jfwambaugh
commented
2 years ago Depending on what you're trying to predict Css might capture most of what you want, since Css is proportional to 1/CLtot, and CLtot = Vd*kelim (for a 1 compartment model).
When you loop you want to use the get_cheminfo() function to identify the chemicals for which a given model will work, like get_cheminfo(model="1compartment").
So to get all the 1compartment parameters use this:
chem.descs <- NULL for (this.chem in get_cheminfo(model="1compartment")) chem.descs <- rbind(chem.descs,c(this.chem,unlist(parameterize_1comp(chem.cas=this.chem))))
To get just Css use: chem.descs <- NULL for (this.chem in get_cheminfo(model="1compartment")) chem.descs<- rbind(chem.descs,c(this.chem,calc_analytic_css(chem.cas=this.chem,model="1compartment")))
The main other descriptor I might want to know is ionization state in plasma (pH=7.4): chem.descs <- NULL for (this.chem in get_cheminfo(model="1compartment")) chem.descs<- rbind(chem.descs,c(this.chem,calc_ionization(chem.cas=this.chem, pH=7.4)))
PARODBE
Hi!
Nice to meet you, and I have a generic question. If you want incorporate toxicokinetic information to qsar models, I think that this tool is very useful, but in your opinion which could be the most interesting variables? Kel,Vd,Css,Cl,Fub? And in this case are there a function? because when I am running a loop for calc_tkstats for all compounds, I am obtaining:
Error in parameterize_pbtk(chem.cas = NULL, chem.name = c("2,2-bis(4-hydroxyphenyl)-1,1,1-trichloroethane (hpte)", : Missing metabolic clearance data for given species.
Thanks! Pablo