Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments.

There is increasing interest to employ in vitro transcriptomics experiments in toxicological testing, for example to determine a point-of-departure (PoD) for chemical safety assessment. However current practices to derive PoD tend to utilise a single exposure time despite the importance of exposure time on the manifestation of toxicity caused by a chemical. Therefore it is important to investigate both concentration and exposure time to determine how these factors affect biological responses, and as a consequence, the derivation of PoDs. In this study, metabolically competent HepaRG cells were exposed to five known toxicants over a range of concentrations and time points for subsequent gene expression analysis, using a targeted RNA expression assay (TempO-Seq). A non-parametric factor-modelling approach was used to model the collective response of all significant genes, which exploited the interdependence of differentially expressed gene responses. This in turn allowed the determination of an isobenchmark response (isoBMR) curve for each chemical in a reproducible manner. For 2 of the 5 chemicals tested, the PoD was observed to vary by 0.5-1 log-order within the 48-h timeframe of the experiment. The approach and findings presented here clearly demonstrate the need to take both concentration and exposure time into account when designing in vitro toxicogenomics experiments to determine PoD. Doing so also provides a means to use concentration-time-response modelling as a basis to extrapolate a PoD from shorter to longer exposure durations, and to identify chemicals of concern that can cause cumulative effects over time.

Gauging innovation and health impact from biomedical research: survey results and interviews with recipients of EU-funding in the fields of Alzheimer's disease, breast cancer and prostate cancer.

Biomedical research on Alzheimer's disease (AD), breast cancer (BC) and prostate cancer (PC) has globally improved our understanding of the etiopathological mechanisms underlying the onset of these diseases, often with the goal to identify associated genetic and environmental risk factors and develop new medicines. However, the prevalence of these diseases and failure rate in drug development remain high. Being able to retrospectively monitor the major scientific breakthroughs and impact of such investment endeavors is important to re-address funding strategies if and when needed. The EU has supported research into those diseases via its successive framework programmes for research, technological development and innovation. The European Commission (EC) has already undertaken several activities to monitor research impact. As an additional contribution, the EC Joint Research Centre (JRC) launched in 2020 a survey addressed to former and current participants of EU-funded research projects in the fields of AD, BC and PC, with the aim to understand how EU-funded research has contributed to scientific innovation and societal impact, and how the selection of the experimental models may have underpinned the advances made. Further feedback was also gathered through in-depth interviews with some selected survey participants representative of the diverse pre-clinical models used in the EU-funded projects. A comprehensive analysis of survey replies, complemented with the information derived from the interviews, has recently been published in a Synopsis report. Here we discuss the main findings of this analysis and propose a set of priority actions that could be considered to help improving the translation of scientific innovation of biomedical research into societal impact.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line.

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 µM to 50 µM; rotenone: from 0.04096 to 10 µM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Effects of spike protein and toxin-like peptides found in COVID-19 patients on human 3D neuronal/glial model undergoing differentiation: Possible implications for SARS-CoV-2 impact on brain development.

The possible neurodevelopmental consequences of SARS-CoV-2 infection are presently unknown. In utero exposure to SARS-CoV-2 has been hypothesized to affect the developing brain, possibly disrupting neurodevelopment of children. Spike protein interactors, such as ACE2, have been found expressed in the fetal brain, and could play a role in potential SARS-CoV-2 fetal brain pathogenesis. Apart from the possible direct involvement of SARS-CoV-2 or its specific viral components in the occurrence of neurological and neurodevelopmental manifestations, we recently reported the presence of toxin-like peptides in plasma, urine and fecal samples specifically from COVID-19 patients. In this study, we investigated the possible neurotoxic effects elicited upon 72-hour exposure to human relevant levels of recombinant spike protein, toxin-like peptides found in COVID-19 patients, as well as a combination of both in 3D human iPSC-derived neural stem cells differentiated for either 2 weeks (short-term) or 8 weeks (long-term, 2 weeks in suspension + 6 weeks on MEA) towards neurons/glia. Whole transcriptome and qPCR analysis revealed that spike protein and toxin-like peptides at non-cytotoxic concentrations differentially perturb the expression of SPHK1, ELN, GASK1B, HEY1, UTS2, ACE2 and some neuronal-, glia- and NSC-related genes critical during brain development. Additionally, exposure to spike protein caused a decrease of spontaneous electrical activity after two days in long-term differentiated cultures. The perturbations of these neurodevelopmental endpoints are discussed in the context of recent knowledge about the key events described in Adverse Outcome Pathways relevant to COVID-19, gathered in the context of the CIAO project (https://www.ciao-covid.net/).

Extension of the Virtual Cell Based Assay from a 2-D to a 3-D Cell Culture Model.

