Reactive oxygen species (ROS) assay-based photosafety screening for complex ingredients: Modification of the ROS assay protocol.

A reactive oxygen species (ROS) assay has been widely used for photosafety assessment; however, the phototoxic potential of complex materials, including plant extracts, essential oils, and functional polymers, is unevaluable because of their undefined molecular weights. The present study was undertaken to modify the ROS assay protocol for evaluating phototoxic potentials of those materials with use of their apparent molecular weight (aMw). On preparing sample solutions for the ROS assay, aMw ranging from 150 to 350 was tentatively employed for test substances. The modified ROS assays were applied to 45 phototoxic and 19 non-phototoxic substances, including 44 chemicals and 20 complex materials (plant extracts) for clarification of the predictive performance. Generation of ROS from photo-irradiated samples tended to increase as aMW grew, resulting in the largest number of false-positive predictions at aMW of 350. Some false-negative predictions were also observed when aMW was set at 200 or less. At aMw of 250, all tested phototoxic substances could be correctly identified as photoreactive with no false-negative predictions. Based on these observations, aMw of 250 was found to be suitable for the ROS assay on complex materials, and the sensitivity, specificity, and positive and negative predictivity for the proposed ROS assay were calculated to be 100, 52.6, 83.3, and 100%, respectively. Thus, the proposed approach may be efficacious for predicting phototoxic potentials of complex materials and contribute to the development of new products with a wide photosafety margin.

StemPanTox: A fast and wide-target drug assessment system for tailor-made safety evaluations using personalized iPS cells.

An alternative model that reliably predicts human-specific toxicity is necessary because the translatability of effects on animal models for human disease is limited to context. Previously, we developed a method that accurately predicts developmental toxicity based on the gene networks of undifferentiated human embryonic stem (ES) cells. Here, we advanced this method to predict adult toxicities of 24 chemicals in six categories (neurotoxins, cardiotoxins, hepatotoxins, two types of nephrotoxins, and non-genotoxic carcinogens) and achieved high predictability (AUC = 0.90-1.00) in all categories. Moreover, we screened for an induced pluripotent stem (iPS) cell line to predict the toxicities based on the gene networks of iPS cells using transfer learning of the gene networks of ES cells, and predicted toxicities in four categories (neurotoxins, hepatotoxins, glomerular nephrotoxins, and non-genotoxic carcinogens) with high performance (AUC = 0.82-0.99). This method holds promise for tailor-made safety evaluations using personalized iPS cells.

In chemico sequential testing strategy for assessing the photoallegic potential.

Several non-animal testing methods to assess photoallergic potential have been developed so far, while none of them have yet to be validated and regulatory accepted. Currently, some photoreactivity assays such as UV-VIS spectral analysis and ROS assay are generally used for initial photosafety assessments because of their high sensitivity. However, they have a low specificity, generating a high percentage of false positive results, and the development of a follow-up assessment method is desired. Therefore, this study aimed to develop an in chemico photoallergy testing method, photo-direct peptide reactivity assay (photo-DPRA). Based on photosafety information, 34 photoallergens and 16 non-photoallergens were selected and subjected to UV-VIS spectral analysis, ROS/micellar ROS assays, photo-DPRA, sequential testing strategy (STS) consisting of all three methods, and 3T3 neutral red uptake phototoxicity testing (3T3 NRU PT). Combination of the methods addressing the key events of photoallergy exhibited high prediction performance. Our results showed the proposed strategy would be useful to predict the photoallergic potential of chemicals as the follow-up assessment for false positive chemicals by UV/VIS spectral analysis and ROS assay.

[Current Issues in Photosafety Evaluation].

