Skip to main content

Advertisement

SpringerLink
Log in

Application of multilayered strategy for variable selection in QSAR modeling of PET and SPECT imaging agents as diagnostic agents for Alzheimer’s disease

  • Original Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

Non-invasive imaging of amyloid beta (Aβ) and tau fibrils in the brain may support an early and precise diagnosis of Alzheimer’s disease. Molecular imaging technologies involving radionuclides such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) against beta amyloid plaques and tau fibrils are among emerging research areas in the field of medicinal chemistry. In the current study, we have developed partial least square (PLS) regression-based two-dimensional quantitative structure-activity relationship (2D-QSAR) models using datasets of 38 PET and 73 SPECT imaging agents targeted against Aβ protein and 31 imaging agents (both PET and SPECT) targeted against tau protein. Following the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have strived to select significant descriptors from the large initial pool of descriptors using multilayered variable selection strategy using the double cross-validation (DCV) method followed by the best subset selection (BSS) method prior to the development of the final PLS models. The developed models showed significant statistical performance and reliability. Molecular docking studies have been performed to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. Furthermore, we have also designed some imaging agents based on the information provided by the models developed and some of them are predicted to be similar to or more active than the most active imaging agents present in the original dataset.

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

Similar content being viewed by others

References

  1. Schilling LP, Zimmer ER, Shin M, Leuzy A, Pascoal TA, Benedet AL, Borelli WV, Palmini A, Gauthier S, Rosa-Neto P (2016) Imaging Alzheimer’s disease pathophysiology with PET. Dement Neuropsychol 10:79–90

    PubMed  PubMed Central  Google Scholar 

  2. Klunk WE (1998) Biological markers of Alzheimer’s disease. Neurobiol Aging 2:145–147

    Google Scholar 

  3. Selkoe DJ (2001) Alzheimer’s disease: genes, proteins, and therapy. Physiol Rev 81:741–766

    CAS  PubMed  Google Scholar 

  4. Wimo A, Winblad B, Aguero-Torres H, von Strauss E (2003) The magnitude of dementia occurrence in the world. Alzheimer Dis Assoc Disord 17:63–67

    PubMed  Google Scholar 

  5. Alzheimer Association. Alzheimer’s and dementia facts and figures https://www.alz.org/alzheimers-dementia/facts-figures. Accessed on 20 Nov 2018

  6. Okamura N, Furumoto S, Harada R, Tago T, Yoshikawa T, Fodero-Tavoletti M, Mulligan RS, Villemagne VL, Akatsu H, Yamamoto T (2013) Novel 18F-labeled arylquinoline derivatives for noninvasive imaging of tau pathology in Alzheimer disease. J Nucl Med 54:1420–1427

    CAS  PubMed  Google Scholar 

  7. Duyckaerts C, Clavaguera F, Potier M-C (2019) The prion-like propagation hypothesis in Alzheimer’s and Parkinson’s disease. Curr Opin Neurol 32:266–271

    CAS  PubMed  Google Scholar 

  8. Hamley IW (2012) The amyloid beta peptide: a chemist’s perspective. Role in Alzheimer’s and fibrillization. Chem Rev 112:5147–5192

    CAS  PubMed  Google Scholar 

  9. Lichtenberg B, Mandelkow EM, Hagestedt T, Mandelkow E (1988) Structure and elasticity of microtubule-associated protein tau. Nature 334:359

    CAS  PubMed  Google Scholar 

  10. Barghorn S, Davies P, Mandelkow E (2004) Tau paired helical filaments from Alzheimer’s disease brain and assembled in vitro are based on β-structure in the core domain. Biochemistry 43:1694–1703

    CAS  PubMed  Google Scholar 

  11. Bondareff W, Mountjoy CQ, Roth M, Hauser DL (1989) Neurofibrillary degeneration and neuronal loss in Alzheimer’s disease. Neurobiol Aging 10:709–715

    CAS  PubMed  Google Scholar 

  12. Bobinski M, Wegiel J, Wisniewski HM, Tarnawski M, Bobinski M, Reisberg B, De Leon MJ, Miller DC (1996) Neurofibrillary pathology—correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiol Aging 17:909–919

