A ground-breaking artificial intelligence (AI) platform has helped researchers to derive an optimal combination of available therapies against SARS-CoV-2, the cause of COVID-19.

Researchers from the National University of Singapore (NUS; Singapore) used their platform known as ‘IDentif.AI’ (Optimizing Infectious Disease Combination Therapy with Artificial Intelligence) to investigate 12 potential drug candidates, representing over 530,000 possible drug combinations. These drugs are: remdesivir, favipiravir, lopinavir, ritonavir (ritonavir and lopinavir are given together for HIV), oseltamivir, hydroxychloroquine, chloroquine, azithromycin, losartan, teicoplanin, ribavirin, and dexamethasone.

In traditional drug screening, a 12-drug set such as this, with 10 different doses studied for each drug, represents a parameter space of one trillion possible combinations. Using IDentif.AI, the research team was able to determine that testing only three different dose levels were needed for each drug. While this still represents 531,000 possible combinations, the team was also able to reduce the numbers of experiments needed by three orders of magnitude and complete the entire study within two weeks.

Given the diversity of different drug candidates that are being studied, and the need to evaluate different permutations of drug combinations and the respective doses, much of the data needed in order to optimize drug development simply does not exist. While AI is being actively explored in the area of therapeutics, current efforts are largely directed towards drug discovery and repurposing. However, repurposed drug candidates are unlikely to be effective on their own. IDentif.AI interrogates extraordinarily large parameter spaces and pinpoints the best possible combinations to give to patients. This can be accomplished rapidly.

Remdesivir, lopinavir, and ritonavir at specific doses represents the top ranked combination, resulting in an almost total inhibition of infection. While remdesivir alone was the best performing single drug relative to the other drugs, the optimal combination increased the inhibition efficiency by 6.5 times. IDentif.AI was able to harness an unforeseen interaction between remdesivir, lopinavir, and ritonavir that experimentally shown to markedly increase efficacy. Therefore, IDentif.AI may be leveraged to realize unexpected drug combinations based on drugs that are ineffective as monotherapies in order to optimize treatment. In addition, the study found that hydroxychloroquine and azithromycin, another widely studied combination, was shown to be relatively ineffective.

The results of this study have demonstrated the power of IDentif.AI to rapidly discover optimal drug combinations for infectious diseases. To provide broader insight into the extensive range of combinations explored by this study, the research team has developed IDentif.AI Online, an interactive resource that allows users to build different drug combinations online and observe corresponding efficacy and safety data for research purposes. This resource will be updated continuously as additional IDentif.AI studies are conducted with additional therapies and viral strains. The team is also preparing to expand IDentif.AI towards locally available therapies to develop novel combinations that can be rapidly deployed and administered easily, and may also use it to find optimal treatments against other infectious diseases in future.

“IDentif.AI is unlike traditional AI as we do not use pre-existing data or in silico modeling to train algorithms and predict drug combinations,” said Professor Dean Ho, Director of The N.1 Institute for Health and Institute for Digital Medicine (WisDM) at NUS. “With IDentif.AI, we will always be ready to rapidly find optimal therapeutic solutions for the next outbreak.”

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