Using AI to Fight a Pandemic: Insilico Medicine Announces Novel Preclinical Candidate for COVID-19 Treatment
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.
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.
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.
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.
The 3CL protease is a protein that is translated early in the COVID-19 viral replication process – without which the virus cannot replicate. Recent studies have shown that other drugs designed to inhibit this protease possessed robust efficacy and safety in clinical trials. Insilico Medicine`s PCC is positioned to advance this class of small molecule inhibitors even further with a drug candidate that is orally available, effective in low dose, and can be synthesized quickly. It is also advantageous in that there is no need for co-dosing with Ritonavir. The PCC with novel small molecule structure was designed using Insilico`s proprietary machine learning platform Pharma.AI capable of rapid, highly-automated end-to-end drug discovery.
Pharma.AI consists of three main pipelines: target discovery (PandaOmics), small molecule drug discovery (Chemistry42), and predictors of clinical trial outcomes (InClinico). This system is designed to achieve maximum automation of drug discovery processes for a broad range of human diseases. The small molecule drug discovery pipeline can be used to generate inhibitors of bacterial and viral protein targets. Multiple publications explaining the basic concepts and approaches in generative chemistry were published by the team. Since there is a known protease target for 2019-nCoV and its sequence and structure are also known, Insilico decided to apply only the generative chemistry pipeline Chemistry42 to perform this drug design project.
Insilico Medicine has created and extensively validated the end-to-end AI-system, Pharma.AI, which is capable of accelerating drug discovery for various indications at a fraction of a typically incurred R&D cost. Since 2021, Insilico has delivered seven preclinical candidates discovered and designed using its AI platform in a variety of disease areas, including fibrosis, inflammation, and oncology, including one for the QPCTL immuno-oncology target in partnership with Fosun Pharma. The company has also successfully completed a Phase 0 microdose trial and entered a Phase I clinical trial with its first internally developed program for fibrosis.
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