Digital Patient
A computational model of a patient that enables in silico drug testing and response prediction
A digital patient is a computational model of a patient built from multi-omics data (genomics, transcriptomics, proteomics, metabolomics) and clinical information that enables in silico drug testing, treatment response prediction, and virtual clinical trials before human studies.
How Digital Patients Work
Digital patients are created by integrating multiple layers of biological data from real patient cohorts. This includes:
- Genomic data: DNA sequences, mutations, copy number variations
- Transcriptomic data: Gene expression levels from RNA sequencing
- Proteomic data: Protein abundance and post-translational modifications
- Metabolomic data: Small molecule metabolite concentrations
- Clinical data: Demographics, disease stage, treatment history, outcomes
Machine learning models learn the complex relationships between these molecular features and disease phenotypes. The resulting digital patient can then be "treated" with virtual drugs to predict responses, identify biomarkers, and optimize therapeutic strategies.
Applications in Drug Discovery
Target Identification
Identify disease-driving molecular targets by analyzing pathway dysregulation in digital patient populations.
Drug Response Prediction
Simulate how digital patients respond to drug candidates before synthesis and testing.
Patient Stratification
Identify subpopulations most likely to respond to specific treatments based on molecular profiles.
Virtual Clinical Trials
Run simulated trials across thousands of digital patients to optimize trial design and predict outcomes.
Omic's Digital Patient Platform
Omic has built one of the most advanced digital patient platforms in the industry, achieving 85%+ accuracy in drug response prediction. Our models integrate data from over 10,000 patient cohorts across multiple disease areas.
Frequently Asked Questions
What is the difference between a digital patient and a digital twin?
A digital twin typically refers to a real-time, continuously updated virtual replica of a specific individual, often used for personalized medicine. A digital patient is a broader term that includes both individual patient models and population-level models used for drug discovery and clinical trial simulation.
How accurate are digital patient simulations?
Modern digital patient platforms achieve 85%+ accuracy in predicting drug response when validated against clinical trial data. Accuracy depends on data quality, disease area, and the specific endpoint being predicted.
Can digital patients replace clinical trials?
Digital patients cannot fully replace clinical trials but can significantly reduce their size, cost, and failure rate. Regulatory agencies are increasingly accepting in silico evidence to support clinical development decisions.