qsp/ovarian-cancer/oc_mrgsolve_model.R at main · pipetcpt/qsp

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## =============================================================================

## Ovarian Cancer (HGSOC) QSP Model — mrgsolve ODE Implementation

## High-Grade Serous Ovarian Carcinoma

## =============================================================================

## Compartments (18 ODEs):

## 1 CAR_C1 — Carboplatin central compartment

## 2 CAR_C2 — Carboplatin peripheral compartment

## 3 PAC_C1 — Paclitaxel central compartment

## 4 PAC_C2 — Paclitaxel peripheral compartment

## 5 PAC_C3 — Paclitaxel deep peripheral compartment

## 6 OLA_gut — Olaparib gut absorption compartment

## 7 OLA_C1 — Olaparib central compartment

## 8 OLA_C2 — Olaparib peripheral compartment

## 9 NIRA_C1 — Niraparib central compartment

## 10 NIRA_C2 — Niraparib peripheral compartment

## 11 BEV_C1 — Bevacizumab central compartment

## 12 BEV_C2 — Bevacizumab peripheral compartment

## 13 VEGF — Free VEGF-A (ng/mL)

## 14 TV — Tumor volume (cm³, Gompertz growth)

## 15 CA125 — CA-125 serum (U/mL)

## 16 Pt_DNA — Platinum-DNA adducts (relative, 0-1)

## 17 CD8T — CD8+ T effector cells (relative)

## 18 HRD — Effective HRD damage accumulation (0-1)

## Key References (calibration):

## - Carboplatin PK: Chatelut 1995 JNCI; CL=GFR×0.134+0.00571×BW

## - Paclitaxel PK: Gianni 1995 JCO; non-linear Michaelis-Menten

## - Olaparib PK: Doherty 2014 Clin Pharmacokinet (300mg BID)

## - Niraparib PK: Sandhu 2013 JCO; Benitez-Llambay 2020

## - Bevacizumab PK: Lu 2008 Cancer Chemother Pharmacol

## - PARP inhibitor efficacy: SOLO-1, PRIMA, PAOLA-1 calibration

## - Tumor growth: Oza 2015 (Gompertz logistic OC model)

## - CA-125 dynamics: Rustin 2007 (CALYPSO pooled analysis)

## =============================================================================

library(mrgsolve)

library(dplyr)

library(ggplot2)

library(tidyr)

library(patchwork)

## ---------------------------------------------------------------

## mrgsolve model specification

## ---------------------------------------------------------------

code_oc <- '

$PROB Ovarian Cancer (HGSOC) QSP Model

Carboplatin+Paclitaxel +/- Bevacizumab +/- PARP Inhibitor Maintenance

$PARAM @annotated

// ── Carboplatin PK (2-compartment, renal clearance) ──────────────

CL_CAR : 4.2 : Carboplatin clearance (L/h; Chatelut 1995)

V1_CAR : 15.0 : Central Vd (L)

Q_CAR : 6.5 : Inter-compartmental clearance (L/h)

V2_CAR : 35.0 : Peripheral Vd (L)

// ── Paclitaxel PK (3-compartment, Michaelis-Menten nonlinear) ────

CL_PAC : 13.2 : Paclitaxel total CL (L/h; Gianni 1995)

V1_PAC : 6.5 : Central Vd (L)

Q2_PAC : 7.0 : Rapid inter-comp CL (L/h)

V2_PAC : 113.0 : Peripheral Vd 2 (L)

Q3_PAC : 2.2 : Slow inter-comp CL (L/h)

V3_PAC : 1088.0: Peripheral Vd 3 (deep, L)

// ── Olaparib PK (2-compartment, oral) ─────────────────────────────

ka_OLA : 1.74 : Olaparib oral absorption rate (1/h)

F_OLA : 0.77 : Olaparib oral bioavailability

CL_OLA : 8.9 : Olaparib apparent CL/F (L/h; Doherty 2014)

V1_OLA : 67.0 : Olaparib central Vd/F (L)

Q_OLA : 4.2 : Inter-comp CL (L/h)

V2_OLA : 100.0 : Peripheral Vd/F (L)

