Read-across and new approach methodologies applied in a 10-step framework for cosmetics safety assessment - A case study with parabens.

Parabens are esters of para-hydroxybenzoic acid that have been used as preservatives in many types of products for decades including agrochemicals, pharmaceuticals, food and cosmetics. This illustrative case study with propylparaben (PP) demonstrates a 10-step read-across (RAX) framework in practice. It aims at establishing a proof-of-concept for the value added by new approach methodologies (NAMs) in read-across (RAX) for use in a next-generation risk assessment (NGRA) in order to assess consumer safety after exposure to PP-containing cosmetics. In addition to structural and physico-chemical properties, in silico information, toxicogenomics, in vitro toxicodynamic, toxicokinetic data from PBK models, and bioactivity data are used to provide evidence of the chemical and biological similarity of PP and analogues and to establish potency trends for observed effects in vitro. The chemical category under consideration is short (C1-C4) linear chain n-alkyl parabens: methylparaben, ethylparaben, propylparaben and butylparaben. The goal of this case study is to illustrate how a practical framework for RAX can be used to fill a hypothetical data gap for reproductive toxicity of the target chemical PP.

New framework for a non-animal approach adequately assures the safety of cosmetic ingredients - A case study on caffeine.

This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine. Based on structural similarities, three primary metabolites of the target chemical caffeine (theophylline, theobromine and paraxanthine) were selected as its most relevant analogues, to estimate a point of departure in order to support a next generation risk assessment (NGRA). On the basis of the pivotal mode of action (MOA) of caffeine and other methylxanthines, theophylline appeared to be the most potent and suitable analogue. A worst-case aggregate exposure assessment determined consumer exposure to caffeine from different sources, such as cosmetics and food/drinks. Using a PBK model to estimate human blood concentrations following exposure to caffeine, an acceptable Margin of Internal Exposure (MOIE) of 27-fold was derived on the basis of a RAX using theophylline animal data, which suggests that the NGRA approach for caffeine is sufficiently conservative to protect human health.

Skin sensitisation testing in practice: Applying a stacking meta model to cosmetic ingredients.

Recently, several non-animal approaches contributing to the identification of skin sensitisation hazard have been introduced. Their validation and acceptance has largely been directed towards regulatory classification. Considering the driving force for replacement of in vivo tests centred on cosmetics, it is reasonable to ask how well the new approaches perform in this respect. In the present study, 219 substances, largely cosmetic raw materials (including dyes, preservatives and fragrances), have been evaluated in our Defined Approach integrating a stacking meta model (version 5), incorporating the individual outcomes of 3 in vitro validated methods (Direct Peptide Reactivity Assay, Keratinosens™, U-SENS™), 2 in silico tools (TIMES SS, TOXTREE) and physicochemical parameters (volatility, pH). Stacking meta model outcomes were compared with existing local lymph node assay (LLNA) data. Non-sensitisers comprised 68/219; 86 were weak/moderate and 65 were stronger sensitisers. The model version revision demonstrate the gain to discriminate sensitizers to non-sensitiser when the in silico TIMES model is incorporated as input parameter. The 85% to 91% accuracy for the cosmetics categories, indicates the stacking meta model offers value for the next generation risk assessment framework. These results pinpoint the power of the stacking meta model relying on a confidence based on the probability given in any individual prediction.

Assessment of a defined approach based on a stacking prediction model to identify skin sensitization hazard.

Skin sensitization is an important toxicological endpoint in the safety assessment of chemicals and cosmetic ingredients. Driven by ethical considerations and European Union (EU) legislation, its assessment has progressed from the reliance on traditional animal models to the use of non-animal test methods. It is generally accepted that the assessment of skin sensitization requires the integration of various non-animal test methods in defined approaches (DAs), to cover the mechanistic key events of the adverse outcomes pathway (AOP) (OECD, 2014). Several case studies for DAs predicting skin sensitization hazard or potency have been submitted to the OECD, including a stacking meta-model developed by L'Oréal Research & Innovation (OECD, 2017b; Del Bufalo et al., 2018; Noçairi et al., 2016). The present study evaluated the predictive performance of the defined approach integrating a stacking meta-model incorporating in silico, in chemico and in vitro assays, using the Cosmetics Europe (CE) skin sensitization database. Based on the optimized prediction cut-offs, the defined approach provided a hazard prediction for 97 chemicals with a sensitivity of 91%, a specificity of 76% and accuracy of 86% (kappa of 0.67) against human skin sensitization hazard data and a sensitivity of 85%, specificity of 91% and accuracy of 87% (kappa of 0.67) against Local Lymph Node Assay (LLNA) hazard data. A comparison of the in vivo LLNA with human hazard data for the same 97 chemicals showed a sensitivity of 92%, specificity of 51% and accuracy of 78% (kappa of 0.48). Thus, the defined approach showed a higher degree of concordance, as compared to the LLNA for predicting human skin sensitization hazard. Moreover, a comparison with the six DAs selected for evaluation of their predictivity in the study by Kleinstreuer et al. (2018) showed a similar high accuracy of 86% for 97 overlapping chemicals. The next step will be an independent evaluation of the DA for its integration in the performances based test guidelines (PBTG) for skin sensitization.

Novel technologies and an overall strategy to allow hazard assessment and risk prediction of chemicals, cosmetics, and drugs with animal-free methods.

Several alternative methods to replace animal experiments have been accepted by legal bodies. An even larger number of tests are under development or already in use for non-regulatory applications or for the generation of information stored in proprietary knowledge bases. The next step for the use of the different in vitro methods is their combination into integrated testing strategies (ITS) to get closer to the overall goal of predictive "in vitro-based risk evaluation processes." We introduce here a conceptual framework as the basis for future ITS and their use for risk evaluation without animal experiments. The framework allows incorporation of both individual tests and already integrated approaches. Illustrative examples for elements to be incorporated are drawn from the session "Innovative technologies" at the 8th World Congress on Alternatives and Animal Use in the Life Sciences, held in Montreal, 2011. For instance, LUHMES cells (conditionally immortalized human neurons) were presented as an example for a 2D cell system. The novel 3D platform developed by InSphero was chosen as an example for the design and use of scaffold-free, organotypic microtissues. The identification of critical pathways of toxicity (PoT) may be facilitated by approaches exemplified by the MatTek 3D model for human epithelial tissues with engineered toxicological reporter functions. The important role of in silico methods and of modeling based on various pre-existing data is demonstrated by Altamira's comprehensive approach to predicting a molecule's potential for skin irritancy. A final example demonstrates how natural variation in human genetics may be overcome using data analytic (pattern recognition) techniques borrowed from computer science and statistics. The overall hazard and risk assessment strategy integrating these different examples has been compiled in a graphical work flow.

Evaluating "fast" micellar monolithic liquid chromatography for high-throughput quantitative structure-retention relationship screening.

The recently introduced monolithic silica columns were tested for their use in micellar liquid chromatography. Micellar methods are utilized in high-throughput quantitative structure-retention relationships to estimate an indicator of the membrane permeability of drugs, namely, the octanol-water partition coefficient, log P. The monolithic column's ability to function at higher flow rates might be useful to speed up these chromatographic methods estimating the log P. Therefore, the elution behavior of diverse basic pharmaceutical substances was determined on a classical particle-based and a monolithic column, both with and without a micellar medium in the mobile phase. Utilizing among others principal component analysis, the extent to which these methods differ in retention characteristics was examined in the context of high-throughput determination of log P. Results indicate that combining monolithic columns with micellar media leads to faster log P and possibly even better permeability predictions.