The Future of Drug Discoveries: Target Identification and Artificial Intelligence

GRC 2023 Global Essay Competition Top 30

By Kevin Chung

As the Human African Trypanosomiasis (HAT) disease unfurls throughout sub-Saharan Africa, target identification using artificial intelligence (AI) powered analytics tools and software could be the key to finding the cure for this endemic. AI technology can not only advance research but also expedite the process of developing treatments and cures for global health issues. To hasten the process of finding an accurate cure, researchers can take advantage of AI to analyze “graphs, which capture detailed information about objects or concepts — such as genes, drugs, diseases or molecular pathways” and find “the relationships between them.” However, the process of creating a remedy for the disease is complicated and time-consuming, for a drug could “target and modulate the disease in a particular way in a particular patient population” and could “turn out to be wrong.” Additionally, with the difficulty of replicating complex body parts, scientists may struggle to find a cure for lung disease — for instance — as they “must be able to model all parts of the lung, including the small airway epithelium, alveoli, fibroblasts and immune cells, and combine them in both structural and functional ways to mimic the disease phenotype.” Even with these difficulties, there are already visible benefits of using AI to improve the speed and accuracy of diagnosis and screening for diseases and to strengthen research and drug development. The advancements in AI technology “make it possible to carry out multi-omic characteri[z]ation of tissues from thousands of patients.” There is tremendous potential for artificial intelligence tools to stimulate progress toward the advancement of global health issues.
HAT, a severe disease in Sub-Saharan Africa, has been infecting nearly half of Africa leading to a high mortality rate. HAT infects around “10,000 recorded cases per year,” and considering non-recorded cases, it could be “around 30,000 cases per year (Brun et al. 2011).” The spread of this endemic demonstrates why the speed and accuracy of target identification using AI can be so significant. Further adding to the desperate situation, HAT has two stages: “[S]tage 1 is the peripheral infection, which has non-specific symptoms and stage 2 occurs when the parasite enters the central nervous system (CNS). HAT is fatal unless treated.” Current treatments are “inappropriate for a rural setting with poor facilities as they all require parenteral administration. There is [a] need for new treatments for HAT, for the reasons given above, but also with the aim of elimination and eradication of this disease.” As eradicating this deadly disease is important, discovering the connections among the vast data may be crucial in developing better treatment methods and even a cure.
Researchers now see great benefits of using AI in dealing with global health issues especially because “artificial intelligence (AI) is playing a growing role in modern drug target identification” by analyzing large datasets and complex biological networks. Whereas scientists could analyze a limited number of patients with inaccuracy, now they can identify the major disease target through AI’s near-perfect analysis of data. More specifically, by integrating AI into health and medical sciences (see Figure 1), researchers can yield a higher success rate in finding an accurate antibiotic. AI’s ability to reduce human errors, coupled with its ability to quickly find the connections among the data will ensure that a solution is discovered. The stages (each indicated with a blue arrow in Figure 1) portray AI’s ability to systematically find a cure for a desired disease. Similar to the AI’s involvement in target identification for the HAT endemic, AI can be used to discover cures for major diseases around the world that need large amounts of data to be processed. As both Figure 1 and Figure 2 demonstrate a step-by-step process of the AI discovering a cure for a target, the possibility of a mistake reduces while the chance of a successful antibiotic being discovered increases. AI can help researchers discover new drugs and treatments (see Figure 2) and ultimately advance the field of health and medical sciences. However, “AI is still in its early stages[, and] it cannot replace the human element.” Even though AI is still relatively new and requires human oversight, its help in assisting doctors and scientists to discover cures is immeasurable.
AI has influenced the medical field tremendously, especially in the field of target identification. As target identification with AI can assess massive amounts of data, find connections with near perfect accuracy, and limit possible errors, the advancement of AI seems to be the perfect antidote to finding novel and improved means to develop cures for diseases and endemics.

Bibliography

Abioye, Helen. “Artificial Intelligence in Drug Delivery and Development.” Medium, September 24, 2022.

https://helendarmi-hd.medium.com/artificial-intelligence-in-drug-delivery-a755c0409 78a.

Davenport, Thomas, and Ravi Kalakota. “The Potential for Artificial Intelligence in Healthcare.” Future Healthcare Journal 6, no. 2 (June 6, 2019): 94–98. https://doi.org/10.7861/futurehosp.6-2-94.

GILBERT, IAN H. “Target-Based Drug Discovery for Human African Trypanosomiasis: Selection of Molecular Target and Chemical Matter.” Parasitology 141, no. 1 (August 9, 2013): 28–36. https://doi.org/10.1017/S0031182013001017.

James, Ted. “How Artificial Intelligence Is Disrupting Medicine and What It Means for Physicians.” postgraduateeducation.hms.harvard.edu, April 13, 2023. https://postgraduateeducation.hms.harvard.edu/trends-medicine/how-artificial-intellig ence-disrupting-medicine-what-means-physicians.

Pun, Frank W, Ivan V Ozerov, and Alex Zhavoronkov. “AI-Powered Therapeutic Target Discovery.” Trends in Pharmacological Sciences 44, no. 9 (July 19, 2023): 561–72. https://doi.org/10.1016/j.tips.2023.06.010.

Rasooly, Danielle, and Muin Khoury. “Artificial Intelligence in Medicine and Public Health: Prospects and Challenges beyond the Pandemic | Blogs | CDC.” Centers for Disease Control and Prevention , March 1, 2022. https://blogs.cdc.gov/genomics/2022/03/01/artificial-intelligence-2/.

Rees, Steve, Henric Olsson, and Benjamin Challis. “Hitting the Bullseye: Finding Better Targets for Drug Development.” AstraZeneca. AstraZeneca, April 21, 2021. https://www.astrazeneca.com/what-science-can-do/topics/disease-understanding/Hitti ng-the-bullseye-finding-better-targets-for-drug-development.html#!.