Abstract
Despite the enormous research efforts that have been put into the development of central nervous system (CNS) drugs, the success rate in this area is still disappointing. To increase the successful rate in the clinical trials, first the problem of predicting human CNS drug distribution should be solved. As it is the unbound drug that equilibrates over membranes and is able to interact with targets, especially knowledge on unbound extracellular drug concentration-time profiles in different CNS compartments is important. The only technique able to provide such information in vivo is microdialysis. Also, obtaining CNS drug distribution data from human subjects is highly limited, and therefore, we have to rely on preclinical approaches combined with physiologically based pharmacokinetic (PBPK) modeling, taking unbound drug CNS concentrations into account. The next step is then to link local CNS pharmacokinetics to target interaction kinetics and CNS drug effects. In this review, system properties and small-molecule drug properties that together govern CNS drug distribution are summarized. Furthermore, the currently available approaches on prediction of CNS pharmacokinetics are discussed, including in vitro, in vivo, ex vivo, and in silico approaches, with special focus on the powerful combination of in vivo microdialysis and PBPK modeling. Also, sources of variability on drug kinetics in the CNS are discussed. Finally, remaining gaps and challenges are highlighted and future directions are suggested.
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INTRODUCTION
There is a huge unmet medical need for central nervous system (CNS) disease therapies because of the growing of chronic and complex diseases associated with aging. However, development of CNS drugs is one of the most challenging tasks for the pharmaceutical industry (1). Actually, drug development for CNS drugs has suffered a higher attrition rate compared to that of other therapeutic areas drugs; it has been reported that only around 8–9% of CNS drugs that entered phase 1 were approved to launch (2). And around 50% of the attrition of potential CNS drugs has resulted due to a lack of efficacy and safety issues in phase 2 (2, 3). Knowledge of human CNS drug concentrations forms the basis for understanding exposure-response relationships; therefore, the lack of appropriate consideration of these target concentrations is one of the factors contributing to this high degree of attrition.
Obtaining the target site concentrations of CNS drugs is not straightforward because plasma concentrations do not adequately reflect CNS exposure, primarily due to the presence of the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barriers (BCSFB), and additional specific physiological characteristics of the CNS. Furthermore, significant variation in the rate and extent of mechanisms that govern target site pharmacokinetics (PK), target engagement, and signal transduction is known to exist, due to differences in system conditions such as species, gender, genetic background, age, diet, disease, and drug treatment (4). Moreover, with regard to CNS drug action, there is a lack of sufficiently established clinical biomarkers and proof-of-concept (5). Thus, it is clear that there is a need for more predictive approaches. These predictive approaches have to be interconnected to the system conditions and must be performed using adequate (including bound and unbound drug) concentrations. Also processes should preferably not be studied in isolation and then combined, but instead studied in conjunction with each other as this will provide insight about the interdependencies of these processes (4). Since measurements on CNS target site concentration in the clinical setting are highly restricted, we have to develop an approach based on integrated preclinical data that is translatable to human.
Even though drug properties have been investigated well, information of CNS system properties (CNS physiology and biochemistry) is sparse and has a large variability. CNS pharmacokinetics of drugs is determined by their interaction. System properties depend on the condition of the system, which means that we have to use approaches to distinguish between system and drug properties, as this would allow us to translate the model to other species and also other disease conditions, by using physiologically based pharmacokinetic (PBPK) modeling.
Currently, many more or less complex semi-PBPK models have been published for CNS drug distribution. At present, four preclinical translational models have been validated with human CNS concentration profiles (6–9). In these models, however, the parameters were estimated using in vivo data to describe CNS distribution of individual drug in animals. Ultimate goal of the PBPK modeling is to build a generic PBPK model in which the parameters are derived from in vitro and/or in silico data. To achieve this, in vivo data is needed to validate the generic PBPK model. Furthermore, an investigation is needed on the relationship between drug physicochemical properties and CNS distribution.
