Skip to main content

Advertisement

SpringerLink
Log in

Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Chagas’s is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi. According to the World Health Organization, 7 million people are infected worldwide leading to 7000 deaths per year. Drugs available, nifurtimox and benzimidazole, are limited due to low efficacy and high toxicity. As a validated target, cruzain represents a major front in drug discovery attempts for Chagas disease. Herein, we describe the development of 2D QSAR (\(r_{{{\text{pred}}}}^{2}\) = 0.81) and a 3D-QSAR-based pharmacophore (\(r_{{{\text{pred}}}}^{2}\) = 0.82) from a series of non-covalent cruzain inhibitors represented mostly by oxadiazoles (lead compound, IC50 = 200 nM). Both models allowed us to map key intermolecular interactions in S1′, S2 and S3 cruzain sub-sites (including halogen bond and C‒H/π). To probe the predictive capacity of obtained models, inhibitors available in the literature from different classes displaying a range of scaffolds were evaluate achieving mean absolute deviation of 0.33 and 0.51 for 2D and 3D models, respectively. CoMFA revealed an unexplored region where addition of bulky substituents to produce new compounds in the series could be beneficial to improve biological activity.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Abbreviations

AutoQSAR:

Automated QSAR

BSSE:

Basis set superposition error

B3LYP-D3:

Becke, three-parameter hybrid functional combined with Lee–Yang–Parr correlation functional including Grimme’s D3 dispersion scheme

CoMFA:

Comparative molecular field analysis

GA:

Genetic algorithm

GALAHAD:

Genetic algorithm with linear assignment for the hypermolecular alignment of datasets

HTS:

High-throughput screening

KPLS:

Kernel-based partial least square

LMP2:

Localized Møller–Plesset perturbation theory

MLR:

Multiple linear regression

OPLS3:

Optimized potentials for liquid simulations

PCR:

Principal component analysis

pIC50 :

Logarithm of the inverse of compound concentration that reduces the enzyme activity by 50% [log (1/IC50)]

PLS:

Partial least square

q 2 :

Leave-one-out cross-validated correlation coefficient

QSAR:

Quantitative structure–activity relationship

r 2 :

Non-cross-validated correlation coefficient

RMSE:

Root-mean-square error of test set predictions

SAR:

Structure–activity relationship

SD:

Standard deviation

SDC:

Standard deviation coefficient

SEE:

Standard error of estimate

SEP:

Standard error prediction

X3LYP:

Extended hybrid functional combined with Lee–Yang–Parr correlation functional

References

  1. Rassi A Jr, Rassi A, Marin-Neto JA (2010) Chagas disease. Lancet 375:1388–1402

    Article  Google Scholar 

  2. Bern C (2015) Chagas’ disease. N Engl J Med 373:456–466

    Article  CAS  Google Scholar 

  3. World Health Organization (2016) Investing to overcome the global impact of neglected tropical diseases: third WHO report on neglected tropical diseases. http://www.who.int/neglected_diseases/9789241564540$4en/. Accessed 9 Oct 2016

  4. Bern C, Montgomery SP (2009) An estimate of the burden of Chagas disease in the United States. Clin Infect Dis 49:e52‒e54

    Article  Google Scholar 

  5. Castro JA, de Mecca MM, Bartel LC (2006) Toxic side effects of drugs used to treat Chagas’ disease (American trypanosomiasis). Hum Exp Toxicol 25:471–479

    Article  CAS  Google Scholar 

  6. Campos MC et al (2014) Benznidazole-resistance in Trypanosoma cruzi: evidence that distinct mechanism can act in concert. Mol Biochem Parasitol 193:17–19

    Article  CAS  Google Scholar 

  7. Doyle PS et al (2011) The Trypanosoma cruzi protease cruzain mediates immune evasion. PLoS Pathog 7:e1002139

    Article  CAS  Google Scholar 

  8. Engel JC et al (1998) Cysteine protease inhibitors alter Golgi complex ultrastructure and function in Trypanosoma cruzi. J Cell Sci 111:597–606

    CAS  Google Scholar 

  9. Doyle PS et al (2007) A cysteine protease inhibitor cures Chagas’ disease in an immunodeficient-mouse model of infection. Antimicrob Agents Chemother 51:3932–3939

    Article  CAS  Google Scholar 

  10. Martinez-Mayorga K et al (2015) Cruzain inhibitors: efforts made, current leads and a structural outlook of new hits. Drug Discov Today 20:890–899

    Article  CAS  Google Scholar 

  11. Ferreira RS et al (2009) Divergent modes of enzyme inhibition in a homologous structure-activity series. J Med Chem 52:5005–5008

    Article  CAS  Google Scholar 

  12. Filho JMS et al (2009) Design, synthesis and cruzain docking of 3-(4-substituted-aryl)-1,2,4-oxadiazole-N-acylhydrazones as anti-Trypanosoma cruzi agents. Bioorg Med Chem 17:6682–6691

