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

model_code <- '
$PARAM @annotated
CL   : 4.28  : Clearance (L/h)
V    : 93.4  : Volume of distribution (L)
DBIL : 2.6   : Direct bilirubin (umol/L)
TINF : 1.0   : Infusion duration (h)

$CMT @annotated
CENT : Central (mg)

$MAIN
double DBILref = 2.6;
double CLi = CL * pow(DBIL / DBILref, -0.40);
double Vi  = V;
D_CENT = TINF;

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

$TABLE
double CP = CENT / Vi;

$CAPTURE CP CLi
'

mod <- mcode("voriconazole_iv", 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 = "Voriconazole IV PopPK Simulator — Critically Ill Patients",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings", width = 340,
    h6("Dosing"),
    checkboxInput("use_loading", "Include Loading Dose (300 mg)", value = TRUE),
    sliderInput("dose", "Maintenance Dose (mg)", min = 100, max = 400, value = 200, step = 50),
    sliderInput("interval", "Dosing Interval (h)", min = 8, max = 24, value = 12, step = 4),
    sliderInput("tinf", "Infusion Duration (h)", min = 0.5, max = 3.0, value = 1.0, step = 0.5),
    numericInput("n_days", "Duration (days)", value = 5, min = 1, max = 14),
    hr(), h6("Patient Characteristics"),
    sliderInput("dbil", "Direct Bilirubin (µmol/L)", min = 0.5, max = 15.0, value = 2.6, step = 0.5),
    hr(),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE)
  ),
  navset_card_tab(
    title = "Voriconazole IV PK Simulator",
    full_screen = TRUE,
    nav_panel("Simulation",
      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 (µg/mL)")),
        div(class="metric-card metric-warning", div(class="metric-value", textOutput("ctrough")), div(class="metric-label","Ctrough,ss (µg/mL)")),
        div(class="metric-card metric-primary", div(class="metric-value", textOutput("auc")), div(class="metric-label","AUC0-τ,ss (µg·h/mL)")),
        div(class="metric-card metric-info", div(class="metric-value", textOutput("thalf")), div(class="metric-label","t½ (h)"))
      ),
      plotOutput("pkPlot", height = "500px")
    ),
    nav_panel("Model Information", markdown("
## Voriconazole IV — Population Pharmacokinetic Model

**Source:** Chen et al. (2015) *Biol. Pharm. Bull.* 38:996-1004

**Population:** Chinese critically ill adult ICU patients with pulmonary diseases (n=62, 240 observations)

**Structure:** One-compartment model with first-order elimination (IV infusion)

**Software:** NONMEM VI with FOCE+I

### Population PK Parameters (Final Model)
| Parameter | Estimate | 95% CI | Bootstrap Median |
|-----------|----------|--------|-----------------|
| CL (L/h) | 4.28 | 3.48–5.08 | 4.30 |
| Vd (L) | 93.4 | 79.4–107.4 | 93.4 |
| θ_DBIL | −0.40 | −0.69 to −0.11 | −0.37 |
| IIV CL (%) | 72.94 | — | 72.67 |
| IIV Vd (%) | 26.50 | — | 26.93 |
| Residual (%) | 13.0 | — | 12.67 |

### Covariate Model
- **CL = 4.28 × (DBIL / 2.6)^(−0.40)**
- Higher direct bilirubin → reduced clearance (impaired hepatic function)
- 1-fold increase in DBIL above average → ~24% decrease in CL

### Therapeutic Drug Monitoring
- **Target trough (Cmin):** 1.5–4.0 µg/mL
- **>80% efficacy probability** at Cmin ≥ 1.5 µg/mL
- **<15% grade 2 hepatotoxicity** at Cmin ≤ 4.0 µg/mL
- Narrower window vs. non-critically ill patients

