Warren Casey

Integrated decision strategies for skin sensitization hazard

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non‐animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co‐operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h‐CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read‐across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty‐four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89–96% for the test set and 96–99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non‐animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

A novel bacterial reversion and forward mutation assay based on green fluorescent protein

We report the first use of green fluorescent protein (GFP) for mutation detection. We have constructed a plasmid-based bacterial system whereby mutated cells fluoresce and non-mutated cells do not fluoresce. Fluorescence is monitored using a simple hand-help UV lamp; no additional cofactors or manipulations are necessary. To develop a reversion system, we introduced a +1 DNA frameshift mutation in the coding region of GFP and the resulting protein is not fluorescent in Escherichia coli. Treatment of bacteria containing the +1 frameshift vector with ICR-191 yields fluorescent colonies, indicating that reversion to the wild-type sequence has occurred. Site-directed mutagenesis was used to insert an additional cytosine into a native CCC sequence in the coding region of GFP in plasmid pBAD-GFPuv, expanding the sequence to CCCC. A dose-related increase in fluorescent colonies was observed when the bacteria were treated with ICR-191, an agent that induces primarily frameshift mutations. The highest dose of ICR-191 tested, 16 microg/ml, produced a mutant fraction of 16 x 10(-5) and 8.8 x 10(-5) in duplicate experiments. The reversion system did not respond to MNNG, an agent that produces mainly single-base substitutions. To develop a forward system, we used GFP under the control of the arabinose PBAD promoter; in the absence of arabinose, GFP expression is repressed and no fluorescent colonies are observed. When cells were treated with MNNG or ENNG, a dose-dependent increase in fluorescent colonies was observed, indicating that mutations had occurred in the arabinose control region that de-repressed the promoter. Treating bacteria with 100 microg/ml MNNG induced mutant fractions as high as 82 x 10(-5) and 40 x 10-5 in duplicate experiments. Treating bacteria with 150 microg/ml ENNG induced a mutant fraction of 2.1 x 10(-5) in a single experiment.

Multivariate models for prediction of human skin sensitization hazard.

One of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) top priorities is the development and evaluation of non‐animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays – the direct peptide reactivity assay (DPRA), human cell line activation test (h‐CLAT) and KeratinoSens™ assay – six physicochemical properties and an in silico read‐across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h‐CLAT and read‐across; (2) DPRA, h‐CLAT, read‐across and KeratinoSens; or (3) DPRA, h‐CLAT, read‐across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63–79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing

Acute systemic toxicity testing provides the basis for hazard labeling and risk management of chemicals. A number of international efforts have been directed at identifying non-animal alternatives for in vivo acute systemic toxicity tests. A September 2015 workshop, Alternative Approaches for Identifying Acute Systemic Toxicity: Moving from Research to Regulatory Testing, reviewed the state-of-the-science of non-animal alternatives for this testing and explored ways to facilitate implementation of alternatives. Workshop attendees included representatives from international regulatory agencies, academia, nongovernmental organizations, and industry. Resources identified as necessary for meaningful progress in implementing alternatives included compiling and making available high-quality reference data, training on use and interpretation of in vitro and in silico approaches, and global harmonization of testing requirements. Attendees particularly noted the need to characterize variability in reference data to evaluate new approaches. They also noted the importance of understanding the mechanisms of acute toxicity, which could be facilitated by the development of adverse outcome pathways. Workshop breakout groups explored different approaches to reducing or replacing animal use for acute toxicity testing, with each group crafting a roadmap and strategy to accomplish near-term progress. The workshop steering committee has organized efforts to implement the recommendations of the workshop participants.

Prediction of skin sensitization potency using machine learning approaches

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non‐sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non‐animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave‐one‐out cross‐validation. A one‐tiered strategy modeled all three categories of response together while a two‐tiered strategy modeled sensitizer/non‐sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two‐tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one‐tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non‐animal methods may provide valuable information for assessing skin sensitization potency. Copyright

Correlation analysis of gene expression and clinical chemistry to identify biomarkers of skeletal myopathy in mice treated with PPAR agonist GW610742X

