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There are substantial gaps in our empirical knowledge of the effects of chemical exposure on aquatic life that are unlikely to be filled by traditional laboratory toxicity testing alone. One possible alternative of generating new toxicity data is cross-species extrapolation (CSE), a statistical approach in which existing data are used to predict the effect of a chemical on untested species. Some CSE models use relatedness as a predictor of chemical sensitivity, but relatively little is known about how strongly shared evolutionary history influences sensitivity across all chemicals. To address this question, we conducted a survey of phylogenetic signal in the toxicity data from aquatic animal species for a large set of chemicals using a phylogeny inferred from taxonomy. Strong phylogenetic signal was present in just nine of thirty-six toxicity datasets, and there were no clear shared properties among those datasets with strong signal. Strong signal was rare even among chemicals specifically developed to target insects, meaning that these chemicals may be equally lethal to non-target taxa, including chordates. When signal was strong, distinct patterns of sensitivity were evident in the data, which may be informative when assembling toxicity datasets for regulatory use. Although strong signal does not appear to manifest in aquatic toxicity data for most chemicals, we encourage additional phylogenetic evaluations of toxicity data in order to guide the selection of CSE tools and as a means to explore the patterns of chemical sensitivity across the broad diversity of life. [ABSTRACT FROM AUTHOR]

The U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) is tasked with assessing chemicals for their potential to perturb endocrine pathways, including those controlled by androgen receptor (AR). To address challenges associated with traditional testing strategies, EDSP is considering in vitro high-throughput screening assays to screen and prioritize chemicals more efficiently. The ability of these assays to accurately reflect chemical interactions in nonmammalian species remains uncertain. Therefore, a goal of the EDSP is to evaluate how broadly results can be extrapolated across taxa. To assess the cross-species conservation of AR-modulated pathways, computational analyses and systematic literature review approaches were used to conduct a comprehensive analysis of existing in silico, in vitro , and in vivo data. First, molecular target conservation was assessed across 585 diverse species based on the structural similarity of ARs. These results indicate that ARs are conserved across vertebrates and are predicted to share similarly susceptibility to chemicals that interact with the human AR. Systematic analysis of over 5000 published manuscripts was used to compile in vitro and in vivo cross-species toxicity data. Assessment of in vitro data indicates conservation of responses occurs across vertebrate ARs, with potential differences in sensitivity. Similarly, in vivo data indicate strong conservation of the AR signaling pathways across vertebrate species, although sensitivity may vary. Overall, this study demonstrates a framework for utilizing bioinformatics and existing data to build weight of evidence for cross-species extrapolation and provides a technical basis for extrapolating hAR-based data to prioritize hazard in nonmammalian vertebrate species. [ABSTRACT FROM AUTHOR]

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

Toxicology is traditionally divided between human and eco-toxicology. In the shared pursuit of environmental health, this separation does not account for discoveries made in the comparative studies of animal genomes. Here, we provide evidence on the feasibility of understanding the health impact of chemicals on all animals, including ecological keystone species and humans, based on a significant number of conserved genes and their functional associations to health-related outcomes across much of animal diversity. We test four conditions to understand the value of comparative genomics data to inform mechanism-based human and environmental hazard assessment: (1) genes that are most fundamental for health evolved early during animal evolution; (2) the molecular functions of pathways are better conserved among distantly related species than the individual genes that are members of these pathways; (3) the most conserved pathways among animals are those that cause adverse health outcomes when disrupted; (4) gene sets that serve as molecular signatures of biological processes or disease-states are largely enriched by evolutionarily conserved genes across the animal phylogeny. The concept of homology is applied in a comparative analysis of gene families and pathways among invertebrate and vertebrate species compared with humans. Results show that over 70% of gene families associated with disease are shared among the greatest variety of animal species through evolution. Pathway conservation between invertebrates and humans is based on the degree of conservation within vertebrates and the number of interacting genes within the human network. Human gene sets that already serve as biomarkers are enriched by evolutionarily conserved genes across the animal phylogeny. By implementing a comparative method for chemical hazard assessment, human and eco-toxicology converge towards a more holistic and mechanistic understanding of toxicity disrupting biological processes that are important for health and shared among animals (including humans).

