Tuberculosis is a serious global health threat that infected more than 10 million people in 2022. Spread through the air and into the lungs, the pathogen that causes “TB” can lead to chronic cough, chest pains, fatigue, fever and weight loss. While infections are more extensive in other parts of the world, a serious tuberculosis outbreak currently unfolding in Kansas has led to two deaths and has become one of the largest on record in the United States. While tuberculosis is typically treated with antibiotics, the rise of drug-resistant strains has led to an urgent need for new drug candidates.

A study published Feb. 6 in the Proceedings of the National Academy of Sciences describes the novel use of artificial intelligence to screen for antimicrobial compound candidates that could be developed into new tuberculosis drug treatments. The study was led by researchers at the University of California San Diego, Linnaeus Bioscience Inc. and the Center for Global Infectious Disease Research at the Seattle Children’s Research Institute.

Linnaeus Bioscience is a San Diego-based biotechnology company founded on technology developed in the UC San Diego School of Biological Sciences laboratories of Professor Joe  Pogliano and Dean Kit Pogliano. Their bacterial cytological profiling (BCP) method provides a shortcut for understanding how antibiotics function by rapidly determining their underlying mechanisms.

The search for new tuberculosis drug targets under traditional laboratory methods has historically proven to be arduous and time-consuming due in part to the difficulty of understanding how new drugs work against Mycobacterium tuberculosis, the bacterium that causes the disease. The new PNAS study describes the development of “MycoBCP,” a next-generation technology developed with funding from the Gates Foundation. The new method adapts BCP with deep learning — a type of artificial intelligence that uses brain-like neural networks — to overcome traditional challenges and open new views of Mycobacterium tuberculosis cells.

This is the first time that this kind of image analysis using machine learning and AI has been applied in this way to bacteria,” said paper co-author Joe Pogliano, a professor in the Department of Molecular Biology. “Tuberculosis images are inherently difficult to interpret by the human eye and traditional lab measurements. Machine learning is much more sensitive in being able to pick up the differences in shapes and patterns that are important for revealing underlying mechanisms.

Source: https://today.ucsd.edu/story/ai-accelerates-the-search-for-new-tuberculosis-drug-targets