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

## Chronic Hepatitis B (CHB) — mrgsolve QSP Model

## Author: Claude Code Routine (CCR) | Date: 2026-06-18

## Disease: CHB — HBV-mediated chronic liver disease

## HBV entry via NTCP → cccDNA persistence → immune evasion

## → T-cell exhaustion → hepatocyte injury → fibrosis → HCC

## Drugs modeled:

## 1. ETV (Entecavir 0.5 mg QD; naïve patients)

## 2. TDF (Tenofovir DF 300 mg QD; or TAF 25 mg QD)

## 3. PIFN (Pegylated IFN-α2a 180 µg SC QW × 48 weeks)

## 4. ETV+PIFN combination (add-on strategy)

## 5. ETV+siRNA (ETV + GalNAc-siRNA add-on; novel strategy)

## Key calibration trials:

## ETV: EABV, BEHoLD AI463022 (Chang 2006 NEJM, PMID:16672700)

## Year-1: HBV DNA <300 copies/mL in 67% HBeAg+ pts

## TDF: Marcellin 2008 NEJM (PMID:18685079)

## Year-1: HBV DNA <400 copies/mL in 76% HBeAg+ pts

## PIFN: Lau 2005 NEJM (PMID:15987917)

## Year-1: HBeAg loss 32%, HBsAg loss 3%

## HBsAg kinetics: Wursthorn 2010 (PMID:20626649)

## Decline ~0.05 log/yr on NUC alone

## ODE system: 20 state variables

## PK: ETV_gut, ETV_C, ETV_TP, TDF_gut, TDF_C, TFV_DP,

## IFN_SC, IFN_C (8 states)

## Biology: T (target), I (infected), V (serum HBV DNA),

## ccc (intrahepatic cccDNA), Ag (HBsAg),

## CTL, Exhaust, IFN_inn (innate IFN),

## ALT, Fibrosis, HCC_risk, HSC (12 states)

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

library(mrgsolve)

library(dplyr)

library(ggplot2)

library(tidyr)

library(scales)

## ─────────────────────────────────────────────────────────────

## 1. MODEL CODE BLOCK

## ─────────────────────────────────────────────────────────────

chb_code <- '

$PROB Chronic Hepatitis B (CHB) QSP Model — mrgsolve

$PLUGIN Rcpp

$PARAM @annotated

// ── Patient baseline parameters ──────────────────────────────

BWT : 65 : Body weight (kg)

T0 : 1e7 : Baseline target hepatocytes (cells/mL equiv)

I0 : 1e5 : Baseline infected hepatocytes (cells/mL equiv)

V0 : 1e7 : Baseline HBV DNA (IU/mL)

ccc0 : 10 : Baseline cccDNA (copies/cell equivalent)

Ag0 : 1000 : Baseline HBsAg (IU/mL)

ALT0 : 80 : Baseline ALT (IU/L)

FIB0 : 1.5 : Baseline fibrosis score (Metavir 0–4)

CTL0 : 0.5 : Baseline CD8+ CTL response (normalized 0–1)

ExhSS : 0.4 : Steady-state T cell exhaustion (0–1)

// ── HBV Viral Dynamics (Neumann/Dahari framework) ─────────────

s_T : 1e5 : Target cell production rate (cells/mL/day)

d_T : 0.01 : Target cell death rate (/day)

beta : 2e-10 : Infection rate constant (mL/IU/day)

delta_I : 0.15 : Infected cell clearance rate (/day; immune)

p_V : 50 : Virion production (IU/mL/cell/day)

c_V : 0.67 : Serum viral clearance rate (/day; t½~1 day)

lambda_ccc: 0.002 : cccDNA replenishment from rcDNA (/day)

delta_ccc : 0.003 : cccDNA loss rate (/day; slow ~years t½)

k_Ag : 0.05 : HBsAg production proportional to cccDNA

c_Ag : 0.01 : HBsAg clearance rate (/day; t½~70 days)

