Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses

Using available in vitro metabolite identification and time course kinetics for β-chloroprene and its metabolite, (1-chloroethenyl) oxirane, to include reactive oxidative metabolites and glutathione depletion in a PBPK model for β-chloroprene

Introduction: ß-chloroprene (2-chloro-1,3-butadiene; CP) causes lung tumors after inhalation exposures in rats and mice. Mice develop these tumors at lower exposures than rats. In rats CP exposures cause depletion of lung glutathione (GSH). Methods: PBPK models developed to relate the appearance of mouse lung tumors with rates of CP metabolism to reactive metabolites or total amounts metabolized during exposures have been expanded to include production of reactive metabolites from CP. The extended PBPK model describes both the unstable oxirane metabolite, 2-CEO, and metabolism of the more stable oxirane, 1-CEO, to reactive metabolites via microsomal oxidation to a diepoxide, and linked production of these metabolites to a PK model predicting GSH depletion with increasing CP exposure. Key information required to develop the model were available from literature studies identifying: 1) microsomal metabolites of CP, and 2) in vitro rates of clearance of CP and 1-CEO from active microsomal preparations from mice, rats, hamsters and humans. Results: Model simulation of concentration dependence of disproportionate increases in reactive metabolite concentrations as exposures increases and decreases in tissue GSH are consistent with the dose-dependence of tumor formation. At the middle bioassay concentrations with a lung tumor incidence, the predicted tissue GSH is less than 50% background. These simulations of reduction in GSH are also consistent with the gene expression results showing the most sensitive pathways are Nrf2-regulation of oxidative stress and GSH metabolism. Discussion: The PBPK model is used to correlate predicted tissue exposure to reactive metabolites with toxicity and carcinogenicity of CP.

Ultrasensitive dose-response for asbestos cancer risk implied by new inflammation-mutation model

Alterations in complex cellular phenotype each typically involve multistep activation of an ultrasensitive molecular switch (e.g., to adaptively initiate an apoptosis, inflammasome, Nrf2-ARE anti-oxidant, or heat-shock activation pathway) that triggers expression of a suite of target genes while efficiently limiting false-positive switching from a baseline state. Such switches exhibit nonlinear signal-activation relationships. In contrast, a linear no-threshold (LNT) dose-response relationship is expected for damage that accumulates in proportion to dose, as hypothesized for increased risk of cancer in relation to genotoxic dose according to the multistage somatic mutation/clonal-expansion theory of cancer, e.g., as represented in the Moolgavkar-Venzon-Knudsen (MVK) cancer model by a doubly stochastic nonhomogeneous Poisson process. Mesothelioma and lung cancer induced by exposure to carcinogenic (e.g., certain asbestos) fibers in humans and experimental animals are thought to involve modes of action driven by mutations, cytotoxicity-associated inflammation, or both, rendering ambiguous expectations concerning the nature of model-implied shape of the low-dose response for above-background increase in risk of incurring these endpoints. A recent Inflammation Somatic Mutation (ISM) theory of cancer posits instead that tissue-damage-associated inflammation that epigenetically recruits, activates and orchestrates stem cells to engage in tissue repair does not merely promote cancer, but rather is a requisite co-initiator (acting together with as few as two somatic mutations) of the most efficient pathway to any type of cancer in any reparable tissue (Dose-Response 2019; 17(2):1-12). This theory is reviewed, implications of this theory are discussed in relation to mesothelioma and lung cancer associated with chronic asbestos inhalation, one of the two types of ISM-required mutations is here hypothesized to block or impede inflammation resolution (e.g., by doing so for GPCR-mediated signal transduction by one or more endogenous autacoid specialized pro-resolving mediators or SPMs), and supporting evidence for this hypothesis is discussed.

Probabilistic methods for non-cancer health effects

Noncancer risk assessment methods and harmonization with cancer assessment methods have advanced from the simple divide a No Observed Adverse Effect Level (NOAEL) by a default safety factor or a linear extrapolation to background of the early 1980's. This advance is due in part due to groups such as the American Industrial Health Council, the National Institute of Environmental Health Sciences, the Society for Risk Analysis, the Society of Toxicology, and the U.S. Environmental Protection Agency (Bogdanffy et al., 2001), the National Academy of Sciences (1983 and 2009), the International Programme on Chemical Safety (2005, 2009), and to many independent researchers outside of and within a workshop series sponsored by the Alliance for Risk Assessment (ARA, 2023) prompted by the NAS (2009). Several of the case studies from this workshop series, and earlier work such as Bogdanffy et al. (2001), demonstrate that the dose response assessment of non-cancer toxicity and the harmonization of cancer and non-cancer methods are more than just a simple reflection of treating all non-cancer toxicity as if it has a threshold, or all cancer toxicity as if it did not. Moreover, one recommendation of NAS (2009) was to develop a problem formulation with risk managers prior to conducting any risk assessment. If the development of this problem formulation only necessitates the determination of a safe, or virtually safe dose, then the estimation of a Reference Dose (RfD) or virtually safe dose (VSD) or similar constructs should be encouraged. Not all of our environmental problems need a precise quantitative solution.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUND

Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets.

OBJECTIVES

In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties.

METHODS

We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process.

FINDINGS

Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa.

