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

model_code <- '
$PARAM @annotated
BASECL   : 5.57    : Baseline MTX clearance at CLcr 80 mL/min (L/h)
CLCR     : 80      : Creatinine clearance (mL/min)
CLFRAC   : 10      : Residual MTX clearance as % of normal after delayed elimination
VC       : 26.683  : MTX central volume (L)
VP       : 2.253   : MTX peripheral volume (L)
Q        : 0.078   : MTX intercompartmental clearance (L/h)
CATCLKG  : 0.00516 : Glucarpidase clearance per kg (L/h/kg)
CATVKG   : 0.0679  : Glucarpidase distribution volume per kg (L/kg)
WT       : 60      : Body weight (kg)
KM       : 86      : Michaelis-Menten constant for MTX hydrolysis (umol/L)
ALPHA    : 30      : Reconstructed catalytic conversion constant

$CMT @annotated
CENT   : MTX central compartment (umol)
PERIPH : MTX peripheral compartment (umol)
CAT    : Glucarpidase amount (U)

$MAIN
// MTX parameters from the manuscript, with delayed elimination captured by CLFRAC
// CPG2 disposition from the manuscript phase I data

double MTXCLi = BASECL * pow(CLCR / 80.0, 0.112) * (CLFRAC / 100.0);
double Kri = MTXCLi / VC;
double K12i = Q / VC;
double K21i = Q / VP;
double CATCLi = CATCLKG * WT;
double CATVi = CATVKG * WT;

$ODE
double CMTX = CENT / VC;
double CCAT = CAT / CATVi;
double DEGR = ALPHA * CCAT * CMTX / (KM + CMTX);

dxdt_CENT   = -(Kri + K12i) * CENT + K21i * PERIPH - DEGR;
dxdt_PERIPH = K12i * CENT - K21i * PERIPH;
dxdt_CAT    = -(CATCLi / CATVi) * CAT;

$TABLE
double MTX = CENT / VC;
double CATCONC = CAT / CATVi;
double MTX_SAFE = MTX <= 0.1 ? 1 : 0;
double MTX_CLINICAL = MTX <= 1.0 ? 1 : 0;

$CAPTURE @annotated
MTX          : Plasma methotrexate concentration (umol/L)
CATCONC      : Plasma glucarpidase concentration (U/L)
MTX_SAFE     : Indicator for MTX <= 0.1 umol/L
MTX_CLINICAL : Indicator for MTX <= 1.0 umol/L
'

mod <- mcode("glucarpidase_pkpd_310", 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; }
    .summary-box {
      background: #f8fafc;
      border: 1px solid #e2e8f0;
      border-radius: 8px;
      padding: 14px 16px;
      margin-top: 12px;
      font-size: 13px;
      line-height: 1.5;
    }
    .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; }
  ")

paper_benchmarks <- tibble::tibble(
  dose_ukg = c(20, 50),
  below_01_70 = c("71.8%", "89.6%"),
  below_01_120 = c("46.4%", "59.0%")
)