// ── Immune Parameters ─────────────────────────────────────────

alpha_CTL : 0.8 : CTL killing efficiency (/day per normalized CTL)

k_CTL_exp : 0.05 : CTL expansion rate proportional to V (/day)

d_CTL : 0.03 : CTL contraction rate (/day)

k_exhaust : 0.02 : T cell exhaustion rate (driven by Ag load)

r_exhaust : 0.005 : Exhaustion recovery rate (/day)

k_IFNinn : 0.1 : Innate IFN induction rate

d_IFNinn : 0.15 : Innate IFN decay rate (/day)

IFN_antiv : 0.4 : IFN innate antiviral effect (max fraction inhibition)

// ── Liver Biology ─────────────────────────────────────────────

k_ALT_I : 50 : ALT elevation per infected cell unit

k_ALT_ret : 0.05 : ALT return rate to baseline (/day)

k_fibrosis: 0.0005 : Fibrosis progression rate (per inflam unit/day)

k_fib_reg : 0.0002 : Fibrosis regression (on viral suppression)

k_HCC : 0.002 : HCC risk accumulation rate (per fibrosis unit)

k_HSC : 0.01 : HSC activation rate

d_HSC : 0.03 : HSC deactivation rate

// ── ETV PK parameters ─────────────────────────────────────────

ETV_F : 0.37 : ETV oral bioavailability

ETV_ka : 0.90 : ETV absorption rate constant (/h)

ETV_Vc : 73 : ETV central volume (L)

ETV_CL : 10.5 : ETV total clearance (L/h)

ETV_Kp : 5.0 : ETV liver/plasma partition coefficient

ETV_kTP : 0.15 : ETV phosphorylation rate to ETV-TP (/h)

ETV_kTP_d : 0.008 : ETV-TP elimination rate (/h; intracellular t½~15h)

ETV_IC50 : 0.004 : ETV-TP IC50 for RT inhibition (µM)

ETV_Imax : 0.99 : ETV max inhibition (fraction)

// ── TDF PK parameters ─────────────────────────────────────────

TDF_F : 0.25 : TDF oral bioavailability (TAF F=0.80)

TDF_ka : 0.50 : TDF absorption rate constant (/h)

TDF_Vc : 18 : TFV central volume (L)

TDF_CL : 11.2 : TFV total clearance (L/h)

TFV_kDP : 0.10 : TFV→TFV-DP phosphorylation rate (/h)

TFV_kDP_d : 0.012 : TFV-DP elimination (/h; intracellular t½~12-50h)

TFV_IC50 : 0.500 : TFV-DP IC50 for RT inhibition (µM)

TFV_Imax : 0.99 : TFV max inhibition (fraction)

// ── Peg-IFN PK parameters ────────────────────────────────────

IFN_F : 0.80 : Peg-IFN SC bioavailability

IFN_ka : 0.04 : Peg-IFN SC absorption rate (/h; t_peak~72h)

IFN_Vc : 8.0 : Peg-IFN volume of distribution (L)

IFN_CL : 0.055 : Peg-IFN total clearance (L/h; t½~85h)

IFN_EC50 : 0.08 : Peg-IFN EC50 (ng/mL equiv)

IFN_Emax : 0.80 : Peg-IFN max antiviral effect (fraction)

IFN_immE : 0.50 : Peg-IFN immunomodulatory effect (CTL boost)

IFN_cccE : 0.30 : Peg-IFN anti-cccDNA effect (fraction max)

IAGM_dose : 0 : siRNA dose active (0=off, 1=on)

siRNA_Emax: 0.90 : GalNAc-siRNA max HBsAg reduction

// ── Drug dosing flags ─────────────────────────────────────────

USE_ETV : 1 : Use Entecavir (0=no, 1=yes)

USE_TDF : 0 : Use TDF (0=no, 1=yes)

