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

# ── PK Model: 2-Compartment IV Infusion ──────────────────────────────────────
# Parameters: Asiimwe et al. CPT:PSP 2025. doi:10.1002/psp4.70013
# MBMA of T-DM1 and T-DXd using summary-level data from 103 clinical trials
model_code <- '
$PARAM @annotated
CL  : 0.809 : Clearance (L/day)
V1  : 3.283 : Central volume of distribution (L)
V2  : 0.748 : Peripheral volume of distribution (L)
Q   : 1.120 : Intercompartment clearance (L/day)
WT  : 70.0  : Body weight (kg)

$CMT @annotated
CENT : Central compartment (mg)
PERI : Peripheral compartment (mg)

$MAIN
double CLi = CL * pow(WT / 70.0, 0.75);
double V1i = V1 * (WT / 70.0);
double V2i = V2 * (WT / 70.0);
double Qi  = Q  * pow(WT / 70.0, 0.75);

$ODE
double k10 = CLi / V1i;
double k12 = Qi  / V1i;
double k21 = Qi  / V2i;
dxdt_CENT = -k10 * CENT - k12 * CENT + k21 * PERI;
dxdt_PERI =  k12 * CENT - k21 * PERI;

$TABLE
double CP = CENT / V1i;

$CAPTURE CP
'

mod <- mcode("adc_2cmt", model_code)

# ── Drug parameter library ────────────────────────────────────────────────────
DRUGS <- list(
  tdm1 = list(
    label  = "T-DM1 (Trastuzumab emtansine / Kadcyla\u00ae)",
    short  = "T-DM1",
    CL = 0.809, V1 = 3.283, V2 = 0.748, Q = 1.120,
    approved_dose = 3.6,
    color  = "#8b5cf6"
  ),
  tdxd = list(
    label  = "T-DXd (Trastuzumab deruxtecan / Enhertu\u00ae)",
    short  = "T-DXd",
    CL = 0.585, V1 = 2.785, V2 = 1.243, Q = 0.652,
    approved_dose = 5.4,
    color  = "#0ea5e9"
  )
)

# ── Helpers ───────────────────────────────────────────────────────────────────
simulate_adc <- function(mod, CL, V1, V2, Q, dose_mgkg, wt, n_cycles) {
  dose_mg <- dose_mgkg * wt
  inf_dur <- 0.0625  # 90 min = 1.5 h = 0.0625 days (Cycle 1 standard infusion)
  ev1 <- ev(
    amt  = dose_mg,
    cmt  = 1,
    rate = dose_mg / inf_dur,
    ii   = 21,
    addl = n_cycles - 1
  )
  mod %>%
    param(CL = CL, V1 = V1, V2 = V2, Q = Q, WT = wt) %>%
    ev(ev1) %>%
    mrgsim(end = n_cycles * 21 + 7, delta = 0.1) %>%
    as.data.frame()
}

calc_thalf <- function(CL, V1, V2, Q, wt = 70) {
  f   <- (wt / 70)^0.75
  g   <- wt / 70
  k10 <- (CL * f) / (V1 * g)
  k12 <- (Q  * f) / (V1 * g)
  k21 <- (Q  * f) / (V2 * g)
  s   <- k10 + k12 + k21
  beta <- (s - sqrt(s^2 - 4 * k10 * k21)) / 2
  round(log(2) / beta, 1)
}

get_metrics <- function(df, n_cycles, dose_interval = 21) {
  last_start <- (n_cycles - 1) * dose_interval
  last_end   <- last_start + dose_interval
  cyc        <- dplyr::filter(df, time >= last_start, time <= last_end)
  cmax    <- round(max(cyc$CP, na.rm = TRUE), 1)
  ctrough <- round(min(cyc$CP, na.rm = TRUE), 3)
  dt      <- diff(cyc$time)
  cm      <- (head(cyc$CP, -1) + tail(cyc$CP, -1)) / 2
  auc     <- round(sum(dt * cm, na.rm = TRUE), 1)
  list(cmax = cmax, ctrough = ctrough, auc = auc)
}

