de Lange EC, Ravenstijn PG, Groenendaal D, van Steeg TJ. Toward the Prediction of CNS Drug-Effect Profiles in Physiological and Pathological Conditions Using Microdialysis and Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling. AAPS Journal. 2005; 7(3): E532-E543. DOI:  10.1208/aapsj070354

Correspondence to:
Elizabeth C.M. de Lange
Tel: +31-71-527-6330
Fax: +31-70-514-1260
Email: l.lange@lacdr.leidenuniv.nl

Received: May 3, 2005;  Accepted: May 15, 2005;  Published: October 7, 2005

Abstract

Our ultimate goal is to develop mechanism-based pharmacokinetic (PK)-pharmacodynamic (PD) models to characterize and to predict CNS drug responses in both physiologic and pathologic conditions. To this end, it is essential to have information on the biophase pharmacokinetics, because these may significantly differ from plasma pharmacokinetics. It is anticipated that biophase kinetics of CNS drugs are strongly influenced by transport across the blood-brain barrier (BBB). The special role of microdialysis in PK/PD modeling of CNS drugs lies in the fact that it enables the determination of free-drug concentrations as a function of time in plasma and in extracellular fluid of the brain, thereby providing important data to determine BBB transport characteristics of drugs. Also, the concentrations of (potential) extracellular biomarkers of drug effects or disease can be monitored with this technique. Here we describe our studies including microdialysis on the following: (1) the evaluation of the free drug hypothesis; (2) the role of BBB transport on the central effects of opioids; (3) changes in BBB transport and biophase equilibration of anti-epileptic drugs; and (4) the relation among neurodegeneration, BBB transport, and drug effects in Parkinson’s disease progression.

Keywords: blood-brain barrier, pharmacokinetics, pharmacodynamics, biophase, microdialysis

Introduction

The biophase kinetics of a CNS drug are an important determinant in the time course and intensity of its CNS effects. Apart from plasma pharmacokinetics (PKs), mechanisms that govern CNS biophase kinetics include the rate and extent of blood-brain barrier (BBB) transport^1,2 and the kinetics of processes of distribution and elimination within the brain. To date, a few studies have applied mechanism-based PK-pharmacodynamic (PD) modeling to in vivo data^3-6 based on only plasma PKs. However, especially for CNS drugs, biophase PKs may differ significantly from plasma PKs, because BBB transport and brain distribution often do not occur instantaneously and to a full extent.^1,7,8 Biophase PKs should, therefore, be taken into consideration in mechanism-based PK/PD modeling.

BBB transport⁹ is related to BBB functionality and occurs by passive diffusion, as well as by active transport. Active transport occurs by many membrane transporters,^10,11 such as the P-glycoprotein (Pgp)^12,13 and the multidrug resistance-associated proteins (MRPs).^14-16 BBB functionality is dynamically controlled^17,18 by blood components and the surrounding brain cells by direct contact or indirectly by their extracellular products. Thus, BBB functionality may vary among different physiologic, pathologic, and chronic drug treatment conditions.^19,20 It is anticipated that such variations in BBB functionality will ultimately affect the biophase kinetics of CNS drugs.

In vivo microdialysis is a valuable technique, because it enables the determination of the free drug in plasma and its concentrations in extracellular fluid (ECF) the brain as a function of time. Herewith, it provides important information for the determination of BBB transport and brain distribution. A prerequisite for this application is that BBB transport characteristics will not be significantly influenced by microdialysis probe implantation and presence in the brain. This was an initial concern. But based on a series of studies performed to validate the usefulness of intracerebral microdialysis in measurements of passive, as well as active, BBB transport,^8,21,26-28 this prerequisite appears to hold, provided that this technique is used under well-controlled surgical and experimental conditions.²⁹

Here, in short, the main principles of mechanism-based PK/PD modeling of CNS active drugs are described, including arguments for the need to include biophase kinetics, followed by the description of factors that govern biophase kinetics of CNS drugs. Then, a short overview is given on current projects in rats that include investigations of different aspects of biophase equilibration of drug, which are the evaluation of the free drug hypothesis, the role of BBB transport on the central effects of opioids, changes in BBB transport and biophase equilibration of anti-epileptic drugs, and the relation among neurodegeneration, BBB transport, and drug effects in Parkinson’s disease progression.

Mechanism-Based PK-PD Modeling of CNS Active Drugs

Our ultimate goal is to predict CNS drug effects, in terms of time course and variability, following different dosage regimens, under a variety of physiologic and pathologic conditions.^30-32 The use of mechanism-based PK/PD modeling in in vivo preclinical studies is anticipated to provide key knowledge for prediction and thereby optimization of the therapeutic regimens of CNS drugs.³³ The simplified relationship among plasma PKs, biophase PKs, receptor occupancy, receptor activation, and effect is shown in Figure 1.

Figure 1. The factors between drug dosing and effect: plasma pharmacokinetics, BBB transport, biophase pharmacokinetics, target binding, and signal transduction, and the resulting effect.

PK/PD Modeling

The model most generally used in PK/PD modeling for a direct (no time delay) and reversible concentration-effect relationship is the Sigmoid E_max model:

E = E 0 + E max C h E C 50 h + C h

(1)

in which E₀ is the baseline response, E is the response observed for a given concentration at time t, C, E_max is the maximal effect of the drug, EC₅₀ is the plasma concentration of the drug that produces 50% of E_max and h is the Hill coefficient, which determines the steepness of the concentration-effect relationship.

The sigmoidal E_max equation used to fit a plasma concentration-effect profile provides estimates of EC₅₀ and E_max values that result from the combined ability of the drug to bind to its receptor (the affinity of the agonist) and the ability of the drug to cause an effect after binding to the receptor (the efficacy of the agonist). Actually, it may estimate identical EC₅₀ and E_max values for a drug with high affinity and low efficacy and a drug with low affinity and high efficacy. It, therefore, lacks the power to predict drug responses under different physiologic or pathologic conditions, where both affinity and efficacy may be affected.

To predict the intrinsic activity and potency of a drug for a particular pharmacological effect or response, a model is required that explicitly distinguishes between drug-specific and system-specific properties. To that end, derived from the receptor occupation theory, the operational model of agonism seems to be very useful^34,35:

E = E 0 + E m · τ h · C h ( K A + C ) h + τ h . C h

(2)

This equation is used to analyze agonist concentration-effect curves in terms of the concentration of the drug (agonist) at time t, C, the baseline response E₀, the maximal tissue response (Em), the slope of the transducer function (h), the agonist-receptor dissociation equilibrium constant (K_A), and the efficacy parameter (τ). The efficacy parameter:

τ = R 0 K E

(3)

is expressed in terms of the total number of available receptors R₀ and the concentration of the number of receptors occupied at the half-maximal effect K_E.Figure 2 shows the drug-specific and tissue-specific properties and the property that is the result from both drug and tissue, K_E. Receptor affinity and intrinsic efficacy, the “drug- specific” properties, can be estimated in in vitro bioassays, with the maximal response of the drug:

E max = E m · τ h τ h + 1

(4)

and the concentration at half-maximal response of the agonist:

E C 50 = K A ( 2 + τ h ) 1 / h − 1

(5)

Figure 2. Scheme on the contribution of drug and tissue-specific parameters, and the hybrid (drug/tissue) parameter KE in relation to the effect.

