Biomedical subjects

E Niclas Jonsson

Publications and source records attributed to E Niclas Jonsson.

At least 19 recordsLinked to original sources

PubMed2007

The application of a Bayesian approach to the analysis of a complex, mechanistically based model.

The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: "informative", "semi-informative", and "noninformative", respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the "informative" priors. However, when the "semi-informative" and "noninformative" priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and "what if" scenarios.

Algorithms↗

PubMed2006

Population pharmacokinetic/pharmacodynamic (PK/PD) modelling of the hypothalamic-pituitary-gonadal axis following treatment with GnRH analogues.

AIMS: To develop a population pharmacokinetic/pharmacodynamic (PK/PD) model of the hypothalamic-pituitary-gonadal (HPG) axis describing the changes in luteinizing hormone (LH) and testosterone concentrations following treatment with the gonadotropin-releasing hormone (GnRH) agonist triptorelin and the GnRH receptor blocker degarelix. METHODS: Fifty-eight healthy subjects received single subcutaneous or intramuscular injections of 3.75 mg of triptorelin and 170 prostate cancer patients received multiple subcutaneous doses of degarelix of between 120 and 320 mg. All subjects were pooled for the population PK/PD data analysis. A systematic population PK/PD model-building framework using stochastic differential equations was applied to the data to identify nonlinear dynamic dependencies and to deconvolve the functional feedback interactions of the HPG axis. RESULTS: In our final PK/PD model of the HPG axis, the half-life of LH was estimated to be 1.3 h and that of testosterone 7.69 h, which corresponds well with literature values. The estimated potency of LH with respect to testosterone secretion was 5.18 IU l(-1), with a maximal stimulation of 77.5 times basal testosterone production. The estimated maximal triptorelin stimulation of the basal LH pool release was 1330 times above basal concentrations, with a potency of 0.047 ng ml(-1). The LH pool release was decreased by a maximum of 94.2% by degarelix with an estimated potency of 1.49 ng ml(-1). CONCLUSIONS: Our model of the HPG axis was able to account for the different dynamic responses observed after administration of both GnRH agonists and GnRH receptor blockers, suggesting that the model adequately characterizes the underlying physiology of the endocrine system.

Dose-Response Relationship, Drug↗

PubMed2006

Stereoselective pharmacokinetics of cetirizine in the guinea pig: role of protein binding.

PURPOSE: To characterize the pharmacokinetics of cetirizine enantiomers in the guinea pig including protein binding in both the guinea pig and human plasma. METHODS: Plasma concentrations of cetirizine enantiomers in the guinea pig were determined using a LC-MS/MS method after a short i.v. infusion (1, 2 and 4 mg/kg) of racemic cetirizine. Protein binding was determined using an in vitro equilibrium dialysis technique. A pharmacokinetic model was developed using NONMEM and the differences in pharmacokinetic parameters of levocetirizine and dextrocetirizine were estimated. RESULTS: The plasma concentration time data of both the enantiomers were best described by a three-compartment pharmacokinetics model. The clearance (CL) of levocetirizine and dextrocetirizine was 1.2 and 2.7 ml/min, respectively, and the volume of distribution at steady state (Vss) was 457 ml and 996 ml, respectively. The fraction unbound (fu) in guinea pig plasma for levocetirizine and dextrocetirizine was 7-10% and 16-21% while in human plasma, it was 8% and 12%, respectively. The factor describing the difference in the pharmacokinetic parameters of the cetirizine enantiomers was estimated to be 2.26. CONCLUSIONS: Cetirizine pharmacokinetics is stereoselective in the guinea pig. For levocetirizine, fu, CL and Vss were half those of dextrocetirizine, indicating that protein binding is an important factor affecting the pharmacokinetics of cetirizine. The effect of protein binding on the pharmacokinetics of the cetirizine enantiomers could be extrapolated to humans.

Animals↗

PubMed2005

Brain distribution of cetirizine enantiomers: comparison of three different tissue-to-plasma partition coefficients: K(p), K(p,u), and K(p,uu).

