Eosinophils promote pulmonary matrix destruction and emphysema via Cathepsin L

Immune Dysregulation in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a disease whose incidence increase with age and is characterised by chronic inflammation and significant immune dysregulation. Inhalation of toxic substances cause oxidative stress in the lung tissue as well as airway inflammation, under the recruitment of chemokines, immune cells gathered and are activated to play a defensive role. However, persistent inflammation damages the immune system and leads to immune dysregulation, which is mainly manifested in the reduction of the body's immune response to antigens, and immune cells function are impaired, further destroy the respiratory defensive system, leading to recurrent lower respiratory infections and progressive exacerbation of the disease, thus immune dysregulation play an important role in the pathogenesis of COPD. This review summarizes the changes of innate and adaptive immune-related cells during the pathogenesis of COPD, aiming to control COPD airway inflammation and improve lung tissue remodelling by regulating immune dysregulation, for further reducing the risk of COPD progression and opening new avenues of therapeutic intervention in COPD.

Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies

Cathepsin L (CTSL) expression is dysregulated in a variety of cancers. Extensive empirical evidence indicates their direct participation in cancer growth, angiogenic processes, metastatic dissemination, and the development of treatment resistance. Currently, no natural CTSL inhibitors are approved for clinical use. Consequently, the development of novel CTSL inhibition strategies is an urgent necessity. In this study, a combined machine learning (ML) and structure-based virtual screening strategy was employed to identify potential natural CTSL inhibitors. The random forest ML model was trained on IC50 values. The accuracy of the trained model was over 90%. Furthermore, we used this ML model to screen the Biopurify and Targetmol natural compound libraries, yielding 149 hits with prediction scores >0.6. These hits were subsequently selected for virtual screening using a structure-based approach, yielding 13 hits with higher binding affinity compared to the positive control (AZ12878478). Two of these hits, ZINC4097985 and ZINC4098355, have been shown to strongly bind CTSL proteins. In addition to drug-like properties, both compounds demonstrated high affinity, ligand efficiency, and specificity for the CTSL binding pocket. Furthermore, in molecular dynamics simulations spanning 200 ns, these compounds formed stable protein-ligand complexes. ZINC4097985 and ZINC4098355 can be considered promising candidates for CTSL inhibition after experimental validation, with the potential to provide therapeutic benefits in cancer management.