README.md

In shirewoman2/Consultancy: Standardized tools for using R within the Simcyp Consultancy Team

Overview

The SimcypConsultancy R package provides tools for analyzing and reporting PBPK data from the Simcyp Simulator. Our goals in making this package are to:

1. Increase accuracy and efficiency overall
2. Easily and accurately make informative tables from Simulator output
3. Create scientifically informative, professional-quality graphs
4. Perform additional analyses of PBPK data

Important: The SimcypConsultancy R package is separate from the Simcyp R package and serves a different purpose. The SimcypConsultancy package does not interact with the simulator and instead is focused on report-writing tasks.

Installation

You can install the SimcypConsultancy package from GitHub like this:

devtools::install_github(repo = "shirewoman2/Consultancy", 
                         upgrade = "never")

Current version: 3.8.9

NOTE: The SimcypConsultancy package requires that you have tidyverse loaded.

A great place to start for getting help:

launch_package_index()

How to use this document

You do not need to read this document from start to finish to follow it. Instead, please do skip around to only the parts that interest you.

TABLE OF CONTENTS

• Check how simulations were set up, e.g., for QCing
• Time savers for setting up workspaces
• Make concentration-time plots for one simulation at a time
• Make concentration-time plots comparing multiple data sets
• Make graphs of enzyme abundance
• Make nice-looking PK summary tables automatically
• Compare the results of two simulations
• Forest plots
• Checking simulation fidelity with simulated vs. observed graphs
• Fit induction data
• Compare demographics
• Create a table of model inputs
• Trial-means plots
• Make a simulation directory
• Automatically extract and graph sensitivity analysis data
• A list of what SimcypConsultancy functions do

We’re using example files in this document, but you generally can substitute whatever Simcyp Simulator files you’re working with instead of the examples. For help on any functions, type a question mark followed by the name of the function. For example, here’s how to get help on the ct_plot function:

?ct_plot

Let’s dive into some examples!

Check how simulations were set up, e.g., for QCing

As we mentioned, one of our goals was to increase accuracy. For this reason, several functions in the SimcypConsultancy package are set up for checking or reporting data in an automated fashion.

extractExpDetails example 1: Check on how all the simulations in a given folder were set up

First, we’ll extract data about how the simulations were set up from the simulation Excel results files and from the workspaces themselves.

Details <- extractExpDetails_mult(sim_data_files = NA, 
                                  exp_details = "all")

Now that we’ve got that information pulled into memory, we can save the data in an Excel file to allow for easy comparisons between simulations. Say you want to know some details about how the experiment was set up such as the start and end times of the simulation, what the substrates and inhibitors were and the dosing regimens. You can use the function “annotateDetails” to do this sort of task. Here’s how you’d get the first few rows with information on the trial design:

annotateDetails(Details, simulator_section = "Trial Design") %>%
   head() %>% formatTable_Simcyp(fontsize = 8)

extractExpDetails example 2: Narrow down the data to focus on one aspect of ADME, one particular compound, etc.

How about some information on what parameters were used for setting up absorption but only for the substrate? We’re again only going to show the first few rows of this, which is what “head() %>%” does in the code below.

annotateDetails(Details, 
                simulator_section = "Absorption",
                compoundID = "substrate") %>% 
   head() %>% formatTable_Simcyp(fontsize = 8)

To see all the possible experimental details, please see the file “All possible experimental details to use with extractExpDetails.csv” (Bold text is a link) or type this into the console:

 view(ExpDetailDefinitions)

For more options and examples, please see the help files for extractExpDetails, extractExpDetails_mult, and annotateDetails and please see “Checking-simulation-experimental-details.docx” (bold text is a link)

Return to TOC

Timesavers for setting up workspaces

If someone else set up some workspaces that they would like you to run but those workspaces included observed data XML files on OneDrive, the Simulator won’t be able to find that file unless you change the path to include your user name instead of the original person’s user name. Here’s how to quickly change all the paths for all the workspaces in a folder so that the Simulator can find the observed data files with your name in the path:

make_xml_path_mine()

That will permanently change the workspaces, so please make sure that’s what you want.

