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

paper_auc_grid <- data.frame(
  daily_dose_mg = c(1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3500, 3750, 4000, 4500, 5000),
  clcr_77 = c(308.1, 375.1, 436.1, 496.6, 560.8, 621.6, 681.7, 740.3, 869.0, 930.7, 996.9, 1118.9, 1239.4),
  clcr_87 = c(274.0, 328.8, 385.9, 439.7, 493.6, 548.0, 600.4, 659.8, 769.4, 825.8, 877.6, 986.5, 1091.6),
  clcr_97 = c(248.0, 295.7, 345.5, 394.6, 446.7, 489.9, 541.3, 596.2, 690.6, 739.4, 794.2, 881.9, 984.1),
  clcr_107 = c(222.6, 267.3, 311.8, 358.1, 400.6, 445.4, 489.6, 536.2, 626.7, 673.9, 712.2, 801.7, 890.1),
  clcr_117 = c(204.7, 243.7, 287.7, 326.5, 366.1, 408.3, 452.4, 488.4, 571.4, 615.3, 650.8, 735.0, 822.5),
  clcr_127 = c(187.8, 226.0, 264.4, 299.4, 337.6, 377.4, 414.5, 452.2, 523.9, 561.8, 600.2, 679.3, 753.6),
  clcr_137 = c(174.7, 210.0, 243.3, 279.6, 314.5, 350.8, 385.3, 420.7, 489.3, 521.6, 559.2, 631.4, 700.5),
  clcr_147 = c(162.3, 195.9, 228.2, 258.5, 291.3, 324.6, 356.9, 389.1, 452.3, 489.4, 517.8, 584.2, 649.0)
)

paper_clcr_values <- c(77, 87, 97, 107, 117, 127, 137, 147)

predict_paper_auc <- function(daily_dose_mg, clcr) {
  dose_idx <- match(daily_dose_mg, paper_auc_grid$daily_dose_mg)

  if (is.na(dose_idx)) {
    return(NA_real_)
  }

  auc_vals <- as.numeric(paper_auc_grid[dose_idx, -1])
  stats::approx(
    x = paper_clcr_values,
    y = auc_vals,
    xout = min(max(clcr, min(paper_clcr_values)), max(paper_clcr_values)),
    rule = 2
  )$y
}

recommended_daily_dose <- function(clcr, target_auc = 400) {
  aucs <- vapply(
    paper_auc_grid$daily_dose_mg,
    function(dose) predict_paper_auc(dose, clcr),
    numeric(1)
  )
  keep <- which(aucs >= target_auc)

  if (length(keep) == 0) {
    return(NA_real_)
  }

  paper_auc_grid$daily_dose_mg[min(keep)]
}

trap_auc <- function(time, conc) {
  if (length(time) < 2 || length(conc) < 2) {
    return(NA_real_)
  }

  sum(diff(time) * (head(conc, -1) + tail(conc, -1)) / 2)
}

build_events <- function(daily_dose_mg, interval_h, infusion_h, n_days) {
  dose_amt <- daily_dose_mg * interval_h / 24
  addl <- max(ceiling(n_days * 24 / interval_h) - 1, 0)

  ev(
    amt = dose_amt,
    cmt = 1,
    rate = dose_amt / infusion_h,
    ii = interval_h,
    addl = addl
  )
}

model_code <- '
$PARAM @annotated
CLREF : 6.54  : Clearance at reference Clcr 125 mL/min/1.73 m2 (L/h)
VWT   : 0.51  : Volume term per kg total body weight (L/kg)
WT    : 147.9 : Total body weight (kg)
CRCL  : 125   : Cockcroft-Gault creatinine clearance normalized to 1.73 m2 (mL/min/1.73 m2)

$CMT @annotated
CENT : Central compartment (mg)

$MAIN
double V = VWT * WT;
double CL = CLREF * (CRCL / 125.0);

if (V < 1.0) V = 1.0;
if (CL < 0.05) CL = 0.05;

