Animal models for connective tissue disease-associated interstitial lung disease: Current status and future
Ziyi Tang1, Hang Yang2, Xiuping Liang1
1Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China; Laboratory of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu 610093, China.
None:
Interstitial lung disease (ILD) significantly contributes to connective tissue disease (CTD) mortality. Although guidelines for managing CTD-associated ILD (CTD-ILD) exist, many patients respond poorly to treatment owing to two key factors: (1) incomplete understanding of subtype-specific pathogenic mechanisms, and (2) reliance on therapies adapted from systemic sclerosis or nonpulmonary rheumatic diseases, which fail to address the unique pathophysiology of distinct CTD-ILD subtypes. This hinders personalized treatment and underscores the need for robust preclinical models that accurately replicate disease-specific mechanisms. However, challenges remain: (1) the lack of models that fully capture the heterogeneity of these disorders and (2) the absence of systematic structured reviews to guide model selection, despite recent advancements in experimental methodologies. These issues often result in mismatches between research goals and model utility. This review summarizes current CTD-ILD animal models, focusing on their construction, characteristics, and limitations, and highlighting differences between each model and the corresponding human disease. We summarize these disparities and propose emerging technologies, including CRISPR/Cas9-mediated genome editing, humanized mice, and lung organoids/lung-on-a-chip systems, that may facilitate the development of next-generation models. By integrating established and emerging strategies, we aim to provide guidance for model selection and development, promote precision modeling, accelerate targeted therapy discovery, and ultimately improve clinical outcomes. We emphasize the importance of developing subtype-specific models to avoid a "one-model-fits-all" approach.
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