Virtual patient models that simulate human physiology for personalized drug response prediction and computational medicine
Digital twins in medicine are computational models that create virtual representations of individual patients. These models integrate genomic data, medical history, lifestyle factors, and real-time physiological measurements to simulate how a specific patient might respond to different treatments.
Unlike traditional clinical trials that treat patients as statistical averages, digital twins enable truly personalized medicine by predicting individual responses before actual treatment begins.
Simulate how a patient will metabolize and respond to specific medications before prescribing
Test multiple treatment protocols virtually to find the optimal therapy for each patient
Model disease trajectories to anticipate complications and intervene proactively
Use virtual populations to optimize trial protocols and reduce required participant numbers
The FDA Modernization Act 2.0 and 3.0 explicitly recognize computational models and digital twins as valid New Approach Methodologies (NAMs) for drug development. Several digital twin platforms have received FDA breakthrough device designation, and the agency has published guidance on using computer modeling for regulatory submissions.
Try our interactive digital twin games and simulations
Drug Predictor Digital Twin Engine All Simulations