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Darwin PBPK — Epistemic Pharmacokinetic Modeling

Physiologically-Based Pharmacokinetic modeling with GUM-compliant uncertainty propagation.

Part of the Sounio ecosystem — the L0 systems + scientific programming language for epistemic computing.

What is this?

A PBPK modeling framework where every value carries its uncertainty, confidence, and provenance as intrinsic properties — not as an afterthought.

Three capabilities that no existing PK/PD tool offers:

  1. GUM-through-ODE — First application of the ISO metrology standard (JCGM 100:2008) to PBPK. Linearized uncertainty propagation through ODE integration via finite-difference Jacobian. 100-1000× faster than Monte Carlo with equivalent accuracy for CV < 40-50%.

  2. Compile-time confidence gates — The type system enforces minimum epistemic confidence thresholds. If a computation chain erodes confidence below the threshold, the program refuses to compile.

  3. Biomaterial → PBPK → uncertainty pipeline — Controlled-release kinetics (Higuchi, zero-order, first-order) integrated with full 14-compartment PBPK via operator splitting, with uncertainty tracked at every step.

Case Study: Sirolimus-Eluting Stent

The framework is validated with rapamycin (sirolimus) — the canonical biomaterial+drug combination (Cypher coronary stent, Cordis 2003):

Metric Value Interpretation
AUC(0-168h) 3.26 ± 0.88 mg·h/L CV=27% — CYP3A4 variability propagated
Brain/blood ratio 0.137 < 0.15 — P-gp efflux at BBB confirmed
s_CL (sensitivity) 0.84 (84%) CL dominates AUC uncertainty
DES/IV AUC ratio 1:103 Implant release correctly orders of magnitude below IV
Mass conservation 5.0 → 3.07 mg 39% eliminated in 7 days

Structure

stdlib/
├── darwin_pbpk/
│   ├── tsit5_pbpk14.sio          # 14-compartment PBPK, adaptive Tsit5 solver
│   ├── epistemic_pbpk14.sio      # GUM uncertainty bridge (finite-difference Jacobian)
│   ├── epistemic_sim.sio         # 4-compartment epistemic PBPK (analytical GUM)
│   ├── release/
│   │   └── biomaterial_release.sio  # Higuchi, zero-order, first-order release models
│   ├── drugs/
│   │   └── rapamycin.sio         # Sirolimus parameters (Ferron 1997, Lampen 1998)
│   ├── core/
│   │   ├── rodgers_rowland.sio   # Tissue partition coefficient prediction
│   │   └── fractal_blood.sio    # CTRW anomalous transport
│   └── validation/
│       └── metrics.sio           # FDA/EMA metrics (GMFE, AAFE, percent-within-2-fold)
├── epistemic/
│   ├── knowledge.sio             # Knowledge<T> type
│   ├── gum.sio                   # GUM Type A/B, Welch-Satterthwaite
│   ├── propagate.sio             # Variance propagation rules (GUM §13.3)
│   └── ...                       # ODE, PDE, MCMC, Sobol, optimization
tests/
└── run-pass/
    └── rapamycin_epistemic_pbpk.sio  # 3-compartment rapamycin validation (6/6 PASS)

Running

Requires the Sounio JIT compiler:

SOUC=./artifacts/omega/souc-bin/souc-linux-x86_64-jit

# Epistemic 14-compartment PBPK (8 tests)
$SOUC run stdlib/darwin_pbpk/epistemic_pbpk14.sio

# Biomaterial release models (8 tests)
$SOUC run stdlib/darwin_pbpk/release/biomaterial_release.sio

# Rapamycin 3-compartment epistemic test (6 tests)
$SOUC run tests/run-pass/rapamycin_epistemic_pbpk.sio

References

  • JCGM 100:2008 — Guide to the Expression of Uncertainty in Measurement (GUM)
  • Ferron GM et al. (1997) Clin Pharmacol Ther 61:696-708 — Sirolimus population PK
  • Lampen A et al. (1998) Biochem Pharmacol — P-gp substrate, BBB transport
  • Higuchi T (1963) J Pharm Sci 52:1145 — Matrix diffusion release model
  • Sousa JE et al. (2003) Circulation 107:2274 — Cypher stent clinical data
  • FDA (2018) — Physiologically Based Pharmacokinetic Analyses Guidance
  • Tsitouras Ch (2011) Comput Math Appl 62:770 — RK 5(4) pairs

License

MIT

Author

Demetrios Chiuratto Agourakis — ORCID 0009-0001-8671-8878

About

Epistemic PBPK modeling — GUM-compliant uncertainty propagation through physiologically-based pharmacokinetic models. Sirolimus DES case study.

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