    Article  Google Scholar 

  13. Ferreira RS et al (2010) Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem 53:4891–4905

    Article  CAS  Google Scholar 

  14. Filho JMS et al (2012) Optimization of anti-Trypanosoma cruzi oxadiazoles leads to identification of compounds with efficacy in infected mice. Bioorg Med Chem 20:6423–6433

    Article  Google Scholar 

  15. Patani GA, LaVoie EJ (1996) Bioisosterism: a rational approach in drug design. Chem Rev 96:3147–3176

    Article  CAS  Google Scholar 

  16. Kaserer T et al (2015) Pharmacophore models and pharmacophore-based virtual screening: concepts and application exemplified on hydroxysteroid dehydrogenases. Molecules 20:22799–22832

    Article  CAS  Google Scholar 

  17. Malvezzi A et al (2009) Pharmacophore model of cruzain inhibitors. QSAR Comb Sci 28:781–784

    Article  CAS  Google Scholar 

  18. Cherkasov A et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  Google Scholar 

  19. Scotti M T et al (2016) Variable-selection approaches to generate QSAR models for a set of antichagasic semicarbazones and analogues. Chemometr Intell Lab 154:137–149

    Article  CAS  Google Scholar 

  20. Belaidi S et al (2015) Structure activity relationship and quantitative structure-activity relationships modeling of antitrypanosomal activities of alkyldiamine cryptolepine derivatives. J Comput Theor Nanosci 12:2421–2427

    Article  CAS  Google Scholar 

  21. Mendez-Lucio O et al (2011) 3D-QSAR studies on purine-carbonitriles as cruzain inhibitors: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). MedChemComm 2:1058–1065

    Article  CAS  Google Scholar 

  22. Freitas RF, Oprea TI, Montanari CA (2008) 2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L. Bioorg Med Chem 16:838–853

    Article  CAS  Google Scholar 

  23. Guido RVC et al (2008) Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi. J Enzym Inhib Med Chem 23:964–973

    Article  CAS  Google Scholar 

  24. Rodrigues CR et al (2011) CoMFA and HQSAR of acylhydrazide cruzain inhibitors. Bioorg Med Chem Lett 12:1537–1541

    Article  Google Scholar 

  25. Leonard JT, Roy K (2006) On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25:235–251

    Article  CAS  Google Scholar 

  26. Wood DJ, Davis AM (2013) Quantitative structure-activity relationship models that stand the test of time. Mol Pharm 10:1183–1190

    Article  Google Scholar 

  27. Dixon SJ et al (2016) AutoQSAR: an automated machine learning tool for best-practice. QSAR modeling. Future Med Chem 8:1825–1839

    Article  CAS  Google Scholar 

  28. Schrödinger Release 2016-2 (2016) Small-Molecule Drug Discovery suite, Schrödinger, LLC, New York

  29. Duan J et al (2010) Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph Model 29:157–170

    Article  CAS  Google Scholar 

  30. Sastry M et al (2010) Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model 50:771–784

    Article  CAS  Google Scholar 

  31. An Y, Sherman W, Dixon SL (2013) Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization. J Chem Inf Model 53:2312–2321

    Article  CAS  Google Scholar 

  32. Richmond NJ et al (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20:567–587

    Article  CAS  Google Scholar 

  33. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450

    Article  CAS  Google Scholar 

  34. Long W et al (2008) 3D-QSAR studies on a class of IKK-2 inhibitors with GALAHAD used to develop molecular alignment models. QSAR Comb Sci 27:1113–1119

    Article  CAS  Google Scholar 

  35. Harder E et al (2015) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12:281–296

    Article  Google Scholar 

  36. Sastry GM et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234

    Article  Google Scholar 

  37. Friesner RA et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196

    Article  CAS  Google Scholar 

  38. Halgren TA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    Article  CAS  Google Scholar 

  39. Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  40. Pettersen EF et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  Google Scholar 

  41. SYBYL®-X Suite Molecular Modeling from Sequence through Lead Optimization, version 2.0, Certara USA, Inc

  42. Melo-Filho CC, Braga RC, Andrade CH (2014) 3D-QSAR approaches in drug design: perspectives to generate reliable CoMFA models. Curr Comput Aided Drug Des 10:148–159

    Article  CAS  Google Scholar 

  43. Bochevarov AD et al (2013) Jaguar: a high-performance quantum chemistry software program with strengths in life and materials sciences. Int J Quantum Chem 113:2110–2142

    Article  CAS  Google Scholar 

  44. Kaminski GA et al (2005) Pseudospectral local second-order Mollet-Plesset methods for computation of hydrogen bonding energies of molecular pairs. J Chem Theory Comput 1:248–254

    Article  CAS  Google Scholar 

  45. Walker JD et al (2003) Guidelines for developing and using quantitative structure-activity relationships. Environ Toxicol Chem 22:1653–1665

