AI Accelerates the Search for New Tuberculosis Drug Targets

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."

Over two years in development, study lead authors Diana Quach and Joseph Sugie shaped the MycoBCP technology by training AI tools known as convolutional neural networks with more than 46,000 images of TB cells (now at Linnaeus Bioscience, Quach and Sugie both received their PhDs from the Shu Chien-Gene Lay Department of Bioengineering and completed postdoctoral appointments in the Pogliano labs in the Department of Molecular Biology).

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

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

"A critical component of progressing towards new drug candidates is defining how they work, which has been technically challenging and takes time," said Parish, a co-author of the study. "This technology expands and accelerates our ability to do this and allows us to prioritize which molecules to work on based on their mode of action. We were excited to collaborate with Linnaeus in their work to develop this technology to M. tuberculosis."

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

"We developed bacterial cytological profiling and it allowed us to look at bacterial cells in a new way," said Joe Pogliano. "It allowed us to really see how cells look after treatment with antibiotics so we could interpret their underlying mechanisms. We describe this method as equivalent to performing an autopsy on a bacterial cell."

Establishing Linnaeus Bioscience in the regional San Diego biotechnology hub allowed Joe and Kit Pogliano to push the BCP technology out into the marketplace, where other companies could have access to it. The company now receives samples from all over the world for rapid analysis and identification of new bacterial drug candidates.

Pogliano credits the biotechnology community, especially the company's early home in the San Diego JLABS incubator, which supports early stage biotech companies, as critical to the company's growth and success.

"We could not have gotten Linnaeus Bioscience off the ground if not for the supportive biotech community and the infrastructure provided at JLABS," said Pogliano. "All of the company's employees at Linnaeus obtained their PhDs at UC San Diego so this has become a great UC San Diego research, alumni and San Diego biotech community success story, culminating in this new AI platform to help solve the antibiotic resistance crisis."

Quach D, Sharp M, Ahmed S, Ames L, Bhagwat A, Deshpande A, Parish T, Pogliano J, Sugie J.
Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in Mycobacterium tuberculosis.
Proc Natl Acad Sci U S A. 2025 Feb 11;122(6):e2419813122. doi: 10.1073/pnas.2419813122

Most Popular Now

Researchers Find Telemedicine may Help R…

Low-value care - medical tests and procedures that provide little to no benefit to patients - contributes to excess medical spending and both direct and cascading harms to patients. A...

AI Revolutionizes Glaucoma Care

Imagine walking into a supermarket, train station, or shopping mall and having your eyes screened for glaucoma within seconds - no appointment needed. With the AI-based Glaucoma Screening (AI-GS) network...

AI may Help Clinicians Personalize Treat…

Individuals with generalized anxiety disorder (GAD), a condition characterized by daily excessive worry lasting at least six months, have a high relapse rate even after receiving treatment. Artificial intelligence (AI)...

Accelerating NHS Digital Maturity: Paper…

Digitised clinical noting at South Tees Hospitals NHS Foundation Trust is creating efficiencies for busy doctors and nurses. The trust’s CCIO Dr Andrew Adair, deputy CCIO Dr John Greenaway, and...

AI can Open Up Beds in the ICU

At the height of the COVID-19 pandemic, hospitals frequently ran short of beds in intensive care units. But even earlier, ICUs faced challenges in keeping beds available. With an aging...

Mobile App Tracking Blood Pressure Helps…

The AHOMKA platform, an innovative mobile app for patient-to-provider communication that developed through a collaboration between the School of Engineering and leading medical institutions in Ghana, has yielded positive results...

Can AI Help Detect Cognitive Impairment?

Mild cognitive impairment (MCI) can be an early indicator of Alzheimer's disease or dementia, so identifying those with cognitive issues early could lead to interventions and better outcomes. But diagnosing...

Customized Smartphone App Shows Promise …

A growing body of research indicates that older adults in assisted living facilities can delay or even prevent cognitive decline through interventions that combine multiple activities, such as improving diet...

AI Model Predicting Two-Year Risk of Com…

AFib (short for atrial fibrillation), a common heart rhythm disorder in adults, can have disastrous consequences including life-threatening blood clots and stroke if left undetected or untreated. A new study...

New Study Shows Promise for Gamified mHe…

A new study published in Multiple Sclerosis and Related Disorders highlights the potential of More Stamina, a gamified mobile health (mHealth) app designed to help people with Multiple Sclerosis (MS)...

Patients' Affinity for AI Messages …

In a Duke Health-led survey, patients who were shown messages written either by artificial intelligence (AI) or human clinicians indicated a preference for responses drafted by AI over a human...

New Research Explores How AI can Build T…

In today’s economy, many workers have transitioned from manual labor toward knowledge work, a move driven primarily by technological advances, and workers in this domain face challenges around managing non-routine...