A step-by-step approach for assessing acute oral toxicity without animal testing for additives of quasi-drugs and cosmetic ingredients.

Animal testing of cosmetic ingredients and products has been banned in the European Union since 2013. However, in Japan, the application of new quasi-drugs requires the generation of data on acute oral toxicity through animal testing. A weight of evidence approach for assessing oral toxicity was challenged. This approach used a combination of safety data, including a neutral red uptake cytotoxicity assay using BALB/c3T3 cells (3T3-NRU cytotoxicity assay), which can assess the acute oral toxicity of quasi-drugs or cosmetic ingredients. We conclude that the step-by-step approach can be used to assess test substances that cause low acute oral toxicity, such as the median lethal dose (LD 50) > 2000 mg/kg, thereby avoiding animal testing.

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.

Causes and countermeasure for blank absorbance increase in the ROS assay.

A reactive oxygen species (ROS) assay is an in chemico photoreactivity test listed in ICH S10 guideline and OECD Test Guideline No. 495. We currently utilize the ROS assay to assess the photosafety of cosmetic ingredients. We have recently confronted a problem that there was an absorbance increase of blank assessing superoxide anion generation after irradiation, whereas this did not occur in the negative control (sulisobenzone), leading to a dissatisfaction of the acceptance criteria. Therefore, we aimed to investigate the causes and find countermeasures. No significant effects of impurities and manufacturer differences of sodium phosphate and DMSO on blank absorbance increases were observed. In contrast, when Cu2+ was added to the buffer, the increase of blank absorbance after irradiation did not occur. We then confirmed the dose-response relationship and found that adding 0.1 µM of Cu2+ (corresponding to 6 ppb of Cu2+) was sufficient in suppressing the blank absorbance increase, suggesting the need of Cu2+ supplementation to the buffer. Finally, we confirmed that the ROS assay using the buffer supplemented with 0.1 µM of Cu2+ obtained stable test results by using 17 proficiency chemicals listed in TG 495. Our results suggest that the modified ROS assay protocol would be useful for obtaining stable test results.

Improved performance of the SH test as an in vitro skin sensitization test with a new predictive model and decision tree.

Demands for the elimination and replacement of animal experiments for cosmetic safety assessment have increased in recent years. Evaluation of skin sensitization, however, is a critical issue in cosmetic safety assessment. The SH test is an in vitro skin sensitization test method that evaluates protein binding of chemical substances, which is an important event in skin sensitization. We previously verified the technical transferability and between-laboratory reproducibility of the SH test, a domestic test method for which no scientific research has been conducted, and improved the protocol, but also noted some unresolved issues. Therefore, in the present study, we successfully improved the operational efficiency and clarity of the final judgment of the SH test by (i) developing a new decision-making system that can make a final judgment without statistical processing, (ii) changing the statistical method, and (iii) evaluating and determining the maximum number of repetitions necessary for optimal efficiency. The improved SH test was verified by comparing it with existing test methods already adopted by the Organization for Economic Cooperation and Development. The results of this study demonstrated excellent performance of the improved SH test, with high reproducibility, reliable predictability, and good operational efficiency. The predictive performance of the improved method does not differ significantly from that of the conventional method, although it is clearer and more efficient. Therefore, the results of the present improved method are consistent with those obtained using the conventional method, with higher efficiency.

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.

Enhancing between-facility reproducibility of the SH test as an in vitro skin sensitization test by the improved test method.

There has been an increased demand to eliminate animal experiments and to replace the experiments with alternative tests for assessing the safety of cosmetics. The SH test is an in vitro skin sensitization test that evaluates the protein binding abilities of a test substance. Skin sensitization must be evaluated by multiple test methods. The SH test uses the same cell line and measuring instruments as the human Cell-Line Activation Test (h-CLAT), which is one of the test methods used to evaluate different key events and is listed in the OECD test guidelines. There are cost advantages to usher the SH test into facilities that are already running the h-CLAT. The SH test is conducted only at a facility that has developed the SH test because studies on the between-facility reproducibility and validity have not been performed. Therefore, to verify the transferability of the SH test and the between-facilities reproducibility, we evaluated the reproducibility of the SH test results at three facilities, including the development facility. After an initial round of testing, the protocol was refined as follows to improve reproducibility among the three facilities: i) determine the optimum pH range, ii) change the maximum applicable concentration of water-soluble substances, and iii) define the appropriate dispersion conditions for evaluating hydrophobic substances. These refinements markedly enhanced the between-facility reproducibility (from 76.0% to 96.0%) for the 25 substances evaluated in this study. This study confirmed that the SH test is an effective skin sensitization test method with high technical transferability and between-facility reproducibility.

Development of a next generation risk assessment framework for the evaluation of skin sensitisation of cosmetic ingredients.

