The Personalized Approach
Every patient is unique. Their genetic makeup, lifestyle, microbiome, and disease history all influence how they respond to drugs. Patient-specific modeling captures this individuality, enabling predictions tailored to each person rather than population averages.
iPSC-Derived Models
Sample Collection
Simple blood draw or skin biopsy provides somatic cells carrying the patient's complete genetic information.
Reprogramming
Yamanaka factors reprogram somatic cells into induced pluripotent stem cells (iPSCs) with unlimited division potential.
Differentiation
Directed differentiation protocols drive iPSCs toward specific lineages: cardiomyocytes, hepatocytes, neurons, or any cell type needed.
Model Assembly
Patient-derived cells populate organ-on-chip systems or form organoids, creating living models of that specific patient.
Digital Twin Integration
Physical patient-derived models are coupled with computational digital twins that integrate the patient's full multi-omic profile. This hybrid approach enables:
Genomic Context
Whole genome/exome data identifies pharmacogenomic variants affecting drug metabolism and response.
Transcriptomic State
RNA-seq captures current gene expression patterns reflecting disease state and treatment history.
Proteomic Profile
Protein abundances reveal post-transcriptional regulation and functional pathway activity.
Clinical History
EHR data including prior treatments, comorbidities, and outcomes inform model predictions.
Clinical Applications
Oncology Treatment Selection
Test multiple cancer drugs on patient-derived tumor organoids to identify the most effective therapy before treatment begins.
Rare Disease Modeling
Create disease models from patients with rare genetic conditions where no animal model exists.
Pharmacogenomics
Validate computational pharmacogenomic predictions with functional testing on patient-derived cells.
Combination Therapy
Optimize drug combinations and sequencing for individual patients based on their specific biology.