use("shiny")
use("bslib")
use("mrgsolve")
use("dplyr")
use("ggplot2")

model_code <- '
$PARAM @annotated
CLTH    : 6.5     : Typical clearance coefficient (L/h)
BSA_EXP : 0.62    : Body surface area exponent on clearance
VC_KG   : 0.36    : Central volume coefficient (L/kg)
Q       : 0.42    : Intercompartmental clearance (L/h)
VP      : 3.2     : Peripheral volume (L)
BSA     : 0.80    : Body surface area (m2)
WT      : 20      : Body weight (kg)

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

$MAIN
double CLi = CLTH * pow(BSA, BSA_EXP);
double VCi = VC_KG * WT;
double Qi = Q;
double VPi = VP;

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

$TABLE
double CP_MG_L = CENT / VCi;
double CP_uM = CP_MG_L / 0.45444;

$CAPTURE @annotated
CP_MG_L : Plasma methotrexate concentration (mg/L)
CP_uM   : Plasma methotrexate concentration (uM)
CLi     : Individual clearance (L/h)
VCi     : Individual central volume (L)
Qi      : Intercompartmental clearance (L/h)
VPi     : Peripheral volume (L)
'

mod <- mrgsolve::mcode("methotrexate_medellin_garibay_2019", model_code)

metric_card <- function(output_id, label, class = "metric-primary") {
  div(
    class = paste("metric-card", class),
    div(class = "metric-value", textOutput(output_id)),
    div(class = "metric-label", label)
  )
}

micro_rates <- function(bsa, wt) {
  cl_i <- 6.5 * bsa^0.62
  vc_i <- 0.36 * wt
  q_i <- 0.42
  vp_i <- 3.2
  k10 <- cl_i / vc_i
  k12 <- q_i / vc_i
  k21 <- q_i / vp_i
  list(cl = cl_i, vc = vc_i, q = q_i, vp = vp_i, k10 = k10, k12 = k12, k21 = k21)
}

terminal_half_life_h <- function(bsa, wt) {
  rates <- micro_rates(bsa, wt)
  a_term <- rates$k10 + rates$k12 + rates$k21
  disc <- a_term^2 - 4 * rates$k10 * rates$k21
  lambda_beta <- 0.5 * (a_term - sqrt(max(disc, 0)))
  if (!is.finite(lambda_beta) || lambda_beta <= 0) {
    return(NA_real_)
  }
  log(2) / lambda_beta
}

simulate_mtx <- function(dose_g_m2 = 3.5, bsa = 0.8, wt = 20,
                         infusion_h = 24, end_h = 72, delta_h = 0.1) {
  total_mg <- dose_g_m2 * 1000 * bsa
  first_mg <- total_mg * 0.20
  second_mg <- total_mg * 0.80

  dose_events <- data.frame(
    time = c(0, 1),
    amt = c(first_mg, second_mg),
    cmt = c(1, 1),
    evid = c(1, 1),
    rate = c(first_mg / 1, second_mg / 23)
  )

  mod |>
    mrgsolve::param(BSA = bsa, WT = wt) |>
    mrgsolve::ev(dose_events) %>%
    mrgsolve::mrgsim(end = end_h, delta = delta_h) |>
    as.data.frame() |>
    dplyr::mutate(
      time_h = time,
      CP_MG_L = pmax(CP_MG_L, 0),
      CP_uM = pmax(CP_uM, 0)
    )
}

value_at_time <- function(data, target_h, column = "CP_uM") {
  data_unique <- data[!duplicated(data$time_h, fromLast = TRUE), c("time_h", column)]
  stats::approx(data_unique$time_h, data_unique[[column]], xout = target_h, rule = 2)$y
}

paper_target_note <- function(risk_group) {
  if (identical(risk_group, "high")) {
    "Paper target: Cpss about 65 uM with C42h <= 1 uM and C60h < 0.2 uM."
  } else {
    "Paper target: Cpss about 33 uM with C42h <= 1 uM and C54h <= 0.2 uM."
  }
}