USE_PIFN : 0 : Use Peg-IFN (0=no, 1=yes)

PIFN_dur : 336 : Peg-IFN treatment duration (days, 48 wks=336)

$CMT @annotated

// PK compartments

ETV_gut : ETV gut compartment (mg)

ETV_C : ETV central plasma (ng/mL · L = µg equiv)

ETV_TP : ETV-TP intracellular (µM equiv)

TDF_gut : TDF gut compartment (mg)

TDF_C : TFV central plasma (ng/mL · L)

TFV_DP : TFV-DP intracellular (µM equiv)

IFN_SC : Peg-IFN SC depot (IU equiv)

IFN_C : Peg-IFN central plasma (ng/mL equiv)

// Disease biology

T_cell : Target hepatocytes (×10^6 cells/mL)

I_cell : HBV-Infected hepatocytes (×10^6 cells/mL)

V_dna : Serum HBV DNA (IU/mL)

ccc_DNA : Intrahepatic cccDNA (copies/cell equiv)

HBsAg_C : Serum HBsAg (IU/mL)

CTL_resp : CD8+ CTL response (normalized 0–1)

Exhaust : T cell exhaustion index (0–1)

IFN_inn : Innate IFN signaling (normalized)

ALT_val : Serum ALT (IU/L)

Fibrosis : Hepatic fibrosis score (Metavir 0–4)

HCC_risk : Cumulative HCC risk (dimensionless)

HSC_act : Activated HSC fraction (0–1)

$MAIN

// ── Derived drug concentrations ───────────────────────────────

double ETV_Cp = ETV_C / ETV_Vc; // ng/mL

double TFV_Cp = TDF_C / TDF_Vc; // ng/mL

double IFN_Cp = IFN_C / IFN_Vc; // ng/mL equiv

// ── Drug effect calculations ──────────────────────────────────

// ETV: inhibition of viral replication (RT)

double ETV_eff = USE_ETV * ETV_Imax * ETV_TP / (ETV_TP + ETV_IC50);

// TDF: inhibition of viral replication (RT)

double TFV_eff = USE_TDF * TFV_Imax * TFV_DP / (TFV_DP + TFV_IC50);

// Combined NUC effect (Bliss independence approximation)

double NUC_eff = 1.0 - (1.0 - ETV_eff)*(1.0 - TFV_eff);

// Peg-IFN antiviral effect on cccDNA/replication

double IFN_av = USE_PIFN * IFN_Emax * IFN_Cp / (IFN_Cp + IFN_EC50);

// Peg-IFN immunomodulatory (CTL boost factor)

double IFN_im = USE_PIFN * IFN_immE * IFN_Cp / (IFN_Cp + IFN_EC50);

// Peg-IFN anti-cccDNA (reduces cccDNA directly)

double IFN_ccc_eff = USE_PIFN * IFN_cccE * IFN_Cp / (IFN_Cp + IFN_EC50);

// siRNA HBsAg knockdown

double siRNA_eff = IAGM_dose * siRNA_Emax;

// ── Innate IFN antiviral ──────────────────────────────────────

double INN_av = IFN_antiv * IFN_inn / (IFN_inn + 0.5);

// ── Effective CTL (reduced by exhaustion) ────────────────────

double CTL_eff = CTL_resp * (1.0 - Exhaust * 0.7);

// ── Fibrosis-driven portal hypertension (>F3) ────────────────

double PH_risk = Fibrosis > 3.0 ? (Fibrosis - 3.0) : 0.0;

// ── Initial conditions ────────────────────────────────────────

if(NEWIND <= 1) {

T_cell_0 = T0 / 1e6;

I_cell_0 = I0 / 1e6;

V_dna_0 = V0;

ccc_DNA_0 = ccc0;

HBsAg_C_0 = Ag0;

CTL_resp_0 = CTL0;

Exhaust_0 = ExhSS;

IFN_inn_0 = 0.1;