The operational model of agonism has been successfully applied in numerous in vitro studies, and it has been shown recently that this approach can also be used for PK/PD analysis of in vivo drug effects.³ For example, affinity and efficacy values of agonists at cardiac adenosine A₁ receptors have been estimated based on in vivo data and appeared to be highly consistent with estimates from in vitro radioligand-binding studies.³⁶.

To estimate the parameters in the operational model, simultaneous analysis of different PK/PD relationships must be performed. These different PK/PD relationships may be obtained for one agonist under control conditions and conditions in which the number of receptors available for binding is reduced. This can be achieved by a compound that irreversibly binds to the receptor to such an extent that the agonist is no longer able to produce its maximal effect.^37,38 Alternatively, simultaneous analysis of the PK/PD relationships that result from a series of drugs with varying degrees of agonism for the specific receptor can be used.³

Biophase PKs

In general, for PK/PD modeling, mostly only plasma PK data are used. However, for CNS drugs, biophase PKs may differ significantly from plasma PKs, because BBB transport and brain distribution often do not occur instantaneously and to a full extent.³⁹ This may result in a delay of the pharmacologic effect versus time profile relative to the plasma profile. For a single drug, incorporation of a hypothetical biophase concentration is an approach to take the time delay into account. This hypothetical biophase concentration is linked to the plasma concentration by a first-order rate constant for influx k_1e and a rate constant k_eo for drug efflux from the hypothetical compartment. However, in the operational model of agonism, the different drugs may have significant differences in time delay between plasma and biophase kinetics. The operational model of agonism does not take into account time-dependent factors, like biophase distribution. Because it is the biophase concentration that is related to the effect, biophase kinetics should be taken into consideration in using the operational model of agonism.

Factors That Govern Biophase Kinetics of CNS Drugs

The biophase kinetics of a CNS drug are governed by plasma PKs, as well as by the kinetics of BBB transport and processes of distribution and elimination within the brain.^1,2

Plasma PKs

After administration, drugs are distributed in body compartments. This occurs either in the free form or associated with 1 or more binding sites, such as plasma proteins. These binding processes vary in rate and extent, and in plasma they determine the time-dependent fraction of a drug that is available for transport into the brain.^40-46 The “free-drug hypothesis” states that it is the free fraction of the drug that is able to pass membranes and to interact with its target.

BBB Transport

Drug transport across the BBB⁴⁷ is determined both by BBB characteristics and by the physicochemical properties of the drug.^48-54 The BBB is located at the brain capillary endothelium and is characterized by the presence of narrow tight junctions, a low rate of vesicular transport, lack of fenestrations and intercellular clefts, and a continuous basal lamina.⁵⁵ Moreover, the brain endothelial cells express numerous influx and efflux transporters.^14,17 Passive transport (diffusion) across the BBB can be either permeability-limited or cerebral blood flow-limited and depends on size, charge, and lipid solubility of the drug. Permeability-limited BBB transport is applicable for the more hydrophilic drugs that depend on the paracellular route for entrance into the brain (and vice versa). This paracellular route is restricted by the presence of the tight junctions between the brain endothelial cells. For the passage of these tight junctions, the size of the drug appears to be important.⁵⁰ Lipophilic, small, and noncharged drugs more easily diffuse transcellularly, and for these drugs the cerebral blood flow may become the determinant in (mainly) the rate of transport across the BBB. Active transport across the BBB occurs by receptor-mediated transport and endocytosis¹⁴ and by numerous polarized transport systems,^56-58 such as the multidrug-resistance efflux pump P-glycoprotein^26,27,59-63 and some members of the family of MRPs. A broad range of drugs has interaction with these active transporters.⁶⁴

Brain Distribution

The brain cannot simply be viewed as a homogeneous tissue, because it is composed of many anatomic structures with different characteristics.^65-67 In general terms, the main compartments are the brain ECF, the brain intracellular space, and the cerebrospinal fluid (CSF). After passage of the BBB, a drug enters the brain ECF and may thereafter distribute into brain intracellular space and the CSF.^2,68-72 Brain intracellular distribution is, in general, quantitatively more profound for the more lipophilic drugs and renders their extracellular brain concentrations relatively lower. Many CNS active drugs have their target at extracellular recognition sites, and, therefore, for those drugs, the extracellular brain concentrations are most closely related to the biophase concentrations.

The interplay between the kinetics of BBB transport and intracellular distribution determines the time to equilibrium between plasma and biophase kinetics. With regard to the extracellular brain concentrations, active transport out of the brain decreases whereas brain tissue binding increases the time to equilibrium. It should be noted that, other than BBB transport, active transporters may also play a role in intracellular distribution in the brain as indicated by the recent findings of localization and functional expression of a number of transporters (Pgp and MRP) in the brain parenchyma.⁷¹

Once within the brain, other than metabolism,⁷³ the kinetics of drug exchange between brain ECF and CSF should also be considered. Diffusion between brain ECF and CSF is governed by the concentration gradient across the layer of ependymal cells that separates the brain ECF from the CSF.⁷⁴ In relative terms, after systemic administration of a drug, only small concentration gradients occur between brain ECF and CSF when transcapillary passage is rapid compared with CSF turnover,⁷⁵ whereas the situation is reversed for drugs with permeability-limited BBB transport. For these compounds, significant concentration gradients between brain ECF and CSF exist after systemic administration.²

Pathologic Conditions

An important feature is that the BBB is under continuous physiologic control by surrounding astrocytes, pericytes, neurons, and plasma components.¹⁷ Altogether, these factors determine the delicate homeostasis of the brain environment. This dynamic regulation of the BBB indicates that different situations may result in different BBB functionalities. Thus, it is known that BBB functionality changes on pathologic conditions.^{23,74-78,80,81} It is also expected that BBB functionality changes in CNS pathologies that include neurodegenerative processes. When, indeed, changes in BBB functionality occur, they may influence drug transport across the BBB and, therefore, they may have important implications for the biophase kinetics (Figure 1).

Current Project Themes

At the Fourth International Symposium of Microdialysis in Drug Research and Development, the following research project themes were presented. Here, the current state is described.

The Evaluation of the Free-Drug Hypothesis

Protein binding can have a major impact on the PKs and PDs of a drug. Drug effects, side effects, and toxic effects are generally assumed to be correlated with free-drug concentrations in plasma.^82,83 It has been suggested that only the free drug in plasma is available for transport across the BBB⁸⁷ and determines the in vivo drug effects (free-drug hypothesis). This, indeed, holds for drugs such as benzodiazepines, certain opioids, and steroids.^6,85-87

However, there are indications that for certain drugs, the total rather than the free concentration determines the pharmacological effect.^36,45 The aim of this project is to develop a theoretical model for the prediction of the role of plasma protein binding as a determinant of in vivo drug effects. To this end, in vivo PK/PD experiments are performed in rats using drugs with varying degrees of plasma protein binding.⁹³ The special role for microdialysis in this project is that the free fraction of the drug in blood can be monitored in vivo as a function of time and concentration.

