Single-cell-level digital twins for preterm birth prevention strategies

Area of Science:

• Computational biology
• Immunology
• Pharmacology

Background:

• Preterm labor (PTL) is a major complication with limited therapeutic options.
• Current digital twin models lack single-cell resolution, hindering personalized PTL treatment development.
• Immune dysregulation is a key driver of PTL, necessitating targeted immunomodulatory therapies.

Purpose of the Study:

• To introduce Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell digital twin framework for PTL.
• To model immunomodulatory treatment effects on labor timing.
• To identify novel drug candidates for PTL prevention.

Main Methods:

• Developed SIMCO, a single-cell digital twin framework utilizing immunome-wide simulations.
• Trained and validated SIMCO on a mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs.
• Applied SIMCO to simulate drug effects on gestational length in an independent cohort.

Main Results:

• SIMCO identified tetrahydrofolate, maprotiline, and the combination of aspirin and lansoprazole as top PTL prevention candidates.
• Simulations revealed enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in CD4+ T cells as key mechanisms.
• The study provides system-level mechanistic insights for each drug candidate.

Conclusions:

• SIMCO offers a powerful tool for low-risk, accelerated therapeutic development in PTL.
• The identified drug candidates show potential for delaying labor onset through specific immune pathway modulation.
• This single-cell approach enables personalized intervention design for PTL.