# ── Theme ─────────────────────────────────────────────────────────────────────
app_theme <- bs_theme(version = 5, bootswatch = "flatly", primary = "#8b5cf6") |>
  bs_add_rules("
    .metric-card  { background:#f8f9fa; border-radius:8px; padding:14px 10px; margin:4px; text-align:center; border:1px solid #dee2e6; }
    .metric-value { font-size:21px; font-weight:bold; color:#2c3e50; }
    .metric-label { font-size:11px; color:#7f8c8d; margin-top:4px; }
    .metric-primary .metric-value { color:#8b5cf6; }
    .metric-info    .metric-value { color:#0ea5e9; }
    .metric-success .metric-value { color:#10b981; }
    .metric-warning .metric-value { color:#f59e0b; }
    .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; }
    .drug-tdm1 { color:#8b5cf6; font-weight:600; }
    .drug-tdxd { color:#0ea5e9; font-weight:600; }
  ")

# ── UI ────────────────────────────────────────────────────────────────────────
ui <- page_sidebar(
  title = "Trastuzumab ADC PK Simulator: T-DM1 & T-DXd",
  theme = app_theme,

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

    h6("Drug Selection"),
    radioButtons("drug_sel", NULL,
      choices = list(
        "T-DM1 (Trastuzumab emtansine)" = "tdm1",
        "T-DXd (Trastuzumab deruxtecan)" = "tdxd",
        "Compare Both" = "both"
      ),
      selected = "both"
    ),
    hr(),

    conditionalPanel("input.drug_sel !== 'tdxd'",
      h6(tags$span(class = "drug-tdm1", "\u25a0 T-DM1 Dose (mg/kg)")),
      sliderInput("dose_tdm1", NULL, min = 0.3, max = 4.8, value = 3.6, step = 0.3),
      tags$small(class = "text-muted", "Approved: 3.6 mg/kg Q3W")
    ),
    conditionalPanel("input.drug_sel !== 'tdm1'",
      h6(tags$span(class = "drug-tdxd", "\u25a0 T-DXd Dose (mg/kg)")),
      sliderInput("dose_tdxd", NULL, min = 0.8, max = 8.0, value = 5.4, step = 0.2),
      tags$small(class = "text-muted", "Approved: 5.4 mg/kg Q3W (breast/NSCLC); 6.4 mg/kg (gastric)")
    ),
    hr(),

    h6("Patient"),
    sliderInput("wt",       "Weight (kg)",              min = 40, max = 120, value = 70, step = 5),
    sliderInput("n_cycles", "Cycles (Q3W, 21-day)",     min = 1,  max = 8,   value = 4,  step = 1),
    hr(),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE)
  ),

  navset_card_underline(
    title = "HER2-Targeted ADC Population PK Simulator",
    full_screen = TRUE,

    # ── Simulation Panel ──────────────────────────────────────────────────────
    nav_panel("Simulation",

      # Single-drug metrics
      conditionalPanel("input.drug_sel !== 'both'",
        layout_columns(
          col_widths = c(3, 3, 3, 3),
          div(class = "metric-card metric-success",
              div(class = "metric-value", textOutput("cmax_s")),
              div(class = "metric-label", "Cmax (\u03bcg/mL)")),
          div(class = "metric-card metric-warning",
              div(class = "metric-value", textOutput("ctrough_s")),
              div(class = "metric-label", "Ctrough (\u03bcg/mL)")),
          div(class = "metric-card metric-primary",
              div(class = "metric-value", textOutput("auc_s")),
              div(class = "metric-label", "AUC\u2080\u208b\u2082\u2081\u1d48 (\u03bcg\u00b7day/mL)")),
          div(class = "metric-card metric-info",
              div(class = "metric-value", textOutput("thalf_s")),
              div(class = "metric-label", "t\u00bd (days)"))
        )
      ),

