Accuracy assessment of time–concentration–effect models in predicting chronic lethality from acute toxicity data

Abstract

Acute-to-chronic (ACE) models (accelerated life testing, ALT; linear regression analysis, LRA) are used to estimate chemical concentrations resulting in low levels of chronic mortality from acute toxicity data, thereby greatly increasing the inferential value of acute data. We applied the ACE models to test data from 72 chemicals and 14 aquatic species (131 acute and 97 chronic tests) and then compared the results with reported chronic no observed effect concentrations (NOEC) and lowest observed effect concentrations (LOEC), as determined by traditional analysis of variance techniques. Acute-to-chronic models produced highly accurate chronic lethality estimates compared with reported chronic NOEC and LOEC values. Lethality estimates fell within two times reported NOEC-LOEC values 71% of the time and within five times 98% of the time. Therefore, ACE models are very appropriate for estimating chronic lethality from acute toxicity data when chronic data are absent and have high applicability in probability-based hazard and risk assessments.