Molecular Target Discovery and Systemic Mechanism Analysis of Teriflunomide for Dry Eye Disease

Background: Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by tear film instability, inflammation, and neurosensory abnormalities. Current therapies remain limited by slow onset and suboptimal efficacy. Teriflunomide, an immunomodulatory agent approved for multiple sclerosis, has shown therapeutic potential in DED, but its multi-target mechanisms remain unclear. Methods: We employed an integrated computational and transcriptomic framework combining ADMET profiling, multi-dataset transcriptomic integration, and single-cell RNA sequencing (scRNA-seq) to identify disease-relevant targets. Candidate genes were further refined through molecular docking and 50 ns molecular dynamics (MD) simulations. The AetherCell virtual cell model was applied to evaluate both the concordance between target perturbation and drug-induced responses and the potential mechanistic roles of candidate targets. Results: Transcriptomic integration identified 16 consensus genes across heterogeneous DED models, which were further localized to disease-relevant epithelial and immune cell populations by scRNA-seq. Molecular simulations prioritized three core targets—CTSS, STAT1, and PTGS1—based on binding stability and affinity. AetherCell simulations demonstrated that perturbation of these targets not only recapitulated teriflunomide-induced transcriptional and pathway changes but also revealed their distinct mechanistic contributions, including epithelial barrier regulation (CTSS), microvascular and lipid homeostasis (PTGS1), and inflammation suppression coupled with tissue repair (STAT1). Conclusions: Teriflunomide exerts therapeutic effects in DED through coordinated multi-target regulation involving inflammation control, barrier restoration, and tissue repair. This study provides a rationale for novel therapeutic targets in dry eye disease, establishes a paradigm for applying virtual cell modeling to elucidate drug mechanisms, and offers a bioinformatics framework for validating drug repositioning outcomes.