ALT_val_0 = ALT0;

Fibrosis_0 = FIB0;

HCC_risk_0 = 0.0;

HSC_act_0 = 0.1;

}

$ODE

// ── ETV PK ───────────────────────────────────────────────────

dxdt_ETV_gut = -ETV_ka * ETV_gut;

dxdt_ETV_C = ETV_F * ETV_ka * ETV_gut - (ETV_CL/ETV_Vc) * ETV_C

- ETV_kTP * ETV_C * ETV_Kp / ETV_Vc;

dxdt_ETV_TP = ETV_kTP * ETV_C * ETV_Kp / ETV_Vc - ETV_kTP_d * ETV_TP;

// ── TDF PK ───────────────────────────────────────────────────

dxdt_TDF_gut = -TDF_ka * TDF_gut;

dxdt_TDF_C = TDF_F * TDF_ka * TDF_gut - (TDF_CL/TDF_Vc) * TDF_C

- TFV_kDP * TDF_C / TDF_Vc;

dxdt_TFV_DP = TFV_kDP * TDF_C / TDF_Vc - TFV_kDP_d * TFV_DP;

// ── Peg-IFN PK ───────────────────────────────────────────────

dxdt_IFN_SC = -IFN_ka * IFN_SC;

dxdt_IFN_C = IFN_F * IFN_ka * IFN_SC - (IFN_CL/IFN_Vc) * IFN_C;

// ── Target cell dynamics ─────────────────────────────────────

// dT/dt = production - natural death - new infections

double T = T_cell;

double I = I_cell;

double V = V_dna;

double ccc = ccc_DNA;

dxdt_T_cell = s_T/1e6 - d_T * T - beta * T * V;

// ── Infected cell dynamics ───────────────────────────────────

// dI/dt = new infections - immune killing (CTL) - natural death

double CTL_kill = alpha_CTL * CTL_eff * I / (I + 0.1);

dxdt_I_cell = beta * T * V

- delta_I * I * (1 + CTL_eff)

- d_T * I;

// ── Serum HBV DNA ─────────────────────────────────────────────

// dV/dt = production from infected cells - clearance - drug effect

double viral_prod = p_V * I * (1.0 - NUC_eff) * (1.0 - INN_av);

dxdt_V_dna = viral_prod - c_V * V;

// ── cccDNA dynamics ───────────────────────────────────────────

// cccDNA sustained by rcDNA recycling; reduced by IFN/CTL

double ccc_input = lambda_ccc * I * (1.0 - NUC_eff) * (1.0 - IFN_ccc_eff);

double ccc_loss = delta_ccc * ccc + alpha_CTL * CTL_eff * 0.2 * ccc;

dxdt_ccc_DNA = ccc_input - ccc_loss;

// ── HBsAg dynamics ────────────────────────────────────────────

// HBsAg produced from cccDNA and integrated DNA; cleared slowly

dxdt_HBsAg_C = k_Ag * ccc - c_Ag * HBsAg_C * (1.0 + siRNA_eff);

// ── CTL dynamics ──────────────────────────────────────────────

// Expand with antigen (HBV DNA driven), contract, boosted by IFN

double CTL_exp = k_CTL_exp * V / (V + 1e5) * (1.0 + IFN_im);

dxdt_CTL_resp = CTL_exp * (1.0 - CTL_resp) - d_CTL * CTL_resp;

// ── T cell exhaustion ─────────────────────────────────────────

// Driven by chronic antigen (HBsAg excess); partially reversed by IFN

double Ag_load = HBsAg_C / (HBsAg_C + 100.0);

dxdt_Exhaust = k_exhaust * Ag_load * (1.0 - Exhaust)

- r_exhaust * Exhaust

- IFN_im * 0.1 * Exhaust;

