Advanced search
Publication Cover
Expert Opinion on Drug Discovery Volume 14, 2019 - Issue 8
Submit an article Journal homepage
644
Views
24
CrossRef citations to date
0
Altmetric
Review

Approaching Target Selectivity by De Novo Drug Design

Thomas FischerCenter of Organic and Medicinal Chemistry, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences ZHAW, Wädenswil, SwitzerlandView further author information
,
Silvia GazzolaDipartimento di Scienza e Alta Tecnologia, Università degli Studi dell’Insubria, Como, ItalyView further author information
&
Rainer RiedlCenter of Organic and Medicinal Chemistry, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences ZHAW, Wädenswil, SwitzerlandCorrespondence[email protected]
View further author information
Pages 791-803 | Received 05 Mar 2019, Accepted 02 May 2019, Published online: 10 Jun 2019

References

  • Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 2004;3:711–716.
  • DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 2016;47:20–33.
  • Böhm H–J, Klebe G. What can we learn from molecular recognition in protein–ligand complexes for the design of new drugs? Angew. Chem. Int. Ed. 1996;35:2588–2614; Angew. Chem. 1996;108:2750–2778.
  • Agnello S, Brand M, Chellat MF, et al. A structural view on medicinal chemistry strategies against drug resistance. Angew. Chem. Int. Ed. 2019;58:3300–3345; Angew. Chem. 2019;131:3336–3383.
  • Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28:235–242.
  • Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. 2005;4:649.
  • Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 2004;3:935–949.
  • Nishibata Y, Itai A. Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 1991;47:8985–8990.
  • Cramer J, Schiebel J, Wulsdorf T, et al. A false-positive screening hit in fragment-based lead discovery: watch out for the red herring. Angew. Chem. Int. Ed. 2017;56:1908–1913; Angew. Chem. 2017;129:1934–1940.
  • Frank AO, Feldkamp MD, Kennedy JP, et al. Discovery of a potent inhibitor of replication protein a protein–protein interactions using a fragment-linking approach. J. Med. Chem. 2013;56:9242–9250.
  • Ichihara O, Barker J, Law RJ, et al. Compound design by fragment-linking. Mol. Inform. 2011;30:298–306.
  • Schneider G. Future de novo drug design. Mol. Inform. 2014;33:397–402.
  • Schneider P, Schneider G. De novo design at the edge of chaos. J. Med. Chem. 2016;59:4077–4086.
  • Hartenfeller M, Schneider G. Enabling future drug discovery by de novo design. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2011;1:742–759.
  • Lanz J, Riedl R. Merging allosteric and active site binding motifs: de novo generation of target selectivity and potency via natural-product-derived fragments. ChemMedChem 2015;10:451–454.
  • Chevillard F, Rimmer H, Betti C, et al. Binding-site compatible fragment growing applied to the design of β2-adrenergic receptor ligands. J. Med. Chem. 2018;61:1118–1129.
  • Liu T, Naderi M, Alvin C, et al. Break down in order to build up: decomposing small molecules for fragment-based drug design with eMolFrag. J. Chem. Inf. Model. 2017;57:627–631.
  • Esposito C, Wiedmer L, Caflisch A. In silico identification of JMJD3 demethylase inhibitors. J. Chem. Inf. Model. 2018;58:2151–2163.
  • Dey F, Caflisch A. Fragment-based de novo ligand design by multiobjective evolutionary optimization. J. Chem. Inf. Model. 2008;48:679–690.
  • Hung AW, Silvestre HL, Wen S, et al. Application of fragment growing and fragment linking to the discovery of inhibitors of Mycobacterium tuberculosis pantothenate synthetase. Angew. Chem. Int. Ed. 2009;48:8452–8456; Angew. Chem. 2009;121:8604–8608.
  • Böhm H-J. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des. 1992;6:61–78.
  • Böhm H-J. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des. 1992;6:593–606.
  • Waszkowycz B, Clark DE, Frenkel D, et al. PRO_LIGAND: an approach to de novo molecular design. 2. design of novel molecules from molecular field analysis (MFA) models and pharmacophores. J. Med. Chem. 1994;37:3994–4002.
  • Clark DE, Frenkel D, Levy SA, et al. PRO_LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules. J. Comput. Aided Mol. Des. 1995;9:13–32.
  • Gillet V, Johnson AP, Mata P, et al. SPROUT: A program for structure generation. J. Comput. Aided Mol. Des. 1993;7:127–153.
  • Gillet VJ, Newell W, Mata P, et al. SPROUT: recent developments in the de novo design of molecules. J. Chem. Inf. Comput. Sci. 1994;34:207–217.
