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Using AI to Fight a Pandemic: Insilico Medicine Announces Novel Preclinical Candidate for COVID-19 Treatment

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May. 23, 2022

Insilico Medicine announced the nomination of a novel preclinical therapeutic candidate for treating COVID-19, designed using the generative chemistry AI platform Chemistry42. The new drug candidate is a 3CL protease inhibitor unique from existing drugs in its class because it can be rapidly produced. While this nomination is a potentially important development for the ongoing COVID-19 pandemic (if successful in clinical trials), what may be even more strategic is the conceptualization of how AI-driven drug discovery and development can be a rapid way of addressing potential future pandemics, with potentially more lethal pathogens than COVID-19.

When the Pandemic Struck
The coronavirus that caused the global COVID-19 pandemic is a positive sense, single stranded RNA beta coronavirus, a member of Beta-CoV lineage B (subgenus Sarbecovirus), possessing high likelihood for human-to-human transmission. The RNA sequence is around 30 kb in length. There is an insightful technical report by Innophore, summarizing information about the coronavirus: its structure, active sites, sequence data, and so on.
While showing similarities to beta coronaviruses found in bats, the specific coronavirus behind the COVID-19 pandemic is genetically different from other coronaviruses such as the previously known Middle East respiratory syndrome-related coronavirus (MERS) and Severe acute respiratory syndrome-related coronavirus (SARS).
The unprecedented speed at which the new coronavirus spread across the globe, disrupting economies and the global healthcare system, revealed the world`s unpreparedness for these kinds of challenges. Healthcare facilities and hospitals were quickly overwhelmed. The world`s inability to react quickly to a pandemic of this size and speed led to a detrimental output: more than half a billion cases of COVID-19 and more than 6 million deaths, according to WHO statistics.

One of the key reasons for the high death toll was the absence of an efficient antiviral remedy at the time the first cases of COVID-19 rapidly spread. The pharmaceutical industry has a notoriously slow business model, where a typical drug discovery program can take many years to successfully progress to a ready-to-prescribe drug. Another standard bottleneck is manufacturing and logistics, even if the innovative solution does become available quickly. For the most part, the industry was largely unable to come up with a rapid solution to stop the pandemic at the very beginning, so it quickly spiraled out of control. The antiviral options available at the time the pandemic struck proved to be of low effectiveness.
The Battlefield for Artificial Intelligence
When dealing with events like the COVID-19 pandemic, time is paramount. The amount of infected people grows exponentially and they need efficient antivirals and vaccines sooner rather than later. Artificial intelligence (AI) is a proven way to dramatically accelerate research. No wonder, when the pandemic struck, people turned to artificial intelligence for various needs associated with the health crisis -- including drug discovery and vaccine development, optimizing production and logistics processes, epidemiologic modeling and prediction, high-throughput diagnostics of patients at scale, and sorting real world data for medical treatment demanding forecasting.

A vivid example of the power of AI-driven research was demonstrated by Moderna (NASDAQ: MRNA), one of the most "digitized" biotechs. A lucky convergence of technologies, such as mRNA itself and its delivery method via LNPs – supported by advanced digital tools and predictive AI algorithms – allowed Moderna to develop a successful COVID-19 vaccine in months. Using technology Moderna produced a vaccine in record time and more than 200 million doses of its vaccine were administered in the US alone.
Beyond Vaccines
But vaccination alone is not enough to tackle the crisis. Since the beginning of the pandemic, the global healthcare system has been in desperate need for efficient antivirals to treat patients infected by the coronavirus.

There are two main strategies for the application of AI technologies in discovering novel efficient antivirals: using AI to sift through tens of thousands of already known potential therapies to identify successful drug repurposing options (e.g. among known antivirals, relevant compounds from commercial compound catalogs, etc). or to create something completely new (de novo drug design).

The first strategy offers a shorter path to the public, which is important. In fact, most of the immediate drug discovery efforts in the early stages of the pandemic were focused on drug repurposing of known clinically approved drugs and virtual screening for the molecules available from chemical libraries. However, this method proved to be of limited efficiency. For example, the IC50 of lopinavir, an HIV protease inhibitor, against the 3C-like protease was found to be approximately 50 micromolar, which was far from ideal.

The second strategy, de novo drug design, is a more complex process, but it can provide a much better eventual solution -- an efficient first-in-class or best-in-class antiviral that can save lives of COVID-19 patients with severe forms of the disease. In an attempt to come up with a bold solution for both the current pandemic and for future pandemics, Insilico Medicine chose to pursue this route. On January 28, 2020, Insilico utilized part of its generative chemistry pipeline Chemistry42 to design novel drug-like inhibitors of COVID-19 and began generation on January 30.

Pioneering AI-Driven COVID-19 Drug Discovery and Development
Insilico wanted to choose the right kind of target for the design of a corresponding molecule. Many potential therapeutics aimed at containing the spread of SARS-CoV-2 have targeted the S, or spike, protein, a surface protein that plays a vital role in viral entry into host cells, since that is the approach that was taken with both SARS and MERS coronaviruses.

However, according to Insilico`s study in collaboration with Nanome, published back in 2020, two-thirds of the SARS-CoV-2 genome comprised non-structural proteins, such as the viral protease (the protein necessary for viral replication). Insilico therefore concluded that such alternative potential targets should not be overlooked, and decided to focus efforts on C30 Endopeptidase, also referred to as the 3C-like proteinase or coronavirus 3C-like protease (3CLP) or coronavirus main protease (Mpro ). 3CLP is a homodimeric cysteine protease and a member of a family of enzymes found in the Coronavirus polyprotein.

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