Non-animal methods to predict skin sensitization (II): an assessment of defined approaches**

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.     

Anchoring molecular mechanisms to the adverse outcome pathway for skin sensitization: Analysis of existing data

Allergic contact dermatitis (ACD) is a hypersensitivity immune response induced by small protein-reactive chemicals. Currently, the murine local lymph node assay (LLNA) provides hazard identification and quantitative estimation of sensitizing potency. Given the complexity of ACD, a single alternative method cannot replace the LLNA, but it is necessary to combine methods through an integrated testing strategy (ITS). In the development of an ITS, information regarding mechanisms and molecular processes involved in skin sensitization is crucial. The recently published adverse outcome pathway (AOP) for skin sensitization captures mechanistic knowledge into key events that lead to ACD. To understand the molecular processes in ACD, a systematic review of murine in vivo studies was performed and an ACD molecular map was constructed. In addition, comparing the molecular map to the limited human in vivo toxicogenomic data available suggests that certain processes are similarly triggered in mice and humans, but additional human data will be needed to confirm these findings and identify differences. To gain insight in the molecular mechanisms represented by various human in vitro systems, the map was compared to in vitro toxicogenomic data. This analysis allows for comparison of emerging in vitro methods on a molecular basis, in addition to mathematical predictive value. Finally, a survey of the current in silico, in chemico, and in vitro methods was used to indicate which AOP key event is modeled by each method. By anchoring emerging classification methods to the AOP and the ACD molecular map, complementing methods can be identified, which provides a cornerstone for the development of a testing strategy that accurately reflects the key events in skin sensitization.     

Applicability of amino acid derivative reactivity assay (4 mM) for the prediction of skin sensitization by combining multiple alternative methods to evaluate key events

The amino acid derivative reactivity assay (ADRA) is an alternative method for evaluating key event 1 (KE-1) in the skin sensitization mechanism included in OECD TG442C (OECD, 2021). Recently, we found that ADRA with a 4-mM test chemical solution had a higher accuracy than the original ADRA (1 mM). However, ADRA (4 mM) has yet to be evaluated using integrated approaches to testing and assessment (IATA), a combination of alternative methods for evaluating KE. In this study, the sensitization potency of three defined approaches (DAs) using ADRA (4 mM) as KE-1 was predicted and compared with those of two additional ADRAs or direct peptide reactivity assay (DPRA): (i) “2 out of 3” approach, (ii) “3 out of 3” approach, and (iii) integrated testing strategy (ITS). In the hazard identification of chemical sensitizers, the accuracy of human data and local lymph node assay (LLNA) remained almost unchanged among the three approaches evaluated. Potency classifications for sensitization were predicted with the LLNA and human data sets using ITS. The potency classifications for the sensitization potency prediction accuracy of LLNA data using any alternative method were almost unchanged, at approximately 70%, and those with ITS were not significantly different. When ITS was performed using DPRA, the prediction accuracy was approximately 73% for human data, which was similar to that of the LLNA data; however, the accuracy tended to increase for all ADRA methods. In particular, when ITS was performed using ADRA (4 mM), the prediction accuracy was approximately 78%, which proved to be a practical level.     

Integrated testing and assessment approaches for skin sensitization: a commentary

A Bayesian integrated testing strategy (ITS) approach, aiming to assess skin sensitization potency, has been presented, in which data from various types of in vitro assays are integrated and assessed in combination for their ability to predict in vivo skin sensitization data. Here we discuss this approach and compare it to our quantitative mechanistic modeling (QMM) approach based on physical organic chemistry. The main findings of the Bayesian study are consistent with our chemistry‐based approach and our previously published assessment of the key determinants of sensitization potency, in particular the relatively high predictive value found for chemical reactivity data and the relatively low predictive value for bioavailability parameters. As it stands at present the Bayesian approach does not utilize the full range of predictive capability that is already available, and aims only to assign potency categories rather than numerical potency values per se. In contrast, for many chemicals the QMM approach can already provide numerical potency predictions. However, the Bayesian approach may have potential for those chemicals where a chemistry modeling approach cannot provide a complete answer (e.g. pro‐electrophiles whose in cutaneo activation cannot currently be modeled confidently). Nonetheless, our main message is of the importance of leveraging chemistry insights and read‐across approaches to the fullest extent possible. Copyright © 2013 John Wiley & Sons, Ltd.     

Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of the REACH legislation. Development of alternative methods requires that the chemicals used to evaluate and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (quantitative) structure-activity relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.     

Structure--activity relationship between the in vivo skin sensitizing potency of analogues of phenyl glycidyl ether and the induction of Nrf2-dependent luciferase activity in the KeratinoSens in vitro assay

Because of regulatory constraints and ethical considerations, research on alternatives to animal testing to predict the skin sensitization potential of novel chemicals has become a high priority. Ideally, these alternatives should not only predict the hazard of novel chemicals but also rate the potency of skin sensitizers. Currently, no alternative method gives reliable potency estimations for a wide range of chemicals in differing structural classes. Performing potency estimations within specific structural classes has thus been proposed. Detailed structure-activity studies for the in vivo sensitization capacity of a series of analogues of phenyl glycidyl ether (PGE) were recently published. These studies are part of an investigation regarding the allergenic activity of epoxy-resin monomers. Here we report data on the same chemicals in the KeratinoSens in vitro assay, which is based on a stable transgenic keratinocyte cell line with a luciferase gene under the control of an antioxidant response element. A strong correlation between the EC3 values in the local lymph node assay (LLNA) and both the luciferase-inducing concentrations and the cytotoxicity in the cell-based assay was established for six analogues of PGE. This correlation allowed the potency in the LLNA of two novel structurally closely related derivatives to be predicted by read-across with errors of 1.4- and 2.6-fold. However, the LLNA EC3 values of two structurally different bifunctional monomers were overpredicted on the basis of this data set, indicating that accurate potency estimation by read-across based on in vitro data might be restricted to a relatively narrow applicability domain.     

Probabilistic hazard assessment for skin sensitization potency by dose–response modeling using feature elimination instead of quantitative structure–activity relationships

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose–response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high‐quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter‐relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose‐informed random forest/hidden Markov model was superior to the dose‐naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose‐informed hidden Markov model strongly reduced " false‐negatives" (i.e. extreme sensitizers as non‐sensitizer) on all data sets. Copyright © 2015 John Wiley & Sons, Ltd.     

An in vitro human skin test for assessing sensitization potential

Sensitization to chemicals resulting in an allergy is an important health issue. The current gold‐standard method for identification and characterization of skin‐sensitizing chemicals was the mouse local lymph node assay (LLNA). However, for a number of reasons there has been an increasing imperative to develop alternative approaches to hazard identification that do not require the use of animals. Here we describe a human in‐vitro skin explant test for identification of sensitization hazards and the assessment of relative skin sensitizing potency. This method measures histological damage in human skin as a readout of the immune response induced by the test material. Using this approach we have measured responses to 44 chemicals including skin sensitizers, pre/pro‐haptens, respiratory sensitizers, non‐sensitizing chemicals (including skin‐irritants) and previously misclassified compounds. Based on comparisons with the LLNA, the skin explant test gave 95% specificity, 95% sensitivity, 95% concordance with a correlation coefficient of 0.9. The same specificity and sensitivity were achieved for comparison of results with published human sensitization data with a correlation coefficient of 0.91. The test also successfully identified nickel sulphate as a human skin sensitizer, which was misclassified as negative in the LLNA. In addition, sensitizers and non‐sensitizers identified as positive or negative by the skin explant test have induced high/low T cell proliferation and IFNγ production, respectively. Collectively, the data suggests the human in‐vitro skin explant test could provide the basis for a novel approach for characterization of the sensitizing activity as a first step in the risk assessment process. Copyright © 2015 John Wiley & Sons, Ltd.     

Novel approach for classifying chemicals according to skin sensitizing potency by non-radioisotopic modification of the local lymph node assay

