Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Nanoscale synthesis and affinity ranking

Abstract

Most drugs are developed through iterative rounds of chemical synthesis and biochemical testing to optimize the affinity of a particular compound for a protein target of therapeutic interest. This process is challenging because candidate molecules must be selected from a chemical space of more than 1060 drug-like possibilities1, and a single reaction used to synthesize each molecule has more than 107 plausible permutations of catalysts, ligands, additives and other parameters2. The merger of a method for high-throughput chemical synthesis with a biochemical assay would facilitate the exploration of this enormous search space and streamline the hunt for new drugs and chemical probes. Miniaturized high-throughput chemical synthesis3,4,5,6,7 has enabled rapid evaluation of reaction space, but so far the merger of such syntheses with bioassays has been achieved with only low-density reaction arrays, which analyse only a handful of analogues prepared under a single reaction condition8,9,10,11,12,13. High-density chemical synthesis approaches that have been coupled to bioassays, including on-bead14, on-surface15, on-DNA16 and mass-encoding technologies17, greatly reduce material requirements, but they require the covalent linkage of substrates to a potentially reactive support, must be performed under high dilution and must operate in a mixture format. These reaction attributes limit the application of transition-metal catalysts, which are easily poisoned by the many functional groups present in a complex mixture, and of transformations for which the kinetics require a high concentration of reactant. Here we couple high-throughput nanomole-scale synthesis with a label-free affinity-selection mass spectrometry bioassay. Each reaction is performed at a 0.1-molar concentration in a discrete well to enable transition-metal catalysis while consuming less than 0.05 milligrams of substrate per reaction. The affinity-selection mass spectrometry bioassay is then used to rank the affinity of the reaction products to target proteins, removing the need for time-intensive reaction purification. This method enables the primary synthesis and testing steps that are critical to the invention of protein inhibitors to be performed rapidly and with minimal consumption of starting materials.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Nanoscale synthesis and affinity ranking (NanoSAR).
Fig. 2: NanoSAR identification of ERK2, MK2 and CHK1 inhibitors.
Fig. 3: NanoSAR mapping of reactivity and bioactivity for diverse CHK1 inhibitors.
Fig. 4: NanoSAR enhances coverage of chemical space.

References

  1. Reymond, J. L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Murray, P. M., Tyler, S. N. G. & Moseley, J. D. Beyond the numbers: charting chemical reaction space. Org. Process Res. Dev . 17, 40–46 (2013).

    Article  CAS  Google Scholar 

  3. Buitrago Santanilla, A. et al. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).

    Article  CAS  PubMed  ADS  Google Scholar 

  4. Shevlin, M. Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Collins, K. D., Gensch, T. & Glorius, F. Contemporary screening approaches to reaction discovery and development. Nat. Chem. 6, 859–871 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Troshin, K. & Hartwig, J. F. Snap deconvolution: an informatics approach to high-throughput discovery of catalytic reactions. Science 357, 175–181 (2017).

    Article  CAS  PubMed  ADS  Google Scholar 

  7. Kutchukian, P. S. et al. Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods. Chem. Sci. 7, 2604–2613 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Werner, M. et al. Seamless integration of dose-response screening and flow chemistry: efficient generation of structure–activity relationship data of β-secretase (BACE1) inhibitors. Angew. Chem. Int. Ed. 53, 1704–1708 (2014).

    Article  CAS  Google Scholar 

  9. Desai, B. et al. Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. J. Med. Chem. 56, 3033–3047 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Guetzoyan, L., Nikbin, N., Baxendale, I. R. & Ley, S. V. Flow chemistry synthesis of zolpidem, alpidem and other GABAA agonists and their biological evaluation through the use of in-line frontal affinity chromatography. Chem. Sci. 4, 764–769 (2013).

    Article  CAS  Google Scholar 

  11. Karageorgis, G., Dow, M., Aimon, A., Warriner, S. & Nelson, A. Activity-directed synthesis with intermolecular reactions: development of a fragment into a range of androgen receptor agonists. Angew. Chem. Int. Ed. 54, 13538–13544 (2015).

