27 research outputs found
Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health
In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups of similar structures and similar toxicological properties. Here we review case studies of grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACh and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria and boundaries of the group; adequacy of methods used to justify the group; inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management
Lehren aus dem Gruppieren von Chemikalien zur Bewertung der Risiken fĂŒr die Gesundheit des Menschen
Analog zum Periodensystem, das Elemente nach ihrer Ăhnlichkeit der Strukturen und chemischen Eigenschaften gruppiert, kann die Gefahr chemischer Stoffe in Gruppen mit Ă€hnlichen Strukturen und Ă€hnlichen toxikologischen Eigenschaften bewertet werden. Im Folgenden werden Fallbeispiele zu Gruppierungsstrategien vorgestellt, welche die Bewertungen von Gefahr, Exposition und Risiko fĂŒr die menschliche Gesundheit unterstĂŒtzen. Sowohl unter EUâREACh als auch im USâTSCA New Chemicals Programm ist in der Regel die strukturelle Ăhnlichkeit die Grundlage fĂŒr eine Gruppierungt. Allerdings ist dieses Kriterium nicht immer angemessen und ausreichend. Auf der Grundlage der gewonnenen Erkenntnisse leiten wir zehn GrundsĂ€tze fĂŒr die Gruppierung ab, darunter: die transparente Darstellung des Zwecks der Gruppierung, die Definition der Kriterien fĂŒr eine Gruppierung und die Grenzen der Gruppen, eine dieEinbeziehung oder der Ausschluss eines Stoffs in oder aus einer Gruppe aufgrund toxikologischer Eigenschaften und eine BegrĂŒndung der Zuordnung durch robuste Daten aus angemessenen Methoden. Diese GrundsĂ€tze gelten sowohl fĂŒr eine erste Gruppierung zur Priorisierung weiterer MaĂnahmen als auch fĂŒr die endgĂŒltige Gruppierung zur Gewinnung von Daten fĂŒr die Risikobewertung. Beides kann ein effektives Risikomanagement forcieren
t4 Workshop Report: Integrated Testing Strategies (ITS) for Safety Assessment
Integrated testing strategies (ITS), as opposed to single definitive tests or fixed batteries of tests, are expected to efficiently combine different information sources in a quantifiable fashion to satisfy an information need, in this case for regulatory safety assessments. With increasing awareness of the limitations of each individual tool and the development of highly targeted tests and predictions, the need for combining pieces of evidence increases. The discussions that took place during this workshop, which brought together a group of experts coming from different related areas, illustrate the current state of the art of ITS, as well as promising developments and identifiable challenges. The case of skin sensitization was taken as an example to understand how possible ITS can be constructed, optimized and validated. This will require embracing and developing new concepts such as adverse outcome pathways (AOP), advanced statistical learning algorithms and machine learning, mechanistic validation and âGood ITS Practicesâ.JRC.I.5-Systems Toxicolog
True Grit: A Story of Perseverance Making Two Out of Three the First Non-Animal Testing Strategy (Adopted as OECD Guideline No. 497)
In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailed—just from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future
True Grit: A Story of Perseverance Making Two Out of Three the First Non-Animal Testing Strategy (Adopted as OECD Guideline No. 497)
In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailedâjust from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future
Improved Method for Stratum Corneum Lipid Analysis by Automated Multiple Development HPTLC
Computer models versus reality: How well do in silico models currently predict the sensitization potential of a substance
Industrial chemicals need to be assessed for their potential to cause skin sensitization. The European chemical and cosmetic legislations have generated increased availability of reliable experimental data on skin sensitization potentials but also a greater demand for non-animal testing methods. In this study, animal data on 55 non-sensitizing and 45 sensitizing chemicals was reviewed and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistical models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox v3.1, DEREK) and a hybrid model (TIMES-SS) were evaluated. Substances were selected which were not expected to be found in the model training sets. This study also explored other aspects, such as ease of use and data interpretation, and applicability for regulatory purposes.JRC.I.5-Systems Toxicolog