FRONT PAGE: AI Tool Pairs Protein Pathways With Clinical Side Effects, Patient Comorbidities To Suggest Targeted Covid-19 Treatments

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Newswise — The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. While some patients experience mild symptoms or a mild immune response to the disease, others are more severe and have long-lasting complications or fatal outcomes.

Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new “decision prioritization tool.”

The team’s new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects — and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher’s latest findings are the focus of a new study published October 21 in Nature Scientific Reports.

Artificial intelligence, protein drivers, and ’24 out of 26 clinical manifestations’ of Covid-19

The team’s new methodology, or tool, is called MOATAI-VIR (Mode Of Action proteins & Targeted therapeutic discovery driven by Artificial Intelligence for VIRuses. Researchers report it predicts 24 out of 26 major clinical manifestations of Covid-19 and their underlying disease-protein-pathway relationships. These clinical manifestations include acute respiratory distress, blood-clotting issues and cytokine storms. Low blood oxygen, white blood cell counts and bone marrow failure are all possible. The commonly reported loss of smell or taste, along with unusual neurological symptoms such as “brain fog” have received widespread attention — as have considerations for patients who have previous health problems, or comorbidities, that could place them in higher risk categories related to Covid-19.

“It’s still the question of, what’s causing the side effects?” says Jeffrey Skolnick, professor and Mary and Maisie Gibson Chair in the School of Biological Sciences, and corresponding author for the study. “So, you lose your sense of smell and get brain fog — another patient has respiratory distress and another cannot remember the day of week. What we’ve identified are the possible mode of action drivers for these various conditions, which is now setting the stage for who’s getting what side effects.”

Skolnick, also Georgia Research Alliance Eminent Scholar in Computational Systems Biology, notes that it makes sense to predict the side effects based on protein interactions.

“Humans can be described as molecular machines. Therefore, it makes sense to predict side effects based on protein interactions. He adds that the AI-based approach was built based on the interactivity of the [novel] coronavirus and the proteins in human bodies. “We then asked ourselves, ‘Could we predict, based on biochemical pathways, which interactive proteins are associated with side effects?’”

Joining Skolnick from the School of Biological Sciences are Ph.D. student Courtney Astore and senior research scientist Hongyi Zhou, both from the Center for the Study of Systems Biology. Joshy Jacob, from the Emory Vaccine Center at Emory School of Medicine, also participated in the study.

MOATAI-VIR Methodology

Skolnick explains that most known diseases are due to the “malfunction and interaction of many proteins,” and notes that it’s a collective effect — a “many-targeted protein effect.” His team’s new AI methodology is identifying as many targets as possible of an interacting nature to predict a complex response from a complex set of interactions.

It’s also well-understood in the medical community that comorbidities — existing and chronic health factors such as diabetes, obesity, autoimmune disorders, and other conditions that affect the immune system — can play an outsized a role in risk factors related to Covid-19. Skolnick also says that these comorbidities could be integrated into the team’s algorithm.

“Alzheimer’s and hyperthyroidism, as well as diabetes, are strongly correlated.” There are six to eight (Covid-19) comorbidities with a patient that has Alzheimer’s,” Skolnick explains. “It’s not just old age — it’s much more complicated.”

The MOATAI-VIR methodology helps identify the common proteins of the comorbidities in relation to the parent disease. The clinician can then use drugs to treat the disease. Researchers report that this specific methodology had 72% success in 123,146 drug-indication pair predictions found by Skolnick’s team. We prioritize proteins for a given disease by their association with the comorbid conditions that lead to that particular complication. He says this identifies the likely (assumed driver proteins) for the given complication. Then, we choose repurposed drug in two ways. First, we screen for the most common comorbid protein to determine their most frequent binding with repurposed medications. For the set of comorbid diseases to a given complication, choose the drugs that treat the most complications.”

It’s critical to find the right drugs for those complications and side effects — and using the new “decision tool” can help do that, Skolnick says.

He warns clinicians and researchers to be objective in approaching this issue. “There are deep reasons why these ‘off target’ interactions occur, where a drug binds with another protein that isn’t its intended target. These algorithms can help to counter these interactions. This methodology is not magical, Skolnick says. It’s fractional help in decision-making processes, which has probabilities [that] it might be useful. If there’s an 80 percent probability, you probably ought to try it.”

The MOATAI-VIR methodology algorithms can be downloaded at: https://sites.gatech.edu/cssb/moatai-vir/

This project was funded by R35GM118039 of the Division of General Medical Sciences of the National Institutes of Health. DOI: http://www.nature.com/articles/s41598-021-00368-6

The Georgia Institute of Technology, or Georgia Tech, is a top 10 public research university developing leaders who advance technology and improve the human condition. The Institute offers degrees in business, computing, engineering, design, and other sciences. Its nearly 40,000 students representing 50 states and 149 countries, study at the main campus in Atlanta, at campuses in France and China, and through distance and online learning. Georgia Tech, a world-class technological university, is a major driver of economic development in Georgia and the Southeast. It conducts more than $1 billion annually in research for industry, government, and society.

Written by: Renay San Miguel

Edited by: Jess Hunt-Ralston and Georgia Parmelee

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