The main focus is the direct competition between plasma protein binding and receptor binding (Figure 3). The β-blockers are used as model drugs in the PK/PD studies, because their target receptors are located in the plasma compartment. Heart rate under isoprenaline-induced tachycardia is used as a biomarker of β-adrenoceptor binding. Three β-adrenoceptor blockers with different combinations of lipophilicity and protein binding characteristics have been selected: atenolol, metoprolol, and propranolol.

Figure 3. Competition between protein and receptor binding will determine whether the free or total drug concentrations are of more importance for the prediction of the drug effect.

The experiments are performed under normal plasma protein-binding conditions and compared with situations of experimentally induced changes in plasma protein binding. The values for plasma protein binding are obtained via combination of total blood concentration, ultrafiltration, and blood microdialysis measurements. The (biomarker of) in vivo β-adrenoceptor binding is analyzed in conjunction with in vitro receptor binding to establish a mechanism-based PK/PD model.

The Role of BBB Transport in the Central Effects of Opioids

Opioids are used in anesthesia, analgesia, and treatment of drug abuse. The usefulness of an opioid for a particular application is determined by its biophase kinetics and receptor binding properties. For the centrally mediated effects of opioids, plasma PKs, BBB transport,^61,76,88-96 and brain distribution determine the biophase kinetics. The objective of this project is to develop a mechanism-based PK/PD model for the central effects of opioids, taking into account the role of BBB transport. The changes in the 0.5 to 4.5 Hz frequency band of the electroencephalogram (EEG) are used as a PD end point in rats and are monitored continuously.

Previously, the EEG effects of the opioids alfentanil, fentanyl, and sufentanil have been subjected to mechanism-based PK/PD analysis, using the operational model of agonism.⁶ However, the extent of the Na-shifts on the in vitro OP₃ receptor binding as a measure of efficacy did not predict in vivo efficacies of these opioids. Moreover, alfentanil, fentanyl, and sufentanil all appeared to behave as high-efficacy (full) agonists. This means that for the development of a mechanism-based PK/PD model for the central effects of opioids, additional PK/PD data on low-efficacy (partial) agonists are needed, as well as information on the biophase equilibration. Therefore, also data on nalbuphine, butorphanol, morphine, and loperamide are included in the data analysis. It is anticipated that this selected set of opioids differ widely in BBB transport characteristics and intrinsic efficacy at the OP₃ receptor.

In vitro transport characteristics, such as passive permeability rates of membrane transport and Pgp interaction, were also included. Studies were performed with cell systems comprised of epithelial cells transfected with either the human MDR1 or the rodent MDR1a gene. The results of these investigations confirmed that morphine and loperamide are substrates for Pgp and that their transport could be inhibited by the Pgp inhibitor GF120918. On the other hand, alfentanil, fentanyl, and sufentanil were found to be inhibitors, although they could not be identified as substrates for Pgp. No interaction with Pgp was observed for butorphanol. In addition, for alfentanil, fentanyl, sufentanil, and butorphanol, the passive permeability across the monolayers was very high, whereas for morphine, nalbuphine, and loperamide, the passive permeability was low.⁹⁷

In vivo, the effect of the Pgp inhibitor GF120918 on the EEG effects of morphine indicated that Pgp-mediated efflux at the BBB has an important role in the central effect-time profile of morphine. To quantitatively determine the influence of BBB transport on the PK/PD relationship of morphine, including Pgp mediated efflux, the combined EEG/microdialysis technique has been developed and used.⁹⁸ All of the data on the opioids are currently mathematically analyzed to ultimately develop the mechanism-based PK/PD model for the central effects of opioids.

Changes in BBB Transport and Biophase Equilibration of Anti-epileptic Drugs

Up to 30% of people with epilepsy continue to experience seizures, despite the best available treatment with anti-epileptic drugs. The causes and mechanisms underlying intractable epilepsy are still elusive and may depend on inadequate drug concentration in crucial brain areas.^99-103 It is of importance to have knowledge on the implications of epilepsy-induced alterations in transporter expression, transporter functionality, ultrastructure, and permeability of the BBB in relation to transport of the different anti-epileptic drugs to their targets in the brain. Such knowledge will provide crucial insight into factors that play a role in intractable epilepsy.

For a long time, it has been known that the ultrastructure and permeability of the BBB are changed by epileptic seizures.^104-106 Furthermore, recently, strong indications have become available on changes in the expression of a variety of active efflux transporters of the BBB during epilepsy.¹⁰⁷ However, a truly quantitative and time-dependent evaluation on the impact of changes in BBB transport mechanisms on the availability of anti-epileptic drugs around the brain target (biophase) is lacking. Biophase concentrations are crucial in the effectiveness of anti-epileptic drug treatment of epilepsy or lack of treatment effects in the pharmacoresistant portion of the epilepsy patients. It is anticipated that a quantitative characterization of the changes in individual transport mechanisms of the BBB will lead to a better understanding of the relation between anti-epileptic drug dosing and effects in different stages of epilepsy.³³

In this project we investigate the changes in BBB functionality as induced by seizures and epileptogenesis, including the time course of the development of these changes in rats. Furthermore, we make a distinction between structural and transient changes by acute seizures and the disease progression. This information will be used to develop mechanism-based PK/PD models that include changes induced by seizure activity and epileptogenesis.

To assess the mechanisms of changes in BBB functionality, we include drugs with limited brain access. In this way, both increases and decreases in brain penetration can be detected. The extracellular drug concentrations in the brain are determined by intracerebral microdialysis. To study the influence of seizure activity per se on the BBB transport, the direct cortical stimulation is used, because this method allows an accurate control of the timing, frequency, and intensity of epileptic activity.^108,109 The first results were obtained for gabapentin. A 4-compartment PK model was developed to describe the available literature data on gabapentin PKs in plasma and the extracellular and intracellular compartment of the brain in the rat.^8,110,111 This model was used to simulate the effect of a 1-hour increase in BBB permeability at different time points after intravenous administration of gabapentin. These in silico investigations were performed in parallel with a pilot in vivo experiment in which BBB transport of gabapentin was investigated after an intravenous bolus dose of 50 mg/kg at time zero. Cortical dialysate was obtained from control rats and from rats that were electrically stimulated on the cortex at time zero to have a generalized seizure. The resulting plasma and dialysate concentration-time profiles of gabapentin are shown in Figure 4. The ratio of area-under-the-curve values in dialysate over those in plasma was 20% higher in the generalized seizure rats, indicating an increase in BBB transport for gabapentin. A full study on BBB transport changes of gabapentin on repetitive induced seizures has been performed recently, and the results are pending analysis.

Figure 4. Concentration-time profiles of gabapentin in brain dialysate and plasma after an intravenous bolus administration of 50 mg/kg in control rats (n = 5) and rats that were electrically stimulated to a generalized seizure at the time of administration of gabapentin (n = 4). Data are presented with SEM.

