NTM-host matched infection models for the classification of drug efficacy against rapid and slow growing nontuberculous mycobacteria species

Nontuberculous mycobacteria (NTM) are increasingly recognized as major causes of pulmonary disease worldwide. However, progress in identifying effective clinical treatments is difficult because standardized, high-burden preclinical models that enable rapid quantitative classification and comparison of drug performance in slow-growing mycobacteria (SGM) and rapid-growing mycobacteria (RGM) species are lacking. This study describes a framework for benchmarking treatment efficacy using NTM species-host matched infection models of Mycobacterium avium 2285 in immunocompetent C57BL/6 mice and Mycobacterium abscessus ATCC 19977 in immunodeficient NOD. CB17-Prkdcscid/NCrCrl (NOD-SCID) mice. A consistent high-burden respiratory infection is established by real-time quantification of viable inoculum, ensuring reproducible bacterial lung burden across experiments. The short-course treatment duration provides a rapid and resource-efficient therapeutic window for assessing pharmacological response. Analytical outputs integrate absolute CFU reduction with variance-adjusted effect size (Hedges’ g), categorical efficacy classification, and an MIC-Adjusted Clearance Index to generate potency-normalized measures of efficacy in these models. Performance was validated using a reference panel of antimicrobials representing diverse drug classes and mechanisms of action, including macrolides, rifamycins, fluoroquinolones, and diarylquinolines, to ensure broad benchmarking across pharmacological targets. The framework revealed consistent NTM species-specific patterns of drug performance, with higher potency-adjusted efficacy in M. avium than in M. abscessus, consistent with known clinical behavior. Together, these data establish a reproducible and standardized preclinical platform for early efficacy evaluation, enabling rapid, quantitative benchmarking across standardized RGM and SGM infection models, improving the translational predictability of NTM drug development.