As the world faces the devastating impact of the COVID-19 pandemic, scientists and researchers are tirelessly searching for solutions to combat future outbreaks. Amid the race to find effective treatments and vaccines, one tool is emerging as a game-changer in the fight against pandemics: algorithmic drug discovery. By leveraging artificial intelligence and machine learning, scientists can rapidly identify existing drugs with the potential to treat a pathogen and reduce the time required to bring a new treatment to market. In this article, we will explore how algorithmic drug discovery works and its potential to revolutionize our approach to tackling the next pandemic.
A team of global researchers has created an algorithmic tool to identify existing drugs to combat future pandemics. The work, which was reported in the Cell Press journal Heliyon, offers the possibility of responding more quickly to public health crises such as COVID-19. The team, which includes immunologists, biologists, chemists, data scientists, and physicians, among others, sought to develop innovative strategies to address COVID-19 through repurposing existing drugs to fight back.
The tool, PHENotype SIMulator (PHENSIM), simulates tissue-specific infection of host cells of SARS-CoV-2 and then performs a series of computer simulations, identifying drugs that could be candidates for repurposing. The algorithm computes, taking into account selected cells, cell lines, and tissues and under an array of contexts, by propagating the effects and alterations of biomolecules—such as differentially expressed genes, proteins, and microRNAs—and then calculates antiviral effects. The team confirmed the validity of the tool by comparing its results with recently published in vitro studies.
COVID-19 has been a daunting challenge over the past three years, even though vaccines and hygienic practices have, over time, lessened its severity. However, despite these tools to combat it, SARS-CoV-2—the virus that causes COVID-19—continues to spread and take lives. This is due, in part, to its ability to rapidly diversify in its target cell types, immune-response pathways, and modes of transmission. These traits make traditional approaches to vaccine and drug design less effective than in the past, especially when the virus co-infects with other pathogens, such as RSV and influenza.
“There is no silver bullet to defeat the COVID pandemic as it takes us over a public-health roller-coaster of deaths and devastation,” explains Naomi Maria, an immunologist, a visiting scientist at New York University’s Courant Institute of Mathematical Sciences, and the paper’s lead author. “However, using this AI tool, coupled with in vitro data and other resources, we’ve been able to model the SARS-CoV-2 infection and identify several COVID-19 drugs currently available as potentially effective in battling the next outbreak.”
Drug repurposing strategies provide an attractive and effective approach for quickly targeting potential new interventions. By identifying and selecting ahead of time the best candidates, prior to costly and laborious in vitro and in vivo experiments and ensuing clinical trials, disease-specific drug development could be significantly improved. Therefore, PHENSIM could significantly improve disease-specific drug development by selecting the best candidates ahead of costly in vitro and in vivo experiments and ensuing clinical trials.
As we continue to navigate the ongoing COVID-19 pandemic, it’s clear that we need to be better prepared for future outbreaks. Fortunately, new tools like an algorithmic drug screening platform offer hope in the fight against future pandemics. By quickly identifying existing drugs that may be effective against emerging diseases, we can better equip ourselves to respond quickly and effectively to prevent the spread of infection. With continued investment and innovation in this field, we can look towards a healthier, safer future for us all.
