Dark chemical matter as a promising starting point for drug lead discovery

ABSTRACT High-throughput screening (HTS) is an integral part of early drug discovery. Herein, we focused on those small molecules in a screening collection that have never shown biological

activity despite having been exhaustively tested in HTS assays. These compounds are referred to as 'dark chemical matter' (DCM). We quantified DCM, validated it in quality control

experiments, described its physicochemical properties and mapped it into chemical space. Through analysis of prospective reporter-gene assay, gene expression and yeast chemogenomics

experiments, we evaluated the potential of DCM to show biological activity in future screens. We demonstrated that, despite the apparent lack of activity, occasionally these compounds can

result in potent hits with unique activity and clean safety profiles, which makes them valuable starting points for lead optimization efforts. Among the identified DCM hits was a new

antifungal chemotype with strong activity against the pathogen _Cryptococcus neoformans_ but little activity at targets relevant to human safety. Access through your institution Buy or

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NATURAL PRODUCTS IN DRUG DISCOVERY: ADVANCES AND OPPORTUNITIES Article 28 January 2021 THE PAN-CANADIAN CHEMICAL LIBRARY: A MECHANISM TO OPEN ACADEMIC CHEMISTRY TO HIGH-THROUGHPUT VIRTUAL

SCREENING Article Open access 06 June 2024 EXPANDING THE SEARCH FOR SMALL-MOLECULE ANTIBACTERIALS BY MULTIDIMENSIONAL PROFILING Article 23 May 2022 ACCESSION CODES ACCESSIONS PROTEIN DATA

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3rd edn., M27-A3 (Clinical and Laboratory Standards Institute, Wayne, Pennsylvania, USA, 2008). Download references ACKNOWLEDGEMENTS A.M.W. and G.L.G. were presidential postdoctoral fellows

supported by the Education Office of the Novartis Institutes for BioMedical Research. The authors thank M. Schirle, R. Nutiu, S. Reiling and E. Gregori-Puigjané for valuable discussions; T.

Aust, O. Galuba and R. Riedl for support with the HIP and follow-up experiments; M. Popov and F. Nigsch for help with data mining; P. Selzer for the cell permeability model; G. Wendel, B.

Burakowska and L. Koppes for help with compound management; and R. Guha, J. Bittker and J. Braisted for help with BARD. AUTHOR INFORMATION Author notes * Anne Mai Wassermann & Meir Glick

Present address: Present addresses: Pfizer Inc., Cambridge, Massachusetts, USA (A.M.W.); Merck Research Laboratories, Boston, Massachusetts, USA (M.G.)., AUTHORS AND AFFILIATIONS * Novartis

Institutes for BioMedical Research Inc., Cambridge, Massachusetts, USA Anne Mai Wassermann, Eugen Lounkine, Gaelle Le Goff, Frederick J King, John M Peltier, Melissa L Grippo, Iain M

Wallace, Shanni Chen, John W Davies & Meir Glick * Novartis Institutes for BioMedical Research Inc., Basel, Switzerland Dominic Hoepfner, Christian Studer, Ansgar Schuffenhauer, Philipp

Krastel, Amanda Cobos-Correa & Christian N Parker * The Genomics Institute of the Novartis Research Foundation, San Diego, California, USA Frederick J King, Vivian Prindle & Jianshi

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A.M.W., E.L., J.W.D. and M.G. conceived the study with contributions from A.S., I.M.W. and C.N.P. A.M.W. carried out the large-scale computational analyses of the Novartis and PubChem HTS

assay results. G.L.G. performed the gene expression experiments. F.J.K. directed and analyzed the reporter-gene assay experiments. D.H. directed and analyzed the _S. cerevisiae_ growth

inhibition and chemogenomics experiments. C.S. performed _S. cerevisiae_ experiments. J.M.P. and M.L.G. conducted the quality control experiments. J.T. and V.P. designed and performed the

antifungal panel experiments. S.C. did safety profiling experiments. P.K. and A.C.-C. supervised the profiling of natural products against the cancer cell line panel. A.M.W., E.L., D.H.,

J.W.D. and M.G. wrote the manuscript with contributions from all authors that read and discussed the manuscript. CORRESPONDING AUTHORS Correspondence to Anne Mai Wassermann or Meir Glick.