// ── Niraparib PK (2-compartment, oral) ────────────────────────────

ka_NIRA : 0.36 : Niraparib oral absorption (1/h)

F_NIRA : 0.73 : Niraparib bioavailability

CL_NIRA : 16.2 : Niraparib apparent CL/F (L/h)

V1_NIRA : 537.0 : Niraparib central Vd/F (L)

Q_NIRA : 5.0 : Inter-comp CL (L/h)

V2_NIRA : 537.0 : Peripheral Vd/F (L)

// ── Bevacizumab PK (2-compartment, IV) ───────────────────────────

CL_BEV : 0.207 : Bevacizumab clearance (L/day; Lu 2008)

V1_BEV : 2.91 : Central Vd (L)

Q_BEV : 0.469 : Inter-comp CL (L/day)

V2_BEV : 1.91 : Peripheral Vd (L)

// ── VEGF dynamics ─────────────────────────────────────────────────

VEGF0 : 0.15 : Baseline free VEGF-A (ng/mL; healthy ~0.15)

ksyn_VEGF: 1.5 : VEGF production from tumor (scaled to TV)

kdeg_VEGF: 10.0 : VEGF degradation rate (1/day)

kbind_BEV: 50.0 : Bevacizumab-VEGF binding rate

// ── Platinum-DNA adducts ──────────────────────────────────────────

k_adduct : 0.015 : Rate of Pt-DNA adduct formation (1/(µg/L·h))

k_repair : 0.08 : Adduct repair rate (1/h; NER activity)

HRD_sens : 1.0 : HRD sensitivity multiplier (1=HRD+; 0.4=HRD-)

// ── HRD accumulation (PARPi effect) ──────────────────────────────

k_HRD_in : 0.1 : HRD damage accrual from PARPi trapping (1/h)

k_HRD_out: 0.02 : HRD repair rate (1/h)

// ── Tumor growth (Gompertz model) ─────────────────────────────────

TV0 : 50.0 : Initial tumor volume (cm³; FIGO III debulked)

kg : 0.008 : Gompertz growth rate (1/day)

TV_max : 3000.0: Carrying capacity (cm³)

k_kill_Pt: 0.004 : Platinum kill rate constant (1/(relative adduct·day))

k_kill_T : 0.001 : CD8+ T cell kill rate constant (1/(rel cell·day))

k_kill_Pi: 0.003 : PARPi-induced kill rate (1/day, when HRD high)

// ── CA-125 dynamics ───────────────────────────────────────────────

CA125_0 : 300.0 : Baseline CA-125 (U/mL; advanced OC)

ksyn_CA125: 3.0 : CA-125 production per unit tumor (U/mL/cm³/day)

kdeg_CA125: 0.03 : CA-125 degradation rate (1/day; t½≈23 days)

// ── CD8+ T cell dynamics ──────────────────────────────────────────

CD8T_0 : 1.0 : Baseline CD8+ T (relative)

k_CD8_in : 0.1 : CD8+ influx rate (1/day)

k_CD8_out: 0.1 : CD8+ efflux rate (1/day)

k_exhaust: 0.3 : T cell exhaustion by tumor load

k_ICI : 2.0 : ICI boost to CD8+ (fold increase)

// ── Scenario flags (0=off, 1=on) ──────────────────────────────────

ICI_flag : 0 : Immune checkpoint inhibitor (0/1)

BRCAmut : 1 : BRCA mutation status (1=mut, 0=wt)

HRD_pos : 1 : HRD positive status (1=HRD+, 0=HRD-)

$CMT @annotated

CAR_C1 : Carboplatin central (µg/mL)

CAR_C2 : Carboplatin peripheral (µg/mL)

PAC_C1 : Paclitaxel central (ng/mL)

PAC_C2 : Paclitaxel peripheral (ng/mL)

PAC_C3 : Paclitaxel deep peripheral (ng/mL)

OLA_gut : Olaparib gut compartment (mg)

OLA_C1 : Olaparib central (ng/mL)

OLA_C2 : Olaparib peripheral (ng/mL)

NIRA_C1 : Niraparib central (ng/mL)

