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

# ── Rifapentine PopPK Model with Autoinduction ──
# Based on: Yang et al. (2026) Int J Infect Dis 162:108229
# PopPK model from: Hibma et al. (2020) Am J Respir Crit Care Med 202(6):866-877
# One-compartment model with transit absorption and enzyme autoinduction

model_code <- '
$PARAM @annotated
CL    : 1.11   : Baseline apparent clearance (L/h)
V     : 36.7   : Apparent volume of distribution (L)
KA    : 1.62   : Absorption rate constant (1/h)
EMAX  : 0.73   : Max autoinduction effect (fractional increase in CL)
EC50  : 4.27   : Conc for half-max induction (mg/L)
GAMMA : 10     : Hill coefficient for induction
KENZ  : 0.00587 : Enzyme production rate constant (1/h)
FHIV  : 1.0    : HIV effect on bioavailability (0.729 if HIV+)
FMEAL : 1.0    : Meal effect (1.0=low-fat, 1.49=high-fat, 0.731=fasting)
FDOSE : 1.0    : Dose-dependent F reduction factor

$CMT @annotated
DEPOT : Oral depot (mg)
CENT  : Central compartment (mg)
ENZ   : Enzyme pool (relative)

$MAIN
double CLi = CL;
double Vi  = V;
double Fi  = FHIV * FMEAL * FDOSE;
F_DEPOT = Fi;
ENZ_0 = 1.0;

$ODE
double CP = CENT / Vi;
double INDUCTION = (EMAX * pow(CP, GAMMA)) / (pow(EC50, GAMMA) + pow(CP, GAMMA));
double KDEG = KENZ;

dxdt_DEPOT = -KA * DEPOT;
dxdt_CENT  = KA * DEPOT - (CLi * ENZ / Vi) * CENT;
dxdt_ENZ   = KENZ * (1.0 + INDUCTION) - KDEG * ENZ;

$TABLE
double DV = CENT / Vi;
DV = DV > 1e-6 ? DV : 1e-6;
double CL_effective = CLi * ENZ;

$CAPTURE @annotated
DV           : Plasma concentration (mg/L)
CL_effective : Effective clearance with induction (L/h)
'

mod <- mcode("rifapentine", 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; }
")

# ── UI ──
ui <- page_sidebar(
  title = "Rifapentine PK Simulator — Ethnic Differences & Drug Interactions",
  theme = app_theme,

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

    h6("Patient Characteristics"),
    radioButtons("hiv_status", "HIV Status",
      choices = c("HIV-negative" = "neg", "HIV-positive" = "pos"),
      selected = "neg", inline = TRUE
    ),

    hr(),
    h6("Dosing"),
    selectInput("dose", "Rifapentine Dose (mg)",
      choices = c(300, 450, 600, 750, 900),
      selected = 600
    ),
    selectInput("regimen", "Regimen",
      choices = c(
        "1HP (daily x 28 days)" = "daily",
        "3HP (weekly x 12 weeks)" = "weekly"
      ),
      selected = "daily"
    ),
    selectInput("meal", "Meal Condition",
      choices = c(
        "Low-fat meal (reference)" = "lowfat",
        "High-fat meal (+49% F)" = "highfat",
        "Fasting (-28% F)" = "fasting"
      ),
      selected = "lowfat"
    ),

    hr(),
    h6("Display"),
    checkboxInput("log_scale", "Log Scale (Y-axis)", value = FALSE),
    checkboxInput("show_induction", "Show CL Induction Profile", value = TRUE),

    hr(),
    card(
      card_header("Model Parameters", class = "bg-light"),
      p(strong("CL/F:"), "1.11 L/h (baseline)"),
      p(strong("V/F:"), "36.7 L"),
      p(strong("t\u00bd:"), "~15 h (pre-induction)"),
      p(strong("Autoinduction:"), "Up to 73% CL increase"),
      p(strong("MIC:"), "0.06 mg/L"),
      p(em("Source: Hibma et al. 2020"))
    )
  ),

