IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

NAM-supported read-across: From case studies to regulatory guidance in safety assessment

The use of new approach methodologies (NAMs) in support of read-across (RAx) approaches for regulatory purposes is a main goal of the EU-ToxRisk project. To bring this forward, EU-ToxRisk partners convened a workshop in close collaboration with regulatory representatives from key organizations including European regulatory agencies, such as the European Chemicals Agency (ECHA) and the European Food Safety Authority (EFSA), as well as the Scientific Committee on Consumer Safety (SCCS), national agencies from several European countries, Japan, Canada and the USA, as well as the Organisation for Economic Cooperation and Development (OECD). More than a hundred people actively participated in the discussions, bringing together diverse viewpoints across academia, regulators and industry. The discussion was organized starting from five practical cases of RAx applied to specific problems that offered the oppor-tunity to consider real examples. There was general consensus that NAMs can improve confidence in RAx, in particular in defining category boundaries as well as characterizing the similarities/dissimilarities between source and target substances. In addition to describing dynamics, NAMs can be helpful in terms of kinetics and metabolism that may play an important role in the demonstration of similarity or dissimilarity among the members of a category. NAMs were also noted as effective in providing quanti-tative data correlated with traditional no observed adverse effect levels (NOAELs) used in risk assessment, while reducing the uncertainty on the final conclusion. An interesting point of view was the advice on calibrating the number of new tests that should be carefully selected, avoiding the allure of "the more, the better". Unfortunately, yet unsurprisingly, there was no single approach befitting every case, requiring careful analysis delineating the optimal approach. Expert analysis and assessment of each specific case is still an important step in the process.

Enhancing Natural Product Clinical Trials (P13-037-19)

Objectives

To discuss good practices and criteria for optimal design and interpretation of pre-clinical and clinical natural product (NP) research in order to increase benefit from our investment in NP clinical trials (CT).

Background: Large, randomized, controlled CT often fail to reject the null hypothesis or show rigorous evidence of benefit. This includes recent large, NIH-supported CT of nutrients such as vitamin D and selenium, and of botanical dietary supplements. Negative and positive outcomes may be equally important for public health, but because large CT cost at least $20 M each, plus opportunity costs, it is important that CT designs maximize the yield of actionable information regardless of outcome.

Methods

Experts and stakeholders from academia, government and the private sector collaboratively developed a broadly attended workshop in which good practices to enhance rigor, reproducibility and translational relevance were discussed.

Results

N/A.

Conclusions

Critical issues in CT design include product identity, reproducibility and pharmacology (where feasible), power to test a primary outcome significant to consumers, and placebo controls. When basing a CT on traditional use or prior in vitro or in vivo studies, similarity of product (e.g., source identity, methods of preparation, form and intake), health outcome and population (e.g., age, sex, genetics, diet and environment), require careful consideration. Appropriate controls for known types of in vitro assay interference (e.g., aggregation, membrane disruption, protein denaturation) are imperative. Compounds with limited bioavailability, or activity only at concentrations above those achievable by ingestion, are likely poor candidates for dietary CT. Translational validity of model systems should be carefully assessed. Appropriate analyses (e.g., p-curve and meta-regression methods) should be used to obtain bias-corrected effect size estimates, and to identify research areas where the evidence base may be weaker than published findings suggest. Finally, CT prioritization should consider expected impact on public health, and whether known NP causal mechanisms of action are such that useful information, e.g., on product bioavailability or biological activity, are generated even if the completed CT fails to reject the null hypothesis.

Funding Sources

NIH, FDA, USDA.

Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.

In vitro and modelling approaches to risk assessment from the U.S. Environmental Protection Agency ToxCast programme

A significant challenge in toxicology is the 'too many chemicals' problem. Human beings and environmental species are exposed to tens of thousands of chemicals, only a small percentage of which have been tested thoroughly using standard in vivo test methods. This study reviews several approaches that are being developed to deal with this problem by the U.S. Environmental Protection Agency, under the umbrella of the ToxCast programme (http://epa.gov/ncct/toxcast/). The overall approach is broken into seven tasks: (i) identifying biological pathways that, when perturbed, can lead to toxicity; (ii) developing high-throughput in vitro assays to test chemical perturbations of these pathways; (iii) identifying the universe of chemicals with likely human or ecological exposure; (iv) testing as many of these chemicals as possible in the relevant in vitro assays; (v) developing hazard models that take the results of these tests and identify chemicals as being potential toxicants; (vi) generating toxicokinetics data on these chemicals to predict the doses at which these hazard pathways would be activated; and (vii) developing exposure models to identify chemicals for which these hazardous dose levels could be achieved. This overall strategy is described and briefly illustrated with recent examples from the ToxCast programme.

Dosimetric anchoring of in vivo and in vitro studies for perfluorooctanoate and perfluorooctanesulfonate

In order to compare between in vivo toxicity studies, dosimetry is needed to translate study-specific dose regimens into dose metrics such as tissue concentration. These tissue concentrations may then be compared with in vitro bioactivity assays to perhaps identify mechanisms relevant to the lowest observed effect level (LOEL) dose group and the onset of the observed in vivo toxicity. Here, we examine the perfluorinated compounds (PFCs) perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS). We analyzed 9 in vivo toxicity studies for PFOA and 13 in vivo toxicity studies for PFOS. Both PFCs caused multiple effects in various test species, strains, and genders. We used a Bayesian pharmacokinetic (PK) modeling framework to incorporate data from 6 PFOA PK studies and 2 PFOS PK studies (conducted in 3 species) to predict dose metrics for the in vivo LOELs and no observed effect levels (NOELs). We estimated PK parameters for 11 combinations of chemical, species, strain, and gender. Despite divergent study designs and species-specific PK, for a given effect, we found that the predicted dose metrics corresponding to the LOELs (and NOELs where available) occur at similar concentrations. In vitro assay results for PFOA and PFOS from EPA's ToxCast project were then examined. We found that most in vitro bioactivity occurs at concentrations lower than the predicted concentrations for the in vivo LOELs and higher than the predicted concentrations for the in vivo NOELs (where available), for a variety of nonimmunological effects. These results indicate that given sufficient PK data, the in vivo LOELs dose regimens, but not necessarily the effects, could have been predicted from in vitro studies for these 2 PFCs.

A computational model predicting disruption of blood vessel development

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology.

Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management

The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.

Zebrafish developmental screening of the ToxCast™ Phase I chemical library

Zebrafish (Danio rerio) is an emerging toxicity screening model for both human health and ecology. As part of the Computational Toxicology Research Program of the U.S. EPA, the toxicity of the 309 ToxCast™ Phase I chemicals was assessed using a zebrafish screen for developmental toxicity. All exposures were by immersion from 6-8 h post fertilization (hpf) to 5 days post fertilization (dpf); nominal concentration range of 1 nM-80 μM. On 6 dpf larvae were assessed for death and overt structural defects. Results revealed that the majority (62%) of chemicals were toxic to the developing zebrafish; both toxicity incidence and potency was correlated with chemical class and hydrophobicity (logP); and inter-and intra-plate replicates showed good agreement. The zebrafish embryo screen, by providing an integrated model of the developing vertebrate, compliments the ToxCast assay portfolio and has the potential to provide information relative to overt and organismal toxicity.