$ODE
dxdt_CENT = -(CL / V) * CENT;

$TABLE
double CP = CENT / V;

$CAPTURE @annotated
CP : Plasma vancomycin concentration (mg/L)
CL : Individual clearance (L/h)
V  : Individual volume of distribution (L)
'

mod <- mcode("vancomycin_adane_2015", model_code, quiet = TRUE)

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; }
  .callout-box {
    background: #f8fafc;
    border: 1px solid #e2e8f0;
    border-radius: 8px;
    padding: 12px 14px;
    margin-top: 10px;
    font-size: 13px;
  }
  .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 = "Vancomycin in Extreme Obesity PK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,

    h6("Patient Characteristics"),
    sliderInput("weight", "Total Body Weight (kg)", min = 110, max = 280, value = 147.9, step = 1),
    sliderInput("crcl", "Creatinine Clearance (mL/min/1.73 m²)", min = 77, max = 147, value = 125, step = 1),
    numericInput("mic", "MIC (mg/L)", value = 1, min = 0.25, max = 4, step = 0.25),

    hr(),
    h6("Dosing Regimen"),
    sliderInput("daily_dose", "Total Daily Dose (mg/day)", min = 1250, max = 5000, value = 4000, step = 250),
    sliderInput("interval", "Dosing Interval (h)", min = 8, max = 24, value = 12, step = 4),
    sliderInput("infusion_h", "Infusion Duration (h)", min = 1, max = 4, value = 2, step = 0.5),
    numericInput("n_days", "Simulation Duration (days)", value = 4, min = 2, max = 14),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE),

    hr(),
    actionButton("apply_reco", "Apply Paper AUC Recommendation", class = "btn-primary"),
    uiOutput("paper_recommendation"),

    div(
      class = "callout-box",
      strong("Paper anchor"),
      tags$br(),
      "Final model: V = 0.51 x TBW and CL = 6.54 x (Clcr / 125).",
      tags$br(),
      "Therapeutic window shown on the plot reflects the 10-20 mg/L trough range discussed in the manuscript."
    )
  ),

  layout_column_wrap(
    width = 1 / 4,
    fill = FALSE,
    div(
      class = "metric-card metric-success",
      div(class = "metric-value", textOutput("cmax_out")),
      div(class = "metric-label", "Cmax (mg/L)")
    ),
    div(
      class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("ctrough_out")),
      div(class = "metric-label", "Ctrough (mg/L)")
    ),
    div(
      class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("auc24_out")),
      div(class = "metric-label", "AUC24 (mg·h/L)")
    ),
    div(
      class = "metric-card metric-info",
      div(class = "metric-value", textOutput("thalf_out")),
      div(class = "metric-label", "t½ (h)")
    )
  ),