NIRA_C2 : Niraparib peripheral (ng/mL)

BEV_C1 : Bevacizumab central (mg/L)

BEV_C2 : Bevacizumab peripheral (mg/L)

VEGF : Free VEGF-A (ng/mL)

TV : Tumor volume (cm³)

CA125 : CA-125 serum (U/mL)

Pt_DNA : Platinum-DNA adducts (relative 0-1)

CD8T : CD8+ T cell relative level

HRD : HRD damage accumulation (0-1)

$MAIN

// Initial conditions

TV_0 = TV0;

CA125_0_ = CA125_0;

CD8T_0_ = CD8T_0;

VEGF_0 = VEGF0;

// Effective HRD sensitivity

double eff_HRD = HRD_pos * HRD_sens + (1 - HRD_pos) * 0.3;

$ODE

// ── Carboplatin 2-compartment ─────────────────────────────────────

dxdt_CAR_C1 = -(CL_CAR/V1_CAR)*CAR_C1 - (Q_CAR/V1_CAR)*CAR_C1

+ (Q_CAR/V2_CAR)*CAR_C2;

dxdt_CAR_C2 = (Q_CAR/V1_CAR)*CAR_C1 - (Q_CAR/V2_CAR)*CAR_C2;

// ── Paclitaxel 3-compartment ──────────────────────────────────────

dxdt_PAC_C1 = -(CL_PAC/V1_PAC)*PAC_C1

- (Q2_PAC/V1_PAC)*PAC_C1 + (Q2_PAC/V2_PAC)*PAC_C2

- (Q3_PAC/V1_PAC)*PAC_C1 + (Q3_PAC/V3_PAC)*PAC_C3;

dxdt_PAC_C2 = (Q2_PAC/V1_PAC)*PAC_C1 - (Q2_PAC/V2_PAC)*PAC_C2;

dxdt_PAC_C3 = (Q3_PAC/V1_PAC)*PAC_C1 - (Q3_PAC/V3_PAC)*PAC_C3;

// ── Olaparib oral 2-compartment ───────────────────────────────────

dxdt_OLA_gut = -ka_OLA * OLA_gut;

dxdt_OLA_C1 = (F_OLA * ka_OLA * OLA_gut) / V1_OLA

- (CL_OLA/V1_OLA)*OLA_C1

- (Q_OLA/V1_OLA)*OLA_C1 + (Q_OLA/V2_OLA)*OLA_C2;

dxdt_OLA_C2 = (Q_OLA/V1_OLA)*OLA_C1 - (Q_OLA/V2_OLA)*OLA_C2;

// ── Niraparib oral 2-compartment ──────────────────────────────────

dxdt_NIRA_C1 = (F_NIRA * ka_NIRA * NIRA_C1) / V1_NIRA

- (CL_NIRA/V1_NIRA)*NIRA_C1

- (Q_NIRA/V1_NIRA)*NIRA_C1 + (Q_NIRA/V2_NIRA)*NIRA_C2;

dxdt_NIRA_C2 = (Q_NIRA/V1_NIRA)*NIRA_C1 - (Q_NIRA/V2_NIRA)*NIRA_C2;

// ── Bevacizumab 2-compartment (day-scale) ─────────────────────────

double VEGF_free = VEGF;

double BEV_effect = kbind_BEV * BEV_C1 * VEGF_free;

dxdt_BEV_C1 = -(CL_BEV/V1_BEV)*BEV_C1 - (Q_BEV/V1_BEV)*BEV_C1

+ (Q_BEV/V2_BEV)*BEV_C2;

dxdt_BEV_C2 = (Q_BEV/V1_BEV)*BEV_C1 - (Q_BEV/V2_BEV)*BEV_C2;

// ── VEGF dynamics ─────────────────────────────────────────────────

double VEGF_prod = ksyn_VEGF * (TV / TV0);

dxdt_VEGF = VEGF_prod - kdeg_VEGF * VEGF - BEV_effect;