MAIN CONCLUSIONS

Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

Time-dependent genomic response in primary human uroepithelial cells exposed to arsenite for up to 60 days

Evidence from both in vivo and in vitro studies suggests that gene expression changes from long-term exposure to arsenite evolve markedly over time, including reversals in the direction of expression change in key regulatory genes. In this study, human uroepithelial cells from the ureter segments of 4 kidney-donors were continuously treated in culture with arsenite at concentrations of 0.1 or 1 μM for 60 days. Gene expression at 10, 20, 30, 40, and 60 days was determined using Affymetrix human genome microarrays and signal pathway analysis was performed using GeneGo Metacore. Arsenic treated cells continued to proliferate for the full 60-day period, whereas untreated cells ceased proliferating after approximately 30 days. A peak in the number of gene changes in the treated cells compared to untreated controls was observed between 30 and 40 days of exposure, with substantially fewer changes at 10 and 60 days, suggesting remodeling of the cells over time. Consistent with this possibility, the direction of expression change for a number of key genes was reversed between 20 and 30 days, including CFOS and MDM2. While the progression of gene changes was different for each subject, a common pattern was observed in arsenic treated cells over time, with early upregulation of oxidative stress responses (HMOX1, NQ01, TXN, TXNRD1) and down-regulation of immune/inflammatory responses (IKKα). At around 30 days, there was a transition to increased inflammatory and proliferative signaling (AKT, CFOS), evidence of epithelial-to-mesenchymal transition (EMT), and alterations in DNA damage responses (MDM2, ATM). A common element in the changing response of cells to arsenite over time appears to involve up-regulation of MDM2 by inflammatory signaling (through AP-1 and NF-κB), leading to inhibition of P53 function.

An eight-compound mixture but not corresponding concentrations of individual chemicals induces triglyceride accumulation in human liver cells

In real life, organisms are exposed to complex mixtures of chemicals at low concentration levels, whereas research on toxicological effects is mostly focused on single compounds at comparably high doses. Mixture effects deviating from the assumption of additivity, especially synergistic effects, are of concern. In an adverse outcome pathway (AOP)-guided manner, we analyzed the accumulation of triglycerides in human HepaRG liver cells by a mixture of eight steatotic chemicals (amiodarone, benzoic acid, cyproconazole, flusilazole, imazalil, prochloraz, propiconazole and tebuconazole), each present below its individual effect concentration at 1-3 μM. Pronounced and significantly enhanced triglyceride accumulation was observed with the mixture, and similar effects were seen at the level of pregnane-X-receptor activation, a molecular initiating event leading to hepatic steatosis. Transcript pattern analysis indicated subtle pro-steatotic changes at low compound concentrations, which did not exert measurable effects on cellular triglycerides. Mathematical modeling of mixture effects indicated potentially more than additive behavior using a model for compounds with similar modes of action. The present data underline the usefulness of AOP-guided in vitro testing for the identification of mixture effects and highlight the need for further research on chemical mixtures and harmonization of data interpretation of mixture effects.

A Simple Toxicokinetic Model Exhibiting Complex Dynamics and Nonlinear Exposure Response

Uncertainty in model predictions of exposure response at low exposures is a problem for risk assessment. A particular interest is the internal concentration of an agent in biological systems as a function of external exposure concentrations. Physiologically based pharmacokinetic (PBPK) models permit estimation of internal exposure concentrations in target tissues but most assume that model parameters are either fixed or instantaneously dose-dependent. Taking into account response times for biological regulatory mechanisms introduces new dynamic behaviors that have implications for low-dose exposure response in chronic exposure. A simple one-compartment simulation model is described in which internal concentrations summed over time exhibit significant nonlinearity and nonmonotonicity in relation to external concentrations due to delayed up- or downregulation of a metabolic pathway. These behaviors could be the mechanistic basis for homeostasis and for some apparent hormetic effects.

Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

One of the key features of a gene circuit is its input/output behavior. A few earlier publications attempted to develop methods to extract this behavior using transient transfection of circuit components in mammalian cells. However, the hitherto developed methods are only suitable for circuit with monomodal output distribution. Moreover, the relationship between the extracted I/O mapping and the "ground truth" that would have obtained with stably-integrated circuits, has not been addressed. Here we explore cell populations easily identifiable in flow cytometry data, namely, the peaks of fluorescent readout distribution in cells binned by the common expression value of the transfection reporter, or marker, gene. Using numerical simulations, we find that the distribution of circuit copy number in these cells deterministically depends on marker fluorescence in the noise-dependent manner. Moreover, we find that this is true also in the case of bi-modal output distribution. Using the peaks of input and output distributions, we are able to reconstruct the I/O mapping of the circuit and relate it to the I/O mapping of the stably-integrated circuit. The reconstruction is enabled by a new computational method we call PFAFF. The method is extensively validated with forward-simulated flow cytometry data from stable and transient transfections, with up to seven different circuits. The results show excellent correlation between the I/O behavior extracted by PFAFF from simulated transient transfection data, and the data simulated for stably integrated circuit.

A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health

The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.