      # Comparison metrics (2 rows of 4)
      conditionalPanel("input.drug_sel === 'both'",
        layout_columns(
          col_widths = c(3, 3, 3, 3, 3, 3, 3, 3),
          div(class = "metric-card metric-primary",
              div(class = "metric-value", textOutput("cmax_tdm1_c")),
              div(class = "metric-label", "T-DM1 Cmax (\u03bcg/mL)")),
          div(class = "metric-card metric-info",
              div(class = "metric-value", textOutput("cmax_tdxd_c")),
              div(class = "metric-label", "T-DXd Cmax (\u03bcg/mL)")),
          div(class = "metric-card metric-primary",
              div(class = "metric-value", textOutput("ctrough_tdm1_c")),
              div(class = "metric-label", "T-DM1 Ctrough (\u03bcg/mL)")),
          div(class = "metric-card metric-info",
              div(class = "metric-value", textOutput("ctrough_tdxd_c")),
              div(class = "metric-label", "T-DXd Ctrough (\u03bcg/mL)")),
          div(class = "metric-card metric-primary",
              div(class = "metric-value", textOutput("auc_tdm1_c")),
              div(class = "metric-label", "T-DM1 AUC\u2080\u208b\u2082\u2081\u1d48")),
          div(class = "metric-card metric-info",
              div(class = "metric-value", textOutput("auc_tdxd_c")),
              div(class = "metric-label", "T-DXd AUC\u2080\u208b\u2082\u2081\u1d48")),
          div(class = "metric-card metric-primary",
              div(class = "metric-value", textOutput("thalf_tdm1_c")),
              div(class = "metric-label", "T-DM1 t\u00bd (days)")),
          div(class = "metric-card metric-info",
              div(class = "metric-value", textOutput("thalf_tdxd_c")),
              div(class = "metric-label", "T-DXd t\u00bd (days)"))
        )
      ),

      # PK Plot
      card(
        full_screen = TRUE,
        height = "500px",
        plotOutput("pkPlot", height = "460px")
      )
    ),

    # ── Model Information ─────────────────────────────────────────────────────
    nav_panel("Model Information",
      markdown("
## Trastuzumab ADC Population PK Models

**Structure:** Two-compartment with linear elimination (IV infusion, ~90 min standard infusion)
**Modeling approach:** Model-Based Meta-Analysis (MBMA) using summary-level PK data from 103 clinical trials
**Software used:** Monolix 2024R1 via R (lixoftConnectors)
**Reference:** Asiimwe et al., *CPT: Pharmacometrics & Systems Pharmacology*, 2025. doi:10.1002/psp4.70013

---

### T-DM1 (Trastuzumab emtansine — Kadcyla®)

| Parameter | Estimate | RSE (%) |
|-----------|----------|---------|
| CL (L/day) | 0.809 | 7.3 |
| V₁ (L) | 3.283 | 4.8 |
| V₂ (L) | 0.748 | 8.3 |
| Q (L/day) | 1.120 | 26.3 |
| BSV CL | 0.334 | 15.5 |
| BSV V₁ | 0.221 | 14.8 |

**Approved dose:** 3.6 mg/kg IV Q3W (HER2+ breast cancer)
**Terminal t½:** ~3.5 days (this app) / ~4 days (reported)
**FDA approval:** 2013 | Median study weight: 69.4 kg

---

### T-DXd (Trastuzumab deruxtecan — Enhertu®)

| Parameter | Estimate | RSE (%) |
|-----------|----------|---------|
| CL (L/day) | 0.585 | 3.7 |
| V₁ (L) | 2.785 | 1.5 |
| V₂ (L) | 1.243 | 9.2 |
| Q (L/day) | 0.652 | 9.7 |
| BSV CL | 0.084 | 48.3 |
| BSV V₁ | 0.038 | 38.8 |

**Approved doses:** 5.4 mg/kg Q3W (breast cancer, NSCLC) | 6.4 mg/kg Q3W (gastric cancer)
**Terminal t½:** ~5.3 days (this app) / ~6 days (reported)
**FDA approval:** 2019 | Median study weight: 59.0 kg

---

### Covariate Scaling (app implementation)
- **CL, Q:** Allometric scaling — (WT/70 kg)^0.75
- **V₁, V₂:** Linear scaling — (WT/70 kg)

### Exposure Metrics Used in CUI Analysis
This paper combined Cmax (PK), ORR (efficacy), and DLT (safety) into a Clinical Utility Index.
The T-DM1 approved dose (3.6 mg/kg) aligns with an **ORR:DLT weight of 81:19** at all trial phases.
These weights successfully predicted the approved T-DXd dose of 5.4 mg/kg for breast cancer.
      ")
    ),

