hb or not hb - When and why accounting for background mortality in toxicological survival models matters?

Decisions in Environmental Risk Assessment (ERA) about impacts of chemical compounds on different species are based on critical effect indicators such as the 50% lethal concentration (LC50). Regulatory documents recommend concentration-response (or concentration-effect) model fitting on standard toxicity test data to get LC50 values. However, toxicokinetic-toxicodynamic (TKTD) models proved their efficiency to better exploit toxicity test data, at Tier-2 but also at Tier-1, delivering time-independent indicators. In particular, LC50 values can be obtained from the reduced General Unified Threshold model of Survival (GUTS-RED) with both variants, Stochastic Death and Individual Tolerance, that include parameter hb, the background mortality. Estimating hb during the fitting process or not depends on studies and fitting habits, while it may strongly influence the other GUTS-RED parameters, and consequently the LC50 estimate. We hypothesized that estimating hb from all data in all replicates over time should provide more precise LC50 estimates. We then explored how estimating hb impacted: (i) GUTS-RED model parameters; (ii) goodness-of-fit criteria (fitting plot, posterior predictive check, parameter correlations); (iii) LC50 accuracy and precision. We finally show that estimating hb does not impact the LC50 precision while providing more accurate and precise GUTS parameter estimates. Hence, estimating hb would lead to a more protective ERA.

rbioacc: An R-package to analyze toxicokinetic data.

The R-package rbioacc allows to analyse experimental data from bioaccumulation tests where organisms are exposed to a chemical (exposure) then put into clean media (depuration). Internal concentrations are measured over time during the experiment. rbioacc provides turnkey functions to visualise and analyse such data. Under a Bayesian framework, rbioacc fits a generic one-compartment toxicokinetic model built from the data. It provides TK parameter estimates (uptake and elimination rates) and standard bioaccumulation metrics. All parameter estimates, bioaccumulation metrics and predictions of internal concentrations are delivered with their uncertainty. Bioaccumulation metrics are provided in support of environmental risk assessment, in full compliance with regulatory requirements required to approve market release of chemical substances. This paper provides worked examples of the use of rbioacc from data collected through standard bioaccumulation tests, publicly available within the scientific literature. These examples constitute step-by-step user-guides to analyse any new data set, uploaded in the right format.

Taking full advantage of modelling to better assess environmental risk due to xenobiotics-the all-in-one facility MOSAIC.

In the European Union, more than 100,000 man-made chemical substances are awaiting an environmental risk assessment (ERA). Simultaneously, ERA of these chemicals has now entered a new era requiring determination of risks for physiologically diverse species exposed to several chemicals, often in mixtures. Additionally, recent recommendations from regulatory bodies underline a crucial need for the use of mechanistic effect models, allowing assessments that are not only ecologically relevant, but also more integrative, consistent and efficient. At the individual level, toxicokinetic-toxicodynamic (TKTD) models are particularly encouraged for the regulatory assessment of pesticide-related risks on aquatic organisms. In this paper, we first briefly present a classical dose-response model to showcase the on-line MOSAIC tool, which offers all necessary services in a turnkey web platform, whatever the type of data analyzed. Secondly, we focus on the necessity to account for the time-dimension of the exposure by illustrating how MOSAIC can support a robust calculation of bioaccumulation metrics. Finally, we show how MOSAIC can be of valuable help to fully complete the EFSA workflow regarding the use of TKTD models, especially with GUTS models, providing a user-friendly interface for calibrating, validating and predicting survival over time under any time-variable exposure scenario of interest. Our conclusion proposes a few lines of thought for an easier use of modelling in ERA.

When the average hides the risk of Bt-corn pollen on non-target Lepidoptera: Application to Aglais io in Catalonia.

Field cultivation of Genetically Modified (GM) Bt-plants has a potential environmental risk toward non-target Lepidoptera (NTLs) larvae through the consumption of Bt-maize pollen. The Bt-maize Cry protein targeting Lepidoptera species detrimental to the crop is also expressed in pollen which is dispersed by wind and can thus reach habitats of NTLs. To better assess the current ecological risk of Bt-maize at landscape scales, we developed a spatially-explicit exposure-hazard model considering (i) the dynamics of pollen dispersal obtained by convolving GM plants emission with a dispersal kernel and (ii) a toxicokinetic-toxicodynamic (TKTD) model accounting for the impact of toxin ingestion on individual lethal effects. We simulated the model using real landscape observations in Catalonia (Spain): GM-maize locations, flowering dates, rainfall time series and larvae emergence date of the European peacock butterfly Aglais io. While in average, the additional mortality appears to be negligible, we show significant additional mortality at sub-population level, with for instance a mortality higher than 40% within the 10m for the 10% most Bt-sensitive individuals. Also, using Pareto optimality we capture the best trade-off between isolation distance and additional mortality: up to 50 m are required to significantly buffer Bt-pollen impact on NTLs survival at the individual level. Our study clears up the narrow line between diverging conclusions: those claiming no risk by only looking at the average regional effect of Bt on NTLs survival and those pointing out a significant threaten when considering the variability of individuals mortality.

COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s).

Discrepancies in population structures, decision making, health systems and numerous other factors result in various COVID-19-mortality dynamics at country scale, and make the forecast of deaths in a country under focus challenging. However, mortality dynamics of countries that are ahead of time implicitly include these factors and can be used as real-life competing predicting models. We precisely propose such a data-driven approach implemented in a publicly available web app timely providing mortality curves comparisons and real-time short-term forecasts for about 100 countries. Here, the approach is applied to compare the mortality trajectories of second-line and front-line European countries facing the COVID-19 epidemic wave. Using data up to mid-April, we show that the second-line countries generally followed relatively mild mortality curves rather than fast and severe ones. Thus, the continuation, after mid-April, of the COVID-19 wave across Europe was likely to be mitigated and not as strong as it was in most of the front-line countries first impacted by the wave (this prediction is corroborated by posterior data).

Recommendations to address uncertainties in environmental risk assessment using toxicokinetic-toxicodynamic models.

Providing reliable environmental quality standards (EQSs) is a challenging issue in environmental risk assessment (ERA). These EQSs are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environmental hazard of chemical compounds released by human activities. These toxicity endpoints include the classical x% effect/lethal concentrations at a specific time t (EC/LC(x, t)) and the new multiplication factors applied to environmental exposure profiles leading to x% effect reduction at a specific time t (MF(x, t), or denoted LP(x, t) by the EFSA). However, classical dose-response models used to estimate toxicity endpoints have some weaknesses, such as their dependency on observation time points, which are likely to differ between species (e.g., experiment duration). Furthermore, real-world exposure profiles are rarely constant over time, which makes the use of classical dose-response models difficult and may prevent the derivation of MF(x, t). When dealing with survival or immobility toxicity test data, these issues can be overcome with the use of the general unified threshold model of survival (GUTS), a toxicokinetic-toxicodynamic (TKTD) model that provides an explicit framework to analyse both time- and concentration-dependent data sets as well as obtain a mechanistic derivation of EC/LC(x, t) and MF(x, t) regardless of x and at any time t of interest. In ERA, the assessment of a risk is inherently built upon probability distributions, such that the next critical step is to characterize the uncertainties of toxicity endpoints and, consequently, those of EQSs. With this perspective, we investigated the use of a Bayesian framework to obtain the uncertainties from the calibration process and to propagate them to model predictions, including LC(x, t) and MF(x, t) derivations. We also explored the mathematical properties of LC(x, t) and MF(x, t) as well as the impact of different experimental designs to provide some recommendations for a robust derivation of toxicity endpoints leading to reliable EQSs: avoid computing LC(x, t) and MF(x, t) for extreme x values (0 or 100%), where uncertainty is maximal; compute MF(x, t) after a long period of time to take depuration time into account and test survival under pulses with different periods of time between them.

Variability in cyanobacteria sensitivity to antibiotics and implications for environmental risk assessment.

Once released into the environment antibiotics can kill or inhibit the growth of bacteria, and in turn potentially have effects on bacterial community structure and ecosystem function. Environmental risk assessment (ERA) seeks to establish protection limits to minimise chemical impacts on the environment, but recent evidence suggests that the current regulatory approaches for ERA for antibiotics may not be adequate for protecting bacteria that have fundamental roles in ecosystem function. In this study we assess the differences in interspecies sensitivity of eight species of cyanobacteria to seven antibiotics (cefazolin, cefotaxime, ampicillin, sufamethazine, sulfadiazine, azithromycin and erythromycin) with three different modes of action. We found that variability in the sensitivity to these antibiotics between species was dependent on the mode of action and varied by up to 70 times for ß-lactams. Probabilistic analysis using species sensitivity distributions suggest that the current predicted no effect concentration PNEC for the antibiotics may be either over or under protective of cyanobacteria dependent on the species on which it is based and the mode of action of the antibiotic; the PNECs derived for the macrolide antibiotics were over protective but PNECs for ß-lactams were generally under protective. For some geographical locations we identify a significant risk to cyanobacteria populations based upon measured environmental concentrations of selected antibiotics. We conclude that protection limits, as determined according to current regulatory guidance, may not always be protective and might be better derived using SSDs and that including toxicity data for a wider range of (cyano-) bacteria would improve confidence for the ERA of antibiotics.

Fit Reduced GUTS Models Online: From Theory to Practice.