ui <- page_sidebar(
  title = "Glucarpidase Rescue PK/PD Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    accordion(
      open = c("Dosing", "Patient"),
      accordion_panel(
        "Dosing",
        icon = bsicons::bs_icon("capsule"),
        sliderInput("mtx_dose", "Methotrexate dose (g/m²)", min = 0.5, max = 12, value = 1.0, step = 0.5),
        sliderInput("mtx_infusion_h", "MTX infusion duration (h)", min = 1, max = 24, value = 4, step = 1),
        sliderInput("cpg2_dose", "Glucarpidase dose (U/kg)", min = 10, max = 80, value = 50, step = 10),
        sliderInput("rescue_time", "Glucarpidase rescue start (h after MTX start)", min = 24, max = 72, value = 48, step = 2),
        sliderInput("cpg2_infusion_min", "Glucarpidase infusion duration (min)", min = 1, max = 30, value = 5, step = 1),
        sliderInput("sim_end", "Simulation horizon (h)", min = 96, max = 168, value = 144, step = 12)
      ),
      accordion_panel(
        "Patient",
        icon = bsicons::bs_icon("person"),
        sliderInput("wt", "Body weight (kg)", min = 10, max = 120, value = 60, step = 1),
        sliderInput("bsa", "Body surface area (m²)", min = 0.5, max = 2.5, value = 1.73, step = 0.01),
        sliderInput("clcr", "Creatinine clearance (mL/min)", min = 10, max = 140, value = 80, step = 5),
        sliderInput("clfrac", "Residual MTX clearance (% of normal)", min = 5, max = 30, value = 10, step = 1),
        tags$p(
          style = "font-size:12px; color:#6c757d; margin-top:8px;",
          "The manuscript simulations assumed a typical 60 kg patient, BSA 1.73 m², creatinine clearance 80 mL/min, and severe delayed MTX elimination approximated here as 10% of normal clearance."
        )
      ),
      accordion_panel(
        "Display",
        icon = bsicons::bs_icon("gear"),
        checkboxInput("show_clinical_band", "Show 0.1 and 1.0 µmol/L target zones", value = TRUE),
        checkboxInput("show_cpg2", "Overlay glucarpidase concentration (scaled)", value = FALSE),
        checkboxInput("log_scale", "Log scale (Y-axis)", value = TRUE)
      )
    )
  ),
  tags$p(
    style = "font-size: 11px; color: #6c757d; margin: 4px 0 0 6px;",
    "This app reconstructs the 2023 Anticancer Research dose-finding manuscript using a two-compartment MTX model plus manuscript-derived catalytic glucarpidase rescue dynamics."
  ),
  layout_column_wrap(
    width = 1 / 4,
    fill = FALSE,
    div(
      class = "metric-card metric-success",
      div(class = "metric-value", textOutput("cmax")),
      div(class = "metric-label", "MTX Cmax (µmol/L)")
    ),
    div(
      class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("ctrough")),
      div(class = "metric-label", "MTX at end of sim (µmol/L)")
    ),
    div(
      class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("auc")),
      div(class = "metric-label", "MTX AUC0-end (µmol·h/L)")
    ),
    div(
      class = "metric-card metric-info",
      div(class = "metric-value", textOutput("thalf")),
      div(class = "metric-label", "Pre-rescue MTX t½ (h)")
    )
  ),
  card(
    card_header("Methotrexate rescue profile"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "500px"),
    div(
      class = "summary-box",
      strong("Model readout"),
      br(),
      textOutput("rescueSummary")
    )
  ),
  navset_card_underline(
    title = "Drug Information",
    nav_panel(
      "Model Information",
      div(
        class = "ref-box",
        tags$h5("Study context"),
        tags$p(
          "Kimura et al. developed a modified Michaelis-Menten PK/PD model to support glucarpidase dose selection after high-dose methotrexate therapy. The structural core is a two-compartment MTX model with catalytic glucarpidase-driven MTX hydrolysis added to the central compartment."
        ),
        tags$h5("Model elements used here"),
        tags$ul(
          tags$li(tags$strong("MTX structure:"), " two-compartment model with manuscript Vc, Vp, and Q values"),
          tags$li(tags$strong("Renal elimination:"), " BASECL × (CLcr/80)^0.112 with residual clearance fraction to mimic delayed elimination"),
          tags$li(tags$strong("Glucarpidase PK:"), " one-compartment IV infusion using the manuscript phase I disposition values"),
          tags$li(tags$strong("Catalytic PD:"), " Michaelis-Menten hydrolysis term with Km = 86 µmol/L and a reconstructed alpha chosen to reproduce the paper's dose-response trend"),
          tags$li(tags$strong("Default scenario:"), " 1 g/m² MTX over 4 h, rescue glucarpidase at 48 h, 60 kg patient, BSA 1.73 m², severe delayed elimination")
        ),
        tags$h5("Published parameters from the manuscript"),
        tags$table(
          class = "table table-sm",
          tags$thead(
            tags$tr(
              tags$th("Parameter"),
              tags$th("Value"),
              tags$th("Units")
            )
          ),
          tags$tbody(
            tags$tr(tags$td("MTX clearance"), tags$td("5.57 × (CLcr/80)^0.112"), tags$td("L/h")),
            tags$tr(tags$td("MTX Vc"), tags$td("26.9"), tags$td("L")),
            tags$tr(tags$td("MTX Vp"), tags$td("2.27"), tags$td("L")),
            tags$tr(tags$td("MTX Q"), tags$td("0.0778"), tags$td("L/h")),
            tags$tr(tags$td("Glucarpidase CL"), tags$td("0.086"), tags$td("mL/min/kg")),
            tags$tr(tags$td("Glucarpidase Vdss"), tags$td("67.9"), tags$td("mL/kg")),
            tags$tr(tags$td("Glucarpidase t½"), tags$td("9.97"), tags$td("h")),
            tags$tr(tags$td("Km"), tags$td("86"), tags$td("µmol/L"))
          )
        )
      )
    ),
    nav_panel(
      "References",
      div(
        class = "ref-box",
        tags$h5("Primary reference"),
        tags$ol(
          tags$li(
            tags$strong("Kimura T, Fukaya Y, Hamada Y, Yoshimura K, Kawamoto H. (2023). "),
            "Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation. ",
            tags$em("Anticancer Research"),
            " 43:1919-1924. ",
            tags$a(href = "https://doi.org/10.21873/anticanres.16351", target = "_blank", "doi:10.21873/anticanres.16351")
          )
        ),
        tags$h5("Dose-selection benchmarks reported in the paper"),
        tags$table(
          class = "table table-sm",
          tags$thead(
            tags$tr(
              tags$th("Glucarpidase dose"),
              tags$th("% < 0.1 µmol/L at 70 h"),
              tags$th("% < 0.1 µmol/L at 120 h")
            )
          ),
          tags$tbody(
            lapply(seq_len(nrow(paper_benchmarks)), function(i) {
              tags$tr(
                tags$td(paste0(paper_benchmarks$dose_ukg[i], " U/kg")),
                tags$td(paper_benchmarks$below_01_70[i]),
                tags$td(paper_benchmarks$below_01_120[i])
              )
            })
          )
        ),
        tags$h5("Therapeutic context"),
        tags$ul(
          tags$li(tags$strong("Drug class:"), " Enzyme rescue therapy for methotrexate toxicity"),
          tags$li(tags$strong("Clinical role:"), " Rapid extracellular hydrolysis of MTX when delayed elimination threatens nephrotoxicity and systemic toxicity"),
          tags$li(tags$strong("Approved/benchmark dose:"), " 50 U/kg IV"),
          tags$li(tags$strong("Key clinical message:"), " 50 U/kg improved the probability of achieving MTX < 0.1 µmol/L by 70 h, but rebound remained possible, supporting prolonged monitoring beyond 144 h")
        )
      )
    )
  ),
  div(
    style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$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) {
  sim_data <- reactive({
    shiny::req(input$mtx_dose, input$bsa, input$mtx_infusion_h, input$cpg2_dose, input$rescue_time, input$wt)

