Advertisement

Quantification of [18F]UCB-H Binding in the Rat Brain: From Kinetic Modelling to Standardised Uptake Value

  • Maria Elisa SerranoEmail author
  • Mohamed Ali Bahri
  • Guillaume Becker
  • Alain Seret
  • Frédéric Mievis
  • Fabrice Giacomelli
  • Christian Lemaire
  • Eric Salmon
  • André Luxen
  • Alain Plenevaux
Research Article
  • 105 Downloads

Abstract

Purpose

[18F]UCB-H is a specific positron emission tomography (PET) biomarker for the Synaptic Vesicle protein 2A (SV2A), the binding site of the antiepileptic drug levetiracetam. With a view to optimising acquisition time and simplifying data analysis with this radiotracer, we compared two parameters: the distribution volume (Vt) obtained from Logan graphical analysis using a Population-Based Input Function, and the Standardised Uptake Value (SUV).

Procedures

Twelve Sprague Dawley male rats, pre-treated with three different doses of levetiracetam were employed to develop the methodology. Three additional kainic acid (KA) treated rats (temporal lobe epilepsy model) were also used to test the procedure. Image analyses focused on: (i) length of the dynamic acquisition (90 versus 60 min); (ii) correlations between Vt and SUV over 20-min consecutive time-frames; (iii) and (iv) evaluation of differences between groups using the Vt and the SUV; and (v) preliminary evaluation of the methodology in the KA epilepsy model.

Results

A large correlation between the Vt issued from 60 to 90-min acquisitions was observed. Further analyses highlighted a large correlation (r > 0.8) between the Vt and the SUV. Equivalent differences between groups were detected for both parameters, especially in the 20–40 and 40–60-min time-frames. The same results were also obtained with the epilepsy model.

Conclusions

Our results enable the acquisition setting to be changed from a 90-min dynamic to a 20-min static PET acquisition. According to a better image quality, the 20–40-min time-frame appears optimal. Due to its equivalence to the Vt, the SUV parameter can be considered in order to quantify [18F]UCB-H uptake in the rat brain. This work, therefore, establishes a starting point for the simplification of SV2A in vivo quantification with [18F]UCB-H, and represents a step forward to the clinical application of this PET radiotracer.

Key Words

SV2A PET [18F]UCB-H Quantification Distribution volume SUV KASE 

Notes

Acknowledgments

We would like to thank Miguel Manuel de Villena for the English revision of the manuscript. The authors are grateful to the Nucleis ULiège spin-off technicians for their help producing [18F]UCB-H.

Funding Information

This work was funded by University of Liège grant 13/17-07 and UCB BioPharma as partners. MES is supported by ULiege ARC 13/17 07 grant. AP is research director from FRS-FNRS Belgium.

Compliance with Ethical Standards

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Ethical Approval

All animal experiments were performed according to the Helsinki declaration and conducted in accordance with the European guidelines for care of laboratory animals (2010/63/EU). All procedures were reviewed and approved by the Institutional Animal Care and Use Ethics Committee of the University of Liege, Belgium.

