In Silico Methods for Hazard Characterization

The adoption of Regulation (EC) No 1223/2009 on the use of cosmetics in the EU has encouraged the development of new methods and alternative tests to evaluate the hazards of a cosmetic ingredient. In silico models, particularly (Quantitative) Structure Activity Relationships [(Q)SARs] and Read-Across models, play a key role in the assessment of toxicological profiles of ingredients, and are a fast and reliable alternative approach to in vivo and in vitro methods.

The use of in silico models is increasingly favored in several regulatory frameworks according to various fields of application (i.e., REACH , biocides, cosmetics, food contact materials regulations, impurities, etc.). It is encouraged by REACH Regulation 1907/2006/EC, as well as by the Scientific Committee on Consumer Safety (SCCS) Notes of Guidance for the evaluation of cosmetic ingredients and products, to gain efficiency in the development and/or evaluation of a substance before it is placed on the market.

In Silico Methods:

In silico toxicology encompasses a wide variety of computational tools, including databases, software to generate molecular descriptors, simulation tools, and modeling methods.

In silico models and tools can predict the efficacy and toxicity of a substance, based on the principle that the characteristics of the substance’s molecular structure can be correlated with its physical and biological properties.

The different in silico methods can be used in combination in a weight-of-evidence (WoE) approach. The three main methods are:

• Read-Across: Comparison between analogue substances
  (grouping, data-gap filing)

Read-across is regarded as a technique for predicting endpoint information for a target substance by using experimental data from the same specific endpoint from surrogate substances. It is applicable to different endpoints such as sensitization, genotoxicity, and/or repeated-dose toxicity.

Read-across studies are performed following the current guidelines set by the European Chemicals Agency (ECHA) and the Organisation for Economic Co-operation and Development (OECD) to meet the requirements for the Read-Across Assessment Framework.

• (Q)SAR: Statistical model based on dataset of molecules
  (quantitative or qualitative)

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models - collectively referred to as(Q)SARs are mathematical models that can be used to predict physical-chemical, biological, and environmental fate properties, as well as toxicity from the physical and structural characteristics of the tested molecules (molecular weight, number of rings, octanol-water partition coefficient, etc.) called molecular descriptors.

• Expert Knowledge Models: Evaluation based on known structures, mode of action, and rules

Expert systems use rules and data complied into models by human experts. Using structures recognized for their toxicity, and the knowledge acquired on substances and their mode of action, target substances are classified into groups according to structural alerts and/or toxic part associated with a toxic activity. Rules and decision trees are then set based on these data to automatically classify substances.

We have the expertise to provide our clients with various in silico approaches for hazard characterization.

Our in silico methods can help you assess chemical substances in various applications. Our services include:

• Assessment of non-intentionally added substances (NIAS)
• Toxicological evaluation of new cosmetic ingredients
• Prediction of metabolite profile and mechanism of action

Intertek also offers in silico methods for the toxicological evaluation of chemical substances used in industrial and commercial applications - for example, TSCA Modeling: Sustainable Futures and (Q)SAR. For more information about this service offering, contact us at chemicals.assuris@intertek.com.