Issue analysis: key characteristics approach for identifying endocrine disruptors

For more than a decade, weight of evidence (WoE) evaluations have been the standard method for determining whether a chemical meets the definition of an endocrine disrupting chemical (EDC). WoE methods consider all data pertinent to satisfying the EDC definition and evaluating those data with respect to relevance, reliability, strength, and coherence with established endocrine physiology and pharmacology. A new approach for identifying EDC hazards has been proposed that organizes and evaluates data according to ten so-called Key Characteristics (KCs) of EDCs. The approach claims to address the lack of a widely accepted, systematic approach for identifying EDC hazards, but completely ignores the WoE literature for EDCs. In contrast to WoE methods, the KC approach fails to apply the consensus definition of EDC and is not amenable to empirical testing or validation, is fungible and ensures inconsistent and unreliable results, ignores principles of hormone action and characteristics of dose-response in endocrine pharmacology and toxicology, lacks a means of distinguishing endocrine-mediated from non-endocrine mediated mechanisms, lacks a means to reach a negative conclusion about a chemical's EDC properties or to distinguish EDCs from non-EDCs, and provides no means for developing a valid consensus among experts nor provides a means of resolving conflicting interpretations of data. Instead of shortcuts like the KC approach, which are prone to bias, error, and arbitrary conclusions, identifying EDCs should rely on WoE evaluations that supply the critical components and scientific rigor lacking in the proposed KCs for EDCs.

Automated summary

Weight of evidence (WoE) evaluations have been the standard method for determining EDCs for more than a decade. A new approach based on Key Characteristics (KCs) has been proposed, but it ignores the WoE literature and lacks scientific rigor. The KC approach is inconsistent, unreliable, and lacks means for reaching negative conclusions or distinguishing EDCs from non-EDCs.