from “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptionst”
Gregory S. Honda, Robert G. Pearce, Ly L. Pham, R. W. Setzer, Barbara A. Wetmore, Nisha S. Sipes, Jon Gilbert, Briana Franz, Russell S. Thomas, John F. Wambaugh
PLoS ONE 14(5): e0217564.
The authors thank James Armitage for providing a revised version of his model ( https://doi.org/10.1021/es501955g).
Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of high-throughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (fup) and intrinsic hepatic clearance (Clint) were measured for rats (for 67 and 77 chemicals, respectively), combined with fup and Clint literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC50 and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets-restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration-outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans.
This produces an updated version of the top panel of Figure 3 from the paper Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol. 2014, 48, 9770-9779.
armitage.dt <- copy(armitage_input) armitage.dt[,well_number:=384] %>% .[,option.bottom:=TRUE] %>% .[,option.plastic:=TRUE] %>% .[,Tsys:=37] %>% .[,Tref:=298.15] %>% .[,FBSf:=0.1] %>% .[,nomconc:=50] # Make sure we have data for the chemical: armitage.dt <- subset(armitage.dt,casrn%in%get_cheminfo()) # These chemicals are missing water solubilities: armitage.dt <- subset(armitage.dt,!(casrn%in%c("71751-41-2","34590-94-8","141517-21-7"))) armitage.dt2 <- armitage_estimate_sarea(tcdata = armitage.dt) armitage_output1 <- armitage_eval(tcdata = armitage.dt2[,ac50:=50]) armitage_output2 <- armitage_eval(tcdata = armitage.dt2[,ac50:=1]) armitage_output3 <- armitage_eval(tcdata = armitage.dt2[,ac50:=0.001])
armitage_output <- rbind(armitage_output1[,xfill:="50 \U00B5M"], armitage_output2[,xfill:="1 \U00B5M"], armitage_output3[,xfill:="1 nM"]) armitage_output[,xfill:=factor(xfill,levels=c("50 \U00B5M","1 \U00B5M","1 nM"))] ggplot(armitage_output) + geom_point(aes(x=gkow,y=log10(ccells/MW*1e9),color=xfill),shape=1) + labs(color=expression("AC"["50"]),x="log P", y = "log(ng chemical/g cells)") + theme_bw()