### Dosing Recommendations
- **Loading:** 300 mg IV then maintenance q12h
- **150 mg BID:** 99.76% in target range (mostly 1.5–2.5 µg/mL)
- **200 mg BID:** 98.76% in target range (mostly 2.5–4.0 µg/mL)
- 200 mg preferred for acute/critical; 150 mg for mild/moderate infection
    ")),
    nav_panel("References", div(class = "ref-box",
      tags$h5("\U0001f4da Key References"),
      tags$ol(
        tags$li("Chen W, Xie H, Liang F, et al. (2015) Population Pharmacokinetics in China: The Dynamics of Intravenous Voriconazole in Critically Ill Patients with Pulmonary Disease. Biol Pharm Bull 38:996-1004"),
        tags$li("Levêque D, Nivoix Y, Jehl F, Herbrecht R. (2006) Clinical pharmacokinetics of voriconazole. Int J Antimicrob Agents 27:274-284"),
        tags$li("Liu P, Mould DR. (2014) Population pharmacokinetic analysis of voriconazole and anidulafungin in adult patients with invasive aspergillosis. Antimicrob Agents Chemother 58:4718-4726"),
        tags$li("Han K, et al. (2011) Population pharmacokinetic evaluation with external validation and Bayesian estimator of voriconazole in liver transplant recipients. Clin Pharmacokinet 50:201-214")
      ),
      tags$h5("\U0001f48a Therapeutic Context"),
      tags$ul(
        tags$li(tags$strong("Class:"), " Triazole antifungal (second-generation, fluconazole derivative)"),
        tags$li(tags$strong("Indication:"), " Invasive aspergillosis, candidemia, Fusarium/Scedosporium infections"),
        tags$li(tags$strong("Route:"), " IV (this model), also available oral (high bioavailability ~96%)"),
        tags$li(tags$strong("Metabolism:"), " Hepatic via CYP2C19 (primary), CYP2C9, CYP3A4; nonlinear at higher doses"),
        tags$li(tags$strong("Key covariates:"), " CYP2C19 polymorphism, hepatic function (DBIL), body weight"),
        tags$li(tags$strong("TDM:"), " Recommended due to high PK variability; target Cmin 1.5-4.0 µg/mL in ICU")
      )
    ))
  ),
  div(style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ", tags$a(href="https://www.pkpdbuilder.com", target="_blank", style="color: #8b5cf6; font-weight: 500;", "PKPDBuilder.com"),
    " \u2022 Built by Sunny \u2600\ufe0f (Husain Attarwala's AI Assistant)", br(),
    tags$span(style="font-size: 10px;", "For research and educational purposes only. Not for clinical decision-making."))
)

server <- function(input, output, session) {
  sim_data <- reactive({
    # Build event schedule
    if (input$use_loading) {
      # Loading dose first, then maintenance starting at next interval
      ev_load <- ev(amt = 300, cmt = 1, rate = 300/input$tinf, time = 0)
      ev_maint <- ev(amt = input$dose, cmt = 1, rate = input$dose/input$tinf,
                     time = input$interval, ii = input$interval,
                     addl = (input$n_days * 24/input$interval) - 2)
      ev_all <- ev_load + ev_maint
    } else {
      ev_all <- ev(amt = input$dose, cmt = 1, rate = input$dose/input$tinf,
                   ii = input$interval,
                   addl = (input$n_days * 24/input$interval) - 1)
    }

    mod %>%
      param(DBIL = input$dbil, TINF = input$tinf) %>%
      ev(ev_all) %>%
      mrgsim(end = input$n_days * 24, delta = 0.1) %>%
      as.data.frame() %>%
      mutate(time_h = time)
  })

  # Compute steady-state metrics from last dosing interval
  ss_data <- reactive({
    d <- sim_data()
    last_dose_time <- max(0, (input$n_days * 24) - input$interval)
    d %>% filter(time >= last_dose_time, time <= last_dose_time + input$interval)
  })

  output$cmax <- renderText({
    sprintf("%.2f", max(ss_data()$CP, na.rm=TRUE))
  })
  output$ctrough <- renderText({
    sprintf("%.2f", min(ss_data()$CP, na.rm=TRUE))
  })
  output$auc <- renderText({
    d <- ss_data()
    if (nrow(d) < 2) return("--")
    auc <- sum(diff(d$time) * (head(d$CP,-1) + tail(d$CP,-1))/2)
    sprintf("%.1f", auc)
  })
  output$thalf <- renderText({
    CLi <- 4.28 * (input$dbil / 2.6)^(-0.40)
    Vi <- 93.4
    sprintf("%.1f", log(2) / (CLi / Vi))
  })

  output$pkPlot <- renderPlot({
    d <- sim_data()
    p <- ggplot(d, aes(x = time_h, y = CP)) +
      annotate("rect", xmin=-Inf, xmax=Inf, ymin=1.5, ymax=4.0, fill="#10b981", alpha=0.12) +
      geom_hline(yintercept = c(1.5, 4.0), linetype = "dashed", color = "#10b981", alpha=0.6) +
      annotate("text", x = max(d$time_h)*0.02, y = 4.3,
               label = "Therapeutic: 1.5-4.0 \u00b5g/mL", hjust=0, size=3.5, color="#10b981") +
      geom_line(color = "#8b5cf6", linewidth = 0.8) +
      labs(x = "Time (hours)",
           y = "Concentration (\u00b5g/mL)",
           title = paste0("Voriconazole IV ", input$dose, " mg Q", input$interval, "H",
                          if(input$use_loading) " (300 mg loading)" else "")) +
      theme_minimal(base_size = 14)
    if (input$log_scale) p <- p + scale_y_log10()
    p
  })
}

shinyApp(ui = ui, server = server)