The Relationship Among Neurodegeneration, BBB Transport, and Drug Effects in Parkinson’s Disease Progression

Current treatment of Parkinson’s disease is still largely focused on symptom alleviation using dopamine-replacing agents, based on the fact that degeneration of nigrostriatal dopamine neurons is the primary histopathological feature of Parkinson’s disease. However, herewith, the underlying neurodegenerative processes are left unattended. Our research efforts are, therefore, directed toward understanding the pathogenesis of Parkinson’s disease to provide knowledge that will contribute to a more effective therapy that will slow down the natural progression (neurodegeneration) of Parkinson’s disease.

It has been posed that excitotoxicity plays a role in neurodegenerative processes via excessive stimulation of, particularly, the N-methyl-D-aspartate receptor by glutamate or NMDA agonists.¹¹¹ Anti-Parkinsonian drugs should, therefore, preferably also include neuroprotective properties.^113-115 Polyamines have a number of functions in the brain,^116-119 and it is known that these endogenous compounds play an important role in excitotoxicity.^120,121 Because BBB functionality is also influenced by polyamines, a relationship among neurodegeneration, polyamines, and BBB function^122-124 is indicated. It is, therefore, hypothesized that BBB functionality in Parkinson’s disease is influenced by the neurodegenerative processes in disease progression.

The objective of this project is to quantify potential changes in BBB transport and effects of anti-Parkinsonian/antineurodegenerative drugs in different stages of Parkinson’s disease, based on the current golden standard ex vivo histopathological markers, and to investigate extracellular polyamine levels as potential biomarkers of in vivo neurodegenerative disease progression.

At the start of the project, we selected the subcutaneous rotenone model as a rat model of Parkinson’s disease.¹²⁵ It has been reported that this new model more closely resembles Parkinson’s disease in humans than other models thus far. Betarbet et al¹²⁵ indicated that about 50% of the rotenone-treated rats developed Parkinsonian signs based on ex vivo histopathologic staining of tyrosine-hydroxylase in relevant brain areas, whereas the other rats either had no such signs or died. In our hands, only 5% of the rotenone-treated rats were positive on this marker, whereas the majority of rats suffered severely from apparent systemic toxicity, and a few died. This means that the data observed so far, such as BBB transport of the permeability marker fluorescein,¹²⁶ cannot be attributed to mechanisms in Parkinson’s disease. For this reason, we discontinued the use of this particular model. Currently, we are trying, as an alternative approach, the intracerebral injection of rotenone, for which the very first results are encouraging.

Conclusions

It is anticipated that the use of mechanism-based PK/PD modeling will improve the characterization and prediction of CNS drug responses in both physiologic and pathologic conditions. In vivo studies on mechanism-based PK/PD modeling of CNS drugs should include the biophase PKs, because this may differ importantly from plasma PKs by factors, such as transport, across the BBB and brain distribution processes. Microdialysis is of special value in the determination of extracellular biophase PKs, whereas it can also be used for monitoring extracellular biomarkers of drug response or disease.

Acknowledgments

The projects are collaborations with and financed by Pfizer UK (free drug hypothesis project), GlaxoSmithKline (opioids project), Eli Lilly (Parkinson project), and Stichting Epilepsie Instellingen Nederland (epilepsy project). The epilepsy project is a collaboration with Dr. Rob Voskuyl (LACDR/Pharmacology, Leiden, The Netherlands) and Dr. Graeme J. Sills (Epilepsy Unit, Glasgow, United Kingdom). The general supervision of the projects by Prof. Meindert Danhof is highly appreciated.

References

1. Hammarlund-Udenaes M, Paalzow LN, de Lange EC.  Drug equilibration across the blood-brain barrier–pharmacokinetic considerations based on the microdialysis method. Pharm Res. 1997;14:128-134.
PubMed  DOI: 10.1023/A:1012080106490

2. De Lange EC, Danhof M.  Considerations in the use of cerebrospinal fluid pharmacokinetics to predict brain target concentrations in the clinical setting: implications of the barriers between blood and brain. Clin Pharmacokinet. 2002;41:691-703.
PubMed 

3. Van Der Graaf PH, Danhof M.  Analysis of drug-receptor interactions in vivo: a new approach in pharmacokinetic-pharmacodynamic modelling. Int J Clin Pharmacol Ther. 1997;35:442-446.
PubMed 

4. Garrido M, Gubbens-Stibbe J, Tukker E, et al.  Pharmacokinetic-pharmacodynamic analysis of the EEG effect of alfentanil in rats following beta-funaltrexamine-induced mu-opioid receptor “knockdown” in vivo. Pharm Res. 2000;17:653-659.
PubMed  DOI: 10.1023/A:1007513812018

5. Cleton A, Odman J, Van der Graaf PH, Ghijsen W, Voskuyl R, Danhof M.  Mechanism-based modeling of functional adaptation upon chronic treatment with midazolam. Pharm Res. 2000;17:321-327.
PubMed  DOI: 10.1023/A:1007505223519

6. Cox EH, Kerbusch T, Van der Graaf PH, Danhof M.  Pharmacokinetic-pharmacodynamic modeling of the electroencephalogram effect of synthetic opioids in the rat. Correlation with binding at the µ-opioid receptor. J Pharmacol Exp Ther. 1998;284:1095-1103.
PubMed 

7. Collins JM, Dedrick LD.  Distributed model for drug delivery to CSF and brain tissue. J Am Physiol. 1983;14:R303-R310.

8. Wang YF, Welty DF.  The simultaneous estimation of the influx and efflux blood-brain barrier permeabilities of gabapentin using a microdialysis-pharmacokinetic approach. Pharm Res. 1996;13:398-403.
PubMed  DOI: 10.1023/A:1016092525901

9. Bodor N, Brewster ME.  Problems of drug delivery of drugs to the brain. Pharmacol Ther. 1982;19:337-386.
PubMed  DOI: 10.1016/0163-7258(82)90073-0

10. Begley DJ.  ABC transporters and the blood-brain barrier. Curr Pharm Des. 2004;10:1295-1312.
PubMed  DOI: 10.2174/1381612043384844

11. Tamai I, Yamashita J, Kido Y, et al.  Limited distribution of new quinolone antibacterial agents into brain caused by multiple efflux transporters at the blood-brain barrier. J Pharmacol Exp Ther. 2000;295:146-152.
PubMed 

12. Cordon-Cardo B, O’Brien JP, Casals D, et al.  Multidrug-resistance gene (P-glycoprotein) is expressed by endothelial cells at blood-brain barrier sites. Proc Natl Acad Sci USA. 1989;86:695-689.
PubMed 

13. Thiebaut F, Tsuruo T, Hamada H, Gottesman MM, Pastan I, Willingham MC.  Cellular localization of the multidrug-resistance gene product P-glycoprotein in normal human tissues. Proc Natl Acad Sci USA. 1987;84:7735-7738.
PubMed 

14. de Boer AG, van der Sandt I, Gaillard PJ.  The role of drug transporters at the blood-brain barrier. Annu Rev Pharmacol Toxicol. 2003;43:629-656.
PubMed  DOI: 10.1146/annurev.pharmtox.43.100901.140204

15. Zhang Y, Schuetz JD, Elmquist WF, Miller DW.  Plasma membrane localization of multidrug resistance-associated protein homologs in brain capillary endothelial cells. J Pharmacol Exp Ther. 2004;311:449-455.
PubMed  DOI: 10.1124/jpet.104.068528

16. Park S, Sinko PJ.  P-glycoprotein and mutlidrug resistance-associated proteins limit the brain uptake of saquinavir in mice. J Pharmacol Exp Ther. 2005;312:1249-1256.
PubMed  DOI: 10.1124/jpet.104.076216

17. Abbott NJ, Revest PA.  Control of brain endothelial permeability. Cerebrovasc Brain Metab Rev. 1991;3:39-72.
PubMed 

18. Cornford EM, Young D, Paxton JW, Sofia RD.  Blood-brain barrier penetration of felbamate. Epilepsia. 1992;33:944-954.
PubMed 

19. Johansson BB. Hypertension. In: Pardridge WM,  ed. Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge, UK: Cambridge University Press; 1998:427-433. 

