Comparison of species sensitivity distributions based on population or individual endpoints

International audienceSpecies sensitivity distributions (SSDs) developed from individual and population endpoints were compared based on simulations and a case study. The simulations were performed with five invertebrate species accounting for the diversity of benthic macroinvertebrate communities in large European lowland rivers and for five benthic invertebrates used as laboratory species. Population growth rate 10% effective concentration (EC10) values were, in most of the simulations, higher than the lowest of the EC10 values at the individual level. However, for the set of ecologically representative species, the fifth percentile level of this distribution (HC5) was more protective for population endpoints than for individual endpoints. This was the opposite for the set of laboratory species. Population and individual SSDs were also compared based on existing data on Cu for the five laboratory invertebrate species. In this case, the calculated population HC5 value was almost twice the individual value, and the authors showed much reduced variability between species sensitivities at population level compared with individual level. They conclude that population-based HC5 would generally be more protective than individual-based HC5. However, the change of level could reveal higher homogeneity at population level than at individual level, supporting the use of population-based HC5 to avoid overprotection. The authors thus advise the derivation of population-based HC5, as soon as it is possible, to derive such value with a relevant panel of species

Substance-tailored testing strategies in toxicology : an in silico methodology based on QSAR modeling of toxicological thresholds and Monte Carlo simulations of toxicological testing

International audienceThe design of toxicological testing strategies aimed at identifying the toxic effects of chemicals without (or with a minimal) recourse to animal experimentation is an important issue for toxicological regulations and for industrial decision-making. This article describes an original approach which enables the design of substance-tailored testing strategies with a specified performance in terms of false-positive and false-negative rates. The outcome of toxicological testing is simulated in a different way than previously published articles on the topic. Indeed, toxicological outcomes are simulated not only as a function of the performance of toxicological tests but also as a function of the physico-chemical Properties of chemicals. The required inputs for Our approach are QSAR predictions for the LOAELs of the toxicological effect of interest and statistical distributions describing the relationship existing between in vivo LOAEL values and results from in vitro tests. Our methodology is able to correctly predict the performance of testing strategies designed to analyze the teratogenic effects of two chemicals: di(2-ethylhexyl)phthalate and Indomethacin. The proposed decision-support methodology can be adapted to any toxicological context as long as a Statistical Comparison between in vitro and in Vivo results is possible and QSAR models for the toxicological effect of interest can be developed

Development of a physiologically based kinetic model for 99m-Technetium-labelled carbon nanoparticles inhaled by humans

International audienceParticulate air pollution is associated with respiratory and cardiovascular morbidity and mortality. Recent studies investigated whether and to which extent inhaled ultrafine particles are able to translocate into the bloodstream in humans. However, their conclusions were conflicting. We developed a physiologically based kinetic model for 99m-technetium-labelled carbon nanoparticles (Technegas). The model was designed to analyse imaging data. It includes different translocation rates and kinetics for free technetium, and small and large technetium-labelled particles. It was calibrated with data from an experiment designed to assess the fate of nanoparticles in humans after inhalation of Technegas. The data provided time courses of radioactivity in the liver, stomach, urine, and blood. Parameter estimation was performed in a Bayesian context with Markov chain Monte Carlo (MCMC) techniques. Our analysis points to a likely translocation of particle-bound technetium from lung to blood, at a rate about twofold lower than the transfer rate of free technetium. Notably, restricting the model so that only free technetium would have been able to reach blood circulation resulted in much poorer fits to the experimental data. The percentage of small particles able to translocate was estimated at 12.7% of total particles. The percentage of unbound technetium was estimated at 6.7% of total technetium. To our knowledge, our model is the first PBPK model able to use imaging data to describe the absorption and distribution of nanoparticles. We believe that our modeling approach using Bayesian and MCMC techniques provides a reasonable description on which to base further model refinement

Predicting in vivo gene expression in macrophages after exposure to benzo(a)pyrene based on in vitro assays and toxicokinetic/toxicodynamic models

International audiencePredictive toxicology aims at developing methodologies to relate the results obtained from in vitro experiments to in vivo exposure. In the case of polycyclic aromatic hydrocarbons (PAHs), a substantial amount of knowledge on effects and modes of action has been recently obtained from in vitro studies of gene expression. In the current study, we built a physiologically based toxicokinetic (PBTK) model to relate in vivo and in vitro gene expression in case of exposure to benzo(a)pyrene (BaP), a referent PAH. This model was calibrated with two toxicokinetic datasets obtained on rats exposed either through intratracheal instillation or through intravenous administration and on an in vitro degradation study. A good agreement was obtained between the model's predictions and the concentrations measured in target organs, such as liver and lungs. Our model was able to relate correctly the gene expression for two genes targeted by PAHs, measured in vitro on primary human macrophages and in vivo in rat macrophages after exposure to BaP. Combining in vitro studies and PBTK modeling is promising for PAH risk assessment, especially for mixtures which are more efficiently studied in vitro than in vivo

