# Add a table of chemical information for use in making httk predictions. This function adds chemical-specific information to the table chem.physical_and_invitro.data. This table is queried by the model parameterization functions when attempting to parameterize a model, so adding sufficient data to this table allows additional chemicals to be modeled. ```r add_chemtable( new.table, data.list, current.table = NULL, reference = NULL, species = NULL, overwrite = FALSE, sig.fig = 4, clint.pvalue.overwrite = TRUE, allow.na = FALSE, suppress.messages = FALSE ) ``` ## Arguments - `new.table`: Object of class data.frame containing one row per chemical, with each chemical minimally described by a CAS number. - `data.list`: This list identifies which properties are to be read from the table. Each item in the list should point to a column in the table new.table. Valid names in the list are: 'Compound', 'CAS', 'DSSTox.GSID' 'SMILES.desalt', 'Reference', 'Species', 'MW', 'logP', 'pKa_Donor', 'pKa_Accept', 'logMA', 'Clint', 'Clint.pValue', 'Funbound.plasma', 'Fabs', 'Fgut', 'Rblood2plasma'. - `current.table`: This is the table to which data are being added. - `reference`: This is the reference for the data in the new table. This may be omitted if a column in data.list gives the reference value for each chemical. - `species`: This is the species for the data in the new table. This may be omitted if a column in data.list gives the species value for each chemical or if the data are not species-specific (e.g., MW). - `overwrite`: If overwrite=TRUE then data in current.table will be replaced by any data in new.table that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way. - `sig.fig`: Sets the number of significant figures stored (defaults to 4) - `clint.pvalue.overwrite`: If TRUE then the Cl_int p-value is set to NA when the Cl_int value is changed unless a new p-value is provided. (defaults to TRUE) - `allow.na`: If TRUE (default is FALSE) then NA values are written to the table, otherwise they are ignored. - `suppress.messages`: Whether or not the output messages are suppressed. ## Returns - **data.frame**: A new data.frame containing the data in current.table augmented by new.table ## Examples ```r library(httk) # Number of chemicals distributed with the package: num.chems <- length(get_cheminfo()) fake <- data.frame(Compound="Tester", CASRN="222-11-1", DTXSID="DTX111222", MW=200, logP=3.5, Fup=0.1, Clint=0.1, Clint.pValue=0.001,stringsAsFactors=FALSE) chem.physical_and_invitro.data <- add_chemtable( fake, current.table=chem.physical_and_invitro.data, data.list=list( Compound="Compound", CAS="CASRN", DTXSID="DTXSID", MW="MW", logP="logP", Funbound.plasma="Fup", Clint="Clint", Clint.pValue="Clint.pValue"), species="Human", reference="Fake") calc_css(chem.name="Tester") #load_sipes2017() # We should have the ADMet Predicted chemicals from Sipes et al. (2017), # this one is a good test since the logP is nearly 10! #calc_css(chem.cas="26040-51-7") #Let's see how many chemicals we have now with the Sipes (2017) data loaded)=: #length(get_cheminfo()) #Now let's reset reset_httk() # We should be back to our original number: num.chems == length(get_cheminfo()) # Now add chemicals A, B, and C: my.new.data <- as.data.frame(c("A","B","C"),stringsAsFactors=FALSE) my.new.data <- cbind(my.new.data,as.data.frame(c( "111-11-2","222-22-0","333-33-5"), stringsAsFactors=FALSE)) my.new.data <- cbind(my.new.data,as.data.frame(c("DTX1","DTX2","DTX3"), stringsAsFactors=FALSE)) my.new.data <- cbind(my.new.data,as.data.frame(c(200,200,200))) my.new.data <- cbind(my.new.data,as.data.frame(c(2,3,4))) my.new.data <- cbind(my.new.data,as.data.frame(c(0.01,0.02,0.3))) my.new.data <- cbind(my.new.data,as.data.frame(c(0,10,100))) colnames(my.new.data) <- c("Name","CASRN","DTXSID","MW","LogP","Fup","CLint") chem.physical_and_invitro.data <- add_chemtable(my.new.data, current.table= chem.physical_and_invitro.data, data.list=list( Compound="Name", CAS="CASRN", DTXSID="DTXSID", MW="MW", logP="LogP", Funbound.plasma="Fup", Clint="CLint"), species="Human", reference="MyPaper 2015") parameterize_steadystate(chem.name="C") calc_css(chem.name="B") # Initialize a column describing proton donors ("acids") my.new.data$pka.a <- NA # set chemical C to an acid (pKa_donor = 5): my.new.data[my.new.data$Name=="C","pka.a"] <- "5" chem.physical_and_invitro.data <- add_chemtable(my.new.data, current.table= chem.physical_and_invitro.data, data.list=list( Compound="Name", CAS="CASRN", DTXSID="DTXSID", pKa_Donor="pka.a"), species="Human", reference="MyPaper 2015") # Note Rblood2plasma and hepatic bioavailability change (relative to above): parameterize_steadystate(chem.name="C") # Initialize a column describing proton acceptors ("bases") my.new.data$pka.b <- NA # set chemical B to a base with multiple pka's (pKa_accept = 7 and 8): my.new.data[my.new.data$Name=="B","pka.b"] <- "7;8" chem.physical_and_invitro.data <- add_chemtable(my.new.data, current.table= chem.physical_and_invitro.data, data.list=list( Compound="Name", CAS="CASRN", DTXSID="DTXSID", pKa_Accept="pka.b"), species="Human", reference="MyPaper 2015") # Note that average and max change (relative to above): calc_css(chem.name="B") ``` ## Author(s) John Wambaugh

add_chemtable function