Toward the prediction of CNS drug effect profiles in physiological and pathological conditions using microdialysis and mechanism-based pharmacokinetic-pharmacodynamic modeling

Leiden/Amsterdam Center for Drug Research, Division of Pharmacology, Gorlaeus Laboratories, 2300 RA, Leiden University, Leiden, The Netherlands.
The AAPS Journal (Impact Factor: 3.8). 02/2005; 7(3):E532-43. DOI: 10.1208/aapsj070354
Source: PubMed


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.

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    • "Generally, small, lipophilic, noncharged drugs diffuse more easily by passive transcellular mechanisms than the hydrophilic ones. In addition, only the free fraction of drug is available to cross the BBB (Lange et al., 2005) and a high lipophilicity also makes compounds more vulnerable to bind to plasma and efflux proteins (Perisic-Janjic et al., 2011). Based on the correlation analysis herein reported, it is important to emphasize that MM and PSA appeared to be the most important determinants of brain/plasma ratios and the AUC 0—12h and C max values obtained in brain were also strongly correlated with PSA. "
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    ABSTRACT: In silico approaches to predict absorption, distribution, metabolism and excretion (ADME) of new drug candidates are gaining a relevant importance in drug discovery programmes. When considering particularly the pharmacokinetics during the development of oral antiepileptic drugs (AEDs), one of the most prominent goals is designing compounds with good bioavailability and brain penetration. Thus, it is expected that in silico models able to predict these features may be applied during the early stages of AEDs discovery. The present investigation was mainly carried out in order to generate in vivo pharmacokinetic data that can be utilized for development and validation of in silico models.
    Epilepsy research 09/2013; 107(1-2). DOI:10.1016/j.eplepsyres.2013.08.013 · 2.02 Impact Factor
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    • "It is important to note that transport across the blood–brain barriers may occur by simple diffusion, facilitated diffusion, vesicle transport, or active transport, or combinations of these, depending on the drug. All these processes occur concomitantly, and will influence each other’s rate and extent, such that these interrelationships need to be considered in ultimately predicting CNS target site concentrations, and resulting drug effects [2, 29]. "
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    ABSTRACT: Human cerebrospinal fluid (CSF) sampling is of high value as the only general applicable methodology to obtain information on free drug concentrations in individual human brain. As the ultimate interest is in the free drug concentration at the CNS target site, the question is what CSF concentrations may tell us in that respect. Studies have been performed in rats and other animals for which concentrations in brain extracellular fluid (brain ECF) as a target site for many drugs, have been compared to (cisterna magna) CSF concentrations, at presumed steady state conditions,. The data indicated that CSF drug concentrations provided a rather good indication of, but not a reliable measure for predicting brain ECF concentrations. Furthermore, comparing rat with human CSF concentrations, human CSF concentrations tend to be higher and display much more variability. However, this comparison of CSF concentrations cannot be a direct one, as humans probably had a disease for which CSF was collected in the first place, while the rats were healthy. In order to be able to more accurately predict human brain ECF concentrations, understanding of the complexity of the CNS in terms of intrabrain pharmacokinetic relationships and the influence of CNS disorders on brain pharmacokinetics needs to be increased. This can be achieved by expanding a currently existing preclinically derived physiologically based pharmacokinetic model for brain distribution. This model has been shown to successfully predict data obtained for human lumbar CSF concentrations of acetaminophen which renders trust in the model prediction of human brain ECF concentrations. This model should further evolute by inclusion of influences of drug properties, fluid flows, transporter functionalities and different disease conditions. Finally the model should include measures of target site engagement and CNS effects, to ultimately learn about concentrations that best predict particular target site concentrations, via human CSF concentrations.
    Journal of Pharmacokinetics and Biopharmaceutics 02/2013; 40(3). DOI:10.1007/s10928-013-9301-9 · 1.86 Impact Factor
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    • "A number of PK-PD modeling efforts have been reported for describing the inflammation [53], endogenous amino acid neurotransmitters [54], and CNS disorder disease progression in animal and human separately [55]. In our study, the present PK-PD model was introduced to investigate the interaction of LPS-PCs-KYN-CNS disorder, as well as incorporating AZI treatment to evaluate the potential involvement of inflammation on depressive progression. "
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    ABSTRACT: A mechanism-based model was developed to describe the time course of lipopolysaccharide-induced depressive-like behavior and azithromycin pharmacodynamics in mice. The lipopolysaccharide-induced disease progression was monitored by lipopolysaccharide, proinflammatory cytokines, and kynrenine concentration in plasma. The depressive-like behavior was investigated by forced swimming test and tail suspension test. Azithromycin was selected to inhibit the surge of proinflammatory cytokines induced by lipopolysaccharide. Disease progression model and azithromycin pharmacodynamics were constructed from transduction and indirect response models. A delay in the onset of increased proinflammatory cytokines, kynrenine, and behavior test compared to lipopolysaccharide was successfully characterized by series transduction models. The inhibition of azithromycin on proinflammatory cytokines was described by an indirect response model. After lipopolysaccharide challenging, the proinflammatory cytokines, kynrenine and behavior tests would peak approximately at 3, 12, and 24 h respectively, and then the time courses slowly declined toward a baseline state after peak response. During azithromycin administration, the peak levels of proinflammatory cytokines, kynrenine and behavior indexes decreased. Model parameters indicated that azithromycin significantly inhibited the proinflammatory cytokines level in plasma and improved the depressive-like behavior induced by inflammation. The integrated model for disease progression and drug intervention captures turnovers of proinflammatory cytokines, kynrenine and the behavior results in the different time phases and conditions.
    PLoS ONE 01/2013; 8(1):e54981. DOI:10.1371/journal.pone.0054981 · 3.23 Impact Factor
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