Three-Layer Integration
The Patient Analog platform architecture represents a paradigm shift in drug discovery infrastructure. By integrating three distinct but interconnected layers, we create a unified system capable of predicting human drug responses with unprecedented accuracy.
Data Infrastructure
Unified data pipelines connecting genomic, proteomic, metabolomic, and phenotypic data with real-time experimental outputs.
Computational Models
AI-powered digital twins integrating biological data to predict individual patient responses to therapeutic interventions.
Physical Systems
Microphysiological systems, organoids, and tissue chips that validate computational predictions with biological reality.
Architectural Principles
Interoperability
All layers communicate through standardized APIs and data formats, enabling seamless integration and third-party extensibility.
Scalability
Cloud-native architecture supports scaling from single experiments to population-wide studies without infrastructure changes.
Reproducibility
Complete provenance tracking ensures every prediction can be traced back to its source data and model versions.
Regulatory Compliance
Built-in audit trails and validation frameworks support FDA 21 CFR Part 11 and ICH guidelines.
Continuous Learning Loop
1. Data Ingestion
Multi-omics and experimental data streams into unified data lake with real-time processing.
2. Model Training
AI models continuously learn from new data, improving prediction accuracy over time.
3. Experimental Validation
Physical systems test predictions, generating new data that feeds back into the loop.
4. Knowledge Synthesis
Validated insights become actionable knowledge for drug discovery decisions.
Explore Each Layer
Dive deeper into the specific components that make up each architectural layer.