Considerations for the Use of Mutation as a Regulatory Endpoint in Risk Assessment

Assessment of a chemical's potential to cause permanent changes in the genetic code has been a common practice in the industry and regulatory settings for decades. Furthermore, the genetic toxicity battery of tests has typically been employed during the earliest stages of the research and development programs of new product development. A positive outcome from such battery has a major impact on the chemical's utility, industrial hygiene, product stewardship practices, and product life cycle analysis, among many other decisions that need to be taken by the industry, even before the registration of a chemical is undertaken. Under the prevailing regulatory paradigm, the dichotomous (yes/no) evaluation of the chemical's genotoxic potential leads to a conservative, linear no-threshold (LNT) risk assessment, unless compelling and undeniable data to the contrary can be provided to satisfy regulators, typically in a number of different global jurisdictions. With the current advent of predictive methods, new testing paradigms, mode-of-action/adverse outcome pathways, and quantitative risk assessment approaches, various stakeholders are starting to employ these state-of-the-science methodologies to further the conversation on decision making and advance the regulatory paradigm beyond the dominant LNT status quo. This commentary describes these novel methodologies, relevant biological responses, and how these can affect internal and regulatory risk assessment approaches. Environ. Mol. Mutagen. 61:84-93, 2020. © 2019 Wiley Periodicals, Inc.

ROS and pentose phosphate pathway: mathematical modelling of the metabolic regulation in response to xenobiotic-induced oxidative stress and the proposed Impact of the gluconate shunt

Elevated intracellular levels of reactive oxygen species (ROS), e.g. resulting from exposure to xenobiotics, can cause severe damages. Antioxidant defence mechanisms, which involve regulation of enzyme activities, protect cells to a certain extent. Nevertheless, continuous or increased exposure can overwhelm this system resulting in an adverse cellular state. To simulate exposure scenarios and to investigate the transition to an adverse cellular state, a mathematical model for the dynamics of ROS in response to xenobiotic-induced oxidative stress has been developed. It is based on exposure experiments of human urothelial cells (RT4) to the nitrated polycyclic aromatic hydrocarbon 3-nitrobenzanthrone (3-NBA), a component of diesel engine exhaust, and takes into account the following metabolic pathways of the antioxidant defence system: glutathione redox cycle scavenging directly ROS, the pentose phosphate pathway and the gluconate shunt as NADPH supplier and the beginning of glycolysis. In addition, ROS generation due to the bioactivation of 3-NBA has been implemented. The regulation of enzyme activities plays an important role in the presented mathematical model. The in silico model consists of ordinary differential equations on the basis of enzyme kinetics and mass action for the metabolism of 3-NBA. Parameters are either estimated from performed in vitro experiments via least-squares fitting or obtained from the literature. The results underline the importance of the pentose phosphate pathway to cope with oxidative stress and suggest an important role of the gluconate shunt during low-dose exposure.

Statistical Approach to Identify Threshold and Point of Departure in Dose-Response Data

The study presents an integrated, rigorous statistical approach to define the likelihood of a threshold and point of departure (POD) based on dose-response data using nested family of bent-hyperbola models. The family includes four models: the full bent-hyperbola model, which allows for transition between two linear regiments with various levels of smoothness; a bent-hyperbola model reduced to a spline model, where the transition is fixed to a knot; a bent-hyperbola model with a restricted negative asymptote slope of zero, named hockey-stick with arc (HS-Arc); and spline model reduced further to a hockey-stick type model (HS), where the first linear segment has a slope of zero. A likelihood-ratio test is used to discriminate between the models and determine if the more flexible versions of the model provide better or significantly better fit than a hockey-stick type model. The full bent-hyperbola model can accommodate both threshold and nonthreshold behavior, can take on concave up and concave down shapes with various levels of curvature, can approximate the biochemically relevant Michaelis-Menten model, and even be reduced to a straight line. Therefore, with the use of this model, the presence or absence of a threshold may even become irrelevant and the best fit of the full bent-hyperbola model be used to characterize the dose-response behavior and risk levels, with no need for mode of action (MOA) information. Point of departure (POD), characterized by exposure level at which some predetermined response is reached, can be defined using the full model or one of the better fitting reduced models.

Dynamics of the Gene Regulatory Network of HIV-1 and the Role of Viral Non-coding RNAs on Latency Reversion

The use of latency reversing agents (LRAs) is currently a promising approach to eliminate latent reservoirs of HIV-1. However, this strategy has not been successful in vivo. It has been proposed that cellular post-transcriptional mechanisms are implicated in the underperformance of LRAs, but it is not clear whether proviral regulatory elements like viral non-coding RNAs (vncRNAs) are also implicated. In order to visualize the complexity of the HIV-1 gene expression, we used experimental data to construct a gene regulatory network (GRN) of latent proviruses in resting CD4+ T cells. We then analyzed the dynamics of this GRN using Boolean and continuous mathematical models. Our simulations predict that vncRNAs are able to counteract the activity of LRAs, which may explain the failure of these compounds to reactivate latent reservoirs of HIV-1. Moreover, our results also predict that using inhibitors of histone methyltransferases, such as chaetocin, together with releasers of the positive transcription elongation factor (P-TEFb), like JQ1, may increase proviral reactivation despite self-repressive effects of vncRNAs.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

NADPH Oxidases and Their Roles in Skin Homeostasis and Carcinogenesis

SIGNIFICANCE

Skin protects the body from dehydration, pathogens, and external mutagens. NADPH oxidases are central components for regulating the cellular redox balance. There is increasing evidence indicating that reactive oxygen species (ROS) generated by members of this enzyme family play important roles in the physiology and pathophysiology of the skin. Recent Advances: NADPH oxidases are active producers of ROS such as superoxide and hydrogen peroxide. Different isoforms are found in virtually all tissues. They play pivotal roles in normal cell homeostasis and in the cellular responses to various stressors. In particular, these enzymes are integral parts of redox-sensitive prosurvival and proapoptotic signaling pathways, in which they act both as effectors and as modulators. However, continuous (re)activation of NADPH oxidases can disturb the redox balance of cells, in the worst-case scenario in a permanent manner. Abnormal NADPH oxidase activity has been associated with a wide spectrum of diseases, as well as with aging and carcinogenesis.

