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

# ── Betamethasone 2-Compartment PopPK Model ──
# Source: Schoenmakers et al. (2025) Br J Clin Pharmacol 91:2327-2339
# Population PK of betamethasone in pre-eclampsia
# 2-cpt model with first-order absorption, eoPE covariate on CL

model_code <- '
$PARAM @annotated
KA    : 1.67   : Absorption rate constant (1/h)
CL    : 15.6   : Apparent clearance without eoPE (L/h)
VC    : 46.1   : Apparent central volume (L)
VP    : 109.0  : Apparent peripheral volume (L)
QQ    : 99.8   : Apparent intercompartmental clearance (L/h)
EOPE  : 0      : Early-onset pre-eclampsia (0=No, 1=Yes)
THETA_PE : 0.606 : Pre-eclampsia effect on CL

$CMT @annotated
DEPOT  : IM depot (mg)
CENT   : Central compartment (mg)
PERIPH : Peripheral compartment (mg)

$MAIN
double CLi = CL;
if (EOPE > 0.5) CLi = CL * THETA_PE;

$ODE
dxdt_DEPOT  = -KA * DEPOT;
dxdt_CENT   = KA * DEPOT - (CLi/VC + QQ/VC) * CENT + (QQ/VP) * PERIPH;
dxdt_PERIPH = (QQ/VC) * CENT - (QQ/VP) * PERIPH;

$TABLE
double CP = CENT / VC;
double CP_ugL = CP * 1000.0;
CP_ugL = CP_ugL > 0 ? CP_ugL : 0;

$CAPTURE @annotated
CP_ugL : Plasma concentration (ug/L)
CLi    : Individual clearance (L/h)
'

mod <- mcode("betamethasone", model_code)

# ── 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-card { font-size: 13px; }
 .ref-card a { color: #8b5cf6; }
")

# ── UI ──
ui <- page_sidebar(
 title = "Betamethasone PK Simulator — Pre-eclampsia",
 theme = app_theme,

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

   h6("Clinical Scenario"),
   radioButtons("eope", "Pre-eclampsia Status",
     choices = c("No Pre-eclampsia" = "0", "Early-Onset Pre-eclampsia" = "1"),
     selected = "0"
   ),

   hr(),
   h6("Dosing Regimen"),
   numericInput("dose", "Dose per administration (mg)", value = 11.4, min = 1, max = 30, step = 0.1),
   numericInput("n_doses", "Number of doses", value = 2, min = 1, max = 4),
   numericInput("interval", "Dosing interval (h)", value = 24, min = 6, max = 48),

   hr(),
   h6("Simulation"),
   numericInput("sim_hours", "Simulation duration (h)", value = 96, min = 24, max = 240),
   checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE),
   checkboxInput("show_threshold", "Show fetal target (1 ng/mL maternal ≈ 1.3 µg/L)", value = TRUE),

   hr(),
   card(
     card_header("Model Parameters", class = "bg-light"),
     p(strong("CL/F:"), "15.6 L/h (no eoPE)"),
     p(strong("CL/F:"), "9.35 L/h (eoPE)"),
     p(strong("Vc/F:"), "46.1 L"),
     p(strong("Vp/F:"), "109 L"),
     p(strong("Q/F:"), "99.8 L/h"),
     p(strong("Ka:"), "1.67 h⁻¹"),
     p(strong("t½:"), "~7.4 h"),
     p(em("Dose: 11.4 mg IM (6 mg phosphate + 5.4 mg acetate)"))
   )
 ),

 # ── Metrics Row ──
 layout_column_wrap(
        width = 1/4,
        fill = FALSE,
   div(class = "metric-card metric-success",
     div(class = "metric-value", textOutput("cmax_val")),
     div(class = "metric-label", "Cmax (µg/L)")
   ),
   div(class = "metric-card metric-warning",
     div(class = "metric-value", textOutput("auc_val")),
     div(class = "metric-label", "AUC₀₋₂₄h (ng·h/mL)")
   ),
   div(class = "metric-card metric-primary",
     div(class = "metric-value", textOutput("cl_val")),
     div(class = "metric-label", "CL/F (L/h)")
   ),
   div(class = "metric-card metric-info",
     div(class = "metric-value", textOutput("thalf_val")),
     div(class = "metric-label", "t½ (h)")
   )
 ),

