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With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in ‘omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other ‘omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during ‘omics data processing and analysis, the TRF will increase confidence processing of ‘omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory ‘omics studies, and it can be executed using different commonly available software tools.

A framework for the quantitative weight-of-evidence (QWoE) analysis of 'omics data for regulatory purposes is presented. The QWoE framework encompasses seven steps to evaluate 'omics data (also together with non-'omics data): (1) Hypothesis formulation, identification and weighting of lines of evidence (LoEs). LoEs conjoin different (types of) studies that are used to critically test the hypothesis. As an essential component of the QWoE framework, step 1 includes the development of templates for scoring sheets that predefine scoring criteria with scores of 0-4 to enable a quantitative determination of study quality and data relevance; (2) literature searches and categorisation of studies into the pre-defined LoEs; (3) and (4) quantitative assessment of study quality and data relevance using the respective pre-defined scoring sheets for each study; (5) evaluation of LoE-specific strength of evidence based upon the study quality and study relevance scores of the studies conjoined in the respective LoE; (6) integration of the strength of evidence from the individual LoEs to determine the overall strength of evidence; (7) characterisation of uncertainties and conclusion on the QWoE. To put the QWoE framework in practice, case studies are recommended to confirm the relevance of its different steps, or to adapt them as necessary.

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field. © 2017, Springer-Verlag GmbH Germany.

Skin absorption testing in vitro is a regulatory accepted alternative method (OECD Guideline 428). Different tests can be applied to evaluate the integrity of the skin samples. Here, we compared the pre- or post-run integrity tests (transepidermal electrical resistance, TEER; transepidermal water loss, TEWL; absorption of the reference compounds water, TWF, or methylene blue, BLUE) and additionally focused on co-absorption of a (3)H-labeled internal reference standard (ISTD) as integrity parameter. The results were correlated to absorption profiles of various test compounds. Limit values of 2kΩ, 10 gm(-2)h(-1) and 4.5∗10(-3)cmh(-1) for the standard methods TEER, TEWL and TWF, respectively, allowed distinguishing between impaired and intact human skin samples in general. Single skin samples did, however, not, poorly and even inversely correlate with the test-compound absorption. In contrast, results with ISTD (e.g. (3)H-testosterone) were highly correlated to the absorption of (14)C-labeled test compounds. Importantly, ISTD did not influence analytics or absorption of test compounds. Therefore, ISTD, especially when adjusted to the physico-chemical properties of test compounds, is a promising concept to assess the integrity of skin samples during the whole course of absorption experiments. However, a historical control dataset is yet necessary for a potential routine application.

‘Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing ‘omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying ‘omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective ‘omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of ‘omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, ‘omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use ‘omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.

This survey by the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) highlights that 'omics technologies are generally not yet applied to meet standard information requirements during regulatory hazard assessment. While they are used within weight-of-evidence approaches to investigate substances' modes-of-action, consistent approaches for the generation, processing and interpretation of 'omics data are not applied. To date, no 'omics technology has been standardised or validated. Best practices for performing 'omics studies for regulatory purposes (e.g., microarrays for transcriptome profiling) remain to be established. Therefore, three frameworks for (i) establishing a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) 'omics data processing; and (iii) quantitative WoE approaches to interpret 'omics data have been developed, that are presented in this journal supplement. Application of the frameworks will enable between-study comparison of results, which will facilitate the regulatory applicability of 'omics data. The frameworks do not constitute prescriptive protocols precluding any other data analysis method, but provide a baseline for analysis that can be applied to all data allowing ready cross-comparison. Data analysis that does not follow the frameworks can be justified and the resulting data can be compared with the Framework-based common analysis output.

Liver toxicity is a leading systemic toxicity of drugs and chemicals demanding more human-relevant, high throughput, cost effective in vitro solutions. In addition to contributing to animal welfare, in vitro techniques facilitate exploring and understanding the molecular mechanisms underlying toxicity. New ‘omics technologies can provide comprehensive information on the toxicological mode of action of compounds, as well as quantitative information about the multi-parametric metabolic response of cellular systems in normal and patho-physiological conditions. Here, we combined mass-spectroscopy metabolomics with an in vitro liver toxicity model. Metabolite profiles of HepG2 cells treated with 35 test substances resulted in 1114 cell supernatants and 3556 intracellular samples analyzed by metabolomics. Control samples showed relative standard deviations of about 10–15%, while the technical replicates were at 5–10%. Importantly, this procedure revealed concentration–response effects and patterns of metabolome changes that are consistent for different liver toxicity mechanisms (liver enzyme induction/inhibition, liver toxicity and peroxisome proliferation). Our findings provide evidence that identifying organ toxicity can be achieved in a robust, reliable, human-relevant system, representing a non-animal alternative for systemic toxicology. Electronic supplementary material The online version of this article (doi:10.1007/s00204-017-2079-6) contains supplementary material, which is available to authorized users.