Objectives: To describe micafungin pharmacokinetic (PK) alterations of sepsis induced in piglets and to determine whether the porcine septic model is able to predict the PK of micafungin in septic patients at the plasma and peritoneal sites. Methods: From healthy (n = 8) and septic piglet group (n = 16), total micafungin concentrations were subject to a population PK analysis using Monolix®. Data from 16 septic humans patients from others studies was used to compare micafungin PK between septic piglets and septic patients. Results: Sepsis induced in piglets slightly alters the total clearance and the volume of distribution, while inter-compartment clearance is increased (from 3.88 to 5.74 L/h) as well as the penetration into peritoneal cavity (from 61 to 90%). In septic human patients, PK parameters are similar except for the Vd, which is corrected by an allometric factor based on the body weight of each species. Micafungin penetration into peritoneal cavity of humans is lower than in septic piglets (40 versus 90%). Conclusions: The sepsis induced in the porcine model alters the PK of micafungin comparable to that in humans. In addition, micafungin PK is similar between these two species at the plasma level taking into account the allometric relationship of the body weight of these species on the central volume of distribution. The porcine septic plasma model would be able to predict the micafungin PK in the septic patients. However, further studies on peritoneal penetration are necessary to characterize this inter-species difference. [ABSTRACT FROM AUTHOR]

The transition from experimental findings in animal models to clinical applications in human patients is a key challenge in pharmacology and toxicology. To date, this step still inhibits a significant level of uncertainty explaining amongst others the continuously high attrition rates in pharmaceutical development programs. Computational modelling bears the promise to support cross-species extrapolation through mechanistic descriptions of relevant physiological processes. In this review, the benefits and limitations of computational approaches for clinical translation are discussed and the needs for future applications are outlined. A particular focus is laid on the differentiation between pharmacokinetics and pharmacodynamics. While the former determines drug exposure in plasma or specific tissues, the latter describes the resulting response, i.e. the therapeutic outcome or an adverse event. Based on a previous study it is argued that the main challenges for cross-species extrapolation is genetic divergence between different animal models and humans which will require novel inter-disciplinary concepts for clinical translation in the future.

Earthworms are important ecosystem engineers, and assessment of the risk of plant protection products toward them is part of the European environmental risk assessment (ERA). In the current ERA scheme, exposure and effects are represented simplistically and are not well integrated, resulting in uncertainty when the results are applied to ecosystems. Modeling offers a powerful tool to integrate the effects observed in lower tier laboratory studies with the environmental conditions under which exposure is expected in the field. This paper provides a summary of the (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop held 28–30 January 2020 in Düsseldorf, Germany. This workshop focused on toxicokinetic–toxicodynamic (TKTD) and population modeling of earthworms in the context of ERA. The goal was to bring together scientists from different stakeholder groups to discuss the current state of soil invertebrate modeling and to explore how earthworm modeling could be applied to risk assessments, in particular how the different model outputs can be used in the tiered ERA approach. In support of these goals, the workshop aimed at addressing the requirements and concerns of the different stakeholder groups to support further model development. The modeling approach included 4 submodules to cover the most relevant processes for earthworm risk assessment: environment, behavior (feeding, vertical movement), TKTD, and population. Four workgroups examined different aspects of the model with relevance for risk assessment, earthworm ecology, uptake routes, and cross‐species extrapolation and model testing. Here, we present the perspectives of each workgroup and highlight how the collaborative effort of participants from multidisciplinary backgrounds helped to establish common ground. In addition, we provide a list of recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products. Integr Environ Assess Manag 2021;17:352–363. © 2020 SETAC KEY POINTS: Earthworms are important ecosystem engineers that increase soil fertility, provide a wide range of ecosystem services, and are therefore included in risk assessments for pesticides.The (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop focused on toxicokinetic–toxicodynamic (TKTD) and population modeling of earthworms in the context of environmental risk assessment.Participants addressed various questions relevant to earthworm mechanistic effect modeling related to risk assessment, earthworm ecology, uptake routes, extrapolation and testing against experimental data sets, and ecotoxicological study needs and data gaps.Recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products are provided. [ABSTRACT FROM AUTHOR]

Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future. [ABSTRACT FROM AUTHOR]

In this study, the transfer of arsenic (As) from soil to corn grain was investigated in 18 soils collected from throughout China. The soils were treated with three concentrations of As and the transfer characteristics were investigated in the corn grain cultivar Zhengdan 958 in a greenhouse experiment. Through stepwise multiple-linear regression analysis, prediction models were developed combining the As bioconcentration factor (BCF) of Zhengdan 958 and soil pH, organic matter (OM) content, and cation exchange capacity (CEC). The possibility of applying the Zhengdan 958 model to other cultivars was tested through a cross-cultivar extrapolation approach. The results showed that the As concentration in corn grain was positively correlated with soil pH. When the prediction model was applied to non-model cultivars, the ratio ranges between the predicted and measured BCF values were within a twofold interval between predicted and measured values. The ratios were close to a 1:1 relationship between predicted and measured values. It was also found that the prediction model (Log [BCF]=0.064 pH-2.297) could effectively reduce the measured BCF variability for all non-model corn cultivars. The novel model is firstly developed for As concentration in crop grain from soil, which will be very useful for understanding the As risk in soil environment. [ABSTRACT FROM AUTHOR]

To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future. [ABSTRACT FROM AUTHOR]

Transfer of knowledge along the different phases of drug development is a fundamental process in pharmaceutical research. In particular, cross-species extrapolation between different laboratory animals and further on to first-in-human trials is challenging because of the uncertain comparability of physiological processes. Physiologically based pharmacokinetic ( PBPK) modeling allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical differences in between. We here evaluated different knowledge-driven approaches for cross-species extrapolation by systematically incorporating specific model parameter domains of a target species into the PBPK model of a reference species. Altogether, 15 knowledge-driven approaches were applied to murine and human PBPK models of 10 exemplary drugs resulting in 300 different extrapolations. Statistical analysis of the quality of the different extrapolations revealed not only species-specific physiology as the key determinant in cross-species extrapolation but also identified a synergistic effect when considering both kinetic rate constants and gene expression profiles of relevant enzymes and transporters. Moreover, we show that considering species-specific physiology, plasma protein binding, enzyme and transport kinetics, as well as tissue-specific gene expression profiles in PBPK modeling increases accuracy of cross-species extrapolations and thus supports first-in-human trials based on prior preclinical knowledge. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:191-206, 2015 [ABSTRACT FROM AUTHOR]

Internal dose metrics, as computed with pharmacokinetic models, are increasingly used as a means for extrapolating animal toxicological data to humans and to extrapolate across routes of administration. These internal dose metrics are thought to provide a more scientific means of comparing toxicological effects across animal species. The use of internal dose metrics is based on the universal assumption that toxic effects are equal across species if and only if the concentration of the toxic moieties in the target tissue is equal across species. Herein it is shown that this assumption is inconsistent with empirical toxicological data. It is shown that measurement of internal dose metrics in chronological time, as is done for AUC (Area under the concentration curve) and rate of metabolite production per kg of target tissue, does not produce equal toxic effects across species. A consequence of this observation is that the application of pharmacokinetics in risk assessments for such important chemicals as trichloroethylene, vinyl chloride, perchloroethylene, and perchlorate may need reassessment. [ABSTRACT FROM AUTHOR]