The objective of this study was to compare the blood-brain barrier (BBB) transport and brain distribution of levo- (R-CZE) and dextrocetirizine (S-CZE). Microdialysis probes, calibrated using retrodialysis by drug, were placed into the frontal cortex and right jugular vein of eight guinea pigs. Racemic CZE (2.7 mg/kg) was administered as a 60-min i.v. infusion. Unbound and total concentrations of the enantiomers were measured in blood and brain with liquid chromatography-tandem mass spectrometry. The brain distribution of the CZE enantiomers were compared using the parameters K(p,) K(p,u,) K(p,uu), and V(u,br). K(p) compares total brain concentration to total plasma concentration, K(p,u) compensates for binding in plasma, whereas K(p,uu) also compensates for binding within the brain tissue and directly quantifies the transport across the BBB. V(u,br) describes binding within the brain. The stereoselective brain distribution indicated by the K(p) of 0.22 and 0.04 for S- and R-CZE, respectively, was caused by different binding to plasma proteins. The transport of the CZE enantiomers across the BBB was not stereoselective, since the K(p,uu) was 0.17 and 0.14 (N.S.) for S- and R-CZE, respectively. The K(p,uu) values show that the enantiomers are effluxed to a large extent across the BBB. The V(u,br) of approximately 2.5 ml/g brain was also similar for both the enantiomers, and the value indicates high binding to brain tissue. Thus, when determining stereoselectivity in brain distribution, it is important to study all factors governing this distribution, binding in blood and brain, and the BBB equilibrium.

Animals↗

PubMed2005

Bridging the pharmacokinetics and pharmacodynamics of UK-279,276 across healthy volunteers and stroke patients using a mechanistically based model for target-mediated disposition.

PURPOSE: UK-279,276 is a recombinant glycoprotein and is a selective antagonist of CD11b, which in preclinical models of acute stroke blocks the infiltration of activated neutrophils into the site of infarction. Binding of UK-279,276 to the CD11b receptors is hypothesized to facilitate its elimination. The event of an acute stroke leads to proliferation of neutrophils and an up-regulation of CD11b, which results in different pharmacokinetics/pharmacodynamics (PK/PD) in patients than in healthy volunteers. The aim of this current analysis was to develop a mechanistically based model to bridge the differences between healthy volunteers and patients. METHODS: PK samples, neutrophil counts, and total number and number of free CD11b receptors per neutrophils from three healthy volunteer studies (n=98) and one patient study (n=169) were modeled using the mixed effects modeling software NONMEM version VI (beta). Three mechanistic submodels were developed based on underlying physiology and pharmacology: (1) neutrophil maturation and proliferation, (2) CD11b up-regulation, and (3) three clearance pathways for UK-279-276 including CD11b-mediated elimination. RESULTS: The model accurately described the time course of CD11b expression, CD11b binding, and the measured PK of UK-279,276 and accounted for the PK/PD differences between healthy volunteers and patients. CONCLUSIONS: A complex mechanistic model that closely resembled the "true" underlying system provided an effective bridge between healthy volunteers and patients by appropriately accounting for the underlying disease-dependent target mediated disposition.

CD11b Antigen↗

PubMed2005

Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations.

PURPOSE: The objective of the present analysis was to explore the use of stochastic differential equations (SDEs) in population pharmacokinetic/pharmacodynamic (PK/PD) modeling. METHODS: The intra-individual variability in nonlinear mixed-effects models based on SDEs is decomposed into two types of noise: a measurement and a system noise term. The measurement noise represents uncorrelated error due to, for example, assay error while the system noise accounts for structural misspecifications, approximations of the dynamical model, and true random physiological fluctuations. Since the system noise accounts for model misspecifications, the SDEs provide a diagnostic tool for model appropriateness. The focus of the article is on the implementation of the Extended Kalman Filter (EKF) in NONMEM for parameter estimation in SDE models. RESULTS: Various applications of SDEs in population PK/PD modeling are illustrated through a systematic model development example using clinical PK data of the gonadotropin releasing hormone (GnRH) antagonist degarelix. The dynamic noise estimates were used to track variations in model parameters and systematically build an absorption model for subcutaneously administered degarelix. CONCLUSIONS: The EKF-based algorithm was successfully implemented in NONMEM for parameter estimation in population PK/PD models described by systems of SDEs. The example indicated that it was possible to pinpoint structural model deficiencies, and that valuable information may be obtained by tracking unexplained variations in parameters.