CRITICAL ISSUES

Sunlight with its beneficial and deleterious effects induces the activation of NADPH oxidases in the skin. Evidence for the important roles of this enzyme family in skin cancer and skin aging, as well as in many chronic skin diseases, is now emerging.

FUTURE DIRECTIONS

Understanding the precise roles of NADPH oxidases in normal skin homeostasis, in the cellular responses to solar radiation, and during carcinogenesis will pave the way for their validation as therapeutic targets not only for the prevention and treatment of skin cancers but also for many other skin-related disorders. Antioxid. Redox Signal. 28, 1238-1261.

Human-relevant potency threshold (HRPT) for ERα agonism

The European Commission has recently proposed draft criteria for the identification of endocrine disrupting chemicals (EDCs) that pose a significant hazard to humans or the environment. Identifying and characterizing toxic hazards based on the manner by which adverse effects are produced rather than on the nature of those adverse effects departs from traditional practice and requires a proper interpretation of the evidence regarding the chemical’s ability to produce physiological effect(s) via a specific mode of action (MoA). The ability of any chemical to produce a physiological effect depends on its pharmacokinetics and the potency by which it acts via the various MoAs that can lead to the particular effect. A chemical’s potency for a specific MoA—its mechanistic potency—is determined by two properties: (1) its affinity for the functional components that comprise the MoA, i.e., its specific receptors, enzymes, transporters, transcriptional elements, etc., and (2) its ability to alter the functional state of those components (activity). Using the agonist MoA via estrogen receptor alpha, we illustrate an empirical method for determining a human-relevant potency threshold (HRPT), defined as the minimum level of mechanistic potency necessary for a chemical to be able to act via a particular MoA in humans. One important use for an HRPT is to distinguish between chemicals that may be capable of, versus those likely to be incapable of, producing adverse effects in humans via the specified MoA. The method involves comparing chemicals that have different ERα agonist potencies with the ability of those chemicals to produce ERα-mediated agonist responses in human clinical trials. Based on this approach, we propose an HRPT for ERα agonism of 1E-04 relative to the potency of the endogenous estrogenic hormone 17β-estradiol or the pharmaceutical estrogen, 17α-ethinylestradiol. This approach provides a practical way to address Hazard Identification according to the draft criteria for identification of EDCs recently proposed by the European Commission.

Linear-No-Threshold Default Assumptions are Unwarranted for Cytotoxic Endpoints Independently Triggered by Ultrasensitive Molecular Switches

Crump's response in this issue to my critique of linear-no-threshold (LNT) default assumptions for noncancer and nongenotoxic cancer risks (Risk Analysis 2016; 36(3):589-604) is rebutted herein. Crump maintains that distinguishing between a low-dose linear dose response and a threshold dose response on the basis of dose-response data is impossible even for endpoints involving increased cytotoxicity. My rebuttal relies on descriptions and specific illustrations of two well-characterized ultrasensitive molecular switches that govern two key cytoprotective responses to cellular stress-heat shock response and antioxidant response element activation, respectively-each of which serve to suppress stress-induced apoptotic cell death unless overwhelmed. Because detailed dose-response data for each endpoint is shown to be J- or inverted-J-shaped with high confidence, and because independent pathways can explain background rates of apoptosis, LNT assumptions for this cytotoxic endpoint are unwarranted, at least in some cases and perhaps generally.

How well can carcinogenicity be predicted by high throughput "characteristics of carcinogens" mechanistic data?

IARC has begun using ToxCast/Tox21 data in efforts to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations and this implicit inference approach also is being considered by USEPA. To determine how well ToxCast/Tox21 data can explicitly predict cancer hazard, this approach was evaluated with statistical analyses and machine learning prediction algorithms. Substances USEPA previously classified as having cancer hazard potential were designated as positives and substances not posing a carcinogenic hazard were designated as negatives. Then ToxCast/Tox21 data were analyzed both with and without adjusting for the cytotoxicity burst effect commonly observed in such assays. Using the same assignments as IARC of ToxCast/Tox21 assays to the seven key characteristics of carcinogens, the ability to predict cancer hazard for each key characteristic, alone or in combination, was found to be no better than chance. Hence, we have little scientific confidence in IARC's inference models derived from current ToxCast/Tox21 assays for key characteristics to predict cancer. This finding supports the need for a more rigorous mode-of-action pathway-based framework to organize, evaluate, and integrate mechanistic evidence with animal toxicity, epidemiological investigations, and knowledge of exposure and dosimetry to evaluate potential carcinogenic hazards and risks to humans.

