster and much cheaper, say researchers writing in the Royal Society journal Interface. ̽��ֱ��team has demonstrated the success of the approach as Eve discovered that a compound shown to have anti-cancer properties might also be used in the fight against malaria.

[Eve's artificial intelligence] reduces the costs, uncertainty, and time involved in drug screening, and has the potential to improve the lives of millions of people worldwide

Steve Oliver

̽��ֱ�� of Manchester

Eve, the Robot Scientist

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New tool in the fight against tropical diseases

gm349 — Wed, 27 Feb 2013 01:01:00 +0000

A novel tool exploits baker’s yeast to expedite the development of new drugs to fight multiple tropical diseases, including malaria, schistosomiasis, and African sleeping sickness. ̽��ֱ��unique screening method uses yeasts which have been genetically engineered to express parasite and human proteins to identify chemical compounds that target disease-causing parasites but do not affect their human hosts.

Parasitic diseases affect millions of people annually, often in the most deprived parts of the world. Every year, malaria alone infects over 200 million people, killing an estimated 655,000 individuals, mostly under the age of five. Unfortunately, our ability to treat malaria, which is caused by Plasmodium parasites, has been compromised by the emergence of parasites that are resistant to the most commonly used drugs. There is also a pressing need for new treatments targeting other parasitic diseases, which have historically been neglected.

Currently, drug-screening methods for these diseases use live, whole parasites. However, this method has several limitations. First, it may be extremely difficult or impossible to grow the parasite, or at least one of its life cycle stages, outside of an animal host. (For example, the parasite Plasmodium vivax, responsible for the majority of cases of malaria in South America and South-East Asia, cannot be continuously cultivated in laboratory conditions.) Second, the current methods give no insight into how the compound interacts with the parasite or the toxicity of the compound to humans.

In an effort to develop new drugs to fight parasitic diseases, scientists from the ̽��ֱ�� of Cambridge have collaborated with computer scientists at Manchester ̽��ֱ�� to create a cheaper and more efficient anti-parasitic drug-screening method. ̽��ֱ��clever screening method identifies chemical compounds which target the enzymes from parasites but not those from their human hosts, thus enabling the early elimination of compounds with potential side effects.

Professor Steve Oliver, from the Cambridge Systems Biology Centre and Department of Biochemistry at the ̽��ֱ�� of Cambridge, said: “Our screening method provides a faster and cheaper approach that complements the use of whole parasites for screening. This means that fewer experiments involving the parasites themselves, often in infected animals, need to be carried out.”

̽��ֱ��new method uses genetically engineered baker’s yeast, which either expresses important parasite proteins or their human counterparts. ̽��ֱ��different yeast cells are labelled with fluorescent proteins to monitor the growth of the individual yeast strains while they grow in competition with one another. High-throughput is provided by growing three to four different yeast strains together in the presence of each candidate compound. This approach also provides high sensitivity (since drug-sensitive yeasts will lose out to drug-resistant strains in the competition for nutrients), reduces costs, and is highly reproducible.

̽��ֱ��scientists can then identify the chemical compounds that inhibit the growth of the yeast strains carrying parasite-drug targets, but fail to inhibit the corresponding human protein (thus excluding compounds that would cause side-effects for humans taking the drugs). ̽��ֱ��compounds can then be explored for further development into anti-parasitic drugs.

In order to demonstrate the effectiveness of their screening tool, the scientists tested it on Trypanosoma brucei, the parasite that causes African sleeping sickness. By using the engineered yeasts to screen for chemicals that would be effective against this parasite, they identified potential compounds and tested them on live parasites cultivated in the lab. Of the 36 compounds tested, 60 per cent were able to kill or severely inhibit the growth of the parasites (under standard lab conditions).

Dr Elizabeth Bilsland, the lead author of the paper from the ̽��ֱ�� of Cambridge, said: “This study is only a beginning. It demonstrates that we can engineer a model organism, yeast, to mimic a disease organism and exploit this technology to perform low-cost, fully-automated drug screens to select and optimise drug candidates as well as identify and validate novel drug targets.”

