Scientists predicted remdesivir’s success with a simulation. Here’s how.

In 2002 pro baseball manager Billy Beane accomplished the impossible. He took the Oakland Athletics, a low-budget team comprised of unknown baseball players to the playoffs. The story’s magic is that he was able to compete with the Goliaths of the baseball world – the New York Yankees – with only a fraction of the budget. The idea is simple – draft players who are statistically successful in their respective positions, no matter their unorthodox or individual quirks. Beane’s secret was hiring baseball outsider Peter Brand, a recently minted economics graduate who would introduce Beane to these statistical strategies. Their use of statistics marks a seismic shift in sports history (and even business), Beane and Brands’ method is now standard for most professional sports teams. Every few years, a group of “nobodies,” engineered on analytics, shocks the “old guard.” Think three-time champion Steph Curry and the Golden State Warriors.

Its revenge of the nerds for sports.

The world of medicine – it’s therapeutics and research – resembles analytics in sports. As an ICU physician, one of the greatest experiences I can share with students is the ability to use “evidence-based medicine,” statistically and rigorously studied treatments, to save patients’ lives. When properly conducted, scientific evidence cuts viciously through the establishment of tyranny, corruption, and falsehoods. A well-designed study can dethrone years of conventional, yet unproven practices.

In February, a laboratory in Taiwan “Moneyballed” their way to success against COVID-19, predicting potential treatments against the virus. During the 2003 SARS outbreak, surrounding countries like Taiwan were left on their own to defend themselves. The World Health Organization (WHO) largely ignored Taiwan’s plea for help researching the virus. China does not recognize the government of Taiwan as a sovereign territory, and the World Health Organization does not recognize Taiwan as an official member.

Once again, the Yankees were not playing fair with the smaller teams.

Almost 20 years after SARS, Taiwanese scientists wasted no time and used a novel tool, 3D simulation, to research therapeutics against COVID-19. The capacity of 3D molecular imaging gives scientists the ability to create images of viruses and therapeutics based on 2-D data. Think Jurassic Park – virtual dinosaur creation – via DNA building. Once these images are created, scientists can then “simulate” what happens when a virus meets a particular medication. The exact molecular mimicry – the shape and curves of viruses and drug structures – are incredibly precise. Shortly after the outbreak, the COVID-19 genetic code was quickly hacked by scientists, making virtual creation possible.

Building upon the limited (in-vitro) data implying HIV antivirals efficacy against SARS, the decision was made to simulate the efficacy of these medications (along with other antivirals) against the COVID-19 virtual structure. Using simulation programs, the scientists now had a total of 10 HIV antivirals and four other antivirals to test. Two antivirals they were testing – remdesivir and favipiravir – are “nucleotide analogue” antivirals which had previously shown modest effect against the Ebola virus.

The stage was set for simulation. Each of the 14 antivirals would run through a simulation 100 times. In the end, the scientists would map out the precision of drug to virus binding. An image is then created – a sort of heat map, or bullseye blueprint, showing how well the drug binds to the virus. The results, published February 17, showed that most antivirals attacked the virus with mild to moderate efficacy (as predicted by the 2003 SARS data). Remdesivir – the nucleotide analogue and Ebola medication – stood out as an optimal match. The virtual simulations predicted remdesivir as a perfect match for COVID-19. In the authors’ words:

… When we tested the active form (CHEMBL2016761) of remdesivir, the docking site revealed a perfect dock in the overlapping region of the NTP binding motif. This result suggests that remdesivir could be a potential therapeutic agent. Clinical trials still must be done in order to confirm the curative effect of these drugs.

On January 31, the first confirmed patient in the U.S. with COVID-19 is successfully treated with remdesivir. In the following weeks, several other reports of clinical improvement in patients receiving remdesivir are published. On April 10, the New England Journal of Medicine publishes a series of patients treated with remdesivir and shows that 68 percent showed clinical improvement.

On April 29, based on the results of an international, randomized placebo-controlled trial of 1063 patients, the FDA announced plans for emergency authorization use of remdesivir. Patients receiving remdesivir recovered 31 percent faster compared to those receiving a placebo. Although the differences in mortality did not approach statistical significance, there was a trend for improvement in survival in the remdesivir group (8 percent vs. 11 percent). A separate randomized trial showing no improvement with remdesivir was published in the Lancet on April 23. This study has been criticized for lacking statistical power (the study enrolled 237 of intended 453).

Although the study Dr. Fauci is citing is yet to be published, an international monitoring group verified the results of the study’s analysis. Based on these results, Dr. Fauci announced remdesivir would be considered the standard of care – medicine’s medicolegal term for optimal treatment of action, given the currently available treatment options. Standard of care is defined as a “… diagnostic and treatment process that a clinician should follow for a certain type of patient, illness, or clinical circumstance.”

Favipiravir (also a nucleotide analog and Ebola antiviral), which showed modest binding efficacy in these simulations, is currently in phase III trials for COVID-19 in Japan. Given the promising results for remdesivir, will the simulations performed in Taiwan prove to be the future of therapeutics in medicine? Will our response to future pandemics be based on the simulation?

In the words of Sun Tzu, one of the greatest military strategists in history, “If you know the enemy and know yourself, you need not fear the result of a hundred battles.” If we can simulate the results of 100 battles, we should not fear the results or the strength of our enemy.

That’s a sports bet worth taking.

Cesar Padilla is an obstetric anesthesiologist and can be reached on Twitter @themillennialmd and on Instagram @doctor_cesar_.

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