For acute inhalation toxicity assessment, I develop a conceptual framework for expressing combinations of intensity (air concentration) and duration that produce equivalent toxicity by examining how the shape of the body-burden uptake curve during a bout of inhalation interacts with various pharmacodynamic measures of the critical body burden needed to produce toxicity. If toxicity depends on attaining a critical tissue concentration, three existing empirical approaches—Haber's Law, the ten Berge equation, and pure air-concentration-dependence—are but local approximations to different parts of an overarching mathematical relationship. The compound-specific half-life of elimination determines the range of durations for which each applies: durations of one half-life or shorter follow Haber's Law, exposures of 4 or more half-lives follow pure air-concentration-dependence, and intermediate durations can be approximated by the ten Berge equation. Better animal-to-human extrapolation is achieved if exposure durations are expressed as number of species-specific half-lives. I consider several alternative pharmacodynamic criteria, such as the dependence of toxicity on time spent above a critical tissue concentration, or on the area under the tissue concentration curve, on the tissue concentration of a toxic metabolite, or on the imbalance of damage and repair processes. [ABSTRACT FROM AUTHOR]

Abstract: As part of a project designed to develop a framework for extrapolating acute central nervous system (CNS) effects of hydrocarbon solvents in animals to humans, experimental studies were conducted in rats and human volunteers in which acute CNS effects were measured and toxicokinetic data were collected. A complex hydrocarbon solvent, white spirit (WS) was used as a model solvent and two marker compounds for WS, 1,2,4-trimethyl benzene (TMB) and n-decane (NDEC), were analyzed to characterize internal exposure after WS inhalation. Toxicokinetic data on blood and brain concentrations of the two marker compounds in the rat, together with in vitro partition coefficients were used to develop physiologically based pharmacokinetic (PBPK) models for TMB and NDEC. The rat models were then allometrically scaled to obtain models for inhalatory exposure for man. The human models were validated with blood and alveolar air kinetics of TMB and NDEC, measured in human volunteers. Using these models, it was predicted that external exposures to WS in the range of 344–771mg/m3 would produce brain concentrations similar to those in rats exposed to 600mg/m3 WS, the no effect level (NOEL) for acute CNS effects. Assuming similar brain concentration–effect relations for humans and rats, the NOEL for acute CNS effects in humans should be in this range. The prediction was consistent with data from a human volunteer study in which the only statistically significant finding was a small change in the simple reaction time test following 4h exposure to approximately 570mg/m3 WS. Thus, the data indicated that the results of animal studies could be used to predict a no effect level for acute CNS depression in humans, consistent with the framework described above. [Copyright &y& Elsevier]

For a particular chemical, one can treat the chemical-by-chemical variation in rel-ative doses for equal toxicity in experimental animals and humans as a characteriza-tion of the likelihoods of extrapolation factors of different magnitudes. An emerging approach to noncancer risk assessment is to use such empirical distributions in place of fixed Uncertainty Factors. This paper discusses dividing the overall variation into two sub-distributions representing pharmacokinetic (PK) and pharmacodynamic (PD) contributions to the variation among chemicals in the animal-to-human toxi-cologically equivalent dose. If a physiologically based pharmacokinetic model (PBPK model) is used to derive a compound specific adjustment factor (CSAF) for the phar-macokinetic component, the deconvolution of the PK and PD components allows one to remove the PK component (to be replaced with the CSAF), while retaining the uncertainty in pharmacodynamics that PBPK models do not address. One must then add back the uncertainty in the PBPK determination of the CSAF (which may be considerable). A candidate criterion for whether one can use an uncertain PBPK model is whether the generic uncertainty about cross-species pharmacokinetics (re-flected in the PK component of the overall empirical distribution) is larger than the chemical-specific uncertainty in the determination of kinetically equivalent doses in experimental animals and humans. [ABSTRACT FROM AUTHOR]