Mechanistic modeling approaches, such as the toxicokinetic-toxicodynamic (TKTD) framework, are promoted by international institutions such as the European Food Safety Authority and the Organization for Economic Cooperation and Development to assess the environmental risk of chemical products generated by human activities. TKTD models can encompass a large set of mechanisms describing the kinetics of compounds inside organisms (e.g., uptake and elimination) and their effect at the level of individuals (e.g., damage accrual, recovery, and death mechanism). Compared to classical dose-response models, TKTD approaches have many advantages, including accounting for temporal aspects of exposure and toxicity, considering data points all along the experiment and not only at the end, and making predictions for untested situations as realistic exposure scenarios. Among TKTD models, the general unified threshold model of survival (GUTS) is within the most recent and innovative framework but is still underused in practice, especially by risk assessors, because specialist programming and statistical skills are necessary to run it. Making GUTS models easier to use through a new module freely available from the web platform MOSAIC (standing for MOdeling and StAtistical tools for ecotoxIClogy) should promote GUTS operability in support of the daily work of environmental risk assessors. This paper presents the main features of MOSAIC_GUTS: uploading of the experimental data, GUTS fitting analysis, and LCx estimates with their uncertainty. These features will be exemplified from literature data. Integr Environ Assess Manag 2018;14:625-630. © 2018 SETAC.

New Insights to Compare and Choose TKTD Models for Survival Based on an Interlaboratory Study for Lymnaea stagnalis Exposed to Cd.

Toxicokinetic-toxicodynamic (TKTD) models, as the General Unified Threshold model of Survival (GUTS), provide a consistent process-based framework compared to classical dose-response models to analyze both time and concentration-dependent data sets. However, the extent to which GUTS models (Stochastic Death (SD) and Individual Tolerance (IT)) lead to a better fitting than classical dose-response model at a given target time (TT) has poorly been investigated. Our paper highlights that GUTS estimates are generally more conservative and have a reduced uncertainty through smaller credible intervals for the studied data sets than classical TT approaches. Also, GUTS models enable estimating any x% lethal concentration at any time (LCx,t), and provide biological information on the internal processes occurring during the experiments. While both GUTS-SD and GUTS-IT models outcompete classical TT approaches, choosing one preferentially to the other is still challenging. Indeed, the estimates of survival rate over time and LCx,t are very close between both models, but our study also points out that the joint posterior distributions of SD model parameters are sometimes bimodal, while two parameters of the IT model seems strongly correlated. Therefore, the selection between these two models has to be supported by the experimental design and the biological objectives, and this paper provides some insights to drive this choice.

The adaptation of generalist predators' diet in a multi-prey context: insights from new functional responses.

The ability for a generalist consumer to adapt its foraging strategy (the multi-species functional response, MSFR) is a milestone in ecology as it contributes to the structure of food webs. The trophic interaction between a generalist predator, as the red fox or the barn owl, and its prey community, mainly composed of small mammals, has been empirically and theoretically widely studied. However, the extent to which these predators adapt their diet according to both multi-annual changes in multiple prey species availability (frequency dependence) and the variation of the total prey density (density dependence) is unexplored.We provide a new general model of MSFR disentangling changes in prey preference according to variation of prey frequency (switching) and of total prey density (we propose the new concept of "rank switching"). We apply these models to two large data sets of red fox and barn owl foraging. We show that both frequency-dependent and density-dependent switching are critical properties of these two systems, suggesting that barn owl and red fox have an accurate image of the prey community in terms of frequency and absolute density. Moreover, we show that negative switching, which can lead to prey instability, is a strong property of the two systems.

Competence of hosts and complex foraging behavior are two cornerstones in the dynamics of trophically transmitted parasites.

Multi-host trophically transmitted parasite (TTP) is a common life cycle where prey and predators are respectively intermediate and definitive hosts of the parasite. In these systems, the foraging response of the predator toward variations in prey community composition underlies the dynamic of the parasite. Therefore, modeling epidemiological dynamic of infectious diseases considering ecological predator-prey interactions is essential to understand the spreading of parasites in ecosystems. However, two important weaknesses of previous TTP models including feeding interaction can be pointed out: (i) the choice of a linear density-dependent contact rate is faintly realistic as it supposes an unlimited ingestion rate with an increase of prey density and (ii) considering only one host prey species prevents the study of host biodiversity effect due to change in the prey community composition where species have different competences to be infected and to transmit the parasite. This article attempts to address the dynamics of parasite in a context of multiple intermediate hosts differentiated by their competences and of complex foraging behavior of the predator. We present and analyze a deterministic one predator-two prey model, which is then used to explore the transmission cycle of the cestode Echinococcus multilocularis. This study examines the foraging condition for the co-existence of the prey, and then, based on the computation of the threshold measure of disease risk, R0, we show that the pattern of feeding interactions changes the relationship between disease risk and prey community composition. Finally, we disentangle the mechanism leading to the counter-intuitive observation of a decrease of disease risk while the population density of intermediate hosts increases.