Phototoxicity is a toxic response elicited by topically applied or systemically administered photoreactive chemicals after exposure to light and can be broadly categorized into photoirritation, photoallergy, photogenotoxicity, and photocarcinogenicity. The need in the 21st century for accurate evaluation of photosafety has led to the publication of a number of guidelines from government agencies in Europe and the U.S.A. as well as the Organisation for Economic Co-operation and Development (OECD). In this review, we first discuss the mechanisms of phototoxicity and how they can be evaluated. We then discuss the state of the art and challenges now faced in photosafety evaluation of pharmaceuticals and cosmetics. Additionally, we describe the latest developments in OECD test guidelines (TG) for assessing photosafety, including revisions to the in vitro 3T3 neutral red uptake (NRU) phototoxicity test (TG 432) and the newly adopted reactive oxigen species (ROS) assay (TG 495). We will emphasize the importance of selecting the most appropriate means of evaluation with reference to the latest guidelines and other legal criteria for conducting photosafety evaluation.

Mechanism-based integrated assay systems for the prediction of drug-induced liver injury.

Drug-induced liver injury (DILI) can cause hepatic failure and result in drug withdrawal from the market. It has host-related and compound-dependent mechanisms. Preclinical prediction of DILI risk is very challenging and safety assessments based on animals inadequately forecast human DILI risk. In contrast, human-derived in vitro cell culture-based models could improve DILI risk prediction accuracy. Here, we developed and validated an innovative method to assess DILI risk associated with various compounds. Fifty-four marketed and withdrawn drugs classified as DILI risks of "most concern", "less concern", and "no concern" were tested using a combination of four assays addressing mitochondrial injury, intrahepatic lipid accumulation, inhibition of bile canalicular network formation, and bile acid accumulation. Using the inhibitory potencies of the drugs evaluated in these in vitro tests, an algorithm with the highest available DILI risk prediction power was built by artificial neural network (ANN) analysis. It had an overall forecasting accuracy of 73%. We excluded the intrahepatic lipid accumulation assay to avoid overfitting. The accuracy of the algorithm in terms of predicting DILI risks was 62% when it was constructed by ANN but only 49% when it was built by the point-added scoring method. The final algorithm based on three assays made no DILI risk prediction errors such as "most concern " instead of "no concern" and vice-versa. Our mechanistic approach may accurately predict DILI risks associated with numerous candidate drugs.

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid.

We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.

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.

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.

Comparative study on prediction performance of photosafety testing tools on photoallergens.

Several testing methods have been established to identify potential phototoxins. The present study was undertaken to clarify the predictive ability of in vitro photosafety assays for photoallergenicity. On the basis of animal and/or clinical photosafety information, 23 photoallergens and 7 non-phototoxic/non-photoallergenic chemicals were selected and subjected to UV/VIS spectral analysis, reactive oxygen species (ROS)/micellar ROS (mROS) assays, and 3T3 neutral red uptake phototoxicity testing (3T3 NRU PT). Of the photoallergens tested, ca. 96% of chemicals had intense UV/VIS absorption with a molar extinction coefficient of over 1000 M(-1) cm(-1), and false-positive predictions were made for 3 non-photoallergenic chemicals. In the ROS assay, all photoallergens were found to be potent ROS generators under exposure to simulated sunlight. In the photosafety prediction based on the ROS assay, the individual specificity was 85.7%, and the positive predictivity and negative predictivity were found to be 95.8% and 100%, respectively. Most of the photoirritant chemicals were correctly identified by the 3T3 NRU PT; however, it provided false predictions for ca. 48% of photoallergens. The orders of sensitivity and specificity for photoallergenicity prediction were estimated to be: [sensitivity] ROS assay>UV/VIS absorption ≫ 3T3 NRU PT, and [specificity] 3T3 NRU PT>ROS assay ≫ UV/VIS absorption. Thus, photochemical assays, in particular the ROS assay, can be used for assessment of photoallergenicity, although there were some false-positive predictions.

Development of an in vitro photosensitization test based on changes of cell-surface thiols and amines as biomarkers: the photo-SH/NH2 test.

