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Essential Thrombocythemia (ET) — QSP Model

Quantitative Systems Pharmacology model for Essential Thrombocythemia (본태성 혈소판 증가증)
Driver mutations: JAK2 V617F / CALR / MPL | Megakaryopoiesis | Thrombosis | Drug PK/PD

Model

Disease Overview

Essential Thrombocythemia (ET) is a Philadelphia chromosome-negative myeloproliferative neoplasm (MPN) characterized by sustained thrombocytosis (platelets ≥450 ×10⁹/L) due to clonal expansion of a mutated hematopoietic stem cell. ET is driven predominantly by somatic mutations in:

Mutation Frequency Key Features
JAK2 V617F ~55–65% Constitutive JAK2 activation; higher thrombosis risk
CALR Type 1 ~15–20% Del52bp; activates MPL; more benign course
CALR Type 2 ~8–12% Ins5bp; activates MPL; similar to JAK2
MPL W515L/K ~3–5% Activates TPOR/JAK2; clinically similar to CALR
Triple negative ~10% Unknown driver; lowest thrombosis risk

Key clinical features:

  • Thrombocytosis (primary criterion; median PLT ~700–900 ×10⁹/L)
  • Thrombotic events (arterial: stroke/MI; venous: DVT/PE; microvascular: erythromelalgia)
  • Hemorrhage at very high platelet counts (>1500 ×10⁹/L, acquired vWD)
  • Splenomegaly (~20–40% of patients)
  • Transformation to post-ET myelofibrosis (~10–15% / 10 years) or AML (~1–3%)

Mechanistic Map

File: et_qsp_model.dot · et_qsp_model.svg · et_qsp_model.png

Clusters (9 subgraphs, ~160 nodes)

# Cluster Key Nodes
① Driver Mutations JAK2 V617F, CALR Type1/2, MPL W515L, TET2, ASXL1, TP53, IDH1/2
② JAK-STAT Signaling TPO·TPOR→JAK2→STAT5/STAT3→PI3K/AKT/mTOR/MAPK
③ Megakaryopoiesis LT-HSC→MPP→CMP→MEP→MKP→BFU-MK→CFU-MK→MK→Proplatelet→PLT
④ Cytokine Milieu IL-3, IL-6, IL-11, TGF-β1, IFN-α/γ, PDGF-AB, VEGF, SCF
⑤ Platelet Biology GPIb-vWF, GPIIb/IIIa, COX-1/TXA2, P2Y12, ADP, aggregation
⑥ Thrombosis/Hemorrhage Arterial/venous events, erythromelalgia, coagulation cascade, acquired vWD
⑦ Drug PK/PD Hydroxyurea (RRM2), Anagrelide (PDE3A), Ruxolitinib (JAK1/2), Peg-IFN-α, Aspirin (COX-1)
⑧ Clinical Endpoints CHR, PHR, CMR, IPSS-ET, ELN response, OS, thrombosis-free survival
⑨ BM Pathology Reticulin/collagen fibrosis, post-ET MF, blast phase, AML transformation

Key Pathway Logic

JAK2 V617F (constitutive) ──► JAK2 activation ──► STAT5 phospho
                                                  ──► PI3K/AKT/mTOR
                                                  ──► BCL-XL / MCL1 (anti-apoptosis)

CALR Type1/2 ──► aberrant MPL activation ──► JAK2 recruitment ──► (same cascade)

LT-HSC ──► MKP ──► IMK ──► PMK ──► Mature MK (endomitosis 8×) ──► Proplatelet ──► PLT
                                                     ↑
                               JAK2/STAT5 drives proliferation & survival
                                                     ↓
                               TGF-β1 secretion ──► fibroblast activation ──► reticulin fibrosis

Platelet > 1500 ──► acquired vWD ──► ultra-large vWF multimers ──► hemorrhage
PLT activation ──► TXA2/ADP ──► aggregation ──► arterial/venous thrombosis

mrgsolve ODE Model

File: et_mrgsolve_model.R

Compartments (17)

# State Description
1 HSC Hematopoietic stem cells (JAK2-mutant pool)
2 MKP Megakaryocyte progenitors
3 MK Mature megakaryocytes
4 PLT Circulating platelets (×10⁹/L)
5 TPO Serum thrombopoietin (pg/mL)
6 JAK2 JAK2 V617F allele burden (fraction 0–1)
7 SPL Spleen size (cm below costal margin)
8–9 HU_C / HU_P Hydroxyurea central/peripheral (µg/mL)
10–11 ANA_C / ANA_P Anagrelide central/peripheral
12–13 RUX_C / RUX_P Ruxolitinib central/peripheral
14–15 pIFN_C / pIFN_P Peg-IFN-α2a central/peripheral
16 RISK_T Cumulative thrombosis risk (AU)
17 RISK_MF Cumulative MF transformation risk (AU)

Key ODEs

dPLT/dt = k_PLT_prod × k_MK_mat × MK - k_PLT_destr × (1 + SPL/10) × PLT
dJAK2/dt = k_JAK2_exp × JAK2 × (1-JAK2) - E_pIFN × 0.015 × JAK2
dRISK_T/dt = λ_T × (PLT/PLT_norm)^α_T × (1 + δ_JAK2 × JAK2) × (1 - E_ASA × 0.4)