20. Hesselink MB, Smolders H, Eilbacher B, De Boer AG, Breimer DD, Danysz W.  The role of probenecid-sensitive organic acid transport in the pharmacokinetics of N-methyl-D-aspartate receptor antagonists acting at the glycine(B)-site: Microdialysis and maximum electroshock seizures studies. J Pharmacol Exp Ther. 1999;290:543-550.
PubMed 

21. Malhotra BK, Lemaire M, Sawchuk RJ.  Investigation of the distribution of EAB 515 to cortical ECF and CSF in freely moving rats utilizing microdialysis. Pharm Res. 1994;11:1223-1231.
PubMed  DOI: 10.1023/A:1018921906993

22. Ooie T, Suzuki H, Terasaki T, Sugiyama Y.  Kinetic evidence for active efflux transport across the blood-brain barrier of quinolone antibiotics. J Pharmacol Exp Ther. 1997;283:293-304.
PubMed 

23. de Lange ECM, De Vries JD, Zurcher C, Danhof M, de Boer AG, Breimer DD.  The use of intracerebral microdialysis for the determination of pharmacokinetic profiles of anticancer drugs in tumor-bearing rat brain. Pharm Res. 1995;12:1924-1931.
PubMed  DOI: 10.1023/A:1016239822287

24. de Lange ECM, Danhof M, de Boer AG, Breimer DD.  Critical factors of intracerebral microdialysis as a technique to determine the pharmacokinetics of drugs in rat brain. Brain Res. 1994;666:1-8.
PubMed  DOI: 10.1016/0006-8993(94)90276-3

25. de Lange ECM, Bouw MR, Danhof M, de Boer AG, Breimer DD.  Application of intracerebral microdialysis to study regional distribution kinetics of drugs in rat brain. Br J Pharmacol. 1995;116:2538-2544.
PubMed 

26. de Lange EC, de Bock G, Schinkel AH, de Boer AG, Breimer DD.  BBB transport and P-glycoprotein functionality using MDR1A (−/−) and wild-type mice. Total brain versus microdialysis concentration profiles of rhodamine-123. Pharm Res. 1998;15:1657-1665.
PubMed  DOI: 10.1023/A:1011988024295

27. Xie R, Hammarlund-Udenaes M, de Boer AG, de Lange EC.  The role of P-glycoprotein in blood-brain barrier transport of morphine: transcortical microdialysis studies in mdr1a (−/−) and mdr1a (+/+) mice. Br J Pharmacol. 1999;128:563-568.
PubMed  DOI: 10.1038/sj.bjp.0702804

28. de Lange ECM, Marchand S, van den Berg D, et al.  In vitro and in vivo investigations on fluoroquinolones; effects of the P-glycoprotein efflux transporter on brain distribution of sparfloxacin. Eur J Pharmacol Sci. 2000;12:85-93.
DOI: 10.1016/S0928-0987(00)00149-4

29. de Lange ECM, Danhof M, de Boer AG, Breimer DD.  Methodological considerations of intracerebral microdialysis in pharmacokinetic studies on blood-brain barrier transport of drugs. Brain Res Brain Res Rev. 1997;25:27-49.
PubMed  DOI: 10.1016/S0165-0173(97)00014-3

30. Breimer DD, Danhof M.  Prediction of the time course of drug effects in vivo in health and disease (intensity and duration). Clin Pharmacokinet. 1997;32:259-267.
PubMed 

31. Breimer DD, Danhof M.  Relevance of the application of pharmacokinetic-pharmacodynamic modelling concepts in drug development - The ‘Wooden Shoe’ paradigm. Clin Pharmacokinet. 1997;32:259-267.
PubMed 

32. Cleton A, Odman J, van der Graaf PH, Ghijsen W, Voskuyl R, Danhof M.  Mechanism-based modeling of functional adaptation upon chronic treatment with midazolam. Pharm Res. 2000;17:321-327.
PubMed  DOI: 10.1023/A:1007505223519

33. Danhof M, Mandema JW, Hoogerkamp A, Mathôt RAA.  Pharmacokinetic-pharmacodynamic modelling in pre-clinical investigations: principles and perspectives. Eur J Drug Metab Pharmacokinet. 1993;18:41-47.
PubMed 

34. Black JW, Leff P.  Operational models of pharmacological agonism. Proc R Soc Lond B Biol Sci. 1983;220:141-162.
PubMed 

35. Black JW, Leff P, Shankley NP, Wood J.  An operational model of pharmacological agonism: the effect of E/[A] curve shape on agonist dissociation constant estimation. Br J Pharmacol. 1985;84:561-571.
PubMed 

36. Van der Graaf PH, Van Schaick EA, Visser SA, De Greef HJ, Ijzerman AP, Danhof M.  Mechanism-based pharmacokinetic-pharmacodynamic modeling of antilipolytic effects of adenosine A(1) receptor agonists in rats: prediction of tissue-dependent efficacy in vivo. J Pharmacol Exp Ther. 1999;290:702-709.
PubMed 

37. Furchgott RF.  The use of β-haloalkylamines in the differentiation of receptors and in the determination of dissociation constants of receptor-agonist complexes. Adv Drug Res. 1966;3:21-55.

38. Christ GJ.  Determination of agonist dissociation constants in isolated vasculature: equivalence of estimates obtained by the method of partial irreversible receptor inactivation and a novel application of the operational model of pharmacological agonism. Life Sci. 1990;47:1867-1874.
PubMed  DOI: 10.1016/0024-3205(90)90290-8

39. Welty DF, Schielke GP, Vartanian MG, Taylor CP.  Gabapentin anticonvulsant action in rats: disequilibrium with peak drug concentrations in plasma and brain microdialysate. Epilepsy Res. 1993;16:175-181.
PubMed  DOI: 10.1016/0920-1211(93)90078-L

40. Danhof M, Mandema JW. Modeling of relationships between pharmacokinetics and pharmacodynamics. In: Welling PG, Tse F,  eds. Pharmacokinetics. New York: Marcel Dekker; 1994:139-174. 

