blood laboratory

Correction of plasma TAC for metabolites

It is common that the PET tracer is metabolized in the liver, kidneys or other parts of the body already during the PET scan, and one or more of the metabolites is still carrying the isotope label. If labelled metabolites are found in the plasma in significant amounts, their proportion has to be subtracted from the plasma curve, because only the concentration of parent tracer can be used as input function in quantitative analysis of the tracer kinetics.

In brain studies the radioactive metabolites, that usually are more polar than the authentic tracer, do not usually pass the blood-brain barrier (BBB). However, the less lipophilic metabolites tend to have lower binding to plasma proteins, which may increase their distribution volume in the brain (Aarnio et al., 2022). In other tissues, not protected by BBB, marked uptake of radioactive metabolite(s) can be observed. When marked proportion of tissue radioactivity concentration is due to metabolits from plasma, the plasma concentrations of both the parent tracer and the radioactive metabolite may have to be included in the compartmental model or spectral analysis (Tomasi et al., 2012; Ichise et al., 2016). Small polar radiometabolites, such as [11C]formaldehyde and [11C]CO2 can pass even the BBB, and substantially affect the brain tissue concentrations and reduce the signal-to-background ratio (Johansen et al., 2018). 18F-labelled radioligands are often defluorinated during the PET study; free [18F]F- and other bone-seeking isotopes, such as Zr4+, may hamper brain PET studies by causing high activity in the skull bone next to the brain cortex.

Metabolite correction in TPC

The fractions of authentic (parent) tracer in plasma must be written in an ASCII file (fraction data). A mathematical function or compartmental model can be fitted to these fractions. Total radioactivity in plasma (PTAC) is measured from arterial plasma samples. With that and the fitted parent fractions, metabolite corrected plasma curve can be calculated using metabcor. TACs of radioactive metabolites in plasma can also be saved, if necessary.

Example of [C-11]flumazenil study - total and parent PTAC
Figure 1. Example of plasma metabolite correction in [11C]flumazenil study: each plasma concentration (black) is multiplied by the parent tracer fraction at each sample time point; result is the curve of unchanged (parent) radioligand concentration in plasma (red).

Alternative metabolite correction methods

Mathematical metabolite correction

For references, see Burger and Buck (1996), and Sanabria-Bohórquez et al. (2000).

Population based methods

Ideally, fractions of plasma metabolites should be measured for each person participating in a PET study. However, the measured fraction curves are sometimes noisy, or there are missing samples. One alternative is to calculate population average curve of the fractions of parent tracer in the plasma, if the inter-individual variation in the rate of metabolism is small. Population average must be determined from a group that is comparable to the study population by their age, sex, and body weight. For example, for rate of metabolism of [18F]FDPN a significant gender difference has been found (Henriksen et al., 2006).

The population average fraction curve can be fitted to a function, for example to the "Hill-type" or power or exponential functions, if there were only few samples or if the fraction curve must be extrapolated. In the fitting, use the weights that were written in the mean fraction curve.

See also:


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Henriksen G, Spilker ME, Sprenger T, Hauser AI, Platzer S, Boecker H, Toelle TR, Schwaiger M, Wester H-J. Gender dependent rate of metabolism of the opioid receptor-PET ligand [18F]fluoroethyldiprenorphine. Nuklearmedizin 2006; 45: 197-200. doi: 10.1055/s-0038-1625219.

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Nagar S, Argikar UA, Tweedie DJ (eds). Enzyme Kinetics in Drug Metabolism - Fundamentals and Applications. Humana Press, 2014, ISBN 978-1-62703-757-0.

Nelissen N, Warwick J, Dupont P (2012). Kinetic modelling in human brain imaging. In: Positron Emission Tomography - Current Clinical and Research Aspects, Dr. Chia-Hung Hsieh (Ed.), ISBN: 978-953-307-824-3, InTech. doi: 10.5772/30052.

Sanabria-Bohórquez SM, Labar D, Levêque P, Bol A, De Volder AG, Michel C, Veraart C. [11C]Flumazenil metabolite measurement in plasma is not necessary for accurate brain benzodiazepine receptor quantification. Eur J Nucl Med. 2000; 27:1674-1683. doi: 10.1007/s002590000336.

Sari H, Erlandsson K, Marner L, Law I, Larsson HBW, Thielemans K, Ourselin S, Arridge S, Atkinson D, Hutton BF. Non-invasive kinetic modelling of PET tracers with radiometabolites using a constrained simultaneous estimation method: evaluation with 11C-SB201745. EJNMMI Res. 2018; 8(1): 58. doi: 10.1186/s13550-018-0412-6.

Sestini S, Halldin C, Mansi L, Castagnoli A, Farde L. Pharmacokinetic analysis of plasma curves obtained after i.v. injection of the PET radioligand [11C] raclopride provides likely explanation for rapid radioligand metabolism. J Cell Physiol. 2012; 227: 1663-1669. doi: 10.1002/jcp.22890.

Tomasi G, Kimberley S, Rosso L, Aboagye E, Turkheimer F. Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology. Phys Med Biol. 2012; 57: 1889-1906. doi: 10.1088/0031-9155/57/7/1889.

Tonietto M, Veronese M, Rizzo G, Zanotti-Fregonara P, Lohith TG, Fujita M, Zoghbi SS, Bertoldo A. Improved models for plasma radiometabolite correction and their impact on kinetic quantification in PET studies. J Cereb Blood Flow Metab. 2015; 35(9): 1462-1469. doi: 10.1038/jcbfm.2015.61.

Tonietto M, Rizzo G, Veronese M, Fujita M, Zoghbi SS, Zanotti-Fregonara P, Bertoldo A. Plasma radiometabolite correction in dynamic PET studies: Insights on the available modeling approaches. J Cereb Blood Flow Metab. 2016; 36(2): 326-339. doi: 10.1177/0271678X15610585.

Veronese M, Gunn RN, Zamuner S, Bertoldo A. A non-linear mixed effect modelling approach for metabolite correction of the arterial input function in PET studies. Neuroimage 2013; 66: 611-22. doi: 10.1016/j.neuroimage.2012.10.048.

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Updated at: 2022-12-02
Created at: 2008-03-02
Written by: Vesa Oikonen