Using Artificial Intelligence for COVID-19 treatment identification.

Six Potential COVID -19 treatments – In an interesting story, two brothers who started their careers in different areas are now working together to harness artificial intelligence (AI) to identify drugs that may be used to fight COVID-19.

In the late 1980s and early 1990s, Aris and Andreas Persidis were studying for their doctorates in different fields — Aris in biochemistry at Cambridge University England, and Andreas in naval architecture and AI at the University of Strathclyde, Scotland.

They had always wanted to work together and at the time, early versions of AI-driven telemedicine, called “medical expert systems,” were being discussed as the next healthcare revolution. The brothers first collaborated by researching and writing a review of the subject, which despite submitting to many journals did not gain widespread acceptance at the time.

Despite these early setbacks, they continued. In 2005, they started ‘Biovista’, a company that initially used early forms of AI to match research areas and scientists which evolved into matching drugs and adverse effects.

Artificial Intelligence

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning.

AI problem solving in drugs and clinical outcomes.

At Biovista, another change into matching any and all drugs against any and all mechanisms, pathways, diseases, and clinical outcomes came next.

COVID outbreak

When COVID-19 hit, the brothers began applying novel AI approaches through their Project Prodigy AI platform to find treatments that may mitigate disease complications that can arise post infection.

The company has just announced that it has identified the antifibrinolytic agent aprotinin and the angiotensin II receptor blocker irbesartan as having potential for reducing the effects of cytokine storm and high viral load associated with COVID-19.

Biovista has also identified caplacizumab and ezetimibe/atorvastatin as potential treatments to address blood clotting and inflammation related to COVID-19.

The AI platform has also identified two bioactive compounds — lycopene and vitamin D — as potentially useful in treating COVID-19, bringing the total to date to six potential treatments.

Drug AI helps find that “needle in the haystack, and we are optimistic that we have found [several] of them to start,” Dr. Aris Persidis, PhD, told Medscape Medical News recently.

These drugs are part of a “rolling release” of possible drugs identified by Project Prodigy that could potentially be repositioned for COVID-19, “COVID-19 has rewritten the book on infectious diseases,” said Persidis.

Treating it has proven especially difficult because it causes multiple complications affecting nearly every organ system in the body.

He noted that typical machine learning AI isn’t designed for a disease like COVID-19. “Machine learning only looks backwards based on what you’ve trained it in. If you change a tiny variable, you have to train it again,” he explained.

In contrast to traditional machine learning AI, Project Prodigy is a “machine building AI that enables us to build, interrogate, and test possible and unanticipated scenarios,” he explained. It’s being used to map all known drugs against every possible mechanism in which SARS-CoV-2 operates to cause complications.

The company maintain that they will continue to publicly release data on potential drugs for COVID-19 for scientists and clinicians to test “until we collectively” solve COVID-19.

Before COVID-19

The first drug Biovista repositioned and tested in animal models was dimebolin, which the company’s AI platform predicted would be helpful for patients with epilepsy and multiple sclerosis. This was confirmed with experiments in animal models, and Biovista was granted patents.

Other repositioned drugs include pirlindole for multiple sclerosis, Friedreich’s ataxia, Leber’s hereditary optical neuropathy, and multiple related rare diseases of mitochondrial dysfunction; and linezolid for rare forms of cancer, including glioblastoma multiforme.

As part of their work with biotech and pharmaceutical companies, Biovista has worked on at least 10 more drugs and drug classes, identifying over 17 diseases and their variations.

Can Artificial Intelligence Speed Time to Clinical Trials?

Many methods are being used to identify currently existing drugs against COVID-19, from traditional clinical experience to different forms of AI.

A team at the National University of Singapore is currently using an AI-based platform called IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) to help accelerate the discovery of optimal combinations of existing drugs that might be effective against COVID-19.

Dean Ho, PhD, head of the university’s Department of Biomedical Engineering and director of the N.1 Institute for Health and Institute for Digital Medicine, is leading the effort and for COVID-19, using drug combinations in lieu of single-drug therapy is likely to be essential, he noted. “This creates additional challenges because selecting the right drugs to combine and the right dose for each drug can mean the difference between maximal efficacy or no efficacy at all.” he said.

For example, testing 12 candidate drugs at 10 different doses each creates one trillion possible drug combinations. Testing this many combinations is insurmountable for any laboratory or even a major pharmaceutical company.

Applying an AI platform toward COVID drug combination design could potentially lead to rapid clinical trial initiation, as the ranked list of combinations can be quite actionable.

Ho’s team has already shown the HIV drug lopinavir/ritonavir (Kaletra) to be “relatively ineffective” against COVID-19. The antiviral remdesivir was shown to be the most effective single drug therapy “but was still not extraordinarily effective.”

It is becoming apparent that for COVID-19 drug combination therapy may be the way forward. It is difficult to remember a time when more groups were working in combination together, for a therapeutic outcome.

[Thanks to Medscape 2020] Dr. Stephen Bray

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