AI-based technology is accelerating the discovery of new tuberculosis drug candidates

AI-based technology is accelerating the discovery of new tuberculosis drug candidates

Tuberculosis is a serious global health threat that has infected more than 10 million people in 2022. Through the air and into the lungs, the pathogen that causes “TB” causes chronic cough, chest pain, fatigue, fever and weight loss. While infections are more extensive in other parts of the world, a serious tuberculosis outbreak in Kansas has resulted in two deaths and has become one of the largest 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 new study published in theProceedings of the National Academy of SciencesDescribes the novel use of artificial intelligence to screen for candidate antimicrobial compounds that could be developed into new tuberculosis drug treatments. The study was led by researchers from the University of California San Diego, Linnaeus Bioscience Inc. and the Center for Global Infectious Disease Research at Seattle Children's Research Institute.

Linnaeus Bioscience is a San Diego-based biotechnology company founded on technology developed at the UC San Diego School of Biological Sciences Laboratories by Professor Joe Pogliano and Dean Kit Pogliano. The BCP (Bacterial Cytological Profiling) method provides a shortcut to understanding how antibiotics work by quickly determining their underlying mechanisms.

Finding new tuberculosis drug targets using traditional laboratory methods has historically proven laborious and time-consuming, due in part to the difficulty of understanding how new drugs workMycobacterium tuberculosisthe bacterium that causes the disease.

The newPNAsThe study describes the development of “MycOBCP,” a next-generation technology developed with funding from the Gates Foundation. The new method adapts BCP to deep learning – a type of artificial intelligence that uses brain-like neural networks – to overcome traditional challenges and open up new perspectivesMycobacterium tuberculosiscells.

This is the first time this type of image analysis using machine learning and AI has been applied to bacteria in this way. Tuberculosis images are inherently difficult to interpret by the human eye and traditional laboratory measurements. Machine learning is much more sensitive when it comes to picking up the differences in shapes and patterns that are important for uncovering the underlying mechanisms. “

Joe Pogliano, paper co-author, professor in the Department of Molecular Biology

In two years in development, lead authors Diana Quach and Joseph Sugie shaped the MycobCP technology through training from KI Shu Chien-Gene Lay Department of Bioengineering and completed postdoctoral appointments in the Pogliano Laboratories in the Department of Molecular Biology).

“Tuberculosis cells are clumpy and always seem to stick close together, so defining cell boundaries didn’t seem possible,” said Sugie, chief technology officer at Linnaeus Bioscience. “Instead, we jumped straight into having the computer analyze the patterns in the images for us.”

Linnaeus teamed up with tuberculosis expert Tanya Parish of the Seattle Children's Research Institute to develop BCP for mycobacteria. The new system has already significantly accelerated the team's TB research capabilities and helped identify optimal candidate compounds for drug development.

“A critical part of advancing new drug candidates is defining how they work, which has been technically challenging and takes time,” said Parish, co-author of the study. “This technology expands and accelerates our ability to do this, allowing us to prioritize which molecules to work on based on how they actM. Tuberculosis. “

UC San Diego Biotech Spinoffs Addresses the Global Health Challenge

Linnaeus Bioscience was developed in 2012 with a UC San Diego that promised to change the face of how antibiotics work.

“We developed bacterial cytological profiling and it allowed us to look at bacterial cells in new ways,” said Joe Pogliano. “It allowed us toReally seeHow cells deal with antibiotic treatment so we can interpret their underlying mechanisms. We describe this method as equivalent to performing an autopsy in a bacterial cell. “

Linnaeus Bioscience's establishment at the San Diego Biotechnology regional hub allowed Joe and Kit Pogliano to bring the BCP technology to market where other companies could have access to it. The company now receives samples from around the world for rapid analysis and identification of new bacterial drug candidates.

Pogliano credits the biotech community, particularly the company's early home at the San Diego JLABS Incubator, for supporting early-stage biotech companies that is critical to the company's growth and success.

“We would not have been able to get Linnaeus Bioscience off the ground if not for the supportive biotech community and infrastructure provided in JLABS,” said Pogliano. “All Linnaeus employees received their doctoral degrees from UC San Diego.

In addition to Quach, Pogliano and Sugie, the newspaper's collaborations include Marc Sharp, Sara Ahmed, Lauren Ames, Amala Bhagwat, Aditi Deshpande and Tanya Parish.

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