Understanding Robust Adaptation Dynamics of Gene Regulatory Network

Robust adaptation is a critical attribute for gene regulatory network (GRN), understanding the relationship between adaptation and the GRN topology, and corresponding parameters is a challenging issue. The work in this paper includes: first, seven constraint multiobjective optimization algorithms are used to find sufficient solutions to get more reliable statistic rules. Meanwhile, the algorithms are compared to facilitate the future algorithm selection; second, a fuzzy c-mean algorithm is used to analyze solutions and to classify the solutions into different groups; third, the histogram analysis for all satisfactory solutions shows the preferred parameter range, i.e., parameter motif. The contributions of this paper includes: 1) Two new adaptation indices i.e., peak time and settle down time, are proposed for the first time to give more accurate description of the robust adaptation. Our conclusion is that some solutions even with satisfactory sensitivity and precision are not practically of robust adaptation because of too long time needed. 2) The relationship between topology, parameter set, and robust adaptation of GRN is discovered in the sense of both preferred topology and parameter motif. Our conclusion is that the robust adaptation depends more on the GRN topology than the model parameter set in two feasible topologies.

Low-Dose Dose-Response for In Vitro Nrf2-ARE Activation in Human HepG2 Cells

Kelch-like ECH-associated protein 1 (Keap1), nuclear factor erythroid 2-like factor 2-related factor 2 (Nrf2), and the antioxidant response element (ARE) are interacting components of a master regulatory signaling pathway that coordinates redox homeostasis, cytoprotective responses, and shifts in stem cell state. This study reexamined detailed dose–response (DR) data reported for in vitro Nrf2-ARE activation in human hepatoblastoma HepG2 cell lines containing either a ARE-bla or ARE-luc reporter at 12 different concentrations of each of 15 chemicals. The normalized study data were combined among chemicals exhibiting a positive response, yielding n = 531 (179) DR data for 9 (7) chemicals using the ARE-bla (ARE-luc) assay. Three-parameter linear/kth-power regression fits obtained to each combined set of ARE-bla- or ARE-luc-assay response data provided good fits (R² = .99 or .91, respectively, P_fit > .99) that each incorporate a highly significant negative initial linear slope (P = 4 × 10⁻⁵ or .00025) and an overall J-shaped DR pattern. Results from this reanalysis of high-resolution ARE response data support the hypothesis that nonlinear ARE-mediated adaptive cellular responses to oxidative stress are governed by an ultrasensitive molecular switch.

Uncertainties in biological responses that influence hazard and risk approaches to the regulation of endocrine active substances

Endocrine-disrupting substances (EDS) may have certain biological effects including delayed effects, multigenerational effects, and may display nonmonotonic dose-response (NMDR) relationships that require careful consideration when determining environmental hazards. Endocrine disrupting substances can have specific and profound effects when exposure occurs during sensitive windows of the life cycle (development, reproduction). This creates the potential for delayed effects that manifest when exposure has ceased, possibly in a different life stage. This potential underscores the need for testing in appropriate (sensitive) life stages and full life cycle designs. Such tests are available in the Organisation for Economic Co-operation and Development (OECD) tool box and should be used to derive endpoints that can be considered protective of all life stages. Similarly, the potential for effects to be manifest in subsequent generations (multigenerational effects) has also been raised as a potential issue in the derivation of appropriate endpoints for EDS. However, multigenerational studies showing increasing sensitivity of successive generations are uncommon. Indeed this is reflected in the design of new higher tier tests to assess endocrine active substances (EAS) that move to extended one-generation designs and away from multi-generational studies. The occurrence of NMDRs is also considered a limiting factor for reliable risk assessment of EDS. Evidence to date indicates NMDRs are more prevalent in in vitro and mechanistic data, not often translating to adverse apical endpoints that would be used in risk assessment. A series of steps to evaluate NMDRs in the context of endocrine hazard and risk assessment procedures is presented. If careful consideration of delayed, multigenerational effects and NMDRs is made, it is feasible to assess environmental endocrine hazards and derive robust apical endpoints for risk assessment procedures ensuring a high level of environmental protection. Integr Environ Assess Manag 2017;13:293-301. © 2016 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

The influence of interindividual variability is not typically assessed in traditional toxicological studies. Given that chemical exposures occur in heterogeneous populations, this knowledge gap has the potential to cause undue harm within the realms of public health and industrial and municipal finances. A recent report from the National Research Council (NRC) suggests that when accounting for interindividual variation in responses, traditionally assumed nonlinear dose-response relationships (DRRs) for noncancer-causing endpoints would better be explained with a linear relationship within the low-dose region. To address this knowledge gap and directly test the NRC's assumption, this study focused on assessing the DRR between 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) exposure and immune suppression in a cohort of unique human donors. Human B cells were isolated from 51 individual donors and treated with logarithmically increasing concentrations of TCDD (0-30 nM TCDD). Two endpoints sensitive to TCDD were assessed: (1) number of IgM-secreting B cells and (2) quantity of IgM secreted. The results show that TCDD significantly suppressed both the number of IgM-secreting B cells and the quantity of IgM secreted (P < .05). Statistical model comparisons indicate that the low-dose region of the two DRRs is best explained with a nonlinear relationship. Rather than assuming low-dose linearity for all noncancer-causing DRRs, our study suggests the need to consider the specific mode of action of toxicants and pharmaceuticals during risk-management decision making.

