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

# ──────────────────────────────────────────────────────────────────────────────
# Tacrolimus Extended-Release (Advagraf®) — Population PK Simulator
# Based on: Benkali et al. (2010) Clin Pharmacokinet 49(10):683-692
# Model: 2-compartment + Erlang absorption (n=3 transit compartments)
# Covariate: CYP3A5*1/*3 genotype on apparent clearance (CL/F)
# ──────────────────────────────────────────────────────────────────────────────

model_code <- '
$PARAM @annotated
KTR      : 3.3  : Transit rate constant — Erlang absorption (1/h)
V1F      : 486  : Apparent central volume V1/F (L)
CLF_NEXP : 19   : Apparent CL/F for CYP3A5 non-expressers (L/h)
THETA2   : 1.15 : CYP3A5 expressor multiplier on CL/F (dimensionless)
K56      : 0.13 : Rate constant central-to-peripheral (1/h)
K65      : 0.09 : Rate constant peripheral-to-central (1/h)
CYP35    : 0    : CYP3A5 status (0=non-expressor 3/*3, 1=expressor)

$CMT @annotated
DEPOT    : Oral dose depot (mg)
TRANSIT1 : Erlang transit compartment 1 (mg)
TRANSIT2 : Erlang transit compartment 2 (mg)
TRANSIT3 : Erlang transit compartment 3 (mg)
CENT     : Central compartment (mg)
PERIPH   : Peripheral compartment (mg)

$ODE
double CLF = CLF_NEXP * (1.0 + THETA2 * CYP35);
dxdt_DEPOT    = -KTR * DEPOT;
dxdt_TRANSIT1 =  KTR * DEPOT    - KTR * TRANSIT1;
dxdt_TRANSIT2 =  KTR * TRANSIT1 - KTR * TRANSIT2;
dxdt_TRANSIT3 =  KTR * TRANSIT2 - KTR * TRANSIT3;
dxdt_CENT     =  KTR * TRANSIT3 - (CLF / V1F + K56) * CENT + K65 * PERIPH;
dxdt_PERIPH   =  K56 * CENT - K65 * PERIPH;

$TABLE
double CP = (CENT / V1F) * 1000.0;

$CAPTURE @annotated
CP : Tacrolimus whole-blood concentration (ng/mL)
'

mod <- mcode("tacrolimus_er", model_code)

TARGET_TROUGH_LOW  <- 4   # ng/mL (stable renal transplant maintenance)
TARGET_TROUGH_HIGH <- 8   # ng/mL

# ── PKPDBuilder Brand Theme ──────────────────────────────────────────────────
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 ───────────────────────────────────────────────────────────────────────
ui <- page_sidebar(
  title = "Tacrolimus ER (Advagraf\u00ae) PK Simulator",
  theme = app_theme,

  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,

    h6("\U0001f48a Dosing"),
    sliderInput("dose", "Once-Daily Dose (mg)",
      min = 0.5, max = 12, value = 5, step = 0.5),
    numericInput("n_days", "Simulation Duration (days)",
      value = 21, min = 7, max = 60),

    hr(),
    h6("\U0001f9ec CYP3A5 Genotype"),
    selectInput("cyp35", "CYP3A5 Status",
      choices = c(
        "Non-expressor — CYP3A5*3/*3"       = "0",
        "Expressor — CYP3A5*1/*1 or *1/*3"  = "1"
      ),
      selected = "0"
    ),
    helpText("Expressors have ~2\u00d7 higher CL/F than non-expressors and",
             " need higher doses to reach target troughs."),

    hr(),
    h6("\U0001f4ca Display"),
    checkboxInput("log_scale",        "Log Scale (Y-axis)",                value = FALSE),
    checkboxInput("show_target",      "Show Target Trough Range (4\u20138 ng/mL)", value = TRUE),
    checkboxInput("show_comparison",  "Compare Both CYP3A5 Genotypes",     value = FALSE)
  ),

