canSAR: update to the cancer translational research and drug discovery knowledgebase

Mitsopoulos, Costas; Micco, Patrizio Di; Fernandez, Eloy Villasclaras; Dolciami, Daniela; Holt, Esty; Mica, Ioan L; Coker, Elizabeth A.; Tym, Joseph E; Campbell, James; Hing, Ka; Özer, Buğra; Kannas, Christos; Antolín, Albert A.; Workman, Paul; Al‐Lazikani, Bissan

doi:10.1093/nar/gky1129

Cited by 84 publications

(51 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysing the expression of potent kinase off-targets of PARP inhibitors in haematological cancers using can-SAR 53 reveals that DYRK1A and DYRK1B are overexpressed at the mRNA level in several types of leukaemia and lymphoma ( Supplementary Figs. 3-6).…”

Section: Discussionmentioning

confidence: 99%

The kinase polypharmacology landscape of clinical PARP inhibitors

Antolín

Ameratunga

Banerji

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

understanding of the activity and liabilities of individual PARP inhibitors is therefore important to aid clinical decisions and benefit cancer patients as well as to guide the design of future PARP inhibitors. Several differences between individual PARP inhibitors have already been reported at the cellular and clinical levels. When used at micromolar concentrations, differences in DNA strand break repair, phosphorylation of several proteins, cell cycle arrest, and anti-proliferative activities have been described between olaparib, rucaparib and veliparib in cancer cell lines 24,25. Moreover, the different capacity of PARP inhibitors to trap PARP at the DNA damage site is widely accepted as important for the action of PARP inhibitors. Yet, the exact molecular mechanism of this is not completely understood 26. Differences in cancer cell sensitivity and synthetic lethality also emerge from recent large-scale profiling experiments, in some instances leading to the prediction of distinct predictive genomic biomarkers (Supplementary Table 1) 27,28. Overall, it seems important to investigate further the differences between PARP inhibitors and to explore the potential impact on clinical use. The selectivity and polypharmacology of PARP inhibitors within the PARP-family was recently characterised in vitro using an enzymatic inhibition assay 29. Of the four approved PARP inhibitors, niraparib was shown to be more selective for PARP1 and PARP2 compared to olaparib, rucaparib and talazoparib which show broader pan-PARP activity (Fig. 1b) 29. However, this differential intra-family PARP selectivity is insufficient to explain all the differences observed between clinical PARP inhibitors. In 2014, we reported for the first time that the different polypharmacology patterns between PARP inhibitors extended beyond the PARP enzyme family 30. We demonstrated that rucaparib inhibited at least nine kinases with micromolar affinity whereas veliparib inhibited only two kinases and olaparib did not exhibit activity against any of the 16 kinases tested 30. From a high-throughput screen for RPS6KB1 kinase inhibitors, we identified a series of carboxamidobenzimidazoles that were confirmed to bind RPS6KB1 by orthogonal methods including X-ray crystallography 31. The carboxamidobenzimidazoles are known inhibitors of PARP 32 and the existence of a crystal structure of a carboxamidobenzimidazole bound to RPS6KB1 kinase 31 prompted our speculation that all PARP inhibitors could have an intrinsic capacity to inhibit kinases. This capacity could result from the ability of their shared benzamide pharmacophore to interact with the highly-conserved kinase hinge region (Fig. 1a) 30,31. Accordingly, depending on its individual molecular size and decoration, each PARP inhibitor could have a unique off-target kinase profile that may remain as yet unexplored and would be important to characterise 4,30. More recently, an unbiased, large scale, mass spectrometry-based chemical proteomics approach uncovered new, low-potency affinities of the PARP inhibitor ...

show abstract

Section: Discussionmentioning

confidence: 99%

The kinase polypharmacology landscape of clinical PARP inhibitors

Antolín

Ameratunga

Banerji

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various other platforms (Gaspar et al, 2018;Coker et al, 2019;Abbot et al, 2020) exist with some conceptual overlaps with EpiGraphDB (Supplementary Table 3). These represent a range of different types of data based on molecular and genetic interactions and drug targets.…”

Section: Discussionmentioning

confidence: 99%

EpiGraphDB: A database and data mining platform for health data science

Liu

Elsworth

Erola

et al. 2020

Preprint

View full text Add to dashboard Cite

Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to sup-port their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to "triangulate" evidence from different sources. Availability: The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for repli-cating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package.

show abstract

“…Druggability assessment was carried out using a comprehensive suite of sophisticated, orthogonal methodologies utilising machine learning and AI predictions. The tools and methodologies used are available at our online canSAR platform [47][48][49]52 .…”

Section: Methodsmentioning

confidence: 99%

Solution structure of the Hop TPR2A domain and investigation of target druggability by NMR, biochemical and in silico approaches

Darby

Vidler

Simpson

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Heat shock protein 90 (Hsp90) is a molecular chaperone that plays an important role in tumour biology by promoting the stabilisation and activity of oncogenic ‘client’ proteins. Inhibition of Hsp90 by small-molecule drugs, acting via its ATP hydrolysis site, has shown promise as a molecularly targeted cancer therapy. Owing to the importance of Hop and other tetratricopeptide repeat (TPR)-containing cochaperones in regulating Hsp90 activity, the Hsp90-TPR domain interface is an alternative site for inhibitors, which could result in effects distinct from ATP site binders. The TPR binding site of Hsp90 cochaperones includes a shallow, positively charged groove that poses a significant challenge for druggability. Herein, we report the apo, solution-state structure of Hop TPR2A which enables this target for NMR-based screening approaches. We have designed prototype TPR ligands that mimic key native ‘carboxylate clamp’ interactions between Hsp90 and its TPR cochaperones and show that they block binding between Hop TPR2A and the Hsp90 C-terminal MEEVD peptide. We confirm direct TPR-binding of these ligands by mapping 1H–15N HSQC chemical shift perturbations to our new NMR structure. Our work provides a novel structure, a thorough assessment of druggability and robust screening approaches that may offer a potential route, albeit difficult, to address the chemically challenging nature of the Hop TPR2A target, with relevance to other TPR domain interactors.

show abstract

canSAR: update to the cancer translational research and drug discovery knowledgebase

Cited by 84 publications

References 11 publications

The kinase polypharmacology landscape of clinical PARP inhibitors

The kinase polypharmacology landscape of clinical PARP inhibitors

EpiGraphDB: A database and data mining platform for health data science

Solution structure of the Hop TPR2A domain and investigation of target druggability by NMR, biochemical and in silico approaches

Contact Info

Product

Resources

About