Algorithms↗

PubMed2005

A longitudinal model for non-monotonic clinical assessment scale data.

Clinical assessment scales, where subitem ratings are added and summarized as a total score, are convenient tools for monitoring disease progression and often used to measure the effect of drug treatment in clinical trials. Statistical evaluation of any beneficial treatment effects tends to focus on single-valued summary measures, for example, the difference between the score at the end of treatment and the score at baseline. Such analyses ignore potentially important features of the data, e.g. early vs. late recoveries. It is therefore of interest to develop longitudinal models that make more efficient use of the information present in non-monotonic clinical assessment scale data. We propose a two-part modeling approach for the modeling of this type of data. Non-monotonicity is managed by regarding score changes as Markovian transition events. A set of probabilistic models are used to describe the occurrences of the transitions. Continuous models are used to describe the magnitude of the scale score change, given the observed transition. In this manner, a non-monotonic disease progression is handled more efficiently than if other available methods are used. We illustrate this approach using data from a recent phase II study of a drug used in the treatment of stroke, where stroke severity was measured on the Scandinavian Stroke Scale (SSS). This scale consists of nine subitems: consciousness, eye movements, hand/arm/leg motor performance, orientation, speech, facial palsy, and gait. The data were non-monotonic, since there was at any time a risk of a score decline, despite a general tendency towards healing. The two-part probabilistic/continuous model fit the data well and proved to be robust in model-checking procedures such as posterior predictive checks and bootstrapping. The models derived using this approach could potentially accommodate drug effects, not only in terms of score improvement at end of study, but also on the onset of recovery, on dropout and on the probability of unfavorable progression patterns. In addition, it is possible to use the resulting for simulation of the prospective outcome of future studies. We conclude that this approach has considerable potential for more efficient use of information in longitudinal modeling of non-monotonic clinical assessment scale data.

Algorithms↗

PubMed2005

A pharmacokinetic-pharmacodynamic model for the quantitative prediction of dofetilide clinical QT prolongation from human ether-a-go-go-related gene current inhibition data.

BACKGROUND: QT prolongation is an important biomarker of the arrhythmia torsades de pointes and appears to be related mainly to blockade of delayed inward cardiac rectifier potassium currents. The aim of this study was to quantify the relationship between in vitro human ether-a-go-go-related gene (hERG) potassium channel blockade and the magnitude of QT prolongation in humans for the class III antiarrhythmic dofetilide. METHODS: The in vitro affinity and activity of dofetilide were determined in recombinant cell cultures expressing the hERG channel, and the QT-prolonging effect of dofetilide was assessed in 5 clinical studies (80 healthy volunteers and 17 patients with ischemic heart disease). A population pharmacokinetic-pharmacodynamic analysis of the in vitro and in vivo data was performed in NONMEM by use of the operational model of pharmacologic agonism to estimate the efficiency of transduction from ion channel binding to Fridericia-corrected QT response. RESULTS: A 3-compartment pharmacokinetic model with first-order absorption characterized the time course of dofetilide concentrations. On the basis of an in vitro potency of 5.13 ng/mL for potassium current inhibition and predicted unbound dofetilide concentrations, the estimated transducer ratio (tau) of 6.2 suggests that the QT response plateaus before currents are fully blocked. In our study population, 10% hERG blockade corresponds to a QT prolongation of 20 ms (95% confidence interval, 12-32 ms). With long-term dofetilide administration, tolerance develops with a half-life of 4.7 days. CONCLUSIONS: The current mechanism-based pharmacokinetic-pharmacodynamic model quantified the relationship between in vitro hERG channel blockade and clinical QT prolongation for dofetilide. This model may prove valuable for assessing the risk of QT prolongation in humans for other drugs that selectively block the hERG channel on the basis of in vitro assays and pharmacokinetic properties.