  # ── Metric Cards ────────────────────────────────────────────────────────────
  layout_column_wrap(
    width = 1/4,
    fill  = FALSE,
    div(class = "metric-card metric-success",
      div(class = "metric-value", textOutput("cmax")),
      div(class = "metric-label", "SS Cmax (ng/mL)")
    ),
    div(class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("ctrough")),
      div(class = "metric-label", "SS Trough / C\u2080 (ng/mL)")
    ),
    div(class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("auc24")),
      div(class = "metric-label", "AUC\u2082\u2084 (ng\u00b7h/mL)")
    ),
    div(class = "metric-card metric-info",
      div(class = "metric-value", textOutput("thalf")),
      div(class = "metric-label", "Terminal t\u00bd (h)")
    )
  ),

  # ── PK Plot ─────────────────────────────────────────────────────────────────
  card(
    card_header("Whole-Blood Concentration-Time Profile"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "500px")
  ),

  # ── Info Tabs ───────────────────────────────────────────────────────────────
  navset_card_underline(
    title = "Drug Information",

    nav_panel("Model Information", markdown("
## Tacrolimus Extended-Release (Advagraf\u00ae) \u2014 Population PK Model

**Based on:** Benkali K et al. (2010), *Clinical Pharmacokinetics* 49(10):683\u2013692
**Drug:** Tacrolimus (FK506) \u2014 Advagraf\u00ae once-daily extended-release capsules
**Indication:** Immunosuppression in adult renal transplant recipients (stable, \u22656 months post-switch)

---

### Model Structure

| Feature | Details |
|---------|---------|
| Structural model | 2-compartment |
| Absorption | Erlang distribution (n = 3 transit compartments) |
| Elimination | First-order |
| Software | NONMEM\u00ae VI (ADVAN5 SS5), FOCE |
| Population | 41 stable adult renal transplant recipients |
| Biosampling | Whole blood (12-point profiles, LC-MS/MS) |

---

### Population PK Parameters (Whole Dataset, n = 41)

| Parameter | Value | IIV (%) | Units |
|-----------|-------|---------|-------|
| Transit rate constant (k\u209c\u1d63) | 3.3 | 52 | h\u207b\u00b9 |
| Apparent central volume (V\u2081/F) | 486 | 53 | L |
| CL/F \u2014 CYP3A5 non-expressers (\u03b8\u2081) | 19 | 35 | L/h |
| CYP3A5 expressor multiplier (\u03b8\u2082) | 1.15 | \u2014 | dimensionless |
| CL/F \u2014 CYP3A5 expressers | ~41 | \u2014 | L/h |
| k central\u2192peripheral (k\u2085\u2086) | 0.13 | \u2014 | h\u207b\u00b9 |
| k peripheral\u2192central (k\u2086\u2085) | 0.09 | \u2014 | h\u207b\u00b9 |
| Residual error (proportional) | 8% | \u2014 | \u2014 |
| Residual error (additive) | 0.7 | \u2014 | ng/mL |

---

### CYP3A5 Pharmacogenomics

- CYP3A5 expressers (*1/*1 or *1/*3) have **~2-fold higher apparent clearance**
- This SNP explains **~35%** of interindividual variability in CL/F
- No other covariates were retained (weight, sex, age, haematocrit, creatinine, prednisolone)
- **Clinical implication:** Expressers require higher doses to achieve the same trough

---

### Therapeutic Drug Monitoring (TDM)

- **Target trough (C\u2080):** 4\u20138 ng/mL (stable maintenance, \u22656 months)
- **Measurement:** Whole blood (tacrolimus ~80% erythrocyte-bound)
- **Key insight:** AUC\u2082\u2084 can vary up to 50% for the same C\u2080 value
- **Bayesian estimation:** 3 samples (C\u2080, C\u2081h, C\u2083h) provides AUC\u2082\u2084 with RMSE 9%