41. Dubey RK, McAllister CB, Inoue M, Wilkinson GR.  Plasma binding and transport of diazepam across the blood-brain barrier. J Clin Invest. 1989;84:1155-1159.
PubMed 

42. Robinson PJ, Rapoport SI.  Kinetics of protein binding determine rates of uptake of drugs by brain. Am J Physiol. 1986;251:R1212-R1220.
PubMed 

43. Rowley M, Kulagowski JJ, Watt AP, et al.  Effect of plasma protein binding on in vivo activity and brain penetration of glycine/NMDA receptor antagonists. J Med Chem. 1997;40:4053-4068.
PubMed  DOI: 10.1021/jm970417o

44. Urien S, Pinquier JL, Paquette B, Chaumet RP, Kiechel JR, Tillement JP.  Effect of the binding of isradipine and darodipine to different plasma proteins on their transfer through the blood-brain barrier. J Pharmacol Exp Ther. 1987;242:349-353.
PubMed 

45. Tanaka H, Mizojiri K.  Drug-protein binding and blood-brain barrier permeability. J Pharmacol Exp Ther. 1999;288:912-918.
PubMed 

46. Pardridge WM, Sakiyama R, Fierer G.  Transport of propanolol and lidocaine through the rat blood-brain barrier. Primary role of globulin-bound drug. J Clin Invest. 1983;71:900-908.
PubMed 

47. Pardridge WM.  Recent advances in blood-brain barrier transport. Annu Rev Pharmacol Toxicol. 1988;28:25-39.
PubMed  DOI: 10.1146/annurev.pa.28.040188.000325

48. Davson H, Segal MB. Physiology of the CSF and blood-brain barriers. Boca Raton, FL: CRC Press; 1996. 

49. Vorbrodt AW.  Ultrastrucutral cytochemistry of blood-brain barrier endothelia. Prog Histochem Cytochem. 1988;18:1-99.
PubMed 

50. van Bree JB, de Boer AG, Danhof M, Ginsel LA, Breimer DD.  Characterization of an “in vitro” blood-brain barrier: effects of molecular size and lipophilicity on cerebrovascular endothelial transport rates of drugs. J Pharmacol Exp Ther. 1988;247:1233-1239.
PubMed 

51. van de Waterbeemd H, Camenisch G, Folkers G, Chretien JR, Raevsky OA.  Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J Drug Target. 1998;6:151-165.
PubMed 

52. Oldendorf WH. Lipid solubility and drug penetration of the blood-brain barrier. Proc Soc Exp Biol Med.; 1974: 813-815. 
PubMed 

53. Oldendorf WH.  Measurement of brain uptake of radiolabelled substances using a tritiated water internal standard. Brain Res. 1970;24:372-376.
PubMed  DOI: 10.1016/0006-8993(70)90123-X

54. Levin VA.  Relationship of octanol/water partition coefficient and molecular weight to rat brain capillary permeability. J Med Chem. 1980;23:682-684.
PubMed  DOI: 10.1021/jm00180a022

55. Rubin LL, Staddon JM.  The cell biology of the blood-brain barrier. Annu Rev Neurosci. 1999;22:11-28.
PubMed  DOI: 10.1146/annurev.neuro.22.1.11

56. Suzuki H, Terasaki T, Sugiyama Y.  Role of efflux transport across the blood-brain barrier and blood-cerebrospinal fluid barrier on the disposition of xenobiotics in the central nervous system. Adv Drug Del Rev. 1997;25:257-285.
DOI: 10.1016/S0169-409X(97)00503-6

57. Scism JL, Powers KM, Artru AA, et al.  Effects of probenecid on brain-cerebrospinal fluid-blood distribution kinetics of E-Delta(2)-valproic acid in rabbits. Drug Metab Dispos. 1997;25:1337-1346.
PubMed 

58. Deguchi Y, Nowaza K, Yamada S, Yokoyama Y, Kimura R.  Quantitative evaluation of brain distribution and blood-brain barrier efflux transport of probenecid in rats by microdialysis. Possible involvement of the monocarboxylic acid transport system. J Pharmacol Exp Ther. 1997;280:551-560.
PubMed 

59. Schinkel AH, Wagenaar E, Mol CAAM, Van Deemter L, Borst P.  P-glycoprotein in the blood-brain barrier of mice influences the brain penetration and pharmacological activity of many drugs. J Clin Invest. 1996;97:2517-2524.
PubMed 

60. Schinkel AH, Smit JJM, van Tellingen O, et al.  Disruption of the mouse mdr1a P-glycoprotein gene leads to a deficiency in the blood-brain barrier and to increased sensitivity to drugs. Cell. 1994;77:491-502.
PubMed  DOI: 10.1016/0092-8674(94)90212-7

61. Wandel C, Kim R, Wood M, Wood A.  Interaction of morphine, fentanyl, sufentanil, alfentanil, and loperamide with the efflux drug transporter P-glycoprotein. Anesthesiology. 2002;96:913-920.
PubMed  DOI: 10.1097/00000542-200204000-00019

62. Uhr M, Steckler T, Yassouridis A, Holsboer F.  Penetration of amitriptyline, but not of fluoxetine, into brain is enhanced in mice with blood-brain barrier deficiency due to Mdr1a P-glycoprotein gene disruption. Neuropsychopharmacology. 2000;22:380-387.
PubMed  DOI: 10.1016/S0893-133X(99)00095-0

63. Mahar Doan KM, Humphreys JE, Webster LO, et al.  Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. J Pharmacol Exp Ther. 2002;303:1029-1037.
PubMed  DOI: 10.1124/jpet.102.039255

64. Kim RB.  Drugs as P-glycoprotein substrates, inhibitors, and inducers. Drug Metab Rev. 2002;34:47-54.
PubMed  DOI: 10.1081/DMR-120001389

65. Gross PM, Sposito NM, Pettersen SE, Fenstermacher JD.  Differences in function and structure of the capillary endothelium in gray matter, white matter, and a circumventricular organ of rat brain. Blood Vessels. 1986;23:261-270.
PubMed 

66. Collins JM, Dedrick LD.  Distributed model for drug delivery to CSF and brain tissue. J Am Physiol. 1983;14:R303-R310.

67. Davson H, Segal MB. Physiology of the CSF and blood-brain barriers. Boca Raton, FL: CRC Press; 1996. 

68. Yu D-W, Gatley SJ, Wolf AP, et al.  Synthesis of carbon-11 labeled ionidated cocaine derivatives and their distribution in baboon brain measured using positron emission tomography. J Med Chem. 1992;35:2178-2183.
PubMed  DOI: 10.1021/jm00090a005

69. Fenstermacher JD, Patlak CS, Blasberg RG.  Transport of material between brain extracellular fluid, brain cells and blood. Fed Proc. 1974;33:2070-2074.
PubMed 

70. Walker MC, Tong X, Perry H, Alavijeh MS, Patsalos PN.  Comparison of serum, cerebrospinal fluid and brain extracellualr fluid pharmacokinetics of lamotrigine. Br J Pharmacol. 2000;130:242-248.
PubMed  DOI: 10.1038/sj.bjp.0703337

71. Lee G, Dallas S, Hong M, Bendayan R.  Drug transporters in the central nervous system: brain barriers and brain parenchyma considerations. Pharmacol Rev. 2001;53:569-596.
PubMed 

72. Wong SL, Van Belle K, Sawchuk RJ.  Distributional transport kinetics of zidovudine between plasma and brain extracellular fluid and cerebrospinal fluid blood-barriers in the rabbit: investigation on the inhibitory effect of probenecid utilizing microdialysis. J Pharmacol Exp Ther. 1993;264:899-909.
PubMed 

73. Ghersi-Egea JF, Leininger-Muller B, Suleman G, Siest G, Minn A.  Localization of drug-metabolizing enzyme activities to blood-brain interfaces and circumventricular organs. J Neurochem. 1994;62:1089-1096.
PubMed 

74. Del Bigio MR.  The ependyma: a protective barrier between brain and cerebrospinal fluid. Glia. 1995;14:1-13.
PubMed  DOI: 10.1002/glia.440140102

75. Cserr HF. Convection of brain interstitial fluid. In: Shapiro K, Marmarou A, Portnoy H,  eds. Hydrocephalus. New York: Raven Press; 1984:59-68. 