In this review, system properties and small-molecule drug properties that together govern CNS drug distribution are summarized, followed by currently available approaches on prediction of CNS pharmacokinetics, including in vitro, in vivo, ex vivo, and in silico approaches, with special focus on the powerful combination of in vivo microdialysis and PBPK modeling. Also, sources of variability on drug kinetics in the CNS are discussed. Finally, remaining gaps and challenges will be discussed and future directions will be provided.
INTERACTION BETWEEN CNS SYSTEM AND DRUG PROPERTIES
Many CNS system properties and drug specific properties are known to influence drug kinetics in the brain, as shown in Fig. 1. Here, we focus on the relevant factors from each that contribute to the drug kinetics and summarize their function.
System and drug properties which govern drug kinetics in brain. Figure is modified from de Lange (4)
CNS SYSTEM PROPERTIES
Physiological Compartments, Flows, and pH
The CNS is a complex system composed of many physiological components and flows (Fig. 2): Physiological compartments are the BBB, the BCSFB, brain extracellular fluid (brainECF), cerebral blood, brain parenchymal cells, and the cerebrospinal fluid (CSF) in the ventricles, the cisterna magna, and the subarachnoid space (4). There are pH differences among the compartments (10–16). Then, there are the CNS fluid flows that include the cerebral blood flow (CBF), brainECF bulk flow, and CSF flow. All relevant physiological parameter values are summarized in Table I.
Brain physiological components and flow. Figure is modified from de Lange (4)
Active Transporters
The localization of transporters and their expression level are also important factors to determine drug distribution in the brain. Transporters are present at the BBB and at the BCSFB, also on the membrane of brain parenchyma. Active transporters on the BBB and BCSFB consist of facilitated transport and ATP-dependent transport. The solute carrier (SLC) family, such as organic anion-transporting polypeptide (OATP) and organic anion transporters (OATs), is categorized as a facilitated transport, while ABC transporters, such as P-glycoprotein (P-gp), multidrug resistance protein (MRPs), and breast cancer-resistant protein (BCRP) are categorized as an ATP-dependent transport (45). Table II summarizes an overview of transporters with their localization and their endogenous and exogenous substrates.
Metabolic Enzymes
Presence and localization of enzymes in the brain are also important factors to determine drug kinetics in the brain. In the brain, the following enzymes are found: oxidoreductases such as cytochrome P450 (CYPs) and monoamine oxidase (MAO), membrane-bound and soluble catechol-O-methyltransferase (COMT), and transferases such as uridine 5-diphospho (UDP) -glucuronosyltransferases (UGTs) and phenol sulfotransferase (PST) (68). In Table III, an overview is provided of the different enzymes with their localization and examples of their endogenous and exogenous substrates.
SMALL-MOLECULE DRUG PROPERTIES AND INTERACTION WITH THE CNS SYSTEM
A combination of CNS system properties and drug properties determines the pharmacokinetics of a drug in the CNS, including the CNS target site. Important physicochemical properties for determination of drug CNS pharmacokinetics are summarized in Fig. 1.
Physicochemical properties of a drug, such as lipophilicity, size, charge, hydrogen binding potential and polar surface area (PSA), are important determinants for pharmacokinetics of a drug. Many studies have investigated the influence of individual physicochemical properties on the BBB penetration in isolation. However, as physicochemical properties are highly inter-correlated, it is more appropriate to consider these properties in combination.
First of all, it should be noted that it is the unbound and neutral form of a drug molecules that is able to diffuse across barriers like the BBB and BCSFB, depending on the concentration gradient of the unbound and neutral form of the drug on either side of a membrane. Lipophilicity relates to the BBB permeability, as transcellular diffusion rate (93,94). Furthermore, as a rule of thumb, higher lipophilicity increases CNS tissue binding. Molecular size is an important factor for paracellular drug diffusion rate and also has an impact on transcellular diffusion rate at the BBB (93, 95, 96). The degree of ionization depends on the pKa of the drug and actual pH in a body compartment. Thus, the BBB permeability rate is influenced by lipophilicity, size, and pKa of a drug (93, 97). Using quantitative structure-activity relationship (QSAR) modeling, it has been shown that the descriptors for the prediction of BBB penetration are different for different charge classes (98). As there are pH differences between plasma, brainECF and CSF (Fig. 2), charge is an important factor for CNS drug disposition (99).