 # ── PK Plot ──
 card(
   card_header("Maternal Betamethasone Concentration-Time Profile"),
   full_screen = TRUE,
   plotOutput("pkPlot", height = "450px")
 ),

 # ── Comparison & Info ──
 navset_card_tab(
   title = "Analysis",
   nav_panel("eoPE vs Non-eoPE Comparison",
     plotOutput("comparePlot", height = "400px")
   ),
   nav_panel("NCA — Ex Vivo Perfusion (Fig 4A)",
     layout_columns(
       col_widths = c(6, 6),
       card(
         card_header("Maternal Betamethasone — Placental Perfusion"),
         plotOutput("ncaPlot", height = "380px")
       ),
       card(
         card_header("NCA Summary (Maternal Circulation)"),
         tableOutput("ncaTable"),
         hr(),
         markdown("
**Data Source:** Figure 4A, Schoenmakers et al. (2025)
*Ex vivo dual-sided placental perfusion, starting conc 1336 nmol/L.*

**Notes:**
- Rapid initial distribution (0–15 min): maternal conc drops from ~800 to ~60 nmol/L
- System reaches equilibrium (~260–300 nmol/L) by 60 min
- Terminal λz estimated from 60–180 min; near-flat profile → very long apparent t½
- AUC extrapolation unreliable (equilibrium, not elimination)
- **Reported foetal:maternal ratio = 0.76 ± 0.05 at 180 min**
         ")
       )
     ),
     card(
       card_header("Perfusion Data (Digitized from Figure 4A)"),
       tableOutput("perfusionDataTable")
     )
   ),
   nav_panel("Clinical Context",
     markdown("
## Antenatal Betamethasone in Pre-eclampsia

**Standard Regimen:** 11.4 mg IM (betamethasone sodium phosphate 6 mg + betamethasone acetate 5.4 mg) × 2 doses, 24 h apart.

**Key Finding:** Women with early-onset pre-eclampsia (eoPE) exhibit **40% lower betamethasone clearance** (9.35 vs 15.6 L/h), resulting in **~40% higher maternal exposure** (AUC₀₋₂₄h: 709 vs 584 ng·h/mL).

### Implications
- Current one-dose-fits-all approach may lead to **overexposure** in eoPE patients
- Fetal target: >1 ng/mL betamethasone for lung maturation — far exceeded with current dosing
- Foetal-to-maternal ratio ≈ 0.76 (from ex vivo perfusion)
- Main placental metabolite: 11-keto-betamethasone (via 11β-HSD)

### Mechanisms of Altered PK in eoPE
- **Decreased 11β-HSD1 mRNA expression** in eoPE placentas (P = 0.015)
- Reduced estrogen levels → impaired CYP3A4-mediated hepatic metabolism
- Elevated hepatic artery resistance → reduced liver blood flow

*Source: Schoenmakers et al. (2025) Br J Clin Pharmacol 91:2327-2339*
     ")
   ),
   nav_panel("References",
     div(class = "ref-card",
       markdown("
## References

1. **Schoenmakers S, Li L, Kluivers ACM, et al.** Pharmacokinetics of betamethasone in pre-eclampsia: An in vivo and ex vivo study. *Br J Clin Pharmacol.* 2025;91:2327-2339. [DOI: 10.1002/bcp.70035](https://doi.org/10.1002/bcp.70035)

2. **Foissac F, Zheng Y, Hirt D, et al.** Maternal betamethasone for prevention of respiratory distress syndrome in neonates: population PK/PD approach. *Clin Pharmacol Ther.* 2020;108(5):1026-1035.

3. **Della Torre M, Hibbard JU, Jeong H, Fischer JH.** Betamethasone in pregnancy: influence of maternal body weight and multiple gestation on pharmacokinetics. *Am J Obstet Gynecol.* 2010;203:254.e1-e12.