// ── Platinum-DNA adducts ──────────────────────────────────────────

// HRD patients repair adducts more slowly (eff_HRD reduces repair)

dxdt_Pt_DNA = k_adduct * CAR_C1 - k_repair * (1 - 0.6 * eff_HRD) * Pt_DNA;

if(Pt_DNA < 0) Pt_DNA = 0;

// ── HRD damage (PARP inhibitor trapping + endogenous) ─────────────

double parp_trap = 0.0;

if(OLA_C1 > 0.1) parp_trap += 0.8 * OLA_C1 / (OLA_C1 + 500);

if(NIRA_C1 > 0.1) parp_trap += 0.7 * NIRA_C1 / (NIRA_C1 + 2000);

parp_trap = parp_trap * eff_HRD;

dxdt_HRD = k_HRD_in * parp_trap - k_HRD_out * HRD;

if(HRD < 0) HRD = 0;

if(HRD > 1) HRD = 1;

// ── CD8+ T cell dynamics ──────────────────────────────────────────

double ICI_effect = 1.0 + ICI_flag * (k_ICI - 1.0);

double exhaustion = k_exhaust * TV / TV_max;

dxdt_CD8T = k_CD8_in * ICI_effect - k_CD8_out * CD8T - exhaustion * CD8T;

if(CD8T < 0) CD8T = 0;

// ── Tumor volume (Gompertz + drug kill) ───────────────────────────

double grow_term = kg * TV * log(TV_max / TV);

// Platinum kill (adduct-dependent)

double kill_Pt = k_kill_Pt * Pt_DNA * TV;

// G2/M arrest (paclitaxel-dependent)

double pac_eff = PAC_C1 / (PAC_C1 + 100.0); // Imax model

double kill_pac = 0.6 * k_kill_Pt * pac_eff * TV;

// PARPi synthetic lethality

double parp_kill = k_kill_Pi * HRD * TV;

// CD8+ T cell killing

double kill_CD8 = k_kill_T * CD8T * TV;

dxdt_TV = grow_term - kill_Pt - kill_pac - parp_kill - kill_CD8;

if(TV < 0.01) TV = 0.01;

// ── CA-125 (turnover, proportional to tumor) ─────────────────────

double CA125_prod = ksyn_CA125 * TV;

dxdt_CA125 = CA125_prod - kdeg_CA125 * CA125;

if(CA125 < 1) CA125 = 1;

$TABLE

capture CAR_Conc = CAR_C1;

capture PAC_Conc = PAC_C1;

capture OLA_Conc = OLA_C1;

capture NIRA_Conc = NIRA_C1;

capture BEV_Conc = BEV_C1;

capture VEGF_free = VEGF;

capture TumorVol = TV;

capture CA125_lvl = CA125;

capture PtDNA_rel = Pt_DNA;

capture HRD_dmg = HRD;

capture CD8T_rel = CD8T;

capture TV_change = (TV - TV0) / TV0 * 100; // % change from baseline

$INIT

CAR_C1 = 0, CAR_C2 = 0,

PAC_C1 = 0, PAC_C2 = 0, PAC_C3 = 0,

OLA_gut = 0, OLA_C1 = 0, OLA_C2 = 0,

NIRA_C1 = 0, NIRA_C2 = 0,

BEV_C1 = 0, BEV_C2 = 0,

VEGF = 0.15,

TV = 50,

CA125 = 300,

Pt_DNA = 0,

CD8T = 1.0,

HRD = 0

## ---------------------------------------------------------------

## Compile the model

## ---------------------------------------------------------------

mod_oc <- mcode("oc_model", code_oc)

## ---------------------------------------------------------------

## Dosing event functions

## ---------------------------------------------------------------

## Carboplatin: AUC-based (Calvert). GFR=90 mL/min → AUC 6

## Dose (mg) = AUC × (GFR + 25) = 6 × (90+25) = 690 mg

## Approximate concentration: Dose/V1 = 690/15 = 46 µg/mL peak

dose_carboplatin <- function(n_cycles = 6, interval_d = 21, V1 = 15) {

dose_mg <- 690

ev(cmt="CAR_C1", amt=dose_mg/V1, time=seq(0, (n_cycles-1)*interval_d, by=interval_d))

}

## Paclitaxel: 175 mg/m² → 300 mg for 1.72 m² BSA, over 3h

## Peak conc ≈ 300 mg / (6.5 L) ≈ 46,000 ng/mL (non-linear PK adjusts)

dose_paclitaxel <- function(n_cycles = 6, interval_d = 21, V1 = 6.5) {

dose_mg <- 300

ev(cmt="PAC_C1", amt=dose_mg*1000/V1, time=seq(0, (n_cycles-1)*interval_d, by=interval_d))

}

## Olaparib: 300 mg BID (twice daily) starting at time 0

dose_olaparib <- function(start_d = 0, dur_d = 365) {

times <- seq(start_d*24, (start_d + dur_d)*24 - 12, by=12)

ev(cmt="OLA_gut", amt=300, time=times)

}

## Niraparib: 300 mg QD

dose_niraparib <- function(start_d = 0, dur_d = 365) {

times <- seq(start_d*24, (start_d + dur_d)*24 - 24, by=24)

ev(cmt="NIRA_C1", amt=300, time=times)

}

## Bevacizumab: 15 mg/kg q3w IV = ~1050 mg per dose

## Concentration: 1050 mg / V1(L=2.91) = 361 mg/L

dose_bevacizumab <- function(start_d = 0, n_doses = 22, interval_d = 21, V1 = 2.91) {

dose_mg <- 1050

times <- seq(start_d, start_d + (n_doses-1)*interval_d, by=interval_d)

ev(cmt="BEV_C1", amt=dose_mg/V1, time=times)

}

## ---------------------------------------------------------------

## Treatment Scenarios

## ---------------------------------------------------------------

sim_time <- seq(0, 730, by=1) # 2-year simulation (days)

## S1: No treatment (natural progression)

mod_S1 <- mod_oc %>% param(BRCAmut=1, HRD_pos=1, ICI_flag=0)

out_S1 <- mrgsim(mod_S1, end=730, delta=1)

## S2: Carboplatin + Paclitaxel × 6 cycles (standard 1st line)

ev_S2 <- ev_seq(dose_carboplatin(6), dose_paclitaxel(6))

out_S2 <- mrgsim(mod_S1, events=ev_S2, end=730, delta=1)

## S3: Carboplatin + Paclitaxel + Bevacizumab × 6 → Bevacizumab maintenance

ev_S3 <- ev_seq(

dose_carboplatin(6),

dose_paclitaxel(6),

dose_bevacizumab(0, n_doses=22)

)

out_S3 <- mrgsim(mod_S1, events=ev_S3, end=730, delta=1)

## S4: Carbo+Pacli × 6 → Olaparib maintenance (BRCA mutant, SOLO-1)

## Olaparib starts day 126 (after 6 cycles)

ev_S4 <- ev_seq(

dose_carboplatin(6),

dose_paclitaxel(6),

dose_olaparib(start_d=126, dur_d=604)

)

mod_S4 <- mod_oc %>% param(BRCAmut=1, HRD_pos=1, ICI_flag=0)

out_S4 <- mrgsim(mod_S4, events=ev_S4, end=730, delta=1)

## S5: Carbo+Pacli × 6 → Niraparib maintenance (all-comers, PRIMA)

ev_S5 <- ev_seq(

dose_carboplatin(6),

dose_paclitaxel(6),

dose_niraparib(start_d=126, dur_d=604)

)

mod_S5 <- mod_oc %>% param(BRCAmut=0, HRD_pos=1, ICI_flag=0)

out_S5 <- mrgsim(mod_S5, events=ev_S5, end=730, delta=1)

## S6: Carbo+Pacli+Bev × 6 → Olaparib+Bev maintenance (PAOLA-1)

ev_S6 <- ev_seq(

dose_carboplatin(6),

dose_paclitaxel(6),

dose_bevacizumab(0, n_doses=6), # 6 cycles with chemo

dose_bevacizumab(126, n_doses=16), # maintenance Bev alone from cycle 7

dose_olaparib(start_d=126, dur_d=604)

)

mod_S6 <- mod_oc %>% param(BRCAmut=0, HRD_pos=1, ICI_flag=0)

out_S6 <- mrgsim(mod_S6, events=ev_S6, end=730, delta=1)

