The iterative application of a large chemical space in the drug discovery process

Authors

DOI:

https://doi.org/10.24959/ophcj.21.244362

Keywords:

chemical space, 1,2,4-triazole, tetrazole, BRD4, thermal shift assay

Abstract

Aim. To demonstrate the advantages of large-scale virtual libraries generated using chemical protocols previously validated in primary steps of the drug discovery process.
Results and discussion. Two validated parallel chemistry protocols reported earlier were used to create the chemical space. It was then sampled based on diversity metric, and the sample was subjected to the virtual screening on BRD4 target. Hits of virtual screening were synthesized and tested in the thermal shift assay.
Experimental part. The chemical space was generated using commercially available building blocks and synthetic protocols suitable for parallel chemistry and previously reported. After narrowing it down, using MedChem filters, the resulting sub-space was clustered based on diversity metrics. Centroids of the clusters were put to the virtual screening against the BRD4 active center. 29 Hits from the docking were synthesized and subjected to the thermal shift assay with BRD4, and 2 compounds showed noticeable dTm.
Conclusions. A combination of cheminformatics and molecular docking was applied to find novel potential binders for BRD4 from a large chemical space. The selected set of predicted molecules was synthesized with a 72 % success rate and tested in a thermal shift assay to reveal a 6 % hit rate. The selection can be performed iteratively to fast support of the drug discovery.

References

Macarron, R.; Banks, M. N.; Bojanic, D.; Burns, D. J.; Cirovic, D. A.; Garyantes, T.; Green, D. V. S.; Hertzberg, R. P.; Janzen, W. P.; Paslay, J. W.; Schopfer, U.; Sittampalam, G. S. Impact of high-throughput screening in biomedical research. Nature Reviews Drug Discovery 2011, 10 (3), 188-195. https://doi.org/10.1038/nrd3368.

Roy, A. Early Probe and Drug Discovery in Academia: A Minireview. High-Throughput 2018, 7 (1), 4. https://doi.org/10.3390/ht7010004.

Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R. K. Artificial intelligence in drug discovery and development. Drug Discov. Today 2021, 26 (1), 80-93. https://doi.org/10.1016/j.drudis.2020.10.010.

Reker, D.; Schneider, P.; Schneider, G. Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors. Chemical Science 2016, 7 (6), 3919-3927. https://doi.org/10.1039/C5SC04272K.

Halberstam, N. M.; Baskin, I. I.; Palyulin, V. A.; Baskin, I. I.; Madzhidov, T. I.; Antipin, I. S.; Varnek, A. Artificial Intelligence in Synthetic Chemistry: Achievements and Prospects. Chem. Rev. 2017, 86 (11), 1127-1156. https://doi.org/10.1070/RCR4746.

Polishchuk, P. G.; Madzhidov, T. I.; Varnek, A. Estimation of the size of drug-like chemical space based on GDB-17 data. Comput. Aided Mol. Des. 2013, 27 (8), 675-679. https://doi.org/10.1007/s10822-013-9672-4.

REAL DATABASE. The largest enumerated database of synthetically feasible molecules. https://enamine.net/compound-collections/real-compounds/real-database (accessed Oct 15, 2021).

Bogolyubsky, A. V.; Savych, O.; Zhemera, A. V.; Pipko, S. E.; Grishchenko, A. V.; Konovets, A. I.; Doroshchuk, R. O.; Khomenko, D. N.; Brovarets, V. S.; Moroz, Y. S.; Vybornyi, M. Facile One-Pot Parallel Synthesis of 3-Amino-1,2,4-triazoles. ACS Comb. Sci. 2018, 20 (7), 461-466. https://doi.org/10.1021/acscombsci.8b00060.

Bogolubsky, A. V.; Moroz, Y. S.; Mykhailiuk, P. K.; Pipko, S. E.; Konovets, A. I.; Sadkova, I. V.; Tolmachev, A. Sulfonyl Fluorides as Alternative to Sulfonyl Chlorides in Parallel Synthesis of Aliphatic Sulfonamides. ACS Comb. Sci. 2014, 16 (4), 192-197. https://doi.org/10.1021/co400164z.

Bogolubsky, A. V.; Moroz, Y. S.; Savych, O.; Pipko, S.; Konovets, A.; Platonov, M. O.; Vasylchenko, O. V.; Hurmach, V. V.; Grygorenko, O. O. An Old Story in the Parallel Synthesis World: An Approach to Hydantoin Libraries. ACS Comb. Sci. 2018, 20 (1), 35-43. https://doi.org/10.1021/acscombsci.7b00163.

