inst/shiny/ui.R

In wwiecek/httkgui: High-Throughput Toxicokinetics: Analtica Laser prototype

# compartment_names <- c("lung", "kidney", "gut", "liver")
parameter_names <- c(
  "BW" = "Body Weight, kg.",
  "Clmetabolismc" = "Hepatic Clearance, L/h/kg BW.",
  "Fgutabs" = "Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gutlumen.",
  "Funbound.plasma" = "Fraction of plasma that is not bound.",
  "Fhep.assay.correction" = "The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)",
  "hematocrit" = "Percent volume of red blood cells in the blood.",
  "kdermabs" = "Rate that chemical is transferred from the skin to the blood, 1/h.",
  "Kgut2pu" = "Ratio of concentration of chemical in gut tissue to unbound concentration in plasma.",
  "kgutabs" = "Rate that chemical enters the gut from gutlumen, 1/h.",
  "kinhabs" = "Rate that the chemical is transferred from the lungs to the blood, 1/h.",
  "Kkidney2pu" = "Ratio of concentration of chemical in kidney tissue to unbound concentration in plasma.",
  "Kliver2pu" = "Ratio of concentration of chemical in liver tissue to unbound concentration in plasma.",
  "Klung2pu" = "Ratio of concentration of chemical in lung tissue to unbound concentration in plasma.",
  "Krbc2pu" = "Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.",
  "Krest2pu" = "Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.",
  "million.cells.per.gliver" = "Millions cells per gram of liver tissue.",
  "MW" = "Molecular Weight, g/mol.",
  "Qcardiacc" = "Cardiac Output, L/h/kg BW^3/4.",
  "Qgfrc" = "Glomerular Filtration Rate, L/h/kg BW^3/4, volume of fluid filtered from kidney and excreted.",
  "Qgutf" = "Fraction of cardiac output flowing to the gut.",
  "Qkidneyf" = "Fraction of cardiac output flowing to the kidneys.",
  "Qliverf" = "Fraction of cardiac output flowing to the liver.",
  "Rblood2plasma" = "The ratio of the concentration of the chemical in the blood to the concentration in the plasma.",
  "Vartc" = "Volume of the arteries per kg body weight, L/kg BW.",
  "Vgutc" = "Volume of the gut per kg body weight, L/kg BW.",
  "Vkidneyc" = "Volume of the kidneys per kg body weight, L/kg BW.",
  "Vliverc" = "Volume of the liver per kg body weight, L/kg BW.",
  "Vlungc" = "Volume of the lungs per kg body weight, L/kg BW.",
  "Vrestc" = "Volume of the rest of the body per kg body weight, L/kg BW.",
  "Vvenc" = "Volume of the veins per kg body weight, L/kg BW.",
  "Vmax" = "Maximal velocity, []",
  "km" = "Michaelis constant"
)

additional_parameters <- c(
  "KTS" = "KTS",
  "FR" = "FR",
  "Clint" = "Clint"
)

shiny::shinyUI(fluidPage(

  # Application title
  div(style = 'position:absolute; top:10px; right:10px; height:100px;', img(src='efsa_logo.png', height='60px')),
  titlePanel("TKPlate: interactive PBTK modelling platform"),
  sidebarLayout(
    sidebarPanel(
      h4("Compound and species"),
        fluidRow(
            column(7, checkboxInput("use_cas", "Use CAS instead of compound name")),
            column(5, checkboxInput("use_add", "Add a new compound"))
        ),
      conditionalPanel("input.use_add == 0",
          conditionalPanel("input.use_cas == 0", selectizeInput("compound", "Compound name", 
                                                                httkgui::chem.physical_and_invitro.data$Compound)),
          conditionalPanel("input.use_cas == 1", selectizeInput("cas", "CAS", 
                                                                httkgui::chem.physical_and_invitro.data$CAS))
      ),
      conditionalPanel("input.use_add == 1", "Please define the compound to use in separate tab on the right"),
      selectInput("species", "Species", c("Human", "Rat", "???")),
      
      h4("Exposure"),
      # checkboxGroupInput("compartments", "Compartments of interest", compartment_names, selected=compartment_names),
      # specify options for solve_pbtk: daily.dose, dose, doses.per.day, species, iv.dose, output.units, tsteps, days
      radioButtons("dose_type", "Dose specification", c("daily dose", "per dose + doses/day"), inline=TRUE),
      conditionalPanel('input.dose_type == "daily dose"', 
        numericInput("solve.daily.dose", "Single dose (mg/kg BW)", 1)
      ),
      conditionalPanel('input.dose_type == "per dose + doses/day"', 
        numericInput("solve.doses.per.day", "Doses per day", 0),
        numericInput("solve.dose", "Per dose/day (mg/kg BW)", 0)
      ),
      checkboxInput("solve.iv.dose", "IV dose (default = oral)", 0),
      