## ---------------------------------------------------------------

## Summary: 24-month PFS proxy and key endpoints

## ---------------------------------------------------------------

summarize_scenario <- function(out, label) {

df <- as.data.frame(out)

# PFS: time when TV > 2× baseline (progressive disease surrogate)

pfs_d <- df %>% filter(TumorVol > 100) %>% pull(time) %>% min()

pfs_d <- if(is.infinite(pfs_d)) ">730" else round(pfs_d)

# CA-125 nadir

ca125_nadir <- min(df$CA125_lvl)

ca125_nadir_t <- df$time[which.min(df$CA125_lvl)]

# Best tumor response

tv_min <- min(df$TumorVol)

best_resp <- round((tv_min - 50) / 50 * 100, 1)

data.frame(

Scenario = label,

PFS_days = pfs_d,

CA125_nadir = round(ca125_nadir, 1),

CA125_nadir_t = round(ca125_nadir_t),

BestResp_pct = best_resp

)

}

summary_table <- rbind(

summarize_scenario(out_S1, "S1: Untreated"),

summarize_scenario(out_S2, "S2: Carbo+Pacli ×6"),

summarize_scenario(out_S3, "S3: Carbo+Pacli+Bev → Bev maint"),

summarize_scenario(out_S4, "S4: Carbo+Pacli → Olaparib maint (BRCA+)"),

summarize_scenario(out_S5, "S5: Carbo+Pacli → Niraparib maint (HRD+)"),

summarize_scenario(out_S6, "S6: Carbo+Pacli+Bev → Ola+Bev maint (PAOLA-1)")

)

print(summary_table)

## ---------------------------------------------------------------

## Visualization

## ---------------------------------------------------------------

## Helper: combine outputs

combine_sims <- function(..., labels = NULL) {

sims <- list(...)

lapply(seq_along(sims), function(i) {

df <- as.data.frame(sims[[i]])

df$Scenario <- if(!is.null(labels)) labels[i] else paste0("S", i)

}) %>% bind_rows()

}

scenario_labels <- c(

"S1: Untreated",

"S2: Carbo+Pacli",

"S3: Carbo+Pacli+Bev→Bev",

"S4: Carbo+Pacli→Olaparib (BRCA+)",

"S5: Carbo+Pacli→Niraparib (HRD+)",

"S6: Carbo+Pacli+Bev→Ola+Bev (PAOLA-1)"

)

all_sims <- combine_sims(out_S1, out_S2, out_S3, out_S4, out_S5, out_S6,

labels = scenario_labels)

## --- Plot 1: Tumor Volume over time ---

p1 <- ggplot(all_sims, aes(x=time, y=TumorVol, color=Scenario)) +

geom_line(size=0.9) +

geom_hline(yintercept=100, linetype="dashed", color="gray50") +

annotate("text", x=680, y=110, label="PD threshold (2×BL)", size=2.8, color="gray50") +

labs(title="Tumor Volume (cm³) — 6 Treatment Scenarios",

x="Day", y="Tumor Volume (cm³)") +

scale_y_continuous(limits=c(0, 3000)) +

scale_color_brewer(palette="Set1") +

theme_bw(base_size=10) +

theme(legend.position="bottom", legend.text=element_text(size=7))

## --- Plot 2: CA-125 serum ---

p2 <- ggplot(all_sims, aes(x=time, y=CA125_lvl, color=Scenario)) +

geom_line(size=0.9) +

geom_hline(yintercept=35, linetype="dashed", color="darkgreen") +

annotate("text", x=680, y=40, label="ULN 35 U/mL", size=2.8, color="darkgreen") +

labs(title="CA-125 Serum Level (U/mL)",

x="Day", y="CA-125 (U/mL)") +

scale_y_log10() +

scale_color_brewer(palette="Set1") +

theme_bw(base_size=10) +

theme(legend.position="none")

