# Concizumab PK/PD Simulator
# Systems PK/PD model for anti-TFPI monoclonal antibody (Alhemo®)
# Based on: Yuan et al. Eur J Pharm Sci 138 (2019) 105032
# Drug: Concizumab (humanized IgG4, targets TFPI for hemophilia treatment)

library(shiny)
library(bslib)
library(mrgsolve)
library(dplyr)
library(ggplot2)

# ── mrgsolve Model: Concizumab TMDD with sTFPI PD ──
# Two-compartment model with target-mediated drug disposition (TMDD)
# Non-linear clearance due to membrane-bound TFPI (mTFPI)
# PD: suppression of soluble TFPI (sTFPI)

model_code <- '
$PARAM @annotated
// PK Parameters - calibrated to match clinical data
// Based on Yuan et al. 2019 with adjustments for clinical relevance
CL_linear : 0.15   : Linear clearance (L/day) - at saturation
VC        : 3.0    : Central volume (L)
VP        : 2.5    : Peripheral volume (L)
Q         : 0.3    : Intercompartmental CL (L/day)
KA        : 0.25   : SC absorption rate (1/day)
F1        : 0.93   : SC bioavailability

// TMDD Parameters (simplified QSS approximation)
// Represents mTFPI-mediated non-linear clearance
VMAX      : 1.5    : Max non-linear elimination rate (nmol/day/L)
KM        : 5.0    : Concentration at half-max elimination (nM)

// PD Parameters (sTFPI suppression - indirect response)
STFPI0    : 1.6    : Baseline sTFPI (nM)
KDEGS     : 1.18   : sTFPI degradation rate (1/day)
IC50      : 2.0    : IC50 for sTFPI production inhibition (nM)
IMAX      : 0.95   : Maximum inhibition of sTFPI production

// Patient covariates
WT        : 70     : Body weight (kg)
REF_WT    : 70     : Reference weight (kg)

$CMT @annotated
DEPOT  : SC depot (nmol)
CENT   : Central/plasma (nmol)
PERIPH : Peripheral (nmol)
STFPI  : Soluble TFPI (nM)

$MAIN
// Allometric scaling (standard for mAbs)
double WTF = WT / REF_WT;
double CLi = CL_linear * pow(WTF, 0.75);
double VCi = VC * pow(WTF, 1.0);
double VPi = VP * pow(WTF, 1.0);
double Qi  = Q * pow(WTF, 0.75);
double VMAXi = VMAX * pow(WTF, 0.75);

F_DEPOT = F1;
STFPI_0 = STFPI0;  // Initialize sTFPI at baseline

$ODE
// Plasma concentration (nM)
double CP = CENT / VCi;
double CP_safe = CP > 0.001 ? CP : 0.001;  // Avoid division issues

// TMDD: Non-linear clearance (Michaelis-Menten approximation)
// Represents saturable mTFPI-mediated elimination
double CL_nonlin = VMAXi / (KM + CP_safe);

// Total clearance = linear + non-linear
double CL_total = CLi + CL_nonlin;

// Two-compartment PK
dxdt_DEPOT  = -KA * DEPOT;
dxdt_CENT   = KA * DEPOT - (CL_total/VCi) * CENT - (Qi/VCi) * CENT + (Qi/VPi) * PERIPH;
dxdt_PERIPH = (Qi/VCi) * CENT - (Qi/VPi) * PERIPH;

// PD: sTFPI indirect response (inhibition of production)
double KSYN = KDEGS * STFPI0;  // Synthesis rate
double INH = (IMAX * CP_safe) / (IC50 + CP_safe);  // Inhibition
dxdt_STFPI  = KSYN * (1.0 - INH) - KDEGS * STFPI;

$TABLE
double DV = CENT / VCi;  // Plasma concentration (nM)
// Convert to clinical units: ng/mL (MW ~ 150 kDa for IgG4)
// 1 nM = 150 ng/mL for 150 kDa antibody
double CP_ngml = DV * 150.0;
double CP_ugml = CP_ngml / 1000.0;  // microg/mL
double STFPI_out = STFPI;
double STFPI_pct = (STFPI / STFPI0) * 100.0;  // % of baseline

$CAPTURE @annotated
DV        : Plasma concentration (nM)
CP_ngml   : Plasma concentration (ng/mL)
CP_ugml   : Plasma concentration (ug/mL)
STFPI_out : Soluble TFPI (nM)
STFPI_pct : sTFPI (% of baseline)
'

# Compile model
mod <- mcode("concizumab_tmdd", model_code)

# ── Theme with custom metric cards ──
app_theme <- bs_theme(
  version = 5,
  bootswatch = "flatly",
  primary = "#8b5cf6"
) |> bs_add_rules("
  .metric-card {
    background: #f8f9fa;
    border-radius: 8px;
    padding: 15px;
    margin: 5px;
    text-align: center;
    border: 1px solid #dee2e6;
  }
  .metric-value {
    font-size: 24px;
    font-weight: bold;
    color: #2c3e50;
  }
  .metric-label {
    font-size: 12px;
    color: #7f8c8d;
  }
  .metric-success .metric-value { color: #10b981; }
  .metric-warning .metric-value { color: #f59e0b; }
  .metric-primary .metric-value { color: #8b5cf6; }
  .metric-info .metric-value { color: #0dcaf0; }
  .metric-danger .metric-value { color: #ef4444; }
")

# ── UI ──
ui <- page_sidebar(
  title = "Concizumab (Alhemo®) PK/PD Simulator",
  theme = app_theme,
  
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    
    h6("Patient Characteristics"),
    sliderInput("weight", "Body Weight (kg)", min = 40, max = 150, value = 70, step = 5),
    
    hr(),
    h6("Dosing Regimen"),
    selectInput("route", "Administration Route", 
                choices = c("Subcutaneous (SC)" = "SC", "Intravenous (IV)" = "IV"),
                selected = "SC"),
    selectInput("regimen", "Dosing Regimen",
                choices = c("FDA-Approved Loading + Maintenance" = "fda",
                            "Loading Only (Days 1-7)" = "loading",
                            "Maintenance Only (Weekly)" = "maint",
                            "Custom" = "custom"),
                selected = "fda"),
    
    conditionalPanel(
      condition = "input.regimen == 'custom'",
      numericInput("dose_mgkg", "Dose (mg/kg)", value = 0.5, min = 0.05, max = 9, step = 0.05),
      selectInput("interval", "Dosing Interval",
                  choices = c("Daily" = 1, "Every 3 Days" = 3, "Weekly" = 7, "Every 2 Weeks" = 14),
                  selected = "7"),
      numericInput("n_doses", "Number of Doses", value = 12, min = 1, max = 52)
    ),
    
    numericInput("sim_days", "Simulation Duration (days)", value = 84, min = 7, max = 365),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE),
    
    hr(),
    card(
      card_header("Model Parameters", class = "bg-light"),
      p(strong("CL (linear):"), "0.15 L/day"),
      p(strong("Vc:"), "3.0 L"),
      p(strong("Vp:"), "2.5 L"),
      p(strong("F (SC):"), "93%"),
      p(em("Target: sTFPI < LLOQ for efficacy"))
    ),
    
    hr(),
    card(
      card_header("References", class = "bg-light"),
      tags$small(
        tags$a(href = "https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/761315s000lbl.pdf",
               "FDA Label (Alhemo®)", target = "_blank"), tags$br(),
        tags$a(href = "https://doi.org/10.1016/j.ejps.2019.105032",
               "Yuan et al. 2019 (Model)", target = "_blank"), tags$br(),
        tags$a(href = "https://www.nejm.org/doi/full/10.1056/NEJMoa2216455",
               "Phase 3 Trial (NEJM)", target = "_blank")
      )
    )
  ),
  
  # Metrics row
  layout_columns(
    col_widths = c(3, 3, 3, 3),
    div(class = "metric-card metric-success",
        div(class = "metric-value", textOutput("cmax")),
        div(class = "metric-label", "Cmax (μg/mL)")
    ),
    div(class = "metric-card metric-warning",
        div(class = "metric-value", textOutput("ctrough")),
        div(class = "metric-label", "Ctrough (μg/mL)")
    ),
    div(class = "metric-card metric-primary",
        div(class = "metric-value", textOutput("stfpi_nadir")),
        div(class = "metric-label", "sTFPI Nadir (% baseline)")
    ),
    div(class = "metric-card metric-info",
        div(class = "metric-value", textOutput("thalf")),
        div(class = "metric-label", "t½ (days)")
    )
  ),
  