      h4("Model settings"),
      radioButtons("output_type", "Simulation type", c("Individual (single)"="single", "Population (Monte Carlo)"="mc"), inline=TRUE),
      selectInput("solve.output.units", "Output units", c("mg/L", "mg", "umol", "uM"), selected="uM"),
      fluidRow(
        column(6, numericInput("solve.tsteps", "time steps / hour", 4)),
        column(6, numericInput("solve.days", "Simulation length (days)", 1, min = 0.25))
      ),
      conditionalPanel("input.output_type == 'mc'", actionButton("run", "Solve PBPK model")),
      h4("Downloading results & automated report"),
      # selectInput("download_choice_mc", 
      # "Choose dataset to download", 
      # c("mean estimates with 95% intervals"))),
      # actionButton("download", "Download dataset as .csv file")
      HTML("<em>All numerical results can be downloaded as .csv file. Automated summary output of all inputs and outputs can be generated below.</em><br>"),
      downloadLink("fileDownload", "Download model solution (.csv file)"),
      conditionalPanel("input.output_type == 'mc'", 
                       "(Values for Monte Carlo are provided as mean over all simulations)"),
      # selectInput("report_format", "Please choose file format", c("html", "pdf", "docx")),
      radioButtons('report_format', 'Please choose file format for report', c('PDF', 'HTML', 'Word'),
                   inline = TRUE),
      downloadButton("report", "Generate report")
    ),

    # Show a plot of the generated distribution
    mainPanel(tabsetPanel(id="main_panel",
      tabPanel("inputs summary",
               h3("Information about the compound"),
               tableOutput("compound_table"),
               fluidRow(
                 column(10, 
                   h3("Population variability"),
                   conditionalPanel("input.output_type == 'single'", HTML("<em>Only allowed for Monte Carlo simulations.</em>")),
                   conditionalPanel("input.output_type == 'mc'", 
                   HTML("<em>Please use unique names to correctly distinguish between different populations</em>"),
                     fluidRow(
                       column(textInput("population_new_name", "Name", value = "Population 1"), width = 2),
                       column(numericInput("population_new_N", "N subjects", value = 100, step = 50), width = 2),
                       column(selectInput("population_new_vartype", "Type of variability", 
                                  c("CL only" = "tk", "CL + more parameters (Monte Carlo tab)" = "tk_physbio")), width = 4),
                       column(numericInput("population_new_multiplier", "CL multiplier", value = 1, step = .1), width = 2),
                       column(numericInput("population_new_cv", "log-normal CV", value = 0.30, step = 0.05), width = 2)
                     ),
                     actionButton("population_new_submit", "Submit values"),
                     tableOutput("custom_subpopulation_table")
                 ))
                 # column(2, 
                 #      h3("Main metabolic pathway"),
                 #      selectInput("compound_pathway", "pathway of interest", c("CYP2C9", "CYP2C19", "renal excretion")))
               ),
               fluidRow(
                 column(5, 
                        # h3("Main metabolic pathway"),
                        # selectInput("compound_pathway", "Main metabolic pathway (for automated settings)", c("CYP2C9", "CYP2C19", "renal excretion")),
                        h3("Model visualisation"),
                        div(style = "margin: 20px auto; width: 300px", imageOutput("model_visual"))
                 ),
                 column(6, 
                        h3("Experimental data"),
                        HTML("<em>Experimental data can be used to validate the model or help inform the model parameters.<br><br></em>"),
                        fileInput("experimental_data_input", "Please choose the file (Excel)"),
                        tableOutput("experimental_data_table"),
                        HTML("Format: .csv file with columns: time, mean, lower, upper (optional), name (optional).<br>
                             Lower and upper denote confidence bounds; 'name' should match subpopulation names in 'Population variability'."))
               )
               ),
      tabPanel("parameters", 
        h3("PBTK model parameter values"),
        conditionalPanel("input.output_type == 'mc'", 
                         HTML("<em>95% interval values will appear here once the results have been generated</em>")),
        checkboxInput("custom_params", "Check here to manually change parameter values", 0, width=500),
        conditionalPanel("input.custom_params == 1",
                         HTML("<em>To erase user-defined values, please restart the application. </em><br>
                              Please note that KTS, FR, Clint parameters are not included in parameterization inputs, 
                              but used to derive other values (renal clearance, hepatic clearance).<br>"),
                         conditionalPanel("input.output_type == 'mc'",
                                          HTML("<b>Note: when setting CV manually, 
                                               this setting will override variability coming from 
                                               physiological parameter variability in another tab.</b><br>")),
                         fluidRow(
                             column(4, selectInput("cparams_select", 
                                                   "Parameter name", 
                                                   c(names(parameter_names), names(additional_parameters))
                                                   )
                                    ),
                             column(2, numericInput("cparams_value", "Mean value", 0)),
                             