ETHICS DECLARATIONS COMPETING INTERESTS As employees of Novartis, the authors do have a perceived financial conflict of interest. SUPPLEMENTARY INFORMATION SUPPLEMENTARY TEXT AND FIGURES

Supplementary Results, Supplementary Tables 1–12, Supplementary Note 1 and Supplementary Figures 1–14. (PDF 1946 kb) SUPPLEMENTARY DATA SET 1 PubChem assay identifiers. All PubChem bioassays

used in the analysis are reported. If two assay identifiers are listed in the same row, the corresponding PubChem bioassays have been combined because they reported different readouts from

the same experiment. (XLS 101 kb) SUPPLEMENTARY DATA SET 2 Compound structures. The file reports InChI keys and SMILES strings for all dark compounds identified in the PubChem data set and a

subset (10,355 structures) of the dark compounds in the Novartis data set (due to intellectual property reasons not all structures can be made available). For each compound, the field “set”

reports whether the compound was identified as dark chemical matter for the PubChem, Novartis or both data sets. (XLSX 7000 kb) SUPPLEMENTARY DATA SET 3 Quality control results. For 623

compound structures identified as dark chemical matter in the Novartis data set, results from our quality control experiments are reported. Purity, identity, concentration, and comments

about how to interpret the observed data for special cases (e.g. highly fluorinated compounds) are given. Compounds are represented by InChI keys and SMLES strings. (XLSX 54 kb)

SUPPLEMENTARY DATA SET 4 DCM scaffolds. The data set lists 95 scaffolds that were significantly enriched in the PubChem DCM set. Scaffolds are reported as SMILES strings. For each scaffold,

numbers of PubChem DCM and ACT compounds that it represents are reported. (XLSX 12 kb) SUPPLEMENTARY DATA SET 5 Dark chemical matter Bayes classifier. We attach the naive Bayes model trained

on the PubChem data set as Pipeline Pilot component (xml file). This component returns a dark matter score for each molecular data record sent to it. (XML 2227 kb) SUPPLEMENTARY DATA SET 6

Reporter gene assay results. For 322 active (“ACT”) and 337 dark (“DCM”) compounds, we make activity readouts from the reporter gene assay panel available. Each row in the data table reports

normalized activities for one compound across the 41 RGAs given in Supplementary Table 10. Activities were obtained 24 hours after compound treatment. If a compound has been tested in

replicates, the reported activity value is the average of the normalized activities obtained for the different replicates. For details on compound activity normalization see the main text

and references provided therein. (XLSX 274 kb) SUPPLEMENTARY DATA SET 7 Gene expression profiles. For 89 active (“ACT”) and 111 dark (“DCM”) compounds, we report measured fold changes and

calculated _R_-scores for the 61 genes in our transcriptional profiling panel. Supplementary Data Set 7 reports gene expression changes after compound treatment with a final compound

concentration of 1 μM. Genes are represented by EntrezGene identifiers, as listed in Supplementary Table 11. (XLSX 516 kb) SUPPLEMENTARY DATA SET 8 Gene expression profiles. For 89 active

(“ACT”) and 111 dark (“DCM”) compounds, we report measured fold changes and calculated _R_-scores for the 61 genes in our transcriptional profiling panel. Supplementary Data Set 7 reports

gene expression changes after compound treatment with a final compound concentration of 10 μM. Genes are represented by EntrezGene identifiers, as listed in Supplementary Table 11. (XLSX 518

kb) SUPPLEMENTARY DATA SET 9 Yeast growth inhibition compound list. The data set lists 178 dark compounds that were tested in yeast growth inhibition experiments. Only compound 1 reported

in the manuscript showed activity in confirmation experiments, i.e., all other compounds are considered as inactive. Compounds are reported as InChI keys and SMILES strings. (XLSX 18 kb)

RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Wassermann, A., Lounkine, E., Hoepfner, D. _et al._ Dark chemical matter as a promising starting point

for drug lead discovery. _Nat Chem Biol_ 11, 958–966 (2015). https://doi.org/10.1038/nchembio.1936 Download citation * Received: 18 February 2015 * Accepted: 10 September 2015 * Published:

19 October 2015 * Issue Date: December 2015 * DOI: https://doi.org/10.1038/nchembio.1936 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get

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