Drug-resistant tuberculosis is on the rise. Software that predicts the specific cocktail of drugs to use in each case will help

Know the genome, kill TB Zackary Canepari/Panos

A DOCTOR in Mumbai, India, puts a spit sample into a handheld device. It whirs away briefly, then a few minutes later a nearby laptop pings. The doctor checks the results to see exactly what kind of drug-resistant tuberculosis the person has, and the precise combination of drugs needed to treat it.

“If you can identify drug-resistant TB in less than a day, you will massively improve treatment“

This is the goal of CRyPTIC, a global project run by a team at the University of Oxford. It aims to speed up the diagnosis and treatment of drug-resistant TB, cutting the wait from months to days, or even minutes. The idea is that the software will prescribe the right medication for TB just by looking at its genome.


“It’s rapid,” says Sarah Hoosdally at the University of Oxford, who is managing the project. Handheld DNA sequencers will make it even quicker – though it may be a few years before such devices hit clinics around the world. “We’re hoping to extract the DNA directly from the sample,” she says.

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Tuberculosis is a bacterial infection that kills by attacking the lungs, until the patient dies of respiratory failure. It ranks alongside HIV as the leading cause of death from infectious disease. In 2014, 9.6 million people became ill with TB and 1.5 million died, according to the World Health Organization.

The WHO wants to end the epidemic by 2030 but that will mean tackling drug resistance. The last 10 years have seen a dramatic rise in drug-resistant bacteria, which spread easily through densely populated cities in poorer parts of the world.

A few years ago there might have been 20 cases of drug-resistant TB a year in Mumbai, says Nerges Mistry, director of the Foundation for Medical Research in the city. That number has shot up. “We now have 3000 to 4000 cases of drug-resistant TB a year – and those are the ones we’re able to catch.”

Resistant bacteria can be defeated with the right cocktail of drugs. But finding out what kind of TB someone has – and thus what drugs they need – can take months. Identifying the bacteria by culturing them in the lab and using dyes can take 3 to 5 weeks, says Marco Schito at the Critical Path Institute in Tucson, Arizona. Then you need to test combinations of drugs to see which ones are going to be effective, and that can take another month.

In the meantime, a person will be given the standard catch-all medication, which may or may not help them. “The way that TB is diagnosed is the same way we were doing it when the disease was identified over 130 years ago,” says Schito. “Often individuals pass away while they’re waiting for their result.”

Speed up

We need a quicker and smarter way to work out exactly what drugs are required – which is where CRyPTIC comes in. “If you can diagnose someone and know their drug resistance profile in less than a day, you’re going to massively improve treatment,” says Hoosdally.

Teams at TB hotspots around the world – including the Chinese Center for Disease Control and Prevention in Beijing, the National Institute for Communicable Diseases in Johannesburg and the Foundation for Medical Research in Mumbai – are collecting data on the TB genomes out there and the specific drugs each mutation responds to.

Mistry and her colleagues at Mumbai’s Hinduja Hospital have started sending TB genomes to a lab in Bangalore for sequencing in addition to running their standard culture analyses. The results from this and several other clinics around the world are then fed into a machine learning system at Oxford that is being taught what drugs work for particular strains of TB – to cut out the slow process of testing cultures in a lab.

Machine learning helps the team untangle the complexity of TB resistance. For example, two bacterial samples with slight differences in their genomes might resist the same drugs without it being clear which genes are involved.

We only know the resistance-conferring gene for a handful of drugs. For example, a gene called katG makes the TB bacterium sensitive to isoniazid, one of the most common drugs used for treatment. With a mutation in katG, TB becomes resistant to the drug. But in most cases, it’s guesswork – something machine learning is good at.

The approach works in much the same way as image recognition software. Just as Google has taught its AI to recognise images of dogs, say, by feeding it huge numbers of images that humans have labelled “dog”, CRyPTIC is teaching its AI to recognise drug resistance by feeding it huge numbers of genomes labelled as resistant to a specific drug. When finished, CRyPTIC’s software will be able to recognise different TB genomes and recommend appropriate drugs automatically.

CRyPTIC’s primary goal is speeding up diagnosis, but the project will also serve as an early warning system for new strains of tuberculosis – and potentially other infectious diseases.

By tracking mutations all over the world, CRyPTIC will provide a bird’s-eye view of the battle between TB and the drugs we throw at it. “The key is getting that catalogue,” says Zamin Iqbal, who works on CRyPTIC’s database in Oxford. “The cherry on top is observation.”

It won’t be easy since getting hold of samples and results from drug tests is expensive. Google needed millions of images to recognise dogs, says Iqbal. The more data, the better. With funding from the Bill and Melinda Gates Foundation and the Wellcome Trust they hope to succeed.

Still, we must not expect AI alone to eradicate tuberculosis, says Mistry. That will require radical social change to address the socio-economic conditions driving infection. “It’s a firefighting tech at the moment,” says Mistry. “But we may bring it down, and I think that’s the right thing to do.”

The AI doctor will see you now It’s not only tuberculosis that artificial intelligence is being pitted against (see main story). Google’s DeepMind is working on several projects with the UK’s National Health Service, including training its AI to spot signs of head and neck cancer in MRI scans of patients having radiotherapy, and diagnose eye disease by looking at retinal scans. This month, San Francisco-based start-up Bay Labs announced that it is developing AI to interpret ultrasound scans. The company is working with doctors in Kenya who are scanning hundreds of children to look for signs of rheumatic heart disease, a chronic condition caused by rheumatic fever. The system can spot signs of the disease in video taken during a scan. Ultrasound scanners are becoming more available in poorer countries, but interpreting the images they produce can take years of training for a doctor.

This article appeared in print under the headline “AI enlists to stop TB”