Characterisation

About

The foundation of modern risk assessment approaches is the characterisation of compounds and materials by which they are evaluated against specific key events for Adverse Outcome Pathways (AOPs). Thereby, the assessment process is structured in a four-step process including¹:

1. Hazard Identification (HI). During this step the molecular biology mechanisms are revealed which underlie adverse effects resulting from exposure to a test compound.
2. Dose Response (DR). During this step the relationship between the dose of the test compound and its effect is determined using toxciokinetic and toxicodynamic models .
3. Exposure Assessment (EA) which evaluates the possible exposure likelihood of organisms to the chemical including geographic place of uptake, biologic route of uptake (oral, per cutaneous uptake etc.) as well as the uptake-frequency describing how often an organism is exposed to the test chemical.
4. Risk characterization which integrates all information from above to determine the overall risk of a chemical to an organism.

As we can draw from the four-step process for risk assessment above, one key element during the assessment process is in vivo studies in which the effect of a test compound on the organism is evaluated. In vivo studies are problematic in many ways. They are not only expensive to conduct and very time consuming, they also violate our scientific responsibility as scientists to protect animal welfare from an ethical and moral point of view. Therefore, moving away from animal experimentation is a great aim but very challenging since complex biological functions such as behaviour (a cause of complex neuronal cell activity) are not simple to mimic ex vivo. It is not only next generation in vitro approaches such as the multi organoid technology which seek solutions to study complex biological mechanisms ex vivo but also in silico methods are aiming to provide solutions for such an approach.² In silico based risk assessment allows to take already existing data of known pharmacophores and their effect on cellular mechanisms and to apply this knowledge in Quantitative Structure-Activity Relationship (QSAR) models to characterise new compounds based on their chemical features including functional groups and molecular structure.³

Key for in silico based risk assessment is a robust data management infrastructure where workflows are standardised and harmonised by Defined Approaches (DAs) and by Integrated Approaches to Testing and Assessment (IATA); this approach ensures that evaluation and validation of compound resultss are conducted with high integrity to be able to trust resulting outcomes. An infrastructure for risk assessment is defined by the following elements:

1. Access to Knowledge. Thereby knowledge is divided into two parts: The first part includes knowledge which is needed to conduct the assessment process and consists of Standard Operating Procedures (SOPs), regulatory guidelines, protocols, scientific publications etc. under which the characterisation of compounds and materials has to be examined. This knowledge can be organised in an easily accessible repository where all the documents are stored.The second part of knowledge includes all the information which is needed for the characterization process itself. This might include existing in vivo and in vitro data for the test chemical which has to be characterised. It also includes the knowledge about the test compound itself such as compound identity (CAS number, SMILES structure etc.), chemical information (functional groups, 3D structures, etc.) and information of biological activity (known pharmacology, known binding sites, etc.). This data is the data which will be used by the machine learning and artificial intelligence algorithms and which delivers the information based on which new compounds are characterised.
2. Harmonised and standardised workflow. A harmonized and standardised workflow ensures the uniformity of the characterisation process and includes the creation of harmonised data throughout different approach methods. One example is the Recommended Metadata for Biological Images (REMBI) which is an international initiative to harmonise microscopy image data which comes from different microscopes so that it is easily Findable, Accessible, Interoperable and Reusable (FAIR). To make data FAIR, standardised templates for experimental protocols and correlating metadata should be elaborated and used.
3. Data storage. Raw and processed data which has been generated throughout the experiments has to be stored in a safe and FAIR data repository. Especially for characterisation purposes, this data should be accessible with computational tools which allow the processing and analysing of all sorts of data formats including harmonised and customised metadata.
4. Characterization output. The outcome of an evaluation can be structured in reports, charts, graphs, papers, heatmaps etc. Also this data has to be stored in a FAIR way and can be added to the existing repository as part of the knowledge base for that experiment.

One possible workflow for compound characterization is visualised in Figure 1 where a compound or material is synthesised and evaluated for specific endpoints in in vitro assays which are then analysed by computational algorithms to give a characterisation profile of that compound as an output.

Figure 1: Possible work flow for a characterization process as described above.

Depending on the kind of repositories, templates, experiments and computational approaches, this characterisation infrastructure can be adapted and customised to specific needs for specific projects.

At SaferWorldbyDesign we have developed a characterisation infrastructure where we integrate newest data management approaches and computational technologies to guarantee efficient risk assessment due to your specific needs. If you want to learn more about our services, please see the «Our solutions» section on this page.

References

(1) A New Paradigm for Environmental Chemistry and Toxicology: From Concepts to Insights; Jiang, G., Liu, X., Eds.; Springer: Singapore, 2020.

(2) Cronin, M. T. D.; Madden, J. C.; Yang, C.; Worth, A. P. Unlocking the Potential of in Silico Chemical Safety Assessment – A Report on a Cross-Sector Symposium on Current Opportunities and Future Challenges. Computational Toxicology 2019, 10, 38–43. https://doi.org/10.1016/j.comtox.2018.12.006.