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; }
    .small-note {
      color: #6c757d;
      font-size: 12px;
      line-height: 1.5;
    }
  ")

ui <- page_sidebar(
  title = "Methotrexate Pediatric PopPK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    accordion(
      open = c("Scenario", "Patient", "Display"),
      accordion_panel(
        "Scenario",
        selectInput(
          "risk_group",
          "Paper scenario",
          choices = c(
            "Standard / low-risk target" = "standard",
            "High-risk target" = "high",
            "Custom dose" = "custom"
          ),
          selected = "standard"
        ),
        numericInput("dose_g_m2", "Methotrexate dose (g/m^2)", value = 3.5, min = 1, max = 10, step = 0.1),
        sliderInput("infusion_h", "Infusion duration (h)", min = 24, max = 24, value = 24, step = 1),
        div(
          class = "small-note",
          "The manuscript model assumes 20% of the total dose in the first hour and 80% over the next 23 hours."
        )
      ),
      accordion_panel(
        "Patient",
        numericInput("bsa", "Body surface area (m^2)", value = 0.80, min = 0.40, max = 1.80, step = 0.01),
        sliderInput("wt", "Body weight (kg)", min = 8, max = 80, value = 20, step = 1),
        div(
          class = "small-note",
          "Paper-average child used in the dosing simulations: BSA 0.8 m^2 and weight 20 kg."
        )
      ),
      accordion_panel(
        "Display",
        sliderInput("end_h", "Monitoring duration (h)", min = 48, max = 96, value = 72, step = 6),
        checkboxInput("log_scale", "Log scale (Y-axis)", value = TRUE)
      )
    )
  ),
  layout_column_wrap(
    width = 1/4,
    fill = FALSE,
    metric_card("c24", "Cpss at 24 h (uM)", "metric-success"),
    metric_card("c42", "Concentration at 42 h (uM)", "metric-warning"),
    metric_card("auc", "AUC0-end (uM*h/L)", "metric-primary"),
    metric_card("thalf", "Terminal t1/2 (h)", "metric-info")
  ),
  card(
    card_header("Methotrexate Concentration-Time Profile"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "500px"),
    div(class = "small-note", textOutput("paperSummary"))
  ),
  navset_card_underline(
    title = "Supporting Details",
    nav_panel(
      "Model Information",
      div(
        class = "ref-box",
        tags$h5("Model Summary"),
        tags$p("Population pharmacokinetic model for high-dose methotrexate in Mexican pediatric patients with acute lymphoblastic leukemia."),
        tags$ul(
          tags$li(tags$strong("Structure:"), " Two-compartment IV infusion model with first-order elimination."),
          tags$li(tags$strong("Clearance:"), " CL (L/h) = 6.5 x BSA^0.62."),
          tags$li(tags$strong("Central volume:"), " Vc (L) = 0.36 x body weight."),
          tags$li(tags$strong("Distribution:"), " Q = 0.42 L/h and Vp = 3.2 L."),
          tags$li(tags$strong("Sampling:"), " Plasma concentrations measured at 24, 36, 42, and 48 h after infusion start."),
          tags$li(tags$strong("Clinical targets:"), " Paper target Cpss about 33 uM for standard-risk and 65 uM for high-risk children.")
        )
      )
    ),
    nav_panel(
      "References",
      div(
        class = "ref-box",
        tags$h5("Key References"),
        tags$ol(
          tags$li(
            "Medellin-Garibay SE, Hernandez-Villa N, Correa-Gonzalez LC, et al. Population pharmacokinetics of methotrexate in Mexican pediatric patients with acute lymphoblastic leukemia. Cancer Chemother Pharmacol. 2019. ",
            tags$a(
              href = "https://doi.org/10.1007/s00280-019-03977-1",
              target = "_blank",
              "doi:10.1007/s00280-019-03977-1"
            )
          ),
          tags$li(
            "Methotrexate Injection prescribing information. ",
            tags$a(
              href = "https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/214121s001lbl.pdf",
              target = "_blank",
              "FDA label"
            )
          )
        ),
        tags$h5("Therapeutic Context"),
        tags$ul(
          tags$li(tags$strong("Drug class:"), " Antifolate antineoplastic"),
          tags$li(tags$strong("Population:"), " Children with acute lymphoblastic leukemia receiving 24-hour high-dose methotrexate infusion"),
          tags$li(tags$strong("Paper recommendations:"), " 3.5 g/m^2 for standard-risk and 7 g/m^2 for high-risk children with the paper-average BSA and body weight"),
          tags$li(tags$strong("Rescue threshold:"), " Leucovorin starts at 42 h when methotrexate concentration is <= 1 uM, then continues until concentration is <= 0.2 uM")
        )
      )
    )
  ),
  div(
    style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$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) {
  observeEvent(input$risk_group, {
    if (identical(input$risk_group, "standard")) {
      updateNumericInput(session, "dose_g_m2", value = 3.5)
    } else if (identical(input$risk_group, "high")) {
      updateNumericInput(session, "dose_g_m2", value = 7.0)
    }
  }, ignoreInit = TRUE)

