Framework for integrating human and animal data in chemical risk assessment

Abstract

Although regulatory agencies formally encourage the integration of all available data in chemical risk assessment, consistent implementation of this practice has been constrained by the lack of a clear, systematic method for doing so. In this paper, we describe a methodology for evaluating, classifying and integrating human and animal data into the risk assessment process that incorporates: (1) a balanced appraisal of human and animal data, (2) relevance to different stages of the risk assessment process, and (3) accommodation for different data quality requirements. The proposed framework offers a flexible, step-wise approach for determining which set of available data best support the chemical risk assessment that involves the rating and relative ranking of human and animal data quality. The evaluation of human data incorporates seven data quality elements, nature and specificity of the lead effect; evaluation of animal data incorporates data quality and relevance to humans. Results of simulations with selected chemicals previously evaluated in a formal risk assessment generally agreed with existing regulatory guidance. Application of the proposed framework across a wider range of chemical agents will improve transparency of the risk assessment process and validity of results, while informing continuous refinements to this evolving methodology.