4. **Krzyzanski W, Milad MA, Jobe AH, et al.** Population PK modeling of IM and oral dexamethasone and betamethasone in Indian women. *J Pharmacokinet Pharmacodyn.* 2021;48(2):261-272.

### FDA Label
- [Betamethasone Prescribing Information (Drugs.com)](https://www.drugs.com/pro/betamethasone.html)
- [FDA Label (PDF)](https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/018741s040lbl.pdf)
       ")
     )
   )
 ),

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

 sim_data <- reactive({
   eope_flag <- as.numeric(input$eope)
   dose_mg <- input$dose
   n <- input$n_doses
   ii <- input$interval
   end_h <- input$sim_hours

   ev_obj <- ev(amt = dose_mg, cmt = 1, ii = ii, addl = n - 1)

   out <- mod %>%
     param(EOPE = eope_flag) %>%
     ev(ev_obj) %>%
     mrgsim(end = end_h, delta = 0.25) %>%
     as.data.frame()

   out
 })

 output$cmax_val <- renderText({
   d <- sim_data()
   sprintf("%.1f", max(d$CP_ugL, na.rm = TRUE))
 })

 output$auc_val <- renderText({
   d <- sim_data()
   d24 <- d %>% dplyr::filter(time <= 24)
   # Trapezoidal AUC in ng*h/mL (CP_ugL is ug/L = ng/mL)
   auc <- sum(diff(d24$time) * (head(d24$CP_ugL, -1) + tail(d24$CP_ugL, -1)) / 2)
   sprintf("%.0f", auc)
 })

 output$cl_val <- renderText({
   d <- sim_data()
   sprintf("%.1f", d$CLi[1])
 })

 output$thalf_val <- renderText({
   # Approximate terminal half-life
   sprintf("%.1f", 7.44)
 })

 output$pkPlot <- renderPlot({
   d <- sim_data()
   d_plot <- if (input$log_scale) d %>% dplyr::filter(CP_ugL > 0.001) else d

   p <- ggplot(d_plot, aes(x = time, y = CP_ugL)) +
     geom_line(color = "#8b5cf6", linewidth = 1.2) +
     labs(
       x = "Time after first dose (h)",
       y = "Betamethasone concentration (µg/L)",
       caption = "Schoenmakers et al. (2025) Br J Clin Pharmacol"
     ) +
     theme_minimal(base_size = 14) +
     theme(
       plot.caption = element_text(color = "grey50", size = 10),
       panel.grid.minor = element_blank()
     )

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

   if (input$show_threshold) {
     # ~1.3 µg/L maternal ≈ 1 ng/mL fetal (ratio 0.76)
     p <- p + geom_hline(yintercept = 1.3, linetype = "dashed", color = "#e74c3c", linewidth = 0.7) +
       annotate("text", x = max(d_plot$time) * 0.85, y = 1.6,
                label = "Fetal target (~1 ng/mL)", color = "#e74c3c", size = 3.5)
   }

   p
 })

 # ── NCA from Figure 4A (Ex Vivo Perfusion) ──
 perfusion_data <- data.frame(
   Time_min = c(0, 15, 30, 60, 90, 120, 150, 180),
   Maternal = c(800, 60, 200, 280, 300, 280, 280, 260),
   Foetal   = c(0, 50, 170, 270, 300, 280, 270, 250)
 )

 output$ncaPlot <- renderPlot({
   d_long <- data.frame(
     Time = rep(perfusion_data$Time_min, 2),
     Conc = c(perfusion_data$Maternal, perfusion_data$Foetal),
     Side = rep(c("Maternal", "Foetal"), each = nrow(perfusion_data))
   )

   ggplot(d_long, aes(x = Time, y = Conc, color = Side, shape = Side)) +
     geom_point(size = 3) +
     geom_line(linewidth = 1) +
     scale_color_manual(values = c("Maternal" = "#e74c3c", "Foetal" = "#3498db")) +
     scale_shape_manual(values = c("Maternal" = 16, "Foetal" = 1)) +
     geom_hline(yintercept = 0, color = "grey80") +
     labs(
       x = "Time (min)", y = "Betamethasone (nmol/L)",
       color = "", shape = "",
       title = "Ex Vivo Placental Perfusion — Figure 4A",
       subtitle = "Starting concentration: 1336 nmol/L (maternal reservoir)",
       caption = "Data digitized from Schoenmakers et al. (2025)"
     ) +
     theme_minimal(base_size = 14) +
     theme(legend.position = "top", panel.grid.minor = element_blank())
 })

 output$ncaTable <- renderTable({
   mat <- perfusion_data$Maternal
   t <- perfusion_data$Time_min

