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

# Rucaparib PopPK Model
# Two-compartment with sequential zero-order release and first-order absorption
# Source: Green et al. Cancer Chemother Pharmacol (2022) 89:671-682
# DOI: 10.1007/s00280-022-04413-7

model_code <- '
$PARAM @annotated
CL   : 10.3   : Clearance (L/h)
VC   : 16.9   : Central volume of distribution (L)
Q    : 17.4   : Intercompartmental clearance (L/h)
VP   : 166    : Peripheral volume of distribution (L)
KA   : 0.0718 : Absorption rate constant (1/h)
D1   : 0.619  : Duration of zero-order release (h)
F1   : 0.372  : Absolute oral bioavailability
CLCR : 83     : Creatinine clearance (mL/min)
ALB  : 41     : Serum albumin (g/L)

$CMT @annotated
DEPOT : Oral depot ~ zero-order release (mg)
CENT  : Central compartment (mg)
PERIPH : Peripheral compartment (mg)

$MAIN
double CLi = CL * pow(CLCR / 83.0, 0.313) * pow(ALB / 41.0, 0.720);
double VCi = VC;
double Qi  = Q;
double VPi = VP;

F_DEPOT = F1;
D_DEPOT = D1;

$ODE
dxdt_DEPOT  = -KA * DEPOT;
dxdt_CENT   =  KA * DEPOT - (CLi/VCi)*CENT - (Qi/VCi)*CENT + (Qi/VPi)*PERIPH;
dxdt_PERIPH =  (Qi/VCi)*CENT - (Qi/VPi)*PERIPH;

$TABLE
double CP = CENT / VCi * 1000.0;

$CAPTURE CP
'

mod <- mcode("rucaparib", 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 = "Rucaparib (Rubraca\u00AE) Population PK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings", width = 340,
    h6("Dosing"),
    sliderInput("dose", "Dose (mg)", min = 200, max = 600, value = 600, step = 100),
    sliderInput("interval", "Dosing Interval (h)", min = 12, max = 24, value = 12, step = 12),
    numericInput("n_days", "Duration (days)", value = 14, min = 1, max = 30),
    hr(), h6("Patient Characteristics"),
    sliderInput("clcr", "Creatinine Clearance (mL/min)", min = 30, max = 180, value = 83, step = 5),
    sliderInput("alb", "Serum Albumin (g/L)", min = 25, max = 50, value = 41, step = 1),
    hr(),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE)
  ),
  navset_card_tab(
    title = "Rucaparib 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 (ng/mL)")),
        div(class = "metric-card metric-warning", div(class = "metric-value", textOutput("ctrough")), div(class = "metric-label", "Ctrough,ss (ng/mL)")),
        div(class = "metric-card metric-primary", div(class = "metric-value", textOutput("auc")), div(class = "metric-label", HTML("AUC<sub>0-\u03C4</sub> (ng\u00B7h/mL)"))),
        div(class = "metric-card metric-info", div(class = "metric-value", textOutput("thalf")), div(class = "metric-label", HTML("t\u00BD (h)")))
      ),
      plotOutput("pkPlot", height = "500px")
    ),
    nav_panel("Model Information", markdown("
## Rucaparib — Two-Compartment Population PK Model

**Structure:** Two-compartment with sequential zero-order release and first-order absorption, first-order elimination  
**Estimation method:** NONMEM FOCEI  
**Patients:** 458 patients across 3 studies (Study 1014, Study 10, ARIEL2)  
**Observations:** 4,518 plasma concentrations  

### Population PK Parameters
| Parameter | Estimate | Description |
|---|---|---|
| CL | 10.3 L/h | Clearance |
| Vc | 16.9 L | Central volume |
| Q | 17.4 L/h | Intercompartmental clearance |
| Vp | 166 L | Peripheral volume |
| Ka | 0.0718 h\u207B\u00B9 | Absorption rate constant |
| D1 | 0.619 h | Zero-order release duration |
| F1 | 37.2% | Absolute oral bioavailability |
| t\u00BD | ~25.9 h | Terminal half-life |

### Covariate Effects
- **Creatinine clearance on CL:** Power model (exponent 0.313, reference 83 mL/min)
- **Albumin on CL:** Power model (exponent 0.720, reference 41 g/L)
- Mild renal impairment: ~15% higher AUC; moderate: ~33% higher AUC
- CYP1A2/CYP2D6 polymorphisms: No clinically significant effect
- Concomitant PPIs: No clinically significant effect on absorption