Hormetic effect and mechanism of imidazolium-based ionic liquids on the nematode Caenorhabditis elegans

In the present study, we used Caenorhabditis elegans assay system to investigate in hormetic effects of imidazolium-based bromide Ionic Liquids (ILs) and explored the possible underlying mechanism. Firstly, C. elegans was treated with ILs with different alkyl chain lengths at different concentrations. We found that exposure to ILs at 0.01 mg/L extended the mean lifespan of C. elegans and the ILs with longer alkyl chain showed more obvious effects. To investigate the possible mechanism, the nematodes were exposed to the three ILs at 0.01 mg/L for 2, 5, 7, 9 and 11 days. The levels of reactive oxygen species (ROS) in C. elegans increased significantly when treated for 2 days and then declined gradually compared to those of respective controls as time went on. After exposure for 11 days, the ROS levels and liposuscin accumulation were significantly lower in the treated groups than those of control group. Meanwhile, the expression of aging-related genes sod-5 and daf-16 were both massively up-regulated for the three ILs examined. Our results show that low concentration of ILs exert hormetic effect on C. elegans. ROS generation and expression of aging-related genes may play important roles in the IL-induced hormetic effect on C. elegans.

Using Transcriptomics to Evaluate Thresholds in Genotoxicity Dose–Response

Several genotoxic chemicals have been reported to produce threshold-shaped dose–response curves for mutation and genotoxicity assays, both in vivo and in vitro, challenging the current default practice for risk assessment of genotoxic chemicals, which assumes a linear dose–response below the lowest tested dose. Statistical methods cannot determine whether a biological threshold exists with sufficient confidence to overturn this assumption of linearity. Indeed, to truly define the shape of the dose–response curves, we must look to the underlying biology and develop targeted experiments to identify and measure the key processes governing the response of the cell to DNA damage. This chapter describes a series of studies aimed at defining the key transcriptional responses. Two approaches were taken to evaluate transcriptional responses preventing micronucleus induction: (1) comparison of gene signatures for several prototype compounds at a single chemical dose that led to a similar activation of the p53-DNA damage pathway (i.e. 1.5-fold increase in total p53); and (2) evaluation of a subset of chemicals with in-depth dose–response studies. The goal of these efforts was to determine the transcriptional pathways responsible for maintaining homeostasis at low levels of DNA damage, i.e., the biological underpinning of threshold-shaped dose–response curves for mutagenicity.

Adaptive Posttranslational Control in Cellular Stress Response Pathways and Its Relationship to Toxicity Testing and Safety Assessment

Although transcriptional induction of stress genes constitutes a major cellular defense program against a variety of stressors, posttranslational control directly regulating the activities of preexisting stress proteins provides a faster-acting alternative response. We propose that posttranslational control is a general adaptive mechanism operating in many stress pathways. Here with the aid of computational models, we first show that posttranslational control fulfills two roles: (1) handling small, transient stresses quickly and (2) stabilizing the negative feedback transcriptional network. We then review the posttranslational control pathways for major stress responses-oxidative stress, metal stress, hyperosmotic stress, DNA damage, heat shock, and hypoxia. Posttranslational regulation of stress protein activities occurs by reversible covalent modifications, allosteric or non-allosteric enzymatic regulations, and physically induced protein structural changes. Acting in feedback or feedforward networks, posttranslational control may establish a threshold level of cellular stress. Sub-threshold stresses are handled adequately by posttranslational control without invoking gene transcription. With supra-threshold stress levels, cellular homeostasis cannot be maintained and transcriptional induction of stress genes and other gene programs, eg, those regulating cell metabolism, proliferation, and apoptosis, takes place. The loss of homeostasis with consequent changes in cellular function may lead to adverse cellular outcomes. Overall, posttranslational and transcriptional control pathways constitute a stratified cellular defense system, handling stresses coherently across time and intensity. As cell-based assays become a focus for chemical testing anchored on toxicity pathways, examination of proteomic and metabolomic changes as a result of posttranslational control occurring in the absence of transcriptomic alterations deserves more attention.

Linear-No-Threshold Default Assumptions for Noncancer and Nongenotoxic Cancer Risks: A Mathematical and Biological Critique

To improve U.S. Environmental Protection Agency (EPA) dose-response (DR) assessments for noncarcinogens and for nonlinear mode of action (MOA) carcinogens, the 2009 NRC Science and Decisions Panel recommended that the adjustment-factor approach traditionally applied to these endpoints should be replaced by a new default assumption that both endpoints have linear-no-threshold (LNT) population-wide DR relationships. The panel claimed this new approach is warranted because population DR is LNT when any new dose adds to a background dose that explains background levels of risk, and/or when there is substantial interindividual heterogeneity in susceptibility in the exposed human population. Mathematically, however, the first claim is either false or effectively meaningless and the second claim is false. Any dose-and population-response relationship that is statistically consistent with an LNT relationship may instead be an additive mixture of just two quasi-threshold DR relationships, which jointly exhibit low-dose S-shaped, quasi-threshold nonlinearity just below the lower end of the observed "linear" dose range. In this case, LNT extrapolation would necessarily overestimate increased risk by increasingly large relative magnitudes at diminishing values of above-background dose. The fact that chemically-induced apoptotic cell death occurs by unambiguously nonlinear, quasi-threshold DR mechanisms is apparent from recent data concerning this quintessential toxicity endpoint. The 2009 NRC Science and Decisions Panel claims and recommendations that default LNT assumptions be applied to DR assessment for noncarcinogens and nonlinear MOA carcinogens are therefore not justified either mathematically or biologically.