  sim_data <- reactive({
    shiny::req(input$dose_g_m2, input$bsa, input$wt, input$infusion_h, input$end_h)
    simulate_mtx(
      dose_g_m2 = input$dose_g_m2,
      bsa = input$bsa,
      wt = input$wt,
      infusion_h = input$infusion_h,
      end_h = input$end_h
    )
  })

  output$c24 <- renderText({
    d <- sim_data()
    sprintf("%.1f", value_at_time(d, 24))
  })

  output$c42 <- renderText({
    d <- sim_data()
    sprintf("%.2f", value_at_time(d, 42))
  })

  output$auc <- renderText({
    d <- sim_data()
    auc_value <- sum(diff(d$time_h) * (head(d$CP_uM, -1) + tail(d$CP_uM, -1)) / 2)
    sprintf("%.0f", auc_value)
  })

  output$thalf <- renderText({
    half_h <- terminal_half_life_h(input$bsa, input$wt)
    if (!is.finite(half_h)) {
      return("NA")
    }
    sprintf("%.1f", half_h)
  })

  output$paperSummary <- renderText({
    d <- sim_data()
    c24 <- value_at_time(d, 24)
    c42 <- value_at_time(d, 42)
    c54 <- value_at_time(d, 54)
    c60 <- value_at_time(d, 60)
    paste0(
      paper_target_note(input$risk_group),
      " Simulated values with the current inputs: C24h ",
      sprintf("%.1f", c24),
      " uM, C42h ",
      sprintf("%.2f", c42),
      " uM, C54h ",
      sprintf("%.2f", c54),
      " uM, C60h ",
      sprintf("%.2f", c60),
      " uM."
    )
  })

  output$pkPlot <- renderPlot({
    d <- sim_data()
    d_plot <- if (input$log_scale) {
      d |> dplyr::filter(CP_uM > 0.001)
    } else {
      d
    }

    p <- ggplot(d_plot, aes(x = time_h, y = CP_uM)) +
      annotate("rect", xmin = 23.5, xmax = 24.5, ymin = 33, ymax = 65, fill = "#10b981", alpha = 0.15) +
      geom_hline(yintercept = c(1, 0.2), linetype = "dashed", color = c("#f59e0b", "#ef4444"), alpha = 0.8) +
      geom_vline(xintercept = c(24, 42, 54, 60), linetype = "dotted", color = "#adb5bd") +
      geom_line(color = "#8b5cf6", linewidth = 1) +
      annotate("text", x = 25.2, y = 63, label = "Target Cpss band", hjust = 0, size = 3.5, color = "#10b981") +
      annotate("text", x = max(d_plot$time_h, na.rm = TRUE) * 0.75, y = 1.1, label = "42 h rescue threshold: 1 uM", hjust = 0, size = 3.3, color = "#f59e0b") +
      annotate("text", x = max(d_plot$time_h, na.rm = TRUE) * 0.75, y = 0.24, label = "Stop rescue threshold: 0.2 uM", hjust = 0, size = 3.3, color = "#ef4444") +
      labs(
        x = "Time after infusion start (hours)",
        y = "Plasma methotrexate (uM)",
        title = paste0("Methotrexate ", sprintf("%.1f", input$dose_g_m2), " g/m^2 over 24 h")
      ) +
      theme_minimal(base_size = 14)

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

    p
  })
}

shinyApp(ui = ui, server = server)