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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 ( f   up  ) and intrinsic hepatic clearance ( Cl   int  ) were measured for rats (for 67 and 77 chemicals, respectively), combined with  f   up   and  Cl   int   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 AC 50  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.

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions