suppressPackageStartupMessages({
  library(shiny)
  library(bslib)
  library(mrgsolve)
  library(dplyr)
  library(ggplot2)
})

paper_reference <- list(
  title = "Population pharmacokinetics of intravenous linezolid in critically ill pediatric patients",
  citation = paste(
    "SANOUFI MR, GUENDOUL K, OUALHA M, ABDALLA S, BERANGER A,",
    "LOUIS LP, FROELICHER-BOURNAUD L, ROUILLON S, TRELUYER JM,",
    "FOISSAC F, BOUAZZA N, BENABOUD S. Manuscript PDF uploaded to PKPDBuilder, 2026."
  )
)

study_constants <- list(
  tvcl_l_h = 1.76,
  tvv_l = 5.3,
  weight_ref_kg = 8,
  egfr_ref = 111.13,
  egfr_power = 0.33,
  transplant_multiplier = 0.51,
  f_birth = 0.66,
  tm50_years = 0.44,
  trough_target_low = 2,
  trough_target_high = 7,
  auc_mic_target = 80
)

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

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

calc_maturation_factor <- function(age_years) {
  with(
    study_constants,
    f_birth + (1 - f_birth) * (1 - exp((-0.693 * age_years) / tm50_years))
  )
}

calc_cl_l_h <- function(weight_kg, egfr, age_years, transplant) {
  with(
    study_constants,
    tvcl_l_h *
      (weight_kg / weight_ref_kg) ^ 0.75 *
      (egfr / egfr_ref) ^ egfr_power *
      transplant_multiplier ^ as.numeric(transplant) *
      calc_maturation_factor(age_years)
  )
}

calc_vd_l <- function(weight_kg) {
  with(study_constants, tvv_l * (weight_kg / weight_ref_kg))
}

calc_half_life_h <- function(weight_kg, egfr, age_years, transplant) {
  vd_l <- calc_vd_l(weight_kg)
  cl_l_h <- calc_cl_l_h(weight_kg, egfr, age_years, transplant)
  log(2) * vd_l / cl_l_h
}

recommended_regimen <- function(weight_kg) {
  if (weight_kg <= 40) {
    list(
      dose_mg = 10 * weight_kg,
      interval_h = 8,
      label = "Published regimen: 10 mg/kg every 8 h"
    )
  } else {
    list(
      dose_mg = 600,
      interval_h = 12,
      label = "Published regimen: 600 mg every 12 h"
    )
  }
}

model_code <- "
$PARAM @annotated
TVCL         : 1.76   : Typical clearance at reference covariates (L/h)
TVV          : 5.3    : Typical volume of distribution at reference weight (L)
WT           : 8      : Body weight (kg)
EGFR         : 111.13 : Estimated glomerular filtration rate (mL/min/1.73 m2)
AGE          : 0.9    : Postnatal age (years)
TRANSPLANT   : 0      : Liver transplant indicator (0/1)
WTREF        : 8      : Reference body weight (kg)
EGFRREF      : 111.13 : Reference eGFR (mL/min/1.73 m2)
BEGFR        : 0.33   : eGFR effect on clearance
TRMULT       : 0.51   : Transplant multiplier on clearance
FBIRTH       : 0.66   : Fraction of mature clearance at birth
TM50         : 0.44   : Postnatal age at half maturation (years)

$CMT @annotated
CENT : Central compartment (mg)

$MAIN
double CLi = TVCL *
  pow(WT / WTREF, 0.75) *
  pow(EGFR / EGFRREF, BEGFR) *
  pow(TRMULT, TRANSPLANT) *
  (FBIRTH + (1.0 - FBIRTH) * (1.0 - exp((-0.693 * AGE) / TM50)));

double VDi = TVV * (WT / WTREF);

$ODE
dxdt_CENT = -(CLi / VDi) * CENT;

$TABLE
double CP = CENT / VDi;