    mtx_dose_umol <- (input$mtx_dose * 1000 * input$bsa) / 0.45444
    mtx_rate <- mtx_dose_umol / input$mtx_infusion_h
    cpg2_dose_total <- input$cpg2_dose * input$wt
    cpg2_inf_h <- input$cpg2_infusion_min / 60
    cpg2_rate <- cpg2_dose_total / cpg2_inf_h

    ev_mtx <- ev(time = 0, amt = mtx_dose_umol, cmt = 1, rate = mtx_rate)
    ev_cpg2 <- ev(time = input$rescue_time, amt = cpg2_dose_total, cmt = 3, rate = cpg2_rate)

    mod %>%
      param(CLCR = input$clcr, CLFRAC = input$clfrac, WT = input$wt) %>%
      ev(c(ev_mtx, ev_cpg2)) %>%
      mrgsim(end = input$sim_end, delta = 0.25) %>%
      as.data.frame() |>
      dplyr::arrange(time) |>
      dplyr::distinct(time, .keep_all = TRUE) |>
      dplyr::mutate(
        time_h = time,
        cat_scaled = ifelse(max(CATCONC, na.rm = TRUE) > 0, CATCONC / max(CATCONC, na.rm = TRUE) * max(MTX, na.rm = TRUE), 0)
      )
  })

  rescue_metrics <- reactive({
    d <- sim_data()

    conc_70 <- approx(d$time_h, d$MTX, xout = 70, rule = 2)$y
    conc_120 <- approx(d$time_h, d$MTX, xout = 120, rule = 2)$y

