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 für die Gesundheit des Menschen

Analog zum Periodensystem, das Elemente nach ihrer Ähnlichkeit der Strukturen und chemischen Eigenschaften gruppiert, kann die Gefahr chemischer Stoffe in Gruppen mit ähnlichen Strukturen und ähnlichen toxikologischen Eigenschaften bewertet werden. Im Folgenden werden Fallbeispiele zu Gruppierungsstrategien vorgestellt, welche die Bewertungen von Gefahr, Exposition und Risiko für die menschliche Gesundheit unterstützen. Sowohl unter EU‐REACh als auch im US‐TSCA New Chemicals Programm ist in der Regel die strukturelle Ähnlichkeit die Grundlage für eine Gruppierungt. Allerdings ist dieses Kriterium nicht immer angemessen und ausreichend. Auf der Grundlage der gewonnenen Erkenntnisse leiten wir zehn Grundsätze für die Gruppierung ab, darunter: die transparente Darstellung des Zwecks der Gruppierung, die Definition der Kriterien für eine Gruppierung und die Grenzen der Gruppen, eine dieEinbeziehung oder der Ausschluss eines Stoffs in oder aus einer Gruppe aufgrund toxikologischer Eigenschaften und eine Begründung der Zuordnung durch robuste Daten aus angemessenen Methoden. Diese Grundsätze gelten sowohl für eine erste Gruppierung zur Priorisierung weiterer Maßnahmen als auch für die endgültige Gruppierung zur Gewinnung von Daten für 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 “Good ITS Practices”.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 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

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