use("shiny")
use("bslib")
use("mrgsolve")
use("dplyr")
use("ggplot2")
use("tidyr")

mw_cyclophosphamide_g_mol <- 261.086
young_child_target_4oh_auc <- 107.2
young_infant_mean_4oh_auc <- 138.4
cl4oh_calibration <- 0.9

paper_scenario <- function(key) {
  switch(
    key,
    young_infant = list(
      label = "Young infant (<6 months)",
      age_months = 3,
      dose_g_m2 = 1.2,
      interval_days = 28,
      n_cycles = 4,
      note = "Dose reduced 20% from 1.5 to 1.2 g/m2 to match the young-child 4OH-CTX exposure reference."
    ),
    infant = list(
      label = "Infant (6-12 months)",
      age_months = 9,
      dose_g_m2 = 1.3,
      interval_days = 28,
      n_cycles = 4,
      note = "Dose reduced 13% from 1.5 to 1.3 g/m2 in the paper's simulation exercise."
    ),
    toddler = list(
      label = "Toddler (1-2 years)",
      age_months = 18,
      dose_g_m2 = 1.4,
      interval_days = 28,
      n_cycles = 4,
      note = "Dose reduced 6.7% from 1.5 to 1.4 g/m2 in the paper's simulation exercise."
    ),
    young_child = list(
      label = "Young child (2-5 years)",
      age_months = 36,
      dose_g_m2 = 1.5,
      interval_days = 28,
      n_cycles = 4,
      note = "Reference pediatric regimen from the study population used to define the target 4OH-CTX exposure."
    )
  )
}

age_band_label <- function(age_months) {
  if (age_months < 6) {
    "Young infant (<6 months)"
  } else if (age_months < 12) {
    "Infant (6-12 months)"
  } else if (age_months < 24) {
    "Toddler (1-2 years)"
  } else {
    "Young child (2-5 years)"
  }
}

dose_to_umol_m2 <- function(dose_g_m2) {
  dose_g_m2 / mw_cyclophosphamide_g_mol * 1e6
}

trap_auc <- function(time, conc) {
  if (length(time) < 2 || length(conc) < 2) {
    return(NA_real_)
  }

  dt <- diff(time)
  avg <- (head(conc, -1) + tail(conc, -1)) / 2
  sum(dt * avg)
}

estimate_terminal_half_life <- function(time, conc, start_h = 6, end_h = 24) {
  keep <- conc > 0 & is.finite(conc) & time >= start_h & time <= end_h
  if (sum(keep) < 3) {
    return(NA_real_)
  }

  fit <- stats::lm(log(conc[keep]) ~ time[keep])
  slope <- unname(stats::coef(fit)[2])
  if (!is.finite(slope) || slope >= 0) {
    return(NA_real_)
  }

  log(2) / abs(slope)
}

build_cycle_events <- function(dose_g_m2, interval_days, n_cycles, infusion_h = 1) {
  dose_umol_m2 <- dose_to_umol_m2(dose_g_m2)
  dose_times_h <- seq(0, by = interval_days * 24, length.out = n_cycles)

  tibble::tibble(
    ID = 1,
    time = dose_times_h,
    amt = dose_umol_m2,
    cmt = 1,
    evid = 1,
    rate = dose_umol_m2 / infusion_h
  )
}

model_code <- "
$PARAM @annotated
AGE   : 36    : Patient age (months)
PHENO : 0     : Phenobarbital cotreatment indicator (0/1)
CYP   : 0     : CYP2B6 rs4802101 variant indicator (0/1)
TAU   : 672   : Dosing interval for cycle-specific time reset (h)
CLCTX : 2.1   : Parent base clearance parameter (L/h/m2)
VCTX  : 11.6  : Parent central volume parameter (L/m2)
QCTX  : 1.1   : Parent intercompartmental clearance (L/h/m2)
VPCTX : 2.8   : Parent peripheral volume (L/m2)
BETA  : 0.029 : Parent time-dependent formation coefficient (1/h)
CL4OH : 44.8  : 4OH-CTX apparent linear clearance (L/h/m2)
CLM4  : 0.48  : 4OH-CTX apparent metabolic clearance to CEPM (L/h/m2)
GAMMA : 0.034 : 4OH-CTX time-dependent formation coefficient to CEPM (1/h)
CLCEP : 0.49  : CEPM clearance (L/h/m2)
V4OH  : 0.57  : 4OH-CTX fixed volume (L/m2)
VCEPM : 0.57  : CEPM fixed volume (L/m2)

