Measuring drug-on-target receptor occupancy
Receptor occupancy (RO) is calculated from PET data as the treatment-induced relative change in the concentration of available (not occupied) receptors (Bavail):
Measurement of Bavail (or "Bmax") is challenging, and other binding parameters are often used instead. If we can assume that treatment does not change the ligand-receptor affinity (1/KD), then useful binding parameters for RO estimation are:
Total volume of distribution (VT) is also related to binding potential (BP), but not linearly. If baseline BP>>(1+k5/k6), then VT can be used to measure receptor occupancy, otherwise VT from reference region must be measured and used to calculate BPND or BPP.
RO estimation based on binding potentials, or ratios, requires that a reference region (devoid of specific binding) is available. For several radioligands there is no true reference region, and in these cases the Lassen plot or its extension can be used (Lassen et al., 1995; Cunningham et al., 2010; Kuwabara et al., 2017), even from SUV (Takano et al., 2014). However, Lassen plot does not always provide satisfactory results (Kågedal et al., 2013; Joshi et al., 2015). Traditional Lassen plot requires that receptor occupancy is similar in all brain regions. When occupancy is not homogeneous, for example in the case of non-selective radioligand and selective drug, a local-neighborhood Lassen plot filter can provide unbiased occupancy and VND estimates (de Laat & Morris, 2021).
In theory, high-affinity PET radioligands can be used to measure receptor occupancy. If there is a state of equilibrium between administrated or endogenous and radiotracer ligands, free receptor and receptor-ligand complexes, radioligand affinity should not affect measurement of occupancy by administrated or endogenous ligand. Yet, a frequent observation from several studies is that the lower affinity radiotracers appear to be more susceptible to competition by synaptic endogenous or administered ligands than radiotracers which have very high receptor affinity.
It has been suggested that the magnitude of the competition is not reduced by the relative difference in ligand affinities, but by failure of the receptor binding of high-affinity radioligands to rapidly attain equilibrium (Gatley et al., 2000; Laruelle, 2000). It is important that equilibrium is achieved within the time scale of the in vivo binding experiment with PET. Under conditions in which the radiotracer binding is still far from reaching equilibrium with the tissue receptors, radiotracer accumulation in the tissue is determined mostly by delivery (perfusion and transport) rather than by density of available receptors.
Even radiotracers which bind their receptors with an affinity so high that the binding is nearly irreversible in the time available for PET can be used to monitor receptor blockade, if proper modelling is applied (Ishizu et al., 2000; Laruelle, 2000).
Instead of different affinities, a possible explanation for differing competition results obtained with different radiopharmaceuticals is matter of different ability to access the internalized receptors (Laruelle, 2000).
Agonist versus antagonist
Agonist ligands may be more useful than antagonists for measuring receptor occupancy by endogenous synaptic neurotransmitters (Cumming et al., 2002).
Partial volume effect
Measured occupancy is independent of partial volume effect (Martinez et al, 2001), if it is similar in baseline and during medication. Notice that when occupancy is very high, image contrast is usually reduced, leading to lower partial volume effect.
Altered perfusion and peripheral clearance do not affect the receptor binding estimates calculated using graphical analysis or compartmental kinetic modelling (Laruelle, 2000). Sander et al. (2018) demonstrated this in a primate PET-fMRI study, where CO2 induced hypercapnia increased cerebral blood flow by ∼2.5-fold, as measured with fMRI, but no changes were observed in the uptake of [11C]raclopride or [18F]fallypride. However, these methods are vulnerable to variations in blood flow or clearance that occur during the PET scan (Laruelle, 2000).
Specific binding in reference region
Modelling receptor occupancy
Aim is to predict receptor occupancy (RO) at any time relative to the blocking drug dosing or when changing the dosage regimen. To achieve this
- conventional PK modelling is needed to relate the drug plasma concentration to drug dosage regime,
- RO as a function of time (measurements with PET) after drug dosing needs to be related to the plasma drug concentration (Zamuner et al., 2010), and
- these two models can be modelled together, using PK data as a link between RO and dose (Vandenhende et al., 2008).
Since PET studies can provide only sparse RO data relative to dosing, it will be challenging to develop a model that could reliably predict the RO. Any model should be validated by simulating with it the RO for dosage regimen that was not used to develop the model.
Receptor occupancy can be related to plasma concentration directly or indirectly.
Often the following Emax model is a reasonable approximation for a direct (sigmoidal) relationship between drug plasma concentration (C) and the level of RO.
Usually E0=0, and then the unconstrained one site binding hyperbola for RO as a function of drug plasma concentration,
, or as a function of drug dose (D),
, can be fitted to estimate Emax and EC50 or
ED50, respectively. Fitting can be done for example in
Also our command-line tool fit_sigm
can be used with options
-EC50 -n=1, or if Emax is constrained to 1 or 100%, with
-EC50 -n1 -A=1 or
-EC50 -n1 -A=100, respectively.
Reliability of the fitting may be improved by fitting all ROIs simultaneously with a common EC50 shared across regions (Graff-Guerrero et al., 2010).
Two site binding model may be necessary if PET radioligand binds to two receptor subtypes, for example dopamine receptors D2 and D2 (Graff-Guerrero et al., 2010):
Indirect response model must be considered when the data suggest a delay in RO compared to plasma concentration. Examples of delayed RO have been reported for example by Tauscher et al. (2002) and Ingman et al. (2005).
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Updated at: 2022-11-21
Created at: 2004-08-09
Written by: Vesa Oikonen