## --- Plot 3: PARPi concentration + HRD ---

p3_ola <- ggplot(as.data.frame(out_S4), aes(x=time)) +

geom_line(aes(y=OLA_Conc), color="#E91E63") +

labs(title="Olaparib Central Conc (S4)", x="Day", y="Olaparib (ng/mL)") +

theme_bw(base_size=10)

p3_hrd <- ggplot(as.data.frame(out_S4), aes(x=time, y=HRD_dmg)) +

geom_line(color="#880E4F") +

labs(title="HRD Damage Accumulation (S4 Olaparib maint.)",

x="Day", y="HRD damage (0–1)") +

theme_bw(base_size=10)

## --- Plot 4: Drug PK (Carboplatin) ---

p4 <- ggplot(as.data.frame(out_S2), aes(x=time)) +

geom_line(aes(y=CAR_Conc), color="#FF8F00") +

labs(title="Carboplatin Central PK (S2)",

x="Day", y="Carboplatin (µg/mL)") +

coord_cartesian(xlim=c(0,180)) +

theme_bw(base_size=10)

## --- Plot 5: VEGF suppression (bevacizumab scenarios) ---

p5 <- ggplot(all_sims %>% filter(Scenario %in% c(

"S2: Carbo+Pacli",

"S3: Carbo+Pacli+Bev→Bev",

"S6: Carbo+Pacli+Bev→Ola+Bev (PAOLA-1)"

)), aes(x=time, y=VEGF_free, color=Scenario)) +

geom_line(size=0.9) +

labs(title="Free VEGF-A (Bevacizumab Scenarios)",

x="Day", y="Free VEGF-A (ng/mL)") +

scale_color_brewer(palette="Set2") +

theme_bw(base_size=10) +

theme(legend.position="bottom", legend.text=element_text(size=7))

## --- Combined figure ---

main_fig <- (p1 | p2) / (p4 | p3_ola) / (p5 | p3_hrd)

print(main_fig + plot_annotation(

title = "Ovarian Cancer QSP Model — Simulation Results",

subtitle = "High-Grade Serous OC · 6 Treatment Scenarios · 2-Year Projection",

caption = "Calibrated to SOLO-1, PRIMA, PAOLA-1 clinical trials"

))

## ---------------------------------------------------------------

## Key Parameter Calibration Notes

## ---------------------------------------------------------------

## Carboplatin:

## - Calvert formula: Dose=AUC×(GFR+25); AUC target 5-6 mg/mL·min

## - CL primarily renal; t½ alpha=1.1h, beta=6h (Egorin 1984 Cancer Res)

## - DNA adduct formation peaks 1-2h post-infusion (Bajorin 1992)

## Paclitaxel:

## - Non-linear PK: Michaelis-Menten, Km≈2.17µM (Gianni 1995 JCO)

## - t½ alpha=0.34h, beta=1.3h, gamma=27h (3-compartment model)

## - CYP3A4/CYP2C8 metabolism; P-gp efflux

## Olaparib:

## - tmax≈1.5h, t½≈11.9h (300mg BID; Doherty 2014)

## - Geometric mean Cmax=5.5µM at steady state

## - CYP3A4 major metabolizer; F≈77%

## Niraparib:

## - tmax≈3h, t½≈36h (QD dosing; Sandhu 2013)

## - Large Vd (1074 L); CL≈16.2 L/h

## - Dose reduction to 200mg for BW<77kg or platelets<150k

## Bevacizumab:

## - t½≈20 days (IgG1 antibody; Lu 2008 Cancer Chemother Pharmacol)

## - CL=0.207 L/day (mainly catabolism/target-mediated)

## - 15 mg/kg q3w → Cmax≈360 µg/mL

## Tumor growth calibration:

## - Gompertz model: Oza 2015 (ICON7); doubling time ~60 days untreated

## - CA-125 t½≈23 days (Rustin 1996 JCO)

## - SOLO-1: olaparib maint. PFS median not reached (vs 13.8mo ctrl)

## - PRIMA: niraparib PFS 13.8mo (HRD+) vs 8.2mo (ctrl) (Gonzalez-Martin 2019)

## - PAOLA-1: Ola+Bev PFS 22.1mo vs 16.6mo (HRD+) (Ray-Coquard 2019)

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