  # Metric cards
  layout_column_wrap(
        width = 1/4,
        fill = FALSE,
    div(class = "metric-card metric-success",
      div(class = "metric-value", textOutput("cmax_ss")),
      div(class = "metric-label", "Cmax,ss (mg/L)")
    ),
    div(class = "metric-card metric-warning",
      div(class = "metric-value", textOutput("cmin_ss")),
      div(class = "metric-label", "Cmin,ss (mg/L)")
    ),
    div(class = "metric-card metric-primary",
      div(class = "metric-value", textOutput("auc_ss")),
      div(class = "metric-label", "AUCss (h\u00b7mg/L)")
    ),
    div(class = "metric-card metric-info",
      div(class = "metric-value", textOutput("cl_induced")),
      div(class = "metric-label", "CL at Steady State (L/h)")
    )
  ),

  # Main PK plot
  card(
    card_header("Concentration-Time Profile"),
    full_screen = TRUE,
    plotOutput("pkPlot", height = "450px")
  ),

  # Tabbed content
  navset_card_tab(
    title = "Analysis",
    nav_panel("Autoinduction Profile",
      plotOutput("inductionPlot", height = "400px")
    ),
    nav_panel("HIV vs Non-HIV Comparison",
      plotOutput("hivComparePlot", height = "400px")
    ),
    nav_panel("Ethnic Comparison",
      card_body(
        markdown("
### No Clinically Significant Ethnic Differences

**Key Finding (Yang et al. 2026):** Integrated 'bottom-up' (PBPK) and 'top-down' (PopPK) modeling 
demonstrated **no clinically significant ethnic differences** in rifapentine PK between Chinese and 
non-Chinese populations.

#### Cross-Validation Results (600 mg daily, 1HP regimen)
| Parameter | PBPK Model | PopPK Model |
|-----------|-----------|-------------|
| Cmin,ss (mg/L) | 7.89 | 6.54 |
| Cmax,ss (mg/L) | 21.07 | 18.17 |
| AUCss (h\u00b7mg/L) | 337.95 | 285.54 |

#### Drug Interaction with Ritonavir
Rifapentine at steady state had **minimal impact** on ritonavir exposure:
- AUCss reduction: ~9%
- Cmax,ss reduction: ~7%
- **No dose adjustment needed** for ritonavir when co-administered

*Ritonavir's mechanism-based inhibition of CYP3A4 counteracts rifapentine-mediated enzyme induction.*
        ")
      )
    ),
    nav_panel("References",
      card_body(
        markdown("
### References

1. **Yang H, Liu W, Xu N, et al.** (2026) Model-informed evidence for rifapentine use in Chinese HIV-positive adults with latent tuberculosis: Assessing ethnic PK differences and interactions with ritonavir. *Int J Infect Dis* 162:108229. [DOI](https://doi.org/10.1016/j.ijid.2025.108229)

2. **Hibma JE, Radtke KK, Dorman SE, et al.** (2020) Rifapentine Population Pharmacokinetics and Dosing Recommendations for Latent Tuberculosis Infection. *Am J Respir Crit Care Med* 202(6):866-877. [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC7491398/)

3. **FDA Label:** [Priftin (rifapentine) tablets](https://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021024s009lbl.pdf)

4. **Swindells S, et al.** (2019) One month of rifapentine plus isoniazid to prevent HIV-related tuberculosis. *N Engl J Med* 380(11):1001-1011.

### Therapeutic Context

- **Indication:** Latent tuberculosis infection (LTBI) prevention
- **Regimens:** 1HP (daily x 28 days) or 3HP (weekly x 12 weeks) with isoniazid
- **Key covariates:** HIV status (\u221227% F), meal type, dose-dependent F
- **MIC target:** 0.06 mg/L (M. tuberculosis)
- **Autoinduction:** CL increases up to 73%, stabilizes by Day 21
        ")
      )
    ),
    nav_panel("Model Information",
      card_body(
        markdown("
### Model Structure

**One-compartment oral PK with enzyme autoinduction**

- Transit absorption simplified to first-order (Ka = 1.62 h\u207b\u00b9)
- Semimechanistic enzyme turnover model for autoinduction
- Rifapentine concentration drives enzyme synthesis via sigmoid Emax

