Since its adoption as a test guideline (EIT, OECD test guideline (TG) 492) in 2015, the EpiOcular™ eye irritation test has been the in vitro method of choice to identify agrochemical formulations non-irritant to the eye (neither category 1 nor 2, according to the Globally Harmonized System of Classification and Labelling of Chemicals, GHS). For serious eye damage (GHS category 1), however, none of the in vitro test methods including the bovine cornea opacity and permeability test (BCOP, OECD TG 437, and protocol modifications), the isolated chicken eye test (ICE, OECD TG 438), the ocular irritation assay (OECD TG 496) or the hen's egg test on the chorioallantoic membrane (HET-CAM) assay proved sufficiently sensitive to correctly identify agrochemical formulations classified as seriously eye damaging in vivo. In 2022, the OECD adopted a new in vitro test guideline for identifying chemicals inducing serious eye damage, eye irritation, and chemicals not requiring classification for eye irritation (OECD TG 492B). However, within the formal validation study of this time-to-toxicity (TTT) test method, agrochemical formulations were not included. To assess the applicability of this test to agrochemical formulations 25 formulations with historical in vivo data were assessed in the TTT. The TTT correctly predicted 0 %, 90 % and 50 % of the agrochemical formulations which were classified by the in vivo data into the three categories 1, 2 and "not classified", respectively. The overall accuracy of the TTT to predict agrochemical formulations of the three categories was 52 %. Therefore, to facilitate non-animal-based identification of the full range of ocular irritation of agrochemical formulations, testing protocols and/or prediction models must be revised or classification guidelines should be adjusted to align with human relevant in vitro testing rather than relying on in vivo Draize data as a reference point.

Since 2020, the REACh regulation requires toxicological data on nanoforms of materials, including the assessment of their skin-sensitizing properties. Small molecules’ skin sensitization potential can be assessed by new approach methodologies (NAMs) addressing three key events (KE: protein interaction, activation of dendritic cells, and activation of keratinocytes) combined in a defined approach (DA) described in the OECD guideline 497. In the present study, the applicability of three NAMs (DPRA, LuSens, and h-CLAT) to nine materials (eight inorganic nanomaterials (NM) consisting of CeO2, BaSO4, TiO2 or SiO2, and quartz) was evaluated. The NAMs were technically applicable to NM using a specific sample preparation (NANOGENOTOX dispersion protocol) and method modifications to reduce interaction of NM with the photometric and flowcytometric read-outs. The results of the three assays were combined according to the defined approach described in the OECD guideline No. 497; two of the inorganic NM were identified as skin sensitizers. However, data from animal studies (for ZnO, also human data) indicate no skin sensitization potential. The remaining seven test substances were assessed as “inconclusive” because all inorganic NM were outside the domain of the DPRA, and the achievable test concentrations were not sufficiently high according to the current test guidelines of all three NAMs. The use of these NAMs for (inorganic) NM and the relevance of the results in general are challenged in three ways: (i) NAMs need modification to be applicable to insoluble, inorganic matter; (ii) current test guidelines lack adequate concentration metrics and top concentrations achievable for NM; and (iii) NM may not cause skin sensitization by the same molecular and cellular key events as small organic molecules do; in fact, T-cell-mediated hypersensitivity may not be the most relevant reaction of the immune system to NM. We conclude that the NAMs adopted by OECD test guidelines are currently not a good fit for testing inorganic NM.

Over the last decade, research into methodologies to identify skin sensitization hazards has led to the adoption of several non-animal methods as OECD test guidelines. However, predictive accuracy beyond the chemical domains of the individual validation studies remains largely untested. In the present study, skin sensitization test results from in vitro and in chemico methods for 12 plant extracts and 15 polymeric materials are reported and compared to available in vivo skin sensitization data. Eight plant extracts were tested in the DPRA and h-CLAT, with the 2 out of 3 approach resulting in a balanced accuracy of 50%. The balanced accuracy for the 11 plant extracts assessed in the SENS-IS was 88%. Excluding 5 polymers inconclusive in vitro, the remainder, assessed using the 2 out of 3 approach, resulted in 63% balanced accuracy. The SENS-IS method, excluding one polymeric material due to technical inapplicability, showed 68% balanced accuracy. Although based on limited numbers, the results presented here indicate that some substance subgroups may not be in the applicability domains of the method used and careful analysis is required before positive or negative results can be accepted.

In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailed-just from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future.

