PK
PK-NCA Suite v2.0
Client-side Only FDA-compliant
Pharmacokinetic Analysis Platform

Non-Compartmental Analysis
& IVIVC in Your Browser

Textbook-compliant NCA calculations, Level A IVIVC analysis, and publication-quality visualization — all running entirely in your browser with zero server dependency.

NCA Analyzer Graph Studio IVIVC Module
Validation Benchmark Disclaimer & Legal Notice
Time (h) Conc (ng/mL) Cmax
12+
PK Parameters
100%
Client-side
Level A
IVIVC Analysis
D3.js
Visualization
Analysis Modules

Four Integrated Modules

Complete pharmacokinetic analysis workflow implemented as a fully client-side architecture.

Stable v1.0

NCA Analyzer

Textbook-compliant NCA with Linear-up Log-down trapezoidal integration and automatic Best Fit λz selection.

  • AUC(0-t), AUC(0-∞), AUMC, MRT
  • CL/F, Vz/F, Vss/F, t½
  • CSV / JSON / Manual input
  • D3.js interactive visualization
  • Theophylline benchmark validation
New v2.0

Graph Studio

Instantly generate journal-ready formal and presentation-ready casual concentration-time graphs.

  • Formal (Journal) / Casual (Report) styles
  • Time unit conversion (min / hr / day)
  • Linear & Semi-log scale
  • SVG / PNG high-resolution export
  • Multi-profile overlay
New v1.0

NPS Superposition

Predict multiple-dose and steady-state concentrations from single-dose profiles using Nonparametric Superposition.

  • Linear-Log interpolation/extrapolation
  • Non-uniform dosing schedule
  • Steady State Cmax/Cmin/Cavg
  • Accumulation ratio & fluctuation
  • Bguide1 built-in validation
New v2.0

IVIVC Level A

FDA guideline-compliant Level A IVIVC analysis with Wagner-Nelson & Loo-Riegelman deconvolution.

  • Wagner-Nelson (1-compartment)
  • Loo-Riegelman (2-compartment)
  • Weibull / Hill dissolution fitting
  • Levy Plot & Fabs vs Fdiss
  • %PE Internal/External validation
Core Features

Why PK-NCA Suite?

Designed for regulatory science, data security, and research efficiency.

🔒
Data Sovereignty
All computations run in the browser. Data is never sent to any server.
⚡
Web Workers
Parallel processing via Web Workers — heavy computations without UI blocking.
📊
Publication Quality
D3.js-based publication-ready graphs with SVG/PNG high-resolution export.
✅
Validated
Automated unit test validation against published pharmacokinetic reference values.
🧮
Precision Numerics
Floating-point error-minimized numerical analysis engine.
📐
FDA Compliance
IVIVC %PE validation criteria — mean <10%, individual <15% auto-calculated.
🔄
Deconvolution
In vivo absorption fraction via Wagner-Nelson & Loo-Riegelman.
📈
Model Fitting
Weibull/Hill nonlinear regression dissolution profile fitting engine.
Validation

Live Benchmark — Theophylline Dataset

Auto-validated NCA computation against published textbook reference values. Score updates on every page load.

— Accuracy

Benchmark Summary

Tests—
Passed—
Failed—
Geo. Mean Acc.—
DatasetTheophylline Subj.1
MethodLin-up Log-down

Standard Dataset

Theophylline Subject 1
Dose: 319.992 mg (oral, extravascular)
11 time points (0 – 24.37 h)
Linear-up Log-down trapezoidal rule
Best-fit λz terminal regression
📖 Gabrielsson & Weiner, Pharmacokinetic & Pharmacodynamic Data Analysis, 5th ed. §10.2
📖 Rowland & Tozer, Clinical Pharmacokinetics, 4th ed. Ch.4
📖 Gibaldi & Perrier, Pharmacokinetics, 2nd ed. §6
Parameter Expected Computed |Δ| Tolerance Accuracy Result Reference
Computing benchmark...
Tolerance Criteria & Regulatory References

Each test parameter is validated against published textbook reference values. PASS criteria are based on absolute tolerance (|Computed − Expected| ≤ Tolerance), calibrated to account for method-dependent variation commonly observed across validated NCA software platforms.

Observed values (Cmax, Tmax, Clast, Tlast): Strict — near-zero tolerance. Direct read from data.
AUC parameters: ≤0.5–2.0 units. Integration method variation (linear vs. log-linear) accounts for typical ±1% differences.
Terminal phase (λz, t½): ≤0.5–2.0 units. λz regression point selection (Best-fit vs. manual) commonly introduces ±1–5% variation across platforms (Gabrielsson §10.3.2).
Derived parameters (CL/F, Vz/F, Vss/F, MRT): Dose-dependent; tolerance scaled to expected magnitude. Variation propagated from AUCinf and λz estimates.
Quality metrics (R²adj, AUC%extrap): Dimensionless. R²adj ≥0.99 required for reliable λz; AUC%extrap <20% per FDA BE guidance.
Cross-validation: All-linear AUC computed independently to verify log-linear method consistency.

Key References: [1] Gabrielsson & Weiner, PK/PD Data Analysis, 5th ed., 2016 — [2] Rowland & Tozer, Clinical Pharmacokinetics, 4th ed., 2010 — [3] Gibaldi & Perrier, Pharmacokinetics, 2nd ed., 1982 — [4] Wagner, Fundamentals of Clinical Pharmacokinetics, 1975 — [5] FDA Guidance: Bioanalytical Method Validation, 2018 — [6] EMA Guideline on the Investigation of Bioequivalence, 2010 — [7] Certara Phoenix WinNonlin User's Guide v8.x

Open Full Benchmark Page →
Architecture

Project Structure

Serverless SPA architecture ensuring fully client-side execution.

File Structure
index.html — Landing page
analyzer.html — NCA Analyzer (5-panel SPA)
graph-studio.html — Graph Studio
nps.html — NPS Superposition
ivivc.html — IVIVC Level A
css/
  style.css — Design system
js/
  pk-engine.js — NCA engine
  pk-worker.js — NCA Web Worker
  pk-chart.js — D3.js chart module
  app.js — App controller
  nps-engine.js — NPS engine
  nps-worker.js — NPS Web Worker
  ivivc-engine.js — IVIVC engine
  ivivc-worker.js — IVIVC Web Worker
  shared.js — Shared utilities
Computation Pipeline
[Main Thread]
  UI Events → Dispatch to Worker
  ← Receive results → Update DOM

[Web Worker Thread]
  NCA: PKEngine.performNCA()
  NPS: NPSEngine.superposition()
  IVIVC: Deconvolution → Correlation
  Math: Linear regression, AUC, Fitting

[Visualization]
  D3.js SVG rendering (main thread)
  Canvas fallback for high-density data