Estimation of no effect concentrations from exposure experiments when values scatter among individuals

Comparing statistical analyses to estimate thresholds in ecotoxicology

Different methods are used in ecotoxicology to estimate thresholds in survival data. This paper uses Monte Carlo simulations to evaluate the accuracy of three methods (maximum likelihood (MLE) and Markov Chain Monte Carlo estimates (Bayesian) of the no-effect concentration (NEC) model and Piecewise regression) in estimating true and apparent thresholds in survival experiments with datasets having different slopes, background mortalities, and experimental designs. Datasets were generated with models that include a threshold parameter (NEC) or not (log-logistic). Accuracy was estimated using root-mean square errors (RMSEs), and RMSE ratios were used to estimate the relative improvement in accuracy by each design and method. All methods had poor performances in shallow and intermediate curves, and accuracy increased with the slope of the curve. The EC5 was generally the most accurate method to estimate true and apparent thresholds, except for steep curves with a true threshold. In that case, the EC5 underestimated the threshold, and MLE and Bayesian estimates were more accurate. In most cases, information criteria weights did not provide strong evidence in support of the true model, suggesting that identifying the true model is a difficult task. Piecewise regression was the only method where the information criteria weights had high support for the threshold model; however, the rate of spurious threshold model selection was also high. Even though thresholds are an attractive concept from a regulatory and practical point of view, threshold estimates, under the experimental conditions evaluated in this work, should be carefully used in survival analysis or when there are any biological reasons to support the existence of a threshold.

A toxic effect at molecular level can be expressed at community level: A case study on toxic hierarchy

This study demonstrated hierarchical toxicity and addressed the relevance and differences of toxic effects at the molecular, individual, population, and community levels. Superoxide dismutase (SOD) activity, photosynthetic oxygen production, filtration rate, life span and densities of Platymonas helgolandica var. tsingtaoensis, Isochrysis galbana, and Brachionus plicatilis in single-species tests and customized community tests were examined in response to a concentration gradient of aniline ranging from 0 to 50.0 mg L^-1. The SOD activity was the most sensitive endpoint with the fastest response to aniline according to the calculated no-detection of toxic effect concentration (NDEC) and the EC₅₀. The individual- and population-level endpoints, showing a lower response to aniline, could be constructed from the SOD activity in a stepwise manner. A multi-scale hierarchical model with endpoints at 4 levels was used to characterize toxic effects, at the scales of time and size. Linkage of SOD activity to toxic effects at a community level was established level by level to express the change in the customized community with the concentration of aniline. The calculated threshold concentration of aniline for the customized community was nearly equal to the minimum NDEC, demonstrating as great an impact on interactions by the toxic effect at subpopulation-level as that at the community level. However, we identified a trend of higher sensitivities of measured endpoints at sub-population level, decreasing sensitivity at higher levels but a great variety of sensitivities at community level. Although the characteristics of toxic effects are different at different levels, the structure and process of endpoints at adjacent levels are related to and interact with each other. The resulted indirect effects, together with direct effect, determine the toxic effect at every levels of biological complexity. The toxic effects at adjacent levels should be studied at the same time to better understand the ecological risk of contaminants.

Reconsidering sufficient and optimal test design in acute toxicity testing

In dose-response analysis, regression analysis and hypothesis testing are the main tools of choice. These methods, however, have specific requirements for the design of acute toxicity experiments. To produce meaningful results, both approaches require a constant exposure concentration over the duration of the test, and regression analysis makes an additional demand for at least two doses with partial mortality at the end of the test. These requirements, however, result from the limitations of the statistical techniques, which only use the observations at the end of the test. In practice, most standard protocols for acute testing prescribe that observations are made at several points in time (often daily). In this contribution, I demonstrate how dynamic modelling can make use of this information to produce robust estimates of LC50 as function of time, with confidence intervals, from data sets that violate the requirements for standard dose-response analysis. This form of modelling invites an entirely different, more flexible, view on experimental design, which could lead to a more efficient use of test animals and, at the same time, a stronger support for environmental risk assessment as well as the science of ecotoxicology.

Ecological nanotoxicology: integrating nanomaterial hazard considerations across the subcellular, population, community, and ecosystems levels