While the skin sensitization hazard of substances can be identified using non-animal methods, the classification of potency into UN GHS sub-categories 1A and 1B remains challenging. The kinetic direct peptide reactivity assay (kDPRA) is a modification of the DPRA wherein the reaction kinetics of a test substance towards a synthetic cysteine-containing peptide are evaluated. For this purpose, several concentrations of the test substance are incubated with the synthetic peptide for several incubation times. The reaction is stopped by addition of monobromobimane, which forms a fluorescent complex with the free cysteine of the model peptide. The relative remaining non-depleted amount of peptide is determined. Kinetic rate constants are derived from the depletion vs concentration and time matrix and used to distinguish between UN GHS sub-category 1A sensitizers and test substances in sub-category 1B/not classified test substances. In this study, we present a ring trial of the kDPRA with 24 blind-coded test substances in seven laboratories. The intra- and inter-laboratory reproducibility were 96% and 88%, respectively (both for differentiating GHS Cat 1A sensitizers from GHS Cat 1B/not classified). Following an independent peer review, the kDPRA was considered to be acceptable for the identification of GHS Cat 1A skin sensitizers. Besides GHS Cat 1A identification, the kDPRA can be used as part of a defined approach(es) with a quantitative data integration procedure for skin sensitization potency assessment. For this aim, next to reproducibility of classification, the quantitative reproducibility and variability of the rate constants were quantified in this study.

Several in vitro OECD test guidelines address key events 1-3 of the adverse outcome pathway for skin sensitization, but none are validated for sensitizer potency assessment. The reaction of sensitizing molecules with skin proteins is the molecular initiating event and appears to be rate-limiting, as chemical reactivity strongly correlates with sensitizer potency. The kinetic direct peptide reactivity assay (kDPRA), a modification of the DPRA (OECD TG 442C), allows derivation of rate constants of the depletion of the cysteine-containing model peptide upon reaction with the test item. Its reproducibility was demonstrated in an inter-laboratory study. Here, we present a database of rate constants, expressed as log k(max), for 180 chemicals to define the prediction threshold to identify strong sensitizers (classified as GHS 1A). A threshold of log k(max) -2 offers a balanced accuracy of 85% for predicting GHS 1A sensitizers according to the local lymph node assay. The kDPRA is proposed as a stand-alone assay for identification of GHS 1A sensitizers among chemicals identified as sensitizers by other tests or defined approaches. It may also be used for the prediction of sensitizer potency on a continuous scale, ideally in combination with continuous parameters from other in vitro assays. We show how the rate constant could be combined with read-outs of other in vitro assays in a defined approach. A decision model based on log kmax alone has, however, a high predictivity and can be used as stand-alone model for identification of GHS 1A sensitizers among chemicals predicted as sensitizers.

While the skin sensitization hazard of substances can already be identified using non-animal methods, the classification of potency sub-categories GHS-1A and 1B is still challenging. Potency can be measured by the dose at which an effect is observed; since the protein-adduct formation is determining the dose of the allergen in the skin, peptide reactivity was used to assess the potency. The Direct Peptide Reactivity Assay (DPRA; one concentration and reaction-time) did not sufficiently discriminate between sub-categories 1A and 1B (56% accuracy compared to LLNA data, n=124). An extended protocol termed 'quantitative DPRA' (three concentrations and one reaction-time), discriminated sub-categories GHS 1A and 1B with an accuracy of 81% or 57% compared to LLNA (n=36) or human (n=14) data, respectively. The analysis of the Cys-adducts was already sufficient; additional analysis of Lys-adducts did not improve the predictivity. An additional modification, the 'kinetic DPRA' (several concentrations and reaction-times) was used to approximate the rate constant of Cys-peptide-adduct formation. 35 of 38 substances were correctly assigned to the potency sub-categories (LLNA data), and the predictivity for 14 human data was equally high. These results warrant the kinetic DPRA for further validation in order to fully replace in vivo testing for assessing skin sensitization including potency sub-classification.

Because of ethical and regulatory reasons, several nonanimal test methods to assess the skin sensitization potential of chemicals have been developed and validated. In contrast to in vivo methods, they lack or provide limited metabolic capacity. For this reason, identification of pro-haptens but also pre-haptens, which require molecular transformations to gain peptide reactivity, is a challenge for these methods. In this study, 27 pre- and pro-haptens were tested using nonanimal test methods. Of these, 18 provided true positive results in the direct peptide reactivity assay (DPRA; sensitivity of 67%), although lacking structural alerts for direct peptide reactivity. The reaction mechanisms leading to peptide depletion in the DPRA were therefore elucidated using mass spectrometry. Hapten-peptide adducts were identified for 13 of the 18 chemicals indicating that these pre-haptens were activated and that peptide binding occurred. Positive results for five of the 18 chemicals can be explained by dipeptide formations or the oxidation of the sulfhydryl group of the peptide. Nine of the 27 chemicals were tested negative in the DPRA. Of these, four yielded true positive results in the keratinocyte and dendritic cell based assays. Likewise, 16 of the 18 chemicals tested positive in the DPRA were also positive in either one or both of the cell-based assays. A combination of DPRA, KeratinoSens, and h-CLAT used in a 2 out of 3 weight of evidence (WoE) approach identified 22 of the 27 pre- and pro-haptens correctly (sensitivity of 81%), exhibiting a similar sensitivity as for directly acting haptens. This analysis shows that the combination of in chemico and in vitro test methods is suitable to identify pre-haptens and the majority of pro-haptens.