“In the future, we hope to engineer entire pathways from pathogens into yeast and also to construct yeast strains that mimic diseased states of human cells.”

̽��ֱ��research, which was funded by the Biotechnology and Biological Sciences Research Council (BBSRC), was published today, 27 February, in the journal Open Biology.

Screening method created to expedite the development of new drugs in the fight against tropical diseases such as malaria and African sleeping sickness.

Our screening method provides a faster and cheaper approach that complements the use of whole parasites for screening.

Steve Oliver

Harry J. Moss

Different yeast cells are labelled with fluorescent proteins to monitor the growth of the individual yeast strains

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New database for vital model organism launched

gm349 — Mon, 28 Nov 2011 16:16:10 +0000

A new database promises to be an invaluable resource to scientists who use a unique single-celled fungus to study human diseases.

̽��ֱ��new database for the fission yeast Schizosaccharomyces pombe, called PomBase, was launched today by a consortium of researchers at the ̽��ֱ�� of Cambridge, the European Bioinformatics Institute (EBI), and ̽��ֱ�� College London (UCL).

Fission yeast is a single-celled fungus (yeast). Because their cells function much like our own, and it is an important model for studying cellular processes frequently associated with heritable diseases and cancers.

Scientists have already discovered that fission yeast has equivalents of many human genes which are known causes of rare genetic diseases and syndromes (including Batten', Bloom's, Birt-Hogg-Dube, Liddle, Lowe, Niemann-Pick). Additionally, fission yeast have counterparts of human genes implicated in diseases with multiple causes, to include many cancers, deafness, neurological diseases, heart disease, Parkinson's, and anaemia.

Biologists today are very dependent on computer databases that catalogue the functions of the genes of the organisms they study and give access to other supporting information. ̽��ֱ��PomBase website will therefore prove to be an important tool for researchers studying fission yeast.

Its launch is the first stage of a 5-year project funded by the Wellcome Trust to provide a model organism database that allows researchers around the world to participate directly in the curation process in addition to using automated procedures based on the genetic blueprint of the fission yeast. ̽��ֱ��project uses Ensembl software for genome browsing, which is already used to present data for many other important experimental species. Novel tools and resources generated by this project will also be available to researchers working on other species, including human pathogens, to create similar databases.

Steve Oliver, Professor of Systems Biology & Biochemistry, who is spearheading the initiative, commented: "Organism specific database projects frequently have limited resources, and large backlogs of uncurated literature. An important novel component of this project is the construction of intuitive tools to allow the research community to involve itself in database curation, and ensure that the scientific information published in their papers is visible to the entire biological research community. These tools can also be shared with other groups and implemented for their organism of interest.”

Valerie Wood, PomBase Manager and co-investigator, said: "PomBase is not only establishing a database for this important model, it is also adapting the EBI's Ensembl Genomes platform and constructing tools to allow the research community to curate their own publications. ̽��ֱ��PomBase protocols will enable other research communities to establish and sustain similar databases for other experimental organisms. We have already identified counterparts for over 300 human disease genes in PomBase and many of these are being studied to elucidate the cellular basis of a diverse range of diseases.”

Jurg Bahler, fission yeast researcher and PomBase co-investigator from UCL, added: “Many basic cellular processes are conserved between yeast and humans, and PomBase will used extensively by biological and biomedical researchers world-wide to study mechanisms involved in cell growth and division.”

Paul Kersey, PomBase co-investigator from EBI, said: “PomBase has adapted the EBI's Ensembl platform to provide a multi-faceted resource dedicated to the needs of fission yeast researchers. These developments will enable other research communities to establish and sustain similar databases for their favourite experimental organisms.”

̽��ֱ��community curation initiative for PomBase will be launched in Spring 2012. ̽��ֱ��database can be found at: www.pombase.org

̽��ֱ��database, PomBase, important new tool for scientists researching fission yeast.

An important novel component of this project is the construction of intuitive tools to allow the research community to involve itself in database curation, and ensure that the scientific information published in their papers is visible to the entire biological research community.

Steve Oliver, Professor of Systems Biology & Biochemistry, who is spearheading the initiative

Image UCL

Pombe

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