    Article  CAS  Google Scholar 

  12. Murray, J. B., Roughley, S. D., Matassova, N. & Brough, P. A. Off-rate screening (ORS) by surface plasmon resonance. An efficient method to kinetically sample hit to lead chemical space from unpurified reaction products. J. Med. Chem. 57, 2845–2850 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Baranczak, A. et al. Integrated platform for expedited synthesis–purification–testing of small molecule libraries. ACS Med. Chem. Lett. 8, 461–465 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Price, A. K., MacConnell, A. B. & Paegel, B. M. hνSABR: photochemical dose–response bead screening in droplets. Anal. Chem. 88, 2904–2911 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Vastl, J., Wang, T., Trinh, T. B. & Spiegel, D. A. Encoded silicon-chip-based platform for combinatorial synthesis and screening. ACS Comb. Sci. 19, 255–261 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Goodnow, R. A. Jr, Dumelin, C. E. & Keefe, A. D. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nat. Rev. Drug Discov. 16, 131–147 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Annis, D. A. et al. An affinity selection–mass spectrometry method for the identification of small molecule ligands from self-encoded combinatorial libraries. Discovery of a novel antagonist of E. coli dihydrofolate reductase. Int. J. Mass Spectrom. 238, 77–83 (2004).

    CAS  Google Scholar 

  18. Andrews, C. L., Ziebell, M. R., Nickbarg, E. & Yang, X. in Protein and Peptide Mass Spectrometry in Drug Discovery (eds Gross, M. L. et al.) 253−286 (John Wiley & Sons, Hoboken, 2012).

  19. O’Connell, T. N., Ramsay, J., Rieth, S. F., Shapiro, M. J. & Stroh, J. G. Solution-based indirect affinity selection mass spectrometry—a general tool for high-throughput screening of pharmaceutical compound libraries. Anal. Chem. 86, 7413–7420 (2014).

    Article  PubMed  Google Scholar 

  20. Annis, D. A. et al. A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures. J. Am. Chem. Soc. 126, 15495–15503 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Cuozzo, J. W. et al. Discovery of a potent BTK inhibitor with a novel binding mode by using parallel selections with a DNA-encoded chemical library. ChemBioChem 18, 864–871 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Schneider, M. et al. Big data from pharmaceutical patents: a computational analysis of medicinal chemists’ bread and butter. J. Med. Chem. 59, 4385–4402 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Brown, D. G. & Boström, J. Analysis of past and present synthetic methodologies on medicinal chemistry: where have all the new reactions gone? J. Med. Chem. 59, 4443–4458 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Aronov, A. M. et al. Flipped out: structure-guided design of selective pyrazolylpyrrole ERK inhibitors. J. Med. Chem. 50, 1280–1287 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Bruno, N. C., Tudge, M. T. & Buchwald, S. L. Design and preparation of new palladium precatalysts for C–C and C–N cross-coupling reactions. Chem. Sci. 4, 916–920 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Anderson, D. R. et al. Pyrrolopyridine inhibitors of mitogen-activated protein kinase-activated protein kinase 2 (MK-2). J. Med. Chem. 50, 2647–2654 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Huang, X. et al. Structure-based design and optimization of 2-aminothiazole-4-carboxamide as a new class of CHK1 inhibitors. Bioorg. Med. Chem. Lett. 23, 2590–2594 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Buitrago Santanilla, A. et al. P2Et phosphazene: a mild, functional group tolerant base for soluble, room temperature Pd-catalyzed C–N, C–O, and C–C cross-coupling reactions. Org. Lett. 17, 3370–3373 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Schneider, P. & Schneider, G. De novo design at the edge of chaos. J. Med. Chem. 59, 4077–4086 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross coupling using machine learning. Science 360, 186–190 (2018).

Download references

Acknowledgements

We are grateful to L. Nogle, D. Smith and M. Pietrafitta (MSD) for assistance with compound purification, S. Bano and X. Bu (MSD) for measuring residual palladium concentrations, A. M. Norris (MSD) for assistance with graphics and Z. Gu (DFKZ German Cancer Research Center) for suggestions for data visualization. N.J.G. was supported by an MRL Postdoctoral Research Fellowship.