If you have run some simulations using the workflow tool in the Simulator, it will include a date/time stamp on the output file name. To remove those quickly, try this to remove that stamp from all the files in your current folder:

remove_file_timestamp()

The R Working Group has a few other tricks up our sleeves for changing workspace parameters quickly, but they do require some care since they permanently change workspace files and since only some parameters work well when changed in an automated fashion. If you find yourself needing to change something like the dose, the inhibition parameters, the induction parameters, etc. for a bunch of workspaces and don’t want to spend hours tediously opening, changing, saving, and closing them all manually, please talk to Laura Shireman.

Return to TOC

Make concentration-time plots for one simulation at a time

The function “ct_plot” will allow you to automatically graph almost any concentration-time data present in Simulator output Excel files. While this function has many options, the default settings will generally create decent graphs that comply with Consultancy Team report templates.

ct_plot example 1: Substrate data showing the arithmetic means for each simulated trial

Let’s make a graph of substrate concentration-time data in plasma, including some observed data, and let’s save the output as “My conc-time graph 1.png”.

First, extract the data from the Simulator output file and also from either the observed-data XML overlay file or the Excel PE template file. We’re using the function “extractConcTime” to extract the data and the function “ct_plot” to make the graphs.

LMV <- extractConcTime(sim_data_file = "letermovir MD.xlsx", 
                       obs_data_file = "Observed data files/letermovir obs data.xlsx")

ct_plot(ct_dataframe = LMV, 
        figure_type = "trial means", 
        save_graph = "My conc-time graph example 1.png")

ct_plot example 2: Zoom in on the last two doses

Let’s zoom in on the last two doses – doses 7 and 8 here – and change the figure type to get percentiles instead of trial means. We’ll save this file, too, and this time, let’s save it as a Word file so that we can get figure heading and caption data already filled in and ready for copying and pasting into a report. If you supply the output from the function “extractExpDetails_mult” here, you’ll get a more informative figure heading and caption. Any text that you’ll want to edit when you paste this into a report will be in bold.

ct_plot(ct_dataframe = LMV, 
        time_range = "doses 7 to 8", 
        figure_type = "percentiles", 
        existing_exp_details = Details, 
        save_graph = "My conc-time graph example 2.docx")

ct_plot example 3: Substrate data with vs. without a perpetrator drug

Next, let’s make a graph of substrate concentration-time data in plasma with and without the presence of a compound in the Inhibitor 1 position in the simulation. First, just like with the other graph, we’ll extract the data from our simulation Excel results file and then we’ll actually make the graph.

Let’s change the figure type to show only the mean predicted concentrations. Let’s include a legend, label it for the fact that our perpetrator molecule is an inducer and not an inhibitor (the default), and let’s only make the semi-log plot here. We’ll save the file so that it is 3 inches high and 5 inches wide. (All of these options are described in more detail in the example files in the folder “Concentration-time plots 1 - one sim at a time”.

MDZ <- extractConcTime(sim_data_file = "QD MDZ QD RIF.xlsx", 
                       obs_data_file = "Observed data files/QD MDZ QD RIF fake observed data.xlsx", 
                       existing_exp_details = Details)

ct_plot(ct_dataframe = MDZ, time_range = "last dose", 
        figure_type = "means only", 
        linear_or_log = "log",
        legend_position = "right",
        legend_label = "Inducer",
        save_graph = "My conc-time graph example 3.docx", 
        fig_height = 3, fig_width = 5)

For more options and examples, please see the help file for ct_plot (type ?ct_plot into the console) and see “Concentration-time-plot-examples-1.docx”.

Return to TOC

Make concentration-time plots comparing multiple data sets

You can also make concentration-time plots with multiple data sets overlaid for easy comparisons.

ct_plot_overlay example: Compare substrate concentrations in multiple tissues

First, let’s get some data. We’re using a variation on the function “extractConcTime” called “extractConcTime_mult” that allows us to pull data for multiple files, tissues, and compounds all at once.

CT <- extractConcTime_mult(
   sim_data_files = c(
      "Example simulator output SD MDZ plus BID RTV.xlsx"), 
   tissues = c("plasma", "blood", "unbound plasma"), 
   ct_dataframe = "CT",
   compoundsToExtract = c("substrate", "inhibitor 1"), 
   existing_exp_details = Details)

Next, graph those data, coloring the lines by whether the perpetrator was present and breaking up the graph into small multiples by the tissue and by what compound we’re plotting. Let’s give a sense of the variability of the data by showing transparent bands for the 5th to 95th percentiles. We’ll save this graph as “My overlaid conc-time graph example 1.docx”. Since we’re saving this as a Word file, we’ll again get some figure heading and caption text filled in.

ct_plot_overlay(ct_dataframe = CT, colorBy_column = Inhibitor,
                facet1_column = Tissue, 
                facet2_column = Compound, 
                figure_type = "percentile ribbon",
                color_set = "Set 1",
                save_graph = "My overlaid conc-time graph example 1.docx", 
                fig_width = 6, fig_height = 6)

For more options and examples, please the help file for ct_plot_overlay (type ?ct_plot_overlay into the console) and see “Concentration-time-plot-examples-3.docx”.

