Powerful and Real-Time Quantification of Antifungal Efficacy against Triazole-Resistant and -Susceptible Aspergillus fumigatus Infections in Galleria mellonella by Longitudinal Bioluminescence Imaging.

Aspergillus fumigatus is an environmental mold that causes life-threatening respiratory infections in immunocompromised patients. The plateaued effectiveness of antifungal therapy and the increasing prevalence of triazole-resistant isolates have led to an urgent need to optimize and expand the current treatment options. For the transition of research to models in the time- and resource-consuming preclinical drug development pipeline, Galleria mellonella larvae have been introduced as a valuable screening intermediate. Despite the high potential of this model, the current readouts of fungal infections in G. mellonella are insensitive, irreproducible, or invasive. To optimize this model, we aimed for the longitudinal quantification of the A. fumigatus burden in G. mellonella using noninvasive bioluminescence imaging (BLI). Larvae were infected with A. fumigatus strains expressing a red-shifted firefly luciferase, and the substrate dosage was optimized for the longitudinal visualization of the fungal burden without affecting larval health. The resulting photon flux was successfully validated for fungal quantification against colony forming units (CFU) analyses, which revealed an increased dynamic range from BLI detection. Comparison of BLI to survival rates and health index scores additionally revealed improved sensitivity for the early discrimination of differences in fungal burdens as early as 1 day after infection. This was confirmed by the improved detection of treatment efficacy against triazole-susceptible and -resistant strains. In conclusion, we established a refined G. mellonella aspergillosis model that enables the noninvasive real-time quantification of A. fumigatus by BLI. This model provides a quick and reproducible system for the evaluation of treatment options and is in line with 3Rs recommendations. Triazole-resistant Aspergillus fumigatus strains are rapidly emerging, and resistant infections are difficult to treat, causing mortality rates of up to 88%. The recent WHO priority list underscores A. fumigatus as one of the most critical fungal pathogens for which innovative antifungal treatment should be (urgently) prioritized. Here, we deliver a Galleria mellonella model for triazole-susceptible and -resistant A. fumigatus infections combined with a statistically powerful quantitative, longitudinal readout of the A. fumigatus burden for optimized preclinical antifungal screening. G. mellonella larvae are a convenient invertebrate model for antifungal screenings, but so far, the model has been limited by variable and insensitive observational readouts. We show that bioluminescence imaging-based fungal burden quantification outperforms these readouts in reliability, sensitivity, and time to the detection of treatment effects in both triazole-susceptible and -resistant infections and can thus lead to better translatability from antifungal screening results to confirmation in mouse and human studies.