One syndrome, many diseases: toward precision pharmacotherapy in acute respiratory distress syndrome

Abstract

Acute respiratory distress syndrome (ARDS) remains a major cause of morbidity and mortality despite advances in supportive care and decades of unsuccessful pharmacologic investigation. A central limitation has been the treatment of ARDS as a uniform clinical syndrome rather than a biologically heterogeneous condition characterized by distinct mechanisms of injury, repair, and therapeutic responsiveness. Recent advances in biomarker profiling, transcriptomics, and data-driven phenotyping have identified reproducible biological subphenotypes, including hyperinflammatory and hypoinflammatory states associated with differing outcomes and treatment responses. Complementary frameworks such as endotypes and treatable traits offer a more mechanistic approach to stratification by linking specific biological processes, including dysregulated inflammation, endothelial dysfunction, coagulation abnormalities, and impaired epithelial repair, to potential therapeutic targets. In this narrative review, we examine how biological heterogeneity may explain the repeated failure of pharmacologic trials in unselected ARDS populations and critically evaluate emerging targeted therapeutic strategies, including anti-inflammatory, anticoagulant and endothelial-directed, epithelial reparative, and adjunctive or repurposed interventions. We also discuss how treatment response may depend not only on biological phenotype, but on disease stage and temporal evolution. Finally, we examine the clinical and methodological requirements for implementing precision pharmacotherapy in ARDS, including biomarker-guided enrichment, adaptive trial design, and integration of artificial intelligence–driven multimodal data analysis. Although major barriers remain, including biomarker validation, bedside feasibility, and prospective clinical testing, the most credible path forward is unlikely to be a universal therapy for ARDS, but rather biologically and temporally tailored treatment strategies matched to the patients most likely to benefit.