Inhalation threshold of toxicological concern (TTC) - Structural alerts discriminate high from low repeated-dose inhalation toxicity

The threshold of toxicological concern (TTC) of a compound represents an exposure value below which the associated human health risk is considered negligible. As such, this approach offers assessing the risk of potential toxicants when little or no toxicological information is available. For the inhalation repeated-dose TTC, the goal was to derive structural alerts that discriminate between high- and low-toxic compounds. A further aim was to identify physicochemical parameters related to the inhalation-specific bioavailability of the compounds, and to explore their use as predictors of high vs low toxicity. 296 compounds with subacute, subchronic and chronic inhalation toxicity NOEC (no-observed effect concentration) values were subdivided into three almost equal-sized high-, medium- and low-toxic (HTox, MTox, LTox) potency classes. Whereas the derived 14 HTox and 7 LTox structural alerts yield an only moderate discrimination between these three groups, the high-toxic vs low-toxic mis-classification is very low: LTox-predicted compounds are not HTox to 97.5%, and HTox-predicted compounds not LTox to 88.6%. The probability of a compound being HTox vs LTox is triggered further by physicochemical properties encoding the tendency to evaporate from blood. The new structural alerts may aid in the predictive inhalation toxicity assessment of compounds as well as in designing low-toxicity chemicals, and provide a rationale for the chemistry underlying the toxicological outcome that can also be used for scoping targeted experimental studies.

How well can in vitro data predict in vivo effects of chemicals? Rodent carcinogenicity as a case study

A recent research article by the National Center for Computational Toxicology (NCCT) (Kleinstreuer et al., 2013), indicated that high throughput screening (HTS) data from assays linked to hallmarks and presumed pathways of carcinogenesis could be used to predict classification of pesticides as either (a) possible, probable or likely rodent carcinogens; or (b) not likely carcinogens or evidence of non-carcinogenicity. Using independently developed software to validate the computational results, we replicated the majority of the results reported. We also found that the prediction model correlating cancer pathway bioactivity scores with in vivo carcinogenic effects in rodents was not robust. A change of classification of a single chemical in the test set was capable of changing the overall study conclusion about the statistical significance of the correlation. Furthermore, in the subset of pesticide compounds used in model validation, the accuracy of prediction was no better than chance for about three quarters of the chemicals (those with fewer than 7 positive outcomes in HTS assays representing the 11 histopathological endpoints used in model development), suggesting that the prediction model was not adequate to predict cancer hazard for most of these chemicals. Although the utility of the model for humans is also unclear because a number of the rodent responses modeled (e.g., mouse liver tumors, rat thyroid tumors, rat testicular tumors, etc.) are not considered biologically relevant to human responses, the data examined imply the need for further research with HTS assays and improved models, which might help to predict classifications of in vivo carcinogenic responses in rodents for the pesticide considered, and thus reduce the need for testing in laboratory animals.

Approaches for characterizing threshold dose-response relationships for DNA-damage pathways involved in carcinogenicity in vivo and micronuclei formation in vitro

Assessing the shape of dose-response curves for DNA-damage in cellular systems and for the consequences of DNA damage in intact animals remains a controversial topic. This overview looks at aspects of the pharmacokinetics (PK) and pharmacodynamics (PD) of cellular DNA-damage/repair and their role in defining the shape of dose-response curves using an in vivo example with formaldehyde and in vitro examples for micronuclei (MN) formation with several test compounds. Formaldehyde is both strongly mutagenic and an endogenous metabolite in cells. With increasing inhaled concentrations, there were transitions in gene changes, from activation of selective stress pathway genes at low concentrations, to activation of pathways for cell-cycle control, p53-DNA damage, and stem cell niche pathways at higher exposures. These gene expression changes were more consistent with dose-dependent transitions in the PD responses to formaldehyde in epithelial cells in the intact rat rather than the low-dose linear extrapolation methods currently used for carcinogens. However, more complete PD explanations of non-linear dose response for creation of fixed damage in cells require detailed examination of cellular responses in vitro using measures of DNA damage and repair that are not easily accessible in the intact animal. In the second section of the article, we illustrate an approach from our laboratory that develops fit-for-purpose, in vitro assays and evaluates the PD of DNA damage and repair through studies using prototypical DNA-damaging agents. Examination of a broad range of responses in these cells showed that transcriptional upregulation of cell cycle control and DNA repair pathways only occurred at doses higher than those causing overt damage fixed damage-measured as MN formation. Lower levels of damage appear to be handled by post-translational repair process using pre-existing proteins. In depth evaluation of the PD properties of one such post-translational process (formation of DNA repair centers; DRCs) has indicated that the formation of DRCs and their ability to complete repair before replication are consistent with threshold behaviours for mutagenesis and, by extension, with chemical carcinogenesis.