Return to TOC

Make graphs of enzyme abundance

You can apply most of the settings from the “ct_plot” function to another function, “enz_plot”, which will automatically make enzyme abundance plots for you. Just as with the ct_plot function, we’re first going to extract the data using a separate function, this one called “extractEnzAbund”.

For this graph, let’s include a legend, color the lines according to whether the perpetrator is present using the colors we want, note in the legend that the perpetrator is an inducer rather than the default inhibitor, and let’s save the output, too.

CYP3A4_liver <- extractEnzAbund(sim_data_file = "Example simulator output - MD MDZ MD EFV.xlsx", 
                                existing_exp_details = Details)

enz_plot(CYP3A4_liver,
         figure_type = "percentile ribbon", 
         line_type = "solid", 
         legend_position = "right", legend_label = "Inducer", 
         line_color = c("dodgerblue3", "darkorchid4"), 
         save_graph = "Enzyme abundance graph example.png", 
         fig_height = 3.5, fig_width = 6)

For more options and examples, please the help file for enz_plot (type ?enz_plot into the console) and see “Enzyme-abundance-plot-examples.docx”.

Return to TOC

Make nice-looking PK summary tables automatically

One of the tasks we would like to automate and make less prone to copy/paste errors is creating tables for reports that summarize simulated PK parameters. The function pk_table does that.

pk_table example 1: Create a table of standard PK parameters for a simulation of a substrate alone

For this, we’ll use a file from a hands-on workshop demonstration with letermovir.

pk_table(
   sim_data_file = "letermovir MD.xlsx", 
   existing_exp_details = Details) %>%
   formatTable_Simcyp()

If you save the output to a Word file, which is what we recommend, you will automatically get a table heading and caption, which will include information about which compound, tissue, and simulation was used to obtain the data, so the table will only include the columns “Statistic”, “AUCtau” and “Cmax”.

pk_table example 2: Adjust which summary stats are included and how they’re formatted

The default setting is to list geometric means and CVs, but you can see arithmetic instead by specifying that with “mean_type”. Additionally, let’s see the range of trial means (includeTrialMeans = TRUE) but not the CVs (includeCV = FALSE).

pk_table(
   sim_data_file = "letermovir MD.xlsx",
   mean_type = "arithmetic",
   includeTrialMeans = TRUE,
   includeCV = FALSE, 
   existing_exp_details = Details, 
   checkDataSource = FALSE) %>% formatTable_Simcyp()

pk_table example 3: Create a table of standard PK parameters for a DDI simulation

If a perpetrator molecule was included, the table will automatically pull parameters sensible for that scenario. This time, let’s additionally ask to check the data source to check where in the simulation Excel results file the data are from. First, here’s the table:

MyTable <- pk_table(
   sim_data_file = "QD MDZ QD RIF.xlsx", 
   existing_exp_details = Details, 
   checkDataSource = TRUE)

MyTable$Table %>% formatTable_Simcyp()

Now, to see where those parameters came from, let’s look at the other item that got included in our output when we said “checkDataSource = TRUE”.

MyTable$QC %>% formatTable_Simcyp(fontsize = 8)

This is a data.frame listing the files, tabs, columns, and rows where the data were found for the purposes of QCing.

For more options and examples, please see the help files for pk_table and see “PK-tables.docx”.

Return to TOC

Compare the results of two simulations

If you are interested in comparing the results from two simulations, e.g., comparing fasted vs. fed or healthy subjects vs. subjects with hepatic impairment, please see the instruction file “Calculating-PK-ratios.docx”

This section under construction. We’ll add examples here soon.