### Key Parameters (Hibma et al. 2020)

| Parameter | Value | Description |
|-----------|-------|-------------|
| CL/F | 1.11 L/h | Baseline apparent clearance |
| V/F | 36.7 L | Apparent volume |
| Ka | 1.62 h\u207b\u00b9 | Absorption rate |
| Emax | 0.73 (73%) | Max CL increase from induction |
| EC50 | 4.27 mg/L | Half-max induction concentration |
| \u03b3 | 10 | Hill coefficient |
| kENZ | 0.00587 h\u207b\u00b9 | Enzyme turnover rate (t\u00bd = 118 h) |

### Covariate Effects on Bioavailability

| Factor | Effect | Relative F |
|--------|--------|-----------|
| HIV infection | \u221227% | 0.729 |
| High-fat meal | +49% | 1.49 |
| Fasting | \u221228% | 0.731 |
| Dose (per 100 mg > 300 mg) | \u22121.67% | Variable |

### Population (N = 863 from 9 studies)
- Median age: 34 years
- Median weight: 59 kg
- 9% HIV-positive
- Doses: 300-1200 mg; daily to weekly
        ")
      )
    )
  ),

  # 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$a(href = "https://www.pkpdbuilder.com", target = "_blank",
           style = "color: #8b5cf6; font-weight: 500;", "PKPDBuilder.com"),
    " \u2022 Built by Sunny \u2600\ufe0f (Husain Attarwala's AI Assistant)",
    br(),
    tags$span(style = "font-size: 10px;",
      "For research and educational purposes only. Not for clinical decision-making.")
  )
)

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

  sim_data <- reactive({
    dose_mg <- as.numeric(input$dose)
    hiv <- ifelse(input$hiv_status == "pos", 0.729, 1.0)
    meal <- switch(input$meal,
      "lowfat" = 1.0,
      "highfat" = 1.49,
      "fasting" = 0.731
    )
    # Dose-dependent F reduction: 1 - 0.0167*(dose/100 - 3) for dose > 300
    fdose <- max(1.0 - 0.0167 * (dose_mg / 100 - 3), 0.5)

    if (input$regimen == "daily") {
      ii <- 24
      addl <- 27
      end_time <- 28 * 24
    } else {
      ii <- 168
      addl <- 11
      end_time <- 12 * 168
    }

    out <- mod %>%
      param(FHIV = hiv, FMEAL = meal, FDOSE = fdose) %>%
      ev(amt = dose_mg, cmt = 1, ii = ii, addl = addl) %>%
      mrgsim(end = end_time, delta = 0.5) %>%
      as.data.frame()

    out
  })

  # Metrics at steady state
  ss_metrics <- reactive({
    d <- sim_data()
    if (input$regimen == "daily") {
      # Last dosing interval (day 27-28)
      ss <- d %>% dplyr::filter(time >= 27 * 24)
    } else {
      # Last dosing interval (week 11-12)
      ss <- d %>% dplyr::filter(time >= 11 * 168)
    }
    list(
      cmax = max(ss$DV, na.rm = TRUE),
      cmin = min(ss$DV[ss$DV > 0.001], na.rm = TRUE),
      auc = sum(diff(ss$time) * (head(ss$DV, -1) + tail(ss$DV, -1)) / 2),
      cl_ss = tail(ss$CL_effective, 1)
    )
  })

  output$cmax_ss <- renderText({
    sprintf("%.1f", ss_metrics()$cmax)
  })
  output$cmin_ss <- renderText({
    sprintf("%.2f", ss_metrics()$cmin)
  })
  output$auc_ss <- renderText({
    sprintf("%.0f", ss_metrics()$auc)
  })
  output$cl_induced <- renderText({
    sprintf("%.2f", ss_metrics()$cl_ss)
  })

  # Main PK plot
  output$pkPlot <- renderPlot({
    d <- sim_data()
    d$time_days <- d$time / 24

    d_plot <- if (input$log_scale) d %>% dplyr::filter(DV > 0.001) else d

    p <- ggplot(d_plot, aes(x = time_days, y = DV)) +
      geom_line(color = "#8b5cf6", linewidth = 1) +
      geom_hline(yintercept = 0.06, linetype = "dashed", color = "#e74c3c", linewidth = 0.7) +
      annotate("text", x = max(d_plot$time_days) * 0.95, y = 0.08,
               label = "MIC = 0.06 mg/L", color = "#e74c3c", hjust = 1, size = 3.5) +
      labs(
        x = "Time (days)",
        y = "Rifapentine Concentration (mg/L)",
        title = paste0("Rifapentine ", input$dose, " mg ",
                       ifelse(input$regimen == "daily", "Daily (1HP)", "Weekly (3HP)"),
                       " — ", ifelse(input$hiv_status == "pos", "HIV+", "HIV\u2212"))
      ) +
      theme_minimal(base_size = 14) +
      theme(plot.title = element_text(face = "bold"))