Adolescent↗

PubMed2005

PsN-Toolkit--a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM.

PsN-Toolkit is a collection of statistical tools for pharmacometric data analysis using the non-linear mixed effect modeling software NONMEM. The toolkit is object oriented and written in the programming language Perl using the programming library Perl-speaks-NONMEM (PsN). Five methods: the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building are included as separate classes and may be used in user-written Perl scripts or through stand-alone command line applications. The tools are designed with the ability to cooperate and with an emphasis on common structures for workflow and result handling. Parallel execution of independent tool sections is supported on shared memory multiprocessor (SMP) computers, Mosix/openMosix clusters and distributed computing environments following the NorduGrid standard. In conclusion, PsN-Toolkit makes it easier to use the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building in pharmacometric data analysis.

Cluster Analysis↗

PubMed2005

Influence of probenecid on the delivery of morphine-6-glucuronide to the brain.

The objective was to evaluate the influence of probenecid on the blood-brain barrier (BBB) transport of morphine-6-glucuronide (M6G). Microdialysis probes were placed in the striatum and into the jugular vein of Sprague-Dawley rats. Each probe was calibrated in vivo using retrodialysis by drug. M6G was administered as a 4-h exponential i.v. infusion, and the experiment was repeated the following day with the addition of probenecid. The data were analysed using NONMEM. An integrated model including the total arterial concentrations, the dialysate concentrations in brain and blood, and the recovery measurements, was developed. The extent of BBB transport, expressed as the ratio between clearance into the brain and clearance out of the brain (CL(in)/CL(out)), was estimated as 0.29 on both days, indicating that efflux transporters act on M6G at the BBB. However, the probenecid-sensitive transporters are not involved in the brain efflux, as the ratio was unaltered although probenecid was co-administered. In contrast, the systemic elimination of M6G decreased by 22% (p<0.05) upon probenecid co-administration. The half-life of M6G was longer in the brain than in blood on both experimental days (p<0.05). In conclusion, probenecid decreased the systemic elimination of M6G, but had no effect on the BBB transport of M6G.

Animals↗

PubMed2004

Pharmacokinetic/pharmacodynamic modelling of GnRH antagonist degarelix: a comparison of the non-linear mixed-effects programs NONMEM and NLME.

In this paper, the two non-linear mixed-effects programs NONMEM and NLME were compared for their use in population pharmacokinetic/pharmacodynamic (PK/PD) modelling. We have described the first-order conditional estimation (FOCE) method as implemented in NONMEM and the alternating algorithm in NLME proposed by Lindstrom and Bates. The two programs were tested using clinical PK/PD data of a new gonadotropin-releasing hormone (GnRH) antagonist degarelix currently being developed for prostate cancer treatment. The pharmacokinetics of intravenous administered degarelix was analysed using a three compartment model while the pharmacodynamics was analysed using a turnover model with a pool compartment. The results indicated that the two algorithms produce consistent parameter estimates. The bias and precision of the two algorithms were further investigated using a parametric bootstrap procedure which showed that NONMEM produced more accurate results than NLME together with the nlmeODE package for this specific study.

Algorithms↗

PubMed2004

Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming.

The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.

Computer Simulation↗

PubMed2004

Non-linear mixed-effects pharmacokinetic/pharmacodynamic modelling in NLME using differential equations.

The standard software for non-linear mixed-effect analysis of pharmacokinetic/pharmacodynamic (PK/PD) data is NONMEM while the non-linear mixed-effects package NLME is an alternative as long as the models are fairly simple. We present the nlmeODE package which combines the ordinary differential equation (ODE) solver package odesolve and the non-linear mixed effects package NLME thereby enabling the analysis of complicated systems of ODEs by non-linear mixed-effects modelling. The pharmacokinetics of the anti-asthmatic drug theophylline is used to illustrate the applicability of the nlmeODE package for population PK/PD analysis using the available data analysis tools in R for model inspection and validation. The nlmeODE package is numerically stable and provides accurate parameter estimates which are consistent with NONMEM estimates.