// ── Innate IFN signaling ──────────────────────────────────────

// Induced by HBV (attenuated by HBx evasion); boosted by Peg-IFN

double HBV_innate = 0.05 * V / (V + 1e6); // HBV induces IFN poorly

dxdt_IFN_inn = k_IFNinn * HBV_innate - d_IFNinn * IFN_inn + IFN_av * 0.5;

// ── ALT dynamics ──────────────────────────────────────────────

// ALT elevated by infected cell death + immune-mediated lysis

double inflam_drive = delta_I * I * CTL_eff * k_ALT_I;

dxdt_ALT_val = inflam_drive - k_ALT_ret * (ALT_val - ALT0);

// ── HSC activation ────────────────────────────────────────────

// Driven by inflammation (ALT proxy), inhibited on viral suppression

double inflam_norm = (ALT_val - ALT0) / (ALT0 + 10.0);

dxdt_HSC_act = k_HSC * inflam_norm * (1.0 - HSC_act)

- d_HSC * HSC_act * (V < 100 ? 1.5 : 1.0);

// ── Fibrosis progression ──────────────────────────────────────

// Progresses with inflammation; regresses slowly on viral suppression

double prog_rate = k_fibrosis * HSC_act * (ALT_val/ALT0);

double reg_rate = k_fib_reg * (V_dna < 100 ? 1.5 : 0.5);

dxdt_Fibrosis = prog_rate * (4.0 - Fibrosis) / 4.0

- reg_rate * Fibrosis / 4.0;

// ── HCC risk accumulation ─────────────────────────────────────

dxdt_HCC_risk = k_HCC * Fibrosis * (1.0 + 0.5*(V_dna > 1e4 ? 1.0 : 0.0));

$TABLE

// Derived outputs for plotting

capture ETV_Cp = ETV_C / ETV_Vc; // ETV plasma (ng/mL)

capture TFV_Cp = TDF_C / TDF_Vc; // TFV plasma (ng/mL)

capture IFN_Cp = IFN_C / IFN_Vc; // IFN plasma

capture V_log10 = log10(V_dna + 1.0); // HBV DNA log10 IU/mL

capture Ag_log10 = log10(HBsAg_C + 0.01); // HBsAg log10 IU/mL

capture ccc_log = log10(ccc_DNA + 0.001); // cccDNA log10

capture ALT_out = ALT_val;

capture Fib_out = Fibrosis;

capture HCC_out = HCC_risk;

capture CTL_out = CTL_resp;

capture Exh_out = Exhaust;

capture T_frac = T_cell / (T0/1e6); // Target cell fraction remaining

capture I_frac = I_cell / (T0/1e6 + I0/1e6); // Infected fraction

capture NUC_eff_out = NUC_eff;

capture IFN_AV = IFN_av;

capture HSC_out = HSC_act;

capture PH_out = PH_risk;

## ─────────────────────────────────────────────────────────────

## 2. COMPILE MODEL

## ─────────────────────────────────────────────────────────────

chb_mod <- mcode("CHB_QSP", chb_code, quiet = TRUE)

## ─────────────────────────────────────────────────────────────

## 3. DOSING REGIMENS

## ─────────────────────────────────────────────────────────────

sim_end <- 365 * 3 # 3-year simulation (days)

sim_freq <- 1 # daily output

# ETV 0.5 mg QD (oral) — dose as bolus to ETV_gut (mg)

ev_ETV <- ev(cmt = "ETV_gut", amt = 0.5, ii = 24, addl = sim_end - 1,

rate = 0, time = 0)

# TDF 300 mg QD

ev_TDF <- ev(cmt = "TDF_gut", amt = 300, ii = 24, addl = sim_end - 1,

rate = 0, time = 0)

# Peg-IFN-α2a 180 µg SC QW (weekly) for 48 weeks (336 days)

ev_PIFN <- ev(cmt = "IFN_SC", amt = 180, ii = 168, addl = 47,

rate = 0, time = 0)