The hydrogen bonding potential reflects the necessary energy for a molecule to move out of the aqueous phase into the lipid phase of a membrane. Recent studies have shown that the relationship between chemical structure and Kp,uu,brain (the ratio of the unbound concentration in the brain over that in plasma at equilibrium which measures the extent of CNS distribution) was dominated by hydrogen bonding (100).
PSA is generally defined as the sum of the van der Waals surface areas of oxygen and nitrogen atoms. Therefore, PSA of a compound can be related to its hydrogen bonding potential. Some studies have shown that PSA is highly correlated with the permeability coefficient (Pc) of membranes (94,101,102). A recent study for Kp,uu,brain has been shown that PSA is one of the important factors to predict the Kp,uu,brain for each compound (103).
BBB and BCSFB Transport
Protein Binding
It is generally accepted that unbound drug in plasma is able to cross the BBB and BCSFB. Two major proteins in plasma are albumin and α1-acid glycoprotein (104). For passive diffusion, the free concentration gradient between plasma and brain determines the rate of transport. The extent of BBB and BCSFB transport are investigated using Kp,uu,brain: If there is only diffusion, Kp,uu,brain is 1. If there is active transport processes, then Kp,uu,brain is larger than 1 (active in) or Kp,uu,brain is smaller than 1 (active out).
Ionization of the Drug in Plasma and in the Brain
There are similar pH differences among the CNS physiological compartments in human and in rat (Table I). Because of the pH differences, the ratio of neutral form of a compound among the compartments is different. It is generally accepted that neutral form can pass barriers; therefore, ionization that is determined by the pKa of a compounds and pH in the physiological compartments will have an impact on drug disposition in the brain.
Cerebral Blood Flow—Flow Versus Permeability-Limited Transport Rate
Lipophilic compounds usually have a large permeability coefficient; therefore, a permeability surface area product (PA), which is determined by the permeability coefficient and surface area of tissue, becomes large. If the PA is larger than the physiological cerebral blood flow, then the physiological cerebral blood flow determines the transport rate of the compound.
Modes of BBB Transport—Different Modes
The combination of transport modes at the BBB, BSCFB, and membrane of brain parenchyma determines the rate and extent of drug exchange at the BBB, BCSFB and membrane of brain parenchyma (105,106). Therefore, the operative transport mechanism(s) may differ for each drug. Each transport mode is summarized in Table IV.
Active Transporter Function
Active transporters mediate influx and efflux of drug transport. The magnitude of interaction of active transport is drug- and species-dependent (107). The functions of individual transporters are summarized in Table II.
Brain Distribution and Elimination
Extra-intracellular Distribution
Once having crossed the BBB, the drug is distributed by brainECF bulk flow into the CSF compartments. At the same time, the drug in brainECF is transported to brain parenchymal cell intracellular fluid (brainICF). It should be noted that also on the brain parenchyma cell membranes active transport may occur (106).
Tissue Binding
Tissue binding can occur as being specific at the target or non-specific to tissue components.
Lysosomal Trapping
In the brain parenchyma cells, there is a physiological pH gradient between the intracellular compartment (cytoplasm) and the lysosome compartment (Fig. 2). Especially basic compounds are known to be trapped in the lysosomes (11).
Drug Dispersion Within CSF
Some studies have shown that intrathecally administered drugs distribute faster than what can be accounted only by molecular diffusion (108, 109). Thus, it is thought that molecular diffusion makes only a small contribution to the total drug dispersion within CSF. This leads to the need to take into account also the convection due to oscillatory CSF flow to adequately explain this dispersion (110). Recently, the drug dispersion has been considered to be enhanced by the CSF pulsatility (heart rate and CSF stroke volume), and it leads to high inter- and intra-patient variability in drug distribution in the brain (110, 111).
