In this project we set up a human cell-based DNT in vitro testing strategy that is based on test methods with high readiness and data generated therefrom. The methods underwent a fit-for-purpose evaluation that considered four key elements: 1. The test system, 2. the exposure scheme, 3. the assay and analytical endpoint(s) and 4. the classification model. This testing battery was challenged with 119 chemicals for which rich toxicological information was available (for some of them also on their DNT hazard). Testing was performed in 5 test systems measuring 10 DNT-specific endpoints and additional 9 viability/ cytotoxicity-related parameters. For approximately half of the compounds, additional and complementary data from DNT in vitro tests was added by the US-EPA. This extended battery was also evaluated. Testing results revealed that the test methods of this current DNT in vitro battery are reliable and reproducible. The endpoints had to a large extent low redundancy. Battery performance, as assessed with compounds well-characterized for DNT hazard had a sensitivity of 82.7 % and a specificity of 88.2 %. Gap analyses suggested that radial, astro-and microglia as well as myelination endpoints may be added to the battery. Two case studies, one for screening and prioritization of 14 flame retardants, and one on hazard characterization of 2 pesticides, were presented. Hypothetical AOPs were developed based on the latter case study. In conclusion, the DNT testing strategy explored here is a very promising first approach for DNT hazard identification and characterization. The performance is encouraging and may be improved by inclusion of further tests. Some uncertainties in DNT in vitro battery testing outcomes could be reduced by incorporating test data and modelling approaches related to in vitro and in vivo toxicokinetics of test compounds.

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.

dossier to the European Chemicals Agency (ECHA) containing an extensive list of data on the intrinsic properties of a substance, ranging from chemical characterization and physicochemical properties to toxicological and ecotoxicological data, with increasing demands depending on the tonnage band of the quantity of the substance placed on the market. In addition, registrants should collect information on use and exposure to perform a chemical safety assessment (CSA) based on the toxicity profile of the substance. The minimum data requirements are described in Annexes VI-X of the regulation. For toxicological testing, the standard require-

Triazoles are interesting templates for novel chemotherapeutic drugs. We synthesized here 17 1,3,4-substituted-1,2,3-triazoles that differed in their 1'-substituent (variable alkyl chain lengths C3-C12), the 3'-substituent (no substituent, -methyl or -propyl) or the salt form obtained. Several of the compounds were cytotoxic (μM range) for tumor cells (HL-60, Jurkat, MCF-7, HCT-116), and when the effect was compared to non-transformed cells (Vero), selectivity ratios of up to 23-fold were obtained. To estimate the liability of these potential drug candidates for triggering neurotoxicity, we used the LUHMES cell-based NeuriTox assay. This test quantifies damage to the neurites of human neurons. The four most potent tumoricidal compounds were found to be neurotoxic in a concentration range similar to the one showing tumor cell toxicity. As the neurites of the LUHMES neurons were affected at >4-fold lower concentrations than the overall cell viability, the novel triazoles were classified as specific neurotoxicants. The structure-activity relationship (SAR) for neurotoxicity was sharply defined and correlated with the one for anti-neoplastic activity. Based on this SAR, two non-neurotoxic compounds were predicted, and testing in the NeuriTox assay confirmed this prediction. In summary, the panel of novel triazoles generated and characterized here, allowed to define structural features associated with cytotoxicity and neurotoxicity. Moreover, the study shows that potential neurotoxic side effects may be predicted early in drug development if highly sensitive test methods for neurite integrity are applied.

In biological systems (cell culture media, cells, body fluids), drugs/toxicants are usually not freely dissolved but partially bound to biomolecules; only a fraction of the chemical is free/unbound (fu). To predict pharmacological effects and toxicity, it is important that the fu of the drug is known. As the differences between free and nominal concentrations are determined by test system parameters (e.g., the protein and lipid content, and the type of surface material), comparison of nominal concentrations between two different new approach methods (NAM) may lead to faulty conclusions. The same problem exists when in vitro concentrations are compared to those in human subjects. Therefore, the respective fu of a chemical in a test system needs to be determined for in vitro-to-in vivo extrapolations (IVIVE). Besides direct measurements, prediction models can help to obtain fu. Here we describe a simplified approach to approximate fu and provide background information on the underlying assumptions. Comparative predictions and measurements of fu of various drugs are shown to exemplify the approach. Basic input data, like protein and lipid concentrations, are also provided. Beyond such test systems data, the only required chemical-specific inputs are the lipophilicity of the candidate drug and its ionization state, as determined by the dissociation constants of its acidic or basic groups. This overview is intended to be used by any lab scientist without specific toxicokinetics training to obtain an estimate of fu in a given cell culture medium.