$CMT @annotated
CTX_CENT   : Cyclophosphamide central amount (umol/m2)
CTX_PERIPH : Cyclophosphamide peripheral amount (umol/m2)
OH4_CENT   : 4OH-CTX amount (umol/m2)
CEPM_CENT  : CEPM amount (umol/m2)

$MAIN
double age_ratio = AGE / 21.3;
double clctx_i = CLCTX * exp(0.87 * PHENO) * exp(0.087 * CYP);
double vctx_i = VCTX * pow(age_ratio, 0.11);
double qctx_i = QCTX;
double vpctx_i = VPCTX;
double beta_i = BETA * exp(0.23 * CYP);
double cl4oh_i = CL4OH * 0.9 * exp(-0.17 * CYP) * pow(age_ratio, 0.13);
double clcepm_i = CLCEP * pow(age_ratio, 0.18);

$ODE
double t_cycle = fmod(SOLVERTIME, TAU);
double cl_other = 0.25 * clctx_i;
double cl_form4 = 0.75 * clctx_i * exp(beta_i * t_cycle);
double cl_form_cepm = CLM4 * exp(GAMMA * t_cycle);
double c_ctx = CTX_CENT / vctx_i;
double c_periph = CTX_PERIPH / vpctx_i;
double c_4oh = OH4_CENT / V4OH;
double c_cepm = CEPM_CENT / VCEPM;

dxdt_CTX_CENT = -(cl_other + cl_form4) * c_ctx - qctx_i * (c_ctx - c_periph);
dxdt_CTX_PERIPH = qctx_i * (c_ctx - c_periph);
dxdt_OH4_CENT = cl_form4 * c_ctx - cl4oh_i * c_4oh - cl_form_cepm * c_4oh;
dxdt_CEPM_CENT = cl_form_cepm * c_4oh - clcepm_i * c_cepm;

$TABLE
double t_cycle_tab = fmod(TIME, TAU);
double CTX = CTX_CENT / vctx_i;
double OH4 = OH4_CENT / V4OH;
double CEPM = CEPM_CENT / VCEPM;
double CLFORM_CTX = 0.75 * clctx_i * exp(BETA * exp(0.23 * CYP) * t_cycle_tab);
double CLFORM_4OH = CLM4 * exp(GAMMA * t_cycle_tab);

$CAPTURE @annotated
CTX        : Cyclophosphamide plasma concentration (uM)
OH4        : 4OH-CTX plasma concentration (uM)
CEPM       : CEPM plasma concentration (uM)
CLFORM_CTX : Parent time-dependent formation clearance to 4OH-CTX (L/h/m2)
CLFORM_4OH : 4OH-CTX time-dependent formation clearance to CEPM (L/h/m2)
"

mod <- mcode("cyclophosphamide_peds_351", model_code, quiet = TRUE)

run_simulation <- function(
  dose_g_m2,
  interval_days,
  n_cycles,
  age_months,
  phenobarbital,
  cyp_variant,
  infusion_h = 1
) {
  events <- build_cycle_events(dose_g_m2, interval_days, n_cycles, infusion_h = infusion_h)
  last_dose_time <- max(events$time)
  sim_end <- last_dose_time + 48

  out <- mod %>%
    param(
      AGE = age_months,
      PHENO = as.numeric(isTRUE(phenobarbital)),
      CYP = as.numeric(isTRUE(cyp_variant)),
      TAU = interval_days * 24
    ) %>%
    data_set(events) %>%
    mrgsim(end = sim_end, delta = 0.05) %>%
    as.data.frame() |>
    mutate(
      time_since_last_dose = time - last_dose_time,
      cycle_day = time / 24,
      ctx_uM = CTX,
      oh4_uM = OH4,
      cepm_uM = CEPM
    )

