Ashenafi Legehar Abstract

A survey of the multi-target testing experimental practices inferred from the ChEMBL data

Ashenafi Legehar1, Leo Ghemtio1, Henri Xhaard1

1Faculty of Pharmacy, Division of Pharmaceutical Chemistry and Technology, University of Helsinki
Polypharmacology is regarded as extremely important in drug discovery (Reddy & Zhang, 2013), however little investigation has been conducted on the practices leading to its discovery. We thus conducted an extensive analysis on the data deposited in ChEMBL(Bender, 2010; Gaulton et al., 2017).
We focus on four journals (J.Med.Chem, E.J.Med.Chem., Bioorg.Med.Chem., Bioorg.Med.Chem.Lett) that forms the bulk (90%) of the deposited compound data over the last 30 years. A set of simple keywords allow to focus on ~8000 articles dealing with synthetic medicinal chemistry and containing ~155000 compounds, most probably congeneric series, i.e. to exclude articles that are perspectives, review, QSAR models, large screens, etc. The number of compounds with registered activities is in the 20-26 range per article over journals and time periods; while the number of targets is in the 6-9 range. Note that this does not account for studies with no apparent targets, For example, antibacterial. The three main tested targets are not surprisingly kinases (13% of the compounds are tested on kinases, whereas kinases are listed as a target in 15% of the articles), GPCRs (30% and 28%), and serine proteases (15% and 13%). We furthermore show that metabolism (CYP450) and toxicity proteins (hERG) are often tested within the same article with these targets. Zooming into GPCRs show intra-family testing as well as monoamine transporters cross-targets, but much less testing of CYPs and hERG, presumably reflecting a longer distance to the clinic. Thus, compounds are not only screened multiple times as part of collections or for repurposing purposed; ADME testing, for example off-target screens (typically kinase panels and GPCR panels), CYP450 and hERG, also provide a considerable yet uncharacterized amount of biological activity data.

This work is to be submitted to the special issue “Multi-Target Drug Discovery: an opportunity for novel and repurposed bioactive compounds” for the Mu.Ta.Lig COST action CA15135 http://www.mutalig.eu

Bender, A. (2010). Compound bioactivities go public. Nature Chemical Biology, 6(5), 309–309. https://doi.org/10.1038/nchembio.354
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., … Leach, A. R. (2017). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954. https://doi.org/10.1093/nar/gkw1074
Reddy, A. S., & Zhang, S. (2013). Polypharmacology: drug discovery for the future. Expert Review of Clinical Pharmacology, 6(1). https://doi.org/10.1586/ECP.12.74