  • Pearlman DA, Murcko MA. CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. J. Med. Chem. 1996;39:1651–1663.
  • Böhm H-J, Flohr A, Stahl M. Scaffold hopping. Drug Discov. Today Technol. 2004;1:217–224.
  • Schneider G, Schneider P, Renner S. Scaffold-hopping: how far can you jump? QSAR Comb. Sci. 2006;25:1162–1171.
  • Sun H, Tawa G, Wallqvist A. Classification of scaffold hopping approaches. Drug Discov. Today 2012;17:310–324.
  • Langdon SR, Ertl P, Brown N. Bioisosteric replacement and scaffold hopping in lead generation and optimization. Mol. Inform. 2010;29:366–385.
  • Wang Y, Zhao H, Brewer JT, et al. De novo design, synthesis, and biological evaluation of 3,4-disubstituted pyrrolidine sulfonamides as potent and selective glycine transporter 1 competitive inhibitors. J. Med. Chem. 2018;61:7486–7502.
  • Levit A, Barak D, Behrens M, et al. Homology model-assisted elucidation of binding sites in GPCRs. In: editors, Vaidehi N, Klein-Seetharaman J. Membrane protein structure and dynamics. methods in molecular biology (methods and protocols). Vol. 914. Totowa, NJ: Humana Press; 2012;179–205.
  • Hartenfeller M, Zettl H, Walter M, et al. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput. Biol. 2012;8:e1002380.
  • Schneider G, Lee M-L, Stahl M, et al. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des. 2000;14:487–494.
  • Vinkers HM, de Jonge MR, Daeyaert FFD, et al. SYNOPSIS: sYNthesize and optimize system in silico. J. Med. Chem. 2003;46:2765–2773.
  • Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 2015;20:318–331.
  • Zhang L, Tan J, Han D, et al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 2017;22:1680–1685.
  • Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol. Inform. 2016;35:3–14.
  • Lima AN, Philot EA, Trossini GHG, et al. Use of machine learning approaches for novel drug discovery. Expert Opin. Drug Discov. 2016;11:225–239.
  • Schneider G, Clark DE. Automated de novo drug design – “Are we nearly there yet?”. Angew. Chem. Int. Ed. 2019. Accepted Author Manuscript. DOI:10.1002/anie.201814681.
  • Huggins DJ, Sherman W, Tidor B. Rational approaches to improving selectivity in drug design. J. Med. Chem. 2012;55:1424–1444.
  • Ghoreschi K, Laurence A, O’Shea JJ. Selectivity and therapeutic inhibition of kinases: to be or not to be? Nat. Immunol. 2009;10:356–360.
  • Malumbres M. Cyclin-dependent kinases. Genome Biol. 2014;15:122.
  • Honma T, Hayashi K, Aoyama T, et al. Structure-based generation of a new class of potent cdk4 inhibitors: new de novo design strategy and library design. J. Med. Chem. 2001;44:4615–4627.
  • Kamb A, Gruis NA, Weaver-Feldhaus J, et al. A cell cycle regulator potentially involved in genesis of many tumor types. Science 1994;264:436–440.
  • Ikuta M, Kamata K, Fukasawa K, et al. Crystallographic approach to identification of cyclin-dependent kinase 4 (CDK4)-specific inhibitors by using CDK4 mimic CDK2 protein. J. Biol. Chem. 2001;276:27548–27554.
  • Takaki T, Echalier A, Brown NR, et al. The structure of CDK4/cyclin D3 has implications for models of CDK activation. Proc. Natl. Acad. Sci. 2009;106:4171–4176.
  • De Bondt HL, Rosenblatt J, Jancarik J, et al. Crystal structure of cyclin-dependent kinase 2. Nature 1993;363:595–602.
  • Nishibata Y, Itai A. Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation. J. Med. Chem. 1993;36:2921–2928.
  • Honma T, Yoshizumi T, Hashimoto N, et al. A novel approach for the development of selective Cdk4 inhibitors: library design based on locations of Cdk4 specific amino acid residues. J. Med. Chem. 2001;44:4628–4640.
  • Park H, Shin Y, Kim J, et al. Application of fragment-based de novo design to the discovery of selective picomolar inhibitors of glycogen synthase kinase-3 beta. J. Med. Chem. 2016;59:9018–9034.
  • Maqbool M, Hoda N. GSK3 Inhibitors in the therapeutic development of diabetes, cancer and neurodegeneration: past, present and future. Curr. Pharm. Des. 2017;23:4332.