Available at: [ISBA ABIS \u2014 ImmunoSuppressant Bayesian Dose Adjustment](https://pharmaco.chu-limoges.fr/)
    ")),

    nav_panel("References", div(class = "ref-box",
      tags$h5("\U0001f4da Primary Reference"),
      tags$ol(
        tags$li(
          "Benkali K, Pr\u00e9maud A, Picard N et al. (2010) Tacrolimus population pharmacokinetic-pharmacogenetic",
          " analysis and Bayesian estimation in renal transplant recipients on a new once-daily formulation. ",
          tags$em("Clinical Pharmacokinetics"), " 49(10):683\u2013692. ",
          tags$a(href = "https://doi.org/10.2165/11535950-000000000-00000",
                 target = "_blank", "DOI: 10.2165/11535950-000000000-00000")
        )
      ),
      tags$h5("\U0001f4cb Supporting Literature"),
      tags$ol(start = 2,
        tags$li("Staatz CE, Tett SE. (2004) Clinical pharmacokinetics and pharmacodynamics of tacrolimus. ",
                tags$em("Clin Pharmacokinet"), " 43(10):623\u2013653."),
        tags$li("Press RR et al. (2009) Explaining variability in tacrolimus pharmacokinetics. ",
                tags$em("Ann Transplant"), " 14(3):57\u201367."),
        tags$li("Barraclough KA et al. (2012) Predicting tacrolimus exposure. ",
                tags$em("Transplantation"), " 93(6):627\u2013633.")
      ),
      tags$h5("\U0001f48a Drug Summary"),
      tags$ul(
        tags$li(tags$strong("Brand names:"), " Advagraf\u00ae (ER, once-daily); Prograf\u00ae (IR, twice-daily)"),
        tags$li(tags$strong("Class:"), " Calcineurin inhibitor (macrolide immunosuppressant)"),
        tags$li(tags$strong("Route:"), " Oral (extended-release capsules)"),
        tags$li(tags$strong("Metabolism:"), " CYP3A4/CYP3A5 (hepatic and intestinal)"),
        tags$li(tags$strong("Molecular weight:"), " 822.05 g/mol"),
        tags$li(tags$strong("Measurement:"), " Whole blood (80% bound to erythrocytes)")
      )
    ))
  ),

  # ── Footer ───────────────────────────────────────────────────────────────────
  div(
    style = "text-align: center; padding: 20px; margin-top: 30px;
             border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$a(href = "https://www.pkpdbuilder.com", target = "_blank",
           style = "color: #8b5cf6; font-weight: 500;", "PKPDBuilder.com"),
    " \u2022 Built by Sunny \u2600\ufe0f (Husain Attarwala's AI Assistant)",
    br(),
    tags$span(
      style = "font-size: 10px;",
      "For research and educational purposes only. Not for clinical decision-making."
    )
  )
)

# ── Server ───────────────────────────────────────────────────────────────────
server <- function(input, output, session) {

  run_sim <- function(cyp35_val) {
    shiny::req(input$dose, input$n_days)
    n_doses <- input$n_days
    ev1 <- ev(amt = input$dose, cmt = 1, ii = 24, addl = n_doses - 1)
    mod %>%
      param(CYP35 = cyp35_val) %>%
      ev(ev1) %>%
      mrgsim(end = input$n_days * 24, delta = 0.5) %>%
      as.data.frame()
  }

  sim_data <- reactive({
    shiny::req(input$dose, input$n_days, input$cyp35)
    cyp35_val <- as.numeric(input$cyp35)
    d <- run_sim(cyp35_val)
    label <- if (cyp35_val == 0) "Non-expressor (*3/*3)" else "Expressor (*1/*3 or *1/*1)"
    d$genotype <- label
    d
  })

  sim_data_compare <- reactive({
    shiny::req(input$dose, input$n_days)
    d0 <- run_sim(0)
    d0$genotype <- "Non-expressor (*3/*3)"
    d1 <- run_sim(1)
    d1$genotype <- "Expressor (*1/*3 or *1/*1)"
    rbind(d0, d1)
  })

  last_interval <- reactive({
    d <- sim_data()
    last_start <- (input$n_days - 1) * 24
    d |> dplyr::filter(time >= last_start)
  })