Return to TOC

Forest plots

There are several options for setting up forest plots; here is one example using example data included in the package: BufForestData_20mg.

forest_plot(forest_dataframe = BufForestData_20mg,
            y_axis_labels = Inhibitor1,
            y_axis_title = "Perpetrator",
            graph_title = "Effects of various DDI perpetrator\ndrugs on bufuralol PK", 
            show_numbers_on_right = TRUE, 
            rel_widths = c(3, 1), 
            save_graph = "Bufuralol forest plot.png", 
            fig_height = 6, fig_width = 7.25)

For more information, please see the instruction file “Examples-for-making-forest-plots.docx”

Return to TOC

Checking simulation fidelity with simulated vs. observed graphs

One quick way to check how well your model is capturing observed data is to visually compare simulated PK values to observed. The function so_graph will use input from making PK tables (from the function pk_table) to create these graphs, including making Guest-style graphs when the PK parameter in question is a ratio for a drug-drug interaction.

so_graph(PKtable = SOdata, 
         axis_title_y = "Predicted", 
         axis_title_x = "Observed")

For more information, please see the instruction file “Examples-for-simulated-vs-observed-graphs.docx”

Return to TOC

Fit induction data

The SimcypConsultancy package function inductFit can fit four possible induction models to in vitro induction data:

Equation 1. Emax model

$$ \text{fold induction} = 1 + \frac{E_{max} \times I}{EC_{50} + I} $$

where I is the concentration of the inducer, Emax is the maximum change in fold induction, and EC50 is the concentration of the inducer that elicits half the maximum fold-change in induction.

Use model = "Emax" in the inductFit call to fit this model to your data.

Equation 2. Emax slope model

$$ \text{fold induction} = 1 + E_{max} \times \frac{I^\gamma}{EC_{50}^\gamma + I^\gamma} $$

where I is the concentration of the inducer, Emax is the maximum change in fold induction, EC50 is the concentration of the inducer that elicits half the maximum fold-change in induction, and gamma is the Hill equation coefficient describing the slope.

Syntax for function: model = "EmaxSlope"

Equation 3. Slope model

$$ \text{fold induction} = 1 + I \times n $$

where I is the concentration of the inducer and n is the slope.

Syntax for function: model = "slope"

Equation 4. Sigmoidal three-parameter model

$$ \text{fold induction} = \frac{Ind_{max}}{1 + e^{ \frac{IndC_{50}-I}{\gamma}}} $$

where I is the concentration of the inducer, Indmax is the maximum fold induction, IndC50 is the concentration of the inducer that elicits half the maximum fold induction, and gamma is the Hill equation coefficient describing the slope.

Syntax for function: model = "Sig3Param"

Important note: The sigmoidal three-parameter model is the ONLY model that determines Indmax rather than Emax. The Simcyp Simulator uses Indmax, so please note that Indmax = Emax + 1

inductFit example 1: Fit 4 models to induction data using the default settings

Let’s fit some induction data to each of 4 possible models: Indmax, Indmax slope, slope, or sigmoid 3 parameter. For details on what these are, please see the instructions fitting induction data.

For now, we’ll call on some pre-existing dummy data we made up for the SimcypConsultancy package called IndData.

We’ll make an object “MyIndFits” to store the output and then look at that output one piece at a time.

MyIndFits <- inductFit(IndData, 
                       conc_column = Concentration_uM, 
                       fold_change_column = FoldInduction, 
                       model = "all", 
                       include_fit_stats = FALSE, 
                       rounding = "significant 3")

Now that we have run the function, let’s see the graphs.

MyIndFits$Graph

You can adjust the look of the graphs; please see the help file or the example Word document for examples.

Let’s see the fitted parameters:

MyIndFits$Fit %>% formatTable_Simcyp()

Return to TOC

Compare demographics

If you’re interested in seeing how the demographics of a population vary – either for comparing multiple simulated populations or a simulated population to an observed population – there are a couple of data visualization tools in the package to help you do that. We’ll focus here on the one for comparing multiple simulated populations, and we’ll compare healthy subjects to subjects with varying degrees of liver disease.

First, we’ll extract demographic data from our simulations. All of these included the “Demographic Data” tab in the results, which is where R is looking for demographic information. If you don’t include that in your results, this will not work.

Demog <- extractDemog(sim_data_files = c("mdz-5mg-sd-hv.xlsx", 
                                         "mdz-5mg-sd-cpa.xlsx", 
                                         "mdz-5mg-sd-cpb.xlsx", 
                                         "mdz-5mg-sd-cpc.xlsx"), 
                      demog_dataframe = Demog)

Here’s an example of the kind of visual comparisons we can make, focusing on just two parameters: a boxplot comparing liver weights and a scatter plot of height vs. overall body weight.

demog_plot_sim(demog_dataframe = Demog, 
               colorBy_column = File, 
               color_labels = c("mdz-5mg-sd-hv.xlsx" = "healthy", 
                                "mdz-5mg-sd-cpa.xlsx" = "Child-Pugh A",
                                "mdz-5mg-sd-cpb.xlsx" = "Child-Pugh B",
                                "mdz-5mg-sd-cpc.xlsx" = "Child-Pugh C"), 
               color_set = "greens", 
               demog_parameters = c("LiverWt_g", 
                                    "Height vs weight"), 
               variability_display = "boxplot", 
               ncol = 1, 
               save_graph = "Demographics - hepatic impairment.png", 
               fig_height = 6, fig_width = 5)

Note: We’re working on the legend but having some trouble with it repeating things at the moment.

For more information and examples, please see Examples-for-checking-subject-demographics.docx

Return to TOC

Create a table of model inputs

Under construction. See the help file for make_Simcyp_inputs_table.

Return to TOC

Trial-means plots

Under construction. See the help file for trial_means_plot and see the instruction file “Trial-means plots.docx”.

Example:

ObservedCmax <- data.frame(PKparameter = "Cmax_dose1", 
                           ObsValue = c(20, 22.5, 16), 
                           ObsVariability = c("10 to 29", "18-25", "12-30"), 
                           Study = c("Kolev et al., 2025", 
                                     "Dinh et al., 2023", 
                                     "Thakur et al., 2020"))

trial_means_plot(sim_data_file = "mdz-5mg-sd.xlsx", 
                 color_option = "by study", 
                 color_set = "viridis", 
                 point_shape = 16, 
                 y_axis_limits_lin = c(0, 60), 
                 observed_PK = ObservedCmax,
                 legend_position = "bottom",
                 save_graph = "Cmax trial means plot.png", 
                 fig_height = 5, fig_width = 6)

Return to TOC

Make a simulation directory

Under construction. See the help file for make_simulation_directory.

Example:

make_simulation_directory(existing_exp_details = Details, 
                          save_table = "simulation directory.xlsx")

Return to TOC

Automatically extract and graph sensitivity analysis data

sensitivity_plot example 1: Make an SA graph showing the effect of your independent variable on Cmax

We’ll supply the Excel file with the SA data to the argument “SA_file”, we’ll tell R which dependent variable we want to see (Cmax), give the graph a title (the “\n” bit is how we can insert a manual carriage return), and then specify whether to save the graph by supplying a file name to “save_graph”.

sensitivity_plot(SA_file = "SA on fa - mdz 5mg sd.xlsx",
                 dependent_variable = "Cmax", 
                 graph_title = "Sensitivity analysis:\nfa effect on Cmax", 
                 save_graph = "SA graph.png")

Other dependent variables are also possible; please see the help file for all options.

sensitivity_plot example 2: Make an SA graph with CL instead and also set the x-axis title manually

Here’s the same sensitivity analysis file but looking at oral clearance and setting something manually for the independent variable label on the x axis.

sensitivity_plot(SA_file = "SA on fa - mdz 5mg sd.xlsx",
                 dependent_variable = "CLpo", 
                 ind_var_label = "fa for the substrate")

sensitivity_plot example 3: Make an SA graph of plasma concentrations

And here is an example of plotting plasma concentrations from a sensitivity-analysis file:

sensitivity_plot(SA_file = "SA on fa - mdz 5mg sd.xlsx",
                 dependent_variable = "plasma", 
                 linear_or_log = "both vertical", 
                 time_range = c(0, 12))

Please see the help file for more information by typing ?sensitivity_plot into the console.

Return to TOC

A list of what SimcypConsultancy functions do

Below are descriptions of all the functions in the package, and the functions most users will want to use most frequently are in bold and listed first in each section.