The murine local lymph node assay (LLNA) is currently recognized as a stand-alone sensitization test for determining the sensitizing potential of chemicals, and it has the advantage of yielding a quantitative endpoint that can be used to predict the sensitization potency of chemicals. The EC3 has been proposed as a parameter for classifying chemicals according to the sensitization potency. We previously developed a non-radioisotopic endpoint for the LLNA based on 5-bromo-2'-deoxyuridine (BrdU) incorporation (non-RI LLNA), and we are proposing a new procedure to predict the sensitization potency of chemicals based on comparisons with known human contact allergens. Nine chemicals (i.e. diphencyclopropenone, p-phenylenediamine, glutaraldehyde, cinnamicaldehyde, citral, eugenol, isopropyl myristate, propyleneglycol and hexane) categorized as human contact allergen classes 1-5 were tested by the non-RI LLNA with the following reference allergens: 2,4-dinitrochlorobenzene (DNCB) as a class 1 human contact allergen, isoeugenol as a class 2 human contact allergen and alpha-hexylcinnamic aldehyde (HCA) as a class 3 human contact allergen. Consequently, nine test chemicals were almost assigned to their correct allergen class. The results suggested that the new procedure for non-RI LLNA can provide correct sensitization potency data. Sensitization potency data are useful for evaluating the sensitization risk to humans of exposure to new chemical products. Accordingly, this approach would be an effective modification of LLNA with regard to its experimental design. Moreover, this procedure can be applied also to the standard LLNA with radioisotopes and to other modifications of the LLNA.     

Relating skin sensitizing potency to chemical reactivity: reactive Michael acceptors inhibit NF-κB signaling and are less sensitizing than S(N)Ar- and S(N)2- reactive chemicals

The skin sensitization potency of chemicals is partly related to their reactivity to proteins. This can be quantified as the rate constant of the reaction with a model peptide, and a kinetic profiling approach to determine rate constants was previously proposed. A linear relationship between the skin sensitization potency in the local lymph node assay (LLNA) and the rate constant for Michael acceptors was reported, characterized by a relatively flat regression line. Thus, a 10-fold increase of reactivity correlates to an increase of the sensitization potential of only 1.7-fold. Here, we first validate this model by repeating previous data and testing additional Michael acceptors and prove that the model is both reproducible and robust to the addition of new data. Chemicals of different mechanistic applicability domains, namely, S(N)Ar- and S(N)2-reactive sensitizers, were then tested with the same kinetic profiling approach. A linear relationship between sensitization potency in the LLNA and rate constants was also found, yet with a much steeper slope, i.e., for S(N)Ar- and S(N)2-reactive sensitizers, increasing reactivity correlates to a much stronger increase in sensitization potency. On the basis of the well-known inhibitory activity of some Michael acceptors on IKK kinase, it was hypothesized that the difference in the slopes is due to the specific anti-inflammatory potential of Michael acceptor chemicals. Therefore, all chemicals were tested for anti-inflammatory activity in a reporter gene assay for the inhibition of NF-κB activation. Increasingly reactive Michael acceptors have increasing anti-inflammatory potential in this assay, whereas no such biological activity was detected for the S(N)Ar and S(N)2 reactive sensitizers. Thus, the increasing reactivity of Michael acceptors confers both anti-inflammatory and skin sensitizing/pro-inflammatory potential, which may partially neutralize each other. This may be the reason for the relatively weak relationship between the potency in the LLNA and the rate constant of this particular group of chemicals.     

Development of a peptide reactivity assay for screening contact allergens.

Allergic contact dermatitis resulting from skin sensitization is a common occupational and environmental health problem. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin sensitization potential of chemicals. In addition to accurate identification of skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency; information that is pivotal in successful management of human health risks. However, even with the significant animal welfare benefits provided by the LLNA, there is still interest in the development of nonanimal test methods for skin sensitization testing. One characteristic of a chemical allergen is its ability to react with proteins prior to the induction of skin sensitization. The majority of chemical allergens is electrophilic and as such reacts with nucleophilic amino acids like cysteine or lysine. In order to determine if reactivity correlates with sensitization potential, 38 chemicals representing allergens of different potencies (weak to extreme) and nonsensitizers were evaluated for their ability to react with glutathione or three synthetic peptides containing either cysteine, lysine, or histidine. Following a 15-min reaction time for glutathione or a 24 h reaction period for the three synthetic peptides, the samples were analyzed by HPLC. UV detection was used to monitor the depletion of glutathione or the peptide following reaction. The results demonstrate that a significant correlation (Spearman correlation) exists between allergen potency and the depletion of glutathione (p = 0.001), lysine (p = 0.025), and cysteine (p = 0.020), but not histidine. The peptide with the highest sensitivity was cysteine (80.8%) whereas histidine was the least sensitive (11.5%). The data presented show that measuring peptide reactivity has utility for screening chemicals for their skin sensitization potency and thus potential for reducing our reliance on animal test methods.     

