Raising diagnostic accuracy and therapeutic perspectives in interstitial lung diseases

儿童和成人间质性肺疾病（ILD）是一组异质性的疾病，包括>100种不同的罕见疾病，其共同的命运是进行性疾病，并最终导致死亡。除了两种新型抗纤维化药物仅被批准用于IPF和类固醇/免疫抑制剂用于炎症驱动的ILD外，治疗选择很少，肺移植是唯一的治愈选择。同样，在大多数情况下，做出正确的诊断是困难的，特别是当病人病得太重而不能接受侵入性手术时。在此背景下，其领域的领先专家加入了这一倡议，以i）开发新的，主要是非侵入性的诊断算法和生物标志物，ii）更好地了解自然病程和间歇性加重的作用，iii）开发更好的预后模型和生物标志物，iv）更好地了解基因型/表型关系，v）通过新的治疗生物标志物先验预测治疗反应。基于现有的儿科（child-EU）和成人（eurIPFreg）登记/生物库，我们的目标是纳入另外5000例ILD患者和> 100.000种生物材料。除了深度表型分析，我们还打算进行日常手持式肺量计、加速度计和饱和度测量，定期捕获基于挥发性有机溶剂（VOC）的特征（通过授予的“Sniffphone”），在呼出气冷凝物（EBC）中应用蛋白质组学/基因组学，在EDTA血液中进行深度测序，使用人工智能进行CT解释，最后在大数据分析的基础上建立疾病模型。

Pediatric and adult Interstitial Lung Diseases (ILD) are a heterogenous group of >100 different, rare diseases, which share the fate of progressivescarring and, ultimately, death. Apart from two novel anti-fibrotic drugs authorized only for IPF and steroids/immunosuppressants used forinflammatory-driven ILDs, therapeutic options are scarce and lung transplantation represents the only curative option. Similarly, settling a correctdiagnosis is difficult in most cases, especially when patients are too sick to undergo invasive procedures. Drawn against this background, leadingexperts in their fields joined this initiative to i) develop novel, primarily non-invasive, diagnostic algorithms and biomarkers, to ii) better understand thenatural course and the role of intermittent exacerbations, to iii) develop better prognostic models and biomarkers, to iv) better understand genotype /phenotype relationships and v) to a priori predict response to therapy via novel therapeutic biomarkers. Based on already existing pediatric (chILD-EU)and adult (eurIPFreg) registries / biobanks, we aim to include another 5000 ILD patients and > 100.000 biomaterials. Next to deep phenotyping, weintend to conduct daily hand-held spirometry, accelerometry and saturation measurement, periodic capture of volatile organic solvent (VOC) basedsignatures (via awarded “Sniffphone”), apply proteomics/genomics in exhaled breath condensates (EBC)s and deep sequencing in EDTA blood, useartificial intelligence for CT interpretation and, finally, establish disease models on the basis of big data analyses.