### Between-Subject Variability
| Parameter | %CV | Shrinkage |
|---|---|---|
| CL | 48.8% | 8.8% |
| Ka | 63.5% | 5.2% |
| D1 | 111% | 11.8% |
    ")),
    nav_panel("References", div(class = "ref-box",
      tags$h5("\U0001F4DA Key References"),
      tags$ol(
        tags$li("Green ML et al. (2022) Cancer Chemother Pharmacol 89:671-682 — Population pharmacokinetics of rucaparib in patients with advanced ovarian cancer or other solid tumors"),
        tags$li("Shapiro GI et al. (2013) Clin Cancer Res 19:268-278 — Phase I dose-escalation study of rucaparib (Study 1014)"),
        tags$li("Kristeleit R et al. (2017) Clin Cancer Res 23:4095-4106 — Phase I-II study of oral rucaparib (Study 10)"),
        tags$li("Swisher EM et al. (2017) Lancet Oncol 18:75-87 — ARIEL2 rucaparib in platinum-sensitive recurrent ovarian cancer"),
        tags$li("Coleman RL et al. (2017) Lancet 376:2271-2281 — ARIEL3 rucaparib maintenance therapy")
      ),
      tags$h5("\U0001F48A Therapeutic Context"),
      tags$ul(
        tags$li(tags$strong("Drug:"), " Rucaparib camsylate (Rubraca\u00AE)"),
        tags$li(tags$strong("Class:"), " Poly(ADP-ribose) polymerase (PARP) inhibitor"),
        tags$li(tags$strong("Targets:"), " PARP1, PARP2, PARP3"),
        tags$li(tags$strong("Indications:"), " Maintenance treatment of recurrent ovarian cancer (platinum-sensitive); BRCA-mutated relapsed ovarian cancer; BRCA-mutated mCRPC"),
        tags$li(tags$strong("Approved dose:"), " 600 mg BID oral"),
        tags$li(tags$strong("Protein binding:"), " ~70%"),
        tags$li(tags$strong("Metabolism:"), " CYP2D6 and CYP1A2; major metabolite M324 (inactive)"),
        tags$li(tags$strong("Excretion:"), " Fecal 71.9%, Urinary 17.4%"),
        tags$li(tags$strong("Steady state:"), " Achieved after ~1 week BID dosing; ~4-fold accumulation")
      )
    ))
  ),
  div(style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ", tags$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({
    n_doses <- floor(input$n_days * 24 / input$interval)
    ev1 <- ev(amt = input$dose, cmt = 1, ii = input$interval, addl = n_doses - 1)
    mod %>%
      param(CLCR = input$clcr, ALB = input$alb) %>%
      ev(ev1) %>%
      mrgsim(end = input$n_days * 24, delta = 0.25) %>%
      as.data.frame() %>%
      mutate(time_h = time)
  })

  output$cmax <- renderText({
    d <- sim_data()
    last_start <- (input$n_days - 2) * 24
    last <- d %>% filter(time >= last_start)
    sprintf("%.0f", max(last$CP, na.rm = TRUE))
  })

  output$ctrough <- renderText({
    d <- sim_data()
    last_start <- (input$n_days - 2) * 24
    last <- d %>% filter(time >= last_start)
    # Get trough: minimum in the last dosing intervals
    sprintf("%.0f", min(last$CP[last$CP > 0], na.rm = TRUE))
  })

  output$auc <- renderText({
    d <- sim_data()
    tau <- input$interval
    last_dose_time <- (input$n_days - 1) * 24
    last <- d %>% filter(time >= last_dose_time, time <= last_dose_time + tau)
    if (nrow(last) > 1) {
      auc <- sum(diff(last$time) * (head(last$CP, -1) + tail(last$CP, -1)) / 2)
      sprintf("%.0f", auc)
    } else {
      "—"
    }
  })

  output$thalf <- renderText({
    CLi <- 10.3 * (input$clcr / 83)^0.313 * (input$alb / 41)^0.720
    Vss <- 16.9 + 166
    sprintf("%.1f", log(2) * Vss / CLi)
  })

  output$pkPlot <- renderPlot({
    d <- sim_data()
    p <- ggplot(d, aes(x = time_h, y = CP)) +
      geom_line(color = "#8b5cf6", linewidth = 0.8) +
      labs(
        x = "Time (hours)",
        y = "Plasma Concentration (ng/mL)",
        title = paste0("Rucaparib ", input$dose, " mg Q", input$interval, "H — Population PK Simulation")
      ) +
      theme_minimal(base_size = 14) +
      theme(
        plot.title = element_text(face = "bold", size = 14),
        panel.grid.minor = element_blank()
      )
    if (input$log_scale) p <- p + scale_y_log10()
    p
  })
}

shinyApp(ui = ui, server = server)