• Get help on using the SimcypConsultancy package
• Get information about your simulations
• PK tables and PK calculations
• Other tables you can make
• Concentration-time plots
• Forest plots
• Fit induction models to in vitro data
• Evaluating model fidelity
• Examining variability between trials
• Comparing demographics
• Other data visualizations that can help you analyze your data
• Interact with the Simulator or with workspaces
• Helper functions for manipulating typical SimcypConsultancy objects
• Miscellany

Get help on using the SimcypConsultancy package

• launch_package_index() - Launch the shiny app for showing an annotated index of what the SimcypConsultancy package can do
• make_example_PK_input() - Examples for supplying PK parameters to the pk_table or calc_PK_ratios functions for the argument PKparameters
• show_code_example() - IN PROGRESS - Show some examples for the code to use for making PK tables or concentration-time plots

Get information about your simulations

• extractExpDetails_mult() - Extract experimental details for multiple files at once
• annotateDetails() - Annotate Simcyp Simulator experimental details
• qc_sims() - Make an Excel file for QCing simulations
• extractExpDetails() - Extract details about the experimental design
• extractExpDetails_DB() - Extract simulation experimental details from a database file - UNDER CONSTRUCTION!!!!!
• extractExpDetails_XML() - Extract experimental details for multiple Simcyp Simulator workspace files at once

PK tables and PK calculations

Make typical PK tables

• pk_table() - Make tables of PK values from Simulator output Excel files

Make PK comparisons not natively available in the Simulator outputs

• calc_PK_ratios_mult() - Calculate the ratio of PK parameters between multiple pairs of simulations
• calc_PK_ratios() - Calculate the ratio of PK parameters between two simulations

Helper functions for PK tables

• convert_units() - Convert concentration and time units as needed
• extractPK() - Extract PK data for specific parameters from a simulator output Excel file
• extractPK_DB() - Extract PK from a Simcyp Simulator database file – UNDER CONSTRUCTION!!!!

Calculate PK

• calc_PK() - Calculate basic PK parameters for concentration-time data
• recalc_PK() - Recalculate the PK for specific concentration-time profiles after running calc_PK
• elimFit() - Calculate the elimination rate(s) for a concentration-time curve
• trapRule() - Calculate the AUC using the trapezoidal rule
• noncompAUC() - Calculate the AUC using the trapezoidal rule

Other tables you can make

• make_simulation_directory() - Make a directory of simulations indicating which simulation file is located in which folder
• insert_copyright() - Insert the standard Certara UK Limited copyright box that appears in compound summary files - Yes, this is actually a table, even though it just looks like a box.
• make_Simcyp_inputs_table() - Summarize a PBPK model using parameters pulled from simulations - UNDER CONSTRUCTION
• make_trial_design_table() - Create a table describing a simulation trial design.

Concentration-time plots

Make concentration-time plots or variations thereof

• ct_plot() - Concentration-time plots to match Consultancy template
• ct_plot_overlay() - Overlay multiple data sets onto a single concentration-time graph
• enz_plot() - Plots of enzyme abundance changes over time to match Consultancy template
• enz_plot_overlay() - Overlay multiple data sets onto a single enzyme-abundance graph
• runShiny() - Launch the shiny app for making PK summary tables and concentration-time plots
• ct_plot3() - Concentration-time plots of the full time range, first dose, and last dose
• ct_plot_1stlast() - Make concentration-time plots of the 1st and last doses only
• ct_plot_mult() - Make graphs for multiple Simulator output files at once
• ct_plot_obs() - Plot observed concentration-time data
• fm_plot() - Make a plot of dynamic fm and fe values

Helper functions for concentration-time plots

• extractConcTime_mult() - Pull concentration-time data from multiple Simcyp Simulator output files
• extractConcTime() - Extract concentration-time data from a simulator output Excel file
• extractConcTime_DB() - Extract concentration-time data from a Simcyp Simulator database file – UNDER CONSTRUCTION!!!
• extractEnzAbund() - Extract enzyme abundance data from a simulator output Excel file
• extractEnzAbund_mult() - Pull enzyme-abundance data from multiple Simcyp Simulator output files
• extractFmFe() - Extract fm and fe valuesthat change with time from a Simulator output Excel file
• match_obs_to_sim() - Match observed concentration-time data to the correct simulated concentration-time data
• extract_fu() - Extract fu,plasma values that change with time from a Simulator output Excel file. UNDER CONSTRUCTION!

