UCLA

Combinational therapy has been one method used to combat the growing concern of multi-antibiotic resistant bacteria. In identifying the interaction type of a drug combination, the focus is often on the overall effect of the combination derived from comparison to each drug alone. These identifications tend away from suppression, where bacteria grow better in combination than when treated with a single drug component. They also miss “hidden” cases of suppression, where the highest-order can be suppressive to a lower-order but not to a single drug. We examined an extensive dataset of 5-drug and all lower-order combinations using computational methods and regression analysis and found that over half of combinations contain hidden suppression. My specific focus was on examining possible structures of hidden suppression at these higher orders. Overall, understanding this is important because of how it can affect our predictions of antibiotic resistance evolution in combinational treatments.