Tissue engineering in cardiovascular regenerative therapy requires the development of an efficient oxygen supply system for cell cultures. However, there are few studies which have examined human cardiomyocytes in terms of oxygen consumption and metabolism in culture. We developed an oxygen measurement system equipped with an oxygen microelectrode sensor and estimated the oxygen consumption rates (OCRs) by using the oxygen concentration profiles in culture medium. The heart is largely made up of cardiomyocytes, cardiac fibroblasts, and cardiac endothelial cells. Therefore, we measured the oxygen consumption of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs), cardiac fibroblasts, human cardiac microvascular endothelial cell and aortic smooth muscle cells. Then we made correlations with their metabolisms. In hiPSC-CMs, the value of the OCR was 0.71 ± 0.38 pmol/h/cell, whereas the glucose consumption rate and lactate production rate were 0.77 ± 0.32 pmol/h/cell and 1.61 ± 0.70 pmol/h/cell, respectively. These values differed significantly from those of the other cells in human heart. The metabolism of the cells that constitute human heart showed the molar ratio of lactate production to glucose consumption (L/G ratio) that ranged between 1.97 and 2.2. Although the energy metabolism in adult heart in vivo is reported to be aerobic, our data demonstrated a dominance of anaerobic glycolysis in an in vitro environment. With our measuring system, we clearly showed the differences in the metabolism of cells between in vivo and in vitro monolayer culture. Our results regarding cell OCRs and metabolism may be useful for future tissue engineering of human heart.

The knowledge-based search engine Go3R, www.Go3R.org, has been developed to assist scientists from industry and regulatory authorities in collecting comprehensive toxicological information with a special focus on identifying available alternatives to animal testing. The semantic search paradigm of Go3R makes use of expert knowledge on 3Rs methods and regulatory toxicology, laid down in the ontology, a network of concepts, terms, and synonyms, to recognize the contents of documents. Search results are automatically sorted into a dynamic table of contents presented alongside the list of documents retrieved. This table of contents allows the user to quickly filter the set of documents by topics of interest. Documents containing hazard information are automatically assigned to a user interface following the endpoint-specific IUCLID5 categorization scheme required, e.g. for REACH registration dossiers. For this purpose, complex endpoint-specific search queries were compiled and integrated into the search engine (based upon a gold standard of 310 references that had been assigned manually to the different endpoint categories). Go3R sorts 87% of the references concordantly into the respective IUCLID5 categories. Currently, Go3R searches in the 22 million documents available in the PubMed and TOXNET databases. However, it can be customized to search in other databases including in-house databanks.

Injection drug use has been identified as a key source of morbidity and mortality, primarily from overdose and the transmission of blood-borne diseases such as HIV. Experts have therefore called for the prioritization of resources toward the prevention of injection drug use. However, these strategies have not been systematically assessed.PRISMA guidelines were used to systematically review and extract findings from the peer-reviewed literature evaluating the effectiveness of interventions to prevent injecting initiation. We searched 10 English language electronic databases (PubMed, PsycINFO, EMBASE, Cochrane CENTRAL, CINAHL, Web of Science, TOXNET, AIDSLINE, AMED and ERIC), the Internet (Google, Google Scholar), and article reference lists, from database inception to June 1st, 2012.Overall, out of 384 studies identified in the initial search, eight met the inclusion criteria. Studies evaluated four different types of interventions: social marketing, peer-based behavior modification, treatment, and drug law enforcement. Four studies observed a significant effect of the intervention on reducing rates of injecting initiation. Peer-based behavior modification and addiction treatment interventions were found to be most effective. Two of three studies assessing the impact of drug law enforcement on patterns of injecting initiation found no impact on injecting initiation, while one study reported inconclusive results.There exists a limited scientific literature on strategies to prevent injecting initiation. Resources should be allocated toward increased research and development of effective interventions to prevent this phenomenon.

While building a predictive quantitative structure-activity relationship (QSAR), validation of the developed model is a very important task. However, a truly new set of data being often unavailable for checking predictability and robustness of the developed model, a typical external validation in QSAR studies is commonly performed by splitting the available data into training and test sets. In the present work we have attempted to explore the impact of training set size on the quality of prediction using different topological descriptors and three different statistical techniques. Three different data sets of moderate size have been used for the present study: cytoprotection data of anti-HIV thiocarbamates (n = 62), HIV reverse transcriptase inhibition data of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives (n = 107) and bioconcentration factor data of diverse functional compounds (n = 122). In each case, the data set was divided into different combinations of training and test sets maintaining different size ratios in several iterations. In cases of the first two data sets, significant impact of reduction of training set size was found on the predictive ability of the models while the first data set showing higher dependence on the size than the second one. However, in case of modeling of bioconcentration factor, no significant impact of training set size on the quality of prediction could be found. Hence, no general rule can be formulated regarding the impact of training set size on the quality of prediction. Optimum size of the training set should be set based on a particular data set and types of descriptors and statistical analysis being used.