Data integration of non-animal tests for the development of a test battery to predict the skin sensitizing potential and potency of chemicals

Recent changes in regulatory restrictions and social views against animal testing have accelerated development of reliable alternative tests for predicting skin sensitizing potential and potency of many chemicals. Lately, a test battery integrated with different in vitro tests has been suggested as a better approach than just one in vitro test for replacing animal tests. In this study, we created a dataset of 101 test chemicals with LLNA, human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and in silico prediction system. The results of these tests were converted into scores of 0–2 and the sum of individual scores provided the accuracy of 85% and 71% for the potential and potency prediction, compared with LLNA. Likewise, the straightforward tiered system of h-CLAT and DPRA provided the accuracy of 86% and 73%. Additionally, the tiered system showed a higher sensitivity (96%) compared with h-CLAT alone, indicating that sensitizers would be detected with higher reliability in the tiered system. Our data not only demonstrates that h-CLAT can be part of a test battery with other methods but also supports the practical utility of a tiered system when h-CLAT and DPRA are the first screening methods for skin sensitization.     

Estimating skin sensitization potency from a single dose LLNA

Skin sensitization is an important aspect of safety assessment. The mouse local lymph node assay (LLNA) developed in the 1990s is an in vivo test used for skin sensitization hazard identification and characterization. More recently a reduced version of the LLNA (rLLNA) has been developed as a means of identifying, but not quantifying, sensitization hazard. The work presented here is aimed at enabling rLLNA data to be used to give quantitative potency information that can be used, inter alia, in modeling and read-across approaches to non-animal based potency estimation. A probit function has been derived enabling estimation of EC3 from a single dose. This has led to development of a modified version of the rLLNA, whereby as a general principle the SI value at 10%, or at a lower concentration if 10% is not testable, is used to calculate the EC3. This version of the rLLNA has been evaluated against a selection of chemicals for which full LLNA data are available, and has been shown to give EC3 values in good agreement with those derived from the full LLNA.     

Chemistry-based risk assessment for skin sensitization: quantitative mechanistic modeling for the S(N)Ar domain

There is a strong impetus to develop nonanimal based methods to predict skin sensitization potency. An approach based on physical organic chemistry, whereby chemicals are classified into reaction mechanistic domains and quantitative models or read-across methods are derived for each domain, has been the basis of several recent publications. This article is concerned with the S(N)Ar reaction mechanistic domain. Electrophiles able to react by the S(N)Ar mechanism have long been recognized as skin sensitizers and have been used extensively in research studies on the biology of skin sensitization. Although qualitative discriminant analysis approaches have been developed for estimating the sensitization potential for S(N)Ar electrophiles on a yes/no qualitative basis, no quantitative mechanistic model (QMM) has so far been developed for this domain. Here, we derive a QMM that correlates skin sensitization potency, quantified by murine local lymph node assay (LLNA) EC3 data on a range of S(N)Ar electrophiles. It is based on the Hammett σ(-) values for the activating groups and the Taft σ* value for the leaving group. The model takes the form pEC3=2.48 Σσ(-) + 0.60 σ* - 4.51. This QMM, generated from mouse LLNA data, provides a reactivity parameter 2.48 Σσ(-) + 0.60 σ*, which was applied to a set of 20 compounds for which guinea pig test results were available in the literature and was found to successfully discriminate the sensitizers from the nonsensitizers. The reactivity parameter correctly predicted a known human sensitizer 2,4-dichloropyrimidine. New LLNA data on two further S(N)Ar electrophiles are consistent with the QMM.     

Accounting for data variability,a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example

When searching for alternative methods to animal testing, confidently rescaling an in vitro result to the corresponding in vivo classification is still a challenging problem. Although one of the most important factors affecting good correlation is sample characteristics, they are very rarely integrated into correlation studies. Usually, in these studies, it is implicitly assumed that both compared values are error‐free numbers, which they are not. In this work, we propose a general methodology to analyze and integrate data variability and thus confidence estimation when rescaling from one test to another. The methodology is demonstrated through the case study of rescaling the in vitro Direct Peptide Reactivity Assay (DPRA) reactivity to the in vivo Local Lymph Node Assay (LLNA) skin sensitization potency classifications. In a first step, a comprehensive statistical analysis evaluating the reliability and variability of LLNA and DPRA as such was done. These results allowed us to link the concept of gray zones and confidence probability, which in turn represents a new perspective for a more precise knowledge of the classification of chemicals within their in vivo OR in vitro test. Next, the novelty and practical value of our methodology introducing variability into the threshold optimization between the in vitro AND in vivo test resides in the fact that it attributes a confidence probability to the predicted classification. The methodology, classification and screening approach presented in this study are not restricted to skin sensitization only. They could be helpful also for fate, toxicity and health hazard assessment where plenty of in vitro and in chemico assays and/or QSARs models are available. Copyright © 2016 John Wiley & Sons, Ltd.     

Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay

This article presents an overview of electrophilic reaction mechanisms relevant to skin sensitization, with reference to a published skin sensitization test data set for 106 chemicals. Where appropriate to aid the interpretation, additional data on a small number of further compounds are also discussed. It is shown that there is a close correspondence in the way differences and similarities in skin sensitization potency of chemicals relate to differences and similarities in their physical organic chemistry and electrophilic reaction mechanistic chemistry. The 106 chemicals are classified into their reaction mechanistic applicability domains, and reactivity-sensitization trends are analyzed for each domain: the Michael acceptor and pro-Michael acceptor electrophile domain; the SNAr electrophile domain; the SN2 electrophile domain; the Schiff base electrophile domain; the acyl transfer electrophile domain; and the non-electrophilic non-pro-electrophilic domain. The last of these domains should be populated mainly by non-sensitizers. Classification of 87 of the 106 compounds, using these domains, was straightforward. In most of the domains and subdomains where there are sufficient compounds, clear trends can be seen, in conformity with the Relative Alkylation Index (RAI) model, between sensitization potential and reactivity/hydrophobicity. Of the remaining 19 compounds, 7 are alpha-X-methyl-gamma-lactones that on the basis of published organic chemistry studies and guinea pig sensitization data can be classed as pro-Michael acceptors by elimination of HX but that are mostly negative in the LLNA, indicating a difference in bioactivation capabilities between mice and guinea pigs. The other 12 compounds, whose chemistry was not immediately obvious, were found after further analysis and literature research to fit into appropriate mechanistic domains that rationalize their skin sensitizing properties.     

Binary test battery with KeratinoSens™ and h-CLAT as part of a bottom-up approach for skin sensitization hazard prediction

Skin sensitization is one of the key safety endpoints for chemicals applied directly to the skin. Several integrated testing strategies (ITS) using multiple non-animal test methods have been developed to accurately evaluate the sensitizing potential of chemicals, but there is no regulatory-accepted ITS to classify a chemical as a non-sensitizer. In this study, the predictive performance of a binary test battery with KeratinoSens™ and h-CLAT compared to the local lymph node assay (LLNA) and human data was examined using comprehensive dataset of 203 chemicals. When two negative results indicate a non-sensitizer, the binary test battery provided sensitivity of 93.4% or 94.4% compared with the LLNA or human data. Taking into account the predictive limitations (i.e. high log Kow, pre-/pro-haptens and acyl transfer agents (or amine-reactive)), the binary test battery had extremely high sensitivity comparable to that of the 3 out of 3 ITS where three negative results of the DPRA, KeratinoSens™ and h-CLAT indicate a non-sensitizer. Therefore, the data from KeratinoSens™ or h-CLAT may provide partly redundant information on the molecular initiating event derived from DPRA. Taken together, the binary test battery of KeratinoSens™ and h-CLAT could be used as part of a bottom-up approach for skin sensitization hazard prediction.     

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three‐step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non‐UV irradiation (UV?). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV? and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens.     

Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays

The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to predict skin sensitization potential of chemicals. For this purpose, combined evaluation using multiple in vitro and in silico parameters that reflect different aspects of the sensitization process seems promising.We previously reported that LLNA thresholds could be well predicted by using an artificial neural network (ANN) model, designated iSENS ver.1 (integrating in vitro sensitization tests version 1), to analyze data obtained from two in vitro tests: the human Cell Line Activation Test (h-CLAT) and the SH test. Here, we present a more advanced ANN model, iSENS ver.2, which additionally utilizes the results of antioxidant response element (ARE) assay and the octanol–water partition coefficient (Log P, reflecting lipid solubility and skin absorption). We found a good correlation between predicted LLNA thresholds calculated by iSENS ver.2 and reported values. The predictive performance of iSENS ver.2 was superior to that of iSENS ver.1. We conclude that ANN analysis of data from multiple in vitro assays is a useful approach for risk assessment of chemicals for skin sensitization.