Using a computer model to stimulate the effect of 3 fluoroquinolones against tuberculosis, researchers were able to narrow down which one was most effective.
Tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis. It is one of the oldest known diseases and remains one of the top 10 causes of death around the world. The infection is typically treated with a combination of antibiotics, and many times the combination includes a fluoroquinolone. Now, in a recent study, researchers have narrowed down which fluoroquinolone may be the better option.
The 3 fluoroquinolones that are available to treat tuberculosis are: moxifloxacin (MOXI), levofloxacin (LEVO), or gatifloxacin (GATI). Because “existing data from clinical trials and animal studies are inadequate to determine which is best or if all three are equivalent,” researchers from the University of Michigan in Ann Arbor, Michigan, and Rutgers University in Newark, New Jersey, “developed a computer model to simulate the effects of the 3 drugs on granulomas—clusters of host cells and bacteria that develop in the lungs of tuberculosis patients,” according to a press release on the study. The researchers created the model to “incorporate experimental data on the 3 drugs, as well as extensive knowledge on the chemistry of their activity.” They used a, “single mechanistic framework, GranSim—a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma, and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data,” according to the study, published in PLOS Computational Biology.
The researchers found that all 3 of the fluoroquinolones “struggled to sterilize non-replicating bacteria residing in caseum;” however, MOXI and LEVO, “had higher granuloma sterilization rates compared to GATI.” In addition, they found that MOXI performed, “better in a simulated non-compliance or treatment interruption scenario.” MOXI worked to kill the granulomas more quickly than the other 2 fluoroquinolones and appeared to perform better in simulations where patients missed doses.
When speaking about the results of the study in the press release, lead author Elsje Pienaar, PhD, from the University of Michigan stated, “The exciting thing about this study is that we are able perform a side-by-side comparison of fluoroquinolones in identical infections. The potential practical application of our findings is to guide selection of individual fluoroquinolones for tuberculosis treatment.” In their paper, the researchers illustrated, “how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for tuberculosis.”
This approach is particularly important as new antibiotics against tuberculosis are being created and tested. To optimize their use, providers need to know the best combination of medications that are most successful against the disease. The authors write, “Systems pharmacology approaches that combine host, pathogen, and antibiotic dynamics are … valuable in identifying promising treatment regimens to advance to animal and clinical studies.”
According to Dr. Pienaar, “the predictions of the simulations [from this study] are now being tested in animal experiments.” In addition, further computer model simulations are being created to test the 3 fluoroquinolones against other tuberculosis drugs to help guide the most successful treatment combinations against the disease.