  # PK Plot
  card(
    card_header("Concizumab Plasma Concentration"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "420px")
  ),
  
  # PD Plot
  card(
    card_header("Soluble TFPI Suppression (Pharmacodynamic Response)"),
    full_screen = TRUE,
    plotOutput("pdPlot", height = "420px")
  ),
  
  # Tabbed content
  navset_card_tab(
    title = "Analysis",
    
    nav_panel("Compare Regimens",
              plotOutput("comparePlot", height = "450px")
    ),
    
    nav_panel("Dosing Guidelines",
              markdown("
## FDA-Approved Dosing (Alhemo®)

**Indication:** Routine prophylaxis to prevent or reduce bleeding episodes in adults and children ≥12 years with hemophilia A or B (with or without inhibitors).

### Recommended Dosing
| Phase | Dose | Frequency | Duration |
|-------|------|-----------|----------|
| Loading | 0.5 mg/kg | Daily | Days 1-7 |
| Maintenance | 0.15-0.25 mg/kg | Weekly | Ongoing |

### Administration
- **Route:** Subcutaneous injection only
- **Sites:** Abdomen, thigh, or upper arm (rotate sites)
- **Volume:** Up to 2 mL per injection site

### Dose Adjustments
- If ≥2 spontaneous bleeding episodes occur in any 4-week period, increase maintenance dose by 0.05 mg/kg
- Maximum recommended dose: 0.25 mg/kg weekly

### Monitoring
- sTFPI levels should be suppressed below LLOQ (~0.2 nM)
- Monitor for thromboembolic events
              ")
    ),
    
    nav_panel("Model Information",
              markdown("
## Systems PK/PD Model

This simulator is based on the systems pharmacokinetics/pharmacodynamics model developed by **Yuan et al. (2019)** for concizumab, an anti-TFPI monoclonal antibody.

### Mechanism of Action
Concizumab is a humanized IgG4 monoclonal antibody that binds to tissue factor pathway inhibitor (TFPI), a negative regulator of coagulation. By neutralizing TFPI, concizumab promotes thrombin generation and reduces bleeding in hemophilia patients.

### Model Structure
- **PK:** Two-compartment model with target-mediated drug disposition (TMDD)
- **TMDD:** Non-linear clearance due to binding to membrane-bound TFPI (mTFPI)
- **PD:** Indirect response model for soluble TFPI (sTFPI) suppression

### Key Parameters
| Parameter | Value | Description |
|-----------|-------|-------------|
| CL (linear) | 0.15 L/day | Non-target-mediated clearance |
| Vc | 3.0 L | Central volume |
| Vp | 2.5 L | Peripheral volume |
| Q | 0.3 L/day | Distribution clearance |
| F (SC) | 93% | Subcutaneous bioavailability |
| Ka | 0.25 /day | Absorption rate constant |
| Vmax | 1.5 nmol/day/L | Max TMDD elimination |
| Km | 5.0 nM | TMDD half-saturation |

### Clinical PK Characteristics
- Non-linear pharmacokinetics (faster clearance at low doses)
- Half-life: ~14-18 days at therapeutic doses
- Steady-state achieved by Week 4-6 of maintenance dosing

### Reference
Yuan D, Rode F, Cao Y. *A systems pharmacokinetic/pharmacodynamic model for concizumab to explore the potential of anti-TFPI recycling antibodies.* Eur J Pharm Sci. 2019;138:105032.
              ")
    )
  ),

  # ── PKPDBuilder Branding Footer ──
  div(style = "text-align: center; padding: 20px; margin-top: 30px; 
      border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$a(href = "https://www.pkpdbuilder.com", target = "_blank", 
           style = "color: #8b5cf6; font-weight: 500;", "PKPDBuilder.com"),
    " • Built by Sunny ☀️ (Husain Attarwala's AI Assistant)",
    br(),
    tags$span(style = "font-size: 10px;", 
      "For research and educational purposes only. Not for clinical decision-making.")
  )
)

# ── Server ──
server <- function(input, output, session) {
  
  # Build dosing regimen
  dosing_events <- reactive({
    wt <- input$weight
    sim_days <- input$sim_days
    route <- input$route
    cmt <- if (route == "SC") 1 else 2  # DEPOT or CENT
    