Drug Effect Functions (Hill equation)

E_drug = Emax × C^γ / (EC50^γ + C^γ)
Drug EC50 Emax Target
Hydroxyurea 3.5 µg/mL 0.85 MKP proliferation (RRM2)
Anagrelide 25 ng/mL 0.75 MK maturation (PDE3A)
Ruxolitinib 150 ng/mL 0.80 JAK2 → pSTAT5 pathway
Peg-IFN-α 8 ng/mL 0.70 JAK2+ clone suppression
Aspirin — 0.80 TXA2/COX-1 (binary)

Treatment Scenarios (7)

Scenario Drug Expected CHR JAK2 Reduction
① No treatment — 0% 0% (slow natural expansion)
② Aspirin only ASA 81 mg/d 0% 0%
③ Hydroxyurea 500 mg HU + ASA ~30% <5%
④ Hydroxyurea 1500 mg HU + ASA ~60% <10%
⑤ Anagrelide 2 mg ANA + ASA ~35–45% <5%
⑥ Ruxolitinib 20 mg RUX + ASA ~60% ~15–20%
⑦ Peg-IFN-α2a 90 µg/wk pIFN + ASA ~45–75% ~30–50% (CMR possible)

Clinical Trial Calibration

Trial Intervention CHR Reference
PT-1 (Harrison 2005 NEJM) HU 1500 mg → ~60% CHR 59% at 1yr NEJM 353:33
PT-1 ANA 2 mg → ~35% CHR 36% at 1yr NEJM 353:33
ANAHYDRET (Gisslinger 2013) HU vs ANA — non-inferior ~65% vs ~62% Blood 121:1720
RESPONSE-2 (ruxolitinib) RUX 10 mg BID ~50–60% PLT norm Leukemia 2020
Kiladjian 2013 Peg-IFN-α2a 45% CHR; JAK2 MR 18% Haematologica

Shiny Dashboard

File: et_shiny_app.R

8 Interactive Tabs

Tab Content
① Patient Profile Baseline PLT, JAK2 AB%, age, prior thrombosis, ASXL1; IPSS-ET score; WHO criteria
② Drug PK Plasma concentration profiles; dose sliders for all 4 cytoreductive agents + aspirin
③ Platelet Dynamics Time-course PLT with CHR/PHR thresholds; response summary table
④ JAK2 Allele Burden JAK2 AB over time; CMR/PMR thresholds; molecular response classification
⑤ Thrombosis Risk Annual hazard over time; cumulative risk area plot; risk factor table
⑥ Scenario Compare Side-by-side multi-scenario plots; checkboxes for any subset of 7 scenarios
⑦ Biomarker Panel 6-panel dashboard: PLT, JAK2 AB, Spleen, TPO, MK pool, Cum MF Risk
⑧ BM & Progression MF transformation risk; pathway summary; co-mutation impact

How to Run

install.packages(c("shiny","shinydashboard","dplyr","ggplot2","plotly","DT","purrr"))
shiny::runApp("essential-thrombocythemia/et_shiny_app.R")

References

File: et_references.md

45 PubMed references organized in 12 sections:

  1. Disease Definition & Epidemiology
  2. Molecular Pathogenesis (JAK2/CALR/MPL)
  3. JAK-STAT Signaling
  4. Megakaryopoiesis
  5. Thrombotic & Hemorrhagic Complications
  6. Hydroxyurea PK/PD & Clinical Data
  7. Anagrelide PK/PD & Clinical Data
  8. Ruxolitinib in ET
  9. Interferon Therapy
  10. Disease Progression & MF Transformation
  11. QSP / Mathematical Modeling
  12. Clinical Guidelines & Risk Stratification

File Summary

File Description Size
et_qsp_model.dot Graphviz mechanistic map source (~160 nodes, 9 clusters) ~18 KB
et_qsp_model.svg Vector map (scalable, full detail) ~120 KB
et_qsp_model.png Raster map (150 dpi) ~180 KB
et_mrgsolve_model.R mrgsolve ODE QSP model (17 compartments, 7 scenarios) ~9 KB
et_shiny_app.R Shiny interactive dashboard (8 tabs) ~14 KB
et_references.md 45 PubMed references (12 sections) ~6 KB

Key Model Assumptions & Limitations

  1. TPO feedback: modeled as platelet-mediated TPO absorption (Kaushansky model); assumes normal hepatic TPO production
  2. JAK2 clone dynamics: logistic growth model; CALR/MPL variants mapped to equivalent JAK2 burden for modeling purposes
  3. Drug PK: steady-state CSS approximation used in Shiny (rapid equilibrium); mrgsolve has 2-compartment ODE
  4. Thrombosis risk: hazard model calibrated to IPSS-ET data; does not model individual clotting factor dynamics
  5. MF progression: simplified risk accumulator; does not include BM biopsy-grade ODE (future extension)
  6. Aspirin effect: modeled as binary 80% TXA2 suppression; does not distinguish COX-1 isoforms