Expanse Simulations Illustrate Combo-Antibiotic Plans for TB Treatments

Published March 28, 2024

By Kimberly Mann Bruch and Advay Shindikar, SDSC Communications

Many promising antibiotics have been discovered that would potentially outperform the current standard treatment for tuberculosis (TB), which kills 1.5 million people every year according to the World Health Organization. However, determining the best combination of medicines for TB treatment is challenging, expensive and time-consuming. For the past three decades, University of Michigan Professor of Microbiology and Immunology Denise Kirschner has been working on an array of research projects related to TB – including how best to configure appropriate treatments using computation modeling for TB patients.

Kirschner and her team most recently utilized ACCESS allocations on Expanse at the San Diego Supercomputer Center (SDSC) at UC San Diego and Anvil at Purdue University to generate models that showed how TB lesions treated with various combinations of drugs respond to various treatments of combined antibiotics. The results from their study were then compared with results from clinical trials and animal studies.

The researchers found that their supercomputer-generated computational models agreed with both the clinical trial and animal study results. They published the study details in CPT: Pharmacometrics & Systems Pharmacology.

“This process is a cheaper and faster alternative to traditional approaches where drugs are tested on humans or animals,” Kirschner said. “Our team’s modeling approach is a vital method of helping physicians narrow down possible drug combinations for TB and could be adapted for cancers that present solid tumors similar to the consistency of TB lesions.”

ACCESS allocations on Anvil at Purdue and Expanse at SDSC were used to show how computational modeling allows researchers to determine the best drug combinations for TB treatment. Credit: Kirschner Lab

The next step for Kirschner’s lab is to simulate drug combinations that haven’t been tested on humans or animals.

“Our ultimate goal is convincing experimental researchers to test our predicted optimal combinations,” she said. “And, without ACCESS resources it would not be possible to do this research, since the time on the other resources we have available would take years.”

The computational work for this project was conducted using ACCESS allocation no. MCB140228.