Skip to main content
SpringerLink
Log in

Multitarget Drug Design for Neurodegenerative Diseases

  • Protocol
  • First Online:
Multi-Target Drug Design Using Chem-Bioinformatic Approaches

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

The quest for new pharmacological treatments of neurodegenerative diseases (NDs) still remains a priority for researchers and caregivers. Being inherently multifactorial, NDs benefited of the paradigm shift from “one-drug-one-target” to “one-drug-more-target” which is typical of the so-called multitarget approach, whose ultimate aim is that of providing a wider pharmacological spectrum to single molecular entities. A multitarget drug should encompass the basic molecular features necessary for an effective interaction with each desired biological target. In this respect, different drug design strategies, mostly inspired by ligand-based and target-based approaches, have been envisaged to achieve this goal. Indeed, huge efforts have been addressed in recent years to harmonically integrate the amount of different (bio)chemical information in the attempt to derive reliable predictive multitarget models. An overview of multitarget computational methods as well as of some successful applications to NDs will be the focus of this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Weinreb O, Amit T, Bar-Am O, Youdim MBH (2012) Ladostigil: a novel multimodal neuroprotective drug with cholinesterase and brain-selective monoamine oxidase inhibitory activities for Alzheimer’s disease treatment. Curr Drug Targets 13:483–494

    Article  CAS  Google Scholar 

  2. Morphy JR, Harris CJ (2012) Designing multi-target drugs. RSC Publishing, London. https://doi.org/10.1039/9781849734912

    Book  Google Scholar 

  3. Morphy R, Rankovic Z (2006) The physicochemical challenges of designing multiple ligands. J Med Chem 49:4961–4970

    Article  CAS  Google Scholar 

  4. Yuan Y, Pei J, Lai L (2011) LigBuilder 2: a practical de novo drug design approach. J Chem Inf Model 51:1083–1091

    Article  CAS  Google Scholar 

  5. Vinkers HM, de Jonge MR, Daeyaert FFD, Heeres J, Koymans LMH, van Lenthe JH et al (2003) SYNOPSIS: SYNthesize and OPtimize System in Silico. J Med Chem 46:2765–2773

    Article  CAS  Google Scholar 

  6. Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E et al (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:e1002380

    Article  CAS  Google Scholar 

  7. Digles D, Ecker GF (2011) Self-organizing maps for in silico screening and data visualization. Mol Inform 30:838–846

    Article  CAS  Google Scholar 

  8. Wang T, Wu M-B, Chen Z-J, Chen H, Lin J-P, Yang L-R (2015) Fragment-based drug discovery and molecular docking in drug design. Curr Pharm Biotechnol 16:11–25

    Article  Google Scholar 

  9. Cavalluzzi MM, Mangiatordi GF, Nicolotti O, Lentini G (2017) Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective. Expert Opin Drug Discov 12:1087–1104

    Article  CAS  Google Scholar 

  10. Singh M, Tam B, Akabayov B (2018) NMR-fragment based virtual screening: a brief overview. Molecules 23:233

    Article  Google Scholar 

  11. Hartshorn MJ, Murray CW, Cleasby A, Frederickson M, Tickle IJ, Jhoti H (2005) Fragment-based lead discovery using X-ray crystallography. J Med Chem 48:403–413

    Article  CAS  Google Scholar 

  12. Retra K, Irth H, van Muijlwijk-Koezen JE (2010) Surface plasmon resonance biosensor analysis as a useful tool in FBDD. Drug Discov Today Technol 7:e181–e187

    Article  CAS  Google Scholar 

  13. Recht MI, Nienaber V, Torres FE (2016) Fragment-based screening for enzyme inhibitors using calorimetry. Methods Enzymol 567:47–69

    Article  CAS  Google Scholar 

  14. Farina R, Pisani L, Catto M et al (2015) Structure-based design and optimization of multitarget-directed 2H-chromen-2-one derivatives as potent inhibitors of monoamine oxidase B and cholinesterases. J Med Chem 58:5561–5578

    Article  CAS  Google Scholar 

  15. Pisani L, Farina R, Soto-Otero R, Denora N, Mangiatordi GF, Nicolotti O et al (2016) Searching for multi-targeting neurotherapeutics against Alzheimer’s: discovery of potent AChE-MAO B inhibitors through the decoration of the 2H-chromen-2-one structural motif. Molecules 21:362

    Article  Google Scholar 

  16. Pisani L, Farina R, Catto M et al (2016) Exploring basic tail modifications of coumarin-based dual acetylcholinesterase-monoamine oxidase B inhibitors: identification of water-soluble, brain-permeant neuroprotective multitarget agents. J Med Chem 59:6791–6806

    Article  CAS  Google Scholar 

  17. Catto M, Nicolotti O, Leonetti F, Carotti A, Favia AD, Soto-Otero R et al (2006) Structural insights into monoamine oxidase inhibitory potency and selectivity of 7-substituted coumarins from ligand- and target-based approaches. J Med Chem 49:4912–4925

    Article  CAS  Google Scholar 

  18. Pisani L, Catto M, Giangreco I, Leonetti F, Nicolotti O, Stefanachi A et al (2010) Design, synthesis and biological evaluation of coumarin derivatives tethered to an edrophonium-like fragment as highly potent and selective dual binding site acetylcholinesterase inhibitors. ChemMedChem 5:1616–1630

    Article  CAS  Google Scholar 

  19. Milletti F, Vulpetti A (2010) Predicting polypharmacology by binding site similarity: from kinases to the protein universe. J Chem Inf Model 50:1418–1431

    Article  CAS  Google Scholar 

  20. Achenbach J, Klingler F-M, Blöcher R et al (2013) Exploring the chemical space of multitarget ligands using aligned self-organizing maps. ACS Med Chem Lett 4:1169–1172

    Article  CAS  Google Scholar 

  21. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2013) Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors. Curr Alzheimer Res 10:117–124

    Article  CAS  Google Scholar 

  22. Reker D, Rodrigues T, Schneider P, Schneider G (2014) Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci U S A 111:4067–4072

    Article  CAS  Google Scholar 

  23. Shahid M, Shahzad Cheema M, Klenner A, Younesi E, Hofmann-Apitius M (2013) SVM based descriptor selection and classification of neurodegenerative disease drugs for pharmacological modeling. Mol Inform 32:241–249

    Article  CAS  Google Scholar 

  24. Lauria A, Bonsignore R, Bartolotta R, Perricone U, Martorana A, Gentile C (2016) Drugs polypharmacology by in silico methods: new opportunities in drug discovery. Curr Pharm Des 22:3073–3081

    Article  CAS  Google Scholar 

  25. Pang X-C, Kang D, Fang J-S, Zhao Y, Xu L-J, Lian W-W et al (2018) Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer’s disease. Chin J Nat Med 16:53–62

    PubMed  Google Scholar 

  26. Adane L, Bharatam PV, Sharma V (2010) A common feature-based 3D-pharmacophore model generation and virtual screening: identification of potential PfDHFR inhibitors. J Enzyme Inhib Med Chem 25:635–645

    Article  CAS  Google Scholar 

  27. Evans DA, Doman TN, Thorner DA, Bodkin MJ (2007) 3D QSAR methods: phase and catalyst compared. J Chem Inf Model 47:1248–1257

    Article  CAS  Google Scholar 

  28. Bender A, Young DW, Jenkins JL, Serrano M, Mikhailov D, Clemons PA et al (2007) Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 10:719–731

    Article  CAS  Google Scholar 

  29. Mangiatordi GF, Alberga D, Pisani L, Gadaleta D, Trisciuzzi D, Farina R et al (2017) A rational approach to elucidate human monoamine oxidase molecular selectivity. Eur J Pharm Sci 101:90–99

    Article  CAS  Google Scholar 

  30. Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K et al (2016) Drug design for CNS diseases: polypharmacological profiling of compounds using cheminformatic, 3D-QSAR and virtual screening methodologies. Front Neurosci 10:265

    Article  Google Scholar 

  31. Zhang W, Pei J, Lai L (2017) Computational multitarget drug design. J Chem Inf Model 57:403–412

    Article  CAS  Google Scholar 

  32. Chaudhari R, Tan Z, Huang B, Zhang S (2017) Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 12:279–291

    Article  CAS  Google Scholar 

  33. Cross S, Baroni M, Carosati E, Benedetti P, Clementi S (2010) FLAP: GRID molecular interaction fields in virtual screening. Validation using the DUD data set. J Chem Inf Model 50:1442–1450

    Article  CAS  Google Scholar 

  34. Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40:W409–W414

    Article  CAS  Google Scholar 

  35. Yuan Y, Pei J, Lai L (2013) Binding site detection and druggability prediction of protein targets for structure-based drug design. Curr Pharm Des 19:2326–2333

    Article  CAS  Google Scholar 

  36. Wang X, Shen Y, Wang S, Li S, Zhang W, Liu X et al (2017) PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 45:W356–W360

    Article  CAS  Google Scholar 

  37. Wei D, Jiang X, Zhou L, Chen J, Chen Z, He C, Yang K et al (2008) Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J Med Chem 51:7882–7888

    Article  CAS  Google Scholar 

  38. Domínguez JL, Fernández-Nieto F, Castro M et al (2015) Computer-aided structure-based design of multitarget leads for Alzheimer’s disease. J Chem Inf Model 55:135–148

    Article  Google Scholar 

  39. Nicolotti O, Gillet VJ, Fleming PJ, Green DVS (2002) Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. J Med Chem 45:5069–5080

    Article  CAS  Google Scholar 

  40. Nicolotti O, Giangreco I, Miscioscia TF, Carotti A (2009) Improving quantitative structure-activity relationships through multiobjective optimization. J Chem Inf Model 49:2290–2302

    Article  CAS  Google Scholar 

  41. Nicolotti O, Giangreco I, Introcaso A, Leonetti F, Stefanachi A, Carotti A (2011) Strategies of multi-objective optimization in drug discovery and development. Expert Opin Drug Discov 6:871–884

    Article  CAS  Google Scholar 

  42. Mangiatordi GF, Alberga D, Altomare CD et al (2016) Mind the Gap! A journey towards computational toxicology. Mol Inform 35:294–308

    Article  CAS  Google Scholar 

  43. Nicolotti O, Benfenati E, Carotti A, Gadaleta D, Gissi A, Mangiatordi GF et al (2014) REACH and in silico methods: an attractive opportunity for medicinal chemists. Drug Discov Today 19:1757–1768

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy

    Marco Catto, Daniela Trisciuzzi, Domenico Alberga, Giuseppe Felice Mangiatordi & Orazio Nicolotti

Authors
  1. Marco Catto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniela Trisciuzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Domenico Alberga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Felice Mangiatordi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Orazio Nicolotti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Orazio Nicolotti .

Editor information

Editors and Affiliations

  1. Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India

    Kunal Roy

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Catto, M., Trisciuzzi, D., Alberga, D., Mangiatordi, G.F., Nicolotti, O. (2018). Multitarget Drug Design for Neurodegenerative Diseases. In: Roy, K. (eds) Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/7653_2018_17

Download citation

  • DOI: https://doi.org/10.1007/7653_2018_17

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8732-0

  • Online ISBN: 978-1-4939-8733-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation