Micro-CT-assisted identification of the optimal time-window for antifibrotic treatment in a bleomycin mouse model of long-lasting pulmonary fibrosis.

Idiopathic Pulmonary Fibrosis (IPF) is a debilitating and fatal lung disease characterized by the excessive formation of scar tissue and decline of lung function. Despite extensive research, only two FDA-approved drugs exist for IPF, with limited efficacy and relevant side effects. Thus, there is an urgent need for new effective therapies, whose discovery strongly relies on IPF animal models. Despite some limitations, the Bleomycin (BLM)-induced lung fibrosis mouse model is widely used for antifibrotic drug discovery and for investigating disease pathogenesis. The initial acute inflammation triggered by BLM instillation and the spontaneous fibrosis resolution that occurs after 3 weeks are the major drawbacks of this system. In the present study, we applied micro-CT technology to a longer-lasting, triple BLM administration fibrosis mouse model to define the best time-window for Nintedanib (NINT) treatment. Two different treatment regimens were examined, with a daily NINT administration from day 7 to 28 (NINT 7-28), and from day 14 to 28 (NINT 14-28). For the first time, we automatically derived both morphological and functional readouts from longitudinal micro-CT. NINT 14-28 showed significant effects on morphological parameters after just 1 week of treatment, while no modulations of these biomarkers were observed during the preceding 7-14-days period, likely due to persistent inflammation. Micro-CT morphological data evaluated on day 28 were confirmed by lung histology and bronchoalveolar lavage fluid (BALF) cells; Once again, the NINT 7-21 regimen did not provide substantial benefits over the NINT 14-28. Interestingly, both NINT treatments failed to improve micro-CT-derived functional parameters. Altogether, our findings support the need for optimized protocols in preclinical studies to expedite the drug discovery process for antifibrotic agents. This study represents a significant advancement in pulmonary fibrosis animal modeling and antifibrotic treatment understanding, with the potential for improved translatability through the concurrent structural-functional analysis offered by longitudinal micro-CT.

Multiphase micro-computed tomography reconstructions provide dynamic respiratory function in a mouse lung fibrosis model.

Micro-computed tomography derived functional biomarkers used in lung disease research can significantly complement end-stage histomorphometric measures while also allowing for longitudinal studies. However, no approach for visualizing lung dynamics across a full respiratory cycle has yet been described. Using bleomycin-induced lung fibrosis and the antifibrotic drug nintedanib as a test model, we implemented a four-dimensional (4D) micro-CT imaging approach consisting of 30 reconstructed volumes per respiratory cycle, coupled with deep-learning-assisted segmentation of lung volumes. 4D micro-CT provided an accurate description of inhalatory and exhalatory lung dynamics under resting conditions and revealed an inflammation-related obstructive pattern at day 7, followed by a restrictive pattern associated with fibrosis development at day 21. A milder restriction and fibrotic pathology resulted from nintedanib treatment. The similarity of 4D micro-CT data with those produced by diagnostic measurements, also points to its great potential as an exploratory tool for the discovery of clinically relevant therapeutic compounds.

A fully automated micro­CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory µCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.