<p>A Stanford Medicine-led study built virtual models of individual maternal immune systems using single-cell data and information from 63 pregnancies. The researchers used the system, called SIMCO, to screen nine possible drug regimens for their predicted effect on the immune processes associated with labor.</p><p>The model highlighted a combination of aspirin and lansoprazole. The researchers then tested the combination in an animal model and received FDA approval to begin a human clinical trial. This is not evidence that the combination is safe or effective for self-treatment; the result remains under clinical evaluation.</p><h2>Why the workflow matters</h2><p>The useful lesson is not that a digital twin replaces a trial. It can help narrow the search and make assumptions explicit before exposing people to a candidate intervention. The human study remains the gate for clinical evidence.</p><h2>A practical next step</h2><p>When evaluating any digital-twin proposal, ask which real-world population trained it, what variation it represents, where it has been validated and which later-stage test can falsify its prediction. Treat simulation as a prioritization tool, not as final acceptance.</p><p><strong>Source:</strong> <a href="https://news.stanford.edu/stories/2026/10/ai-digital-twins-preterm-labor" target="_blank" rel="noopener noreferrer">Read the source</a></p><p>For a broader procurement framework, see the <a href="https://supplybridge.com/procurement-transformation">procurement transformation guide</a>.</p>