   # AUC trapezoidal
   auc_0_180 <- sum(diff(t) * (head(mat, -1) + tail(mat, -1)) / 2)

   # Terminal slope from 60-180 min
   idx <- which(t >= 60)
   fit <- lm(log(mat[idx]) ~ t[idx])
   lz <- -coef(fit)[2]
   thalf <- ifelse(lz > 0, log(2) / lz, NA)

   data.frame(
     Parameter = c(
       "Cmax", "Tmax", "C_last (180 min)",
       "AUC(0–180 min)", "AUC(0–180 min)",
       "λz (terminal)", "t½ (terminal)*",
       "Foetal:Maternal (180 min)"
     ),
     Value = c(
       "800 nmol/L", "0 min (start of perfusion)", "260 nmol/L",
       sprintf("%.0f nmol·min/L", auc_0_180),
       sprintf("%.0f nmol·h/L", auc_0_180 / 60),
       sprintf("%.5f 1/min", lz),
       sprintf("%.0f min (%.1f h)*", thalf, thalf / 60),
       sprintf("%.2f (published: 0.76 ± 0.05)", 250 / 260)
     )
   )
 }, striped = TRUE, hover = TRUE, width = "100%")

 output$perfusionDataTable <- renderTable({
   df <- perfusion_data
   df$FM_Ratio <- round(df$Foetal / df$Maternal, 2)
   df$FM_Ratio[1] <- NA  # 0/800 not meaningful
   names(df) <- c("Time (min)", "Maternal (nmol/L)", "Foetal (nmol/L)", "F:M Ratio")
   df
 }, striped = TRUE, hover = TRUE, digits = 0, width = "100%", na = "—")

 output$comparePlot <- renderPlot({
   dose_mg <- input$dose
   n <- input$n_doses
   ii <- input$interval
   end_h <- input$sim_hours

   ev_obj <- ev(amt = dose_mg, cmt = 1, ii = ii, addl = n - 1)

   out_no <- mod %>%
     param(EOPE = 0) %>%
     ev(ev_obj) %>%
     mrgsim(end = end_h, delta = 0.25) %>%
     as.data.frame() %>%
     mutate(Group = "No Pre-eclampsia")

   out_pe <- mod %>%
     param(EOPE = 1) %>%
     ev(ev_obj) %>%
     mrgsim(end = end_h, delta = 0.25) %>%
     as.data.frame() %>%
     mutate(Group = "Early-Onset Pre-eclampsia")

   d <- bind_rows(out_no, out_pe)

   ggplot(d, aes(x = time, y = CP_ugL, color = Group)) +
     geom_line(linewidth = 1.1) +
     scale_color_manual(values = c("No Pre-eclampsia" = "#3498db",
                                    "Early-Onset Pre-eclampsia" = "#e74c3c")) +
     geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey50", linewidth = 0.5) +
     annotate("text", x = end_h * 0.85, y = 1.6,
              label = "Fetal target", color = "grey50", size = 3.5) +
     labs(
       x = "Time after first dose (h)",
       y = "Betamethasone concentration (µg/L)",
       color = "",
       title = "Impact of Pre-eclampsia on Betamethasone Exposure",
       subtitle = sprintf("Dose: %.1f mg IM × %d doses, %dh apart", dose_mg, n, ii),
       caption = "eoPE: CL reduced by 40% → higher maternal exposure"
     ) +
     theme_minimal(base_size = 14) +
     theme(
       legend.position = "top",
       plot.caption = element_text(color = "grey50", size = 10),
       panel.grid.minor = element_blank()
     )
 })
}

shinyApp(ui = ui, server = server)