(3) Astuto, M. C.; Di Nicola, M. R.; Tarazona, J. V.; Rortais, A.; Devos, Y.; Liem, A. K. D.; Kass, G. E. N.; Bastaki, M.; Schoonjans, R.; Maggiore, A.; Charles, S.; Ratier, A.; Lopes, C.; Gestin, O.; Robinson, T.; Williams, A.; Kramer, N.; Carnesecchi, E.; Dorne, J.-L. C. M. In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives. In In Silico Methods for Predicting Drug Toxicity; Benfenati, E., Ed.; Methods in Molecular Biology; Springer US: New York, NY, 2022; Vol. 2425, pp 589–636. https://doi.org/10.1007/978-1-0716-1960-5_23.

Our services

At SaferWorldbyDesign we developed an integrated characterisation infrastructure. Implemented in this infrastructure are the following tools:

The eNanoMapper environment, which includes a database containing protocols and experimental templates which are standardised and harmonised to make data generated throughout the experimental process easily Findable, Accessible, Interoperable and Reusable (FAIR) what is key to ensure integrity and validity of experimental outcomes. The eNanoMapper environment also provides a FAIR user guide which helps you to make your data FAIR.

The ACENano Infrastructure is a knowledge base which manages protocols, SOPs, articles and linked datasets which can be explored through a user-friendly interface. It allows you to collect and organise knowledge which is needed to conduct characterisation experiments.

EdelweissData is a data management infrastructure solution developed and provided by Edelweiss Connect. It can provide a repository where your data can be stored in a FAIR and safe way with high integrity and harmonisation. Specific to your needs we can develop computational tools such as observable notebooks and user friendly interfaces to easily manage your data and its analysis.

Computational characterisation tools which are based on the newest machine learning and artificial intelligence technologies. Thereby, we analyse your raw or processed data for your endpoint of interest and summarise the outcoming results for instance in detailed reports, graphs and heatmaps. We also integrate knowledge in knowledge graphs where the newly generated knowledge can be explored interactively.

With this we offer a characterisation workflow that covers all needs from the beginning of the assessment process throughout the end. One possible workflow is visualised in Figure 1, showing the procedure from template development and knowledge management over the experimental phase throughout the analysis of the experimental results.

Figure 1: Possible work flow for a characterization process at SaferWorldbyDesign as described above.

Furthermore, we provide a large variety of next generation in vitro and in silico technologies in collaboration with our partners. Those technologies include organ specific cell and tissue models such as the HepaRG cell model provided by Biopredic to conduct liver specific experiments or the multi organ on a chip model provided by TissUse with which compounds can be characterised in a multi organ in vitro environment. Also computational characterisation toolboxes such as the MultiCASE CASE Ultra toolbox developed by MultiCASE are implemented in our solutions. MultiCASE CASE Ultra is a toolbox with which compounds can be characterised in a QSAR approach against different endpoints such as skin irritation, skin sensitisation, endocrine disruption and much more. To explore the whole collection of services which we offer, please visit the «partners» section on this homepage.

To provide a good and user-friendly overview over our solutions, we collect our services in solution packages for specific organ endpoints such as skin, liver, kidney etc. which you can explore in the «solutions» part of this portal. Thereby, we can adapt our approaches and services to your specific needs and interests to always provide you with the best solution that serves you the best. If you are interested in taking advantage of our services, please feel free to reach out to us.

Partner contribution

EdelweissConnect

Edelweiss Connect (EwC) - as the provider of this SaferWorldbyDesign platform - offers the expertise and experience to initiate, coordinate and manage large collaborative research projects, with partners from industry, government, and academia. Our goal is to incubate high-impact products, services, and solutions at the forefront of innovation, with sustainability and responsibility.

>Read more

Regulatory framework

Defined Approaches (DA) and Integrated Approaches to Testing and Assessment (IATA) which are defined and controlled by authorities are key to ensure comparable and valuable characterisation results during the risk assessment process for test compounds to ensure their integrity, trust and validity. Therefore, the assessment processes at SaferWorldbyDesign are based on such guidelines. One example is SaferSkin where the OECD has elaborated detailed procedures¹ which have to be followed to characterise compounds in concern of their potential to induce skin sensitisation and which we have implemented in our assessment process. For characterising compounds as part of the risk assessment process, the following guidelines are important:

1. The OECD Guidelines for the Testing of Chemicals
2. The OECD series on testing and assessment
3. United States Environmental Protection Agency (EPA) Science Policy Council Handbook on Risk Characterization

References

(1) OECD. Guideline No. 497: Defined Approaches on Skin Sensitisation; OECD Guidelines for the Testing of Chemicals, Section 4; OECD, 2021. https://doi.org/10.1787/b92879a4-en.