Death Dilemma and Organism Recovery in Ecotoxicology

被引:46
作者
Ashauer, Roman [1 ,2 ]
O'Connor, Isabel [1 ]
Hintermeister, Anita [1 ]
Escher, Beate I. [1 ,3 ,4 ]
机构
[1] Eawag Swiss Fed Inst Aquat Sci & Technol, Dept Environm Toxicol, CH-8600 Dubendorf, Switzerland
[2] Univ York, Dept Environm, York YO10 5DD, N Yorkshire, England
[3] UFZ, Helmholtz Ctr Environm Res, Cell Toxicol, D-04318 Leipzig, Germany
[4] Univ Tubingen, Ctr Appl Geosci, Environm Toxicol, D-72074 Tubingen, Germany
基金
瑞士国家科学基金会;
关键词
BIOLOGY-BASED APPROACH; BASE-LINE TOXICITY; EXPOSURE DURATION; MIXTURE TOXICITY; ENVIRONMENTAL-POLLUTANTS; INTERMITTENT EXPOSURE; AQUATIC TOXICITY; POLAR NARCOSIS; GAMMARUS-PULEX; TIME;
D O I
10.1021/acs.est.5b03079
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Why do some individuals survive after exposure to chemicals while others die? Either, the tolerance threshold is distributed among the individuals in a population, and its exceedance leads to certain death, or all individuals share the same threshold above which death occurs stochastically. The previously published General Unified Threshold model of Survival (GUTS) established a mathematical relationship between the two assumptions. According to this model stochastic death would result in systematically faster compensation and damage repair mechanisms than individual tolerance. Thus, we face a circular conclusion dilemma because inference about the death mechanism is inherently linked to the speed of damage recovery. We provide empirical evidence that the stochastic death model consistently infers much faster toxicodynamic recovery than the individual tolerance model. Survival data can be explained by either, slower damage recovery and a wider individual tolerance distribution, or faster damage recovery paired with a narrow tolerance distribution. The toxicodynamic model parameters exhibited meaningful patterns in chemical space, which is why we suggest toxicodynamic model parameters as novel phenotypic anchors for in vitro to in vivo toxicity extrapolation. GUTS appears to be a promising refinement of traditional survival curve analysis and dose response models.
引用
收藏
页码:10136 / 10146
页数:11
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