Challenges in predicting the change in the cumulative exposure of new tobacco and related products based on emissions and toxicity dose–response data

Funding: This work was funded by the Netherlands Food and Consumer Product Safety Authority (NVWA), Utrecht, the Netherlands. Project number 9.7.1Many novel tobacco products have been developed in recent years. Although many may emit lower levels of several toxicants, their risk in the long term remains unclear. We previously published a method for the exposure assessment of mixtures that can be used to compare the changes in cumulative exposure to carcinogens among tobacco products. While further developing this method by including more carcinogens or to explore its application to non-cancer endpoints, we encountered a lack of data that are required for better-substantiated conclusions regarding differences in exposure between products. In this special communication, we argue the case for more data on adverse health effects, as well as more data on the composition of the emissions from tobacco products. Such information can be used to identify significant changes in relevance to health using the cumulative exposure method with different products and to substantiate regulatory decisions.Publisher PDFPeer reviewe

Methodological Approaches for Risk Assessment of Tobacco and Related Products.

Health risk assessment of tobacco and related products (TRPs) is highly challenging due to the variety in products, even within the product class, the complex mixture of components in the emission and the variety of user behaviour. In this paper, we summarize methods that can be used to assess the health risks associated with the use of TRPs. The choice of methods to be used and the data needed are dependent on the aim. Risk assessment can be used to identify the emission components of highest health concern. Alternatively, risk assessment methods can be used to determine the absolute risk of a TRP, which is the health risk of a product, not related to other products, or to determine the relative risk of a TRP, which is the health risk of a TRP compared to, for example, a cigarette. Generally, health risk assessment can be based on the effects of the complete mixture (whole smoke) or based on the (added) effects of individual components. Data requirements are dependent on the method used, but most methods require substantial data on identity and quantity of components in emissions and on the hazards of these components. Especially for hazards, only limited data are available. Currently, due to a lack of suitable data, quantitative risk assessment methods cannot be used to inform regulation

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area. Given the impressive progress in mechanistic knowledge of human biology and toxicology, the time is right for a conceptual approach for designing testing strategies that cover the integral mechanistic landscape of developmental neurotoxicity. The ontology approach provides a framework for defining this landscape, upon which an integral in silico model for predicting toxicity can be built. It subsequently directs the selection of in vitro assays for rate-limiting events in the biological network, to feed parameter tuning in the model, leading to prediction of the toxicological outcome. Validation of such models requires primary attention to coverage of the biological domain, rather than classical predictive value of individual tests. Proofs of concept for such an approach are already available. The challenge is in mining modern biology, toxicology and chemical information to feed intelligent designs, which will define testing strategies for neurodevelopmental toxicity testing

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area. Given the impressive progress in mechanistic knowledge of human biology and toxicology, the time is right for a conceptual approach for designing testing strategies that cover the integral mechanistic landscape of developmental neurotoxicity. The ontology approach provides a framework for defining this landscape, upon which an integral in silico model for predicting toxicity can be built. It subsequently directs the selection of in vitro assays for rate-limiting events in the biological network, to feed parameter tuning in the model, leading to prediction of the toxicological outcome. Validation of such models requires primary attention to coverage of the biological domain, rather than classical predictive value of individual tests. Proofs of concept for such an approach are already available. The challenge is in mining modern biology, toxicology and chemical information to feed intelligent designs, which will define testing strategies for neurodevelopmental toxicity testing

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area. Given the impressive progress in mechanistic knowledge of human biology and toxicology, the time is right for a conceptual approach for designing testing strategies that cover the integral mechanistic landscape of developmental neurotoxicity. The ontology approach provides a framework for defining this landscape, upon which an integral in silico model for predicting toxicity can be built. It subsequently directs the selection of in vitro assays for rate-limiting events in the biological network, to feed parameter tuning in the model, leading to prediction of the toxicological outcome. Validation of such models requires primary attention to coverage of the biological domain, rather than classical predictive value of individual tests. Proofs of concept for such an approach are already available. The challenge is in mining modern biology, toxicology and chemical information to feed intelligent designs, which will define testing strategies for neurodevelopmental toxicity testing