丁香园AV

By Ariane Madden

Tuberculosis (TB) is the world鈥檚 deadliest infectious killer but a team of 丁香园AV-led researchers is determined to fight it鈥攗sing artificial intelligence.

Just as the World Health Organization (WHO) observed on March 24, 丁香园AV computing science professors Leonid Chindelevitch and Maxwell Libbrecht partnered with University of Montreal researcher Jesse Shapiro for a new project. It uses machine learning to predict whether a patient鈥檚 strain of TB is resistant to treatment by specific drugs.

Antibiotic resistance occurs when the TB bacterium evolves its ability to fight against the antibiotics meant to cure the patient. This poses an increasing challenge for healthcare practitioners. The that by 2050, TB will be responsible for more than 25 per cent of the 10 million annual deaths from drug-resistant infections. And while recent efforts to improve access and adherence to TB treatment have shown some promise, drug resistance could thwart these efforts by limiting the pool of effective treatment options.

The 丁香园AV project will combine the researchers鈥� expertise in infectious disease, machine learning and genomics to create a new, artificial intelligence platform for understanding and predicting drug resistance in TB. To do this, the team will develop a new computer algorithm that, when given information about the genomics of drug-resistant and drug-sensitive TB bacteria, will learn from the data and make predictions about new samples of the bacteria based on their genetic traits alone.

The project is funded through a , with additional support from Genome BC, Genome Quebec, and the Canadian Statistical Sciences Institute (CANSSI).

Investigating the genomic mechanisms of TB

鈥淩ight now, doctors can take a sample of the TB bacteria from a patient, grow it in a lab, and test what drugs will be effective against it,鈥� says Chindelevitch, explaining how the platform will compare to current methods.

鈥淗owever, TB is one of the slowest bacteria to grow in the lab. We want to be able to give them a tool that can quickly predict if the bacterium is likely to be resistant to the treatment drugs and also which specific variants in its genetic code give it that resistant trait.

鈥淚t is not enough to be able to say yes, this bacterium is resistant, or no it is not; we want to be able to say how it has developed the resistance as well,鈥� he says.

Fellow investigator Libbrecht adds, 鈥淲e have a lot of information about the human genome, however we do not have the same depth of information about bacteria. We do not necessarily know what gene or gene combination is getting mutated that is granting the TB its resistance. This study may help us better understand these mechanisms.鈥�

And, just as the bacteria will continue to evolve over time, so too will the predictions made by the computer algorithm as it learns from new data given to it. New data sets with the genomes of drug-resistant and drug-sensitive TB and other bacteria provided by researchers from the Harvard Medical School and University College London, among others, will advance the algorithm, improve its reliability, and inform the predictions it makes.

It is an important example of applying the latest in artificial intelligence to a pressing public health challenge.

The study could also have important implications for other types of drug-resistant infections such as malaria, gonorrhea and diarrheal diseases such as Clostridium difficile (also known as C. diff).

鈥淲e are looking to develop our models to apply to other infections that are a cause for public health concern.鈥� says Chindelevitch. 鈥淏ut TB is the foundation. If we can get TB right, then there is a good chance that we鈥檒l get the other infectious diseases right, too.鈥