Modeling explicitly and mechanistically median lethal concentration as a function of time for risk assessment

Semiempirical models are useful for interpreting the response of aquatic organisms to toxicants as a function of exposure concentration and duration. Most applications predict cumulative mortality at the end of the test for constant exposure concentrations. Summary measures, such as the median lethal concentration, are then estimated as a function of concentration. Real-world exposures are not constant. Effects may depend on pulse timing, and cumulative analysis based only on integrated exposure concentration is not sufficient to interpret results. We undertook a series of pulsed-exposure experiments using standard toxicological protocols and interpreted the results (mortality, biomass, and reproduction) using a dynamic generalization of a Mancini/Breck--type model that includes two compartments, one for internal concentration as a function of exposure and one for site-of-action concentration or accumulated damage as a function of the internal dose. At exposure concentrations near the effects level, the model explained approximately 50% of the variability in the observed time history of survival, 43% of the change in biomass, and 83% of the variability in net reproduction. Unexplained variability may result from differences in organism susceptibility, amplified by the effects of small sample sizes in standard tests. The results suggest that response is sensitive to prior conditions and that constant-exposure experiments can underestimate the risk from intermittent exposures to the same concentration. For pulsed exposures, neither the average nor the maximum concentration alone is an adequate index of risk, which depends on both the magnitude, duration, and timing of exposure pulses. Better understanding about the impacts of pulsed exposures will require use of experimental protocols with significantly greater numbers of replicates.

Section: Methodsmentioning

confidence: 57%

“…The kinetic toxicity model of Bonnomet et al [25] uses a hazard rate approach to toxicity, with the assumption of a threshold internal concentration, D sorg , below which risk is not incurred. The hazard rate is then assumed to be a linear function of the internal dose, such that for D ( t ) > D sorg , and zero otherwise.…”

Section: Methodsmentioning

confidence: 99%

Toxicity models of pulsed copper exposure to Pimephales promelas and Daphnia magna

Butcher

Diamond

Bearr

et al. 2006

“…In this first phase, the parameters of the relations between acute and chronic data were therefore estimated (see Eqns. 12–15). These relations are now used to transform a sample of acute toxicity values into a sample of predicted chronic values.…”

Section: Resultsmentioning

confidence: 99%

“…This relation is based on a reduction in interspecies variation when moving from acute to chronic data. Indeed, according to Bonnomet et al [14,15], the differences in sensitivity to a toxicant for short and medium‐term exposure that are observed between the taxonomic groups vertebrates, crustaceans, insects, and mollusks are mainly caused by differences in the toxicokinetics (the kinetics of uptake and elimination of a toxicant from an organism) and kinetics of the toxicant's action. With regard to the acute–chronic comparison, species with slow kinetics appear to be the least sensitive in the short term: Equilibrium between internal concentration and external concentration of the toxicant is reached slowly for these organisms.…”

Section: Discussionmentioning

confidence: 99%

Acute‐to‐chronic species sensitivity distribution extrapolation

Duboudin¹,

Ciffroy²,

Magaud

2004

Self Cite

Seeking to make greater use of available data for risk assessment of substances, we constructed, for the situation in which chronic data are limited or even nonexistent but acute data are relatively large, an acute to chronic transformation (ACT) methodology based on the concept of species sensitivity distributions (SSDs). This ACT methodology uses a comparison of acute and chronic SSDs, separately for vertebrate data (with 22 substances) and for invertebrate data (with 15 substances). Rather than comparing an acute toxicity value with a chronic value, as when calculating an acute to chronic ratio (ACR), samples of acute and chronic data corresponding to the same category of species were compared. Starting from a sample of acute data, the ACT methodology showed relationships that enable the creation of a sample of predicted chronic values. This sample can then be used to calculate a predicted chronic hazardous concentration potentially affecting 5% of species (HC5%), just as with a sample of real chronic toxicity values. This ACT approach was tested on 11 substances. For each substance, the real chronic HC5% and the predicted chronic HC5% were calculated and compared. The ratio between chronic HC5% and ACT HC5% was, on average, 1.6 and did not exceed 4.4 for the 11 substances studied.

“…Toxicokinetics‐based models have been continually developed for the understanding of bioassay data [2]. Of note are compartment models [3], such as the one‐compartment model [4] and the multiple‐compartment model [5], that are frequently used for analysis of toxicity data.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of an accelerated life‐test model and a toxicokinetics‐based model for the analysis of Porcellio scaber survival data

Ren

2004

Statistical models have long been used for reliability analysis and risk assessment. In the present study, an accelerated life-test model was used to analyze a set of dose-time-response data obtained with the terrestrial isopod Porcellio scaber. Survival data were experimentally obtained by exposing P. scaber to diazinon (a nonpersistent insecticide) at six concentrations between 2 and 11.31 microg/g (toxicant/soil). Survival data are presented on a weekly basis. The accelerated life-test model assumed a log-normal distribution and constant variance across all diazinon concentrations. Model parameters were obtained by maximum likelihood estimation. The accelerated life-test model was compared to a toxicokinetics-based model reported in the literature. Survival predictions made by both models were compared with the observed data. Both the accelerated life-test model and the toxicokinetics-based model underestimated toxicity at a diazinon concentration of 8 microg/g. Overall, however, the accelerated life-test model outperformed the toxicokinetics-based model, with survival predictions closer to the observed data in most cases and a stronger correlation between predicted and observed survivals. However, as a statistical model, the accelerated life-test model did not reveal mechanistic information, and only statistical and distributional interpretations of its model parameters could be made.