Crowdsourced mapping of unexplored target space of kinase inhibitors.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Application of Integrated Drug Screening/Kinome Analysis to Identify Inhibitors of Gemcitabine-Resistant Pancreatic Cancer Cell Growth.

Continuous exposure of a pancreatic cancer cell line MIA PaCa-2 (MiaS) to gemcitabine resulted in the formation of a gemcitabine-resistant subline (MiaR). In an effort to discover kinase inhibitors that inhibited MiaR growth, MiaR cells were exposed to kinase inhibitors (PKIS-1 library) in a 384-well screening format. Three compounds (UNC10112721A, UNC10112652A, and UNC10112793A) were identified that inhibited the growth of MiaR cells by more than 50% (at 50 nM). Two compounds (UNC10112721A and UNC10112652A) were classified as cyclin-dependent kinase (CDK) inhibitors, whereas UNC10112793A was reported to be a PLK inhibitor. Dose-response experiments supported the efficacy of these compounds to inhibit growth and increase apoptosis in 2D cultures of these cells. However, only UNC10112721A significantly inhibited the growth of 3D spheroids composed of MiaR cells and GFP-tagged cancer-associated fibroblasts. Multiplexed inhibitor bead (MIB)-mass spectrometry (MS) kinome competition experiments identified CDK9, CLK1-4, DYRK1A, and CSNK1 as major kinase targets for UNC10112721A in MiaR cells. Another CDK9 inhibitor (CDK-IN-2) replicated the growth inhibitory effects of UNC10112721A, whereas inhibitors against the CLK, DYRK, or CSNK1 kinases had no effect. In summary, these studies describe a coordinated approach to discover novel kinase inhibitors, evaluate their efficacy in 3D models, and define their specificity against the kinome.

The importance of chronobiology to drug discovery.

Drug discovery scientists, faced with the myriad challenges involved in developing novel therapeutics as medicines, have tended to overlook the question of the most beneficial time to administer the drug. Recent developments in our understanding of circadian biology and the availability of tools to characterise the molecular clock indicate that time and duration of dosing may have profound consequences for the efficacy and safety of new and existing therapeutic agents. Progress in the field also suggests that many key physiological mechanisms are remarkably dependent on the circadian clock. It has also become clear that a number of diseases with important unmet medical need display marked circadian variation in their symptoms and severity. These discoveries now reveal opportunities for new therapeutic strategies to be developed that act by modulation of biological rhythms. These novel therapeutic approaches are likely to be facilitated by the continuing development of chemical probes and synthetic ligands targeted to an increasing number of the key proteins that regulate the molecular clock.

The phytoestrogen coumestrol is a naturally occurring antagonist of the human pregnane X receptor.

Antagonizing the action of the human nuclear xenobiotic receptor pregnane X receptor (PXR) may have important clinical implications in preventing drug-drug interactions and improving therapeutic efficacy. We provide evidence that a naturally occurring phytoestrogen, coumestrol, is an antagonist of the nuclear receptor PXR (NR1I2). In transient transfection assays, coumestrol was able to suppress the agonist effects of SR12813 on human PXR activity. PXR activity was assessed and correlated with effects on the metabolism of the anesthetic tribromoethanol and on gene expression in primary human hepatocytes. We found that coumestrol was able to suppress the effects of PXR agonists on the expression of the known PXR target genes, CYP3A4 and CYP2B6, in primary human hepatocytes as well as inhibit metabolism of tribromoethanol in humanized PXR mice. Coumestrol at concentrations above 1.0 microm competed in scintillation proximity assays with a labeled PXR agonist for binding to the ligand-binding cavity. However, mammalian two-hybrid assays and transient transcription data using ligand-binding-cavity mutant forms of PXR show that coumestrol also antagonizes coregulator recruitment. This effect is likely by binding to a surface outside the ligand-binding pocket. Taken together, these data imply that there are antagonist binding site(s) for coumestrol on the surface of PXR. These studies provide the basis for development of novel small molecule inhibitors of PXR with the ultimate goal of clinical applications toward preventing drug-drug interactions.

Selection, application, and validation of a set of molecular descriptors for nuclear receptor ligands.

A methodology for the selection and validation of nuclear receptor ligand chemical descriptors is described. After descriptors for a targeted chemical space were selected, a virtual screening methodology utilizing this space was formulated for the identification of potential NR ligands from our corporate collection. Using simple descriptors and our virtual screening method, we are able to quickly identify potential NR ligands from a large collection of compounds. As validation of the virtual screening procedure, an 8, 000-membered NR targeted set and a 24, 000-membered diverse control set of compounds were selected from our in-house general screening collection and screened in parallel across a number of orphan NR FRET assays. For the two assays that provided at least one hit per set by the established minimum pEC(50) for activity, the results showed a 2-fold increase in the hit-rate of the targeted compound set over the diverse set.