Imagine you have 20 new compounds that have shown some effectiveness in treating a disease like tuberculosis (TB), which infects 10 million people worldwide and kills 1.5 million each year. For effective treatment, patients will need to take a combination of three or four drugs for months or even years because tuberculosis bacteria behave differently in different environments in cells — and in some cases develop to become drug-resistant. Twenty compounds in combinations of three and four drugs offer nearly 6,000 possible combinations. How do you decide which drugs to test together?
In a recent study published in the September issue of Medicine Cell ReportsIn this study, researchers from Tufts University used data from large studies containing laboratory measurements of combinations of two 12 anti-tuberculosis drugs. Using mathematical models, the team discovered a set of rules that drug pairs must meet to be good potential treatments as part of a three- and four-drug combination.
The use of drug pairs rather than measuring the combination of three and four drugs significantly reduces the amount of testing that must be performed before moving the drug combination to further study.
Using the design rules we have created and tested, we can substitute one drug pair for another drug pair and know with a high degree of confidence that the drug pair must work in concert with the other drug pair to kill tuberculosis bacteria in the rodent model. The selection process we developed is much simpler and more accurate in predicting success than previous processes, which are essentially fewer groups.”
Bree Aldridge, Tufts University
Brie Aldridge is Associate Professor of Molecular Biology and Microbiology in the Tufts University School of Medicine and Biomedical Engineering in the School of Engineering, and a faculty member in the Molecular Immunology and Microbiology Program in the Graduate School of Biomedical Sciences.
The Aldridge lab, who is the corresponding author on the paper and co-director of the Tufts Stuart B. Levy Center for the Integrated Management of Antimicrobial Resistance, previously developed and used DiaMOND, or the n-way diagonal measurement of drug interactions, a systematic method for studying pairwise and high-order drug interactions to identify shorter treatment regimens The most efficient for tuberculosis and other potential bacterial infections. With the design rules established in this new study, the researchers believe they can increase the speed at which scientists identify drug combinations that are most effective in treating tuberculosis, the world’s second leading infectious killer.
The research in this article was supported by the Bill & Melinda Gates Foundation under award number OPP1189457 and by the National Institutes of Health under award number 1U54CA225088. Full information on authors, funders, and conflicts of interest is available in the published paper. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.