The governance structure for data access in the DIRECT consortium: an innovative medicines initiative (IMI) project.

Biomedical research projects involving multiple partners from public and private sectors require coherent internal governance mechanisms to engender good working relationships. The DIRECT project is an example of such a venture, funded by the Innovative Medicines Initiative Joint Undertaking (IMI JU). This paper describes the data access policy that was developed within DIRECT to support data access and sharing, via the establishment of a 3-tiered Data Access Committee.

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: rationale and design of the epidemiological studies within the IMI DIRECT Consortium.

Aims/hypothesis: The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT.

Comprehensive analysis of kinase inhibitor selectivity.

We tested the interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome. Our data show that, as a class, type II inhibitors are more selective than type I inhibitors, but that there are important exceptions to this trend. The data further illustrate that selective inhibitors have been developed against the majority of kinases targeted by the compounds tested.

High-throughput kinase profiling: a more efficient approach toward the discovery of new kinase inhibitors.

Selective protein kinase inhibitors have only been developed against a small number of kinase targets. Here we demonstrate that "high-throughput kinase profiling" is an efficient method for the discovery of lead compounds for established as well as unexplored kinase targets. We screened a library of 118 compounds constituting two distinct scaffolds (furan-thiazolidinediones and pyrimido-diazepines) against a panel of 353 kinases.

A quantitative analysis of kinase inhibitor selectivity.

Kinase inhibitors are a new class of therapeutics with a propensity to inhibit multiple targets. The biological consequences of multi-kinase activity are poorly defined, and an important step toward understanding the relationship between selectivity, efficacy and safety is the exploration of how inhibitors interact with the human kinome. We present interaction maps for 38 kinase inhibitors across a panel of 317 kinases representing >50% of the predicted human protein kinome.

Inhibition of drug-resistant mutants of ABL, KIT, and EGF receptor kinases.

To realize the full potential of targeted protein kinase inhibitors for the treatment of cancer, it is important to address the emergence of drug resistance in treated patients. Mutant forms of BCR-ABL, KIT, and the EGF receptor (EGFR) have been found that confer resistance to the drugs imatinib, gefitinib, and erlotinib. The mutations weaken or prevent drug binding, and interestingly, one of the most common sites of mutation in all three kinases is a highly conserved "gatekeeper" threonine residue near the kinase active site.

A small molecule-kinase interaction map for clinical kinase inhibitors.

Kinase inhibitors show great promise as a new class of therapeutics. Here we describe an efficient way to determine kinase inhibitor specificity by measuring binding of small molecules to the ATP site of kinases. We have profiled 20 kinase inhibitors, including 16 that are approved drugs or in clinical development, against a panel of 119 protein kinases.

Prediction of deleterious functional effects of amino acid mutations using a library of structure-based function descriptors.

An automated, active site-focused, computational method is described for use in predicting the effects of engineered amino acid mutations on enzyme catalytic activity. The method uses structure-based function descriptors (Fuzzy Functional Forms trade mark or FFFs trade mark ) to automatically identify enzyme functional sites in proteins. Three-dimensional sequence profiles are created from the surrounding active site structure.