Abstract Data from individual animals were used to identify genes in mouse skeletal muscle whose expression correlated with a known serum marker of skeletal myopathy, creatine kinase activity (CK), after treatment with a peroxisome proliferator-activated receptors (PPAR) agonist, GW610742X. Six genes had correlation coefficients of ≥0.90: Mt1a (metallothionein 1a), Rrad (Ras-related associated with diabetes), Ankrd1 (ankyrin repeat domain 1), Stat3 (signal transducer and activator of transcription 3), Socs3 (suppressor of cytokine signalling 3) and Mid1ip1 (Mid1 interacting protein 1). The physiological function of these genes provides potentially useful information relating to the mechanism of PPAR-induced skeletal myopathy, with oxidative stress and disruption of glycolysis most closely associated with myopathic damage. Some of the muscle genes most highly correlated with serum CK in mice also appear to be good indicators of PPAR-induced myopathy in rat skeletal muscle, demonstrating the translational potential of this approach. This study clearly shows the utility of using correlation analysis as a simple tool for identifying novel biomarkers and investigating mechanisms of toxicity.

A robotic MCF-7:WS8 cell proliferation assay to detect agonist and antagonist estrogenic activity.

Endocrine-disrupting chemicals with estrogenic activity (EA) or anti-EA (AEA) have been extensively reported to possibly have many adverse health effects. We have developed robotized assays using MCF-7:WS8 cell proliferation (or suppression) to detect EA (or AEA) of 78 test substances supplied by the Interagency Coordinating Committee on the Validation of Alternative Methods and the National Toxicology Programs Interagency Center for the Evaluation of Alternative Toxicological Methods for validation studies. We also assayed ICI 182,780, a strong estrogen antagonist. Chemicals to be assayed were initially examined for solubility and volatility to determine optimal assay conditions. For both EA and AEA determinations, a Range-Finder assay was conducted to determine the concentration range for testing, followed by a Comprehensive assay. Test substances with potentially positive results from an EA Comprehensive assay were subjected to an EA Confirmation assay that evaluated the ability of ICI 182,780 to reverse chemically induced MCF-7 cell proliferation. The AEA assays examined the ability of chemicals to decrease MCF-7 cell proliferation induced by nonsaturating concentrations of 17β-estradiol (E2), relative to ICI or raloxifene, also a strong estrogen antagonist. To be classified as having AEA, a saturating concentration of E2 had to significantly reverse the decrease in cell proliferation produced by the test substance in nonsaturating E2. We conclude that our robotized MCF-7 EA and AEA assays have accuracy, sensitivity, and specificity values at least equivalent to validated test methods accepted by the U.S. Environmental Protection Agency and the Organisation for Economic Co-operation and Development.

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

Abstract 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.

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substances physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances.

Murine J774 Macrophages Recognize LPS/IFN-g, Non-CpG DNA or Two-CpG DNA-containing Sequences as Immunologically Distinct

Specific bacterial lipopolysaccharides (LPS), IFN-gamma, and unmethylated cytosine or guanosine-phosphorothioate containing DNAs (CpG) activate host immunity, influencing infectious responses. Macrophages detect, inactivate and destroy infectious particles, and synthetic CpG sequences invoke similar responses of the innate immune system. Previously, murine macrophage J774 cells treated with CpG induced the expression of nitric oxide synthase 2 (NOS2) and cyclo-oxygenase 2 (COX2) mRNA and protein. In this study murine J774 macrophages were exposed to vehicle, interferon gamma+lipopolysaccharide (IFN-g/LPS), non-CpG (SAK1), or two-CpG sequence-containing DNA (SAK2) for 0-18h and gene expression changes measured. A large number of immunostimulatory and inflammatory changes were observed. SAK2 was a stronger activator of TNFalpha- and chemokine expression-related changes than LPS/IFN-g. Up regulation included tumor necrosis factor receptor superfamily genes (TNFRSFs), IL-1 receptor signaling via stress-activated protein kinase (SAPK), NF-kappaB activation, hemopoietic maturation factors and sonic hedgehog/wingless integration site (SHH/Wnt) pathway genes. Genes of the TGF-beta pathway were down regulated. In contrast, LPS/IFN-g-treated cells showed increased levels for TGF-beta signaling genes, which may be linked to the observed up regulation of numerous collagens and down regulation of Wnt pathway genes. SAK1 produced distinct changes from LPS/IFN-g or SAK2. Therefore, J774 macrophages recognize LPS/IFN-g, non-CpG DNA or two-CpG DNA-containing sequences as immunologically distinct.