Reviewer information

Nature thanks J. Janey, D. Young and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Contributions

N.J.G., P.J.D. and T.C. conceived the study and wrote the manuscript. N.J.G., B.S., P.J.C., P.J.D. and T. C. designed and executed the experiments and analysed the data. C.L.A. and M.P.R. developed the protein titration method. T.C. supervised the work.

Corresponding author

Correspondence to Tim Cernak.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Additional details of the nanoscale synthesis and affinity ranking workflow.

a, Reaction solutions are dosed using a TTP mosquito liquid-handling robot that handles increments of 20 nl. b, c, UPLC–MS analysis confirms the presence of products from crude reactions (b), which is visualized in heat maps (c). d, For the ASMS assay, productive reactions are mass-encoded, pooled and incubated with a protein of interest. e, f, Elution through a size-exclusion column separates protein-bound compounds from unbound compounds (e), and binders can be observed by high-performance liquid chromatography–mass spectrometry (HPLC–MS) following denaturation of the protein–ligand complex (f). g, Decreasing the concentration of protein in the ASMS assay increases the competition for binding so that ligands can be categorized as high, medium or low affinity. h, Scale-up of compounds and purification by using a traditional approach. i, Measurement of IC50 in a functional assay enables comparison of the new method to existing assay technology. See Supplementary Information for additional details.

Extended Data Fig. 2 Structures, yields, Ki values and ASMS comparison plots for the ERK2 library.

a, A library of 19 unique ERK2 inhibitors (2, 3, A1A17) was produced by coupling 1 to diverse amines under a single reaction condition (HATU, iPr2NEt, NMP; see Fig. 2a). b, Crude nanoscale reactions were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of ERK2 to increase the competition for binding. The lowest ERK2 concentration at which the mass signal of the compound was still observed is shown on the abscissa. The Ki value reported24 from purified product samples generated on a 500-times-larger reaction scale is shown on the ordinate. The colour shading groups compounds into low affinity (purple), modest affinity (magenta) or high affinity (yellow), as determined by the ASMS assay. rxn, reaction. c, Comparison of the results of the ASMS affinity-ranking assay (abscissa) to the reported ERK2 Ki values (ordinate). Both datasets are from purified product samples produced on a 50-μmol scale. d, Comparison of results of ASMS affinity ranking from crude reaction mixtures generated on a 100-nmol scale with those from purified product samples generated on a 50-μmol scale. Points are coloured by Ki, and jittering was applied to this categorical data to reveal overlapping data. e, Product structures with reaction conditions used, isolated yields and Ki values. See Supplementary Information for additional details.

Source Data

Extended Data Fig. 3 Structures, yields, IC50 values and ASMS comparison plots for the MK2 Library.

a, A library of 18 unique MK2 inhibitors (5, 6, B1B16) was produced by coupling 4 to diverse boronates under eight reaction conditions (see Fig. 2b). be, As in Extended Data Fig. 2, but for MK2 and IC50 values instead of ERK2 and Ki values.

Source Data

Extended Data Fig. 4 Structures, yields, IC50 values and ASMS comparison plots for the CHK1 Library.

a, A library of 20 unique CHK1 inhibitors (8, 9, C1C18) was produced by coupling 7 to diverse boronates and amines under eight or nine reaction conditions (see Fig. 2c). be, As in Extended Data Fig. 2, but for CHK1 and IC50 values instead of ERK2 and Ki values.

Source Data

Extended Data Fig. 5 ASMS comparison plots for different reaction workup protocols and residual levels of palladium from the CHK1 library.

Reactions with different workup protocols were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of CHK1 to increase the competition for binding. The axes of the plots are as in Extended Data Fig. 4b, c. The reaction and workup protocols are as follows: a, nanoscale reactions submitted to the affinity-ranking assay with no purification; b, nanoscale reactions submitted to the affinity-ranking assay following treatment with SiliaMetS dimercaptotriazine (DMT) resin; and c, 50-mmol-scale reactions in which the product samples were purified by reverse-phase HPLC before submission to the affinity-ranking assay. See Supplementary Information for additional details.

Source Data

Extended Data Fig. 6 Catalyst–base survey.

