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

model_code <- '
$PARAM @annotated
CL   : 0.235 : Typical clearance (L/day)
VC   : 3.11  : Central volume (L)
Q    : 0.534 : Intercompartmental clearance (L/day)
VP   : 2.46  : Peripheral volume (L)
ALBU : 3.9   : Baseline serum albumin (g/dL)
LBW  : 49    : Lean body weight (kg)

$CMT @annotated
CENT   : Central compartment (mg)
PERIPH : Peripheral compartment (mg)

$MAIN
double CLi = CL * pow(ALBU / 3.9, -1.06) * pow(LBW / 49.0, 0.516);
double VCi = VC * pow(LBW / 49.0, 0.747);
double Qi  = Q;
double VPi = VP * pow(LBW / 49.0, 0.83);

$ODE
dxdt_CENT   = -(CLi / VCi) * CENT - (Qi / VCi) * CENT + (Qi / VPi) * PERIPH;
dxdt_PERIPH =  (Qi / VCi) * CENT - (Qi / VPi) * PERIPH;

$TABLE
double CP = CENT / VCi;

$CAPTURE @annotated
CP  : Serum pertuzumab concentration (mg/L)
CLi : Individual clearance (L/day)
VCi : Individual central volume (L)
VPi : Individual peripheral volume (L)
'

mod <- mcode("pertuzumab_pkpd_302", model_code)

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; }
  .ref-box {
    background: #f0f4ff;
    border-left: 4px solid #8b5cf6;
    padding: 12px 16px;
    border-radius: 4px;
    margin-top: 10px;
    font-size: 13px;
  }
  .ref-box a { color: #8b5cf6; }
")

ui <- page_sidebar(
  title = "Pertuzumab (Perjeta) Population PK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    accordion(
      open = c("Dosing", "Patient"),
      accordion_panel(
        "Dosing",
        icon = bsicons::bs_icon("capsule"),
        radioButtons(
          "dose_strategy",
          "Dosing strategy",
          choices = c(
            "Fixed 840/420 mg q3w" = "fixed",
            "Weight-based 12/6 mg/kg q3w" = "weight"
          ),
          selected = "fixed"
        ),
        conditionalPanel(
          "input.dose_strategy === 'fixed'",
          numericInput("loading_dose", "Loading dose (mg)", value = 840, min = 420, max = 2000, step = 10),
          numericInput("maintenance_dose", "Maintenance dose (mg)", value = 420, min = 210, max = 2000, step = 10)
        ),
        conditionalPanel(
          "input.dose_strategy === 'weight'",
          sliderInput("loading_mgkg", "Loading dose (mg/kg)", min = 6, max = 20, value = 12, step = 0.5),
          sliderInput("maintenance_mgkg", "Maintenance dose (mg/kg)", min = 3, max = 12, value = 6, step = 0.5)
        ),
        sliderInput("interval_days", "Dosing interval (days)", min = 14, max = 28, value = 21, step = 1),
        numericInput("n_cycles", "Number of cycles", value = 6, min = 1, max = 12),
        sliderInput("infusion_h", "Infusion duration (hours)", min = 0.5, max = 2.0, value = 1.0, step = 0.25)
      ),
      accordion_panel(
        "Patient",
        icon = bsicons::bs_icon("person"),
        sliderInput("wt", "Actual body weight (kg)", min = 36, max = 150, value = 72, step = 1),
        sliderInput("lbw", "Lean body weight (kg)", min = 25, max = 90, value = 49, step = 1),
        sliderInput("albu", "Serum albumin (g/dL)", min = 2.0, max = 5.5, value = 3.9, step = 0.1),
        tags$p(
          style = "font-size:12px; color:#6c757d; margin-top:8px;",
          "Lean body weight and albumin drive PK covariate effects in the final model. Actual body weight is used for the weight-based dosing option."
        )
      ),
      accordion_panel(
        "Display",
        icon = bsicons::bs_icon("gear"),
        checkboxInput("show_target", "Show target trough threshold (20 mg/L)", value = TRUE),
        checkboxInput("log_scale", "Log scale (Y-axis)", value = FALSE)
      )
    )
  ),
  tags$p(
    style = "font-size: 11px; color: #6c757d; margin: 4px 0 0 6px;",
    "Default values reflect the approximate median study population: WT 72 kg, LBW 49 kg, albumin 3.9 g/dL."
  ),
  layout_column_wrap(
    width = 1/4,
    fill = FALSE,
    div(class = "metric-card metric-success",
        div(class = "metric-value", textOutput("cmax")),
        div(class = "metric-label", "Cmax,ss (mg/L)")),
    div(class = "metric-card metric-warning",
        div(class = "metric-value", textOutput("ctrough")),
        div(class = "metric-label", "Ctrough,ss (mg/L)")),
    div(class = "metric-card metric-primary",
        div(class = "metric-value", textOutput("auc")),
        div(class = "metric-label", "AUCtau,ss (mg.day/L)")),
    div(class = "metric-card metric-info",
        div(class = "metric-value", textOutput("thalf")),
        div(class = "metric-label", "t1/2 (days)"))
  ),
  card(
    card_header("Serum Concentration-Time Profile"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "500px")
  ),
  navset_card_underline(
    title = "Drug Information",
    nav_panel("Model Information", markdown("## Pertuzumab population PK model\n\n**Primary paper:** Garg A et al. *Cancer Chemotherapy and Pharmacology* (2014) 74:819-829  \n**PMID:** 25119184  \n**Drug class:** HER2-directed monoclonal antibody  \n**Route:** Intravenous infusion\n\n### Final structural model\n- Two-compartment linear model with first-order elimination from the central compartment\n- Population built from 4,525 serum concentrations in 481 patients with solid tumors\n- Terminal half-life approximately 18.0 days\n\n### Typical PK parameters\n| Parameter | Estimate | Units |\n|-----------|----------|-------|\n| CL | 0.235 | L/day |\n| Vc | 3.11 | L |\n| Q | 0.534 | L/day |\n| Vp | 2.46 | L |\n\n### Covariate effects in the final model\n- Clearance increases with lower albumin and higher lean body weight\n- Central and peripheral volumes increase with lean body weight\n- Covariate effects were statistically significant but not clinically large enough to require dose adjustment\n\n### Dosing conclusion from the paper\n- Fixed dosing: **840 mg loading dose, then 420 mg every 3 weeks**\n- Weight-based comparator: **12 mg/kg loading, then 6 mg/kg every 3 weeks**\n- The fixed regimen maintained most patients above the target **Ctrough >= 20 mg/L** threshold\n\n### Exposure context\nAt steady state with fixed dosing, the paper reported median trough concentrations around **51.6 mg/L** overall, with the 5th percentile at **16.0 mg/L** and about **7.6%** of patients below **20 mg/L**.\n")),
    nav_panel("References", div(class = "ref-box",
      tags$h5("Primary reference"),
      tags$ol(
        tags$li(
          tags$strong("Garg A, Quartino A, Li J, et al. (2014)."),
          " Population pharmacokinetic and covariate analysis of pertuzumab, a HER2-targeted monoclonal antibody, and evaluation of a fixed, non-weight-based dose in patients with a variety of solid tumors. ",
          tags$em("Cancer Chemother Pharmacol"),
          " 74(4):819-829. ",
          tags$a(href = "https://doi.org/10.1007/s00280-014-2560-3", target = "_blank", "doi:10.1007/s00280-014-2560-3")
        ),
        tags$li(
          "PERJETA (pertuzumab) prescribing information. Genentech/Roche."
        )
      ),
      tags$h5("Therapeutic context"),
      tags$ul(
        tags$li(tags$strong("Class:"), " HER2-directed monoclonal antibody"),
        tags$li(tags$strong("Therapeutic area:"), " Oncology, HER2-positive solid tumors and breast cancer regimens"),
        tags$li(tags$strong("Typical regimen:"), " 840 mg loading dose followed by 420 mg every 3 weeks"),
        tags$li(tags$strong("Clinical PK message:"), " Fixed dosing is supported despite statistically significant albumin and lean body weight effects"),
        tags$li(tags$strong("Target used in the paper:"), " Steady-state trough concentration >= 20 mg/L")
      )
    ))
  ),
  div(
    style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$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 <- function(input, output, session) {
  regimen_label <- reactive({
    if (input$dose_strategy == "fixed") {
      paste0(input$loading_dose, " mg load, then ", input$maintenance_dose, " mg q", input$interval_days, "d")
    } else {
      paste0(input$loading_mgkg, " mg/kg load, then ", input$maintenance_mgkg, " mg/kg q", input$interval_days, "d")
    }
  })

  regimen_events <- reactive({
    shiny::req(input$infusion_h, input$n_cycles, input$interval_days, input$wt)