A PGC-1α-Mediated Transcriptional Network Maintains Mitochondrial Redox and Bioenergetic Homeostasis against Doxorubicin-Induced Toxicity in Human Cardiomyocytes: Implementation of TT21C

Chemical toxicity testing is fast moving in a direction that relies increasingly on cell-basedin vitroassays anchored on toxicity pathways according to the toxicity testing in the 21st century vision. Identifying points of departure (POD) via these assays and revealing their mechanistic underpinnings via computational modeling of the relevant pathways are critical and challenging steps. Here we used doxorubicin (DOX) as a prototype chemical to study mitochondrial toxicity in human AC16 cells. Mitochondrial toxicity has been linked to cardiovascular risk of DOX, which has limited its clinical use as an antitumor drug. Ourin vitrostudy revealed a well-defined POD concentration of DOX below which adaptive induction of proliferator-activated receptor-γ coactivator-1α (PGC-1α) -mediated mitochondrial genes, including NRF-1, MnSOD, UCP2, and COX1, concurred with negligible changes in mitochondrial superoxide and cytotoxicity. At higher DOX concentrations adversity became significant with elevated superoxide and suppressed ATP levels. A computational model was formulated to simulate the PGC-1α-mediated transcriptional network comprising multiple negative feedback loops that underlie redox and bioenergetics homeostasis in the mitochondrion. The model recapitulated the transition phase from adaptive to adverse responses, supporting the notion that saturated induction of PGC-1α-mediated gene network underpins POD. The model further predicts (follow-up experiments verified) that silencing PGC-1α compromises the adaptive function of the transcriptional network, leading to disruption of mitochondria and cytotoxicity at lower DOX concentrations. In summary, our study demonstrates that combining pathway-focusedin vitroassays and computational simulation of relevant biochemical network is synergistic for understanding dose-response behaviors in the low-dose region and identifying POD.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Contributions of DNA repair and damage response pathways to the non-linear genotoxic responses of alkylating agents

From a risk assessment perspective, DNA-reactive agents are conventionally assumed to have genotoxic risks at all exposure levels, thus applying a linear extrapolation for low-dose responses. New approaches discussed here, including more diverse and sensitive methods for assessing DNA damage and DNA repair, strongly support the existence of measurable regions where genotoxic responses with increasing doses are insignificant relative to control. Model monofunctional alkylating agents have in vitro and in vivo datasets amenable to determination of points of departure (PoDs) for genotoxic effects. A session at the 2013 Society of Toxicology meeting provided an opportunity to survey the progress in understanding the biological basis of empirically-observed PoDs for DNA alkylating agents. Together with the literature published since, this review discusses cellular pathways activated by endogenous and exogenous alkylation DNA damage. Cells have evolved conserved processes that monitor and counteract a spontaneous steady-state level of DNA damage. The ubiquitous network of DNA repair pathways serves as the first line of defense for clearing of the DNA damage and preventing mutation. Other biological pathways discussed here that are activated by genotoxic stress include post-translational activation of cell cycle networks and transcriptional networks for apoptosis/cell death. The interactions of various DNA repair and DNA damage response pathways provide biological bases for the observed PoD behaviors seen with genotoxic compounds. Thus, after formation of DNA adducts, the activation of cellular pathways can lead to the avoidance of a mutagenic outcome. The understanding of the cellular mechanisms acting within the low-dose region will serve to better characterize risks from exposures to DNA-reactive agents at environmentally-relevant concentrations.

Dose and Effect Thresholds for Early Key Events in a PPARα-Mediated Mode of Action

Current strategies for predicting adverse health outcomes of environmental chemicals are centered on early key events in toxicity pathways. However, quantitative relationships between early molecular changes in a given pathway and later health effects are often poorly defined. The goal of this study was to evaluate short-term key event indicators using qualitative and quantitative methods in an established pathway of mouse liver tumorigenesis mediated by peroxisome proliferator-activated receptor alpha (PPARα). Male B6C3F1 mice were exposed for 7 days to di (2-ethylhexyl) phthalate (DEHP), di-n-octyl phthalate (DNOP), and n-butyl benzyl phthalate (BBP), which vary in PPARα activity and liver tumorigenicity. Each phthalate increased expression of select PPARα target genes at 7 days, while only DEHP significantly increased liver cell proliferation labeling index (LI). Transcriptional benchmark dose (BMDT) estimates for dose-related genomic markers stratified phthalates according to hypothetical tumorigenic potencies, unlike BMDs for non-genomic endpoints (relative liver weights or proliferation). The 7-day BMDT values for Acot1 as a surrogate measure for PPARα activation were 29, 370, and 676 mg/kg/day for DEHP, DNOP, and BBP, respectively, distinguishing DEHP (liver tumor BMD of 35 mg/kg/day) from non-tumorigenic DNOP and BBP. Effect thresholds were generated using linear regression of DEHP effects at 7 days and 2-year tumor incidence values to anchor early response molecular indicators and a later phenotypic outcome. Thresholds varied widely by marker, from 2-fold (Pdk4 and proliferation LI) to 30-fold (Acot1) induction to reach hypothetical tumorigenic expression levels. These findings highlight key issues in defining thresholds for biological adversity based on molecular changes.

Toxicology: a discipline in need of academic anchoring--the point of view of the German Society of Toxicology

The paper describes the importance of toxicology as a discipline, its past achievements, current scientific challenges, and future development. Toxicological expertise is instrumental in the reduction of human health risks arising from chemicals and drugs. Toxicological assessment is needed to evaluate evidence and arguments, whether or not there is a scientific base for concern. The immense success already achieved by toxicological work is exemplified by reduced pollution of air, soil, water, and safer working places. Predominantly predictive toxicological testing is derived from the findings to assess risks to humans and the environment. Assessment of the adversity of molecular effects (including epigenetic effects), the effects of mixtures, and integration of exposure and biokinetics into in vitro testing are emerging challenges for toxicology. Toxicology is a translational science with its base in fundamental science. Academic institutions play an essential part by providing scientific innovation and education of young scientists.

The human toxome project

The Human Toxome Project, funded as an NIH Transformative Research grant 2011-2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectroscopy-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies.