A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.

Summary

UNAGI models time-evolving single-cell states to elucidate idiopathic pulmonary fibrosis progression and prioritizes drug candidates; nifedipine’s anti-fibrotic effect was confirmed in human precision-cut lung slices. The framework generalizes across diseases (including COVID), combining computational innovation with proteomic and ex vivo validation.

Key Findings

• UNAGI captured time-resolved single-cell disease dynamics and improved drug perturbation modeling.
• In idiopathic pulmonary fibrosis, UNAGI identified candidate therapeutics; nifedipine’s anti-fibrotic effect was validated in human precision-cut lung slices.
• Proteomic data supported the inferred cellular dynamics, and the approach generalized to other diseases including COVID.

Clinical Implications

While not yet clinical, UNAGI can prioritize repurposable agents (e.g., nifedipine) and new targets for idiopathic pulmonary fibrosis, informing preclinical pipelines and the design of early-phase trials.

Limitations

• Validation of drug predictions was limited in scope (e.g., nifedipine) and lacks in vivo/clinical outcomes.
• Model performance and generalizability across diverse patient populations and tissue contexts require further evaluation.

Future Directions

Prospective preclinical testing of prioritized candidates (dose-response, mechanism), multi-center single-cell cohorts for external validation, and early-phase trials guided by model-informed biomarkers.