Preclinical murine models for the testing of antimicrobials against Mycobacterium abscessus pulmonary infections: Current practices and recommendations

Abstract

Mycobacterium abscessus, a rapidly growing nontuberculous mycobacterium, is increasingly recognized as an important pathogen of the human lung, disproportionally affecting people with cystic fibrosis (CF) and other susceptible individuals with non-CF bronchiectasis and compromised immune functions. M. abscessus infections are extremely difficult to treat due to intrinsic resistance to many antibiotics, including most anti-tuberculous drugs. Current standard-of-care chemotherapy is long, includes multiple oral and parenteral repurposed drugs, and is associated with significant toxicity. The development of more effective oral antibiotics to treat M. abscessus infections has thus emerged as a high priority. While murine models have proven instrumental in predicting the efficacy of therapeutic treatments for M. tuberculosis infections, the preclinical evaluation of drugs against M. abscessus infections has proven more challenging due to the difficulty of establishing a progressive, sustained, pulmonary infection with this pathogen in mice. To address this issue, a series of three workshops were hosted in 2023 by the Cystic Fibrosis Foundation (CFF) and the National Institute of Allergy and Infectious Diseases (NIAID) to review the current murine models of M. abscessus infections, discuss current challenges and identify priorities toward establishing validated and globally harmonized preclinical models. This paper summarizes the key points from these workshops. The hope is that the recommendations that emerged from this exercise will facilitate the implementation of informative murine models of therapeutic efficacy testing across laboratories, improve reproducibility from lab-to-lab and accelerate preclinical-to-clinical translation.

Keywords: Mycobacterium abscessus; Nontuberculous mycobacteria; cystic fibrosis; drug efficacy studies; lung infection; mouse models of infection.