How can you validate nanotoxicology and risk assessment models?

Nanotoxicology and risk assessment are crucial fields of study for the safe and sustainable development of nanomaterials and nanotechnologies. Nanomaterials have unique properties and interactions with biological systems that may pose potential hazards to human health and the environment. Therefore, it is important to validate the models and methods that are used to predict and evaluate the effects of nanomaterials on living organisms and ecosystems. In this article, we will discuss some of the challenges and strategies for validating nanotoxicology and risk assessment models, as well as some of the emerging tools and approaches that can enhance their reliability and applicability.

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1 Model validation

Model validation is the process of verifying that a model is accurate, consistent, and relevant for its intended purpose and context. Validation can be performed at different levels, such as data, structure, output, and application. Data validation involves checking the quality, completeness, and representativeness of the data that are used to build or calibrate the model. Structure validation involves testing the logic, assumptions, and parameters of the model. Output validation involves comparing the model predictions with experimental or observational results. Application validation involves assessing the suitability and usefulness of the model for a specific problem or scenario.

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Olayemi J. Fakayode

Researcher at Institute for Nanotechnology and Water Sustainability, University of South Africa, Florida
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First of all , a proper design is needed for to achieve a functional model. This include all necessary factors that describe the success route to achieving the goals and objectives of the model. In a nanotoxicology model, for example, pollution indices, biological effect factors, e.g. bioavailability to plants and lower or higher animals, risk effects and relationship of these factors among themselves and within food chain are all important. Afterwards, validation can be carried out. Data, structure, output and application validations are all related to analytical validation. i.e., excellent repeatability, precision, accuracy outputs, etc. are all needed to ensure good validation is achieved.

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2 Validation challenges

One of the main challenges for validating nanotoxicology and risk assessment models is the lack of standardized and harmonized data and methods. Nanomaterials are diverse and complex, and their properties and effects may vary depending on the synthesis, processing, characterization, exposure, and environmental conditions. Therefore, it is difficult to compare and integrate data from different sources and studies, and to extrapolate results from one system or scale to another. Moreover, there is a need for more data on the exposure, fate, transport, transformation, and bioaccumulation of nanomaterials in realistic settings, as well as on the mechanisms and pathways of their toxicity and ecotoxicity.

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3 Validation strategies

To overcome some of the challenges for validating nanotoxicology and risk assessment models, some strategies can be adopted. These include developing common protocols and guidelines for nanomaterial characterization, exposure assessment, toxicity testing, and data reporting; establishing and maintaining databases and repositories of nanomaterial data and metadata with quality control and accessibility features; performing interlaboratory comparisons and proficiency tests to evaluate the reproducibility and variability of nanomaterial measurements and methods; conducting sensitivity and uncertainty analyses to identify and quantify the sources and impacts of errors and variability on model outputs; as well as applying multiple models and methods to the same problem or scenario, comparing, and reconciling the results.

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4 Emerging tools and approaches

In addition to existing strategies for validating nanotoxicology and risk assessment models, emerging tools and approaches can be used to improve their performance and applicability. For instance, machine learning and artificial intelligence techniques can be used to analyze large and complex datasets, discover patterns and relationships, and generate and optimize models. Incorporating mechanistic and biological information into models, such as molecular interactions, cellular responses, and adverse outcome pathways, can also enhance their explanatory and predictive power. Furthermore, multiscale and multiphysics models can capture the dynamics and interactions of nanomaterials and biological systems at different levels of organization and complexity. Finally, integrating models with experiments and observations through feedback loops and adaptive mechanisms can refine and validate the models in real time.

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Hesbon Amenya Zablon, PhD, DABT

Toxicologist at ExxonMobil
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There are efforts to develop and validate nanoparticle PBPK models that hold promise in translating in vitro to human-relevant dose levels at a throughput scale.

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5 Implications and outlook

Validating nanotoxicology and risk assessment models is essential for ensuring their credibility and utility for decision making and risk management. By addressing the challenges and applying the strategies and tools discussed in this article, we can enhance the quality and reliability of the models, and increase their confidence and acceptance by the stakeholders and the society. Moreover, we can advance the scientific understanding and innovation of nanomaterials and nanotechnologies, and support their safe and sustainable development and application.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you validate nanotoxicology and risk assessment models?

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1 Model validation

2 Validation challenges

3 Validation strategies

4 Emerging tools and approaches

5 Implications and outlook

6 Here’s what else to consider

Materials Science

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