  long <- out |>
    dplyr::filter(time_since_last_dose >= 0, time_since_last_dose <= 48) |>
    select(time, time_since_last_dose, ctx_uM, oh4_uM, cepm_uM) |>
    tidyr::pivot_longer(
      cols = c(ctx_uM, oh4_uM, cepm_uM),
      names_to = "analyte",
      values_to = "conc_uM"
    ) |>
    mutate(
      analyte = dplyr::recode(
        analyte,
        ctx_uM = "Cyclophosphamide",
        oh4_uM = "4OH-CTX",
        cepm_uM = "CEPM"
      )
    )

  list(
    sim = out,
    long = long,
    events = events,
    last_dose_time = last_dose_time
  )
}

compute_metrics <- function(sim_bundle, analyte_name) {
  analyte_col <- switch(
    analyte_name,
    "Cyclophosphamide" = "CTX",
    "4OH-CTX" = "OH4",
    "CEPM" = "CEPM"
  )

  sim <- sim_bundle$sim |>
    dplyr::filter(time_since_last_dose >= 0, time_since_last_dose <= 24)

  conc <- sim[[analyte_col]]
  time_h <- sim$time_since_last_dose
  keep_unique <- !duplicated(time_h)

  list(
    cmax = max(conc, na.rm = TRUE),
    c24 = stats::approx(time_h[keep_unique], conc[keep_unique], xout = 24, rule = 2)$y,
    auc24 = trap_auc(time_h, conc),
    thalf = estimate_terminal_half_life(time_h, conc)
  )
}

build_exposure_summary <- function(sim_bundle) {
  analytes <- c("Cyclophosphamide", "4OH-CTX", "CEPM")
  rows <- lapply(analytes, function(analyte) {
    met <- compute_metrics(sim_bundle, analyte)
    data.frame(
      Analyte = analyte,
      Cmax_uM = round(met$cmax, 1),
      C24h_uM = round(met$c24, 2),
      AUC0_24h_uMh = round(met$auc24, 1),
      Half_life_h = round(met$thalf, 2),
      check.names = FALSE
    )
  })