    infusion_days <- input$infusion_h / 24

    if (input$dose_strategy == "fixed") {
      loading_amt <- input$loading_dose
      maintenance_amt <- input$maintenance_dose
    } else {
      loading_amt <- input$loading_mgkg * input$wt
      maintenance_amt <- input$maintenance_mgkg * input$wt
    }

    events <- list(
      ev(time = 0, amt = loading_amt, cmt = 1, rate = loading_amt / infusion_days)
    )

    if (input$n_cycles > 1) {
      for (i in seq_len(input$n_cycles - 1)) {
        events[[length(events) + 1]] <- ev(
          time = i * input$interval_days,
          amt = maintenance_amt,
          cmt = 1,
          rate = maintenance_amt / infusion_days
        )
      }
    }

    do.call(c, events)
  })

  sim_data <- reactive({
    shiny::req(input$lbw, input$albu)

    total_days <- input$interval_days * input$n_cycles

    mod %>%
      param(ALBU = input$albu, LBW = input$lbw) %>%
      ev(regimen_events()) %>%
      mrgsim(end = total_days, delta = 0.1) %>%
      as.data.frame() |>
      dplyr::mutate(time_days = time)
  })

  last_interval_data <- reactive({
    d <- sim_data()
    interval_start <- input$interval_days * (input$n_cycles - 1)
    d |>
      dplyr::filter(time_days >= interval_start, time_days <= interval_start + input$interval_days)
  })

  output$cmax <- renderText({
    d <- last_interval_data()
    shiny::req(nrow(d) > 0)
    sprintf("%.1f", max(d$CP, na.rm = TRUE))
  })

  output$ctrough <- renderText({
    d <- last_interval_data() |>
      dplyr::filter(CP > 0)
    shiny::req(nrow(d) > 0)
    sprintf("%.1f", tail(d$CP, 1))
  })

  output$auc <- renderText({
    d <- last_interval_data()
    shiny::req(nrow(d) > 1)
    auc_val <- sum(diff(d$time_days) * (head(d$CP, -1) + tail(d$CP, -1)) / 2)
    sprintf("%.0f", auc_val)
  })

  output$thalf <- renderText({
    cl_i <- 0.235 * (input$albu / 3.9)^(-1.06) * (input$lbw / 49)^0.516
    vc_i <- 3.11 * (input$lbw / 49)^0.747
    q_i <- 0.534
    vp_i <- 2.46 * (input$lbw / 49)^0.83

    k10 <- cl_i / vc_i
    k12 <- q_i / vc_i
    k21 <- q_i / vp_i
    beta <- 0.5 * ((k10 + k12 + k21) - sqrt((k10 + k12 + k21)^2 - 4 * k10 * k21))

    sprintf("%.1f", log(2) / beta)
  })

  output$pkPlot <- renderPlot({
    d <- sim_data()
    shiny::req(nrow(d) > 0)

    plot_data <- d
    if (isTRUE(input$log_scale)) {
      plot_data <- plot_data |>
        dplyr::filter(CP > 0)
    }

    ymax <- max(plot_data$CP, na.rm = TRUE)

    p <- ggplot(plot_data, aes(x = time_days, y = CP))

    if (isTRUE(input$show_target) && is.finite(ymax) && ymax > 20) {
      p <- p +
        annotate("rect", xmin = -Inf, xmax = Inf, ymin = 20, ymax = ymax * 1.02, fill = "#10b981", alpha = 0.08) +
        geom_hline(yintercept = 20, linetype = "dashed", color = "#10b981", linewidth = 0.7) +
        annotate(
          "text",
          x = max(plot_data$time_days) * 0.02,
          y = 20 * 1.08,
          label = "Target Ctrough >= 20 mg/L",
          hjust = 0,
          size = 3.5,
          color = "#10b981"
        )
    }

    p <- p +
      geom_line(color = "#8b5cf6", linewidth = 0.9) +
      labs(
        x = "Time (days)",
        y = "Pertuzumab concentration (mg/L)",
        title = paste("Pertuzumab PK Profile -", regimen_label()),
        subtitle = paste0(
          "LBW ", input$lbw, " kg, albumin ", input$albu, " g/dL",
          if (input$dose_strategy == "weight") paste0(", actual weight ", input$wt, " kg") else ""
        )
      ) +
      theme_minimal(base_size = 14)

    if (isTRUE(input$log_scale)) {
      p <- p + scale_y_log10()
    }

    p
  })
}

shinyApp(ui = ui, server = server)