$CAPTURE CP CLi VDi
"

mod <- mcode("linezolid_pediatric_critically_ill", model_code)

simulate_profile <- function(
  weight_kg,
  egfr,
  age_years,
  transplant,
  dose_mg,
  interval_h,
  infusion_h,
  duration_days,
  delta = 0.1
) {
  total_hours <- duration_days * 24
  n_doses <- max(1, floor(total_hours / interval_h))
  dose_event <- ev(
    amt = dose_mg,
    ii = interval_h,
    addl = max(n_doses - 1, 0),
    rate = dose_mg / infusion_h,
    cmt = 1
  )

  mod |>
    param(
      WT = weight_kg,
      EGFR = egfr,
      AGE = age_years,
      TRANSPLANT = as.numeric(transplant)
    ) %>%
    ev(dose_event) %>%
    mrgsim(end = total_hours, delta = delta) %>%
    as.data.frame() |>
    mutate(time_h = time)
}

metric_frame <- function(sim_df, interval_h, mic) {
  last_time <- max(sim_df$time_h, na.rm = TRUE)
  last_interval_start <- max(last_time - interval_h, 0)
  last_day_start <- max(last_time - 24, 0)

  interval_df <- sim_df |>
    dplyr::filter(.data$time_h >= last_interval_start)
  day_df <- sim_df |>
    dplyr::filter(.data$time_h >= last_day_start)

  auc24 <- trap_auc(day_df$time_h, day_df$CP)
  cmax <- max(interval_df$CP, na.rm = TRUE)
  ctrough <- min(interval_df$CP, na.rm = TRUE)

  data.frame(
    cmax = cmax,
    ctrough = ctrough,
    auc24 = auc24,
    auc_mic = auc24 / mic
  )
}

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

app_theme <- bs_theme(
  version = 5,
  bootswatch = "flatly",
  primary = "#8b5cf6"
) |> bs_add_rules("
  .metric-card {
    background: #f8f9fa;
    border-radius: 10px;
    padding: 16px;
    margin: 6px;
    text-align: center;
    border: 1px solid #dee2e6;
    box-shadow: 0 8px 24px rgba(15, 23, 42, 0.05);
  }
  .metric-value {
    font-size: 24px;
    font-weight: 700;
    color: #1f2937;
  }
  .metric-label {
    font-size: 12px;
    color: #6b7280;
    text-transform: uppercase;
    letter-spacing: 0.04em;
  }
  .metric-success .metric-value { color: #059669; }
  .metric-warning .metric-value { color: #d97706; }
  .metric-primary .metric-value { color: #8b5cf6; }
  .metric-info .metric-value { color: #0284c7; }
  .info-box {
    background: #f8fafc;
    border: 1px solid #e2e8f0;
    border-left: 4px solid #8b5cf6;
    border-radius: 10px;
    padding: 16px 18px;
    margin-top: 14px;
  }
  .info-box h5 {
    margin-top: 0;
    margin-bottom: 10px;
    color: #111827;
  }
  .status-chip {
    display: inline-block;
    padding: 6px 10px;
    border-radius: 999px;
    font-size: 12px;
    font-weight: 600;
    margin-right: 8px;
    margin-bottom: 8px;
  }
  .status-pass {
    background: rgba(16, 185, 129, 0.12);
    color: #047857;
  }
  .status-warn {
    background: rgba(245, 158, 11, 0.15);
    color: #b45309;
  }
  .status-fail {
    background: rgba(239, 68, 68, 0.12);
    color: #b91c1c;
  }
  .ref-box {
    background: #f0f4ff;
    border-left: 4px solid #8b5cf6;
    padding: 14px 18px;
    border-radius: 6px;
    margin-top: 12px;
    font-size: 13px;
  }
  .ref-box a { color: #8b5cf6; }
")