See Fig. 3. a, Diverse model nucleophiles (D1D17) were screened against combinations of catalysts (1126) and bases (2734) in NMP, DMSO or DMF solvent. b, The details beside the heat maps show the mapping of nucleophiles, solvents, catalysts and bases. NA, no catalyst used. Reactions were run on a 100-nmol scale and analysed by UPLC–MS for conversion to products of the form of 10 compared to a biphenyl internal standard. Productive reaction conditions from this screen were selected for use in the subsequent library synthesis campaign. aD1 is compound C2 in Fig. 2c; bD2 is compound C3 in Fig. 2c and 43 in Fig. 4; cD3 is compound C5 in Fig. 2c; dD4 is compound C6 in Fig. 2c; eD5 is compound 8 in Fig. 2c; fprepared from the boronic acid. See Supplementary Information for additional details. 

Source Data

Extended Data Fig. 7 Exemplary compounds from the synthesis of the library.

See Fig. 3. Structures for 62 diverse coupling products (of the form of 10) are shown, which were selected from the 345 productive reactions identified in a library synthesis campaign targeting 384 products (Fig. 3). Diverse nucleophiles were coupled to 7 using the four reaction conditions selected in Fig. 3 and Extended Data Fig. 6. See Supplementary Information for additional details.

Extended Data Fig. 8 Comparison of the results of affinity ranking with IC50 values for inhibition of CHK1 functional activity.

The 62 exemplary compounds (Extended Data Fig. 6) were selected from 345 that were submitted to affinity ranking (Fig. 3). Crude nanoscale reactions were submitted to the ASMS assay, with affinity ranking determined by reducing the concentration of CHK1 to increase the competition for binding. The axes of the plots are as in Extended Data Fig. 4b, c. Points are coloured by the IC50 value for inhibition of CHK1 function (as in Extended Data Fig. 4d). See Supplementary Information for additional details.

Source Data

Extended Data Fig. 9 Dose response curves for exemplary compounds.

The compounds shown are 3541 and 43. The inhibition of CHK1 functional activity by diverse coupling products (10) from the reaction of 7 with nucleophiles under various reaction conditions is shown. Isolated yields were achieved when the reaction conditions shown were used. The dose response curves and IC50 values shown were measured on purified product samples generated on a 50-μmol scale and could be predicted from affinity-ranking results of crude nanoscale reactions as shown in Extended Data Fig. 8. See Supplementary Information for dose response curves of additional compounds.

Source Data

Extended Data Fig. 10 Reaction metrics and chemical ‘dark’ space.

a, Histogram of reaction performance for 384 diverse coupling reactions with bromide 7 using a single reaction condition (10 mol% tBuXPhos Pd G3, P2Et, NMP). Only 158 of the 384 targeted products were observed by mass spectrometry and the majority of the reactions failed (0% conversion to product). b, Histogram of reaction performance for 384 diverse coupling reactions with bromide 7 using the best of four reaction conditions, as described in Extended Data Figs. 6 and 7. Of the 384 targeted products, 345 were observed by mass spectrometry, with a more even distribution of reaction yields and the majority of reactions succeeding (100% conversion to product). c, Principal-component (PC) analysis of chemical ‘dark’ space, with each dot representing a compound that was not formed under a single reaction condition (0% yield) but that had affinity to CHK1. By using the best of four reaction conditions, 187 additional products were produced and assayed that bound to CHK1; the colour of the dots reflects the affinity ranking of the compounds. The boundaries of this space are identical to those depicted in Fig. 4a, in which purple shading highlights additional regions where the majority of reactions failed (0% yield). Areas where dots are shown in purple shaded regions depict products with affinity to CHK1 that were formed in 0% yield in a but in more than 1% yield in b. d, Potent CHK1 inhibitors that were produced in 0% yield under a single condition (10 mol% tBuXPhos Pd G3, P2Et, NMP), but in modest to good yields following reaction-condition screening.

Source Data

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gesmundo, N.J., Sauvagnat, B., Curran, P. et al. Nanoscale synthesis and affinity ranking. Nature 557, 228–232 (2018). https://doi.org/10.1038/s41586-018-0056-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-018-0056-8

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing