httk (version 2.7.2)

add_chemtable: Add a table of chemical information for use in making httk predictions.

Description

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.

Usage

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
)

Value

data.frame

A new data.frame containing the data in current.table augmented by new.table

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.

Author

John Wambaugh

Examples

# \donttest{
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")     
# }