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

  # Autoinduction plot
  output$inductionPlot <- renderPlot({
    d <- sim_data()
    d$time_days <- d$time / 24

    ggplot(d, aes(x = time_days, y = CL_effective)) +
      geom_line(color = "#e67e22", linewidth = 1.2) +
      geom_hline(yintercept = 1.11, linetype = "dashed", color = "#95a5a6") +
      annotate("text", x = 2, y = 1.15, label = "Baseline CL = 1.11 L/h",
               color = "#95a5a6", hjust = 0, size = 3.5) +
      labs(
        x = "Time (days)",
        y = "Effective Clearance (L/h)",
        title = "Autoinduction of Rifapentine Clearance Over Time",
        subtitle = "Enzyme turnover model: CL increases as rifapentine induces its own metabolism"
      ) +
      theme_minimal(base_size = 14) +
      theme(plot.title = element_text(face = "bold"))
  })

  # HIV comparison plot
  output$hivComparePlot <- renderPlot({
    dose_mg <- as.numeric(input$dose)
    meal <- switch(input$meal, "lowfat" = 1.0, "highfat" = 1.49, "fasting" = 0.731)
    fdose <- max(1.0 - 0.0167 * (dose_mg / 100 - 3), 0.5)

    if (input$regimen == "daily") {
      ii <- 24; addl <- 27; end_time <- 28 * 24
    } else {
      ii <- 168; addl <- 11; end_time <- 12 * 168
    }

    sim_neg <- mod %>%
      param(FHIV = 1.0, FMEAL = meal, FDOSE = fdose) %>%
      ev(amt = dose_mg, cmt = 1, ii = ii, addl = addl) %>%
      mrgsim(end = end_time, delta = 0.5) %>%
      as.data.frame() %>%
      mutate(Group = "HIV\u2212")

    sim_pos <- mod %>%
      param(FHIV = 0.729, FMEAL = meal, FDOSE = fdose) %>%
      ev(amt = dose_mg, cmt = 1, ii = ii, addl = addl) %>%
      mrgsim(end = end_time, delta = 0.5) %>%
      as.data.frame() %>%
      mutate(Group = "HIV+")

    # Also simulate HIV+ with 30% higher dose
    dose_hiv <- round(dose_mg * 1.3 / 150) * 150  # round to nearest 150
    fdose_hiv <- max(1.0 - 0.0167 * (dose_hiv / 100 - 3), 0.5)
    sim_pos_high <- mod %>%
      param(FHIV = 0.729, FMEAL = meal, FDOSE = fdose_hiv) %>%
      ev(amt = dose_hiv, cmt = 1, ii = ii, addl = addl) %>%
      mrgsim(end = end_time, delta = 0.5) %>%
      as.data.frame() %>%
      mutate(Group = paste0("HIV+ (", dose_hiv, " mg)"))

    combined <- bind_rows(sim_neg, sim_pos, sim_pos_high) %>%
      mutate(time_days = time / 24)

    ggplot(combined, aes(x = time_days, y = DV, color = Group)) +
      geom_line(linewidth = 0.9) +
      geom_hline(yintercept = 0.06, linetype = "dashed", color = "#e74c3c", alpha = 0.5) +
      scale_color_manual(values = c("HIV\u2212" = "#2ecc71",
                                     "HIV+" = "#e74c3c",
                                     setNames("#3498db", paste0("HIV+ (", dose_hiv, " mg)")))) +
      labs(
        x = "Time (days)",
        y = "Concentration (mg/L)",
        title = "Impact of HIV Status on Rifapentine Exposure",
        subtitle = "HIV+ patients need ~30% higher doses to match HIV\u2212 exposure",
        color = "Group"
      ) +
      theme_minimal(base_size = 14) +
      theme(plot.title = element_text(face = "bold"),
            legend.position = "top")
  })
}

shinyApp(ui = ui, server = server)