Research into the health and environmental safety of nanotechnology has seriously lagged behind its emergence in industry. While humans have often adopted synthetic chemicals without considering ancillary consequences, the lessons learned from worldwide pollution should motivate making nanotechnology compatible with environmental concerns. Researchers and policymakers need to understand exposure and harm of engineered nanomaterials (ENMs), currently nanotechnology's main products, to influence the ENM industry toward sustainable growth. Yet, how should research proceed? Standard toxicity testing anchored in single-organism, dose-response characterizations does not adequately represent real-world exposure and receptor scenarios and their complexities. Our approach is different: it derives from ecology, the study of organisms' interactions with each other and their environments. Our approach involves the characterization of ENMs and the mechanistic assessment of their property-based effects. Using high throughput/content screening (HTS/HCS) with cells or environmentally-relevant organisms, we measure the effects of ENMs on a subcellular or population level. We then relate those effects to mechanisms within dynamic energy budget (DEB) models of growth and reproduction. We reconcile DEB model predictions with experimental data on organism and population responses. Finally, we use microcosm studies to measure the potential for community- or ecosystem-level effects by ENMs that are likely to be produced in large quantities and for which either HTS/HCS or DEB modeling suggest their potential to harm populations and ecosystems. Our approach accounts for ecological interactions across scales, from within organisms to whole ecosystems. Organismal ENM effects, if propagated through populations, can alter communities comprising multiple populations (e.g., plant, fish, bacteria) within food webs. Altered communities can change ecosystem services: processes that cycle carbon, nutrients, and energy, and regulate Earth's waters and atmosphere. We have shown ENM effects on populations, communities, and ecosystems, including transfer and concentration of ENMs through food chains, for a range of exposure scenarios; in many cases, we have identified subcellular ENM effects mechanisms. To keep pace with ENM development, rapid assessment of the mechanisms of ENM effects and modeling are needed. DEB models provide a method for mathematically representing effects such as the generation of reactive oxygen species and their associated damage. These models account for organism-level effects on metabolism and reproduction and can predict outcomes of ENM-organism combinations on populations; those predictions can then suggest ENM characteristics to be avoided. HTS/HCS provides a rapid assessment tool of the ENM chemical characteristics that affect biological systems; such results guide and expand DEB model expressions of hazard. Our approach addresses ecological processes in both natural and managed ecosystems (agriculture) and has the potential to deliver timely and meaningful understanding towards environmentally sustainable nanotechnology.

Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

In ecotoxicology, critical effect concentrations are the most common indicators to quantitatively assess risks for species exposed to contaminants. Three types of critical effect concentrations are classically used: lowest/ no observed effect concentration (LOEC/NOEC), LC( x) (x% lethal concentration) and NEC (no effect concentration). In this article, for each of these three types of critical effect concentration, we compared methods or models used for their estimation and proposed one as the most appropriate. We then compared these critical effect concentrations to each other. For that, we used nine survival data sets corresponding to D. magna exposition to nine different contaminants, for which the time-course of the response was monitored. Our results showed that: (i) LOEC/NOEC values at day 21 were method-dependent, and that the Cochran-Armitage test with a step-down procedure appeared to be the most protective for the environment; (ii) all tested concentration-response models we compared gave close values of LC50 at day 21, nevertheless the Weibull model had the lowest global mean deviance; (iii) a simple threshold NEC-model both concentration and time dependent more completely described whole data (i.e. all timepoints) and enabled a precise estimation of the NEC. We then compared the three critical effect concentrations and argued that the use of the NEC might be a good option for environmental risk assessment.

General unified threshold model of survival--a toxicokinetic-toxicodynamic framework for ecotoxicology

Toxicokinetic-toxicodynamic models (TKTD models) simulate the time-course of processes leading to toxic effects on organisms. Even for an apparently simple endpoint as survival, a large number of very different TKTD approaches exist. These differ in their underlying hypotheses and assumptions, although often the assumptions are not explicitly stated. Thus, our first objective was to illuminate the underlying assumptions (individual tolerance or stochastic death, speed of toxicodynamic damage recovery, threshold distribution) of various existing modeling approaches for survival and show how they relate to each other (e.g., critical body residue, critical target occupation, damage assessment, DEBtox survival, threshold damage). Our second objective was to develop a general unified threshold model for survival (GUTS), from which a large range of existing models can be derived as special cases. Specific assumptions to arrive at these special cases are made and explained. Finally, we illustrate how special cases of GUTS can be fitted to survival data. We envision that GUTS will help increase the application of TKTD models in ecotoxicological research as well as environmental risk assessment of chemicals. It unifies a wide range of previously unrelated approaches, clarifies their underlying assumptions, and facilitates further improvement in the modeling of survival under chemical stress.

Understanding toxicity as processes in time

Studies in ecotoxicology usually focus on a single end point (typically mortality, growth, or reproduction) at a standardized exposure time. The exposure time is chosen irrespective of the properties of the chemical under scrutiny, but should depend on the organism of choice in combination with the compound(s) of interest. This paper discusses the typical patterns for toxic effects in time that can be observed for the most encountered endpoints growth reproduction and survival. Ignoring the fact that toxicity is a process in time can lead to severe bias in environmental risk assessment. We show that especially EC(x) values for sublethal endpoints can show very distinct patterns in time. We recommend that the test duration for survival as an endpoint should be extended till the incipient LC(50) is observed. Given the fact that toxicity data for single compounds show clear patterns in time, it is to be expected that effects of mixtures will also be strongly dependent on time. The few examples that have been published support this statement.