  navset_card_underline(
    title = "Vancomycin PopPK Explorer",
    nav_panel(
      "Simulation",
      card(
        full_screen = TRUE,
        plotOutput("pk_plot", height = "500px")
      )
    ),
    nav_panel(
      "Exposure Summary",
      card(
        full_screen = TRUE,
        card_header("Paper-based exposure check"),
        tableOutput("dose_table")
      ),
      div(
        class = "callout-box",
        textOutput("auc_mic_out"),
        tags$br(),
        textOutput("dose_note_out")
      )
    ),
    nav_panel(
      "Model Information",
      markdown(
        paste0(
          "## Adane et al. 2015 Vancomycin PopPK Simulator\n\n",
          "**Source:** Adane ED, Herald M, Koura F. *Pharmacokinetics of Vancomycin in Extremely Obese Patients with Suspected or Confirmed Staphylococcus aureus Infections.* Pharmacotherapy. 2015;35:127-139. doi:10.1002/phar.1531.\n\n",
          "### Model structure\n",
          "- **Structural model:** one-compartment intravenous infusion model\n",
          "- **Estimation:** NONMEM 7.3, ADVAN1 TRANS2\n",
          "- **Population:** 29 analyzed adults with BMI >= 40 kg/m2\n",
          "- **Sampling:** peak, midpoint, and trough at steady state\n\n",
          "### Final covariate model (Table 3)\n",
          "- **V = 0.51 x TBW** (L)\n",
          "- **CL = 6.54 x (Clcr / 125)** (L/h)\n",
          "- **Reference patient:** TBW 147.9 kg and Clcr 125 mL/min/1.73 m2\n",
          "- **Between-subject variability:** 23.9% on V and 26.7% on CL\n\n",
          "### Published cohort summary\n",
          "- Median weight: **147.9 kg**\n",
          "- Median BMI: **49.5 kg/m2**\n",
          "- Median Clcr: **124.8 mL/min/1.73 m2**\n",
          "- Median daily dose: **4000 mg/day**\n",
          "- Median AUC24: **582.9 mg·h/L**\n\n",
          "### Clinical interpretation\n",
          "- The manuscript supports dosing in extreme obesity using **total body weight** for V and **Cockcroft-Gault Clcr** for CL.\n",
          "- Simulations in the paper found that **4000-5000 mg/day** gave >=93% probability of **AUC24/MIC >= 400** for MIC = 1 mg/L.\n",
          "- Trough monitoring remains important because AUC-only targets can still produce troughs >20 mg/L in some patients.\n"
        )
      )
    ),
    nav_panel(
      "References",
      div(
        class = "ref-box",
        tags$h5("Key references"),
        tags$ol(
          tags$li("Adane ED, Herald M, Koura F. Pharmacotherapy. 2015;35:127-139."),
          tags$li("Rybak MJ et al. Therapeutic monitoring of vancomycin in adult patients. Am J Health Syst Pharm. 2009;66:82-98."),
          tags$li("Brown DL, Lalla CD, Masselink AJ. AUC versus peak-trough dosing of vancomycin in the morbidly obese. Ann Pharmacother. 2013;47:1264-1273.")
        ),
        tags$h5("Drug context"),
        tags$ul(
          tags$li(tags$strong("Drug class:"), " Glycopeptide antibiotic"),
          tags$li(tags$strong("Route modeled:"), " Intravenous infusion"),
          tags$li(tags$strong("Primary elimination:"), " Renal"),
          tags$li(tags$strong("Population:"), " Extremely obese adults with suspected or confirmed S. aureus infection")
        )
      )
    )
  ),

  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$apply_reco, {
    reco <- recommended_daily_dose(input$crcl, target_auc = 400)

    if (is.finite(reco)) {
      updateSliderInput(session, "daily_dose", value = reco)
    }
  })

  output$paper_recommendation <- renderUI({
    reco <- recommended_daily_dose(input$crcl, target_auc = 400)
    per_dose <- input$daily_dose * input$interval / 24
    paper_auc <- predict_paper_auc(input$daily_dose, input$crcl)

    div(
      class = "callout-box",
      strong("Paper-based minimum daily dose for AUC24 >= 400"),
      tags$br(),
      if (is.finite(reco)) {
        paste0(sprintf("%.0f", reco), " mg/day at current renal function")
      } else {
        "No dose in the published grid reached the selected target."
      },
      tags$br(),
      paste0("Current regimen: ", sprintf("%.0f", per_dose), " mg every ", input$interval, " h"),
      tags$br(),
      paste0("Published table AUC24 estimate for current daily dose: ", sprintf("%.1f", paper_auc), " mg·h/L")
    )
  })

  sim_data <- reactive({
    shiny::req(input$weight, input$crcl, input$daily_dose, input$interval, input$infusion_h, input$n_days)

    reg <- build_events(
      daily_dose_mg = input$daily_dose,
      interval_h = input$interval,
      infusion_h = input$infusion_h,
      n_days = input$n_days
    )

