Analytical method for simultaneously measuring ex vivo drug receptor occupancy and dissociation rate: Application to (R)-dimethindene occupancy of central histamine H-1 receptors
ABSTRACT We introduce a novel experimental method to determine both the extent of ex vivo receptor occupancy of administered compound and its dissociation rate constant (k4). [Here, we reference k4 as the rate of offset of unlabeled ligand in convention with Motulsky and Mahan (1)]. We derived a kinetic rate equation based on the dissociation rate constant for an unlabeled compound competing for the same site as a labeled compound and describe a model to simulate fractional occupancy. To validate our model, we performed in vitro kinetics and ex vivo occupancy experiments in rat cortex with varying concentrations of (R)-dimethindene, a sedating antihistamine. Brain tissue was removed at various times post oral administration, and histamine H1 receptor ligand [3H]-doxepin binding to homogenates from drug-treated or vehicle-treated rats was measured at multiple time points at room temperature. Fractional occupancy and k4 for (R)-dimethindene binding to H1 receptors were calculated by using our proposed model. Rats dosed with 30 and 60 mg/kg (R)-dimethindene showed 42% and 67% occupancy of central H1 receptors, respectively. These results were comparable to occupancy data determined by equilibrium radioligand binding. In addition, drug k4 rate determined by using our ex vivo method was equivalent to k4 determined by in vitro competition kinetics (dissociation half-life t(1/2) approximately 30 min). The outlined method can be used to assess, by simulation and experiment, occupancy for compounds based on dissociation rate constants and contributes to current efforts in drug optimization to profile antagonist efficacy in terms of its kinetic drug-target binding parameters. Data described by the method may be analyzed with commercially available software. Suggested fitting procedures are given in the appendix.
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- "Method 3 (i.e., " delayed association " method) differs from the previous ones by keeping the washout time as short as possible and by (finally) incubating the receptors with a fixed concentration of radioligand for increasing periods of time. The drug's dissociation rate can be estimated by comparing such obtained radioligand association curves with those obtained without drug pretreatment or calculated by the equation provided by Malany et al. (2009). The number of time periods could be reduced to only one for the purpose of high-throughput screening (Tresadern et al. 2011). "
ABSTRACT: Drug-receptor interactions are traditionally quantified in terms of affinity and efficacy, but there is increasing awareness that the drug-on-receptor residence time also affects clinical performance. While most interest has hitherto been focused on slow-dissociating drugs, D(2) dopamine receptor antagonists show less extrapyramidal side effects but still have excellent antipsychotic activity when they dissociate swiftly. Fast dissociation of clozapine, the prototype of the "atypical antipsychotics", has been evidenced by distinct radioligand binding approaches both on cell membranes and intact cells. The surmountable nature of clozapine in functional assays with fast-emerging responses like calcium transients is confirmatory. Potential advantages and pitfalls of the hitherto used techniques are discussed, and recommendations are given to obtain more precise dissociation rates for such drugs. Surmountable antagonism is necessary to allow sufficient D(2) receptor stimulation by endogenous dopamine in the striatum. Simulations are presented to find out whether this can be achieved during sub-second bursts in dopamine concentration or rather during much slower, activity-related increases thereof. While the antagonist's dissociation rate is important to distinguish between both mechanisms, this becomes much less so when contemplating time intervals between successive drug intakes, i.e., when pharmacokinetic considerations prevail. Attention is also drawn to the divergent residence times of hydrophobic antagonists like haloperidol when comparing radioligand binding data on cell membranes with those on intact cells and clinical data.Archiv für Experimentelle Pathologie und Pharmakologie 02/2012; 385(4):337-72. DOI:10.1007/s00210-012-0734-2 · 2.36 Impact Factor
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- "Radioligand association curves may acquire complex shapes when the receptors were pretreated with competitor (including a delayed onset in binding such as in panel A and biphasic curves such as in panel C). Despite their complexity, such binding data can now adequately be analysed by the fitting procedure developed by Malany et al. (2009) to yield the competitor's dissociation rate (k4) and the amount of competitor-bound receptors at the onset of the radioligand incubation step, provided that the radioligand's k1 and k2 values are known. An interesting situation arises when the same binding data are plotted as a function of the initial free competitor concentration instead of the incubation time (Figure 1B and D). "
ABSTRACT: Because the in vivo effectiveness of ligands may also be determined by the rate by which they dissociate from their target receptors, drug candidates are being increasingly screened for this kinetic property. The dissociation rate of unlabelled ligand-receptor complexes can be estimated indirectly from their ability to slow the association of subsequently added radioligand molecules. We used the 'two-step competition' binding approach consisting of pre-incubating the receptor preparation with a wide range of ligand concentrations, washing off free ligand molecules, adding radioligand and monitoring its receptor binding after a fixed time. Based on the rationale that binding of both ligands is mutually exclusive and that they bind according to the law of mass action to a single class of sites, the unlabelled ligand's dissociation rate can be estimated from the upward shift that the competition curve experiences after washing. The relevance of the 'two-step competition' approach was explored by computer simulations and by comparing the dissociation behaviour of unlabelled D(2) dopamine and CB(1) cannabinoid receptor antagonists in this and alternative approaches. Besides providing satisfactory estimations of dissociation rates, the method also detects the ability of the unlabelled ligand molecules to be released from 'sinks' such as the cell membrane. As the 'two-step competition' requires rapid intermediate washing steps and needs radioligand binding to be measured at only one time point, this approach is particularly suited for binding studies on intact plated cells. LINKED ARTICLES: This article is part of a themed section on Analytical Receptor Pharmacology in Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2010.161.issue-6.British Journal of Pharmacology 06/2010; 161(6):1311-28. DOI:10.1111/j.1476-5381.2010.00931.x · 4.99 Impact Factor
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ABSTRACT: The physical barrier between blood and the CNS (the blood-brain barrier, the blood-spinal cord barrier and the blood-CSF barrier) protects the CNS from both toxic and pathogenic agents in the blood. It is now clear that disruption of the blood-CNS barrier plays a key role in a number of CNS disorders, particularly those associated with neurodegeneration. Such disruption is inevitably accompanied by inflammatory change, as immune cells and immune mediators gain access to the brain or spinal cord. The blood-CNS barrier also presents a major obstacle for potential CNS medicines. Robust methods to assess CNS permeation are therefore essential for CNS drug discovery, particularly when brain pharmacokinetics are taken into account and especially when such measures are linked to neurochemical, physiological, behavioural or neuroimaging readouts of drug action. Drug candidates can be successfully designed to cross the blood-CNS barrier, but for those that can't there is the possibility of entry with a delivery system that facilitates the movement of drug candidate across the blood-CNS barrier.Neurobiology of Disease 09/2009; 37(1):3-12. DOI:10.1016/j.nbd.2009.07.029 · 5.20 Impact Factor