Antidepressants can boost antibiotic resistance

"Sertraline, duloxetine, and fluoxetine had the strongest impact on bacterial resistance to antibiotics among the drugs we tested," says Jianhua Guo. "Our study showed a marked increase in antibiotic resistance from those three, even at very low doses." (Credit: Michał Parzuchowski/Unsplash)

A range of commonly prescribed antidepressants can increase bacteria’s resistance to antibiotic medications, a new study finds.

The researchers focused on prescription drugs used to treat depressive disorders, anxiety disorders, and other psychological conditions.

More than 42 million prescriptions were dispensed for antidepressant medications in Australia in 2021 and the researchers investigated bacterial exposure to five of the most common drugs: sertraline (Zoloft), escitalopram (Lexapro), bupropion (Welbutrin), duloxetine (Cymbalta), and agomelatine (Valdoxan).

“While the overuse of antibiotics is acknowledged as the major driver of bacterial resistance, we wanted to investigate if other common medications were contributing to the problem,” says Jianhua Guo, a professor at the University of Queensland’s Australian Centre for Water and Environmental Biotechnology.

“Sertraline, duloxetine, and fluoxetine had the strongest impact on bacterial resistance to antibiotics among the drugs we tested. Our study showed a marked increase in antibiotic resistance from those three, even at very low doses.

“Notably, the antibiotic resistance appears to be antidepressant-dependent, which may be due to oxidative stress in bacteria posed by antidepressants. Further studies need to evaluate the potential effects on the microbiomes of people given antidepressants and assess their risk gastrointestinal disturbances or diseases.”

It is estimated 1.27 million people die every year from infections which do not respond to medication and the figure is predicted to reach 10 million by 2050 unless global action is taken.

The study, published in the Proceedings of the National Academy of Sciences, was funded by the Australian Research Council Discovery Project, and the UQ Foundation Research Excellence Awards.

Source: University of Queensland