  • Berg S, Bergh M, Hellberg S, et al. Discovery of novel potent and highly selective glycogen synthase kinase-3β (GSK3β) inhibitors for alzheimer’s disease: design, synthesis, and characterization of pyrazines. J. Med. Chem. 2012;55:9107–9119.
  • Wang R, Gao Y, Lai L. LigBuilder: a multi-purpose program for structure-based drug design. Mol. Model. Annu. 2000;6:498–516.
  • Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25.1. Adv. Drug Deliv. Rev. 2001;46:3–26.
  • Ji H, Stanton BZ, Igarashi J, et al. Minimal pharmacophoric elements and fragment hopping, an approach directed at molecular diversity and isozyme selectivity. Design of selective neuronal nitric oxide synthase inhibitors. J. Am. Chem. Soc. 2008;130:3900–3914.
  • Mukherjee P, Cinelli MA, Kang S, et al. Development of nitric oxide synthase inhibitors for neurodegeneration and neuropathic pain. Chem. Soc. Rev. 2014;43:6814–6838.
  • Li H, Shimizu H, Flinspach M, et al. The novel binding mode of N-Alkyl-N‘-hydroxyguanidine to neuronal nitric oxide synthase provides mechanistic insights into NO biosynthesis. Biochemistry 2002;41:13868–13875.
  • Raman CS, Li H, Martásek P, et al. Crystal structure of constitutive endothelial nitric oxide synthase: a paradigm for pterin function involving a novel metal center. Cell 1998;95:939–950.
  • Crane BR, Arvai AS, Gachhui R, et al. The structure of nitric oxide synthase oxygenase domain and inhibitor complexes. Science 1997;278:425–431.
  • Steinert JR, Chernova T, Forsythe ID. Nitric oxide signaling in brain function, dysfunction, and dementia. The Neuroscientist 2010;16:435–452.
  • Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985;28:849–857.
  • Miranker A, Karplus M. Functionality maps of binding sites: A multiple copy simultaneous search method. Proteins Struct. Funct. Bioinforma. 1991;11:29–34.
  • Ji H, Li H, Flinspach M, et al. Computer modeling of selective regions in the active site of nitric oxide synthases: implication for the design of isoform-selective inhibitors. J. Med. Chem. 2003;46:5700–5711.
  • Flinspach ML, Li H, Jamal J, et al. Structural basis for dipeptide amide isoform-selective inhibition of neuronal nitric oxide synthase. Nat. Struct. Mol. Biol. 2004;11:54–59.
  • Ji H, Li H, Martásek P, et al. Discovery of highly potent and selective inhibitors of neuronal nitric oxide synthase by fragment hopping. J. Med. Chem. 2009;52:779–797.
  • Lawton GR, Ranaivo HR, Chico LK, et al. Analogues of 2-aminopyridine-based selective inhibitors of neuronal nitric oxide synthase with increased bioavailability. Bioorg. Med. Chem. 2009;17:2371–2380.
  • Nagase H, Woessner JF, Matrix Metalloproteinases. J. Biol. Chem. 1999;274:21491–21494.
  • Bertini I, Calderone V, Fragai M, et al. Snapshots of the reaction mechanism of matrix metalloproteinases. Angew. Chem. Int. Ed. 2006;45:7952–7955; Angew. Chem. 2006;118:8120–8123.
  • Rowan AD, Litherland GJ, Hui W, et al. Metalloproteases as potential therapeutic targets in arthritis treatment. Expert Opin. Ther. Targets 2008;12:1–18.
  • Fischer T, Senn N, Riedl R. Design and structural evolution of matrix metalloproteinase inhibitors. Chem. Eur. J. 2019. Accepted Author Manuscript. DOI:10.1002/chem.201805361.
  • Fischer T, Riedl R. Strategic targeting of multiple water-mediated interactions: a concise and rational structure-based design approach to potent and selective MMP-13 inhibitors. ChemMedChem 2013;8:1457–1461.
  • Johnson AR, Pavlovsky AG, Ortwine DF, et al. Discovery and characterization of a novel inhibitor of matrix metalloprotease-13 that reduces cartilage damage in vivo without joint fibroplasia side effects. J. Biol. Chem. 2007;282:27781–27791.
  • Fischer T, Riedl R. Molecular recognition of the catalytic zinc(II) ion in MMP-13: structure-based evolution of an allosteric inhibitor to dual binding mode inhibitors with improved lipophilic ligand efficiencies. Int. J. Mol. Sci. 2016;17:314.
  • Fischer T, Riedl R. Targeted fluoro positioning for the discovery of a potent and highly selective matrix metalloproteinase inhibitor. ChemistryOpen 2017;6:192–195.
