Understanding allergen potency: role of protein kinase C activation in the vigor of dendritic cell
Over the last decade, an incredible progress has been made in the development of non-animal tests to assess contact allergy. While a number of in vitro methods are at various stages of development and use, currently it is not possible to establish the relative skin sensitizing potency of contact allergens, an issue important for a safety assessment. The identification of mechanisms influencing the vigor of T cell responses, that can explain the strength of allergic contact dermatitis reactions to weak, moderate, strong, and extreme sensitizers is a challenge still to be solved. This will require a better understanding of molecular events that lead to cell activation following exposure to contact allergens. Experience suggests that the overall magnitude of T cell proliferation in vivo provides a good correlation with skin sensitization potency for the purpose of risk assessment. This project aims to characterize in vitro the extent of chemical allergens-induced immune cells maturation/activation by assessing specific markers related to signals necessary to achieve a full T cell activation. This project, investigating the quantitative relation between several markers and potency in dendritic cells, and their qualitative and quantitative relation with T cell responses, will give insights into the process of sensitization and vigor of cell activation associated with different types of allergens. We aim to provide a simple assay able to provide potency information, necessary for full replacement of animals in the assessment of the allergenic potential of chemicals.
Maria Teresa Cruz
In chemico, in silico and in vitro modeling to predict human respiratory allergens
The prevalence of respiratory allergies has dramatically increased over the last decades due to environmental and occupational factors, being occupational asthma the most prevalent occupational lung disease in developed countries with high levels of morbidity. More than 300 substances have been shown to cause occupational asthma, and a large proportion of these are low molecular weight (LMW) organic compounds. There is currently no widely accepted animal or non-animal method able to identify potential LMW respiratory sensitizers for regulatory purposes, despite the risk to human health. In addition, there is significant social, scientific and economic pressure to replace animal testing where possible. Therefore, development of non-animal assays for identifying potential respiratory allergenic chemicals is highly warranted to protect public health and will be of uttermost importance for the pharmaceutical, chemical, cosmetic, pesticide, and food industries. In this context, and supported by our expertise in the development of predictive in vitro toxicity tests, we intend to develop an innovative platform to predict respiratory sensitization hazard. For that, in chemico measurements of respiratory allergens with model peptides will be performed to assess the reactivity of respiratory allergens. In addition, the adjuvanticity/irritancy and immunogenicity triggered by respiratory allergens will be assessed in cells representative of the respiratory system. Finally, we will design a mathematical framework, derived from the readouts described above for the identification and classification of respiratory sensitizers.
Epithelix Sarl has provided a line of credit to Dr. Cruz for these studies. Epithelix produces standardized in vitro human lung tissue. We offer our sincere appreciation to Epithelix for this contribution.
A michanism-based in vitro alternative to the mouse histamine sensitization test: pertussis toxin-induced kinome response pathways in himan barrier cells.
Every year, numerous animal tests are performed by manufacturers to ensure the safety of vaccines, as demanded by regulatory authorities. This also holds true for vaccines directed against the highly contagious pertussis disease (Whooping cough). The main ingredient of these vaccines is inactivated pertussis toxin, the major infective component from the bacterium that causes pertussis disease. To ensure the absence of any residual activity of this toxin, lethal animal tests are performed that cause a high degree of suffering. Therefore, our project will focus on the development of an alternative animal-free safety test for pertussis vaccines.
It has been shown that a certain type of mammalian cells respond to active pertussis toxin by changing their growth pattern by an unknown mechanism. We hypothesize that these changes are mediated by kinases, enzymes involved in cellular pathways that transduce signals. Using a innovative technique called kinomics, we propose to analyze all kinase activity present in cells exposed to pertussis toxin. The data we will obtain will enable us to better understand the underlying pathways, and furthermore, the kinases found to be activated will serve as potential biomarkers for pertussis toxin activity. We will develop an assay in which kinase activity can be measured and correlated to the presence of active pertussis toxin in vaccine preparations. Hopefully, this test can then be used by manufacturers to replace the lethal animal tests.
Advance Predictive Modeling of Acute Toxicity by Big Data
Utilizing computational models to directly predict the animal toxicity of new compounds before conducting organic synthesis is a promising strategy to reduce the use of animals. Our recent studies have shown that the predictivity of oral bioavailability models could be greatly improved by integrating the data obtained from the modeling results of intestinal transportation. Inspired by these important findings, this project will develop novel predictive models for animal acute toxicity endpoints of small molecules by applying hybrid modeling approaches on the comprehensive bioprofiles generated from the current big data resources. First we will curate a comprehensive database for the target molecules with animal acute toxicity data from public big data resources that contain over a million different assay response data points. Secondly, we will use a hybrid modeling workflow to study various acute toxicity endpoints. The in vitro-in vivo relationship analysis will give insight into the mechanisms relevant to the toxicity effects of molecules. We will validate and use our computational predictors to directly evaluate the toxicity effects of molecules and prioritize candidates for future experimental testing. To the best of our knowledge, the implementation of this project will lead to the publicly available in silico toxicity modeling framework and predictors based on the public big data pool, which will have a significant impact on alternative research to animal testing by not only predicting the acute toxicity of chemicals but also revealing the MOAs from the available big data.