Prediction of chemical toxicity is very useful in risk assessment. With the current paradigm shift towards the use of in vitro and in silico systems, we present herein a theoretical mathematical description of a quasi-diffusion process to predict chemical concentrations in 3-D spheroid cell cultures. By extending a 2-D Virtual Cell Based Assay (VCBA) model into a 3-D spheroid cell model, we assume that cells are arranged in a series of concentric layers within the sphere. We formulate the chemical quasi-diffusion process by simplifying the spheroid with respect to the number of cells in each layer. The system was calibrated and tested with acetaminophen (APAP). Simulated predictions of APAP toxicity were compared with empirical data from in vitro measurements by using a 3-D spheroid model. The results of this first attempt to extend the VCBA model are promising - they show that the VCBA model simulates close correlation between the influence of compound concentration and the viability of the HepaRG 3-D cell culture. The 3-D VCBA model provides a complement to current in vitro procedures to refine experimental setups, to fill data gaps and help in the interpretation of in vitro data for the purposes of risk assessment.

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics.

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument's deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform.

INTRODUCTION: High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing. OBJECTIVE: In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well. METHODS: Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass. RESULTS: The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids. CONCLUSIONS: Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Settling dynamics of nanoparticles in simple and biological media.

The biological response of organisms exposed to nanoparticles is often studied in vitro using adherent monolayers of cultured cells. In order to derive accurate concentration-response relationships, it is important to determine the local concentration of nanoparticles to which the cells are actually exposed rather than the nominal concentration of nanoparticles in the cell culture medium. In this study, the sedimentation-diffusion process of different sized and charged gold nanoparticles has been investigated in vitro by evaluating their settling dynamics and by developing a theoretical model to predict the concentration depth profile of nanoparticles in solution over time. Experiments were carried out in water and in cell culture media at a range of controlled temperatures. The optical phenomenon of caustics was exploited to track nanoparticles in real time in a conventional optical microscope without any requirement for fluorescent labelling that potentially affects the dynamics of the nanoparticles. The results obtained demonstrate that size, temperature and the stability of the nanoparticles play a pivotal role in regulating the settling dynamics of nanoparticles. For gold nanoparticles larger than 60 nm in diameter, the initial nominal concentration did not accurately represent the concentration of nanoparticles local to the cells. Finally, the theoretical model proposed accurately described the settling dynamics of the nanoparticles and thus represents a promising tool to support the design of in vitro experiments and the study of concentration-response relationships.

Putting Science into Standards workshop on standards for organ-on-chip.

The European Commission Joint Research Centre and the European Standardization Organizations CEN and CENELEC organized the "Putting Science into Standards" workshop, focusing on organ-on-chip technologies. The workshop, held online on 28-29 April, 2021, aimed at identifying needs and priorities for standards development and suggesting possible ways forward.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Standardisation needs for organ on chip devices.

Organ on chip (OoC) devices represent the cutting edge of biotechnologies, combining advanced cell and tissue culture with microengineering. OoC is accelerating innovation in the life sciences and has the potential to revolutionise many fields including biomedical research, drug development and chemical risk assessment. In order to gain acceptance by end-users of OoC based methods and the data derived from them, and to establish OoC approaches as credible alternatives to animal testing, OoC devices need to go through an extensive qualification process. In this context, standardisation can play a key role in ensuring proper characterisation of individual devices, benchmarking against appropriate reference elements and aiding efficient communication among stakeholders. The development of standards for OoC will address several important issues such as basic terminology, device classification, and technical and biological performance. An analysis of technical and biological aspects related to OoC is presented here to identify standardisation areas specific for OoC, focusing on needs and opportunities. About 90 standards are already available from related fields including microtechnologies, medical devices and in vitro cell culture, laying the basis for future work in the OoC domain. Finally, two priority areas for OoC are identified that could be addressed with standards, namely, characterisation of small molecule absorption and measurement of microfluidic parameters.

Extrapolating from acute to chronic toxicity in vitro.

Chemical safety assessment requires information on both chronic and acute effects of toxicants. Traditionally, such information has been provided by a set of animal studies conducted over different durations, ranging from a single dose with observation of effects over a few days, to repeat daily dosing and observations made over many months. With the advent of modern mechanistic approaches to toxicology, the role of in vitro studies within alternative approaches has never been more prominent. Typical in vitro experiments are conducted over short durations with measurements of response at a single time point, with a focus on providing effect and concentration-response information as input to hazard and risk assessment. This limits the usefulness of such data since potential chronic effects that cumulate over time are not usually considered. To address this, an experimental design is presented to characterise the toxicodynamics of a response not only in terms of concentration, but also as a function of time. Generation of concentration-time-effect responses allows both the extrapolation of points of departure from an acute to chronic exposure, and the determination of a chronicity index that provides a quantitative measure of a chemical's potential to cause cumulative effects over time. In addition, the approach provides a means to characterise the dynamics of key event relationships for the development of quantitative adverse outcome pathways.

The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application.

