Occupational and Non-Occupational Allergic Contact Dermatitis: A Follow-Up Study

Macan, Jelena; Rimac, Davor; Kežić, Sanja; Varnai, Veda Marija

doi:10.1159/000354763

Cited by 14 publications

(13 citation statements)

References 42 publications

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“…ACD can adversely affect the quality of life (Brutti et al, 2013;Heisterberg et al, 2011;Kadyk et al, 2003). The prognosis for ACD includes the development of new skin allergies and the persistence of clinical symptoms for approximately 10 years after diagnosis, with occupational cases of ACD generally having poorer prognoses than non-occupational ACD cases (Macan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of skin sensitization potency using machine learning approaches

Zang

Paris

Lehmann

et al. 2017

J of Applied Toxicology

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The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for U.S. federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or nonsensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: the direct peptide reactivity assay, human cell line activation test, and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/nonsensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60/87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency.

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Section: Introductionmentioning

confidence: 99%

Prediction of skin sensitization potency using machine learning approaches

Zang

Paris

Lehmann

et al. 2017

J of Applied Toxicology

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“…Skin sensitization is a key step in the complex immune-mediated inflammatory skin reaction called allergic contact dermatitis. Although not life threatening, this disease has a profound impact on individual working ability and quality of life . The murine local lymph node assay (LLNA) is regarded as the preferred in vivo test for evaluating skin sensitization hazard and the potency of chemicals regulated not only by REACH, but various other regulatory agencies, such as the UK Health and Safety Executive and the US EPA .…”

Section: Introductionmentioning

confidence: 99%

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals

Braga

Alves

Muratov

et al. 2017

J. Chem. Inf. Model.

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Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70-0.81 and 0.72-0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure-activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal ( http://labmol.com.br/predskin/ ), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available.

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“…1 ACD is caused by topical exposure to chemical allergens 2 and their abundance in commerce demands that hazardous chemicals must be identified and replaced by “greener”, i.e. , safer, alternatives.…”

Section: Introductionmentioning

confidence: 99%

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

et al. 2016

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Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

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Occupational and Non-Occupational Allergic Contact Dermatitis: A Follow-Up Study

Cited by 14 publications

References 42 publications

Prediction of skin sensitization potency using machine learning approaches

Prediction of skin sensitization potency using machine learning approaches

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

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