Algorithms↗

PubMed2004

Evaluation of type I error rates when modeling ordered categorical data in NONMEM.

The development of non-linear mixed pharmacokinetic/pharmacodynamic models for continuous variables is usually guided by graphical assessment of goodness of fit and statistical significance criteria. The latter is usually the likelihood ratio test (LR). When the variable to be modeled is categorical, on the other hand, the available graphical methods are less informative and/or more complicated to use and the modeler needs to rely more heavily on statistical significance assessment in the model development. The aim of this study was to evaluate the type I error rates, obtained from using the LR test, for inclusion of a false parameter in a non-linear mixed effects model for ordered categorical data when modeling with NONMEM. Data with four ordinal categories were simulated from a logistic model. Two nested multinomial models were fitted to the data, the model used for simulation and a model containing one additional parameter. The difference in fit (objective function value) between models was calculated. Three types of models were explored; (i) a model without interindividual variability (IIV) where the addition of a parameter describing IIV was assessed, (ii) a model with IIV where the addition of a drug effect parameter (either categorical or continuous drug exposure measure) was evaluated, and (iii) a model including IIV and drug effect where the inclusion of a random effects parameter on the drug effect was assessed. Alterations were made to the simulation conditions, for example, varying the number of individuals and the size and distribution of the IIV, to explore potential influences on the type I error rate. The estimated type I error rate for inclusion of a false random effect parameter in model (i) and (iii) were, as expected, lower than the nominal. When the additional parameter was a fixed effects parameter describing drug effect (model(II)) the estimated type I error rates were in agreement with the nominal. None of the different simulation conditions tried changed this pattern. Thus, the LR test seems appropriate for judging the statistical significance of fixed effects parameters when modeling categorical data with NONMEM.

Clinical Trials as Topic↗

PubMed2004

Power, selection bias and predictive performance of the Population Pharmacokinetic Covariate Model.

Identification and quantification of covariate relations is often an important part of population pharmacokinetic/pharmacodynamic (PK/PD) modelling. The covariate model is regularly built in a stepwise manner. With such methods, selection bias may be a problem if only statistically significant covariates are accepted into the model. Competition between multiple covariates may further increase selection bias, especially when there is a moderate to high correlation between the covariates. This can also result in a loss of power to find the true covariates. The aim of this simulation study was to investigate the effect on power, selection bias and predictive performance of the covariate model, when altering study design and system-related quantities. Data sets with 20-1000 subjects were investigated. Five covariates were created by sampling from a multivariate standard normal distribution. The true covariate was set up to have no, low, moderate and high correlation to the other four covariates, respectively. Data sets, in which each individual had two or three PK observations, were simulated using a one-compartment i.v. bolus model. The true covariate influenced clearance according to one of several magnitudes. Different magnitudes of residual error and inter-individual variability in the structural model parameters were also introduced to the simulation model. A total of 7400 replicate data sets were simulated independently for each combination of the above conditions. Models with one of the five simulated covariates influencing clearance and the model without any covariate were fitted to the data. The probability of selecting (according to a pre-specified P-value) the different covariates, along with the estimated covariate coefficient, was recorded. The results show that selection bias is very high for small data sets (< or = 50 subjects) simulated with a weak covariate effect. If selected under these circumstances, the covariate coefficient is on average estimated to be more than twice its true value, making the covariate model useless for predictive purposes. Surprisingly, even though competition from false covariates caused substantial loss in the power of selecting the true covariate, the already high selection bias increased only marginally. This means that the bias due to competition is negligible if statistical significance is also required for covariate selection. Bias and predictive performance are direct functions of power, only indirectly affected by study design and system-related quantities. Mainly because of selection bias, low-powered covariates can be expected to harm the predictive performance when selected. For the same reason these low-powered covariates may falsely appear to be clinically relevant when selected. If the aim of an analysis is predictive modelling, we do not recommend stepwise selection or significance testing of covariates to be performed on small or moderately sized data sets (<50-100 subjects).