New approaches in toxicology based on in vitro methods and computational modelling offer considerable potential to improve the efficiency and effectiveness of chemical hazard and risk assessment in a variety of regulatory contexts. However, this presents challenges both for developers and regulatory assessors because often these two communities do not share the same level of confidence in a new approach. To address this challenge, various assessment frameworks have been developed over the past 20 years with the aim of creating harmonised and systematic approaches for evaluating new methods. These frameworks typically focus on specific methodologies and technologies, which has proven useful for establishing the validity and credibility of individual methods. However, given the increasing need to compare methods and combine their use in integrated assessment strategies, the multiplicity of frameworks is arguably becoming a barrier to their acceptance. In this commentary, we explore the concepts of model validity and credibility, and we illustrate how a set of seven credibility factors provides a method-agnostic means of comparing different kinds of predictive toxicology approaches. It is hoped that this will facilitate communication and cross-disciplinarity among method developers and users, with the ultimate aim of increasing the acceptance and use of predictive approaches in toxicology.

Bringing together scientific disciplines for collaborative undertakings: a vision for advancing the adverse outcome pathway framework.

BACKGROUND: Decades of research to understand the impacts of various types of environmental occupational and medical stressors on human health have produced a vast amount of data across many scientific disciplines. Organizing these data in a meaningful way to support risk assessment has been a significant challenge. To address this and other challenges in modernizing chemical health risk assessment, the Organisation for Economic Cooperation and Development (OECD) formalized the adverse outcome pathway (AOP) framework, an approach to consolidate knowledge into measurable key events (KEs) at various levels of biological organisation causally linked to disease based on the weight of scientific evidence (http://oe.cd/aops). Currently, AOPs have been considered predominantly in chemical safety but are relevant to radiation. In this context, the Nuclear Energy Agency's (NEA's) High-Level Group on Low Dose Research (HLG-LDR) is working to improve research co-ordination, including radiological research with chemical research, identify synergies between the fields and to avoid duplication of efforts and resource investments. To this end, a virtual workshop was held on 7 and 8 October 2020 with experts from the OECD AOP Programme together with the radiation and chemical research/regulation communities. The workshop was a coordinated effort of Health Canada, the Electric Power Research Institute (EPRI), and the Nuclear Energy Agency (NEA). The AOP approach was discussed including key issues to fully embrace its value and catalyze implementation in areas of radiation risk assessment. CONCLUSIONS: A joint chemical and radiological expert group was proposed as a means to encourage cooperation between risk assessors and an initial vision was discussed on a path forward. A global survey was suggested as a way to identify priority health outcomes of regulatory interest for AOP development. Multidisciplinary teams are needed to address the challenge of producing the appropriate data for risk assessments. Data management and machine learning tools were highlighted as a way to progress from weight of evidence to computational causal inference.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research.

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.

New ideas for non-animal approaches to predict repeated-dose systemic toxicity: Report from an EPAA Blue Sky Workshop.

The European Partnership for Alternative Approaches to Animal Testing (EPAA) convened a 'Blue Sky Workshop' on new ideas for non-animal approaches to predict repeated-dose systemic toxicity. The aim of the Workshop was to formulate strategic ideas to improve and increase the applicability, implementation and acceptance of modern non-animal methods to determine systemic toxicity. The Workshop concluded that good progress is being made to assess repeated dose toxicity without animals taking advantage of existing knowledge in toxicology, thresholds of toxicological concern, adverse outcome pathways and read-across workflows. These approaches can be supported by New Approach Methodologies (NAMs) utilising modern molecular technologies and computational methods. Recommendations from the Workshop were based around the needs for better chemical safety assessment: how to strengthen the evidence base for decision making; to develop, standardise and harmonise NAMs for human toxicity; and the improvement in the applicability and acceptance of novel techniques. "Disruptive thinking" is required to reconsider chemical legislation, validation of NAMs and the opportunities to move away from reliance on animal tests. Case study practices and data sharing, ensuring reproducibility of NAMs, were viewed as crucial to the improvement of non-animal test approaches for systemic toxicity.

[Molecular Pathway and AOP Development Using Gene Network Analysis].

The Organisation for Economic Co-operation and Development (OECD) has initiated the adverse outcome pathway (AOP) Development Program in which the concept of AOP is applied to evaluate the safety of molecules such as chemicals. This program aims to assist regulatory needs and construct a knowledge base by accumulating AOP case studies. AOP consists of a molecular initiating event (MIE) as the initiating event of the pathway; key events (KEs) as the events themselves, such as cellular-molecular interactions; and adverse outcome (AO), such as signaling transduction-induced toxicity, as adverse events. KEs are extracted as important events at various levels, such as the molecular, cellular, tissue, organ, individual, and species levels; measurement of KEs and key event relationships (KERs), including mechanisms, plausibility, species differences, and empirical support information, are gathered. The development status of the AOP relating to histone deacetylase inhibition-induced testicular toxicity, currently being reviewed by the OECD, has been introduced. The AOP describing malignancies by Wnt ligand stimulation and Wnt signaling activation using gene expression network analysis-based mechanisms in molecular pathway elucidation has been suggested.