Energy-based modelling to assess effects of chemicals on Caenorhabditis elegans: A case study on uranium

International audienceThe ubiquitous free-living nematode Caenorhabditis elegans is a powerful animal model for measuring the evolutionary effects of pollutants which is increasingly used in (eco)toxicological studies. Indeed, toxicity tests with this nematode can provide in a few days data on the whole life cycle. These data can be analysed with mathematical tools such as toxicokinetic-toxicodynamic modelling approaches. In this study, we assessed how a chronic exposure to a radioactive heavy metal (uranium) affects the life-cycle of C. elegans using a mechanistic model. In order to achieve this, we exposed individuals to a range of seven concentrations of uranium. Growth and reproduction were followed daily. These data were analysed with a model for nematodes based on the Dynamic Energy Budget theory, able to handle a wide range of plausible biological parameters values. Parameter estimations were performed using a Bayesian framework. Our results showed that uranium affects the assimilation of energy from food with a no-effect concentration (NEC) of 0.42 mM U which would be the threshold for effects on both growth and reproduction. The sensitivity analysis showed that the main contributors to the model output were parameters linked to the feeding processes and the actual exposure concentration. This confirms that the real exposure concentration should be measured accu-rately and that the feeding parameters should not be fixed, but need to be reestimated during the parameter estimation process

A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

International audienceIn this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy

Fluoxetine effects assessment on the life cycle of aquatic invertebrates

International audienceFluoxetine is a serotonin re-uptake inhibitor, generally used as an antidepressant. It is suspected to provoke substantial effects in the aquatic environment. This study reports the effects of fluoxetine on the life cycle of four invertebrate species, Daphnia magna, Hyalella azteca and the snail Potamopyrgus antipodarum exposed to fluoxetine spiked-water and the midge Chironomus riparius exposed to fluoxetine-spiked sediments. For D. magna, a multi-generational study was performed with exposition of newborns from exposed organisms. Effects of fluoxetine could be found at low measured concentrations (around 10 micro g l(-1)), especially for parthenogenetic reproduction of D. magna and P. antipodarum. For daphnids, newborns length was impacted by fluoxetine and the second generation of exposed individuals showed much more pronounced effects than the first one, with a NOEC of 8.9 micro g l(-1). For P. antipodarum, significant decrease of reproduction was found for concentrations around 10 micro g l(-1). In contrast, we found no effect on the reproduction of H. azteca but a significant effect on growth, which resulted in a NOEC of 33 micro g l(-1), expressed in nominal concentration. No effect on C. riparius could be found for measured concentrations up to 59.5 mg kg(-1). General mechanistic energy-based models showed poor relevance for data analysis, which suggests that fluoxetine targets specific mechanisms of reproduction

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

International audienceFor more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA

Stochasticity in Physiologically Based Kinetics Models : implications for cancer risk assessment

International audienceIn case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level

La toxicocinétique prédictive

Dose-effect relationships in chemical risk assessment are commonly derived through simple mathematical models linking directly effects with exposure dose. These models, usually calibrated with animal data, are specific to the tested chemical, the endpoint and the experimental protocol. Accounting for toxicokinetics permit to extrapolate results for different chemicals and different scenarios of exposure. Among toxicokinetics models, physiologically based pharmacockinetic (PBPK) models are based on a realistic description of anatomy, physiology and of the mechanisms involved in the disposition of a compound within an organism, i.e. absorption, distribution, metabolism, and excretion (ADME processes). We present three examples of use of toxicokinetics models in risk assessment. In the first one, we contributed to an INRS study aiming at evaluating workers cobalt exposure. The second one consists in the recent development of a generic lifetime PBPK model accounting for physiological differences between individuals. The third one exposes the calibration of a PBPK model based on imaging data to assess the kinetics of inhaled particles.Les relations « dose-effet » développées en évaluation de risques des substances chimiques sont des modèles mathématiques simples liant la dose extérieure à un effet observé. Des modèles de toxicocinétique relient les doses et les scénarios d’exposition avec les concentrations au niveau des tissus cibles des substances. Ils permettent d’intégrer explicitement le temps et de prédire la réponse pour différentes durées d’expositions ou pour des expositions variables au cours du temps. Mais pour extrapoler entre différentes voies d’exposition, entre différentes espèces ou entre différents âges, il est nécessaire d’intégrer la physiologie de l’organisme étudié. L’étude en collaboration avec l’INRS à laquelle nous avons contribué consistait à développer et à exploiter un modèle toxicocinétique pour relier l’exposition atmosphérique de travailleurs au cobalt et sa concentration dans leurs urines