Make observed concentration-time plots

• ct_plot_obs() - Plot observed concentration-time data

Observed concentration-time plot helper functions

• create_doses() - Create a csv file of dosing rows for XML files
• create_doses_from_file() - Create a csv file of dosing rows for XML files using a study file to get subject information
• clean_obs_data() - Tidy up messy observed concentration-time data. UNDER CONSTRUCTION!!!
• extractObsConcTime() - Extract observed concentration-time data from an Excel file
• extractObsConcTime_mult() - Extract observed concentration-time data from multiple Excel files
• extractObsConcTime_xlsx() - INTERNAL - Extract observed conc-time data from an xlsx file
• extractObsConcTime_XML() - INTERNAL - Extract observed conc-time data from an xml file
• format_obs_for_XML() - Format generic observed data for making an XML overlay file

Forest plots

• forest_plot() - Create a forest plot
• extractForestData() - Extract pertinent data from Simulator output files for creating forest plots

Fit induction models to in vitro data

• inductFit() - Fit induction data to calculate EC50, Emax, and/or slope

Evaluating model fidelity

• so_graph() - Graph of simulated vs. observed PK

Examining variability between trials

• trial_means_plot() - Make graphs comparing center statistic and variability in PK across trials and, optionally, against observed PK data as well

Comparing demographics

• demog_table() - Make a table of demographics for a set of simulations
• demog_plot_sim() - Make plots for comparing populations across simulations
• demog_plot_SO() - Make a set of plots showing the demographics used in a simulation and optionally compare them to observed demographics. UNDER CONSTRUCTION.
• extractDemog() - Extract simulated demographic data from the Demographic Data tab of a Simcyp Simulator ouptut Excel file

Other data visualizations that can help you analyze your data

• sensitivity_plot() - Make graphs of sensitivity analysis results
• checkSS() - Create a graph of simulated concentrations to check for whether a perpetrator drug is at steady-state when the victim drug is dosed
• fm_treemap() - For comparing fm values, make a treemap as opposed to the standard and arguably inferior pie chart
• dissolution_profile_plot() - Make a graph of the dissolution profiles of a compound
• graph_boxplot() - Make boxplots or boxplots overlaid with individual points
• ontogeny_plot() - Graph the ontogenies of drug-metabolizing enzymes and transporters
• release_profile_plot() - Make a graph of the release profiles of a compound

Helper functions for graphing

• blues() - Create a vector of blues
• blueGreens()- Create a vector of blues fading into greens
• purples() - Create a vector of purples
• chartreuse() - Create a vector of yellow-greens
• rainbow() - Create a vector of a rainbow of colors
• SimcypColors() - Simcyp colors used in the PowerPoint template
• log10_breaks() - Create nice breaks for graphs of log-transformed data
• make_log_breaks() - Make pretty breaks and labels for a log axis
• theme_consultancy() - Apply the standard Consultancy Team graphing styles to your graph
• scale_x_time() - Automatically scale a ggplot graph x axis for time

Interact with the Simulator or with workspaces

Read or change workspaces

NB: Most functions that actually change workspaces are only available in the beta version of the package.

• make_xml_path_mine() - Make the user name in the path of observed data overlay XML files and fixed trial design XML files match that of the current user
• remove_file_timestamp() - Remove date/time stamps from Excel results files created by the Autorunner
• change_wksz_interactions() - Change interaction parameters in Simcyp Simulator workspace files
• change_wksz_trial_design() - Change a limited set of trial-design parameters in Simcyp Simulator workspace files. UNDER CONSTRUCTION.
• change_xml_path() - Set the user name in the path of observed data overlay XML files and fixed trial design XML files
• detect_file_timestamp() - Figure out what date/time stamps on Excel results files would be removed by the function remove_file_timestamp

Helper functions for manipulating typical SimcypConsultancy objects

• add_sims() - Add simulations to an R object
• filter_sims() - Selectively include or omit specific simulations from an R object
• sort_sims() - Sort the simulation file order in an object

Miscellany

Formatting and saving tables

• formatTable_Simcyp() - Format tables according to Simcyp Consultancy Team specifications, e.g.,
• format_table_simple() - Format a table rather simply to look nice in a Word file
• save_table_to_Word() - Save a bespoke PK table to Word using the pksummary_mult rmarkdown template

Helper functions that call on the Simcyp package

• check_simulator_initialized() - check whether the Simcyp Simulator has been initialized

Getting data from a pdf table

• pdf_to_csv() - Convert a page from a pdf file to a csv file

Examining DDIs

• list_interactions() - Find all the possible drug-drug interactions between compounds included in a single simulation