                             conditionalPanel("input.output_type == 'mc'",
                                              column(2, sliderInput("cparams_cv", "Monte Carlo CV", min=0, max=1, 0))
                                              # column(2, numericInput("cparams_uci", "97.5% value", 0))
                             )
                         ),
                         actionButton("cparams_submit", "Submit values"),
                         DT::dataTableOutput("custom_param_table"),
                         h3("Original parameter values"),
                         em("These values will be replaced by user-defined values.")
        ),
        
        tableOutput("parameters_df")
      ),
      tabPanel("add compound",
               conditionalPanel("input.use_add == 1", 
                                h3("Define new compound and press submit when all inputs ready"),
                                column(4,
                                    textInput("add_compound", "Compound name", ""),
                                    textInput("add_cas", "Compound CAS", ""),
                                    textInput("add_reference", "Reference(s)", value=""),
                                    HTML("Species can be selected in the input panel on the left <br><br>"),
                                    HTML("<em>If data is not available, please check the NA box instead of inputting a value.</em> <br><br>"),
                                    actionButton("add_submit", "Submit")
                                ),
                                column(4, 
                                    numericInput("add_mw", "MW", value=0),
                                    checkboxInput("add_mw_na", "MW NA", value=0),
                                    numericInput("add_logp", "LogP", value=0),
                                    checkboxInput("add_logp_na", "LogP NA", value=0),
                                    numericInput("add_funbound", "Funbound plasma", value=0),
                                    checkboxInput("add_funbound_na", "Funbound plasma NA", value=0),
                                    numericInput("add_fgutabs", "Fgutabs", value=0),
                                    checkboxInput("add_fgutabs_na", "Fgutabs NA", value=0),
                                    textInput("add_pka_donor", "pKa donor", value=""),
                                    checkboxInput("add_pka_donor_na", "pKa donor NA", value=0),
                                    textInput("add_pka_accept", "pKa accept", value=""),
                                    checkboxInput("add_pka_accept_na", "pKa accept NA", value=0)
                                ),
                                column(4, 
                                    numericInput("add_clint", "Clint", value=0),
                                    checkboxInput("add_clint_na", "Clint NA", value=0),
                                    numericInput("add_kts", "KTS", value=0),
                                    checkboxInput("add_kts_na", "KTS NA", value=0),
                                    numericInput("add_fr", "FR", value=0),
                                    checkboxInput("add_fr_na", "FR NA", value=0),
                                    numericInput("add_vmax", "Vmax", value=0),
                                    checkboxInput("add_vmax_na", "Vmax NA", value=0),
                                    numericInput("add_km", "Km", value=0),
                                    checkboxInput("add_km_na", "Km NA", value=0)
                                )
                                
                                
                                
               ),
               conditionalPanel("input.use_add == 0", "Please select 'add compound' on the left to manually enter compound data'")
      ),
      tabPanel("Monte Carlo settings", 
        conditionalPanel("input.output_type == 'mc'", 
                       h3("Define Monte Carlo simulation parameters"),
                       # numericInput("nSimulations", "Number of draws", 50), #this has been superseeded by subpopulation generation
                       checkboxInput("mc_use_log", "Use log-normal distributions", 1),
                       h3("Coefficient of variation values: physio-bio parameters"),
                       fluidRow(
                       column(4,
                        sliderInput("cv.water",		'Total Body Water' 			, 0, 1, .3), 
                        sliderInput("cv.plasma",	    'Plasma Volume' 			, 0, 1, .3),
                        sliderInput("cv.cardiac",	'Cardiac Output' 			, 0, 1, .3),
                        sliderInput("cv.bw",		    'Average BW' 				, 0, 1, .16)
                       ), column(4,
                       sliderInput("cv.tpp", 		'Total Plasma Protein' 		, 0, 1, .14),
                       sliderInput("cv.albumin",	'Plasma albumin' 			, 0, 1, .1),
                       sliderInput("cv.a1agp",		'Plasma a-1-AGP' 			, 0, 1, .3),
                       sliderInput("cv.hematocrit", 'Hematocrit'				, 0, 1, .3)
                       ), column(4,
                       sliderInput("cv.urine",		'Urine' 					, 0, 1, .3),
                       sliderInput("cv.bile",		'Bile' 						, 0, 1, .3),
                       sliderInput("cv.gfr",		'GFR' 						, 0, 1, .3),
                       sliderInput("cv.abt",		'Average Body Temperature' 	, 0, 1, 0)
                       )
                       )
                       # h3("Additional variability on CLh parameter"),
                       # sliderInput("cv.clh", "CLh (log-normal CV)", 0, 1, 0)
          ),
          conditionalPanel("input.output_type == 'single'",
                           "Please select Monte Carlo mode on the left to define parameter variability.")
      ),
      tabPanel("results", 
        h3("PBTK model results"),
        fluidRow(
          column(4, htmlOutput("choose_plot_ui")),
          column(4, conditionalPanel("input.output_type == 'mc'", 
                                     sliderInput("display_ci", "Uncertainty interval", 
                                                 min = 0, max = 1, value = 0.95, step = .05))),
          column(4, htmlOutput("choose_plot_type_ui"))
        ),
        plotOutput("results_plot_single"),
        fluidRow(
            column(5, 
                h3("Cmax, AUC, Half-life calculations"),
                tableOutput("results_numerical")
                # plotOutput("results_plot")
            ),
            column(6,
                htmlOutput("validation_results")
            ))
        
        # uiOutput("results_plot_ui")
      )
    )
  ))
))