Nanomaterials (NM) offer great technological advantages but their risks to human health are still under discussion. For toxicological testing and evaluation, information on the toxicokinetics of NM is essential as it is different from that of most other xenobiotics. This review provides an overview on the toxicokinetics of NM available to date. The toxicokinetics of NM depends on particle size and shape, protein binding, agglomeration, hydrophobicity, surface charge and protein binding. In most studies with topical skin application, unintentional permeation and systemic availability were not observed; permeation for some NM with distinct properties was observed in animals. Upon inhalation, low levels of primary model nanoparticles became systemically available, but many real-world engineered NM aggregate in aerosols, do not disintegrate in the lung, and do not become systemically available. NM are prone to lymphatic transport, and many NM are taken up by the mononuclear phagocyte system (MPS) acting as a depot. Their half-life in blood depends on their uptake by MPS rather than their elimination from the body. NM reaching the GI tract are excreted with the feces, but of some NM low levels are absorbed and become systemically available. Some quantum dots were not observably excreted in urine nor in feces. Some model quantum dots, however, were efficiently excreted by the kidneys below, but not above 5-6 nm hydrodynamic diameter, while nanotubes 20-30 nm thick and 500-2,000 nm long were abundant in urine. NM are typically not metabolized. Some NM cross the blood-brain barrier favored by a negative surface charge.

A major health concern for nanomaterials is their potential toxic effect after inhalation of dusts. Correspondingly, the core element of tier 1 in the currently proposed integrated testing strategy (ITS) is a short-term rat inhalation study (STIS) for this route of exposure. STIS comprises a comprehensive scheme of biological effects and marker determination in order to generate appropriate information on early key elements of pathogenesis, such as inflammatory reactions in the lung and indications of effects in other organs. Within the STIS information on the persistence, progression and/or regression of effects is obtained. The STIS also addresses organ burden in the lung and potential translocation to other tissues. Up to now, STIS was performed in research projects and routine testing of nanomaterials. Meanwhile, rat STIS results for more than 20 nanomaterials are available including the representative nanomaterials listed by the Organization for Economic Cooperation and Development (OECD) working party on manufactured nanomaterials (WPMN), which has endorsed a list of representative manufactured nanomaterials (MN) as well as a set of relevant endpoints to be addressed. Here, results of STIS carried out with different nanomaterials are discussed as case studies. The ranking of different nanomaterials potential to induce adverse effects and the ranking of the respective NOAEC are the same among the STIS and the corresponding subchronic and chronic studies. In another case study, a translocation of a coated silica nanomaterial was judged critical for its safety assessment. Thus, STIS enables application of the proposed ITS, as long as reliable and relevant in vitro methods for the tier 1 testing are still missing. Compared to traditional subacute and subchronic inhalation testing (according to OECD test guidelines 412 and 413), STIS uses less animals and resources and offers additional information on organ burden and progression or regression of potential effects.

The fate of nano-TiO(2) particles in the body was investigated after inhalation exposure or intravenous (i.v.) injection, and compared with pigmentary TiO(2) and quartz. For this purpose, a 5-day inhalation study (6h/day, head/nose exposure) was carried out in male Wistar rats using nano-TiO(2) (100mg/m(3)), pigmentary TiO(2) (250mg/m(3)) and quartz dust DQ 12 (100mg/m(3)). Deposition in the lung and tissue distribution was evaluated, and histological examination of the respiratory tract was performed upon termination of exposure, and 2 weeks after the last exposure. Broncho-alveolar lavage (BAL) was carried out 3 and 14 days after the last exposure. Rats were also injected with a single intravenous dose of a suspension of TiO(2) in serum (5mg/kg body weight), and tissue content of TiO(2) was determined 1, 14 and 28 days later. The majority of the inhaled nano-TiO(2) was deposited in the lung. Translocation to the mediastinal lymph nodes was also noted, although to smaller amounts than following inhalation of pigmentary TiO(2), but much higher amounts than after exposure to quartz. Systemically available nano-TiO(2), as simulated by the i.v. injection, was trapped mainly in the liver and spleen. The (agglomerate) particle size of lung deposited nano-TiO(2) was virtually the same as in the test atmosphere. Changes in BAL fluid composition and histological examination indicated mild neutrophilic inflammation and activation of macrophages in the lung. The effects were reversible for nano- and pigmentary TiO(2), but progressive for quartz. The effects observed after 5-day inhalation exposure to nano-TiO(2) were qualitatively similar to those reported in sub-chronic studies.

The tissue distribution and toxicity of intravenously administered nanoparticles of titanium dioxide (TiO2) (>10 wt.% at

Allergic contact dermatitis can develop following repeated exposure to allergenic substances. To date, hazard identification is still based on animal studies as non-animal alternatives have not yet gained global regulatory acceptance. Several non-animal methods addressing key-steps of the adverse outcome pathway (OECD, 2012) will most likely be needed to fully address this effect. Among the initial cellular events is the activation of keratinocytes and currently only one method, the KeratinoSens™, has been formally validated to address this event. In this study, a further method, the LuSens assay, that uses a human keratinocyte cell line harbouring a reporter gene construct composed of the antioxidant response element (ARE) of the rat NADPH:quinone oxidoreductase 1 gene and the luciferase gene. The assay was validated in house using a selection of 74 substances which included the LLNA performance standards. The predictivity of the LuSens assay for skin sensitization hazard identification was comparable to other non-animal methods, in particular to the KeratinoSens™. When used as part of a testing battery based on the OECD adverse outcome pathway for skin sensitization, a combination of the LuSens assay, the DPRA and a dendritic cell line activation test attained predictivities similar to that of the LLNA.