Pharmacokinetics from PET plasma curves
This page covers the pharmacokinetics of PET ligand in the plasma, not in tissue, and not the pharmacokinetics or pharmacodynamics of the drug that is studied with PET ligand.
AUC is the area under the plot (integral) of radioactivity concentration of authentic (metabolite corrected) PET tracer against time after tracer infusion. The AUC from 0 to infinite time, AUC0-∞, can be used to estimate the total clearance of labeled PET ligands, CIT.
AUC can be determined by ”trapezoidal rule”:
the data points are connected by straight line segments, perpendiculars are erected from
the abscissa to each data point, and the sum of the areas of the trapezoids is computed.
This can be done e.g. using program interpol with option
However, the last measured concentration is usually not zero, sometimes not even close to zero.
Then the AUC from 0 to infinite time,
AUC0-∞, must be estimated using the slope of the end-part of the plot of
the natural logarithm of tracer concentration against time. This can be done e.g. using program
paucinf, or with Excel (Zhang et al., 2010), if dedicated
software for pharmacokinetic analysis is not available.
If blood sampling is not started right after i.v. injection, the plasma peak will be missed. In that case the initial concentration, C0, can be estimated for instance by back-extrapolating to t=0 from log-linear regression of the two first concentration values. Small-molecular compounds usually have two phases (fast and slow) in the decrease of the plasma concentration curve, and biexponential decay model is then used to describe the plasma concentration as a function of time:
The unit of AUC is the unit of time multiplied by the unit of radioactivity concentration, usually min*kBq/mL.
The total clearance of PET ligand after a single intravenous dose can be calculated as
The estimation of AUC0-∞ was explained above.
The unit of clearance depends on the unit of dose, but in principle the unit of radioactivity is cancelled out, and thus the unit of clearance will be volume/unit time. Both of the measures, patient dose and AUC0-∞, must be specified either as radioactivity (Bq, Ci) or as moles or mass (mol, g). The specific radioactivity (SA, mCi/µmol or MBq/µmol) can be used to radioactivity-mass conversion, if necessary.
In mice and rat studies each animal is often sampled only once, and plasma curve must be constructed from separate animals. Variable injected dose and animal weight must then be taken into account by using SUV or %ID/g as concentration. When AUC0-∞ is calculated from the SUV curve, then CIT is calculated as
, and the outcome has unit (mL of plasma)*min-1*(animal weight (g))-1. If AUC0-∞ is calculated from %ID/g curves, then
, and its unit is normal (g or mL of plasma)*min-1.
Another possibility is to fit a sum of three exponentials to the (protein-free) plasma data and calculate the clearance from the fitted parameters (Abi-Dargham et al., 1994).
When the natural logarithm of tracer concentration is plotted against time from bolus infusion, the plot becomes linear in the end phase, as the tracer is eliminated according to the laws of first-order reaction kinetics; linearity should be verified from the plot. The slope of the linear part of the plot equals -kel. The kel can be estimated e.g. using program paucinf, or with Excel (Zhang et al., 2010)
This is the amount of time required for the concentration of the PET ligand in plasma to be halved. If the ligand is eliminated from the plasma according to the laws of first-order kinetics, it can be calculated as
Notice that this t1/2 has nothing in common with the physical half-life of positron emitting isotope labels (T1/2), except the name. For pharmacokinetic calculations all curves are corrected for decay, so that the curves represent the concentrations of PET ligands, and not concentrations of the radioactive label.
In principle, the maximal concentration of PET tracer in plasma (Cmax) and the time of maximum concentration (Tmax) could be determined from plasma TAC directly, or after fitting a function to plasma TAC. However, these parameters would be dependent on the injected dose and the bolus infusion protocol, and thus less useful than kel or t1/2.
Mean residence time (MRT) and mean transit time (MTT) are frequently used in pharmacokinetic literature, including system moment and system matrix MRTs, and MRTs of individual compartments such as central/plasma compartment (which is discussed here), tissue compartments, and the MRT of an absorption site (Wagner, 1988). By definition, MRT of drug molecules in a kinetic space is the average total time the drug molecules injected into a kinetic system at a given point spend in the kinetic space; MTT of drug molecules in a kinetic space is the average time taken by drug molecules injected into the kinetic system at a given point to leave the kinetic space after first and possible subsequent entries into that space (Veng-Pedersen, 1989a). In PET studies, MTT is sometimes estimated in tissue perfusion studies, for example as the inverse of cerebral perfusion pressure.
Based on a number of (often overlooked) assumptions, MTT, in the case of intravenous bolus injection, is often estimated using formula
, where AUMC is the area under the moment curve, and AUC is the area under the concentration-time curve. The limitations and assumptions behind this formula are reviewed by Kasuya et al. (1987) and Veng-Pedersen (1989a). An additional problem is that MRT is often calculated from peripheral venous data, and is then dependent on the sampling site (Chiou, 1989a).
In PET studies MTT is often calculated from tissue concentration curves, which can be measured only limited time, and input function is delayed. Corrections for these can be included in the equations (Choi et al., 1993).
MTT is related to the clearance. For the central (plasma) compartment,
, where Vc is the distribution volume of the central compartment (Veng-Pedersen, 1989b).
- Input function preprocessing
- Metabolite correction
- Fitting the input TAC
- Area under curve (AUC)
- Pharmacokinetic two-compartment model
- Receptor occupancy
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Created at: 2006-03-21
Updated at: 2018-12-08
Written by: Vesa Oikonen, Anne Roivainen