LeadScope (United States)

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models’ performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.

There are a wide variety of established toxicity databases being routinely used to support hazard and risk assessment. The increasing number and size of toxicology databases and methods for automatically recording toxicology studies has led to a diverse series of formats for organisation of the information. Currently, such approaches span from simple one-to-one data exchange formats to complex formats incorporating semantic representations and data models covering additional information (such as experimental design, results and findings). Such developments support the volume, variety and value of (big) data and the transition from insular, isolated and simple structures to big data technologies for big toxicology data resources. This chapter reviews current solutions for the most known resources and identifies opportunities and gaps that such resources show in the era of big data. It covers a variety of approaches to data exchange and database design for organising toxicity and related data, highlights a number of databases with toxicity data as well as information on alternative approaches, and discusses project management and regulatory compliance databases. The chapter concludes with reflections on how these systems can integrate diverse toxicology-related data in preparation for the adoption of new big data technologies.

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.

ABSTRACT Chemical toxicity data at all levels of description, from treatment-level dose response data to a high-level summarized toxicity “endpoint,” effectively circumscribe, enable, and limit predictive toxicology approaches and capabilities. Several new and evolving public data initiatives focused on the world of chemical toxicity information—as represented here by ToxML (Toxicology XML standard), DSSTox (Distributed Structure-Searchable Toxicity Database Network), and ACToR (Aggregated Computational Toxicology Resource)—are contributing to the creation of a more unified, mineable, and modelable landscape of public toxicity data. These projects address different layers in the spectrum of toxicological data representation and detail and, additionally, span diverse domains of toxicology and chemistry in relation to industry and environmental regulatory concerns. For each of the three projects, data standards are the key to enabling “read-across” in relation to toxicity data and chemical-indexed information. In turn, “read-across” capability enables flexible data mining, as well as meaningful aggregation of lower levels of toxicity information to summarized, modelable endpoints spanning sufficient areas of chemical space for building predictive models. By means of shared data standards and transparent and flexible rules for data aggregation, these and related public data initiatives are effectively spanning the divides among experimental toxicologists, computational modelers, and the world of chemically indexed, publicly available toxicity information.

Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.

To facilitate a systematic study of chemoresistance across diverse classes of anticancer drug candidates, we performed correlation analyses between cytotoxic drug potency and gene expression in 60 tumor cell lines (NCI-60; NCI-National Cancer Institute). Ellipticine analogs displayed a range of correlation coefficients (r) with MDR1 (ABCB1, encoding multidrug resistance (MDR) protein MDR1 or P-glycoprotein). To determine MDR1 interactions of five ellipticines with diverse MDR1-r values, we employed MDR1-transport and cytotoxicity assays, using MDR1 inhibitors and siRNA-mediated MDR1 downregulation, in MDR1-overexpressing cells. Ellipticines with negative correlations-indicative of MDR1-mediated resistance-were shown to be MDR1 substrates, whereas those with neutral or positive correlations served as MDR1 inhibitors, which escape MDR1-mediated chemoresistance. Correlation with additional genes in the NCI-60 confirmed topoisomerases as ellipticine targets, but suggested distinct mechanisms of action and chemoresistance among them, providing a guide for selecting optimal drug candidates.

Statistical data mining methods have proven to be powerful tools for investigating correlations between molecular structure and biological activity. Recursive partitioning (RP), in particular, offers several advantages in mining large, diverse data sets resulting from high throughput screening. When used with binary molecular descriptors, the standard implementation of RP splits on single descriptors. We use simulated annealing (SA) to find combinations of molecular descriptors whose simultaneous presence best separates off the most active, chemically similar group of compounds. The search is incorporated into a recursive partitioning design to produce a regression tree for biological activity on the space of structural fingerprints. Each node is characterized by a specific combination of structural features, and the terminal nodes with high average activities correspond, roughly, to different classes of compounds. Using LeadScope structural features as descriptors to mine a database from the National Cancer Institute, the merging of RP and SA consistently identifies structurally homogeneous classes of highly potent anticancer agents.