    Article  CAS  Google Scholar 

  46. Desiraju GR et al (2013) Definition of the halogen bond (IUPAC recommendations 2013). Pure Appl Chem 85:1711–1713

    Article  CAS  Google Scholar 

  47. Cavallo G et al (2016) The halogen bond. Chem Rev 116:2478–2601

    Article  CAS  Google Scholar 

  48. Wilckern R et al (2012) Principles and applications of halogen bonding in medicinal chemistry and chemical biology. J Med Chem 56:1363–1388

    Article  Google Scholar 

  49. Ferreira RS et al (2014) Synthesis, biological evaluation, and structure–activity relationships of potent noncovalent and nonpeptidic cruzain inhibitors as anti-Trypanosoma cruzi agents. J Med Chem 57:2380–2392

    Article  CAS  Google Scholar 

  50. Tsuzuki S (2000) Origin of the attraction and directionality of the NH/π interaction: comparison with OH/π and CH/π interactions. J Am Chem Soc 122:11450–11458

    Article  CAS  Google Scholar 

  51. Tsuzuki S (2006) Magnitude and directionality of the interaction energy and aliphatic CH/π interaction: significant difference from hydrogen bond. J Phys Chem A 110:10163–10168

    Article  CAS  Google Scholar 

  52. Barreiro EJ, Kummerle AE, Fraga CAM (2011) The methylation effect in medicinal chemistry. Chem Rev 11:5215–5246

    Article  Google Scholar 

  53. Wiggers HJ (2013) Non-peptidic cruzain inhibitors with Trypanocidal activity discovered by virtual screening and in vitro assay. PLoS Negl Trop Dis 7:e2370

    Article  Google Scholar 

  54. Carvalho SA et al (2012) Design and synthesis of new (E)-cinnamic N-acylhydrazones as potent antirypanosomal agents. Eur J Med Chem 54:512–521

    Article  CAS  Google Scholar 

  55. Trossini GH et al (2010) Cruzain inhibition by hydroxymethylnitrofurazone and nitrofurazone: investigation of a new target in Trypanosoma cruzi. J Enzym Inhib Med Chem 25(1):62–67

    Article  CAS  Google Scholar 

  56. Aguilera E et al (2016) Potent and selective inhibitors of Trypanosoma cruzi triosephosphate isomerase with concomitant inhibition of cruzipain: inhibition of parasite growth through multitarget activity. ChemMedChem 11(12):1328–1338

    Article  CAS  Google Scholar 

  57. Sassano MF et al (2103) Colloidal aggregation causes inhibition of G protein-coupled receptors. J Med Chem 56(6):2406–2414

    Article  Google Scholar 

  58. Braga SFP et al (2017) Synthesis and biological evaluation of potential inhibitors of the cysteine proteases cruzain and rhodesain designed by molecular simplification. Bioorg Med Chem 25(6):1889–1890

    Article  CAS  Google Scholar 

  59. Couto M et al (2015) 2-H-[1,2]Dithiole as a new anti-Trypanosoma cruzi chemotype: biological and mechanism of action studies. Molecules 20(8):14595–14610

    Article  CAS  Google Scholar 

  60. Burger, MCM et al (2014) Structures and bioactivities of dihydrochalcones from Metrodorea stipularis. J Nat Prod 77(11):2418–2422

    Article  CAS  Google Scholar 

  61. Serafim RAM et al (2017) Molecular modeling and structure-activity relationships studies of bioisoster hybrids on N-acylhydrazone. Med Chem Res 26(4):760–769

    Article  CAS  Google Scholar 

  62. Silva-Junior EF et al (2016) Design, synthesis, molecular docking and biological evaluation of thiophen-2-iminothiazolidine derivatives for use against Trypanosoma cruzi. Bioorg Med Chem 24(18):4228–4240

    Article  CAS  Google Scholar 

  63. Bellara CL et al (2013) Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. J Chem Inf Model 53(9):2402–2408

    Article  Google Scholar 

  64. Schonherr H, Cernak T (2013) Profound methyl effects in drug discovey and a call for new C–H methylation reactions. Angew Chem Int Ed 52:12256–12267

    Article  Google Scholar 

Download references

Funding

Financial support was provided by the State of São Paulo Research Foundation (FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo), Grant 2013/07600-3. A.S.S. and M.T.O acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for institutional grants.

Author information

Authors and Affiliations

  1. Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São Carlos, Universidade de São Paulo, Avenida João Dagnone 1100, São Carlos, SP, 13563-120, Brazil

    Anacleto S. de Souza, Marcelo T. de Oliveira & Adriano D. Andricopulo

  2. Instituto de Química de São Carlos, Universidade de São Paulo, Avenida Trabalhador são-carlense 400, São Carlos, SP, 13566-590, Brazil

    Marcelo T. de Oliveira

Authors
  1. Anacleto S. de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcelo T. de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adriano D. Andricopulo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adriano D. Andricopulo.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 1639 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Souza, A.S., de Oliveira, M.T. & Andricopulo, A.D. Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies. J Comput Aided Mol Des 31, 801–816 (2017). https://doi.org/10.1007/s10822-017-0039-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-017-0039-0

Keywords

Search

Navigation