SafePack · Assessing safety of food packaging through computational toxicology

Assessing safety of food packaging through computational toxicology

Food packaging is generally recognized as contributing directly to product safety through prevention of biological (pathogens) and chemical contaminations. In addition, packaging indirectly impacts food safety by providing consumers with health related information on ingredients (e.g. nutritional, composition and allergen information), mode of preparation and shelf-life. It is important to ensure to packaging does not act as a major source of chemical exposure. Indeed, the application of continuously improving analytical methods has revealed that many chemicals may migrate from the food contact material into the food, resulting in undesirable consumer exposure. For most of these chemicals, no toxicological data is available. The most consistent data gap deals with migrating substances, which originate from impurities in starting raw materials and from reaction and degradation of substances formed during manufacturing (the so-called Non-Intentionally Added Substances, NIAS). In this context, there is an urgent need to acquire more insight on the actual safety concern associated with the thousands of chemicals associated with food contact materials (FCM). It is imperative to avoid the use of laboratory animal experiments in such studies. Accordingly computational toxicology (a branch of toxicology concerned with the development and use of computer-based models to predict toxicological end-points, also called in silico methodology), represents a promising solution and is increasingly applied by academic and regulatory scientists to cope with the data gap in establishing safety of chemicals. Computational models have become popular in 2006 since the introduction of regulations, such as the European REACH regulation. Nowadays their use is strongly encouraged by regulatory authorities and agencies all over the world for reducing animal studies. This project proposes a proactive approach to address the data gaps identified above, by proposing and applying a strategy to study rapidly and cost-efficiently the level of safety concern of large sets of food contact chemicals using in silico methods. Following a step-wise approach mimicking the standard chemical risk assessment workflow (exposure assessment, hazard identification, hazard characterization and risk characterization), in silico predictions were used for filling the experimental data gaps related to the key toxicological endpoints needed to perform food safety evaluation (mutagenicity, carcinogenic potency, chronic toxicity, developmental and reproductive toxicity). Then, applying the Margin of exposure approach (MoE, the ratio between predicted toxicity value and exposure estimate) will allow establishing the level of safety concern for chemicals in food without the need for toxicity testing.

Data: CORDIS, © European Union