76. Bouw R, Ederoth P, Lundberg J, Ungerstedt U, Nordstrom CH, Hammarlund-Udenaes M.  Increased blood-brain barrier permeability of morphine in a patient with severe brain lesions as determined by microdialysis. Acta Anaesthesiol Scand. 2001;45:390-392.
PubMed  DOI: 10.1034/j.1399-6576.2001.045003390.x

77. Bolwig TG, Hertz MM, Paulson OB, Spotoft H, Rafaelsen OJ.  The permeability of the blood-brain barrier during electrically induced seizures in man. Eur J Clin Invest. 1977;7:87-93.
PubMed 

78. Brosman CF, Claudio L. Brain microvasculature in multiple sclerosis. In: Pardridge WM,  ed. Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge, UK: Cambridge University Press; 1998:386-400. 

79. de Vries HE, Kuiper J, de Boer AG, van Berkel TJ, Breimer DD.  The blood-brain barrier in neuro-inflammatory diseases. Pharmacol Rev. 1997;49:143-156.
PubMed 

80. Povlishock JT. The pathophysiology of blood-brain barrier dysfunction due to traumatic brain injury. In: Pardridg WM,  ed. Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge, UK: Cambridge University Press; 1998:441-453. 

81. Steward PA, Mikulis D. The blood-brain barrier in brain tumours. In: Pardridge WM,  ed. Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge, UK: Cambridge University Press; 1998:434-440. 

82. de Rick AF, Belpaire FM, Dello C, Bogaert MG. Influence of enhanced AAG concentration on protein binding, pharmacokinetics and anti-arrhythmic effect of lidocaine in the dog. In: J Pharmacol Exp Ther. vol. 241. 1987:289-293. 

83. Jolliet P, Simon N, Bree F, et al.  Blood-to-brain transfer of various oxicams: effects of plasma binding on their brain delivery. Pharm Res. 1997;14:650-656.
PubMed  DOI: 10.1023/A:1012165414610

84. Lin TH, Sawada Y, Sugiyama Y, Iga T, Hanano M.  Effects of albumin and alpha 1-acid glycoprotein on the transport of imipramine and desipramine through the blood-brain barrier in rats. Chem Pharm Bull (Tokyo). 1987;35:294-301.
PubMed 

85. Mandema JW, Sansom LN, Dios-Viéitez MC, Hollander-Jansen M, Danhof M.  Pharmacokinetic-pharmacodynamic modelling of the EEG effects of benzodiazepines. Correlation with receptor binding and anticonvulsant activity. J Pharmacol Exp Ther. 1991;257:472-478.
PubMed 

86. Mandema JW, Tukker HJ, Danhof M.  Pharmacokinetic- Pharmacodynamic Modelling of the EEG effects of midazolam in individual rats: influence of rate and route of administration. Br J Pharmacol. 1991;102:663-668.
PubMed 

87. Derendorf H, Hochhaus G, Mollmann H, et al.  Receptor-based pharmacokinetic-pharmacodynamic analysis of corticosteroids. J Clin Pharmacol. 1993;33:115-123.
PubMed 

88. van Steeg T, Macintyre F, Danhof M, de Lange ECM. The influence of an increase in plasma protein binding on the pharmacokinetics and pharmacodynamics of S(-)-Propranolol. Pamplona, Spain: Poster presentation at the Population Approach Group Europe (PAGE); 2005. 

89. Tunblad K, Ederoth P, Gardenfors A, Hammarlund-Udenaes M, Nordstrom CH.  Altered brain exposure of morphine in experimental meningitis studied with microdialysis. Acta Anaesthesiol Scand. 2004;48:294-301.
PubMed  DOI: 10.1111/j.0001-5172.2003.0311.x

90. Tunblad K, Jonsson EN, Hammarlund-Udenaes M.  Morphine blood-brain barrier transport is influenced by probenecid co-administration. Pharm Res. 2003;20:618-623.
PubMed  DOI: 10.1023/A:1023250900462

91. Bouw MR, Xie R, Tunblad K, Hammarlund-Udenaes M.  Blood-brain barrier transport and brain distribution of morphine-6-glucuronide in relation to the antinociceptive effect in rats—pharmacokinetic/ pharmacodynamic modelling. Br J Pharmacol. 2001;134:1796-804.
PubMed  DOI: 10.1038/sj.bjp.0704406

92. Bouw MR, Gardmark M, Hammarlund-Udenaes M.  Pharmacokinetic-pharmacodynamic modelling of morphine transport across the blood-brain barrier as a cause of the antinociceptive effect delay in rats—a microdialysis study. Pharm Res. 2000;17:1220-1227.
PubMed  DOI: 10.1023/A:1026414713509

93. Dagenais C, Graff CL, Pollack GM.  Variable modulation of opioid brain uptake by P-glycoprotein in mice. Biochem Pharmacol. 2004;67:269-276.
PubMed  DOI: 10.1016/j.bcp.2003.08.027

94. Henthorn TK, Liu Y, Mahapatro M, Ng KY.  Active transport of fentanyl by the blood-brain barrier. J Pharmacol Exp Ther. 1999;289:1084-1089.
PubMed 

95. Letrent SP, Pollack GM, Brouwer KR, Brouwer KL.  Effect of GF120918, a potent P-glycoprotein inhibitor, on morphine pharmacokinetics and pharmacodynamics in the rat. Pharm Res. 1998;15:599-605.
PubMed  DOI: 10.1023/A:1011938112599

96. Letrent SP, Pollack GM, Brouwer KR, Brouwer KL.  Effects of a potent and specific P-glycoprotein inhibitor on the blood-brain barrier distribution and antinociceptive effect of morphine in the rat. Drug Metab Dispos. 1999;27:827-834.
PubMed 

97. Groenendaal D, de Mik D, Nicholls G, et al.  Towards a mechanism-based pharmacokinetic/pharmacodynamic model for opioids: influence of P-glycoprotein and apparent permeability in vitro . Submitted, 2005. Jake has AQ out on status.

