A Family of Water-Immiscible, Dipolar Aprotic, Diamide Solvents from Succinic Acid.

Three dipolar aprotic solvents were designed to possess high dipolarity and low toxicity: N,N,N',N'-tetrabutylsuccindiamide (TBSA), N,N'-diethyl-N,N'-dibutylsuccindiamide (EBSA), and N,N'-dimethyl-N,N'-dibutylsuccindiamide (MBSA). They were synthesized catalytically by using a K60 silica catalyst in a solventless system. Their water immiscibility stands out as an unusual and useful property for dipolar aprotic solvents. They were tested in a model Heck reaction, metal-organic framework syntheses, and a selection of polymer solubility experiments in which their performances were found to be comparable to traditional solvents. Furthermore, MBSA was found to be suitable for the production of an industrially relevant membrane from polyethersulfone. An integrated approach involving in silico analysis based on available experimental information, prediction model outcomes and read across data, as well as a panel of in vitro reporter gene assays covering a broad range of toxicological endpoints was used to assess toxicity. These in silico and in vitro tests suggested no alarming indications of toxicity in the new solvents.

Prediction of carcinogenic potential of chemicals using repeated-dose (13-week) toxicity data.

Sub-chronic toxicity studies of 163 non-genotoxic chemicals were evaluated in order to predict the tumour outcome of 24-month rat carcinogenicity studies obtained from the EFSA and ToxRef databases. Hundred eleven of the 148 chemicals that did not induce putative preneoplastic lesions in the sub-chronic study also did not induce tumours in the carcinogenicity study (True Negatives). Cellular hypertrophy appeared to be an unreliable predictor of carcinogenicity. The negative predictivity, the measure of the compounds evaluated that did not show any putative preneoplastic lesion in de sub-chronic studies and were negative in the carcinogenicity studies, was 75%, whereas the sensitivity, a measure of the sub-chronic study to predict a positive carcinogenicity outcome was only 5%. The specificity, the accuracy of the sub-chronic study to correctly identify non-carcinogens was 90%. When the chemicals which induced tumours generally considered not relevant for humans (33 out of 37 False Negatives) are classified as True Negatives, the negative predictivity amounts to 97%. Overall, the results of this retrospective study support the concept that chemicals showing no histopathological risk factors for neoplasia in a sub-chronic study in rats may be considered non-carcinogenic and do not require further testing in a carcinogenicity study.

Supporting read-across using biological data.

Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

The ChemScreen project to design a pragmatic alternative approach to predict reproductive toxicity of chemicals.

There is a great need for rapid testing strategies for reproductive toxicity testing, avoiding animal use. The EU Framework program 7 project ChemScreen aimed to fill this gap in a pragmatic manner preferably using validated existing tools and place them in an innovative alternative testing strategy. In our approach we combined knowledge on critical processes affected by reproductive toxicants with knowledge on the mechanistic basis of such effects. We used in silico methods for prescreening chemicals for relevant toxic effects aiming at reduced testing needs. For those chemicals that need testing we have set up an in vitro screening panel that includes mechanistic high throughput methods and lower throughput assays that measure more integrative endpoints. In silico pharmacokinetic modules were developed for rapid exposure predictions via diverse exposure routes. These modules to match in vitro and in vivo exposure levels greatly improved predictivity of the in vitro tests. As a further step, we have generated examples how to predict reproductive toxicity of chemicals using available data. We have executed formal validations of panel constituents and also used more innovative manners to validate the test panel using mechanistic approaches. We are actively engaged in promoting regulatory acceptance of the tools developed as an essential step towards practical application, including case studies for read-across purposes. With this approach, a significant saving in animal use and associated costs seems very feasible.

Evaluation of an alternative in vitro test battery for detecting reproductive toxicants in a grouping context.

Previously we showed a battery consisting of CALUX transcriptional activation assays, the ReProGlo assay, and the embryonic stem cell test, and zebrafish embryotoxicity assay as 'apical' tests to correctly predict developmental toxicity for 11 out of 12 compounds, and to explain the one false negative [7]. Here we report on applying this battery within the context of grouping and read across, put forward as a potential tool to fill data gaps and avoid animal testing, to distinguish in vivo non- or weak developmental toxicants from potent developmental toxicants within groups of structural analogs. The battery correctly distinguished 2-methylhexanoic acid, monomethyl phthalate, and monobutyltin trichloride as non- or weak developmental toxicants from structurally related developmental toxicants valproic acid, mono-ethylhexyl phthalate, and tributyltin chloride, respectively, and, therefore, holds promise as a biological verification model in grouping and read across approaches. The relevance of toxicokinetic information is indicated.

A high throughput screening system for predicting chemically-induced reproductive organ deformities.

There is a great need for alternative testing methods for reproductive toxicants that are practical, fast, cost-effective and easy to interpret. Previously we followed a pragmatic approach using readily available tests, which was successful in predicting reproductive toxicity of chemicals [13]. This initial battery still contained apical tests and is fairly complex and low in its throughput. The current study aimed to simplify this screening battery using a mechanistic approach and a panel of high throughput CALUX reporter gene assays. A mechanistic approach was taken to validate this high throughput test battery. To this end it was challenged with two preselected sets of chemicals addressing two major apical effect classes relevant in reproductive toxicity. We found selectivity in this battery in that 82% of the compounds inducing reproductive organ deformities were predicted correctly, while for compounds inducing neural tube defects this was the case in 47% only. This is consistent with the mechanisms of toxicity covered in the battery. The most informative assays in the battery were ERalpha CALUX to measure estrogenicity and the AR-anti CALUX assay to measure androgen receptor antagonism.

Towards a pragmatic alternative testing strategy for the detection of reproductive toxicants.

In spite of extensive research in the area over many decades, there is still a shortage of accepted alternative testing methods in reproductive toxicology. Of the variety of alternative methods developed for reproductive toxicity testing not a single one has reached regulatory acceptance. Although various standardized tests have been described, their predictability and applicability domains have so far not satisfactorily been defined. In the near future this situation will only change if new approaches are explored. Current regulatory needs in combination with technological innovations set the scene for rapid progress in this area.