Optimizing Antibiotic Treatments by Targeting Bacteria

A team from Duke University, led by Dr. Lingchong You, has investigated the growing problem of antibiotic-resistant pathogens and their responses to combination treatments. With resistance to frontline antibiotics rising globally, understanding bacterial adaptation is crucial for effective therapies.

Their study, published in Nature Communications, identifies the bacteria's level of "selfishness" as key to how they adapt to treatments. This insight can help clinicians tailor therapies to minimize the selection for resistance and develop new antibiotic resistance inhibitors.

Dr. You highlighted a notable increase in antibiotic-resistant infections in the American South, stressing the need for effective combination therapies to combat this global crisis. Understanding how to implement these treatments is essential to reducing resistance.

Beta-lactam antibiotics, such as penicillin, are among the most commonly used. Bacteria often become resistant by producing enzymes that degrade these drugs, prompting researchers to create inhibitors that target these enzymes, aiming to restore the antibiotics' effectiveness.

Previous research has yielded mixed results regarding how resistant infections respond to combination therapies. Some resistant bacteria thrived, while others were depleted, allowing sensitive bacteria to benefit instead.

The study distinguishes between selfish and generous bacterial behaviors regarding resistance enzymes. Selfish bacteria retain their enzymes better and thrive post-treatment, while generous strains share their resources, benefiting sensitive bacteria.

Dr. You's findings suggest that clinicians should consider the specific bacterial strain when using beta-lactam resistance inhibitors. By focusing on how these inhibitors penetrate bacterial membranes, doctors can more effectively target selfish strains, reducing the risk of further resistance development. Creating a database to quantify strain reactions to treatment combinations could enhance treatment outcomes significantly.