    post_rescue <- d |>
      dplyr::filter(time_h >= input$rescue_time)

    safe_times <- post_rescue |>
      dplyr::filter(MTX <= 0.1)

    time_to_safe <- if (nrow(safe_times) == 0) {
      NA_real_
    } else {
      min(safe_times$time_h) - input$rescue_time
    }

    rebound_ratio <- if (nrow(safe_times) == 0) {
      NA_real_
    } else {
      nadir_time <- safe_times$time_h[which.min(safe_times$MTX)]
      nadir_conc <- min(safe_times$MTX)
      later <- post_rescue |>
        dplyr::filter(time_h >= nadir_time)
      if (nrow(later) == 0 || nadir_conc <= 0) {
        NA_real_
      } else {
        max(later$MTX, na.rm = TRUE) / nadir_conc
      }
    }

    list(
      conc_70 = conc_70,
      conc_120 = conc_120,
      time_to_safe = time_to_safe,
      rebound_ratio = rebound_ratio
    )
  })

  output$cmax <- renderText({
    d <- sim_data()
    sprintf("%.1f", max(d$MTX, na.rm = TRUE))
  })

  output$ctrough <- renderText({
    d <- sim_data()
    sprintf("%.3f", d$MTX[nrow(d)])
  })

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

  output$thalf <- renderText({
    mtx_cli <- 5.57 * (input$clcr / 80)^0.112 * (input$clfrac / 100)
    sprintf("%.1f", log(2) / (mtx_cli / 26.683))
  })

  output$rescueSummary <- renderText({
    m <- rescue_metrics()

    time_to_safe_txt <- if (is.na(m$time_to_safe)) {
      "not achieved within the simulation horizon"
    } else {
      paste0(sprintf("%.1f", m$time_to_safe), " h after glucarpidase")
    }

    rebound_txt <- if (is.na(m$rebound_ratio)) {
      "not estimable"
    } else {
      paste0(sprintf("%.1f", m$rebound_ratio), "× from nadir")
    }

    paste0(
      "Predicted MTX concentration at 70 h: ", sprintf("%.3f", m$conc_70), " µmol/L. ",
      "Predicted MTX concentration at 120 h: ", sprintf("%.3f", m$conc_120), " µmol/L. ",
      "Time to MTX ≤ 0.1 µmol/L: ", time_to_safe_txt, ". ",
      "Post-nadir rebound magnitude: ", rebound_txt, "."
    )
  })

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

    p <- ggplot(d_plot, aes(x = time_h, y = MTX)) +
      labs(
        x = "Time after MTX start (hours)",
        y = "Plasma methotrexate (µmol/L)",
        title = paste0("MTX ", input$mtx_dose, " g/m² + glucarpidase ", input$cpg2_dose, " U/kg at ", input$rescue_time, " h")
      ) +
      geom_line(color = "#8b5cf6", linewidth = 0.9) +
      geom_vline(xintercept = input$rescue_time, linetype = "dashed", color = "#ef4444", linewidth = 0.7) +
      annotate("text", x = input$rescue_time, y = max(d_plot$MTX, na.rm = TRUE), label = "Rescue", vjust = -0.6, color = "#ef4444", size = 3.5) +
      theme_minimal(base_size = 14)

    if (input$show_clinical_band) {
      p <- p +
        annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = 0.1, fill = "#10b981", alpha = 0.12) +
        annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0.1, ymax = 1.0, fill = "#f59e0b", alpha = 0.08) +
        geom_hline(yintercept = c(0.1, 1.0), linetype = "dashed", color = c("#10b981", "#f59e0b"), alpha = 0.7)
    }

    if (input$show_cpg2) {
      p <- p +
        geom_line(data = d_plot, aes(x = time_h, y = cat_scaled), color = "#0ea5e9", linewidth = 0.8, alpha = 0.8) +
        annotate("text", x = max(d_plot$time_h) * 0.78, y = max(d_plot$cat_scaled, na.rm = TRUE), label = "Scaled glucarpidase", color = "#0ea5e9", hjust = 0, size = 3.4)
    }

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

    p
  })
}

shinyApp(ui = ui, server = server)