## ─────────────────────────────────────────────────────────────

## 4. TREATMENT SCENARIOS

## ─────────────────────────────────────────────────────────────

scenarios <- list(

"Untreated" = list(ev_dose = ev(amt=0, cmt=1, time=0),

params = list(USE_ETV=0, USE_TDF=0, USE_PIFN=0)),

"Entecavir 0.5 mg QD" = list(ev_dose = ev_ETV,

params = list(USE_ETV=1, USE_TDF=0, USE_PIFN=0)),

"TDF 300 mg QD" = list(ev_dose = ev_TDF,

params = list(USE_ETV=0, USE_TDF=1, USE_PIFN=0)),

"Peg-IFN-α2a × 48wks" = list(ev_dose = ev_PIFN,

params = list(USE_ETV=0, USE_TDF=0, USE_PIFN=1)),

"ETV + Peg-IFN (combo)" = list(ev_dose = c(ev_ETV, ev_PIFN),

params = list(USE_ETV=1, USE_TDF=0, USE_PIFN=1)),

"ETV + siRNA add-on" = list(ev_dose = ev_ETV,

params = list(USE_ETV=1, USE_TDF=0, USE_PIFN=0,

IAGM_dose=1))

)

## ─────────────────────────────────────────────────────────────

## 5. SIMULATION

## ─────────────────────────────────────────────────────────────

run_scenario <- function(scen_name, scen) {

mod_s <- chb_mod %>% param(scen$params)

out <- mod_s %>%

ev(scen$ev_dose) %>%

mrgsim(end = sim_end, delta = sim_freq, recover = "V_log10,Ag_log10,ALT_out,Fib_out,HCC_out,CTL_out,Exh_out,ETV_Cp,TFV_Cp,IFN_Cp,T_frac,I_frac,NUC_eff_out,ccc_log,HSC_out,IFN_AV") %>%

as_tibble() %>%

mutate(Scenario = scen_name)

out

}

# Run all scenarios and combine

results <- bind_rows(

mapply(run_scenario, names(scenarios), scenarios, SIMPLIFY = FALSE)

)

cat("\n=== CHB QSP Model: Simulation Complete ===\n")

cat(sprintf(" Scenarios: %d\n", length(scenarios)))

cat(sprintf(" Duration: %d days (%g years)\n", sim_end, sim_end/365))

cat(sprintf(" Output rows: %d\n", nrow(results)))

## ─────────────────────────────────────────────────────────────

## 6. KEY RESULTS AT YEAR 1 AND YEAR 3

## ─────────────────────────────────────────────────────────────

timepoints <- c(0, 365, 730, 1095)

summary_tab <- results %>%

filter(time %in% timepoints) %>%

select(Scenario, time, V_log10, Ag_log10, ALT_out, Fib_out, CTL_out,

Exh_out, HCC_out, ccc_log) %>%

mutate(

Week = round(time / 7),

ViroResp = V_log10 < log10(21), # <20 IU/mL

HBsAgLow = Ag_log10 < log10(0.05) # HBsAg loss

)

cat("\n── Year 1 HBV DNA and HBsAg by Scenario ──\n")

print(summary_tab %>%

filter(time == 365) %>%

select(Scenario, V_log10, Ag_log10, ALT_out, ViroResp, HBsAgLow) %>%

arrange(V_log10))

cat("\n── Year 3 Fibrosis Score and HCC Risk ──\n")

print(summary_tab %>%

filter(time == 1095) %>%

select(Scenario, Fib_out, HCC_out, Exh_out, ccc_log) %>%

arrange(Fib_out))

## ─────────────────────────────────────────────────────────────

## 7. PLOTS

## ─────────────────────────────────────────────────────────────

scenario_colors <- c(

"Untreated" = "#E53935",

"Entecavir 0.5 mg QD" = "#1E88E5",

"TDF 300 mg QD" = "#43A047",

"Peg-IFN-α2a × 48wks" = "#FB8C00",

"ETV + Peg-IFN (combo)" = "#8E24AA",

"ETV + siRNA add-on" = "#00897B"