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

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

  output$auc24 <- renderText({
    # Analytical SS AUC: Dose / CL/F (mg / (L/h)) × 1000 = ng·h/mL
    cyp35_val <- as.numeric(input$cyp35)
    clf <- 19 * (1 + 1.15 * cyp35_val)
    auc <- input$dose / clf * 1000
    sprintf("%.0f", auc)
  })

  output$thalf <- renderText({
    cyp35_val <- as.numeric(input$cyp35)
    clf  <- 19 * (1 + 1.15 * cyp35_val)
    k10  <- clf / 486          # elimination micro-rate constant
    k12  <- 0.13               # k central → peripheral
    k21  <- 0.09               # k peripheral → central
    sum_k  <- k10 + k12 + k21
    prod_k <- k10 * k21
    discriminant <- pmax(sum_k^2 - 4 * prod_k, 0)
    beta   <- (sum_k - sqrt(discriminant)) / 2
    t_half <- log(2) / beta
    sprintf("%.0f", t_half)
  })

  output$pkPlot <- renderPlot({
    shiny::req(input$dose, input$n_days, input$cyp35)

    color_vals <- c(
      "Non-expressor (*3/*3)"       = "#8b5cf6",
      "Expressor (*1/*3 or *1/*1)"  = "#ef4444"
    )

    if (isTRUE(input$show_comparison)) {
      d_all  <- sim_data_compare()
      d_plot <- if (input$log_scale) d_all |> dplyr::filter(CP > 0.01) else d_all
      p <- ggplot(d_plot, aes(x = time, y = CP, color = genotype)) +
        geom_line(linewidth = 0.9) +
        scale_color_manual(values = color_vals, name = "CYP3A5 Genotype") +
        theme(legend.position = "bottom")
      title_text <- paste0("Tacrolimus ER ", input$dose,
                           " mg QD \u2014 CYP3A5 Genotype Comparison")
    } else {
      d      <- sim_data()
      d_plot <- if (input$log_scale) d |> dplyr::filter(CP > 0.01) else d
      cyp35_val <- as.numeric(input$cyp35)
      geno_label <- if (cyp35_val == 0) "CYP3A5 Non-expressor (*3/*3)"
                    else                 "CYP3A5 Expressor (*1/*3 or *1/*1)"
      geno_color <- if (cyp35_val == 0) "#8b5cf6" else "#ef4444"
      p <- ggplot(d_plot, aes(x = time, y = CP)) +
        geom_line(color = geno_color, linewidth = 0.9) +
        theme(legend.position = "none")
      title_text <- paste0("Tacrolimus ER ", input$dose, " mg QD \u2014 ", geno_label)
    }

    x_max <- max(d_plot$time, na.rm = TRUE)

    if (isTRUE(input$show_target)) {
      p <- p +
        annotate("rect",
          xmin = -Inf, xmax = Inf,
          ymin = TARGET_TROUGH_LOW, ymax = TARGET_TROUGH_HIGH,
          fill = "#10b981", alpha = 0.12) +
        geom_hline(
          yintercept = c(TARGET_TROUGH_LOW, TARGET_TROUGH_HIGH),
          linetype = "dashed", color = "#059669", linewidth = 0.5, alpha = 0.8) +
        annotate("text",
          x = x_max * 0.02, y = TARGET_TROUGH_HIGH + 0.5,
          label = "Target trough: 4\u20138 ng/mL",
          hjust = 0, size = 3.5, color = "#059669")
    }

    p <- p +
      labs(
        x     = "Time (hours)",
        y     = "Whole-blood tacrolimus concentration (ng/mL)",
        title = title_text
      ) +
      theme_minimal(base_size = 14)

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

shinyApp(ui = ui, server = server)