  • Gall FM, Hohl D, Frasson D, et al. Drug design inspired by nature: crystallographic detection of auto-tailored protease inhibitor template. Angew. Chem. Int. Ed. 2019;58:4051–4055; Angew. Chem. 2019;131:4091–4096.
  • Senn N, Ott M, Lanz J, et al. Targeted polypharmacology: discovery of a highly potent non-hydroxamate dual matrix metalloproteinase (MMP)-10/-13 inhibitor. J. Med. Chem. 2017;60:9585–9598.
  • Brady RM, Vom A, Roy MJ, et al. De-novo designed library of benzoylureas as inhibitors of bcl-xl: synthesis, structural and biochemical characterization. J. Med. Chem. 2014;57:1323–1343.
  • Adams JM, Cory S. The BCL-2 arbiters of apoptosis and their growing role as cancer targets. Cell Death Differ. 2018;25:27–36.
  • Huska JD, Lamb HM, Hardwick JM. Overview of BCL-2 Family Proteins and Therapeutic Potentials. In: editor, Gavathiotis E. BCL-2 Family Proteins. Methods in Molecular Biology. Vol. 1877. New York, NY: Humana Press; 2019;1–21.
  • Ashkenazi A, Fairbrother WJ, Leverson JD, et al. From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors. Nat. Rev. Drug. Discov. 2017;16:273–284.
  • Sattler M, Liang H, Nettesheim D, et al. Structure of Bcl-xL-bak peptide complex: recognition between regulators of apoptosis. Science 1997;275:983–986.
  • Petros AM, Nettesheim DG, Wang Y, et al. Rationale for Bcl-XL/Bad peptide complex formation from structure, mutagenesis, and biophysical studies. Protein Sci. 2000;9:2528–2534.
  • Rodriguez JM, Ross NT, Katt WP, et al. Structure and function of benzoylurea-derived α-helix mimetics targeting the Bcl-xL/Bak binding interface. ChemMedChem 2009;4:649–656.
  • Ernst JT, Becerril J, Park HS, et al. Design and application of an α-helix-mimetic scaffold based on an oligoamide-foldamer strategy: antagonism of the bak BH3/Bcl-xL complex. Angew. Chem. Int. Ed. 2003;42:535–539; Angew. Chem. 2003;115:553–557.
  • Kutzki O, Park HS, Ernst JT, et al. Development of a potent Bcl-xL antagonist based on α-helix mimicry. J. Am. Chem. Soc. 2002;124:11838–11839.
  • Rodrigues T, Reker D, Welin M, et al. De novo fragment design for drug discovery and chemical biology. Angew. Chem. Int. Ed. 2015;54:15079–15083; Angew. Chem. 2015;127:15294–15298.
  • Brognard J, Zhang Y-W, Puto LA, et al. Cancer-associated loss-of-function mutations implicate DAPK3 as a tumor‐suppressing kinase. Cancer Res. 2011;71:3152–3161.
  • Kawai T, Matsumoto M, Takeda K, et al. ZIP kinase, a novel serine/threonine kinase which mediates apoptosis. Mol. Cell. Biol. 1998;18:1642–1651.
  • Page G, Kögel D, Rangnekar V, et al. Interaction partners of Dlk/ZIP kinase: co-expression of Dlk/ZIP kinase and Par-4 results in cytoplasmic retention and apoptosis. Oncogene 1999;18:7265–7273.
  • Haystead TAJ. ZIP kinase, a key regulator of myosin protein phosphatase 1. Cell. Signal. 2005;17:1313–1322.
  • Alshaker H, Srivats S, Monteil D, et al. Field template-based design and biological evaluation of new sphingosine kinase 1 inhibitors. Breast Cancer Res. Treat. 2018;172:33–43.
  • Pitman MR, Costabile M, Pitson SM. Recent advances in the development of sphingosine kinase inhibitors. Cell. Signal. 2016;28:1349–1363.
  • Pyne NJ, Tonelli F, Lim KG, et al. Targeting sphingosine kinase 1 in cancer. Adv. Biol. Regul. 2012;52:31–38.
  • Xiang Y, Asmussen G, Booker M, et al. Discovery of novel sphingosine kinase 1 inhibitors. Bioorg. Med. Chem. Lett. 2009;19:6119–6121.
  • Simonin C, Awale M, Brand M, et al. Optimization of TRPV6 calcium channel inhibitors using a 3D ligand-based virtual screening method. Angew. Chem. Int. Ed. 2015;54:14748–14752; Angew. Chem. 2015;127:14961–14965.
  • Awale M, Jin X, Reymond J-L. Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints. J. Cheminformatics 2015;7:3.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.