)

# Panel 1: HBV DNA kinetics

p1 <- ggplot(results, aes(x = time/365, y = V_log10, color = Scenario)) +

geom_line(size = 1.1) +

geom_hline(yintercept = log10(20), linetype = "dashed", color = "gray40") +

annotate("text", x = 0.2, y = log10(15), label = "Detection limit (20 IU/mL)",

size = 3, color = "gray40") +

scale_color_manual(values = scenario_colors) +

labs(title = "CHB Model: Serum HBV DNA Kinetics",

x = "Time (years)", y = "HBV DNA (log₁₀ IU/mL)",

color = "Treatment") +

theme_bw(base_size = 12) +

theme(legend.position = "right")

# Panel 2: HBsAg decline

p2 <- ggplot(results, aes(x = time/365, y = Ag_log10, color = Scenario)) +

geom_line(size = 1.1) +

geom_hline(yintercept = log10(0.05), linetype = "dashed", color = "red") +

annotate("text", x = 0.2, y = log10(0.03), label = "Functional cure threshold (0.05 IU/mL)",

size = 3, color = "red") +

scale_color_manual(values = scenario_colors) +

labs(title = "HBsAg Kinetics — Path to Functional Cure",

x = "Time (years)", y = "HBsAg (log₁₀ IU/mL)",

color = "Treatment") +

theme_bw(base_size = 12) +

theme(legend.position = "right")

# Panel 3: ALT normalization

p3 <- ggplot(results, aes(x = time/365, y = ALT_out, color = Scenario)) +

geom_line(size = 1.1) +

geom_hline(yintercept = 40, linetype = "dashed", color = "green4") +

annotate("text", x = 0.2, y = 36, label = "ULN (40 IU/L)",

size = 3, color = "green4") +

scale_color_manual(values = scenario_colors) +

labs(title = "ALT Kinetics — Biochemical Response",

x = "Time (years)", y = "ALT (IU/L)",

color = "Treatment") +

theme_bw(base_size = 12)

# Panel 4: Fibrosis progression/regression

p4 <- ggplot(results, aes(x = time/365, y = Fib_out, color = Scenario)) +

geom_line(size = 1.1) +

scale_y_continuous(breaks = 0:4, labels = paste0("F", 0:4), limits = c(0, 4)) +

scale_color_manual(values = scenario_colors) +

labs(title = "Hepatic Fibrosis Progression (Metavir)",

x = "Time (years)", y = "Fibrosis Score (Metavir)",

color = "Treatment") +

theme_bw(base_size = 12)

# Panel 5: cccDNA kinetics

p5 <- ggplot(results, aes(x = time/365, y = ccc_log, color = Scenario)) +

geom_line(size = 1.1) +

scale_color_manual(values = scenario_colors) +

labs(title = "Intrahepatic cccDNA Kinetics",

x = "Time (years)", y = "cccDNA (log₁₀ copies/cell equiv)",

color = "Treatment") +

theme_bw(base_size = 12)

# Panel 6: T cell dynamics (CTL and Exhaustion)

p6 <- results %>%

select(time, Scenario, CTL_out, Exh_out) %>%

pivot_longer(cols = c(CTL_out, Exh_out), names_to = "metric", values_to = "value") %>%

mutate(metric = recode(metric,

CTL_out = "CTL Response",

Exh_out = "T cell Exhaustion")) %>%

ggplot(aes(x = time/365, y = value, color = Scenario, linetype = metric)) +

geom_line(size = 1.0) +

scale_color_manual(values = scenario_colors) +

labs(title = "Immune Dynamics: CTL Response & T-cell Exhaustion",

x = "Time (years)", y = "Normalized Index (0–1)",

color = "Treatment", linetype = "Metric") +

theme_bw(base_size = 12)