All cosmetic products placed onto the market must undergo a risk assessment for human health to ensure they are safe for consumers, including an assessment of skin sensitisation risk. Historically, in vivo animal test methods were used to identify and characterise skin sensitisation hazard, however non-animal and other new approach methodologies (NAMs) are now the preferred and mandated choice for use in risk assessment for cosmetic ingredients. The experience gained over the last three decades on how to conduct risk assessments based upon NAMs has allowed us to develop a non-animal, next generation risk assessment (NGRA) framework for the assessment of skin sensitisers. The framework presented here is based upon the principles published by the International Cooperation on Cosmetic Regulation (ICCR) and is human relevant, exposure led, hypothesis driven and designed to prevent harm. It is structured in three tiers and integrates all relevant information using a weight of evidence (WoE) approach that can be iterated when new information becomes available. The initial tier (TIER 0) involves a thorough review of the existing information including; identification of the use scenario/consumer exposure; characterisation of the chemical purity and structure; in silico predictions; existing data pertaining to skin sensitisation hazard (historical or non-animal); the identification of suitable read-across candidates with supporting hazard identification/characterisation information and application of exposure-based waiving. Considering all information identified in TIER 0, the next step is the generation of a hypothesis (TIER 1). All data are considered in an exposure-led WoE approach, taking into account an initial view on whether a chemical is likely to be a skin sensitiser or not, choice of defined approach (DA) and availability of read-across candidates. If existing information is insufficient for concluding the risk assessment, the generation of additional information may be required to proceed (TIER 2). Such targeted testing could involve refinement of the exposure estimation or generation of data from in vitro or in chemico NAMs. Once sufficient information is available, the final stage of the NGRA framework is the determination of a point of departure (POD), characterising uncertainty and comparing to the consumer exposure in a WoE. Thorough evaluation of the sources of uncertainty is essential to ensure transparency and build trust in new risk assessment approaches. Although significant progress has been made, industry must continue to share its experience in skin sensitisation NGRA via case studies to demonstrate that this new risk assessment approach is protective for consumers. Dialogue and collaboration between key stakeholders, i.e. risk assessors, clinicians and regulators are important to gain mutual understanding and grow confidence in new approaches.

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.

Chemical photoallergy: photobiochemical mechanisms, classification, and risk assessments.

Chemical photosensitivity can be elicited by exposure of the skin to various pharmaceutical substances, foods, cosmetics and other environmental chemicals, followed by exposure to sunlight. There are at least three types of chemical photosensitivity, i.e., photoirritancy (narrowly defined as phototoxicity), photogenotoxicity and photoallergenicity, and their clinical characteristics and mechanisms are quite different. Concerns about chemical photoallergy is increasing, and various studies have been made to clarify the photobiochemical characteristics of photoallergens and the mechanisms involved. Various methodologies, including in silico prediction models, photochemical assay systems, and in vitro phototoxicity prediction tools, have been developed to predict the photoallergenic potential of chemicals over the past few years. The aim of this manuscript is to review the clinical characteristics, pathogenetic mechanisms and photobiochemical features of photoallergens, with special emphasis on the current status about development of screening systems for predicting photoallergenic potential of chemicals.

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.

Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization.

The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years public concern about animal welfare has led to a requirement for non-animal risk assessment systems for the prediction of skin sensitization potential, to replace LLNA. Selection of an appropriate in vitro test or in silico model descriptors is critical to obtain good predictive performance. Here, we investigated the utility of artificial neural network (ANN) prediction models using various combinations of descriptors from several in vitro sensitization tests. The dataset, collected from published data and from experiments carried out in collaboration with the Japan Cosmetic Industry Association (JCIA), consisted of values from the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA), SH test and antioxidant response element (ARE) assay for chemicals whose LLNA thresholds have been reported. After confirming the relationship between individual in vitro test descriptors and the LLNA threshold (e.g. EC3 value), we used the subsets of chemicals for which the requisite test values were available to evaluate the predictive performance of ANN models using combinations of h-CLAT/DPRA (N = 139 chemicals), the DPRA/ARE assay (N = 69), the SH test/ARE assay (N = 73), the h-CLAT/DPRA/ARE assay (N = 69) and the h-CLAT/SH test/ARE assay (N = 73). The h-CLAT/DPRA, h-CLAT/DPRA/ARE assay and h-CLAT/SH test/ARE assay combinations showed a better predictive performance than the DPRA/ARE assay and the SH test/ARE assay. Our data indicates that the descriptors evaluated in this study were all useful for predicting human skin sensitization potential, although combinations containing h-CLAT (reflecting dendritic cell-activating ability) were most effective for ANN-based prediction.

Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals.

To develop a testing strategy incorporating the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and DEREK, we created an expanded data set of 139 chemicals (102 sensitizers and 37 non-sensitizers) by combining the existing data set of 101 chemicals through the collaborative projects of Japan Cosmetic Industry Association. Of the additional 38 chemicals, 15 chemicals with relatively low water solubility (log Kow > 3.5) were selected to clarify the limitation of testing strategies regarding the lipophilic chemicals. Predictivities of the h-CLAT, DPRA and DEREK, and the combinations thereof were evaluated by comparison to results of the local lymph node assay. When evaluating 139 chemicals using combinations of three methods based on integrated testing strategy (ITS) concept (ITS-based test battery) and a sequential testing strategy (STS) weighing the predictive performance of the h-CLAT and DPRA, overall similar predictivities were found as before on the 101 chemical data set. An analysis of false negative chemicals suggested a major limitation of our strategies was the testing of low water-soluble chemicals. When excluded the negative results for chemicals with log Kow > 3.5, the sensitivity and accuracy of ITS improved to 97% (91 of 94 chemicals) and 89% (114 of 128). Likewise, the sensitivity and accuracy of STS to 98% (92 of 94) and 85% (111 of 129). Moreover, the ITS and STS also showed good correlation with local lymph node assay on three potency classifications, yielding accuracies of 74% (ITS) and 73% (STS). Thus, the inclusion of log Kow in analysis could give both strategies a higher predictive performance.

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.

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.

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.