    CAS  PubMed  Google Scholar 

  13. Ono M, Hayashi S, Matsumura K, Kimura H, Okamoto Y, Ihara M, Takahashi R, Mori H, Saji H (2011) Rhodanine and thiohydantoin derivatives for detecting tau pathology in Alzheimer’s brains. ACS Chem Neurosci 2:269–275

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang Y, Klunk WE, Debnath ML, Huang G-F, Holt DP, Shao L, Mathis CA (2004) Development of a PET/SPECT agent for amyloid imaging in Alzheimer’s disease. J Mol Neurosci 24:55–62

    PubMed  Google Scholar 

  15. Yang Y, Cui M, Jin B, Wang X, Li Z, Yu P, Jia J, Fu H, Jia H, Liu B (2013) 99mTc-labeled dibenzylideneacetone derivatives as potential SPECT probes for in vivo imaging of β-amyloid plaque. Eur J Med Chem 64:90–98

    CAS  PubMed  Google Scholar 

  16. Kung HF, Choi SR, Qu W, Zhang W, Skovronsky D (2009) 18F stilbenes and styrylpyridines for PET imaging of Aβ plaques in Alzheimer’s disease: a miniperspective. J Med Chem 53:933–941

    Google Scholar 

  17. Rojo LE, Alzate-Morales J, Saavedra IN, Davies P, Maccioni RB (2010) Selective interaction of lansoprazole and astemizole with tau polymers: potential new clinical use in diagnosis of Alzheimer’s disease. J Alzheimers Dis 19:573–589

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Jensen JR, Cisek K, Funk KE, Naphade S, Schafer KN, Kuret J (2011) Research towards tau imaging. J Alzheimers Dis 26:147–115

    PubMed  Google Scholar 

  19. Fodero-Tavoletti MT, Okamura N, Furumoto S, Mulligan RS, Connor AR, McLean CA, Cao D, Rigopoulos A, Cartwright GA, O’keefe G (2011) 18F-THK523: a novel in vivo tau imaging ligand for Alzheimer’s disease. Brain 134:1089–1100

    PubMed  Google Scholar 

  20. Villemagne VL, Furumoto S, Fodero-Tavoletti M, Harada R, Mulligan RS, Kudo Y, Masters CL, Yanai K, Rowe CC, Okamura N (2012) The challenges of tau imaging. Future Neurol 7:409–421

    CAS  Google Scholar 

  21. Ono M, Saji H (2011) SPECT imaging agents for detecting cerebral β-amyloid plaques. Int J Mol Imaging 2011. https://doi.org/10.1155/2011/543267

    Google Scholar 

  22. Small GW, Agdeppa ED, Kepe V, Satyamurthy N, Huang S-C, Barrio JR (2002) In vivo brain imaging of tangle burden in humans. J Mol Neurosci 19:321–327

    Google Scholar 

  23. Shoghi-Jadid K, Small GW, Agdeppa ED, Kepe V, Ercoli LM, Siddarth P, Read S, Satyamurthy N, Petric A, Huang S-C (2002) Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer disease. Am J Geriatr Psychiatry 10:24–35

    PubMed  Google Scholar 

  24. Hansch C, Leo A, Hoekman DH (1995) Exploring QSAR: fundamentals and applications in chemistry and biology. American Chemical Society, Washington, DC

    Google Scholar 

  25. Hansch C, Leo A, Mekapati SB, Kurup A (2004) Qsar and Adme. Bioorg Med Chem 12:3391–3400

    CAS  PubMed  Google Scholar 

  26. Klein C, Kaiser D, Kopp S, Chiba P, Ecker GF (2002) Similarity based SAR (SIBAR) as tool for early ADME profiling. J Comput Aided Mol Des 16:785–793

    CAS  PubMed  Google Scholar 

  27. Toropova MA (2017) Drug metabolism as an object of computational analysis by the Monte Carlo method. Curr Drug Metab 18:1123–1131

    CAS  PubMed  Google Scholar 

  28. Toropova AP, Toropov AA (2018) CORAL: Monte Carlo method to predict endpoints for medical chemistry. Mini Rev Med Chem 18:382–391

    CAS  PubMed  Google Scholar 

  29. Toropova AP, Toropov AA, Begum S, Achary PGR (2018) Blood brain barrier and Alzheimer’s disease: similarity and dissimilarity of molecular alerts. Curr Neuropharmacol 16:769–785