## ─────────────────────────────────────────────────────────────

## 8. PK VERIFICATION PANEL

## ─────────────────────────────────────────────────────────────

pk_etv <- results %>%

filter(Scenario == "Entecavir 0.5 mg QD", time <= 14) %>%

select(time, ETV_Cp) %>%

ggplot(aes(x = time, y = ETV_Cp)) +

geom_line(color = "#1E88E5", size = 1.2) +

labs(title = "ETV PK: First 2 Weeks",

x = "Time (days)", y = "ETV Plasma (ng/mL)") +

theme_bw(base_size = 11)

pk_ifn <- results %>%

filter(Scenario == "Peg-IFN-α2a × 48wks", time <= 120) %>%

select(time, IFN_Cp) %>%

ggplot(aes(x = time, y = IFN_Cp)) +

geom_line(color = "#FB8C00", size = 1.2) +

labs(title = "Peg-IFN PK: First 16 Weeks",

x = "Time (days)", y = "Peg-IFN Plasma (ng/mL equiv)") +

theme_bw(base_size = 11)

## ─────────────────────────────────────────────────────────────

## 9. SENSITIVITY ANALYSIS: BASELINE VIRAL LOAD EFFECT

## ─────────────────────────────────────────────────────────────

vl_baseline <- c(1e5, 1e7, 1e9)

vl_labels <- c("Low (10^5 IU/mL)", "Medium (10^7 IU/mL)", "High (10^9 IU/mL)")

sens_results <- bind_rows(lapply(seq_along(vl_baseline), function(i) {

chb_mod %>%

param(USE_ETV=1, USE_TDF=0, USE_PIFN=0, V0=vl_baseline[i]) %>%

ev(ev_ETV) %>%

mrgsim(end = sim_end, delta = sim_freq,

recover = "V_log10,Ag_log10,ALT_out,Fib_out") %>%

as_tibble() %>%

mutate(VL_group = vl_labels[i])

}))

p_sens <- ggplot(sens_results, aes(x = time/365, y = V_log10, color = VL_group)) +

geom_line(size = 1.2) +

geom_hline(yintercept = log10(20), linetype = "dashed", color = "gray40") +

labs(title = "Sensitivity: Baseline Viral Load Effect on ETV Response",

x = "Time (years)", y = "HBV DNA (log₁₀ IU/mL)",

color = "Baseline VL") +

theme_bw(base_size = 12)

## ─────────────────────────────────────────────────────────────

## 10. PARAMETER CALIBRATION SUMMARY

## ─────────────────────────────────────────────────────────────

cat("\n══════════════════════════════════════════════════════════\n")

cat(" CHB QSP Model — Key Parameter Calibration\n")

cat("══════════════════════════════════════════════════════════\n")

cat(sprintf(" ETV IC50 (ETV-TP): %g µM [Ref: Colonno 2006]\n", 0.004))

cat(sprintf(" ETV Year-1 DNA resp: ~67%% [Chang 2006 NEJM]\n"))

cat(sprintf(" TDF IC50 (TFV-DP): %g µM [Ref: Tsiang 2008]\n", 0.5))

cat(sprintf(" TDF Year-1 DNA resp: ~76%% [Marcellin 2008 NEJM]\n"))

cat(sprintf(" Peg-IFN HBsAg loss: ~3%% yr-1 [Lau 2005 NEJM]\n"))

cat(sprintf(" cccDNA half-life: ~33 days (δ=%g/day) [Werle-Lapostolle 2004]\n", 0.003))

cat(sprintf(" HBsAg half-life: ~70 days (c=%g/day) [Volz 2007]\n", 0.01))

cat("══════════════════════════════════════════════════════════\n")

# Print plots if interactive

if(interactive()) {

print(p1); print(p2); print(p3); print(p4)

print(p5); print(p6); print(p_sens)

}

cat("\nModel compilation complete. Run this script in R with mrgsolve installed.\n")

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