As a part of our studies to develop a cell-based in vitro photosensitization assay, we examined whether changes of cell-surface thiols and amines on human monocytic cell line THP-1 could be used to predict photosensitizing potential of chemicals. First, we identified a suitable ultraviolet A (UV-A) irradiation dose to be 5.0 J/cm(2) by investigating the effect of UV-A on the levels of cell-surface thiols and amines in ketoprofen (KP; a representative photoallergen)-treated THP-1 cells. Next, we confirmed that phenol red, a known photoirritant used as a pH indicator in the culture medium, did not affect the KP-induced changes of cell-surface thiols and amines. Using the criterion of more than 15% change of cell-surface thiols and/or amines in response to UV-A irradiation, 22 of 26 known photosensitizers (15 of 18 photoallergens, 7 of 8 photoirritants) were judged positive. Seven of 7 known non-phototoxins did not alter cell-surface thiols or amines. The accuracy for predicting photosensitizers was 87.9% (sensitivity/specificity; 84.6%/100%), and the accuracy for predicting photoallergens was 69.7% (sensitivity/specificity; 83.3%/53.3%). Our results suggest that changes of cell-surface thiols and/or amines may be useful biomarkers for predicting photosensitization potential, including photoallergenicity, of compounds. We designate this test as the photo-SH/NH2 test.

Development of novel in vitro photosafety assays focused on the Keap1-Nrf2-ARE pathway.

Although photoallergens require UV energy for antigen formation, the subsequent immune response is considered to be the same as in ordinary skin sensitization. Therefore, in vitro tests for skin sensitization should also be applicable for photoallergy testing. In this study, we examined whether activation of the Keap1 (Kelch-like ECH-associated protein 1)-Nrf2 (nuclear factor-erythroid 2-related factor 2)-ARE (antioxidant response element) pathway could be used to assess the photoallergenic potential of chemicals, using the reporter cell line AREc32 or KeratinoSens(TM) . First, we identified an appropriate UVA irradiation dose [5 J cm(-2) irradiation in phosphate-buffered saline (PBS)] by investigating the effect of UV irradiation on ARE-dependent gene induction using untreated or 6-methylcoumarin (6-MC)-treated cells. Irradiation of well-known photoallergens under this condition increased ARE-dependent gene expression by more than 50% compared with both vehicle and non-irradiated controls. When the cut-off value for detecting photoallergens was set at 50% induction, the accuracy of predicting photoallergenic/phototoxic chemicals was 70% in AREc32 cells and 67% in KeratinoSens(TM) cells, and the specificity was 100% in each case. We designate these assays as a photo-ARE assay and photo-KeratinoSens(TM) , respectively. Our results suggest that activation of the Keap1-Nrf2-ARE pathway is an effective biomarker for evaluating both photoallergenic and phototoxic potentials. Either of the above tests might be a useful component of a battery of in vitro tests/in silico methods for predicting the photoallergenicity and phototoxicity of chemicals. Copyright © 2015 John Wiley & Sons, Ltd.

Non-animal photosafety screening for complex cosmetic ingredients with photochemical and photobiochemical assessment tools.

Previously, a non-animal screening approach was proposed for evaluating photosafety of cosmetic ingredients by means of in vitro photochemical and photobiochemical assays; however, complex cosmetic ingredients, such as plant extracts and polymers, could not be evaluated because their molecular weight is often poorly defined and so their molar concentration cannot be calculated. The aim of the present investigation was to establish a photosafety screen for complex cosmetic ingredients by using appropriately modified in vitro photosafety assays. Twenty plant extracts were selected as model materials on the basis of photosafety information, and their phototoxic potentials were assessed by means of ultraviolet (UV)/visible light (VIS) spectral analysis, reactive oxygen species (ROS)/micellar ROS (mROS) assays, and 3T3 neutral red uptake phototoxicity testing (3T3 NRU PT). The maximum UV/VIS absorption value was employed as a judgment factor for evaluating photoexcitability of samples, and the value of 1.0 was adopted as a tentative criterion for photosafety identification. The ROS/mROS assays were conducted at 50 µg/mL, and no false negative prediction was obtained. Furthermore, the ROS/mROS assays at 50 µg/mL had a similar predictive capacity to the ROS/mROS assays in the previous study. A systematic tiered approach for simple and rapid non-animal photosafety evaluation of complex cosmetic ingredients can be constructed using these modified in vitro photochemical assays.

Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system.

Genotoxicity is the most commonly used endpoint to predict the carcinogenicity of chemicals. The International Conference on Harmonization (ICH) M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk offers guidance on (quantitative) structure-activity relationship ((Q)SAR) methodologies that predict the outcome of bacterial mutagenicity assay for actual and potential impurities. We examined the effectiveness of the (Q)SAR approach with the combination of DEREK NEXUS as an expert rule-based system and ADMEWorks as a statistics-based system for the prediction of not only mutagenic potential in the Ames test, but also genotoxic potential in mutagenicity and clastogenicity tests, using a data set of 342 chemicals extracted from the literature. The prediction of mutagenic potential or genotoxic potential by DEREK NEXUS or ADMEWorks showed high values of sensitivity and concordance, while prediction by the combination of DEREK NEXUS and ADMEWorks (battery system) showed the highest values of sensitivity and concordance among the three methods, but the lowest value of specificity. The number of false negatives was reduced with the battery system. We also separately predicted the mutagenic potential and genotoxic potential of 41 cosmetic ingredients listed in the International Nomenclature of Cosmetic Ingredients (INCI) among the 342 chemicals. Although specificity was low with the battery system, sensitivity and concordance were high. These results suggest that the battery system consisting of DEREK NEXUS and ADMEWorks is useful for prediction of genotoxic potential of chemicals, including cosmetic ingredients.

Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

In silico risk assessment for skin sensitization using artificial neural network analysis.

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 the skin sensitization potential of chemicals. For this purpose, an integrated evaluation system employing 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. 2 (integrating in vitro sensitization tests version 2), to analyze data obtained from in vitro tests focused on different aspects of skin sensitization. Here, we examined whether LLNA thresholds could be predicted by ANN using in silico-calculated descriptors of the three-dimensional structures of chemicals. We obtained a good correlation between predicted LLNA thresholds and reported values. Furthermore, combining the results of the in vitro (iSENS ver. 2) and in silico models reduced the number of chemicals for which the potency category was under-estimated. In conclusion, the ANN model using in silico parameters was shown to be have useful predictive performance. Further, our results indicate that the combination of this model with a predictive model using in vitro data represents a promising approach for integrated risk assessment of skin sensitization potential of chemicals.

Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties.

An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log Kp), taking account of the physicochemical properties of the vehicle, and the apparent diffusion coefficient (log D). Molecular weight and octanol-water partition coefficient (log P) of chemicals, and log P of the vehicles, were used as molecular descriptors for predicting log Kp and log D of 359 samples, for which literature values of either or both of log Kp and log D were available. Adaptivity of the ANN model was evaluated in comparison with a multiple linear regression model (MLR) by calculating the root-mean-square (RMS) errors. Accuracy and robustness were confirmed by 10-fold cross-validation. The predictive RMS errors of the ANN model were smaller than those of the MLR model (log Kp; 0.675 vs 0.887, log D; 0.553 vs 0.658), indicating superior performance. The predictive RMS errors for log Kp and log D with the ANN model after 10-fold cross-validation analysis were 0.723 and 0.606, respectively. Moreover, we estimated the cumulative amounts of chemicals permeated into the skin during 24 hr (Q24hr) from the values of log Kp and log D by applying Fick's law of diffusion. Our results suggest that this newly established ANN analysis method, taking account of the property of the vehicle, could contribute to non-animal risk assessment of cosmetic ingredients by providing a tool for calculating Q24hr, which is required for evaluating the margin of safety.

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 (LogP, 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.

Non-animal photosafety assessment approaches for cosmetics based on the photochemical and photobiochemical properties.