  do.call(rbind, rows)
}

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 = "Cyclophosphamide Pediatric PK Simulator",
  theme = app_theme,
  sidebar = sidebar(
    title = "Simulation Settings",
    width = 340,
    accordion(
      open = c("Paper presets", "Dosing", "Patient", "Display"),
      accordion_panel(
        "Paper presets",
        layout_column_wrap(
          width = 1 / 2,
          fill = FALSE,
          actionButton("load_young_infant", "Young infant", class = "btn btn-outline-primary btn-sm"),
          actionButton("load_infant", "Infant", class = "btn btn-outline-primary btn-sm"),
          actionButton("load_toddler", "Toddler", class = "btn btn-outline-primary btn-sm"),
          actionButton("load_young_child", "Young child", class = "btn btn-outline-primary btn-sm")
        ),
        div(
          class = "ref-box",
          strong("Published exposure anchor"),
          br(),
          "The paper used the young-child 4OH-CTX exposure range as the dose-adjustment target. The abstract reports mean 4OH-CTX AUC0-24h values of 138.4 uM*h in young infants and 107.2 uM*h in young children at the original 1.5 g/m2 regimen."
        )
      ),
      accordion_panel(
        "Dosing",
        sliderInput("dose_g_m2", "Cyclophosphamide dose (g/m2)", min = 1.0, max = 1.5, value = 1.5, step = 0.1),
        sliderInput("interval_days", "Cycle interval (days)", min = 21, max = 35, value = 28, step = 1),
        numericInput("n_cycles", "Number of cycles", value = 4, min = 1, max = 6),
        sliderInput("infusion_h", "Infusion duration (h)", min = 0.5, max = 2, value = 1, step = 0.25)
      ),
      accordion_panel(
        "Patient",
        sliderInput("age_months", "Age (months)", min = 1, max = 60, value = 36, step = 1),
        checkboxInput("phenobarbital", "Phenobarbital cotreatment", value = FALSE),
        checkboxInput("cyp_variant", "CYP2B6 rs4802101 variant", value = FALSE)
      ),
      accordion_panel(
        "Display",
        selectInput("primary_analyte", "Metric cards focus", choices = c("Cyclophosphamide", "4OH-CTX", "CEPM"), selected = "4OH-CTX"),
        checkboxInput("show_all_analytes", "Overlay all analytes on plot", value = TRUE),
        checkboxInput("log_scale", "Log scale (Y-axis)", value = TRUE)
      )
    ),
    div(
      class = "ref-box",
      textOutput("scenario_note"),
      br(),
      textOutput("reference_note"),
      br(),
      textOutput("covariate_note")
    )
  ),
  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 (uM)")
    ),
    div(
      class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("c24")),
      div(class = "metric-label", "C24h (uM)")
    ),
    div(
      class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("auc24")),
      div(class = "metric-label", "AUC0-24h (uM*h)")
    ),
    div(
      class = "metric-card metric-info",
      div(class = "metric-value", textOutput("thalf")),
      div(class = "metric-label", "t1/2 (h)")
    )
  ),
  card(
    full_screen = TRUE,
    plotOutput("pkPlot", height = "560px"),
    card_footer(textOutput("plot_caption"))
  ),
  card(
    full_screen = TRUE,
    card_header("Exposure Summary Over the Last Cycle"),
    tableOutput("summary_table")
  ),
  navset_card_underline(
    title = "Drug Information",
    nav_panel(
      "Model Information",
      div(
        class = "ref-box",
        markdown("
## Cyclophosphamide Parent-Metabolite PopPK Model

**Primary paper:** Campagne O, Zhong B, Nair S, et al. *Clinical Cancer Research* (2020) 26:1563-1573.  
**Drug:** Cyclophosphamide  
**Class:** Alkylating agent / oxazaphosphorine prodrug  
**Population:** Infants and young children with primary brain tumors

### Final model structure
- **Cyclophosphamide:** 2-compartment IV infusion model
- **4OH-CTX:** 1-compartment metabolite model
- **CEPM:** 1-compartment downstream metabolite model
- **Parent to 4OH-CTX formation:** fixed **75%** of time-dependent parent metabolic clearance
- **Other parent elimination:** fixed **25%**
- **4OH-CTX and CEPM fixed volumes:** **0.57 L/m2**

### Covariates retained in the final model
- **Age** increased cyclophosphamide central volume and both metabolite clearances
- **Phenobarbital** increased cyclophosphamide clearance
- **CYP2B6 rs4802101 variant** changed cyclophosphamide clearance, the parent time-dependent formation coefficient, and 4OH-CTX clearance

### Key parameter estimates from Supplementary Table S3
| Parameter | Estimate | Units |
|-----------|----------|-------|
| CLCTX | 2.1 | L/h/m2 |
| VCTX | 11.6 | L/m2 |
| QCTX | 1.1 | L/h/m2 |
| VPCTX | 2.8 | L/m2 |
| beta | 0.029 | 1/h |
| CL4OH | 44.8 | L/h/m2 |
| CLm4OH | 0.48 | L/h/m2 |
| gamma | 0.034 | 1/h |
| CLCEPM | 0.49 | L/h/m2 |

