There are no in vivo repeated-dose data for the vast majority of β-olefinic alcohols.However, there are robust and consistent ex vivo data suggesting many of these chemicals are metabolically transformed, especially in the liver, to reactive electrophilic toxicants which react in a mechanistically similar manner to acrolein, the reactive metabolite of 2-propen-1-ol. Hence, an evaluation was conducted to determine suitability of 2-propen-1-ol as a read-across analogue for other β-olefinic alcohols. The pivotal issue to applying read-across to the proposed category is the confirmation of the biotransformation to metabolites having the same mechanism of electrophilic reactivity, via the same metabolic pathway, with a rate of transformation sufficient to induce the same in vivo outcome. The applicability domain for this case study was limited to small (C3 to C6) primary and secondary -olefinic alcohols. Mechanistically, these -unsaturated alcohols are considered to be readily metabolised by alcohol dehydrogenase to polarised α, -unsaturated aldehydes and ketones. These metabolites are able to react via the Michael addition reaction mechanism with thiol groups in proteins resulting in cellular apoptosis and/or necrosis. The addition of the non-animal in chemico reactivity data (50% depletion of free glutathione) reduced the uncertainty so the read-across prediction for the straight-chain olefinic -unsaturated alcohols is deemed equivalent to a standard test. Specifically, the rat oral 90-day repeated-dose No Observed Adverse Effect Level (NOAEL) for 2-propen-1-ol of 6 mg/kg body weight bw/d in males based on increase in relative weight of liver and 25 mg/kg bw/d in females based on bile duct hyperplasia and periportal hepatocyte hypertrophy in the liver, is read across to fill data gaps for the straight-chained analogues.

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.

Highlights The transparent and systematic reporting of computational models facilitates their regulatory acceptance and use. A reporting format for physiologically based kinetic, toxicodynamic and environmental fate models was developed. The QSAR Model Reporting Format (QMRF) was adapted to describe QSARs for nanomaterials. The model documentation is stored in the publicly accessible JRC Data Catalogue. The model documentation was used to give an overview of the model landscape for nanomaterials.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework.To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy = 81%, sensitivity = 85% and specificity = 76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q Graphical abstract Highlights

The development of physiologically based (PB) models to support safety assessments in the field of nanotechnology has grown steadily during the last decade. This review reports on the availability of PB models for toxicokinetic (TK) and toxicodynamic (TD) processes, including in vitro and in vivo dosimetry models applied to manufactured nanomaterials (MNs). In addition to reporting on the state-of-the-art in the scientific literature concerning the availability of physiologically based kinetic (PBK) models, we evaluate their relevance for regulatory applications, mainly considering the EU REACH regulation. First, we performed a literature search to identify all available PBK models. Then, we systematically reported the content of the identified papers in a tailored template to build a consistent inventory, thereby supporting model comparison. We also described model availability for physiologically based dynamic (PBD) and in vitro and in vivo dosimetry models according to the same template. For completeness, a number of classical toxicokinetic (CTK) models were also included in the inventory. The review describes the PBK model landscape applied to MNs on the basis of the type of MNs covered by the models, their stated applicability domain, the type of (nano-specific) inputs required, and the type of outputs generated. We identify the main assumptions made during model development that may influence the uncertainty in the final assessment, and we assess the REACH relevance of the available models within each model category. Finally, we compare the state of PB model acceptance for chemicals and for MNs. In general, PB model acceptance is limited by the absence of standardised reporting formats, psychological factors such as the complexity of the models, and technical considerations such as lack of blood:tissue partitioning data for model calibration/validation.