Digital Patient Cohort Simulation

Simulate drug response across virtual patient populations to predict efficacy, identify responder subgroups, and de-risk clinical development.

Overview

Digital patient simulation enables pharmaceutical companies to test drug candidates in silico before committing to expensive clinical trials. By building computational models of patient populations from multi-omics data, Omic can predict how drugs will perform across diverse patient subgroups.

This workflow covers the complete process from population definition through response analysis, including pharmacokinetic/pharmacodynamic modeling and subgroup identification.

Define Patient Population

Specify the characteristics of your virtual patient cohort including disease context, demographics, and genomic features.

from omic import Omic

client = Omic(api_key="your_api_key")

# Define patient population
population = client.create_population(
    size=1000,
    disease="non_small_cell_lung_cancer",
    demographics={
        "age_range": (45, 75),
        "sex_ratio": 0.55,  # proportion male
        "ethnicity_distribution": {
            "european": 0.6,
            "asian": 0.25,
            "african": 0.15
        }
    },
    genomic_features={
        "EGFR_mutation_rate": 0.15,
        "KRAS_mutation_rate": 0.25,
        "ALK_fusion_rate": 0.05,
        "PD_L1_high_rate": 0.30
    },
    clinical_features={
        "stage_distribution": {"IIIA": 0.3, "IIIB": 0.3, "IV": 0.4},
        "prior_therapy_rate": 0.4
    }
)

Configure Treatment Protocol

Define the drug compound, dosing regimen, and treatment duration. Multiple treatment arms can be configured for comparison.

# Define treatment protocol
treatment = client.create_treatment(
    drug_smiles="CC1=C(C=C(C=C1)NC2=NC=NC3=CC(=C(C=C32)OC)OC)NC(=O)C=C",
    drug_name="Erlotinib",
    dosing={
        "dose": 150,
        "unit": "mg",
        "frequency": "daily",
        "route": "oral"
    },
    duration_weeks=12
)

# Optional: Add comparator arm
control = client.create_treatment(
    drug_smiles=None,  # Placebo
    drug_name="Placebo",
    dosing={"dose": 0, "unit": "mg", "frequency": "daily"},
    duration_weeks=12
)

Run Pharmacokinetic Simulation

Simulate drug absorption, distribution, metabolism, and excretion across the virtual population.

# Run PK simulation
pk_results = client.simulate_pharmacokinetics(
    population=population,
    treatment=treatment,
    include_metabolites=True,
    include_tissue_distribution=True
)

# View population PK parameters
print(f"Mean Cmax: {pk_results.cmax_mean:.2f} ng/mL")
print(f"Mean AUC: {pk_results.auc_mean:.2f} ng*h/mL")
print(f"Mean t1/2: {pk_results.half_life_mean:.2f} hours")

# Check for PK variability by genotype
for genotype in pk_results.cyp_genotype_groups:
    print(f"  {genotype.name}: AUC = {genotype.auc_mean:.2f}")

Model Pharmacodynamic Response

Systems biology models predict drug effects on disease pathways and clinical outcomes.

# Run full simulation
simulation = client.simulate_treatment(
    population=population,
    treatment=treatment,
    pk_results=pk_results,
    endpoints=[
        "tumor_response",
        "progression_free_survival",
        "overall_survival",
        "adverse_events"
    ]
)

# Wait for results
results = simulation.wait()

# View efficacy predictions
print(f"Overall response rate: {results.response_rate:.1%}")
print(f"Median PFS: {results.median_pfs:.1f} months")
print(f"Disease control rate: {results.disease_control_rate:.1%}")

Analyze Results and Subgroups

Identify responder subgroups and predictive biomarkers from the simulation results.

# Identify responder subgroups
for subgroup in results.responder_subgroups:
    print(f"{subgroup.name}:")
    print(f"  Response rate: {subgroup.response_rate:.1%}")
    print(f"  Median PFS: {subgroup.median_pfs:.1f} months")
    print(f"  Defining features: {subgroup.features}")

# Get predictive biomarkers
biomarkers = results.get_predictive_biomarkers()
for biomarker in biomarkers[:5]:
    print(f"{biomarker.name}: AUC={biomarker.auc:.3f}")

# Export results
results.to_csv("simulation_results.csv")
results.plot_survival_curves("survival_curves.png")
results.generate_report("simulation_report.pdf")

Use Cases

Clinical Trial Design

Optimize trial design by predicting response rates and identifying enrichment strategies before patient enrollment.

Dosing Optimization

Find optimal dose levels that maximize efficacy while minimizing toxicity across patient subgroups.

Biomarker Discovery

Identify predictive biomarkers for patient selection and companion diagnostic development.

Combination Therapy

Simulate drug combinations to predict synergy, antagonism, and optimal sequencing strategies.