The main purpose of the present study was to establish a non-animal photosafety assessment approach for cosmetics using in vitro photochemical and photobiochemical screening systems. Fifty-one cosmetics, pharmaceutics and other chemicals were selected as model chemicals on the basis of animal and/or clinical photosafety information. The model chemicals were assessed in terms of photochemical properties by UV/VIS spectral analysis, reactive oxygen species (ROS) assay and 3T3 neutral red uptake phototoxicity testing (3T3 NRU PT). Most phototoxins exhibited potent UV/VIS absorption with molar extinction coefficients of over 1000M(-1)cm(-1), although false-negative prediction occurred for 2 cosmetic phototoxins owing to weak UV/VIS absorption. Among all the cosmetic ingredients, ca. 42% of tested chemicals were non-testable in the ROS assay because of low water solubility; thereby, micellar ROS (mROS) assay using a solubilizing surfactant was employed for follow-up screening. Upon combination use of ROS and mROS assays, the individual specificity was 88.2%, and the positive and negative predictivities were estimated to be 94.4% and 100%, respectively. In the 3T3 NRU PT, 3 cosmetics and 4 drugs were incorrectly predicted not to be phototoxic, although some of them were typical photoallergens. Thus, these in vitro screening systems individually provide false predictions; however, a systematic tiered approach using these assays could provide reliable photosafety assessment without any false-negatives. The combined use of in vitro assays might enable simple and fast non-animal photosafety evaluation of cosmetic ingredients.

Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicals.

In order to develop in vitro risk assessment systems for skin sensitization, it is important to predict a threshold from the murine local lymph node assay (LLNA). We first confirmed that the combination of the human Cell Line Activation Test (h-CLAT) and the SH test improved the accuracy and sensitivity of prediction of LLNA data compared with each individual test. Next, we assessed the mutual correlations among maximum amount of change of cell-surface thiols (MAC value) in the SH test, CV75 value (concentration giving 75% cell viability) in a cytotoxicity assay, EC150 and EC200 values (thresholds concentrations of CD86 and CD54 expression, respectively) in h-CLAT and published LLNA thresholds of 64 chemicals. Based on the results, we selected MAC value and the minimum of CV75, EC150 (CD86) and EC200 (CD54) as descriptors for the input layer of an artificial neural network (ANN) system. The ANN-predicted values were well correlated with reported LLNA thresholds. We also found a correlation between the SH test and the peptide-binding assay used to evaluate hapten-protein complex formation. Thus, this model, which we designate as the "iSENS ver. 1", may be useful for risk assessment of skin sensitization potential of chemicals from in vitro test data.

Changes of cell-surface thiols and intracellular signaling in human monocytic cell line THP-1 treated with diphenylcyclopropenone.

Changes of cell-surface thiols induced by chemical treatment may affect the conformations of membrane proteins and intracellular signaling mechanisms. In our previous study, we found that a non-toxic dose of diphenylcyclopropene (DPCP), which is a potent skin sensitizer, induced an increase of cell-surface thiols in cells of a human monocytic cell line, THP-1. Here, we examined the influence of DPCP on intracellular signaling. First, we confirmed that DPCP induced an increase of cell-surface thiols not only in THP-1 cells, but also in primary monocytes. The intracellular reduced-form glutathione/oxidized-form glutathione ratio (GSH/GSSG ratio) was not affected by DPCP treatment. By means of labeling with a membrane-impermeable thiol-reactive compound, Alexa Fluor 488 C5 maleimide (AFM), followed by two-dimensional gel electrophoresis and analysis by liquid chromatography coupled with electrospray tandem mass spectrometry (LC/MS/MS), we identified several proteins whose thiol contents were modified in response to DPCP. These proteins included cell membrane components, such as actin and ß-tubulin, molecular chaperones, such as heat shock protein 27A and 70, and endoplasmic reticulum (ER) stress-inducible proteins. Next, we confirmed the expression in DPCP-treated cells of spliced XBP1, a known marker of ER stress. We also detected the phosphorylation of SAPK/JNK and p38 MAPK, which are downstream signaling molecules in the IRE1α-ASK1 pathway, which is activated by ER stress. These data suggested that increase of cell-surface thiols might be associated with activation of ER stress-mediated signaling.