The interest in the lung microbiome and virome and their contribution to the pathogenesis, perpetuation and progression of idiopathic pulmonary fibrosis (IPF) has been increasing during the last decade. The utilization of high-throughput sequencing to detect microbial and/or viral genetic material in bronchoalveolar lavage fluid or lung tissue samples has amplified the ability to identify and quantify specific microbial and viral populations. In stable IPF, higher microbial burden is associated with worse prognosis but no specific microbe has been identified to contribute to this. Additionally, no causative relation has been established. Regarding viral infections, although in the past they have been associated with IPF, causation has not been proved. Although in the past the diagnosis of acute exacerbation of IPF (AE-IPF) was not considered in patients with overt infection, this was amended in the last few years and infection is considered a cause for exacerbation. Besides this, a higher microbial burden has been found in the lungs of patients with AE-IPF and an association with higher morbidity and mortality has been confirmed. In contrast, an association of AE-IPF with viral infection has not been established. Despite the progress during the last decade, a comprehensive knowledge of the microbiome and virome in IPF and their role in disease pathogenesis are yet elusive. Although association with disease severity, risk for progression and mortality has been established, causation has not been proven and the potential use as a biomarker or the benefits of antimicrobial therapeutic strategies are yet to be determined.

Introduction: Treatment of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) often includes systemic corticosteroids. Use of steroid-sparing agents is amenable to avoid potential side effects. Methods: Functional indices and high-resolution computed tomography (HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two independent radiologists and a machine learning software system (Imbio 1.4.2.) evaluated HRCT patterns. Results: Fifty-five (n = 55) patients were included in the analysis (male: 30 [55%], median age: 65.0 [95% CI: 59.7–70.0], mean forced vital capacity %predicted [FVC %pred.] ± standard deviation [SD]: 69.4 ± 18.3, mean diffusing capacity of lung for carbon monoxide %pred. ± SD: 40.8 ± 14.3, hypersensitivity pneumonitis: 26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7). There was no significant difference in mean FVC %pred. post-6 months (1.59 ± 2.04) and 1 year (−0.39 ± 2.49) of treatment compared to baseline. Radiographic evaluation showed no significant difference between baseline and post-1 year %ground glass opacities (20.0 [95% CI: 14.4–30.0] vs. 20.0 [95% CI: 14.4–25.6]) and %reticulation (5.0 [95% CI: 2.0–15.6] vs. 7.5 [95% CI: 2.0–17.5]). A similar performance between expert radiologists and Imbio software analysis was observed in assessing ground glass opacities (intraclass correlation coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen patients (25.5%) reported at least one side effect and 8 patients (14.5%) switched to antifibrotics due to disease progression. Conclusion: Our data suggest that MMF is a safe and effective steroid-sparing agent leading to disease stabilization in a proportion of patients with non-IPF ILDs. Machine learning software systems may exhibit similar performance to specialist radiologists and represent fruitful diagnostic and prognostic tools.