AI is becoming ever more intertwined with every area of medicine, from family practice to radiology. That influence includes the field of autoimmune and autoinflammatory disorders (AIIDs).
Increasingly, the field of immunology is benefiting from AI and machine learning, as existing and new models yield fresh insights into immune diseases. In particular, the creation of a new AI algorithm has the potential to generate new treatments and improve the batting average of disease prediction (1).
With AI on the AIID case, the potential for future breakthroughs seems limitless, especially withf identifying genes involved with immune diseases and then developing drugs to treat those diseases. With AI modeling, such drugs can be tested with confidence before they are ever used on a real person. It’s a win-win for researchers and patients (2).
Artificial Intelligence Pinpoints Autoimmune Factors
In most medical areas, AI tends to improve efficiency and shorten time to diagnosis, but there are some specific pluses in the realm of immunology. Philippe Moingeon writes about some of the benefits of AI for AIID study:
“AI-based predictive models are used to (i) stratify patients into homogeneous clusters, (ii) represent the pathophysiology as a perturbed biological system with inferences of causality, (iii) design and optimize drug candidates or combination therapies, and (iv) evaluate the efficacy and safety of drug candidates in virtual patient models” (2).
The first point is a huge time saver, sparing any manual or otherwise less efficient method of sorting patients into similar groups. The second point allows the imaginative potential of AI to go to work with full predictive power. The third and fourth points, obviously, can pay dividends for patients in the form of creating and evaluating potential drugs. All of the above equals a huge gain for the field of AIID and a greater gain for patients.
Moingeon adds,
“AI fosters the evolution of a computational precision medicine aiming to relate individual patient characteristics with predicted properties of drug candidates so as to offer more personalized treatments for AIIDs” (2).
That personalized treatment could make all the difference in the world for people with AIIDs. Regarding AIIDS, treatment is far from one-size-fits-all, and the potential for such individualized, gene-focused, AI-driven care is a game-changer.
A New AI Algorithm Cracks the Code
A recent study is a powerful example of AI’s potential with AIIDs. A new AI algorithm has proven fruitful in identifying key genes related to AIIDs. Lida Wang et al explain the context of their achievement:
“Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants” (1).
In other words, EXPRESSO is a new AI model that can analyze the latest autoimmune data—data that is similarly on the cutting edge of science. As a result of this new method and data, new insights are possible: in particular, the identification of “958 novel gene x trait associations, which is 26% more than the second-best method” (1).
Understanding which genes and traits are linked clears the way to develop more precise drug treatments. Autoimmune diseases have always been tough to treat because we didn’t have detailed knowledge of the genes involved. It’s like trying to hit a target you can’t see (1).
With better insights into these genes, now we can create drugs that genuinely help people instead of just taking a random shot.
One Size May Fit Not All, But Some
One giant benefit of AI-propelled research is the potential for individualized, tailored treatment. But such research also yields some intriguing commonalities, as Moingeon wrote of recent gene discoveries:
“Common signatures involved upregulated genes associated with inflammatory responses, type I IFN signaling, cytokine-mediated signaling, and mitotic cell-cycle and apoptotic processes. These results have important implications because they indicate that well-designed drugs could be used to treat multiple AIIDs” (1).
In other words, finding similarities between genes of people with different immune diseases is a discovery with great promise. If a new drug can treat more than one AIID, all the better, since more people could potentially be helped. And AI can help identify the genes and design the drugs.
Predicting AIIDs with AI
Through identifying relevant genes and developing targeted drugs, AI could mean a sea change in the treatment of immune diseases. Drug treatments tailored to individuals may be possible, and as more knowledge is gained in a broader sense, more and more patients may be helped.
But such gene research can also help predict who is likely to develop an AIID. An ounce of prevention could be worth many pounds of treatment for patients with markers for AIID.
Such sufferers have gained a powerful ally in AI. As Siddiqui, F. et al write,
“Programs having AI capabilities can contextualize and analyze data to deliver information or automatically initiate operations without the need for human intervention…to make ever-better judgments using algorithms to discover patterns and acquire insights from data automatically (3).
When the human factor is added on top of those automatic insights, it’s a good day at the office for doctors who can identify AIIDs, or the potential for AIIDs, sooner than ever before. That’s a seriously good day for patients as well, thanks to researchers putting the AI in AIID.
A Light at the End of the Tunnel
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Understanding the intricacies of AI’s impact on immunology can be challenging. Our team excels at distilling this complex information into digestible, engaging content that informs and inspires action.
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References
Wang, L., Khunsriraksakul, C., Markus, H. et al. (2024). Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nature Communications, 15, 4260.
Moingeon, P. (2023). Artificial intelligence-driven drug development against autoimmune diseases. Trends in Pharmacological Sciences, 44(7), 411-424.
Siddiqui, F., Aslam, D., Tanveer, K., Soudy, M. (2024). The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. In: Raza, K., Singh, S. (eds) Artificial Intelligence and Autoimmune Diseases. Studies in Computational Intelligence, vol 1133. Springer, Singapore.