References

  1. 1.
    Heurling K, Leuzy A, Jonasson M, Frick A, Zimmer ER, Nordberg A, Lubberink M (2017) Quantitative positron emission tomography in brain research. Brain Res 1670:220–234CrossRefGoogle Scholar
  2. 2.
    Maria Moresco R, Messa C, Lucignani G, Rizzo G G, Todde S, Carla Gilardi M, Grimaldi A, Fazio F (2001) PET in psychopharmacology. Pharmacol Res 44:151–159CrossRefGoogle Scholar
  3. 3.
    Vāvere AL, Scott PJH (2017) Clinical applications of small-molecule PET radiotracers: current progress and future outlook. Semin Nucl Med 47:429–453CrossRefGoogle Scholar
  4. 4.
    Acton PD, Zhuang H, Alavi A (2004) Quantification in PET. Radiol Clin N Am 42:1055–1062 viiiCrossRefGoogle Scholar
  5. 5.
    Cunningham VJ, Gunn RN, Matthews JC (2004) Quantification in positron emission tomography for research in pharmacology and drug development. Nucl Med Commun 25:643–646CrossRefGoogle Scholar
  6. 6.
    Fang Y-H, Kao T, Liu R-S, Wu L-C (2004) Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data. Eur J Nucl Med Mol Imaging 31:692–702CrossRefGoogle Scholar
  7. 7.
    Li F, Joergensen JT, Hansen AE, Kjaer A (2014) Kinetic modeling in PET imaging of hypoxia. Am J Nucl Med Mol Imaging 4:490–506PubMedPubMedCentralGoogle Scholar
  8. 8.
    Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, MacGregor RR, Hitzemann R, Bendriem B, Gatley SJ, Christman DR (1990) Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(−)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab 10:740–747CrossRefGoogle Scholar
  9. 9.
    Varga J, Szabo Z (2002) Modified regression model for the Logan plot. J Cereb Blood Flow Metab 22:240–244CrossRefGoogle Scholar
  10. 10.
    Tomasi G, Turkheimer F, Aboagye E (2012) Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future. Mol Imaging Biol 14:131–146CrossRefGoogle Scholar
  11. 11.
    Kinahan PE, Fletcher JW (2010) Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MR 31:496–505CrossRefGoogle Scholar
  12. 12.
    Bahri MA, Plenevaux A, Aerts J, Bastin C, Becker G, Mercier J, Valade A, Buchanan T, Mestdagh N, Ledoux D, Seret A, Luxen A, Salmon E (2017) Measuring brain synaptic vesicle protein 2A with positron emission tomography and [18F]UCB-H. TRCI 3:481–486Google Scholar
  13. 13.
    Zanotti-Fregonara P, Chen K, Liow J-S, Fujita M, Innis RB (2011) Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab 31:1986–1998CrossRefGoogle Scholar
  14. 14.
    Rissanen E, Tuisku J, Luoto P, Arponen E, Johansson J, Oikonen V, Parkkola R, Airas L, Rinne JO (2015) Automated reference region extraction and population-based input function for brain [11C]TMSX PET image analyses. J Cereb Blood Flow Metab 35:157–165CrossRefGoogle Scholar
  15. 15.
    Mabrouk R, Strafella AP, Knezevic D, Ghadery C, Mizrahi R, Gharehgazlou A, Koshimori Y, Houle S, Rusjan P (2017) Feasibility study of TSPO quantification with [18F]FEPPA using population-based input function. PLoS One 12:e0177785CrossRefGoogle Scholar
  16. 16.
    Zanotti-Fregonara P, Hirvonen J, Lyoo CH, Zoghbi SS, Rallis-Frutos D, Huestis MA, Morse C, Pike VW, Innis RB (2013) Population-based input function modeling for [18F]FMPEP-d 2, an inverse agonist radioligand for cannabinoid CB1 receptors: validation in clinical studies. PLoS One 8:e60231CrossRefGoogle Scholar
  17. 17.
    Zanotti-Fregonara P, Hines CS, Zoghbi SS, Liow JS, Zhang Y, Pike VW, Drevets WC, Mallinger AG, Zarate CA Jr, Fujita M, Innis RB (2012) Population-based input function and image-derived input function for [11C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder. Neuroimage 63:1532–1541CrossRefGoogle Scholar
  18. 18.
