Predicting full thickness skin sensitization using a support vector machine

Abstract

To assess the public’s propensity for allergic contact dermatitis (ACD), many alternatives to in vivo chemical screening have been developed which generally incorporate a small panel of cell surface and secreted dendritic cell biomarkers. However, given the underlying complexity of ACD, one cell type and limited cellular metrics may be insufficient to predict contact sensitizers accurately. To identify a molecular signature that can further characterize sensitization, we developed a novel system using RealSkin, a full thickness skin equivalent, in co-culture with MUTZ-3 derived Langerhan’s cells. This system was used to distinguish a model moderate pro-hapten isoeugenol (IE) and a model strong pre-hapten p-phenylenediamine (PPD) from irritant, salicylic acid (SA). Commonly evaluated metrics such as CD86, CD54, and IL-8 secretion were assessed, in concert with a 27-cytokine multi-plex screen and a functional chemotaxis assay. Data were analyzed with feature selection methods using ANOVA, hierarchical cluster analysis, and a support vector machine to identify the best molecular signature for sensitization. A panel consisting of IL-12, IL-9, VEGF, and IFN-γ predicted sensitization with over 90% accuracy using this co-culture system analysis. Thus, a multi-metric approach that has the potential to identify a molecular signature may be more predictive of contact sensitization.

Tri-culture system for pro-hapten sensitizer identification and potency classification

“…However, current in vitro assays have particularly low accuracy in predicting pre- and pro-hapten sensitizers, which require chemical transformation or metabolic activation prior to inducing sensitization. We previously developed a full-thickness skin model that showed promise in predicting sensitization by pre- and pro-haptens, but this model was expensive and low-throughput 22 . The current studies were designed to develop a system containing the relevant metabolizing and responsive skin components, in conjunction with computational tools, to identify and ultimately predict sensitization potential of pre and pro-haptens in a high-throughput, cost-effective manner.…”

“…The 5637 human bladder carcinoma line was purchased from ATCC (Manassas, VA) and cultured as previously described 22 . Conditioned medium from 5637 culture was supplemented into the MUTZ-3 culture medium as per the guidelines from DSMZ.…”

“…The MUTZ-3 cell line was a donation from Massachusetts General Hospital (Boston, MA) and is available for purchase from DSMZ (Brauncshweig, Germany) and cultured and differentiated as previously described 22 . Differentiated MUTZ-LCs utilized in experiments were maintained in Maturation Medium (complete media without conditioned media supplement).…”

“…A machine learning method using a previously developed support vector machine (SVM) in MATLAB® was used to compute and rank the margin distance for each cytokine measured in the bio-plex assay 22 . We trained and tested the SVM with leave-one out cross-validation; 90% of the dataset was used to train and 10% to test.…”

“…Previously, we established a full-thickness skin model co-cultured with LCs to be used in concert with computational support vector machine analysis to identify a panel of predictive biomarkers for the classification of skin sensitizers 22 . Given the high cost and low through-putness of full-thickness skin models, the current studies were designed to determine the efficacy of a simpler tri-culture approach, which preserves the benefits of keratinocyte and fibroblasts, especially for pre- and pro-sensitizer classification.…”