The threshold of toxicological concern (TTC) is a pragmatic risk assessment tool that is based on the principle of establishing a human exposure threshold value for all chemicals, below which there is a very low probability of an appreciable risk to human health. The concept that there are levels of exposure that do not cause adverse effects is inherent in setting acceptable daily intakes (ADIs) for chemicals with known toxicological profiles. The TTC principle extends this concept by proposing that a de minimis value can be identified for many chemicals, in the absence of a full toxicity database, based on their chemical structures and the known toxicity of chemicals which share similar structural characteristics. The establishment and application of widely accepted TTC values would benefit consumers, industry and regulators. By avoiding unnecessary toxicity testing and safety evaluations when human intakes are below such a threshold, application of the TTC approach would focus limited resources of time, cost, animal use and expertise on the testing and evaluation of substances with the greatest potential to pose risks to human health and thereby contribute to a reduction in the use of animals. An Expert Group of the European branch of the International Life Sciences Institute-ILSI Europe-has examined the TTC principle for its wider applicability in food safety evaluation. The Expert Group examined metabolism and accumulation, structural alerts, endocrine disrupting chemicals and specific endpoints, such as neurotoxicity, teratogenicity, developmental toxicity, allergenicity and immunotoxicity, and determined whether such properties or endpoints had to be taken into consideration specifically in a step-wise approach. The Expert Group concluded that the TTC principle can be applied for low concentrations in food of chemicals that lack toxicity data, provided that there is a sound intake estimate. The use of a decision tree to apply the TTC principle is proposed, and this paper describes the step-wise process in detail. Proteins, heavy metals and polyhalogenated-dibenzodioxins and related compounds were excluded from this approach. When assessing a chemical, a review of prior knowledge and context of use should always precede the use of the TTC decision tree. The initial step is the identification and evaluation of possible genotoxic and/or high potency carcinogens. Following this step, non-genotoxic substances are evaluated in a sequence of steps related to the concerns that would be associated with increasing intakes. For organophosphates a TTC of 18microg per person per day (0.3 microg/kg bw/day) is proposed, and when the compound is not an OP, the TTC values for the Cramer structural classes III, II and I, with their respective TTC levels (e.g. 1800, 540 and 90 microg per person per day; or 30, 9 and 1.5 microg/kg bw /day), would be applied sequentially. All other endpoints or properties were shown to have a distribution of no observed effect levels (NOELs) similar to the distribution of NOELs for general toxicity endpoints in Cramer classes I, II and III. The document was discussed with a wider audience during a workshop held in March 2003 (see list of workshop participants).

Structure-Based Thresholds of Toxicological Concern (TTC): Guidance for Application to Substances Present at Low Levels in the Diet

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

/pdf/in-silico-prediction-of-organ-level-toxicity-linking-3x0v5vh8rq.pdf

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

A Review of In Silico Toxicology Approaches to Support the Safety Assessment of Cosmetics-Related Materials

The chemical landscape of high-throughput new approach methodologies for exposure.

Going back to the 19th century, there is a long history of developing an understanding of how the physicochemical properties and chemical structure of a molecule affect its biological activity. Such approaches, commonly now referred to as being in silico , are formalised into (quantitative) structure–activity relationships ((Q)SARs) and read-across to predict toxicity and Physiologically-Based Pharmacokinetic models, to predict the distribution of chemicals in vivo and to allow for the extrapolation from in vitro effects. There have been many drivers for the development of in silico approaches to predict toxicity, fate and distribution. Notable among these have been the needs of various industrial sectors to assess the hazard of chemicals rapidly and efficiently, in terms of cost and animal use. These needs have been amplified globally by legislation aiming to improve animal welfare, and to respond to ethical concerns, as well as to regulate new and existing chemicals. In addition, key advances in chemoinformatics, computational power, and the connectivity of the internet have all played a role in the advancement of in silico approaches.

Computational Methods to Predict Toxicity

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COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

COSMOS DB as an international share point for exchanging regulatory and toxicity data of cosmetics ingredients and related substances