    # ── References ────────────────────────────────────────────────────────────
    nav_panel("References",
      div(class = "ref-box",
        tags$h5("\U0001f4da Key References"),
        tags$ol(
          tags$li(
            "Asiimwe I, et al. Postmarketing Assessment of Antibody Drug Conjugates: Proof-of-Concept Using Model-Based Meta-Analysis and a Clinical Utility Index Approach. ",
            tags$em("CPT Pharmacometrics Syst Pharmacol."), " 2025;11. ",
            tags$a(href = "https://doi.org/10.1002/psp4.70013", target = "_blank", "doi:10.1002/psp4.70013")
          ),
          tags$li("Lu D, et al. Population pharmacokinetics of trastuzumab emtansine (T-DM1) in HER2-positive metastatic breast cancer. Cancer Chemother Pharmacol. 2014;74(2):399-409."),
          tags$li("Yin O, et al. Population pharmacokinetics of trastuzumab deruxtecan. Clin Pharmacokinet. 2020;59(11):1461-1473."),
          tags$li("Verma S, et al. Trastuzumab emtansine for HER2-positive advanced breast cancer (EMILIA). N Engl J Med. 2012;367(19):1783-1791."),
          tags$li("Cortés J, et al. Trastuzumab deruxtecan versus trastuzumab emtansine (DESTINY-Breast03). N Engl J Med. 2022;386(12):1143-1154.")
        ),
        tags$h5("\U0001f48a Drug Information"),
        tags$ul(
          tags$li(tags$strong("T-DM1 (Kadcyla\u00ae):"), " HER2-targeting mAb (trastuzumab) + DM1 (maytansinoid). DAR ~3.5. FDA approved 2013. 3.6 mg/kg Q3W."),
          tags$li(tags$strong("T-DXd (Enhertu\u00ae):"), " HER2-targeting mAb (trastuzumab) + DXd (topoisomerase I inhibitor). DAR ~8. FDA approved 2019. 5.4 or 6.4 mg/kg Q3W."),
          tags$li(tags$strong("Drug class:"), " Antibody-Drug Conjugates (ADC), HER2-targeted"),
          tags$li(tags$strong("Route:"), " IV infusion — 90 min (Cycle 1), 30 min (subsequent cycles if tolerated)"),
          tags$li(tags$strong("Indication:"), " HER2-positive breast cancer, NSCLC (T-DXd), gastric/GEJ cancer (T-DXd)")
        )
      )
    )
  ),

  div(
    style = "text-align:center; padding:20px; margin-top:30px; border-top:1px solid #e9ecef; color:#6c757d; font-size:12px;",
    "Powered by ",
    tags$a(href = "https://www.pkpdbuilder.com", target = "_blank",
           style = "color:#8b5cf6; font-weight:500;", "PKPDBuilder.com"),
    " \u2022 For research and educational purposes only. Not for clinical decision-making.", tags$br(),
    tags$span(style = "font-size:10px;",
      "Model: Asiimwe et al. CPT:PSP 2025 \u2022 T-DM1: CL=0.809 L/d, V\u2081=3.28 L \u2022 T-DXd: CL=0.585 L/d, V\u2081=2.79 L")
  )
)

# ── Server ────────────────────────────────────────────────────────────────────
server <- function(input, output, session) {
  # thematic removed — not available on shinyapps.io

  # Reactive simulations
  sim_tdm1 <- reactive({
    shiny::req(input$dose_tdm1, input$wt, input$n_cycles)
    p <- DRUGS$tdm1
    simulate_adc(mod, p$CL, p$V1, p$V2, p$Q,
                 input$dose_tdm1, input$wt, input$n_cycles)
  })

  sim_tdxd <- reactive({
    shiny::req(input$dose_tdxd, input$wt, input$n_cycles)
    p <- DRUGS$tdxd
    simulate_adc(mod, p$CL, p$V1, p$V2, p$Q,
                 input$dose_tdxd, input$wt, input$n_cycles)
  })

  sim_active <- reactive({
    if (input$drug_sel == "tdm1") sim_tdm1() else sim_tdxd()
  })

  # ── Single-drug metrics ────────────────────────────────────────────────────
  output$cmax_s <- renderText({
    m <- get_metrics(sim_active(), input$n_cycles)
    sprintf("%.1f", m$cmax)
  })
  output$ctrough_s <- renderText({
    m <- get_metrics(sim_active(), input$n_cycles)
    sprintf("%.3f", m$ctrough)
  })
  output$auc_s <- renderText({
    m <- get_metrics(sim_active(), input$n_cycles)
    sprintf("%.1f", m$auc)
  })
  output$thalf_s <- renderText({
    shiny::req(input$wt)
    p <- if (input$drug_sel == "tdm1") DRUGS$tdm1 else DRUGS$tdxd
    sprintf("%.1f", calc_thalf(p$CL, p$V1, p$V2, p$Q, input$wt))
  })

