# About DC ITS SkinSens

DC ITS SkinSens provides an open implementation of the integrated testing strategy (ITS) that assesses skin sensitisation potency based on the previously described approach developed at Procter & Gamble (Jaworska et al.). The DC-ITS implementation combines information from three validated alternative assays (DPRA, KeratinoSens and h-CLAT) with in silico predictions for bioavailability using open cheminformatics tools from the community.

Allergic contact dermatitis (aka., skin sensitization) accounts for 20% of all contact dermatitis cases^{1} and hence has an estimated annual cost up to $200 million^{2}. It is also a public health problem, responsible for more than seven million outpatient visits annually. Currently, there are more than 3700 substances that are identified as contact allergens.

This implementation of an integrated testing strategy assesses skin sensitisation potency based on a previously described approach by Jaworska et al^{3}. Integrated Testing Strategy (ITS-3) is an approach that assesses skin sensitization potency by combining information from three validated alternative assays, DPRA, KeratinoSens and h-CLAT as well as in silico predictions for bioavailability. The prediction of chemical potency for sensitization is first calculated in the form of a probability distribution over four potency classes (non-sensitizer, weak, moderate and strong). Such probability distribution is then transformed into Bayes factors to remove prediction bias from training set distribution and to calculate uncertainty quantitatively giving an objective measure for judging confidence in prediction. ITS-3 is based on a database of 207 chemicals for which the complete set of in vivo and in vitro data are available. Table 1 shows the prediction accuracy for the DC ITS as compared to the results published by Jaworska et al.

Table 1 Calculated accuracy per pEC3 class for the original ITS-3 network in article as well as the reproduced Bayesian network. The statistics of the original network are based on the published confusion matrix (statistics for class 3 should be verified as the confusion matrix reports on 50 instances despite having only 40 chemicals with such observed potency).

Class | Jaworska et al. | DC ITS SkinSens |
---|---|---|

C1 | 92% | 95% |

C2 | 82% | 77% |

C3 | 70% | 68% |

C4 | 72% | 79% |

overall |
79.6% |
79.6% |

DC ITS SkinSens allows scientists to evaluate the skin sensitisation hazard of their chemicals using a Bayesian network approach utilizing the expert-knowledge embedded in the skin sensitization adverse outcome pathway (AOP) as published by OECD.

**Advantages of the Bayesian network approach include:**

- It tolerates missing information and conveys a probabilistic hypothesis of skin sensitization based on accumulative evidence from data.
- It assesses the uncertainty in prediction given the input data using Bayes Factors.
- It directs consequent testing by value of information calculations, i.e. suggest the experiments to be conducted to achieve maximum information gain reducing uncertainty in prediction.

- (1)Coman, G.; Zinsmeister, C.; Norris, P. Occupational Contact Dermatitis.
*Allergy, Asthma, Clin. Immunol.***2008**, 4, 59–65. - (2)CDC - Skin- Occupational Dermatoses Slides 6 to 10 - NIOSH Workplace Safety and Health Topic https://www.cdc.gov/niosh/topics/skin/occderm-slides/ocderm2.html (accessed Jan 18, 2017).
- (3)Jaworska, J. S.; Natsch, A.; Ryan, C.; Strickland, J.; Ashikaga, T.; Miyazawa, M. Bayesian Integrated Testing Strategy (ITS) for Skin Sensitization Potency Assessment: A Decision Support System for Quantitative Weight of Evidence and Adaptive Testing Strategy.
*Arch. Toxicol.***2015**, 89, 2355–2383.