98. Groenendaal D, de Mik D, Freijer J, et al. Towards a mechanism-based pharmacokinetic/pharmacodynamic model for opioids: combined microdialysis/EEG studies on morphine. Vienna, Austria: Poster presentation at the 4th International Symposium on Microdialysis in Drug Research and Development; 2004. 

99. Sills GJ, Kwan P, Butler E, de Lange EC, van den Berg DJ, Brodie MJ.  P-glycoprotein-mediated efflux of anti-epileptic drugs: preliminary studies in mdr1a knockout mice. Epilepsy Behav. 2002;3:427-432.
PubMed  DOI: 10.1016/S1525-5050(02)00511-5

100. Kwan P, Brodie MJ.  Potential role of drug transporters in the pathogenesis of medically intractable epilepsy. Epilepsia. 2005;46:224-235.
PubMed  DOI: 10.1111/j.0013-9580.2005.31904.x

101. Gibbs JP, Adeyeye MC, Yang Z, Shen DD.  Valproic acid uptake by bovine brain microvessel endothelial cells: role of active efflux transport. Epilepsy Res. 2004;58:53-66.
PubMed  DOI: 10.1016/j.eplepsyres.2003.12.008

102. Volk HA, Burkhardt K, Potschka H, Chen J, Becker A, Loscher W.  Neuronal expression of the drug efflux transporter P-glycoproteinin the rat hippocampus after limbic seizures. Neuroscience. 2004;123:751-759.
PubMed  DOI: 10.1016/j.neuroscience.2003.10.012

103. Marroni M, Marchi N, Cucullo L, Abbott NJ, Signorelli K, Janigro D.  Vascular and parenchymal mechanisms in multiple drug resistance: a lesson from human epilepsy. Curr Drug Targets. 2003;4:297-304.
PubMed  DOI: 10.2174/1389450033491109

104. Seiffert E, Dreier JP, Ivens S, et al.  Lasting blood-brain barrier disruption induces epileptic focus in the rat somatosensory cortex. J Neurosci. 2004;24:7829-7836.
PubMed  DOI: 10.1523/JNEUROSCI.1751-04.2004

105. Nitsch C, Klatzo I.  Regional patterns of blood-brain barrier breakdown during epileptiform seizures induced by various convulsive agents. J Neurol Sci. 1983;59:305-322.
PubMed  DOI: 10.1016/0022-510X(83)90016-3

106. Petito CK, Schaefer JA, Plum F.  Ultrastructural characteristics of the brain and blood-brain barrier in experimental seizures. Brain Res. 1977;127:251-267.
PubMed  DOI: 10.1016/0006-8993(77)90539-X

107. Tishler DM, Weinberg KI, Hinton DR, Barbaro N, Annett GM, Raffel C.  MDR1 gene expression in brain of patients with medically intractable epilepsy. Epilepsia. 1995;36:1-6.
PubMed 

108. Voskuyl RA, Hoogerkamp A, Danhof M.  Properties of the convulsive threshold determined by direct cortical stimulation in rats. Epilepsy Res. 1992;12:111-120.
PubMed  DOI: 10.1016/0920-1211(92)90031-N

109. Hoogerkamp A, Vis PW, Danhof M, Voskuyl RA.  Characterization of the pharmacodynamics of several anti-epileptic drugs in a direct cortical stimulation model of anticonvulsant effect in the rat. J Pharmacol Exp Ther. 1994;269:521-528.
PubMed 

110. Radulovic LL, Turck D, von Hodenberg A, et al.  Disposition of gabapentin (neurontin) in mice, rats, dogs, and monkeys. Drug Metab Dispos. 1995;23:441-448.
PubMed 

111. Vollmer KO, von Hodenberg A, Kolle EU.  Pharmacokinetics and metabolism of gabapentin in rat, dog and man. Arzneimittelforschung. 1986;36:830-839.
PubMed 

112. Doble RR.  Excitatory amino acid receptors and neurodegeneration. Therapie. 1995;50:319-337.
PubMed 

113. Gerlach M, Desser H, Youdim MB, Riederer P.  New horizons in molecular mechanisms underlying Parkinson’s disease and in our understanding of the neuroprotective effects of selegiline. J Neural Transm Suppl. 1996;48:7-21.
PubMed 

114. Gerlach M, Youdim MB, Riederer P.  Pharmacology of selegiline. Neurology. 1996;47:S137-S145.
PubMed 

115. Mitchell IJ, Carroll CB.  Reversal of parkinsonian symptoms in primates by antagonists of excitatory amino acids transmission: potential mechanism of action. Neurosci Biobehav Rev. 1997;21:469-475.
PubMed  DOI: 10.1016/S0149-7634(96)00036-X

116. Paschen W.  Polyamine metabolism in different pathological states of the brain. Mol Chem Neuropathol. 1992;16:241-271.
PubMed 

117. Bernstein HG, Müller M.  The cellular localization of the L-ornithine decarboxylase/polyamine system in normal and diseased central nervous systems. Prog Neurobiol. 1999;57:485-505.
PubMed  DOI: 10.1016/S0301-0082(98)00065-3

118. Kauppinen RA, Alhonen LI.  Transgenic animals as models in the study of the neurobiological role of polyamines. Prog Neurobiol. 1995;47:545-563.
PubMed  DOI: 10.1016/0301-0082(95)00037-2

119. Williams K.  Modulation and block of ion channels: a new biology of polyamines. Cell Signal. 1997;9:1-13.
PubMed  DOI: 10.1016/S0898-6568(96)00089-7

120. Williams K, Romano C, Dichter MA, Molinoff PB.  Modulation of the NMDA receptor by polyamines. Life Sci. 1991;48:469-498.
PubMed  DOI: 10.1016/0024-3205(91)90463-L

121. Baskaya MK, Rao AM, Prasad MR, Dempsey RJ.  Regional activity of ornithine decarboxylase and edema formation after traumatic brain injury. Neurosurgery. 1996;38:140-145.
PubMed 

122. Koenig H, Trout JJ, Goldstone AD, Lu CY.  Capillary NMDA receptors regulate blood-brain barrier function and breakdown. Brain Res. 1992;588:297-303.
PubMed 

123. Poduslo JF, Curran GL.  Polyamine modification increases the permeability of proteins at the blood-nerve and blood-brain barriers. J Neurochem. 1996;66:1599-1609.
PubMed 

124. Trout JJ, Koenig H, Goldstone AD, Lu CY.  Blood-brain barrier breakdown in cold injury. Polyamine signals mediate active stimulation of endocytosis, vesicular transport, and microvillus formation in rat cerebral capillaries. Lab Invest. 1986;55:622-631.
PubMed 

125. Betarbet R, Sherer TB, MacKenzie G, Garcia-Osuna M, Panov AV, Greenamyre JT.  Chronic systemic pesticide exposure reproduces features of Parkinson’s disease. Nat Neurosci. 2000;3:1301-1306.
PubMed  DOI: 10.1038/81834

126. Ravenstijn P, Merlini M, Hameetman M, et al. Blood-brain barrier permeability in Parkinson’s disease progression using the rotenone rat model. Poster presentation at the 4th International Symposium on Microdialysis in Drug Research and Development; Vienna, Austria. 2004