Lyu, J.; Wang, S.; Balius, T. E.; Singh, I.; Levit, A.; Moroz, Y. S.; O’Meara, M. J.; Che, T.; Algaa, E.; Tolmachova, K.; Tolmachev, A. A.; Shoichet, B. K.; Roth, B. L.; Irwin, J. J. Ultra-large library docking for discovering new chemotypes. Nature 2019, 566 (7743), 224-229. https://doi.org/10.1038/s41586-019-0917-9.

Irwin, J. J.; Tang, K. G.; Young, J.; Dandarchuluun, C.; Wong, B. R.; Khurelbaatar, M.; Moroz, Y. S.; Mayfield, J.; Sayle, R. A. ZINC20—A Free Ultra-large-Scale Chemical Database for Ligand Discovery. Journal of Chemical Information and Modeling 2020, 60 (12), 6065-6073. https://doi.org/10.1021/acs.jcim.0c00675.

Vogt, M. How do we optimize chemical space navigation? Expert Opinion on Drug Discovery 2020, 15 (5), 523-525. https://doi.org/10.1080/17460441.2020.1730324.

Savych, O.; Kuchkovska, Y. O.; Bogolyubsky, A. V.; Konovets, A. I.; Gubina, K. E.; Pipko, S. E.; Zhemera, A. V.; Grishchenko, A. V.; Khomenko, D. N.; Brovarets, V. S.; Doroschuk, R.; Moroz, Y. S.; Grygorenko, O. O. One-Pot Parallel Synthesis of 5-(Dialkylamino)tetrazoles. ACS Comb. Sci. 2019, 21 (9), 635-642. https://doi.org/10.1021/acscombsci.9b00120.

Filippakopoulos, P.; Knapp, S. Targeting bromodomains: epigenetic readers of lysine acetylation. Nature Reviews Drug Discovery 2014, 13 (5), 337-356. https://doi.org/10.1038/nrd4286.

Cochran, A. G.; Conery, A. R.; Sims, R. J. Bromodomains: a new target class for drug development. Nature Reviews Drug Discovery 2019, 18 (8), 609-628. https://doi.org/10.1038/s41573-019-0030-7.

Duan, Y.; Guan, Y.; Qin, W.; Zhai, X.; Yu, B.; Liu, H. Targeting Brd4 for cancer therapy: inhibitors and degraders. MedChemComm 2018, 9 (11), 1779-1802. https://doi.org/10.1039/C8MD00198G.

Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Drug Deliv. Rev. 2012, 64, 4-17. https://doi.org/10.1016/j.addr.2012.09.019.

Veber, D. F.; Johnson, S. R.; Cheng, H.-Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D. Molecular Properties That Influence on the Oral Bioavailability of Drug Candidates. Med. Chem. 2002, 45 (12), 2615-2623. https://doi.org/10.1021/jm020017n.

UniProt. https://www.uniprot.org/ (accessed Sep 13, 2021).

Target Report Card. Bromodomain-Containing Protein 4. https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL1163125/ (accessed Sep 4, 2021).

Protein Data Bank. https://www.rcsb.org/ (accessed Sep 17, 2021).

Filippakopoulos, P.; Picaud, S.; Fedorov, O.; Keller, M.; Wrobel, M.; Morgenstern, O.; Bracher, F.; Knapp, S. Benzodiazepines and benzotriazepines as protein interaction inhibitors targeting bromodomains of the BET family. Med. Chem. 2012, 20 (6), 1878-1886. https://doi.org/10.1016/j.bmc.2011.10.080.

Zhao, H.; Gartenmann, L.; Dong, J.; Spiliotopoulos, D.; Caflisch, A. Discovery of BRD4 bromodomain inhibitors by fragment-based high-throughput docking. Med. Chem. Lett. 2014, 24 (11), 2493-2496. https://doi.org/10.1016/j.bmcl.2014.04.017.

Duffy, B. C.; Liu, S.; Martin, G. S.; Wang, R.; Hsia, M. M.; Zhao, H.; Guo, C.; Ellis, M.; Quinn, J. F.; Kharenko, O. A.; Norek, K.; Gesner, E. M.; Young, P. R.; McLure, K. G.; Wagner, G. S.; Lakshminarasimhan, D.; White, A.; Suto, R. K.; Hansen, H. C.; Kitchen, D. B. Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design. Med. Chem. Lett. 2015, 25 (14), 2818-2823. https://doi.org/10.1016/j.bmcl.2015.04.107.

2N0 on PDB. https://www.rcsb.org/ligand/2T0 (accessed May 11, 2021)

1A6 on PDB. https://www.rcsb.org/ligand/1A6 (accessed May 14, 2021)

Mohs, R. C.; Greig, N. H. Drug discovery and development: Role of basic biological research. Alzheimer's & Dementia: Translational Research & Clinical Interventions 2017, 3 (4), 651-657. https://doi.org/10.1016/j.trci.2017.10.005.

