To more quickly identify drug combinations, such as those that might treat COVID-19, researchers have come up with an artificial intelligence platform called IDentif.AI.
Traditionally, when dangerous new bacterial and viral infections emerge, the response is to develop a treatment that combines several different drugs. However, this process is laborious and time-consuming, with drug combinations chosen sub-optimally, and selection of doses a matter of trial and error. This costly and inefficient way of developing a treatment presents problems when a rapid response becomes crucial to tackle a global pandemic and resources need to be conserved.
The work appears in Advanced Therapeutics.
Conventional selection of drugs for treatment involves examining virus or bacteria growth in response to different potential candidates.
Researchers treat the bacteria or viruses with increasing doses of drugs until they reach maximum growth prevention. They then add additional drugs to amplify the effect. However, these methods become ineffective when several drugs are simultaneously under investigation as candidates. Also, these approaches often result in positive outcomes for in vitro studies, but not in human studies.
“If 10 or more drugs are examined, it is virtually impossible to study the effects of all the possible drug combinations and dosages needed to identify the best possible combination using traditional methods,” says professor Dean Ho, director of the N.1 Institute for Health and Institute for Digital Medicine (WisDM) at the National University of Singapore.
Furthermore, in traditional screening, if a drug from a pool of candidate therapies demonstrates no apparent effect on the pathogen, this drug will generally no longer be under consideration. “However, if this drug is systematically combined with more drugs, each at the correct doses, this could very well result in the best possible combination. Unfortunately, this remarkable level of required precision cannot be arbitrarily derived,” adds Ho.
To avoid the drawbacks of traditional drug combination therapy development, Ho and his team, together with collaborators from Shanghai Jiao Tong University, harnessed the processing power of AI.
Drug combinations for a lung infection
The research team carefully selected 12 drugs that are viable candidates for treating an infection in lung cells caused by the vesicular stomatitis virus (VSV). They then used IDentif.AI to markedly reduce the number of experiments needed to interrogate the full range of combinations and optimal dosages of these 12 drugs.
“Using IDentif.AI, we took three days to identify multiple optimal drug regimens out of billions of possible combinations that reduced the VSV infection to 1.5% with no apparent adverse impact. This speed and accuracy in discovering new drug combination therapies is completely unprecedented,” says Ho.
Importantly, the team saw that when they optimally dosed the top-ranked drug combination, it proved seven times more effective compared to sub-optimal doses. This shows the critical importance of ideal drug and dose identification.
Similarly, when researchers substituted a single drug out from the top-ranked drug combination, and administered the new combination at sub-optimal doses, the combination showed 14 times less effectiveness.
“There is a notion in drug discovery that if you discover the right molecule, the work is done. Our results with IDentif.AI prove that it is critically important to think about how the drug is developed into a combination and subsequently administered. How do you combine it with the right drugs? How do you dose this drug properly? Answering these questions can dramatically increase efficacy at the clinical stage of drug development,” says Ho.
Now, IDentif.AI vs. COVID-19
Having proved the effectiveness of IDentif.AI to rapidly provide treatments for infectious diseases, the team is currently setting their sights on COVID-19.
“As the development of vaccines and antibody therapies for COVID-19 are ongoing, we will need a rapid therapeutic strategy that addresses the virus which may evolve over time,” says Ho. “Our strength is that we can perform one experiment and come out with a list of drug combinations for treatment within days. And in time, if patients do not respond well to the first combinations of drugs, we can derive new combinations within days to re-optimize their care.
“Our platform is useful to address the possibility that patients will need different drug combinations depending on when treatment was initiated, and if downstream infection with a different strain occurs.”
Furthermore, researchers could immediately deploy IDentif.AI to address any other infectious diseases in the future.
“When an aggressive pathogen hits, a rapid response is needed, and this response may need to evolve quickly as the pathogen evolves,” says Ho. “Now, with IDentif.AI, we will be ready.”
The study also includes insights from a team of experts in operations research and healthcare economics from NUS Business School and KPMG Global Health and Life Sciences Centre of Excellence, as well as global health security and surveillance experts from EpiPointe LLC and MRIGlobal. They say that strategies such as IDentif.AI, which can rapidly optimize drug repurposing under austere economic conditions amid pandemics, could play a key role in improving patient outcomes compared to standard approaches.