ui <- page_sidebar(
  title = "Linezolid PopPK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    h6("Patient Characteristics"),
    sliderInput("weight_kg", "Weight (kg)", min = 0.8, max = 60, value = 8, step = 0.1),
    sliderInput("age_years", "Postnatal Age (years)", min = 0.03, max = 16, value = 0.9, step = 0.01),
    sliderInput("egfr", "eGFR (mL/min/1.73 m²)", min = 15, max = 220, value = 111, step = 1),
    checkboxInput("transplant", "Liver transplant recipient", value = FALSE),
    hr(),
    h6("Dosing"),
    radioButtons(
      "dose_mode",
      "Regimen",
      choices = c(
        "Published standard regimen" = "published",
        "Custom regimen" = "custom"
      ),
      selected = "published"
    ),
    conditionalPanel(
      condition = "input.dose_mode === 'custom'",
      sliderInput("dose_mg", "Dose (mg)", min = 25, max = 1200, value = 80, step = 5),
      sliderInput("interval_h", "Interval (hours)", min = 6, max = 24, value = 8, step = 1)
    ),
    sliderInput("infusion_h", "Infusion duration (hours)", min = 0.5, max = 2, value = 1, step = 0.25),
    numericInput("duration_days", "Simulation duration (days)", value = 5, min = 2, max = 10),
    selectInput("mic", "MIC (mg/L)", choices = c(0.5, 1, 2, 4, 8, 16), selected = 1),
    checkboxInput("log_scale", "Log scale concentration axis", value = FALSE)
  ),
  navset_card_underline(
    title = "Intravenous Linezolid in Critically Ill Pediatric Patients",
    nav_panel(
      "Simulation",
      layout_column_wrap(
        width = 1 / 4,
        fill = FALSE,
        metric_card("cmax", "Cmax at steady state (mg/L)", "metric-success"),
        metric_card("ctrough", "Ctrough at steady state (mg/L)", "metric-warning"),
        metric_card("auc24", "AUC0-24 at steady state (mg·h/L)", "metric-primary"),
        metric_card("thalf", "Half-life (hours)", "metric-info")
      ),
      card(
        full_screen = TRUE,
        card_header(textOutput("regimen_label")),
        plotOutput("pk_plot", height = "500px")
      ),
      card(
        card_header("Target Evaluation"),
        uiOutput("target_status"),
        div(class = "info-box", textOutput("clinical_note"))
      )
    ),
    nav_panel(
      "Model Information",
      div(
        class = "info-box",
        tags$h5("Model structure"),
        tags$p("One-compartment IV infusion model with first-order elimination."),
        tags$ul(
          tags$li("Clearance: 1.76 × (WT/8)^0.75 × (eGFR/111.13)^0.33 × 0.51^transplant × maturation"),
          tags$li("Maturation: 0.66 + (1 - 0.66) × (1 - exp(-0.693 × age / 0.44))"),
          tags$li("Volume: 5.3 × (WT/8) L"),
          tags$li("Population: 47 critically ill pediatric patients, 109 concentrations")
        )
      ),
      div(
        class = "info-box",
        tags$h5("Study interpretation"),
        tags$ul(
          tags$li("Published trough target: 2-7 mg/L."),
          tags$li("Published efficacy target: AUC0-24/MIC ≥ 80."),
          tags$li("Standard dosing underexposed many non-transplant children, especially at high eGFR and low weight."),
          tags$li("Liver transplant status reduced clearance by about 49%, shifting some patients toward overexposure.")
        )
      )
    ),
    nav_panel(
      "References",
      div(
        class = "ref-box",
        tags$h5("Source manuscript"),
        tags$ol(
          tags$li(paper_reference$citation)
        ),
        tags$h5("Targets used in the manuscript"),
        tags$ul(
          tags$li("Trough target: 2-7 mg/L for balancing efficacy and thrombocytopenia risk."),
          tags$li("AUC0-24/MIC target: ≥ 80 for antimicrobial efficacy."),
          tags$li("Published regimens evaluated: 10 mg/kg q8h at ≤ 40 kg and 600 mg q12h at > 40 kg.")
        )
      )
    )
  ),
  div(
    style = paste(
      "text-align:center; padding:20px; margin-top:24px;",
      "border-top:1px solid #e5e7eb; color:#6b7280; font-size:12px;"
    ),
    "Powered by ",
    tags$a(
      href = "https://www.pkpdbuilder.com",
      target = "_blank",
      style = "color:#8b5cf6; font-weight:600;",
      "PKPDBuilder.com"
    ),
    " • Built by Sunny for Husain Attarwala",
    br(),
    tags$span(
      style = "font-size:10px;",
      "For research and educational use only. Not for direct clinical dosing decisions."
    )
  )
)

server <- function(input, output, session) {
  regimen <- reactive({
    if (identical(input$dose_mode, "published")) {
      recommended_regimen(input$weight_kg)
    } else {
      list(
        dose_mg = input$dose_mg,
        interval_h = input$interval_h,
        label = paste0("Custom regimen: ", input$dose_mg, " mg every ", input$interval_h, " h")
      )
    }
  })

  sim_data <- reactive({
    shiny::req(input$weight_kg, input$age_years, input$egfr, input$duration_days, input$mic)

