GARD – Genomic Allergen Rapid Detection : From Biomarker Discovery towards a State-of-the-art Testing Platform for Chemical Sensitizers

Chemical hypersensitivity reactions induced in the skin or in the respiratory tract are important health concerns and develops following repeated exposure to certain chemicals, termed sensitizers. To prevent such hazardous compounds from entering the consumer market, legal frameworks within EU require chemicals to be tested for their capacity to induce hypersensitivity. This type of testing has traditionally been performed in animals, but a recent paradigm shift has been initiated to promote the development of in vitro alternatives. However, currently, such proposed strategies can only be used for identification of skin sensitizing hazard, and are unable to inform on other endpoints of regulatory concern, such as skin sensitizing potency and respiratory sensitization. The GARD assay was developed as a multiparametric alternative to current in vitro tests. The assay measures chemically-induced changes within a predictive biomarker signature of 200 genes using transcriptome-wide microarray profiling and has proven to be highly accurate for identification of skin sensitizers. The overall ambition of the work presented in this thesis has been to utilize novel transcriptomic technologies, advanced computational tools, and powerful machine learning strategies to further transform GARD into a versatile and highly standardized tool for regulatory decision-making.The first part of this thesis addressed the need for a more versatile test platform capable of targeting additional toxicological endpoints. By measuring complete transcriptomes of cells following chemical stimulations, two separate biomarker signatures could be identified and demonstrated to have potent ability to predict respiratory sensitizing properties and skin sensitizing potency, respectively. The second part of this thesis addressed modifications to current GARD protocols to facilitate progression from biomarker discovery into a highly standardized tool for chemical risk assessment. A novel streamlined workflow was presented, where gene expression measurements had been transferred from transcriptome-wide profiling into the NanoString nCounter platform, measuring only genes in the biomarker signatures, which enabled for increased sample throughput, simplified protocols, and reduced assay costs in a format adapted to routine testing.To conclude, by combining the novel biomarker signatures identified in this thesis with the previous biomarker signature for identification of skin sensitizers, the GARD platform demonstrates a unique possibility to simultaneously screen for skin sensitizing hazard and potency, as well as respiratory sensitizing properties in a single test sample. Following the introduction of a novel pipeline to progress from initial biomarker discovery into a highly standardized testing format, results presented in this thesis shows that GARD is a state-of-the-art platform ready to replace animal experimentation for testing of chemical sensitizers

Predicting skin sensitizers with confidence — Using conformal prediction to determine applicability domain of GARD

GARD — Genomic Allergen Rapid Detection is a cell based alternative to animal testing for identification of skin sensitizers. The assay is based on a biomarker signature comprising 200 genes measured in an in vitro model of dendritic cells following chemical stimulations, and consistently reports predictive performances ~90% for classification of external test sets. Within the field of in vitro skin sensitization testing, definition of applicability domain is often neglected by test developers, and assays are often considered applicable across the entire chemical space. This study complements previous assessments of model performance with an estimate of confidence in individual classifications, as well as a statistically valid determination of the applicability domain for the GARD assay. Conformal prediction was implemented into current GARD protocols, and a large external test dataset (n = 70) was classified at a confidence level of 85%, to generate a valid model with a balanced accuracy of 88%, with none of the tested chemical reactivity domains identified as outside the applicability domain of the assay. In conclusion, results presented in this study complement previously reported predictive performances of GARD with a statistically valid assessment of uncertainty in each individual prediction, thus allowing for classification of skin sensitizers with confidence

Evaluation of high throughput gene expression platforms using a genomic biomarker signature for prediction of skin sensitization.

Allergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an in vitro alternative to animal testing for identification of skin sensitizers, classifying chemicals by evaluating transcriptional levels of a genomic biomarker signature. During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms - nCounter®, BioMark HD™ and OpenArray® - and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms

From genome-wide arrays to tailor-made biomarker readout – Progress towards routine analysis of skin sensitizing chemicals with GARD

Allergic contact dermatitis (ACD) initiated by chemical sensitizers is an important public health concern. To prevent ACD, it is important to identify chemical allergens to limit the use of such compounds in various products. EU legislations, as well as increased mechanistic knowledge of skin sensitization have promoted development of non-animal based approaches for hazard classification of chemicals. GARD is an in vitro testing strategy based on measurements of a genomic biomarker signature. However, current GARD protocols are optimized for identification of predictive biomarker signatures, and not suitable for standardized screening. This study describes improvements to GARD to progress from biomarker discovery into a reliable and cost-effective assay for routine testing. Gene expression measurements were transferred to NanoString nCounter platform, normalization strategy was adjusted to fit serial arrival of testing substances, and a novel strategy to correct batch variations was presented. When challenging GARD with 29 compounds, sensitivity, specificity and accuracy could be estimated to 94%, 83% and 90%, respectively. In conclusion, we present a GARD workflow with improved sample capacity, retained predictive performance, and in a format adapted to standardized screening. We propose that GARD is ready to be considered as part of an integrated testing strategy for skin sensitization

In vitro assessment of mechanistic events induced by structurally related chemical rubber sensitizers

Allergic contact dermatitis (ACD) is one of the most common forms of immunotoxicity, and increased understanding of how chemicals trigger these adverse reactions is needed in order to treat or design testing strategies to identify and subsequently avoid exposure to such substances. In this study, we investigated the cellular response induced by rubber chemicals in a dendritic cell (DC) model, focusing on the structurally similar chemicals diethylthiocarbamylbenzothiazole sulfide and dimethylthiocarbamylbenzothiazole sulfide, with regard to regulation of microRNA, and messenger RNA expression. Only a few miRNAs were found to be commonly regulated by both rubber chemicals, among them miR1973, while the overall miRNA expression profiles were diverse. Similarly, out of approximately 500 differentially regulated transcripts for each chemical, about 60% overlapped, while remaining were unique. The pathways predicted to be enriched in the cell model by stimulation with the rubber chemicals were linked to immunological events, relevant in the context of ACD. These results suggest that small structural differences can trigger specific activation of the immune system in response to chemicals. The here presented mechanistic data can be valuable in explaining the immunotoxicological events in DC activation after exposure to skin sensitizing chemicals, and can contribute to understanding, preventing and treating ACD

The GARD platform for potency assessment of skin sensitizing chemicals

Contact allergy induced by certain chemicals is a common health concern, and several alternative methods have been developed to fulfill the requirements of European legislation with regard to hazard assessment of potential skin sensitizers. However, validated methods, which provide information about the potency of skin sensitizers, are still lacking. The cell-based assay Genomic Allergen Rapid Detection (GARD), targeting key event 3, dendritic cell activation, of the skin sensitization AOP, uses gene expression profiling and a machine learning approach for the prediction of chemicals as sensitizers or non-sensitizers. Based on the GARD platform, we here expanded the assay to predict three sensitizer potency classes according to the European Classification, Labelling and Packaging (CLP) Regulation, targeting categories 1A (strong), 1B (weak) and no cat (non-sensitizer). Using a random forest approach and 70 training samples, a potential biomarker signature of 52 transcripts was identified. The resulting model could predict an independent test set consisting of 18 chemicals, six from each CLP category and all previously unseen to the model, with an overall accuracy of 78%. Importantly, the model was shown to be conservative and only underestimated the class label of one chemical. Furthermore, an association of defined chemical protein reactivity with distinct biological pathways illustrates that our transcriptional approach can reveal information contributing to the understanding of underlying mechanisms in sensitization

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers.Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers.We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample

Establishment of a predictive biomarker signature for prediction of respiratory sensitization.

<p>(A) Unsupervised learning was used to construct the representation of the dataset. The method was visualized using PCA based on 999 transcripts identified by one-way ANOVA p-value filtering between respiratory sensitizers (blue, n = 29) and non-respiratory sensitizers (green, n = 74). (B) The 999 transcripts identified by p-value filtering were used as input into an algorithm for backward elimination. A breakpoint in Kullback-Leibler divergence was observed after removal of 610 transcripts. (C) The remaining 389 transcripts were used as input variables into a PCA. As illustrated in the figure, a complete seperation between respiratory sensitizers and non-respiratory sensitizers was achieved in the training data.</p