Tidbits for making quick calculations

• calcKi() - Calculate the Ki of an inhibitor with the Cheng-Prusoff equation
• centerBin() - Cut a set of numeric data into bins based on the middle value of the bin
• check_doseint() - Check whether the AUC interval matches the dosing interval and create warnings if not
• check_file_name() - Check whether the simulation file names comply with regulatory requirements for submitting PBPK data
• countWords() - Useful for guessing at good widths for columns in Excel output
• cutNumeric() - Cut numeric data into bins with vector output
• dateConv() - Convert dates from numbers to Date
• difftime_sim() - Calculate the time difference between two times formatted as in simulator output
• getReferences() - Pull references from a DataRec folder
• gm_mean() - Calculate the geometric mean. Related: gm_conf, gm_CV, gm_sd, confInt.
• rounding
• Several functions for rounding, including round_consultancy, round_up, round_down, round_down_unit, etc.
• timeConv() - Convert times from numbers to POSIXct
• str_comma() - Collapse a vector of character strings into one readable string, separated by commas and by “and” and “or”

Internal functions you’ll probably never need to use but listed just in case you’ve seen them and are curious about what they do

• addObsPoints() - INTERNAL PACKAGE USE: Add observed data points to a concentration-time plot
• calc_dosenumber() - Calculate the dose number for each time in a set of concentration-time data given the experimental details for that simulation
• check_include_dose_num() - INTERNAL PACKAGE USE ONLY. Check on whether to include the dose number in the PK table headings
• ct_x_axis() - Set up x axis in a conc-time plot
• ct_y_axis() - Set up y axis in a conc-time plot
• deannotateDetails() - De-annotate a data.frame of experimental details that was previously annotated
• determine_myperpetrator() - Determine the (optionally pretty) name of all perpetrators included in a simulation, all concatenated nicely
• eCT_harmonize() - Harmonize the compound names on sim_data_xl to made finding the correct rows easier later.
• eCT_pulldata() - Pull the appropriate data from the harmonized Excel tab
• eCT_readxl() - Read into memory an Excel sheet for extracting conc-time data.
• extractAUCXtab() - FOR INTERNAL USE ONLY: Extract PK data from a generic AUC tab, e.g., NOT one formatted like the AUC tab from Simcyp V21 and earlier
• format_scripts() - Format common PBPK model parameters to have appropriate subscripts and superscripts. UNDER CONSTRUCTION.
• guess_col_widths() - INTERNAL guess appropriate column widths
• harmonize_details() - FOR INTERNAL PACKAGE USE. Take as input an existing data.frame or list of experimental details and make it be a list with standard items
• harmonize_PK_names() - Harmonize user input for PK parameters
• make_color_set() - Create a set of colors for use in graphs
• make_ct_caption() - INTERNAL - make caption for conc-time plots
• make_table_annotations() - Make table headings and captions for use internally when saving PK tables to Word
• make_text_legos() - INTERNAL - Assemble pieces of text for headings, captions and trial design descriptions
• move_asterisks() - INTERNAL - Move asterisks around so that you can put asterisks around that string and things will be italicized appropriately
• pk_table_subfun() - Subfunction for pk_table. FOR INTERNAL PACKAGE USE.
• prettify_column_names() - Convert the column names in a table of PK parameters into more human-readable names
• prettify_compound_name() - Make a compound name prettier and more human readable
• renameStats() - Rename the statistics listed in Simulator output to R-friendly versions
• round_opt() - INTERNAL: Rounding function
• scale_x_time_setup() - Get the information needed to automatically scale a ggplot graph x axis for time
• set_aesthet() - Set up certain aesthetics a conc-time plot
• set_boundary_colors() - INTERNAL: Create a set of boundary colors based on the color set and the boundaries the user needs.
• tidy_input_PK() - Tidy input PK data as needed
• tidy_PKparameters_names() - INTERNAL USE. tidy the column names of PKparameters supplied to, e.g., pk_table.
• tidyPop() - Tidying Simcyp simulator population names for reports, etc.
• wrapn() - INTERNAL PACKAGE USE. Wrap warning text and add a carriage return at the end.

Final notes

If you encounter a bug, if you would like help using any of these functions, or if there’s an additional feature you would like to have, please contact a member of the R Working Group.

shirewoman2/Consultancy documentation built on Feb. 18, 2025, 10 p.m.