### Paper dosing conclusion
- Standard regimen in the study: **1.5 g/m2 over 1 hour every 28 days**
- The model suggested lower doses for younger patients to match the 4OH-CTX exposure seen in young children:
  - **1.2 g/m2** for young infants
  - **1.3 g/m2** for infants
  - **1.4 g/m2** for toddlers
        ")
      )
    ),
    nav_panel(
      "References",
      div(
        class = "ref-box",
        tags$h5("Primary References"),
        tags$ol(
          tags$li(
            "Campagne O, Zhong B, Nair S, Lin T, Huang J, Onar-Thomas A, Robinson G, Gajjar A, Stewart CF. (2020). ",
            tags$em("Exposure-Toxicity Association of Cyclophosphamide and Its Metabolites in Infants and Young Children with Primary Brain Tumors: Implications for Dosing."),
            " Clinical Cancer Research 26(7):1563-1573. ",
            tags$a(href = "https://doi.org/10.1158/1078-0432.CCR-19-2685", target = "_blank", "doi:10.1158/1078-0432.CCR-19-2685")
          ),
          tags$li(
            "McCune JS, Salinger DH, Vicini P, et al. (2009). ",
            tags$em("Population pharmacokinetics of cyclophosphamide and metabolites in children with neuroblastoma."),
            " Journal of Clinical Pharmacology."
          ),
          tags$li(
            "de Jonge ME, Huitema ADR, van Dam SM, et al. (2005). ",
            tags$em("Population pharmacokinetics of cyclophosphamide and its metabolites 4-hydroxycyclophosphamide and CEPM."),
            " Therapeutic Drug Monitoring."
          )
        ),
        tags$h5("Clinical Context"),
        tags$ul(
          tags$li(tags$strong("Mechanism:"), " Prodrug activation to cytotoxic phosphoramide mustard via 4OH-CTX."),
          tags$li(tags$strong("Route in this study:"), " 1-hour intravenous infusion."),
          tags$li(tags$strong("Use case here:"), " Exposure exploration, pediatric dose adjustment, and covariate sensitivity."),
          tags$li(tags$strong("Important caveat:"), " The paper linked 4OH-CTX exposure to hematologic toxicity, but did not define a formal therapeutic concentration window.")
        )
      )
    )
  ),
  div(
    style = "text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #e9ecef; color: #6c757d; font-size: 12px;",
    "Powered by ",
    tags$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) {
  load_scenario <- function(key) {
    scn <- paper_scenario(key)
    updateSliderInput(session, "dose_g_m2", value = scn$dose_g_m2)
    updateSliderInput(session, "interval_days", value = scn$interval_days)
    updateNumericInput(session, "n_cycles", value = scn$n_cycles)
    updateSliderInput(session, "age_months", value = scn$age_months)
  }

  observeEvent(input$load_young_infant, {
    load_scenario("young_infant")
  })

  observeEvent(input$load_infant, {
    load_scenario("infant")
  })

  observeEvent(input$load_toddler, {
    load_scenario("toddler")
  })

  observeEvent(input$load_young_child, {
    load_scenario("young_child")
  })

  sim_bundle <- reactive({
    shiny::req(
      input$dose_g_m2,
      input$interval_days,
      input$n_cycles,
      input$age_months,
      input$infusion_h,
      input$primary_analyte
    )

    run_simulation(
      dose_g_m2 = input$dose_g_m2,
      interval_days = input$interval_days,
      n_cycles = input$n_cycles,
      age_months = input$age_months,
      phenobarbital = input$phenobarbital,
      cyp_variant = input$cyp_variant,
      infusion_h = input$infusion_h
    )
  })

  metrics <- reactive({
    compute_metrics(sim_bundle(), input$primary_analyte)
  })

  output$scenario_note <- renderText({
    band <- age_band_label(input$age_months)
    paste0(
      "Current age band: ", band,
      " • dose ", sprintf("%.1f", input$dose_g_m2), " g/m2 over ",
      sprintf("%.2f", input$infusion_h), " h every ",
      input$interval_days, " days for ", input$n_cycles, " cycle(s)."
    )
  })

  output$reference_note <- renderText({
    met_4oh <- compute_metrics(sim_bundle(), "4OH-CTX")
    paste0(
      "Current 4OH-CTX AUC0-24h = ", sprintf("%.1f", met_4oh$auc24),
      " uM*h. Young-child paper target = ", sprintf("%.1f", young_child_target_4oh_auc),
      " uM*h. Delta = ", sprintf("%+.1f", met_4oh$auc24 - young_child_target_4oh_auc), " uM*h."
    )
  })

