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. The process was intended to allow quick access to data, whilst enabling strong oversight of how data were being accessed and by whom, and any subsequent analyses, to contribute to the overall objectives of the consortium.

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. METHODS: Prediabetic participants (target sample size 2,200-2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires. CONCLUSIONS/INTERPRETATION: DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.

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. Analysis of the interaction patterns reveals a class of 'group-selective' inhibitors broadly active against a single subfamily of kinases, but selective outside that subfamily. The data set suggests compounds to use as tools to study kinases for which no dedicated inhibitors exist. It also provides a foundation for further exploring kinase inhibitor biology and toxicity, as well as for studying the structural basis of the observed interaction patterns. Our findings will help to realize the direct enabling potential of genomics for drug development and basic research about cellular signaling.

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 distinct kinase selectivity profile was observed for each scaffold. Selective inhibitors were identified with submicromolar cellular activity against PIM1, ERK5, ACK1, MPS1, PLK1-3, and Aurora A,B kinases. In addition, we identified potent inhibitors for so far unexplored kinases such as DRAK1, HIPK2, and DCAMKL1 that await further evaluation. This inhibitor-centric approach permits comprehensive assessment of a scaffold of interest and represents an efficient and general strategy for identifying new selective kinase inhibitors.

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. The data constitute the most comprehensive study of kinase inhibitor selectivity to date and reveal a wide diversity of interaction patterns. To enable a global analysis of the results, we introduce the concept of a selectivity score as a general tool to quantify and differentiate the observed interaction patterns. We further investigate the impact of panel size and find that small assay panels do not provide a robust measure of selectivity.

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. We have identified existing clinical compounds that bind and inhibit drug-resistant mutant variants of ABL, KIT, and EGFR. We found that the Aurora kinase inhibitor VX-680 and the p38 inhibitor BIRB-796 inhibit the imatinib- and BMS-354825-resistant ABL(T315I) kinase. The KIT/FLT3 inhibitor SU-11248 potently inhibits the imatinib-resistant KIT(V559D/T670I) kinase, consistent with the clinical efficacy of SU-11248 against imatinib-resistant gastrointestinal tumors, and the EGFR inhibitors EKB-569 and CI-1033, but not GW-572016 and ZD-6474, potently inhibit the gefitinib- and erlotinib-resistant EGFR(L858R/T790M) kinase. EKB-569 and CI-1033 are already in clinical trials, and our results suggest that they should be considered for testing in the treatment of gefitinib/erlotinib-resistant non-small cell lung cancer. The results highlight the strategy of screening existing clinical compounds against newly identified drug-resistant mutant variants to find compounds that may serve as starting points for the development of next-generation drugs, or that could be used directly to treat patients that have acquired resistance to first-generation targeted therapy.

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. We find that specificity varies widely and is not strongly correlated with chemical structure or the identity of the intended target. Many novel interactions were identified, including tight binding of the p38 inhibitor BIRB-796 to an imatinib-resistant variant of the ABL kinase, and binding of imatinib to the SRC-family kinase LCK. We also show that mutations in the epidermal growth factor receptor (EGFR) found in gefitinib-responsive patients do not affect the binding affinity of gefitinib or erlotinib. Our results represent a systematic small molecule-protein interaction map for clinical compounds across a large number of related proteins.

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. The computationally derived active site profile is used to analyze the effect of each amino acid change by defining three key features: proximity of the change to the active site, degree of amino acid conservation at the position in related proteins, and compatibility of the change with residues observed at that position in similar proteins. The features were analyzed using a data set of individual amino acid mutations occurring at 128 residue positions in 14 different enzymes. The results show that changes at key active site residues and at highly conserved positions are likely to have deleterious effects on the catalytic activity, and that non-conservative mutations at highly conserved residues are even more likely to be deleterious. Interestingly, the study revealed that amino acid substitutions at residues in close contact with the key active site residues are not more likely to have deleterious effects than mutations more distant from the active site. Utilization of the FFF-derived structural information yields a prediction method that is accurate in 79-83% of the test cases. The success of this method across all six EC classes suggests that it can be used generally to predict the effects of mutations and nsSNPs for enzymes. Future applications of the approach include automated, large-scale identification of deleterious nsSNPs in clinical populations and in large sets of disease-associated nsSNPs, and identification of deleterious nsSNPs in drug targets and drug metabolizing enzymes.