    mod %>%
      param(WT = input$weight, CRCL = input$crcl) %>%
      ev(reg) %>%
      mrgsim(end = input$n_days * 24, delta = 0.1) %>%
      as.data.frame() |>
      mutate(time_h = time)
  })

  last_interval <- reactive({
    d <- sim_data()
    end_time <- max(d$time_h)
    start_time <- max(end_time - input$interval, 0)
    d |>
      dplyr::filter(time_h >= start_time, time_h <= end_time)
  })

  last_24h <- reactive({
    d <- sim_data()
    end_time <- max(d$time_h)
    start_time <- max(end_time - 24, 0)
    d |>
      dplyr::filter(time_h >= start_time, time_h <= end_time)
  })

  output$cmax_out <- renderText({
    d <- last_interval()
    sprintf("%.1f", max(d$CP, na.rm = TRUE))
  })

  output$ctrough_out <- renderText({
    d <- last_interval()
    sprintf("%.1f", d$CP[nrow(d)])
  })

  output$auc24_out <- renderText({
    d <- last_24h()
    sprintf("%.1f", trap_auc(d$time_h, d$CP))
  })

  output$thalf_out <- renderText({
    d <- sim_data()
    sprintf("%.1f", log(2) * d$V[1] / d$CL[1])
  })

  output$pk_plot <- renderPlot({
    d <- sim_data()
    d_plot <- if (input$log_scale) {
      d |>
        dplyr::filter(CP > 0)
    } else {
      d
    }

    p <- ggplot(d_plot, aes(x = time_h, y = CP)) +
      annotate("rect", xmin = -Inf, xmax = Inf, ymin = 10, ymax = 20, fill = "#10b981", alpha = 0.10) +
      geom_hline(yintercept = c(10, 20), linetype = "dashed", color = "#10b981", alpha = 0.7) +
      geom_line(color = "#8b5cf6", linewidth = 0.9) +
      labs(
        x = "Time (hours)",
        y = "Vancomycin Concentration (mg/L)",
        title = paste0(
          "Vancomycin ",
          sprintf("%.0f", input$daily_dose * input$interval / 24),
          " mg every ",
          input$interval,
          " h"
        ),
        subtitle = "Green band shows the 10-20 mg/L trough range discussed in the manuscript"
      ) +
      theme_minimal(base_size = 14)

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

    p
  })

  output$dose_table <- renderTable({
    dose_choices <- c(2000, 2500, 2750, 3000, 3500, 4000, 4500, 5000)
    auc_vals <- vapply(
      dose_choices,
      function(dose) predict_paper_auc(dose, input$crcl),
      numeric(1)
    )

    data.frame(
      `Daily dose (mg/day)` = dose_choices,
      `Paper AUC24 (mg·h/L)` = round(auc_vals, 1),
      `AUC24/MIC` = round(auc_vals / input$mic, 1),
      `Target >= 400?` = ifelse(auc_vals / input$mic >= 400, "Yes", "No"),
      check.names = FALSE
    )
  }, striped = TRUE, bordered = TRUE, digits = 1)

  output$auc_mic_out <- renderText({
    d <- last_24h()
    auc24 <- trap_auc(d$time_h, d$CP)
    paste0(
      "Simulated steady-state AUC24/MIC at MIC ",
      sprintf("%.2f", input$mic),
      " mg/L: ",
      sprintf("%.1f", auc24 / input$mic)
    )
  })

  output$dose_note_out <- renderText({
    reco <- recommended_daily_dose(input$crcl, target_auc = 400 * input$mic)

    if (is.finite(reco)) {
      paste0(
        "Published Table 4 suggests a minimum of ",
        sprintf("%.0f", reco),
        " mg/day to reach AUC24/MIC >= 400 at the selected MIC."
      )
    } else {
      "The selected MIC is higher than the published dose grid can reliably support."
    }
  })
}

shinyApp(ui = ui, server = server)