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Non-Compartmental Analysis

The neopkpd.nca module provides FDA/EMA-compliant non-compartmental analysis with comprehensive exposure metrics.

Overview

Quick Start

Basic NCA

from neopkpd.nca import run_nca

# Concentration-time data
times = [0.0, 0.5, 1.0, 2.0, 4.0, 8.0, 12.0, 24.0]
conc = [0.0, 1.8, 2.0, 1.5, 1.0, 0.5, 0.25, 0.06]
dose = 100.0

# Run NCA
result = run_nca(times, conc, dose)

# Access results
print(f"Cmax: {result.cmax:.2f} mg/L")
print(f"Tmax: {result.tmax:.2f} h")
print(f"AUC0-t: {result.auc_0_t:.2f} mg·h/L")
print(f"AUC0-inf: {result.auc_0_inf:.2f} mg·h/L")
print(f"t½: {result.t_half:.2f} h")
print(f"CL/F: {result.cl_f:.2f} L/h")

With Configuration

from neopkpd.nca import run_nca, NCAConfig

config = NCAConfig(
    method="log_linear",           # AUC calculation method
    lambda_z_min_points=3,         # Min points for λz
    lambda_z_r2_threshold=0.9,     # R² threshold
    extrapolation_max_pct=20.0,    # Warning threshold
    blq_handling="zero"            # BLQ handling
)

result = run_nca(times, conc, dose, config=config)
print(f"λz R²: {result.lambda_z_r_squared:.4f}")

NCA Metrics

Metric Attribute Units Description
Cmax .cmax mg/L Maximum observed concentration
Tmax .tmax h Time of Cmax
AUC0-t .auc_0_t mg·h/L AUC to last observation
AUC0-inf .auc_0_inf mg·h/L AUC extrapolated to infinity
t½ .t_half h Terminal half-life
λz .lambda_z 1/h Terminal elimination rate
CL/F .cl_f L/h Apparent clearance
Vz/F .vz_f L Apparent volume
MRT .mrt h Mean residence time

AUC Methods

Method Description Use Case
"linear" Linear trapezoidal Ascending phase
"log_linear" Log-linear trapezoidal Descending phase
"lin_log_mixed" Mixed (recommended) General purpose 
# Mixed method (default)
config = NCAConfig(method="lin_log_mixed")

Individual Functions

from neopkpd import nca

# Peak metrics
cmax = nca.nca_cmax(times, conc)
tmax = nca.nca_tmax(times, conc)

# AUC calculations
auc_0_t = nca.auc_0_t(times, conc, method="log_linear")
auc_0_inf, extra_pct = nca.auc_0_inf(times, conc, lambda_z)

# Terminal phase
lambda_z, t_half, r_squared = nca.estimate_lambda_z(times, conc)

Population NCA

from neopkpd.nca import run_population_nca, summarize_population_nca

# Run NCA for all subjects
pop_results = run_population_nca(population_result, dose=100.0)

# Summarize
summary = summarize_population_nca(pop_results)

print(f"Cmax: {summary['cmax']['mean']:.2f} (CV: {summary['cmax']['cv']:.1f}%)")
print(f"AUC: {summary['auc_0_inf']['mean']:.2f} (CV: {summary['auc_0_inf']['cv']:.1f}%)")

Bioequivalence

from neopkpd.nca import bioequivalence_90ci, tost_analysis, be_conclusion

# 90% CI for geometric mean ratio
lower, upper = bioequivalence_90ci(test_auc, reference_auc)
print(f"90% CI: ({lower:.3f}, {upper:.3f})")

# TOST analysis
result = tost_analysis(
    test_auc,
    reference_auc,
    theta_lower=0.80,
    theta_upper=1.25
)

# Conclusion
is_be = be_conclusion(lower, upper, theta_lower=0.80, theta_upper=1.25)
print(f"Bioequivalent: {is_be}")

NCA Result Object

class NCAResult:
    # Primary metrics
    cmax: float
    tmax: float
    auc_0_t: float
    auc_0_inf: float
    t_half: float

    # Terminal phase
    lambda_z: float
    lambda_z_r_squared: float
    lambda_z_n_points: int

    # PK parameters
    cl_f: float
    vz_f: float
    mrt: float

    # Quality metrics
    auc_extrapolated_pct: float

    # Metadata
    dose: float
    method: str

Next Steps