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Toropova MA, Toropov AA, Raška Jr I, Rašková M (2015) Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Comput Biol Med 64:148–154

    CAS  PubMed  Google Scholar 

  31. Roy K, Ambure P (2016) The “double cross-validation” software tool for MLR QSAR model development. Chemom Intell Lab Syst 159:108–126

    CAS  Google Scholar 

  32. Herholz K, Ebmeier K (2011) Clinical amyloid imaging in Alzheimer’s disease. Lancet Neurol 10:667–670

    CAS  PubMed  Google Scholar 

  33. Cohen AD, Rabinovici GD, Mathis CA, Jagust WJ, Klunk WE, Ikonomovic MD (2012) Using Pittsburgh compound B for in vivo PET imaging of fibrillar amyloid-beta. Adv Pharmacol 64:27–81

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhu L, Ploessl K, Kung HF (2014) PET/SPECT imaging agents for neurodegenerative diseases. Chem Soc Rev 43:6683–6691

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Mathis CA, Wang Y, Holt DP, Huang G-F, Debnath ML, Klunk WE (2003) Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem 46:2740–2754

    CAS  PubMed  Google Scholar 

  36. Ono M, Kawashima H, Nonaka A, Kawai T, Haratake M, Mori H, Kung M-P, Kung HF, Saji H, Nakayama M (2006) Novel benzofuran derivatives for PET imaging of β-amyloid plaques in Alzheimer’s disease brains. J Med Chem 49:2725–2730

    CAS  PubMed  Google Scholar 

  37. Qu W, Kung M-P, Hou C, Jin L-W, Kung HF (2007) Radioiodinated aza-diphenylacetylenes as potential SPECT imaging agents for β-amyloid plaque detection. Bioorg Med Chem Lett 17:3581–3584

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Ono M, Cheng Y, Kimura H, Watanabe H, Matsumura K, Yoshimura M, Iikuni S, Okamoto Y, Ihara M, Takahashi R (2013) Development of novel 123I-labeled pyridyl benzofuran derivatives for SPECT imaging of β-amyloid plaques in Alzheimer’s disease. PLoS One 8:e74104

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Fuchigami T, Yamashita Y, Kawasaki M, Ogawa A, Haratake M, Atarashi R, Sano K, Nakagaki T, Ubagai K, Ono M (2016) Characterisation of radioiodinated flavonoid derivatives for SPECT imaging of cerebral prion deposits. Sci Rep 5:18440

    Google Scholar 

  40. Maya Y, Ono M, Watanabe H, Haratake M, Saji H, Nakayama M (2008) Novel radioiodinated aurones as probes for SPECT imaging of β-amyloid plaques in the brain. Bioconjug Chem 20:95–101

    Google Scholar 

  41. Alagille D, DaCosta H, Baldwin RM, Tamagnan GD (2011) 2-Arylimidazo [2, 1-b] benzothiazoles: a new family of amyloid binding agents with potential for PET and SPECT imaging of Alzheimer’s brain. Bioorg Med Chem Lett 21:2966–2968

    CAS  PubMed  Google Scholar 

  42. Maya Y, Okumura Y, Kobayashi R, Onishi T, Shoyama Y, Barret O, Alagille D, Jennings D, Marek K, Seibyl J (2015) Preclinical properties and human in vivo assessment of 123 I-ABC577 as a novel SPECT agent for imaging amyloid-β. Brain 139:193–203

    PubMed  PubMed Central  Google Scholar 

  43. Kung M-P, Hou C, Zhuang Z-P, Skovronsky DM, Zhang B, Gur TL, Trojanowski JQ, Lee VMY, Kung HF (2002) Radioiodinated styrylbenzene derivatives as potential SPECT imaging agents for amyloid plaque detection in Alzheimer’s disease. J Mol Neurosci 19:7–10

    CAS  PubMed  Google Scholar 

  44. Pan J, Mason NS, Debnath ML, Mathis CA, Klunk WE, Lin K-S (2013) Design, synthesis and structure–activity relationship of rhenium 2-arylbenzothiazoles as β-amyloid plaque binding agents. Bioorg Med Chem Lett 23:1720–1726