Shen, J.; Zhang, H. Synthesis of 1-substituted 3-amino-1H-1,2,4-triazoles from ethyl N-(5-phenyl-1,2,4-oxadiazol-3-yl)formimidate. Tetrahedron 2015, 71 (36), 6164-6169. https://doi.org/10.1016/j.tet.2015.06.094.

Lipinski, C. A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies 2004, 1 (4), 337-341. https://doi.org/10.1016/j.ddtec.2004.11.007.

RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org/ (accessed May 27, 2021).

DOCK Blaster. http://blaster.docking.org/ http://blaster.docking.org/ (accessed May 25, 2021).

Coleman, R. G.; Carchia, M.; Sterling, T.; Irwin, J. J.; Shoichet, B. K. Ligand Pose and Orientational Sampling in Molecular Docking. PLOS ONE 2013, 8 (10), e75992. https://doi.org/10.1371/journal.pone.0075992.

Word, J. M.; Lovell, S. C.; Richardson, J. S.; Richardson, D. C. Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation11Edited by J. Thornton. Mol. Biol. 1999, 285 (4), 1735-1747. https://doi.org/10.1006/jmbi.1998.2401.

Case, D. A.; Aktulga, H. M.; Belfon, K.; Ben-Shalom, I. Y.; Brozell, S. R.; Cerutti, D. S.; Cheatham, T. E.; Cisneros, G. A.; Cruzeiro, V. W. D.; Darden, T. A.; Duke, R. E.; Giambasu, G.; Gilson, M. K.; Gohlke, H.; Goetz, A. W.; Harris, R.; Izadi, S.; Izmailov, S. A.; Jin, C.; Kasavajhala, K.; Kaymak, M. C.; King, E.; Kovalenko, A.; Kurtzman, T.; Lee, T. S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Machado, M.; Man, V.; Manathunga, M.; Merz, K. M.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; O’Hearn, K. A.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Rahnamoun, A.; Roe, D. R.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shen, J.; Simmerling, C. L.; Skrynnikov, N. R.; Smith, J.; Swails, J.; Walker, R. C.; Wang, J.; Wei, H.; Wolf, R. M.; Wu, X.; Xue, Y.; York, D. M.; Zhao, S.; P. A. Kollman, Amber 2021, University of California, San Francisco.

Gallagher, K.; Sharp, K. Electrostatic Contributions to Heat Capacity Changes of DNA-Ligand Binding. J. 1998, 75 (2), 769-776. https://doi.org/10.1016/S0006-3495(98)77566-6.

Sharp, K. A. Polyelectrolyte electrostatics: Salt dependence, entropic, and enthalpic contributions to free energy in the nonlinear Poisson–Boltzmann model. Biopolymers 1995, 36 (2), 227-243. https://doi.org/10.1002/bip.360360210.

Meng, E. C.; Shoichet, B. K.; Kuntz, I. D. Automated docking with grid-based energy evaluation. Comput. Chem. 1992, 13 (4), 505-524. https://doi.org/10.1002/jcc.540130412.

Mysinger, M. M.; Shoichet, B. K. Rapid Context-Dependent Ligand Desolvation in Molecular Docking. Journal of Chemical Information and Modeling 2010, 50 (9), 1561-1573. https://doi.org/10.1021/ci100214a.

Borysko, P.; Moroz, Y. S.; Vasylchenko, O. V.; Hurmach, V. V.; Starodubtseva, A.; Stefanishena, N.; Nesteruk, K.; Zozulya, S.; Kondratov, I. S.; Grygorenko, O. O. Straightforward hit identification approach in fragment-based discovery of bromodomain-containing protein 4 (BRD4) inhibitors. Med. Chem. 2018, 26 (12), 3399-3405. https://doi.org/10.1016/j.bmc.2018.05.010.

Klingler, F.-M.; Gastreich, M.; Grygorenko, O. O.; Savych, O.; Borysko, P.; Griniukova, A.; Gubina, K. E.; Lemmen, C.; Moroz, Y. S. SAR by Space: Enriching Hit Sets from the Chemical Space. Molecules 2019, 24 (17), 3096. https://doi.org/10.3390/molecules24173096.

Downloads

Additional Files

Published

2021-12-23

How to Cite

(1)
Savych, O. V.; Gryniukova, A. V.; Alieksieieva, D. O.; Dziuba, I. M.; Borysko, P. O.; Dudenko, D. V.; Brovarets, V. S.; Moroz, Y. S. The Iterative Application of a Large Chemical Space in the Drug Discovery Process. J. Org. Pharm. Chem. 2021, 19, 3-11.

Issue

Section

Advanced researches