Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter

Abstract

It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential.

Computational approaches for skin sensitization prediction

“…The no expected sensitization induction level (NESIL) was thereby derived from predicted EC3 values. Hirota et al (2017) utilized artificial neural networks for the prediction of LLNA EC3 values (binned into four potency classes) by integrating results obtained from the h-CLAT, DPRA, and KeratinoSens TM with predictions from Toxtree and TIMES. Several models taking into account different combinations of input variables were trained on 134 and tested on 28 compounds annotated with LLNA data.…”

“…U-SENS TM and h-CLAT assess the induction of cell surface marker (CD54/CD86) expression in dendritic-like cells (U937 and THP-1, respectively) as a measure for immunogenic cell activation, and the IL-8 Luc assay measures dendritic cell activation through changes in IL-8 cytokine secretion. In recent studies, these non-animal testing methods obtained accuracies (or correct classification rates, CCRs) in the range of 65% to 80% when measured against LLNA and human data (Hirota et al 2017;Hoffmann et al 2018).…”

Theoretical studies to estimate the skin sensitization potential of chemicals of the Schiff base domain

“…Methods TIMES, Toxtree, Derek Nexus etc . have proved useful in compound assessment in their own right, and as part of multitiered testing strategies with in silico models as the first tier approach …”

“…[15,16] Methods TIMES, Toxtree, Derek Nexus etc. [13,17] have proved useful in compound assessment in their own right, [15,18] and as part of multitiered testing strategies with in silico models as the first tier approach. [15,19] Several different quantitative structure activity relationship (QSAR) models, which relate chemical properties to the degree of sensitization, have been reported in the literature.…”

Application of Spectro‐DPRA, KeratinoSens™ and h‐CLAT to estimation of the skin sensitization potential of cosmetics ingredients

“…The Organization for Economic Co‐operation and Development (OECD) presented the concept of adverse outcome pathway (AOP), which represents a series of processes from the molecular initiation event and key event (OECD, ). Currently, there are several ongoing approaches to develop an integrated testing strategy (ITS) for achieving higher predictivity for skin sensitization potential combined with OECD Test Guideline methods or individual laboratory assay development (Hirota, Ashikaga, & Kouzuki, ; Jaworska, Dancik, Kern, Gerberick, & Natsch, ; Jaworska, Harol, Kern, & Gerberick, ; Jaworska et al, ; Macmillan, Canipa, Chilton, Williams, & Barber, ; Macmillan & Chilton, ; Nukada, Miyazawa, Kazutoshi, Sakaguchi, & Nishiyama, ; Otsubo et al, ; Takenouchi et al, ; Urbisch et al, ). In addition, the OECD in collaboration with EURL ECVAM, proposed a roadmap for developing an AOP‐based integrated approach to testing and assessment (IATA)/ITS and has attempted to develop formally validated and regulated strategies in the approach to skin sensitization predictivity (Kinsner‐Ovaskainen et al, ).…”