  # ── Comparison metrics ─────────────────────────────────────────────────────
  output$cmax_tdm1_c    <- renderText({ m <- get_metrics(sim_tdm1(), input$n_cycles); sprintf("%.1f",  m$cmax)    })
  output$cmax_tdxd_c    <- renderText({ m <- get_metrics(sim_tdxd(), input$n_cycles); sprintf("%.1f",  m$cmax)    })
  output$ctrough_tdm1_c <- renderText({ m <- get_metrics(sim_tdm1(), input$n_cycles); sprintf("%.3f",  m$ctrough) })
  output$ctrough_tdxd_c <- renderText({ m <- get_metrics(sim_tdxd(), input$n_cycles); sprintf("%.3f",  m$ctrough) })
  output$auc_tdm1_c     <- renderText({ m <- get_metrics(sim_tdm1(), input$n_cycles); sprintf("%.1f",  m$auc)     })
  output$auc_tdxd_c     <- renderText({ m <- get_metrics(sim_tdxd(), input$n_cycles); sprintf("%.1f",  m$auc)     })
  output$thalf_tdm1_c   <- renderText({ sprintf("%.1f", calc_thalf(DRUGS$tdm1$CL, DRUGS$tdm1$V1, DRUGS$tdm1$V2, DRUGS$tdm1$Q, input$wt)) })
  output$thalf_tdxd_c   <- renderText({ sprintf("%.1f", calc_thalf(DRUGS$tdxd$CL, DRUGS$tdxd$V1, DRUGS$tdxd$V2, DRUGS$tdxd$Q, input$wt)) })

  # ── PK Plot ────────────────────────────────────────────────────────────────
  output$pkPlot <- renderPlot({
    shiny::req(input$drug_sel, input$wt, input$n_cycles)

    if (input$drug_sel == "both") {
      shiny::req(input$dose_tdm1, input$dose_tdxd)
      d1 <- sim_tdm1() |> mutate(drug = paste0("T-DM1 (", input$dose_tdm1, " mg/kg)"))
      d2 <- sim_tdxd() |> mutate(drug = paste0("T-DXd (", input$dose_tdxd, " mg/kg)"))
      d  <- bind_rows(d1, d2)

      p <- ggplot(d, aes(x = time, y = CP, color = drug)) +
        geom_line(linewidth = 0.9) +
        scale_color_manual(
          values = setNames(
            c(DRUGS$tdm1$color, DRUGS$tdxd$color),
            c(paste0("T-DM1 (", input$dose_tdm1, " mg/kg)"),
              paste0("T-DXd (", input$dose_tdxd, " mg/kg)"))
          )
        ) +
        labs(
          x     = "Time (days)",
          y     = "Concentration (\u03bcg/mL)",
          title = paste0("ADC PK Comparison \u2014 ", input$n_cycles, " Cycles Q3W \u2014 ", input$wt, " kg"),
          color = NULL
        ) +
        theme_minimal(base_size = 14) +
        theme(legend.position = "top")

    } else {
      dose_v  <- if (input$drug_sel == "tdm1") input$dose_tdm1 else input$dose_tdxd
      p_drug  <- DRUGS[[input$drug_sel]]
      d       <- sim_active()

      p <- ggplot(d, aes(x = time, y = CP)) +
        geom_line(color = p_drug$color, linewidth = 0.9) +
        labs(
          x     = "Time (days)",
          y     = "Concentration (\u03bcg/mL)",
          title = paste0(p_drug$short, " \u2014 ", dose_v, " mg/kg Q3W \u00d7 ",
                         input$n_cycles, " cycles \u2014 ", input$wt, " kg")
        ) +
        theme_minimal(base_size = 14)
    }

    if (input$log_scale) {
      p <- p + scale_y_log10() + annotation_logticks(sides = "l", alpha = 0.4)
    }
    p
  })
}

shinyApp(ui = ui, server = server)