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Okamura N, Suemoto T, Furumoto S, Suzuki M, Shimadzu H, Akatsu H, Yamamoto T, Fujiwara H, Nemoto M, Maruyama M (2005) Quinoline and benzimidazole derivatives: candidate probes for in vivo imaging of tau pathology in Alzheimer’s disease. J Neurosci 25:10857–10862

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Declercq L, Celen S, Lecina J, Ahamed M, Tousseyn T, Moechars D, Alcazar J, Ariza M, Fierens K, Bottelbergs A (2016) Comparison of new tau PET-tracer candidates with [18F] T808 and [18F] T807. Mol Imaging 15:1536012115624920

    PubMed  PubMed Central  Google Scholar 

  47. Tago T, Furumoto S, Okamura N, Harada R, Adachi H, Ishikawa Y, Yanai K, Iwata R, Kudo Y (2016) Structure–activity relationship of 2-arylquinolines as PET imaging tracers for tau pathology in Alzheimer disease. J Nucl Med 57:608–614

    CAS  PubMed  Google Scholar 

  48. Hashimoto H, Kawamura K, Takei M, Igarashi N, Fujishiro T, Shiomi S, Watanabe R, Muto M, Furutsuka K, Ito T (2015) Identification of a major radiometabolite of [11C] PBB3. Nucl Med Biol 42:905–910

    CAS  PubMed  Google Scholar 

  49. Tago T, Furumoto S, Okamura N, Harada R, Ishikawa Y, Arai H, Yanai K, Iwata R, Kudo Y (2014) Synthesis and preliminary evaluation of 2-arylhydroxyquinoline derivatives for tau imaging. J Label Compd Radiopharm 57:18–24

    CAS  Google Scholar 

  50. Matsumura K, Ono M, Hayashi S, Kimura H, Okamoto Y, Ihara M, Takahashi R, Mori H, Saji H (2011) Phenyldiazenyl benzothiazole derivatives as probes for in vivo imaging of neurofibrillary tangles in Alzheimer’s disease brains. MedChemComm 2:596–600

    CAS  Google Scholar 

  51. MarvinSketch software, https://www.chemaxon.com. Accessed 28 Dec 2018

  52. Dragon version 7, Kodesrl, Milan, Italy, 2016; software available at http://www.talete.mi.it/index.htm. Accessed 03 Jan 2019

  53. Golbraikh A, Shen M, Xiao Z, Xiao Y-D, Lee K-H, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253

    CAS  PubMed  Google Scholar 

  54. Golmohammadi H, Dashtbozorgi Z, Acree Jr WE (2012) Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine. Eur J Pharm Sci 47:421–429

    CAS  PubMed  Google Scholar 

  55. Park H-S, Jun C-H (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341

    Google Scholar 

  56. Khan PM, Roy K (2018) Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR). Expert Opin Drug Discov 13:1075–1089

    CAS  PubMed  Google Scholar 

  57. Pope PT, Webster JT (1972) The use of an F-statistic in stepwise regression procedures. Technometrics 14:327–340

    Google Scholar 

  58. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130

    CAS  Google Scholar 

  59. Baumann D, Baumann K (2014) Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation. J Cheminform 6:47

    PubMed  PubMed Central  Google Scholar 

  60. Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, New York

    Google Scholar 

  61. Todeschini R, Ballabio D, Grisoni F (2016) Beware of unreliable Q2! A comparative study of regression metrics for predictivity assessment of QSAR models. J Chem Inf Model 56:1905–1913

    CAS  PubMed  Google Scholar 

  62. Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52:2044–2058

    CAS  PubMed  Google Scholar 

  63. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408

    CAS  PubMed  Google Scholar 

  64. Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33

    CAS  Google Scholar 

  65. Paravastu AK, Leapman RD, Yau W-M, Tycko R (2008) Molecular structural basis for polymorphism in Alzheimer’s β-amyloid fibrils. Proc Natl Acad Sci 105:18349–18354

    CAS  PubMed  Google Scholar 

  66. Andrei SA, Meijer FA, Neves JF, Brunsveld L, Landrieu I, Ottmann C, Milroy L-G (2018) Inhibition of 14-3-3/Tau by hybrid small-molecule peptides operating via two different binding modes. ACS Chem Neurosci 9:2639–2654

