Orthologue chemical space and its influence on target prediction

Motivation In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice. Results Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance

Therapeutic opportunities within the DNA damage response

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Simvastatin Sodium Salt and Fluvastatin Interact with Human Gap Junction Gamma-3 Protein

Finding pleiomorphic targets for drugs allows new indications or warnings for treatment to be identified. As test of concept, we applied a new chemical genomics approach to uncover additional targets for the widely prescribed lipid-lowering pro-drug simvastatin. We used mRNA extracted from internal mammary artery from patients undergoing coronary artery surgery to prepare a viral cardiovascular protein library, using T7 bacteriophage. We then studied interactions of clones of the bacteriophage, each expressing a different cardiovascular polypeptide, with surface-bound simvastatin in 96-well plates. To maximise likelihood of identifying meaningful interactions between simvastatin and vascular peptides, we used a validated photo-immobilisation method to apply a series of different chemical linkers to bind simvastatin so as to present multiple orientations of its constituent components to potential targets. Three rounds of biopanning identified consistent interaction with the clone expressing part of the gene GJC3, which maps to Homo sapiens chromosome 7, and codes for gap junction gamma-3 protein, also known as connexin 30.2/31.3 (mouse connexin Cx29). Further analysis indicated the binding site to be for the N-terminal domain putatively ‘regulating’ connexin hemichannel and gap junction pores. Using immunohistochemistry we found connexin 30.2/31.3 to be present in samples of artery similar to those used to prepare the bacteriophage library. Surface plasmon resonance revealed that a 25 amino acid synthetic peptide representing the discovered N-terminus did not interact with simvastatin lactone, but did bind to the hydrolysed HMG CoA inhibitor, simvastatin acid. This interaction was also seen for fluvastatin. The gap junction blockers carbenoxolone and flufenamic acid also interacted with the same peptide providing insight into potential site of binding. These findings raise key questions about the functional significance of GJC3 transcripts in the vasculature and other tissues, and this connexin’s role in therapeutic and adverse effects of statins in a range of disease states