Release and toxicity assessment of carbon nanomaterial reinforced polymers during the use and end-of-life phases: A comparative review.

The research on carbon-based nanomaterial (C-NM) composites has increased in the last two decades. This family of functional materials shows outstanding mechanical, thermal and electrical properties, and are being used in a variety of applications. An important challenge remains before C-NM can be fully integrated in our production industries and our lives: to assess the release of debris during production, use, and misuse of composites and the effect they may have on the environment and on human health. During their lifecycle, composites materials can be subjected to a variety of stresses which may release particles from the macroscopic range to the nanoscale. In this review, the release of debris due to abrasion, weathering and combustion as well as their toxicity is evaluated for the three most used C-NM: Carbon Black, Carbon Nanotubes and Graphene-related materials. The goal is to stimulate a Safe-By-Design approach by guiding the selection of carbon nano-fillers for specific applications based of safety and performance.

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies-the case of nanomaterials.

The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system.

Structure-activity relationship of graphene-related materials: A meta-analysis based on mammalian in vitro toxicity data.

To support a safe application of graphene-related materials (GRMs) it is necessary to understand the potential negative impacts they could have on human health, in particular on the lung - one of the most sensitive exposure routes. Machine learning (ML) approaches can help analyse the results of multiple toxicity studies to understand the structure-activity relationship and the effect of experimental conditions, thus supporting predictive nanotoxicology. In this work we collected in vitro cytotoxicity data obtained from studies using lung cells; we then fitted multiple regression models to predict this endpoint based on the material properties and experimental conditions. Moreover, the data set was used to calculate the Benchmark Dose Lower Confidence Interval (BMDL), a dose descriptor widely used in risk assessment. Regression and classification models were applied for the prediction of the BMDL value and BMDL range. The analyses show that both cytotoxicity and the BMDL range can be predicted well (Q2 = 0.77 and accuracy = 0.71, respectively). Both physico-chemical characteristics such as the lateral size, number of layers, and functionalization, and experimental conditions such as the assay and media used were important predicting features, confirming the need for thorough characterization and reporting of these parameters.

Approach toward In Vitro-Based Human Toxicity Effect Factors for the Life Cycle Impact Assessment of Inhaled Low-Solubility Particles.

Today's scarcity of animal toxicological data for nanomaterials could be lifted by substituting in vivo data with in vitro data to calculate nanomaterials' effect factors (EF) for Life Cycle Assessment (LCA). Here, we present a step-by-step procedure to calculate in vitro-to-in vivo extrapolation factors to estimate human Benchmark Doses and subsequently in vitro-based EFs for several inhaled nonsoluble nanomaterials. Based on mouse data, the in vitro-based EF of TiO2 is between 2.76 · 10-4 and 1.10 · 10-3 cases/(m2/g·kg intake), depending on the aerodynamic size of the particle, which is in good agreement with in vivo-based EFs (1.51 · 10-4-5.6 · 10-2 cases/(m2/g·kg intake)). The EF for amorphous silica is in a similar range as for TiO2, but the result is less robust due to only few in vivo data available. The results based on rat data are very different, confirming the importance of selecting animal species representative of human responses. The discrepancy between in vivo and in vitro animal data in terms of availability and quality limits the coverage of further nanomaterials. Systematic testing on human and animal cells is needed to reduce the variability in toxicological response determined by the differences in experimental conditions, thus helping improve the predictivity of in vitro-to-in vivo extrapolation factors.

Combined in vitro-in vivo dosimetry enables the extrapolation of in vitro doses to human exposure levels: A proof of concept based on a meta-analysis of in vitro and in vivo titanium dioxide toxicity data.

Evaluating the potential risks of nanomaterials on human health is fundamental to assure their safety. To do so, Human Health Risk Assessment (HHRA) relies mostly on animal studies to provide information about nanomaterials toxicity. The scarcity of such data, due to the shift of the nanotoxicology field away from a phenomenological, animal-based approach and towards a mechanistic understanding based on in vitro studies, represents a challenge for HHRA. Implementing in vitro data in the HHRA methodology requires an extrapolation strategy; combining in vitro dosimetry and lung dosimetry can be an option to estimate the toxic effects on lung cells caused by inhaled nanomaterials. Since the two dosimetry models have rarely been used together, we developed a combined dosimetry model (CoDo) that estimates the air concentrations corresponding to the in vitro doses, extrapolating in this way in vitro doses to human doses. Applying the model to a data set of in vitro and in vivo toxicity data about titanium dioxide, we demonstrated CoDo's multiple applications. First, we confirmed that most in vitro doses are much higher than realistic human exposures, considering the Swiss Occupational Exposure Limit as benchmark. The comparison of the Benchmark Doses (BMD) extrapolated from in vitro and in vivo data, using the surface area dose metric, showed that despite both types of data had a quite wide range, animal data were overall more precise. The high variability of the results may be due both to the dis-homogeneity of the original data (different cell lines, particle properties, etc.) and to the high level of uncertainty in the extrapolation procedure caused by both model assumptions and experimental conditions. Moreover, while the surface area BMDs from studies on rodents and rodent cells were comparable, human co-cultures showed less susceptibility and had higher BMDs regardless of the titanium dioxide type. Last, a Support Vector Machine classification model built on the in vitro data set was able to predict the BMD-derived human exposure level range for viability effects based on the particle properties and experimental conditions with an accuracy of 85%, while for cytokine release in vitro and neutrophil influx in vivo the model had a lower performance.

Understanding Nanomaterial Biotransformation: An Unmet Challenge to Achieving Predictive Nanotoxicology.

More than a decade has passed since the first concepts of predictive nanotoxicology were formulated. During this time, many advancements have been achieved in multiple disciplines, including the success stories of the fiber paradigm and the oxidative stress paradigm. However, important knowledge gaps are slowing down the development of predictive nanotoxicology and require a mutidisciplinary effort to be overcome. Among these gaps, understanding, reproducing, and modeling of nanomaterial biotransformation in biological environments is a central challenge, both in vitro and in silico. This dynamic and complex process is still a challenge for today's bioanalytics. This work explores and discusses selected approaches of the multidisciplinary efforts taken in the last decade and the challenges that remain unmet, in particular concerning nanomaterial biotransformation. It highlights some future advancements that, together, can help to understand such complex processes and accelerate the development of predictive nanotoxicology.

An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials.

In line with the 3R concept, nanotoxicology is shifting from a phenomenological to a mechanistic approach based on in vitro and in silico methods, with a consequent reduction in animal testing. Risk Assessment (RA) and Life Cycle Assessment (LCA) methodologies, which traditionally rely on in vivo toxicity studies, will not be able to keep up with the pace of development of new nanomaterials unless they adapt to use this new type of data. While tools and models are already available and show a great potential for future use in RA and LCA, currently none is able alone to quantitatively assess human hazards (i.e. calculate chronic NOAEL or ED50 values). By highlighting which models and approaches can be used in a quantitative way with the available knowledge and data, we propose an integrated pathway for the use of in vitro data in RA and LCA. Starting with the characterization of nanoparticles' properties, the pathway then investigates how to select relevant in vitro human data, and how to bridge in vitro dose-response relationships to in vivo effects. If verified, this approach would allow RA and LCA to stir up the development of nanotoxicology by giving indications about the data and quality requirements needed in risk methodologies.