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How IT accelerated the COVID-19 vaccines

Medical science now relies on IT playing a pivotal role in solving global problems. We will have, in a manner of speaking, more IT coursing through our veins than we can imagine, helping us stay safe and keeping us free.

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We have been witnessing the fiercest race in the history of medicine. It is the race to save millions of lives by making a COVID-19 vaccine available to every one of the 7.8 billion people on earth as quickly as possible. To do this, medical researchers and scientists, technologists and regulators, have been using a fascinating mix of biotechnology and IT. Hopefully, the resulting vaccine will reduce the intensity of the crisis, cut the loss of lives and livelihoods and put us on the path to normalcy.

By the end of the first week of January 2021, the coronavirus had ravaged 220 countries and territories, recording 87,763,513 cases with 1,893,873 deaths. By then, medical researchers had created 132 vaccine candidates of which 64 were in clinical trials and another 85 in pre-clinical trials. Hope was on the rise.

The EU and Israel had authorized the use of the Moderna vaccine, India’s Bharat Biotech had developed COVAXIN while another from Zydus Cadila had moved to Phase 3 of trials, China had the Sinopharma vaccine, Taiwan had Midigen in Phase 2 of trials, Mexico, India, Argentina, and Britain had authorized the Oxford-AstraZeneca vaccine for use, and the World Health Organization had validated the Pfizer-BioNTech vaccine. By the time you read this, thousands would have already received a vaccine.

It normally takes over a decade to develop a vaccine, but we got these in just over eight months, thanks to the incredible technology that accelerates research in modern medicine.

Let’s start at the beginning. It was BlueDot, a Canadian AI start-up that identified an unusual flu around the market in Wuhan, China, nine days before the World Health Organization (WHO) released its statement on the threat of coronavirus. BlueDot uses Artificial Intelligence (AI), Machine Learning (ML) and Big Data to predict and track the outbreak of diseases and their spread. It gathers data on over 150 diseases around the world every 15 minutes, 24x7, from sources like the Centre for Disease Control, the WHO, the movement of 4 billion people on commercial flights every year, population and climate data from satellites and by scanning more than 100,000 articles in 65 languages published online each day by journalists and healthcare workers.i How good was the BlueDot prediction? To its credit, BlueDot accurately predicted eight of the first ten cities that would import the coronavirus.

But the world urgently needed to move to cures, prevention and eradication. To quickly identify effective therapeutics for COVID-19 from existing and available drugs and molecules, a team led by Anandasankar Ray and Joel Kowalewski (a graduate in Ray’s lab), at the University of

California, used ML. Their work did not use trial-and-error to predict viral entry, replication and inhibitory activity. Instead, it used computational methodologies “to screen FDA registered chemicals and approved drugs (~100,000) and ~14 million purchasable chemicals.” Their work helped repurpose drugs such as Remdesivir to combat COVID-19, saving scores of lives.

Researchers like Ray, and others hoping to understand the pathophysiology of COVID-19 and develop efficacious therapeutics and vaccines for the virus, used publicly available databases to accelerate their work. One such database came from the European Union (EU) and Canadian-funded project called iReceptor Plus. This is a consortium that gathered 200 million T and B cell receptor sequences from COVID-19 patients. It is the largest database of its kind and the sequencing data is open source, available through the iReceptor Gateway. T and B cell receptors are related to the human immune system and are critical in modern immunotherapy such as vaccine development. Without the aid of technology, critical and reliable information of this kind could not have been shared so quickly and effectively. Neither would it have been ready for analysis by researchers at the push of a button.

Incidentally, it was AI that identified why people with cardio-vascular diseases were more prone to the virus. Murali Aravamudan and Venky Soundararajan, co-founders of Nference, an artificial intelligence start-up used genetic data from 10,967 samples and discovered that a snippet of DNA code (RRARSVAS) mimics a protein that regulates salt and fluid balance in humans. This discovery helped create a deeper understanding of how the virus acts and why it makes those with cardio-vascular diseases more vulnerable than others. As an aside, deep neural networks at Nference identified that loss of smell and taste were early symptoms of the infection. Barely a decade ago, before technologies like deep neural networks became widespread, a loss of smell and taste would have been anecdotal evidence that could have waited years to be confirmed as indicators of COVID-19.

Working with the large datasets involved in understanding the nature of the COVID-19 virus requires massive computational capabilities. Researchers across the world used cloud environments to beat the race against time. One such team at Australia’s Flinders University worked with Oracle cloud technology and computational models once the genomic sequence of COVID-19 became available to develop a vaccine that blocks the Spike (S) protein which makes the virus so dangerous.

Now, as the world crosses its fingers and prepares to deploy the vaccines, technology is being put in place to analyze its adverse effects. The UK government, for example, has invested in an AI tool that will examine the reports on reactions to COVID-19 vaccines. These are new vaccines. Their outcomes could be unpredictable. Adverse events will have to be identified and acted on quickly. We need systems that can analyze data in minutes, identifying even that which the most highly trained medical mind may miss. Again, this is made possible by pressing advanced IT into service.

We tend to think of advanced technologies like Big Data, Cloud, Analytics, AI, ML and Automation as the means to bring down costs; as tools to create products and services that keep consumers locked in through personalization and engagement; as tactical arms to sometimes gain unfair—and ethically questionable—intelligence on people, businesses and governments. We have become naturally skeptical about technology. But used judiciously it can, literally, save mankind.

Medical science now relies on IT playing a pivotal role in solving global problems. We will have, in a manner of speaking, more IT coursing through our veins than we can imagine, helping us stay safe and keeping us free.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

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Pradeep Kar

The author is Microland's Founder, Chairman and Managing Director, setting the foundation for excellence as Microland guides enterprises in adopting nextGen technologies to achieve the highest possible levels of reliability, stability, and predictability.

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