Kramer J.,Chantest |
Obejero-Paz C.A.,Chantest |
Myatt G.,Leadscope, Inc. |
Kuryshev Y.A.,Chantest |
And 3 more authors.
Scientific Reports | Year: 2013
Drug-induced block of the cardiac hERG (human Ether-à-go-go-Related Gene) potassium channel delays cardiac repolarization and increases the risk of Torsade de Pointes (TdP), a potentially lethal arrhythmia. A positive hERG assay has been embraced by regulators as a non-clinical predictor of TdP despite a discordance of about 30%. To test whether assaying concomitant block of multiple ion channels (Multiple Ion Channel Effects or MICE) improves predictivity we measured the concentration-responses of hERG, Nav1.5 and Cav1.2 currents for 32 torsadogenic and 23 non-torsadogenic drugs from multiple classes. We used automated gigaseal patch clamp instruments to provide higher throughput along with accuracy and reproducibility. Logistic regression models using the MICE assay showed a significant reduction in false positives (Type 1 errors) and false negatives (Type 2 errors) when compared to the hERG assay. The best MICE model only required a comparison of the blocking potencies between hERG and Cav1.2.
Jennings P.,Innsbruck Medical University |
Schwarz M.,University of Tubingen |
Landesmann B.,EU Joint Research Centre |
Maggioni S.,Istituto di Ricerche Farmacologiche Mario Negri |
And 4 more authors.
Archives of Toxicology | Year: 2014
There is an urgent need for the development of alternative methods to replace animal testing for the prediction of repeat dose chemical toxicity. To address this need, the European Commission and Cosmetics Europe have jointly funded a research program for ‘Safety Evaluation Ultimately Replacing Animal Testing.’ The goal of this program was the development of in vitro cellular systems and associated computational capabilities for the prediction of hepatic, cardiac, renal, neuronal, muscle, and skin toxicities. An essential component of this effort is the choice of appropriate reference compounds that can be used in the development and validation of assays. In this review, we focus on the selection of reference compounds for liver pathologies in the broad categories of cytotoxicity and lipid disorders. Mitochondrial impairment, oxidative stress, and apoptosis are considered under the category of cytotoxicity, while steatosis, cholestasis, and phospholipidosis are considered under the category of lipid dysregulation. We focused on four compound classes capable of initiating such events, i.e., chemically reactive compounds, compounds with specific cellular targets, compounds that modulate lipid regulatory networks, and compounds that disrupt the plasma membrane. We describe the molecular mechanisms of these compounds and the cellular response networks which they elicit. This information will be helpful to both improve our understanding of mode of action and help in the selection of appropriate mechanistic biomarkers, allowing us to progress the development of animal-free models with improved predictivity to the human situation. © 2014, Springer-Verlag Berlin Heidelberg.
Wang Y.-J.,U.S. Food and Drug Administration |
Wang Y.-J.,GlobalNet Services Inc. |
Dou J.,U.S. Food and Drug Administration |
Cross K.P.,Leadscope, Inc. |
Valerio L.G.,U.S. Food and Drug Administration
Regulatory Toxicology and Pharmacology | Year: 2011
Black cohosh, red clover, hops, and chasteberry are botanicals commonly used to alleviate menopausal symptoms in the US, and are examined in this study as part of a FDA Office of Women's Health research collaboration to expand knowledge on the safety of these botanical products. Computational approaches using classic (quantitative) structure-activity relationships ((Q)SAR), probabilistic reasoning, machine learning methods, and human expert rule-based systems were employed to deliver human hepatobiliary adverse effect predictions. The objective is to profile and analyze constituents that are alerting for the human hepatobiliary adverse effects. Computational analysis of positively predicted constituents showed that common structural features contributing to the hepatobiliary adverse effect predictions contain phenolic, flavone, isoflavone, glucoside conjugated flavone and isoflavone, and 4-hydroxyacetophenone structures. Specifically, protocatechuic acid from black cohosh, benzofuran and 4-vinylphenol from chasteberry, and xanthohumol I from hops were botanical constituents predicted positive for liver toxicity endpoints and were also confirmed with literature findings. However, comparison between the estimated human exposure to these botanical constituents and the LOAEL and NOAEL in published animal liver toxicology studies for these constituents demonstrated varying margins of safety. This study will serve as regulatory decision support information for regulators at the FDA to help with the process of prioritizing chemicals for testing. © 2010.
Valerio L.G.,U.S. Food and Drug Administration |
Cross K.P.,Leadscope, Inc.
Toxicology and Applied Pharmacology | Year: 2012
Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure-activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. © 2012.
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 200.00K | Year: 2002
"Gene-to-drug" is a new paradigm in drug discovery based on the novel methodology of chemical genomics. This approach provides a means to systematically analyze the vast quantities of genomics information generated by high throughput experiments to develop new pharmacophores. The methodology seeks to match biological targets and drug candidates by establishing information feedback loops to associate genes and compounds. This proposal envisions a novel molecular informatics analysis method to correlate biological information such as gene expression patterns with activity profiles of therapeutic agents to extract molecular-level information on cellular mechanisms. Expected outcomes of the proposed research are: 1) development of a novel informatics methodology to link gene expressions and structural features of compounds; 2) a unique database containing experiments results from direct testing of the effects of compound treatment on gene expression patterns for various cell lines. Phase II will focus on the further development of informatics software and accompanying database. This product will find widespread use by medicinal and pharmaceutical chemists in the identification of potential pairs of compound classes and specific gene subsets, and will also help determine the cellular pathways for molecular-level manipulation of pharmacology so that novel cancer therapeutics can be discovered. PROPOSED COMMERCIAL APPLICATIONS: The proposed research will result in two commercial products: (1) a database of the effects of various compounds on gene expression levels; (2) a novel bioinformatics software tool for identifying chemical features within compounds that are responsible for specific compound-gene or compound-protein interactions.