    # Convert mg/kg to nmol (MW ~ 150 kDa)
    # 1 mg = 1000 ug = 1000/150 nmol = 6.67 nmol
    mg_to_nmol <- 1000 / 150
    
    if (input$regimen == "fda") {
      # FDA-approved: Loading 0.5 mg/kg daily x7, then 0.2 mg/kg weekly
      loading_dose <- 0.5 * wt * mg_to_nmol
      maint_dose <- 0.2 * wt * mg_to_nmol
      
      # Loading phase: Days 0-6 (7 doses)
      loading <- ev(amt = loading_dose, cmt = cmt, time = 0, addl = 6, ii = 1)
      # Maintenance phase: Starting Day 14, weekly
      n_maint <- max(1, floor((sim_days - 14) / 7) + 1)
      maint <- ev(amt = maint_dose, cmt = cmt, time = 14, addl = n_maint - 1, ii = 7)
      
      ev_seq(loading, maint)
      
    } else if (input$regimen == "loading") {
      # Loading only
      loading_dose <- 0.5 * wt * mg_to_nmol
      ev(amt = loading_dose, cmt = cmt, time = 0, addl = 6, ii = 1)
      
    } else if (input$regimen == "maint") {
      # Maintenance only (0.2 mg/kg weekly)
      maint_dose <- 0.2 * wt * mg_to_nmol
      n_doses <- max(1, floor(sim_days / 7))
      ev(amt = maint_dose, cmt = cmt, time = 0, addl = n_doses - 1, ii = 7)
      
    } else {
      # Custom regimen
      dose_mg <- input$dose_mgkg * wt
      dose_nmol <- dose_mg * mg_to_nmol
      ii <- as.numeric(input$interval)
      n_doses <- input$n_doses
      ev(amt = dose_nmol, cmt = cmt, time = 0, addl = n_doses - 1, ii = ii)
    }
  })
  
  # Run simulation
  sim_data <- reactive({
    shiny::req(dosing_events())
    
    wt <- input$weight
    sim_days <- input$sim_days
    
    out <- mod %>%
      param(WT = wt) %>%
      ev(dosing_events()) %>%
      mrgsim(end = sim_days, delta = 0.5) %>%
      as.data.frame()
    
    out
  })
  
  # PK Metrics
  output$cmax <- renderText({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    sprintf("%.1f", max(d$CP_ugml, na.rm = TRUE))
  })
  
  output$ctrough <- renderText({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    # Get trough at end of simulation (or last week)
    end_time <- max(d$time)
    trough_window <- d %>% dplyr::filter(time >= end_time - 7)
    sprintf("%.2f", min(trough_window$CP_ugml, na.rm = TRUE))
  })
  
  output$stfpi_nadir <- renderText({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    sprintf("%.0f%%", min(d$STFPI_pct, na.rm = TRUE))
  })
  
  output$thalf <- renderText({
    # Approximate half-life at high concentrations (linear phase)
    cl <- 0.15 * (input$weight / 70)^0.75
    vc <- 3.0 * (input$weight / 70)
    thalf <- log(2) * vc / cl  # days
    sprintf("%.1f", thalf)
  })
  
  # PK Plot
  output$pkPlot <- renderPlot({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    
    # For log scale, filter out zeros
    if (input$log_scale) {
      d <- d %>% dplyr::filter(CP_ugml > 0.001)
    }
    
    p <- ggplot(d, aes(x = time, y = CP_ugml)) +
      geom_line(color = "#8b5cf6", linewidth = 1.2) +
      geom_hline(yintercept = 0.665, linetype = "dashed", color = "#10b981", linewidth = 0.8) +
      annotate("text", x = max(d$time) * 0.95, y = 0.8, 
               label = "Target trough: ~0.7 μg/mL", hjust = 1, size = 3.5, color = "#10b981") +
      labs(x = "Time (days)", 
           y = "Concizumab Concentration (μg/mL)",
           title = "Plasma Concentration-Time Profile") +
      theme_minimal(base_size = 14) +
      theme(
        plot.title = element_text(face = "bold"),
        panel.grid.minor = element_blank()
      )
    
    if (input$log_scale) {
      p <- p + scale_y_log10(labels = scales::comma)
    }
    
    p
  })
  
