Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity

Improving the safety assessment of chemicals and drug candidates by the integrating bioinformatics and chemoinformatics

The application of read-across and in silico tools for regulatory decision making has been limited for pharmaceutical compounds to the assessment of genotoxic impurity. In contrast, the broad availability of toxicity data for industrial chemicals has triggered regulatory frameworks for read-across (e.g. ECHA read-across assessment framework), software tools and public databases for an automated process of gap filling for safety assessment framework. This review provides an overview of the currently existing read-across approaches for chemicals and pharmaceutical compounds highlighting particularly the different requirements in the safety assessment of these two fields. The biggest hurdle for establishing preclinical safety databases for pharma compounds are the unwillingness to share proprietary data and lack of published data sets. In a consortial approach thirteen pharmaceutical companies, eleven academic partners and six small to medium size enterprises (SMEs) of the bioinformatics sector joined forces over the last seven years within the European Innovative Medicines Initiative project eTOX ("electronic toxicity") to design and implement a strategy for leveraging these preclinical data and sharing them across project partners. The eTOX database has evolved as the largest preclinical toxicity database for drugs and drug candidates and currently contains more than 1900 different chemical structures and more than 8000 in vivo toxicity study data. Use cases based on chemical or biological similarity are presented. It can be foreseen that the development and application of such databases for drugs or drug candidates will in the future also cross-fertilize the read-across and in silico assessment of industrial or consumer chemicals particularly as soon as human safety data from clinical trials are integrated, too

The eTOX Consortium: To Improve the Safety Assessment of New Drug Candidates

After their incorporation into human risk assessment, reports on preclinical animal studies in most cases get buried in the archives. The wealth of these data is hardly accessible. The European Innovative Medicines Initiative project eTOX ("electronic toxicity") over the last years designed and implemented a strategy for leveraging these preclinical data and sharing them across pharmaceutical companies and academic institutions. The shared toxicological data can now be used by the participating companies to perform early safety assessments of new drug candidates and new pharmacological targets. In addition, the data are used to build in silico predictive tools for specific toxicological endpoint