The toxicity of four quinones, 2,3-dimethoxy-1,4-naphthoquinone (DMONQ), 2-methyl-1,4-naphthoquinone (MNQ), 1,4-naphthoquinone (NQ), and 1,4-benzoquinone (BQ), which redox cycle or arlyate in mammalian cells, was determined in isolated trout (Oncorhynchus mykiss) hepatocytes. More than 70% of cells died in 3 h when exposed to BQ or NQ; 50% died in 7 h when exposed to MNQ, with no mortality compared to controls after 7 h DMONQ exposure. A suite of biochemical parameters was assessed for ability to discriminate these reactivity pathways in fish. Rapid depletion of glutathione (GSH) with appearance of glutathione disulfide (GSSG) and increased dichlorofluoroscein fluorescence were used as indicators of redox cycling, noted with DMONQ, MNQ, and NQ. Depletion of GSH with no GSSG accumulation, and loss of free protein thiol (PrSH) groups (nonreducible) indicated direct arylation by BQ. All toxicants rapidly oxidized NADH, with changes in NADPH noted later (BQ, NQ, MNQ) or not at all (DMONQ). Biochemical measures including cellular energy status, cytotoxicity, and measures of reactive oxygen species, along with the key parameters of GSH and PrSH redox status, allowed differentiation of responses associated with lethality. Chemicals that arylate were more potent than redox cyclers. Toxic pathway discrimination is needed to group chemicals for potency predictions and identification of structural parameters associated with distinct types of reactive toxicity, a necessary step for development of mechanistically based quantitative structure-activity relationships (QSARs) to predict chemical toxic potential. The commonality of reactivity mechanisms between rodents and fish was also demonstrated, a step essential for species extrapolations. [ABSTRACT FROM PUBLISHER]

The scaling of administered doses to achieve equal degrees of toxic effect in different species has been relatively poorly examined for noncancer toxicity, either empirically or theoretically. We investigate empirical patterns in the correspondence of single oral dose LD50 values across several mammalian species for a large number of chemicals based on data reported in the RTECS(R) database maintained by the National Institute for Occupational Safety and Health. We find a good correspondence of LD50 values across species when the dose levels are expressed in terms of mg administered per kg of body mass. Our findings contrast with earlier analyses that supportscaling doses by the 3/4-power of body mass to achieve equal subacute toxicity of antineoplastic agents. We suggest that, especially for severe toxicity, single- and repeated-dosing regimes may have different cross-species scaling properties, as they may depend on standing levels of defenses and rate of regeneration of defenses, respectively. [ABSTRACT FROM AUTHOR]

Ecosystems are usually populated by many species. Each of these species carries the potential to show a different sensitivity towards all of the numerous chemical compounds that can be present in their environment. Since experimentally testing all possible species-chemical combinations is impossible, the ecological risk assessment of chemicals largely depends on cross-species extrapolation approaches. This review overviews currently existing cross-species extrapolation methodologies, and discusses i) how species sensitivity could be described, ii) which predictors might be useful for explaining differences in species sensitivity, and iii) which statistical considerations are important. We argue that risk assessment can benefit most from modelling approaches when sensitivity is described based on ecologically relevant and robust effects. Additionally, specific attention should be paid to heterogeneity of the training data (e.g. exposure duration, pH, temperature), since this strongly influences the reliability of the resulting models. Regarding which predictors are useful for explaining differences in species sensitivity, we review interspecies-correlation, relatedness-based, traits-based, and genomic-based extrapolation methods, describing the amount of mechanistic information the predictors contain, the amount of input data the models require, and the extent to which the different methods provide protection for ecological entities. We develop a conceptual framework, incorporating the strengths of each of the methods described. Finally, the discussion of statistical considerations reveals that regardless of the method used, statistically significant models can be found, although the usefulness, applicability, and understanding of these models varies considerably. We therefore recommend publication of scientific code along with scientific studies to simultaneously clarify modelling choices and enable elaboration on existing work. In general, this review specifies the data requirements of different cross-species extrapolation methods, aiming to make regulators and publishers more aware that access to raw- and meta-data needs to be improved to make future cross-species extrapolation efforts successful, enabling their integration into the regulatory environment. Unlabelled Image • Methods for the cross-species extrapolation of chemical sensitivity were overviewed. • Various descriptors of species sensitivity were surveyed. • Relatedness-, traits-, and genomic-predictors added mechanistic information. • An integrated framework combining approaches is suggested. • Statistical considerations important when extrapolating sensitivity are described. [ABSTRACT FROM AUTHOR]