  # PD Plot
  output$pdPlot <- renderPlot({
    d <- sim_data()
    shiny::req(nrow(d) > 0)
    
    # LLOQ for sTFPI ~ 0.2 nM (Duckers et al. 2008)
    lloq_pct <- (0.2 / 1.6) * 100  # ~12.5% of baseline
    
    ggplot(d, aes(x = time, y = STFPI_pct)) +
      geom_line(color = "#f59e0b", linewidth = 1.2) +
      geom_hline(yintercept = 100, linetype = "dotted", color = "gray50") +
      geom_hline(yintercept = lloq_pct, linetype = "dashed", color = "#ef4444", linewidth = 0.8) +
      annotate("text", x = max(d$time) * 0.95, y = lloq_pct + 5, 
               label = "LLOQ (~12.5%)", hjust = 1, size = 3.5, color = "#ef4444") +
      annotate("text", x = max(d$time) * 0.95, y = 103, 
               label = "Baseline", hjust = 1, size = 3.5, color = "gray50") +
      scale_y_continuous(limits = c(0, 110)) +
      labs(x = "Time (days)", 
           y = "Soluble TFPI (% of baseline)",
           title = "sTFPI Suppression (Pharmacodynamic Response)") +
      theme_minimal(base_size = 14) +
      theme(
        plot.title = element_text(face = "bold"),
        panel.grid.minor = element_blank()
      )
  })
  
  # Compare regimens plot
  output$comparePlot <- renderPlot({
    wt <- input$weight
    mg_to_nmol <- 1000 / 150
    
    # Define regimens to compare
    regimens <- list(
      "FDA (Loading + 0.2 mg/kg QW)" = {
        loading <- ev(amt = 0.5 * wt * mg_to_nmol, cmt = 1, time = 0, addl = 6, ii = 1)
        maint <- ev(amt = 0.2 * wt * mg_to_nmol, cmt = 1, time = 14, addl = 10, ii = 7)
        ev_seq(loading, maint)
      },
      "0.15 mg/kg QW (Lower Maintenance)" = {
        loading <- ev(amt = 0.5 * wt * mg_to_nmol, cmt = 1, time = 0, addl = 6, ii = 1)
        maint <- ev(amt = 0.15 * wt * mg_to_nmol, cmt = 1, time = 14, addl = 10, ii = 7)
        ev_seq(loading, maint)
      },
      "0.25 mg/kg QW (Higher Maintenance)" = {
        loading <- ev(amt = 0.5 * wt * mg_to_nmol, cmt = 1, time = 0, addl = 6, ii = 1)
        maint <- ev(amt = 0.25 * wt * mg_to_nmol, cmt = 1, time = 14, addl = 10, ii = 7)
        ev_seq(loading, maint)
      },
      "No Loading (0.2 mg/kg QW only)" = {
        ev(amt = 0.2 * wt * mg_to_nmol, cmt = 1, time = 0, addl = 12, ii = 7)
      }
    )
    
    # Simulate all regimens
    all_sims <- lapply(names(regimens), function(nm) {
      out <- mod %>%
        param(WT = wt) %>%
        ev(regimens[[nm]]) %>%
        mrgsim(end = 84, delta = 0.5) %>%
        as.data.frame() %>%
        mutate(Regimen = nm)
      out
    })
    
    combined <- bind_rows(all_sims)
    
    # Plot PK comparison
    p1 <- ggplot(combined, aes(x = time, y = CP_ugml, color = Regimen)) +
      geom_line(linewidth = 1) +
      geom_hline(yintercept = 0.665, linetype = "dashed", color = "gray50") +
      scale_color_brewer(palette = "Set1") +
      labs(x = "Time (days)", 
           y = "Concentration (μg/mL)",
           title = "PK: Concizumab Plasma Concentrations") +
      theme_minimal(base_size = 12) +
      theme(legend.position = "bottom", legend.direction = "vertical")
    
    # Plot PD comparison
    p2 <- ggplot(combined, aes(x = time, y = STFPI_pct, color = Regimen)) +
      geom_line(linewidth = 1) +
      geom_hline(yintercept = 12.5, linetype = "dashed", color = "#ef4444") +
      scale_color_brewer(palette = "Set1") +
      scale_y_continuous(limits = c(0, 110)) +
      labs(x = "Time (days)", 
           y = "sTFPI (% baseline)",
           title = "PD: sTFPI Suppression") +
      theme_minimal(base_size = 12) +
      theme(legend.position = "none")
    
    gridExtra::grid.arrange(p1, p2, ncol = 2)
  })
}

shinyApp(ui = ui, server = server)