    CAS  PubMed  PubMed Central  Google Scholar 

  67. BIOVIA Discovery studio 2018. http://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/requirements/technical-requirements-410.html. Accessed 08 Feb 2019

  68. Wu G, Robertson DH, Brooks Iii CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER—a CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562

    CAS  PubMed  Google Scholar 

  69. Benfenati E (2011) Quantitative structure-activity relationships (QSAR) for pesticide regulatory purposes. Elsevier, Amsterdam

    Google Scholar 

  70. Chartrand G, Johns GL, Tian S (1993) Detour distance in graphs. Ann Discrete Math 55:127–136

    Google Scholar 

  71. Akarachantachote N, Chadcham S, Saithanu K (2014) Cutoff threshold of variable importance in projection for variable selection. Int J Pure Appl Math 94:307–322

    Google Scholar 

  72. Jackson JE (2005) A user’s guide to principal components. John Wiley & Sons, New Jersey

    Google Scholar 

  73. Gadaleta D, Mangiatordi GF, Catto M, Carotti A, Nicolotti O (2016) Applicability domain for QSAR models: where theory meets reality. IJQSPR 1:45–63

    Google Scholar 

  74. U. Simca-P, 10.0, info@umetrics.com, www.umetrics.com, Umea, Sweden, 2002. Accessed 22 Jan 2019

  75. Rücker C, Rücker G, Meringer M (2007) Y-randomization and its variants in QSPR/QSAR. J Chem Inf Model 47:2345–2357

    PubMed  Google Scholar 

  76. Alkorta I, Rozas I, Elguero J (1998) Non-conventional hydrogen bonds. Chem Soc Rev 27:163–170

    CAS  Google Scholar 

  77. Ribas J, Cubero E, Luque FJ, Orozco M (2002) Theoretical study of alkyl-π and aryl-π interactions. Reconciling theory and experiment. J Org Chem 67:7057–7065

    CAS  PubMed  Google Scholar 

  78. Echeverría J (2017) Alkyl groups as electron density donors in π-hole bonding. CrystEngComm 19:6289–6296

    Google Scholar 

  79. Martinez CR, Iverson BL (2012) Rethinking the term “pi-stacking”. Chem Sci 3:2191–2201

    CAS  Google Scholar 

  80. Shiri F, Shahraki S, Baneshi S, Nejati-Yazdinejad M, Majd MH (2016) Synthesis, characterization, in vitro cytotoxicity, in silico ADMET analysis and interaction studies of 5-dithiocarbamato-1, 3, 4-thiadiazole-2-thiol and its zinc (ii) complex with human serum albumin: combined spectroscopy and molecular docking investigations. RSC Adv 6:106516–106526

    CAS  Google Scholar 

  81. Darras FH, Pang Y-P (2017) On the use of the experimentally determined enzyme inhibition constant as a measure of absolute binding affinity. Biochem Biophys Res Commun 489:451–454

    CAS  PubMed  Google Scholar 

  82. De P, Roy K (2018) Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors. SAR QSAR Environ Res 29:319–337

    CAS  PubMed  Google Scholar 

  83. Khan K, Benfenati E, Roy K (2019) Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: ranking and prioritization of the DrugBank database compounds. Ecotoxicol Environ Saf 168:287–297

    CAS  PubMed  Google Scholar 

Download references

Funding

PD received financial assistance from the Department of Atomic Energy—Board of Research in Nuclear Sciences (DAE-BRNS).

Author information

Authors and Affiliations

  1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India

    Priyanka De & Kunal Roy

  2. Computational Science Division, Saha Institute of Nuclear Physics, Kolkata, 700064, India

    Dhananjay Bhattacharyya

Authors
  1. Priyanka De
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dhananjay Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kunal Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunal Roy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 10268 kb)

ESM 2

(XLSX 20 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De, P., Bhattacharyya, D. & Roy, K. Application of multilayered strategy for variable selection in QSAR modeling of PET and SPECT imaging agents as diagnostic agents for Alzheimer’s disease. Struct Chem 30, 2429–2445 (2019). https://doi.org/10.1007/s11224-019-01376-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-019-01376-z

Keywords

Search

Navigation