    Zanotti-Fregonara P, Maroy R, Peyronneau M-A, Trebossen R, Bottlaender M (2012) Minimally invasive input function for 2-18F-fluoro-A-85380 brain PET studies. Eur J Nucl Med Mol Imaging 39:651–659CrossRefGoogle Scholar
  19. 19.
    Zanderigo F, Ogden RT, Parsey RV (2013) Reference region approaches in PET: a comparative study on multiple radioligands. J Cereb Blood Flow Metab 33:888–897CrossRefGoogle Scholar
  20. 20.
    Lammertsma AA (2017) Forward to the past: the case for quantitative PET imaging. J Nucl Med 58:1019–1024CrossRefGoogle Scholar
  21. 21.
    Lucignani G, Paganelli G, Bombardieri E (2004) The use of standardized uptake values for assessing FDG uptake with PET in oncology: a clinical perspective. Nucl Med Commun 25:651–656CrossRefGoogle Scholar
  22. 22.
    Strauss LG, Dimitrakopoulou-Strauss A, Haberkorn U (2003) Shortened PET data acquisition protocol for the quantification of 18F-FDG kinetics. J Nucl Med 44:1933–1939PubMedGoogle Scholar
  23. 23.
    Dimitrakopoulou-Strauss A, Pan L, Strauss LG (2012) Quantitative approaches of dynamic FDG-PET and PET/CT studies (dPET/CT) for the evaluation of oncological patients. Cancer Imaging 12:283–289CrossRefGoogle Scholar
  24. 24.
    Lockhart SN, Baker SL, Okamura N, Furukawa K, Ishiki A, Furumoto S, Tashiro M, Yanai K, Arai H, Kudo Y, Harada R, Tomita N, Hiraoka K, Watanuki S, Jagust WJ (2016) Dynamic PET measures of tau accumulation in cognitively normal older adults and Alzheimer’s disease patients measured using [18F] THK-5351. PLoS One 11:e0158460CrossRefGoogle Scholar
  25. 25.
    Lopes Alves I, Vállez García DV, Parente A, et al. (2017) Parametric imaging of [11C]flumazenil binding in the rat brain. Mol Imaging Biol 1–10Google Scholar
  26. 26.
    Bretin F, Warnock G, Bahri MA, Aerts J, Mestdagh N, Buchanan T, Valade A, Mievis F, Giacomelli F, Lemaire C, Luxen A, Salmon E, Seret A, Plenevaux A (2013) Preclinical radiation dosimetry for the novel SV2A radiotracer [18F]UCB-H. EJNMMI Res 3:35CrossRefGoogle Scholar
  27. 27.
    Warnock GI, Aerts J, Bahri MA, Bretin F, Lemaire C, Giacomelli F, Mievis F, Mestdagh N, Buchanan T, Valade A, Mercier J, Wood M, Gillard M, Seret A, Luxen A, Salmon E, Plenevaux A (2014) Evaluation of 18F-UCB-H as a novel PET tracer for synaptic vesicle protein 2A in the brain. J Nucl Med 55:1336–1341CrossRefGoogle Scholar
  28. 28.
    Bretin F, Bahri MA, Bernard C, Warnock G, Aerts J, Mestdagh N, Buchanan T, Otoul C, Koestler F, Mievis F, Giacomelli F, Degueldre C, Hustinx R, Luxen A, Seret A, Plenevaux A, Salmon E (2015) Biodistribution and radiation dosimetry for the novel SV2A radiotracer [18F]UCB-H: first-in-human study. Mol Imaging Biol 17:557–564CrossRefGoogle Scholar
  29. 29.
    Becker G, Warnier C, Serrano ME, Bahri MA, Mercier J, Lemaire C, Salmon E, Luxen A, Plenevaux A (2017) Pharmacokinetic characterization of [18F]UCB-H PET radiopharmaceutical in the rat brain. Mol Pharm 14:2719–2725CrossRefGoogle Scholar
  30. 30.
    Kaminski RM, Gillard M, Klitgaard H (2012) Targeting SV2A for discovery of antiepileptic drugs. In: Jasper’s Basic Mechanisms of the EpilepsiesGoogle Scholar
  31. 31.
    Klitgaard H, Verdru P (2007) Levetiracetam: the first SV2A ligand for the treatment of epilepsy. Expert Opin Drug Discovery 2:1537–1545CrossRefGoogle Scholar
  32. 32.
    Nicolas J-M, Hannestad J, Holden D, Kervyn S, Nabulsi N, Tytgat D, Huang Y, Chanteux H, Staelens L, Matagne A, Mathy FX, Mercier J, Stockis A, Carson RE, Klitgaard H (2016) Brivaracetam, a selective high-affinity synaptic vesicle protein 2A (SV2A) ligand with preclinical evidence of high brain permeability and fast onset of action. Epilepsia 57:201–209CrossRefGoogle Scholar
  33. 33.