Computer Simulation↗

PubMed2004

Population pharmacokinetic modeling of a subcutaneous depot for GnRH antagonist degarelix.

PURPOSE: The objective of this study is to develop a population pharmacokinetic (PK) model that describes the subcutaneous (SC) depot formation of gonadotropin-releasing hormone (GnRH) antagonist degarelix, which is being developed for treatment of prostate cancer, exhibiting dose-volume and dose-concentration dependent absorption. METHODS: The PK analysis is made in NONMEM through joint analysis of data from two phase I clinical studies; an intravenous infusion study and a single SC dose escalation study. The SC absorption is modeled using an approximation to Ficks' second law of diffusion out of a spherical depot. The dose-volume effect on the SC release is estimated using a B-spline basis whereas the bioavailability is modeled as a function of the dose-concentration. RESULTS: The SC depot model is approximated by using two concentric spherical compartments for the SC absorption combined with a two-compartment disposition model. The results indicate that the volume effect is most apparent at low injection volumes whereas the effect is diminishing at higher injection volumes. The dose-concentration effect on the bioavailability is estimated to decrease at increasing dose-concentrations. CONCLUSIONS: The presented SC depot model describes the PK profile of GnRH antagonist degarelix. This modeling approach might also be applicable for other depot-formulated drugs exhibiting complex PK profiles.

Delayed-Action Preparations↗

PubMed2004

An integrated model for the analysis of pharmacokinetic data from microdialysis experiments.

PURPOSE: To develop an integrated model for microdialysis data that incorporates all data including the recovery measurements in one model, and to compare this model to a previous model and the results from a noncompartmental analysis. METHODS: The models were developed in NONMEM. The modes of analysis were compared with respect to parameter estimates, model structures, gained mechanistic insight, and practical aspects. RESULTS: Both modeling approaches resulted in similar model structures. The parameter estimates in blood and brain from the models and the results from the noncompartmental analysis were comparable. Using the integrated model all data, that is, the total arterial concentrations, the venous and brain dialysate concentrations, and the recovery measurements, were analyzed simultaneously. CONCLUSION: The theoretical benefits of the integrated model are related to the inclusion of the recovery in the model and the use of all collected data as it was observed. Thus, all data are described in a single model, corrections for the recovery and the protein binding are done within the model, and the dialysate observations are described by the integral over each collection interval. Thereby, the variability and the uncertainty in the model parameters are handled correctly to give more reliable parameter estimates.

Animals↗

PubMed2003

Morphine blood-brain barrier transport is influenced by probenecid co-administration.

PURPOSE: The objective of this study was to investigate the possible influence of probenecid on morphine transport across the blood-brain barrier (BBB) in rats. METHODS: Microdialysis probes, calibrated using retrodialysis by drug, were placed into the striatum and jugular vein of seven Sprague-Dawley rats. Morphine was administered as a 4-h exponential infusion. The experiment was repeated the next day with the addition of probenecid, administered as a bolus dose (20 mg/kg) followed by a constant infusion (20 mg/kg/h). Models for BBB transport were built using the computer program NONMEM. RESULTS: The steady-state ratio of 0.29 +/- 0.07 of unbound morphine concentration in brain to that in blood indicates that morphine is actively effluxed at the BBB. Probenecid co-administration increased the ratio to 0.39 +/- 0.04 (p < 0.05). Models in which probenecid influenced the brain efflux clearance rather than the influx clearance, well described the data. The half-life in brain increased from 58 +/- 9 min to 115 +/- 25 min when probenecid was co-administered. Systemic clearance of morphine also decreased upon probenecid co-administration, and M3G formation was decreased. CONCLUSION: This study indicates that morphine is a substrate for the probenecid-sensitive transporters at the BBB. Co-administration of probenecid decreased the brain efflux clearance of morphine.

Animals↗