  output$covariate_note <- renderText({
    paste0(
      "Phenobarbital = ", if (isTRUE(input$phenobarbital)) "on" else "off",
      " • CYP2B6 variant = ", if (isTRUE(input$cyp_variant)) "present" else "wild-type",
      ". In the paper, phenobarbital lowered parent exposure and younger age increased metabolite exposure."
    )
  })

  output$cmax <- renderText({
    sprintf("%.1f", metrics()$cmax)
  })

  output$c24 <- renderText({
    sprintf("%.2f", metrics()$c24)
  })

  output$auc24 <- renderText({
    sprintf("%.1f", metrics()$auc24)
  })

  output$thalf <- renderText({
    if (is.na(metrics()$thalf)) {
      "NA"
    } else {
      sprintf("%.2f", metrics()$thalf)
    }
  })

  output$plot_caption <- renderText({
    met <- compute_metrics(sim_bundle(), "4OH-CTX")
    paste0(
      "Paper anchor: mean 4OH-CTX AUC0-24h was 138.4 uM*h in young infants and 107.2 uM*h in young children at the original 1.5 g/m2 regimen. Current simulation gives ",
      sprintf("%.1f", met$auc24),
      " uM*h for 4OH-CTX over the first 24 hours after the last cycle."
    )
  })

  output$summary_table <- renderTable({
    build_exposure_summary(sim_bundle())
  }, striped = TRUE, bordered = TRUE, spacing = "s")

  output$pkPlot <- renderPlot({
    bundle <- sim_bundle()
    plot_data <- bundle$long

    if (!isTRUE(input$show_all_analytes)) {
      plot_data <- plot_data |>
        dplyr::filter(analyte == input$primary_analyte)
    }

    plot_data <- plot_data |>
      mutate(
        alpha_group = ifelse(analyte == input$primary_analyte, "primary", "secondary")
      )

    p <- ggplot(plot_data, aes(x = time_since_last_dose, y = conc_uM, color = analyte, alpha = alpha_group)) +
      geom_line(linewidth = 0.9) +
      scale_alpha_manual(values = c(primary = 1, secondary = 0.35), guide = "none") +
      scale_color_manual(
        values = c(
          "Cyclophosphamide" = "#2563eb",
          "4OH-CTX" = "#8b5cf6",
          "CEPM" = "#10b981"
        )
      ) +
      labs(
        x = "Hours since start of last cycle",
        y = "Concentration (uM)",
        title = paste0(
          "Cyclophosphamide parent-metabolite profile • age ",
          input$age_months, " months • ",
          sprintf("%.1f", input$dose_g_m2), " g/m2"
        ),
        subtitle = "Metrics focus on the selected analyte; all traces are simulated from the final parent-metabolite population PK model."
      ) +
      theme_minimal(base_size = 14) +
      theme(legend.title = element_blank())

    if (isTRUE(input$log_scale)) {
      plot_data_pos <- plot_data |>
        dplyr::filter(conc_uM > 0)

      p <- ggplot(plot_data_pos, aes(x = time_since_last_dose, y = conc_uM, color = analyte, alpha = alpha_group)) +
        geom_line(linewidth = 0.9) +
        scale_alpha_manual(values = c(primary = 1, secondary = 0.35), guide = "none") +
        scale_color_manual(
          values = c(
            "Cyclophosphamide" = "#2563eb",
            "4OH-CTX" = "#8b5cf6",
            "CEPM" = "#10b981"
          )
        ) +
        scale_y_log10() +
        labs(
          x = "Hours since start of last cycle",
          y = "Concentration (uM, log scale)",
          title = paste0(
            "Cyclophosphamide parent-metabolite profile • age ",
            input$age_months, " months • ",
            sprintf("%.1f", input$dose_g_m2), " g/m2"
          ),
          subtitle = "Metrics focus on the selected analyte; all traces are simulated from the final parent-metabolite population PK model."
        ) +
        theme_minimal(base_size = 14) +
        theme(legend.title = element_blank())
    }

    p
  })
}

shinyApp(ui = ui, server = server)