    Mercier J, Provins L, Valade A (2017) Discovery and development of SV2A PET tracers: potential for imaging synaptic density and clinical applications. Drug Discov Today Technol 25:45–52CrossRefGoogle Scholar
  34. 34.
    Rabiner EIA (2017) Imaging synaptic density: a different look at neurological diseases. J Nucl MedGoogle Scholar
  35. 35.
    Faul F, Erdfelder E, Lang A-G, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39:175–191CrossRefGoogle Scholar
  36. 36.
    Hellier JL, Patrylo PR, Buckmaster PS, Dudek FE (1998) Recurrent spontaneous motor seizures after repeated low-dose systemic treatment with kainate: assessment of a rat model of temporal lobe epilepsy. Epilepsy Res 31:73–84CrossRefGoogle Scholar
  37. 37.
    Racine RJ (1972) Modification of seizure activity by electrical stimulation. II. Motor seizure. Electroencephalogr Clin Neurophysiol 32:281–294CrossRefGoogle Scholar
  38. 38.
    Gano LB, Liang L-P, Ryan K, Michel CR, Gomez J, Vassilopoulos A, Reisdorph N, Fritz KS, Patel M (2018) Altered mitochondrial acetylation profiles in a kainic acid model of temporal lobe epilepsy. Free Radic Biol Med 123:116–124CrossRefGoogle Scholar
  39. 39.
    Bertoglio D, Amhaoul H, Van Eetveldt A et al (2017) Kainic acid-induced post-status epilepticus models of temporal lobe epilepsy with diverging seizure phenotype and neuropathology. Front Neurol 8:588CrossRefGoogle Scholar
  40. 40.
    Van Nieuwenhuyse B, Raedt R, Sprengers M et al (2015) The systemic kainic acid rat model of temporal lobe epilepsy: long-term EEG monitoring. Brain Res 1627:1–11CrossRefGoogle Scholar
  41. 41.
    Warnier C, Lemaire C, Becker G, Zaragoza G, Giacomelli F, Aerts J, Otabashi M, Bahri MA, Mercier J, Plenevaux A, Luxen A (2016) Enabling efficient positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) with a robust and one-step radiosynthesis of a highly potent 18F-labeled ligand (18[F]UCB-H). J Med Chem 59:8955–8966CrossRefGoogle Scholar
  42. 42.
    Kim JS, Lee JS, Im KC, Kim SJ, Kim SY, Lee DS, Moon DH (2007) Performance measurement of the microPET focus 120 scanner. J Nucl Med 48:1527–1535CrossRefGoogle Scholar
  43. 43.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (London, England) 1:307–310CrossRefGoogle Scholar
  44. 44.
    Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160CrossRefGoogle Scholar
  45. 45.
    Ludbrook J (2008) Statistics in biomedical laboratory and clinical science: applications, issues and pitfalls. Med Princ Pract 17:1–13CrossRefGoogle Scholar
  46. 46.
    Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd editio. Lawrence Erlbaum AssociatesGoogle Scholar
  47. 47.
    van Vliet EA, Aronica E, Redeker S, Boer K, Gorter JA (2009) Decreased expression of synaptic vesicle protein 2A, the binding site for levetiracetam, during epileptogenesis and chronic epilepsy. Epilepsia 50:422–433CrossRefGoogle Scholar
  48. 48.
    Bajjalieh SM, Peterson K, Linial M, Scheller RH (1993) Brain contains two forms of synaptic vesicle protein 2. Proc Natl Acad Sci U S A 90:2150–2154CrossRefGoogle Scholar
  49. 49.
    Ziai P, Hayeri MR, Salei A, Salavati A, Houshmand S, Alavi A, Teytelboym OM (2016) Role of optimal quantification of FDG PET imaging in the clinical practice of radiology. RadioGraphics 36:481–496CrossRefGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Maria Elisa Serrano
    • 1
    Email author
  • Mohamed Ali Bahri
    • 1
  • Guillaume Becker
    • 1
  • Alain Seret
    • 1
  • Frédéric Mievis
    • 2
  • Fabrice Giacomelli
    • 2
  • Christian Lemaire
    • 1
  • Eric Salmon
    • 1
  • André Luxen
    • 1
  • Alain Plenevaux
    • 1
  1. 1.GIGA – CRC In Vivo ImagingUniversity of LiègeLiègeBelgium
  2. 2.NucleisUniversity of LiègeLiègeBelgium

Personalised recommendations