Nanomaterials are small particles that can be found everywhere, including in the air we breathe, which can have detrimental effects on health and the environment.
Dr Ernesto Alfaro-Moreno of the International Iberian Nanotechnology Laboratory in Braga, Portugal, has been involved in continued research on the toxicology of nanomaterials, with promising application in our daily lives.
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Read the original research: doi.org/10.1186/s12989-023-00530-0
Image Source: Deposit Photos / Bonninturina
Hello and welcome to Research Pod! Thank you for listening and joining us today.
In this episode, we look at the research of Dr Ernesto Alfaro-Moreno of the International Iberian Nanotechnology Laboratory in Braga, Portugal, who has been involved in continued research on the toxicology of nanomaterials. The Nanosafety Group of the Laboratory aim to incorporate machine learning strategies to predict the potential risks of several nanomaterials with promising application in our daily life. Their findings indicate that computational tools could be the future of faster toxicology assessment.
Nanomaterials can be found anywhere, from cosmetics to paints and coatings, textiles to food and drugs, and several other applications and objects that we use every day. Since the introduction of the concept of nanomaterials in the late 20th century and the blooming of nanotechnology and its applications from that point on, one way or another, we are all inevitably exposed to nanomaterials. This exposure could be either through active choices, such as use of cosmetics or household commodities, or passively, through the wear of paint layers or car protective coatings. As with many technological advancements, the big question arises: how can nanomaterials affect our health and the environment?
The Nanosafety Group of International Iberian Nanotechnology Laboratory in Braga, Portugal, evaluates nanomaterials and their potential adverse effects. The group is led by Dr Ernesto Alfaro-Moreno, in collaboration with fellow researchers, and their latest study centres around the toxicological effect nanomaterials and nanoparticles can have on human lungs and other biological targets.
Labels, such as ‘Not suitable for children under 3 years’, which warn against small particles in items that we use every day are a fairly common occurrence. Even for adults, this warning is implicit. But what happens when the object in question is invisible to the naked eye? What precautions do we take then, and how do we comprehend the effect of something we cannot see?
An emerging area of research called ‘nanosafety’ deals with the assessment of the potential harmful effects of nanomaterials, including its toxicity, and their impact on human health and the environment, including their production, chemistry, structural properties, different uses, and end-of-life handling.
Among the many ways to evaluate the potential adverse effects that nanomaterials may have on health, there are three main approaches that are widely used. These approaches are classified as in vivo, in vitro, or in silico methods. The difference between the three methods is quite substantial in terms of importance to life and resources required. During in vivo studies, the effect of the nanomaterial under examination is assessed using living organisms. In vitro studies involve assessing the effect practically, but outside living organisms, under laboratory conditions mimicking the conditions of a living organism as closely as possible. In silico studies use computational modelling and theoretical approaches, without employing any sort of live or laboratory-based samples.
Over the past few years, in silico studies have seen significant development with the advancement of technology. When combined with in vivo or in vitro studies, they accelerate our ability to reach meaningful predictions about possible adverse effects of nanomaterials with different physicochemical nature, that had previously not been studied.
In silico studies enable the assessment of numerous factors for several nanomaterials and build representative predictive models with the use of significantly less time and resources compared to in vitro and especially, in vivo studies. The rise of artificial intelligence and machine learning concepts has further boosted the development of in silico models, expanding the capabilities of computational tools in many fields, including nanosafety and the assessment of nanomaterials’ toxicology. While the research in the field of nanomaterial toxicology has been limited, there is an abundance of in vivo or in vitro data for many different nanomaterials and the possible health or environmental effects. This existing information can be used in combination with computational tools to build in silico models that can efficiently predict the potential adverse effects of nanomaterials.
The study of nanomaterials is ever evolving, as new nanomaterials that have been engineered to have improved properties with different coatings or addition of extra elements to the ‘traditional’ chemical structure, emerge. When the development of new, engineered nanomaterials keeps thriving, it is imperative that nanosafety must keep up to ensure that the ones that make it into our everyday lives are assessed for their potential effects on health and the environment.
INL Nanosafety’s latest work has been on the development of an in silico model to predict the toxicity of specific types of engineered nanomaterials and assess their cytotoxicity, or the toxicity of substances to specific human cells, on human lungs. The research team used available data sets from in vitro studies on the toxicity of nanomaterials on human lung cells to develop and train a model to predict the toxicity of nanomaterials. Using machine learning, the team developed this model to quantitatively predict toxicity based on the specific structure and physical and chemical properties of widely used engineered nanomaterials, from a library of ten inorganic core nanomaterial types, five possible added elements, and nine possible coating types.
The accuracy of the model was validated by matching the predicted toxicology with the existing toxicological data – using parts of the original data set that were not used during the initial development of the model or during the training phase, all under rigorous statistical examination.
Although the team used set combinations of nanomaterial structures to develop and train the in silico model, the model could be applied for the prediction of toxicity of nanomaterials with structures that were not examined but are structurally similar to the examined ones. The team emphasises the need to set boundaries on the model’s applicability very carefully, as they dictate its use and accuracy.
Alfaro-Moreno and team’s recent work shows the possibility of harnessing the power of computers towards building tools to understand and predict the effects of nanomaterials to protect our health and the environment, ultimately benefitting the society and advancing sustainable economic growth. While the highlight of this research has been the development of an in silico model for the prediction of nanomaterial toxicology, Alfaro-Moreno has previously also worked around developing and highlighting in vitro models with the same scope.
In recent publications, INL’s Nanosafety group have not only reviewed available literature on the area of safety, but also on the development of organ-on-chip systems as in vitro models to assess the toxicity of nanomaterials. From their research, it is evident that toxicology assessment, at least in the case of nanomaterials, is moving from frequently relying on in vivo models to other practices that are less resource-heavy or bound by ethical considerations. From in vivo to in vitro, and more recently to in silico, assessment of toxicological effects of nanomaterials on human health and the environment is becoming faster, while retaining reliability.
When asked if it is possible to adapt this model for assessment of nanomaterial toxicology to distinct parts of the body, Alfaro-Moreno concluded, showing great promise for the future:
‘In order to adapt the model, we need to analyse data regarding the behaviour of cells from other tissues first. The specialisation of each organ is related to the metabolic activity of each cell type, and also to the probability of materials reaching other organs, which may not be the first target of different materials. That is why there is a focus on developing specific models for specific targets.’
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