文件列表:

ACD_labs.R (6414, 2022-09-28)
Additional_data.R (7957, 2022-09-28)
Dermal_WorkspaceMechKim.wksz (1008219, 2022-09-28)
Dermal_WorkspaceNonMechkim.wksz (1007566, 2022-09-28)
IVBolus_WorkspaceMechKim.wksz (1007551, 2022-09-28)
IVBolus_WorkspaceNonMechkim.wksz (1007855, 2022-09-28)
Oral_WorkspaceMechKim.wksz (1008794, 2022-09-28)
Oral_WorkspaceNonMechkim.wksz (1008469, 2022-09-28)
PredictParams.R (2387, 2022-09-28)
R Workflow.R (30260, 2022-09-28)
additional_functions.R (12023, 2022-09-28)
app.R (105810, 2022-09-28)
chembl_search.R (2189, 2022-09-28)
data_files (0, 2022-09-28)
data_files\Exemplar_Compound_File.xlsx (22291, 2022-09-28)
data_files\Exemplar_Experimental_file.xlsx (14090, 2022-09-28)
data_files\Norman_susdat.zip (9342784, 2022-09-28)
data_files\chembl_29_sqlite (1, 2022-09-28)
experimental_data_search.R (10321, 2022-09-28)
httk_search.R (24905, 2022-09-28)
input_query.R (2113, 2022-09-28)
organise_simulation_data.R (12922, 2022-09-28)
placeholder.R (14499, 2022-09-28)
plotting_functions.R (11496, 2022-09-28)
susdat_search.R (11002, 2022-09-28)

# SimRFlow A high throughput workflow comprising data collection and simulation of compounds using Certara's Simcyp Simulator. # The following is an example use of SimRFlow functions ```bash #set the working directory to the working directory of the scripts setwd(dirname(rstudioapi::getSourceEditorContext()$path)) ``` ```bash #import the scripts source('input_query.R') source('chembl_search.R') source('susdat_search.R') source('ACD_Labs.R') source('httk_search.R') source('experimental_data_search.R') source('organise_simulation_data.R') source('R Workflow.R') source('PredictParams.R') ``` ```bash #set the file directory of the compound file file_dir <- 'data_files/compound_file.xlsx' ``` ```bash #preprocess the compound file, extract only columns of interest data <- ProcessInputs(file_dir) ``` ```bash #query the chembl database chembl_data <- CHEMBLSearch(data) ``` ```bash #determine the compounds not found in chembl nf_in_chembl <- CompoundsNotFound(data, chembl_data) ``` ```bash #query the Norman suspect database for the compounds not found in Chembl sus_data <- SusdatSearch(data, nf_in_chembl, chembl_data) ``` ```bash #determine the compounds not found in the Norman suspect list NOT_FOUND <- NotFoundInsusdat(nf_in_chembl, sus_data) ``` ```bash ### This step can be skipped if users do not wish to query httk as part of their workflow #extract CAS numbers and DTXSID values to query the httk database CAS_DTXSID <- CAS_and_DTXSID(data) ``` ```bash ### This step is for users who wish to upload additional data ACD_data_directory<- 'data_files/acd_output.xls' acd_data<-ACD_inputs(data,nf_in_chembl,sus_data,missing_info=T) physchem_data <- ACD_outputs(data,ACD_data_directory,sus_data) ``` ```bash ### For users who do not have additional data #physchem_data <- sus_data ``` ```bash #search httk library for experimental data using CAS and DTXSIDs httk_data <- httkSearch(physchem_data, CAS_DTXSID, data) ``` ```bash ### For users who have additional experimental data experimental_data_directory<-'data_files/experimental_data.xlsx' ``` ```bash #incorporate experimental data with data from httk httk_exp_data <- ExpDataSearch(httk_data, experimental_data_directory, CL_threshold = 3.8) ``` ```bash #organise the data in preparation for running it through Simcyp organised_data <- OrganiseInputData(httk_data,info = info, admin_route = 'IV Bolus') ```bash #use the prediction module of SimRFlow to predict fu, BP, Vss and Kd predictions<- PredictParameters(organised_data) ``` ```bash #run the simulations for each compound in bulk output_profiles<-SimcypSimulation(organised_data, trials = 1, subjects = 5, Time = 24) ``` ```bash #extract additional outputs (which will be used for plotting) simcyp_outputs <- AdditionalOutputs(organised_data) ``` ```bash #summarise outputs into human-readable table containing simulated compounds summary_simcyp <-SummaryOutputs(simcyp_outputs) ``` ```bash #plot concentration-time profiles. Units can be either 'uM' or 'ng/mL'. plot_profile('CompoundID', output_profiles, curated_data = httk_data, units = 'uM', logy=F) ``` ```bash #plot a scatter plot to see the trend between two simulated parameters plot_parameters(simcyp_outputs, 'CompoundID', plot_type = 'Relationship', 'BW', 'Tmax', 'blue') ``` ```bash #plot a distribution plot to see variation of a parameter across a populationf for a given compound plot_parameters(simcyp_outputs, 'CompoundID', plot_type = 'Distribution', 'Age', 'Tmax', 'blue') ``` ```bash #create a chart to compare a given parameter across simulated compounds compare_simulated_compound(summary_simcyp,'Vss','compound_code','salmon') ```

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