No abstract

Success in small molecule screening relies heavily on the preselection of compounds. Here, we present a strategy for the enrichment of chemical libraries with potentially bioactive compounds integrating the collected knowledge of medicinal chemistry. Employing a genetic algorithm, substructures typically occurring in bioactive compounds were identified using the World Drug Index. Availability of compounds containing the selected substructures was analysed in vendor libraries, and the substructure-specific sublibraries were assembled. Compounds containing reactive, undesired functional groups were omitted. Using a diversity filter for both physico-chemical properties and the substructure composition, the compounds of all the sublibraries were ranked. Accordingly, a screening collection of 16,671 compounds was selected. Diversity and chemical space coverage of the collection indicate that it is highly diverse and well-placed in the chemical space spanned by bioactive com-

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.

Dairy cows in modern production systems are at risk to develop metabolic disorders during the transition period. Reasons for individual differences in susceptibility, as well as the underlying pathomechanisms, are still only partially understood. The development of metaphylactic treatment protocols is needed. In this context, an on-farm prospective 3-fold blinded randomized study involving 80 German Holstein cows was performed throughout 1 yr. The trial involved a thorough recording of the production and clinical traits, clinical chemistry, and liver biopsies and blood and urine sampling at d 14 (mean: 12 d, range: 1-26 d) antepartum (AP), and d 7 (7, 4-13) and 28 (28, 23-34) postpartum (PP) for metabolomics analyses. Two groups received a treatment with butaphosphan and cyanocobalamin (BCC) at either the dosage recommended by the manufacturer or the double dosage (5 or 10 mL/100 kg of body weight 10% butaphosphan and 0.005% cyanocobalamin (Catosal, Bayer Animal Health), n = 20 in each group, parity: 4.2 ± 2.0 and 3.4 ± 1.3, respectively (mean ± SD)] and one group a placebo treatment (NaCl 0.9%, n = 40, parity: 4.0 ± 1.9). The animals were treated at 6 time points (7, 6, and 5 d AP, and 1, 2, and 3 d PP) via intravenous injection. Mass spectroscopy-based targeted metabolomics analysis of blood plasma and liver samples were performed using the AbsoluteIDQ p180 kit (Biocrates Life Sciences), whereas the urine samples were analyzed by nuclear magnetic resonance

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.