Abstract
The toxic equivalency factors (TEFs) approach for dioxin-like chemicals (DLCs) is currently based on a qualitative assessment of a heterogeneous data set of relative estimates of potency (REPs) spanning several orders of magnitude with highly variable study quality and relevance. An effort was undertaken to develop a weighting framework to systematically evaluate and quantitatively integrate the quality and relevance for development of more robust TEFs. Six main-study characteristics were identified as most important in characterizing the quality and relevance of an individual REP for human health risk assessment: study type, study model, pharmacokinetics, REP derivation method, REP derivation quality, and endpoint. Subsequently, a computational approach for quantitatively integrating the weighting framework parameters was developed and applied to the REP(2004) database. This was accomplished using a machine learning approach which infers a weighted TEF distribution for each congener. The resulting database, weighted for quality and relevance, provides REP distributions from >600 data sets (including in vivo and in vitro studies, a range of endpoints, etc.). This weighted database provides a flexible platform for systematically and objectively characterizing TEFs for use in risk assessment, as well as providing information to characterize uncertainty and variability. Collectively, this information provides risk managers with information for decision making.