Machines are getting better at beating HIV drug resistance

Scientists have improved machine learning algorithms to help finding the right antiretroviral medications for patients who are developing drug resistance.

In 10 seconds? HIV is a highly mutating virus capable of rendering useless the drugs taken by patients. Computer modelling using machine learning is getting better at predicting who is at risk, helping to save lives and money. (Read the science)

What’s the breakthrough? Scientists have developed a computer model to simulate how HIV mutates in people to suggest the best possible treatments. Working with a number of variables, ranging from peoples’ behaviour to how drugs work on HIV mutations, the algorithm predicts which alternative drugs would save more lives and money in the long run. (Find out more)

Why do we need computing power to keep HIV at bay? Because the virus has been adapting against antiretroviral drugs in the past decades. Predicting where in the world this resistance occurs needs a lot of data crunching. A recent WHO study found that in Uganda, almost 16% of newly infected people were resistant to a key class of first-line drugs, usually the doctors’ first choice. (Learn more)

And what do these algorithms do? For example, they predicted that HIV-related deaths could be reduced by administering dolutegravir as a first-line antiretroviral drug in sub-Saharan Africa. The choice is very important as it can affect the future behaviour and mutations of HIV in patients. (Find out more)

How do they do it? They sift through through massive volumes of data about HIV mutations to find the ones that will be drug-resistant. Currently, laboratory testing methods are still widely used for this task. By using algorithms, scientists have managed to improve the prediction rate of the current gold standard resistance prediction test — known as ANRS — by up to 28%. (Read more)

Are there other applications of algorithms against HIV? Yes, for example, algorithms can quickly determine ‘viral load’ patterns in large groups of people (i.e. how much HIV there is in their bloodstreams). This classifies patients into groups to help assign the right therapies to them. (Find out more)

So can machine learning help to cure HIV? We hope so! We mentioned broadly neutralising antibodies (bNAbs) in an earlier Digest — scientists are using machine learning techniques to build and combine more effective bNAbs. The algorithms look through millions of parameters to seek common patterns or weak points in HIV that can be targeted by a vaccine. (Find out more)

Why AI is good news for medicine
From diagnosing cancers and predicting bipolar episodes to establishing personalised doses, machine learning can do the heavy lifting that is needed for better medical outcomes.
Being able to find patterns in masses of data, it can be used to direct drug development by suggesting ideal candidates for medical trials and help scientists better understand the progression of various diseases, such as MS or diabetes.
Also, machine learning offers an unexpected benefit: it’s good at recognising letters, even doctors’ notoriously bad handwriting.

Endre Szvetnik is Senior Editor at Sparrho. Endre works with Sparrho Heroes to curate, translate and disseminate scientific research to the wider public.

(Psst, Endre distilled 11 research papers to save you 816.2 min)


Originally published at www.sparrho.com.

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