A comparison of dermal toxicity models; assessing suitability for safe(r)-by-design decision-making and for screening nanomaterial hazards.

The objective of Safe-by-Design (SbD) is to support the development of safer products and production processes, and enable safe use throughout a materials' life cycle; an intervention at an early stage of innovation can greatly benefit industry by reducing costs associated with the development of products later found to elicit harmful effects. Early hazard screening can support this process, and is needed for all of the expected nanomaterial exposure routes, including inhalation, ingestion and dermal. In this study, we compare in vitro and ex vivo cell models that represent dermal exposures (including HaCaT cells, primary keratinocytes, and reconstructed human epidermis (RhE)), and when possible consider these in the context of regulatory accepted OECD TG for in vitro dermal irritation. Various benchmark nanomaterials were used to assess markers of cell stress in each cell model. In addition, we evaluated different dosing strategies that have been used when applying the OECD TG for dermal irritation in assessment of nanomaterials, and how inconsistencies in the approach used can have considerable impact of the conclusions made. Although we could not demonstrate alignment of all models used, there was an indication that the simpler in vitro cell model aligned more closely with RhE tissue than ex vivo primary keratinocytes, supporting the use of HaCaT cells for screening of dermal toxicity of nanomaterials and in early-stage SbD decision-making.

Evaluation of the Influence of Astrocytes on In Vitro Blood-Brain Barrier Models.

In vitro blood-brain barrier (BBB) models are a useful tool to screen the permeability and toxicity of new drugs. Currently, many different in vitro BBB models coexist, but none stands out as being notably better than the rest. Therefore, there is still a need to evaluate the quality of BBB models under various conditions and assess their ability to mimic the in vivo situation. In this study, two brain endothelial cell lines (bEnd.3 and hCMEC/D3) and two epithelial-like cell lines (MDCKII and Caco-2) were selected for BBB modelling purposes. They were grown as monolayers of a single cell type, under the following conditions: in coculture with either primary or immortalised astrocytes; or in the presence of primary or immortalised astrocyte-derived conditioned media. A total of 20 different BBB models were established in this manner, in order to assess the effects of the astroglial components on the BBB phenotype in each case. To this end, six parameters were studied: the expression of selected tight junction proteins; the enzyme activities of alkaline phosphatase and of gamma glutamyl transpeptidase; the transendothelial/transepithelial electrical resistance (TEER); restriction in paracellular transport; and efflux transporter inhibition were each evaluated and correlated. The results showed that coculturing with either primary or immortalised astrocytes led to a general improvement in all parameters studied, evidencing the contribution of this cell type to effective BBB formation. Furthermore, the permeability coefficient (P e) of the tracer molecule, Lucifer Yellow, correlated with three of the six parameters studied. In addition, this study highlights the potential for the use of the Lucifer Yellow P e value as an indicator of barrier integrity in in vitro BBB models, which could be useful for screening the permeability of new drugs.

Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices.

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

Identification of a biomarker panel for colorectal cancer diagnosis.

BACKGROUND: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. METHODS: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. RESULTS: After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. CONCLUSIONS: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).