    simulate_profile(
      weight_kg = input$weight_kg,
      egfr = input$egfr,
      age_years = input$age_years,
      transplant = input$transplant,
      dose_mg = regimen()$dose_mg,
      interval_h = regimen()$interval_h,
      infusion_h = input$infusion_h,
      duration_days = input$duration_days
    )
  })

  metrics <- reactive({
    metric_frame(sim_data(), interval_h = regimen()$interval_h, mic = as.numeric(input$mic))
  })

  output$regimen_label <- renderText({
    paste0(
      regimen()$label,
      " • Infusion ",
      format(input$infusion_h, trim = TRUE),
      " h"
    )
  })

  output$cmax <- renderText({
    sprintf("%.1f", metrics()$cmax[[1]])
  })

  output$ctrough <- renderText({
    sprintf("%.1f", metrics()$ctrough[[1]])
  })

  output$auc24 <- renderText({
    sprintf("%.1f", metrics()$auc24[[1]])
  })

  output$thalf <- renderText({
    sprintf(
      "%.1f",
      calc_half_life_h(
        weight_kg = input$weight_kg,
        egfr = input$egfr,
        age_years = input$age_years,
        transplant = input$transplant
      )
    )
  })

  output$target_status <- renderUI({
    current_metrics <- metrics()
    current_trough <- current_metrics$ctrough[[1]]
    current_auc_mic <- current_metrics$auc_mic[[1]]

    trough_class <- if (current_trough < study_constants$trough_target_low) {
      "status-fail"
    } else if (current_trough > study_constants$trough_target_high) {
      "status-warn"
    } else {
      "status-pass"
    }

    auc_class <- if (current_auc_mic >= study_constants$auc_mic_target) {
      "status-pass"
    } else {
      "status-fail"
    }

    tagList(
      div(
        class = paste("status-chip", trough_class),
        paste0(
          "Ctrough ",
          sprintf("%.1f", current_trough),
          " mg/L"
        )
      ),
      div(
        class = paste("status-chip", auc_class),
        paste0(
          "AUC0-24/MIC ",
          sprintf("%.1f", current_auc_mic),
          " at MIC ",
          input$mic,
          " mg/L"
        )
      )
    )
  })

  output$clinical_note <- renderText({
    current_auc_mic <- metrics()$auc_mic[[1]]
    current_trough <- metrics()$ctrough[[1]]

    if (current_trough < study_constants$trough_target_low && current_auc_mic < study_constants$auc_mic_target) {
      return("Predicted steady-state trough and AUC/MIC are both below manuscript targets, consistent with likely underexposure.")
    }

    if (current_trough > study_constants$trough_target_high) {
      return("Predicted steady-state trough exceeds the manuscript target range, suggesting higher overexposure risk.")
    }

    if (current_auc_mic < study_constants$auc_mic_target) {
      return("Predicted trough is acceptable, but AUC/MIC remains below the efficacy target for the selected MIC.")
    }

    "Predicted steady-state exposure satisfies both manuscript targets for the selected MIC and covariate profile."
  })

  output$pk_plot <- renderPlot({
    cutoff_h <- max(sim_data()$time_h) - 48
    plot_df <- sim_data() |>
      dplyr::filter(.data$time_h >= cutoff_h)

    if (isTRUE(input$log_scale)) {
      plot_df <- plot_df |>
        dplyr::filter(.data$CP > 0)
    }

    p <- ggplot(plot_df, aes(x = .data$time_h, y = .data$CP)) +
      annotate(
        "rect",
        xmin = -Inf,
        xmax = Inf,
        ymin = study_constants$trough_target_low,
        ymax = study_constants$trough_target_high,
        fill = "#10b981",
        alpha = 0.12
      ) +
      geom_hline(
        yintercept = c(study_constants$trough_target_low, study_constants$trough_target_high),
        linetype = "dashed",
        color = "#10b981",
        alpha = 0.8
      ) +
      geom_line(color = "#8b5cf6", linewidth = 1) +
      labs(
        x = "Time (hours)",
        y = "Linezolid concentration (mg/L)",
        title = "Predicted concentration-time profile",
        subtitle = "Last 48 hours of repeated dosing"
      ) +
      theme_minimal(base_size = 14) +
      theme(
        plot.title = element_text(face = "bold"),
        panel.grid.minor = element_blank()
      )

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

    p
  })
}

app <- shinyApp(ui = ui, server = server)
app