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

model_code <- '
$PARAM @annotated
CL   : 2.94  : Clearance (L/h) [0.042 L/h/kg x 70 kg]
V    : 35    : Volume of distribution (L) [0.50 L/kg x 70 kg]
KA   : 1.4   : Absorption rate constant (1/h)
F1   : 1.0   : Bioavailability
WT   : 70    : Body weight (kg)
SMOK : 1.0   : Smoking status (1=non-smoker, 1.5=smoker)

$CMT @annotated
DEPOT : Oral depot (mg)
CENT  : Central (mg)

$MAIN
double CLi = CL * (WT/70.0) * SMOK;
double Vi  = V * (WT/70.0);
F_DEPOT = F1;

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

$TABLE
double CP = CENT / Vi;

$CAPTURE CP
'

mod <- mcode("theophylline_rovei", 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 = "Theophylline PK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings", width = 340,
    h6("Dosing"),
    sliderInput("dose", "Dose (mg)", min = 100, max = 600, value = 250, step = 25),
    sliderInput("interval", "Dosing Interval (h)", min = 6, max = 24, value = 12, step = 1),
    numericInput("n_days", "Duration (days)", value = 5, min = 1, max = 14),
    hr(), h6("Patient Characteristics"),
    sliderInput("wt", "Weight (kg)", min = 40, max = 120, value = 70, step = 5),
    selectInput("smoking", "Smoking Status", choices = c("Non-smoker" = 1.0, "Smoker (+50% CL)" = 1.5), selected = 1.0),
    hr(),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE)
  ),
  navset_card_tab(
    title = "Theophylline 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 (mg/L)")),
        div(class="metric-card metric-warning", div(class="metric-value", textOutput("ctrough")), div(class="metric-label","Ctrough (mg/L)")),
        div(class="metric-card metric-primary", div(class="metric-value", textOutput("auc")), div(class="metric-label","AUC0-τ (mg·h/L)")),
        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("
## Theophylline — 1-Compartment Oral PK Model

### Source
Rovei V, Chanoine F, Strolin Benedetti M. *Pharmacokinetics of theophylline: a dose-range study.* Br J Clin Pharmacol. 1982;14:769-778.

### Study Design
- **Subjects:** 8 healthy adult volunteers (non-smokers, xanthine-free diet)
- **Doses:** 125, 250, 375, and 500 mg single oral doses (crossover)
- **Formulation:** Theodel® tablets
- **Model:** One-compartment open model with first-order absorption

### PK Parameters (Mean ± SD across doses)
| Parameter | Value | Unit |
|-----------|-------|------|
| CL | 0.042 ± 0.010 | L/h/kg |
| Vd | 0.50 ± 0.07 | L/kg |
| Ka | ~1.4 | 1/h |
| F | ~1.0 | (complete absorption) |
| t½ (elimination) | 8.2 ± 1.5 | h |
| Protein binding | 53–65% | — |

### Therapeutic Window
- **Therapeutic range:** 10–20 mg/L (bronchodilator effect)
- **Toxicity risk:** > 20 mg/L (GI, CNS, cardiovascular adverse effects)

### Key Findings
- Linear pharmacokinetics across 125–500 mg dose range
- Clearance unchanged across all doses (no dose-dependent kinetics)
- Primary elimination via hepatic metabolism (~85% of clearance)
- Renal clearance: 13–17% of total clearance
- Smoking increases theophylline clearance by ~50%
    ")),
    nav_panel("References",
      div(class = "ref-box",
        tags$p(tags$strong("Primary Reference:")),
        tags$p("Rovei V, Chanoine F, Strolin Benedetti M. Pharmacokinetics of theophylline: a dose-range study. Br J Clin Pharmacol. 1982;14:769-778."),
        tags$p(tags$strong("FDA Label:")),
        tags$p(tags$a(href="https://www.accessdata.fda.gov/drugsatfda_docs/label/2009/019211s042lbl.pdf", target="_blank", "Theophylline FDA Prescribing Information")),
        tags$p(tags$strong("Population PK Review:")),
        tags$p(tags$a(href="https://pubmed.ncbi.nlm.nih.gov/27578172/", target="_blank", "Ma et al. Theophylline: a review of population pharmacokinetic analyses. J Clin Pharm Ther. 2016.")),
        tags$p(tags$strong("Additional References:")),
        tags$ul(
          tags$li("Ogilvie RI. Clinical pharmacokinetics of theophylline. Clin Pharmacokinet. 1978;3:267-293."),
          tags$li("Hendeles L, Weinberger M, Johnson G. Monitoring serum theophylline levels. Clin Pharmacokinet. 1978;3:294-312.")
        )
      )
    )
  ),
  # ── PKPDBuilder Branding Footer ──
  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({
    doses <- expand.grid(amt = as.numeric(input$dose), time = seq(0, by = as.numeric(input$interval), length.out = as.numeric(input$n_days) * 24 / as.numeric(input$interval)))
    doses$evid <- 1
    doses$cmt  <- 1
    doses$ID   <- 1

    total_h <- as.numeric(input$n_days) * 24
    out <- mod %>%
      param(WT = as.numeric(input$wt), SMOK = as.numeric(input$smoking)) %>%
      data_set(doses) %>%
      mrgsim(end = total_h, delta = 0.25) %>%
      as.data.frame()
    out
  })

  last_interval <- reactive({
    d <- sim_data()
    tau <- as.numeric(input$interval)
    total_h <- as.numeric(input$n_days) * 24
    start <- total_h - tau
    d %>% filter(time >= start & time <= total_h)
  })

  output$cmax <- renderText({
    sprintf("%.1f", max(last_interval()$CP))
  })
  output$ctrough <- renderText({
    li <- last_interval()
    sprintf("%.1f", li$CP[nrow(li)])
  })
  output$auc <- renderText({
    li <- last_interval()
    auc_val <- sum(diff(li$time) * (head(li$CP, -1) + tail(li$CP, -1)) / 2)
    sprintf("%.1f", auc_val)
  })
  output$thalf <- renderText({
    ke <- (as.numeric(input$wt) * 0.042 * as.numeric(input$smoking)) / (as.numeric(input$wt) * 0.50)
    sprintf("%.1f", log(2)/ke)
  })

  output$pkPlot <- renderPlot({
    d <- sim_data()
    p <- ggplot(d, aes(x = time, y = CP)) +
      annotate("rect", xmin = -Inf, xmax = Inf, ymin = 10, ymax = 20,
               fill = "#10b981", alpha = 0.10) +
      annotate("rect", xmin = -Inf, xmax = Inf, ymin = 20, ymax = Inf,
               fill = "#ef4444", alpha = 0.08) +
      geom_hline(yintercept = c(10, 20), linetype = "dashed", color = c("#10b981","#ef4444"), linewidth = 0.5) +
      geom_line(color = "#8b5cf6", linewidth = 1.2) +
      annotate("text", x = max(d$time)*0.98, y = 10.5, label = "Therapeutic min (10 mg/L)",
               hjust = 1, size = 3, color = "#10b981") +
      annotate("text", x = max(d$time)*0.98, y = 20.5, label = "Toxic threshold (20 mg/L)",
               hjust = 1, size = 3, color = "#ef4444") +
      labs(x = "Time (h)", y = "Plasma Concentration (mg/L)",
           title = "Theophylline Plasma Concentration–Time Profile") +
      theme_minimal(base_size = 14) +
      theme(plot.title = element_text(face = "bold", size = 16))

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

shinyApp(ui, server)