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

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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.

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Figure 1: Dark matter definition and characterization.
Figure 2: Dark matter in chemical space.
Figure 3: Hit rates and selectivity.
Figure 4: Prospective experiments.
Figure 5: Identification of Hem14 (protoporphyrinogen oxidase) as target for compound 1 and derivatives.

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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.

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Authors

Contributions

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.

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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)

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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

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