ABCA3 (ATP-binding cassette subfamily A member 3) is a lipid transporter expressed in alveolar type II cells and localized in the limiting membrane of lamellar bodies. It is crucial for pulmonary surfactant storage and homeostasis. Mutations in the ABCA3 gene are the most common genetic cause of respiratory distress syndrome in mature newborns and of interstitial lung disease in children. Apart from lung transplant, there is no cure available. To address the lack of causal therapeutic options for ABCA3 deficiency, a rapid and reliable approach is needed to investigate variant-specific molecular mechanisms and to identify pharmacologic modulators for monotherapies or combination therapies. To this end, we developed a phenotypic cell-based assay to autonomously identify ABCA3 wild-type-like or mutant-like cells by using machine learning algorithms aimed at identifying morphologic differences in wild-type and mutant cells. The assay was subsequently used to identify new drug candidates for ABCA3-specific molecular correction by using high-content screening of 1,280 Food and Drug Administration-approved small molecules. Cyclosporin A was identified as a potent corrector, specific for some but not all ABCA3 variants. Results were validated by using our previously established functional small-format assays. Hence, cyclosporin A may be selected for orphan drug evaluation in controlled repurposing trials in patients.
Keywords: ABCA3; childhood interstitial lung disease; cyclosporin A; high-content screening; machine learning.