Elimination from the Brain
Apart from transport across the BBB and BCSFB as discussed earlier, drug may leave the brain via the BBB, but also via CSF reflux into the blood stream at the level of the arachnoid villi.
Metabolism
In the brain, several metabolic enzymes are present. Enzyme interaction with drugs is important information not only on the drug PK profile but also the drug pharmacological effect in the brain since it may create active metabolites. Presence and localization of several enzymes have been reported in the brain (Table III), although their activity is reported to be relatively small compared to the liver (68, 87).
CURRENT APPROACHES TO INVESTIGATE CNS DRUG DISTRIBUTION
Since obtaining a human drug target site concentration in the brain is not feasible in most of the clinical studies, quantitative prediction of target site concentration is important. To achieve this, we need information from in vitro, ex vivo, in vivo, and in silico approaches. Here, we summarize the current approaches to obtain the necessary information to predict human drug target site concentration.
IN SILICO APPROACHES
For decades, QSAR studies have been performed using Kp,brain (total concentration ratio of the brain to plasma) or log BB, either of which may not reflect the relevant drug exposure in the brain to assess the efficacy of the drug since this efficacy is influenced by binding of compounds to plasma proteins and brain tissue. Eventually, log BB was replaced by the PA, as an estimate of the net BBB influx clearance (112). However, it has been argued that the PA cannot predict the unbound drug concentration in the CNS by itself. Recently, the most relevant parameter Kp,uu,brain has been used, with QSAR being conducted to model this parameter (100,103,113,114). Other than Kp,uu,brain, physiological meaningful parameter, Vu,brain (the volume of distribution of the unbound drug in the brain) or Kp,uu,cell (unbound concentration ration between brainECF and brainICF) are also reported using molecular descriptors (103).
IN VITRO APPROACHES
In vitro approaches to investigate the BBB permeability have been conducted using BBB models (115). BBB models can be classified into non-cell based surrogate models, such as parallel artificial membrane permeability assay (PAMPA), and cell-based models such as primary cultures cells, immortalized brain endothelial cells, or human-derived stem cells (116). Although primary cultured cells from human tissue have been reported, acquiring human brain tissue is difficult as it can be obtained postmortem and should be fresh enough (117). Therefore, alternative models based on immortalized brain endothelial cells or human-derived stem cells are also used (118,119). Even though some models have been developed for measuring the BBB permeability, an ideal cell culture model of the BBB is yet to be developed. Furthermore, reliable in vitro-in vivo correlation data is needed to enable the use of in vitro results for the prediction of in vivo permeability. However, in vitro results have not been consistent in their ability to predict in vivo permeability, probably because of different in vitro models and different sets of compounds used in the in vitro studies (120).
Currently, the biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are used for CNS drugs. The BDDCS is a modification of BCS that utilizes drug metabolism to predict drug disposition and potential drug-drug interactions in the brain (121). However, this classification approach needs to be further investigated because of inconsistencies. For example, it was proposed that 98% of BDDCS class 1 drugs would be able to get into the brain even though the drugs were P-gp substrates based on in vitro studies (122), while it has also been reported that the in vitro efflux ratio reflects the in vivo brain penetration regardless of the class in BDDCS (123).
EX VIVO APPROACHES
As mentioned before, it is the unbound drug molecules that are able to pass membranes and to interact with the target (21). Thus, measuring unbound drug concentrations is very important. Vu,brain or Fu,brain (the unbound fraction in the brain) is used to investigate unbound fraction of drugs in the brain. Fu,brain can be derived from brain homogenate (124), and Vu,brain can be obtained from the brain slice technique (125). The brain slice method is more physiologically relevant because the cell-cell interactions, pH gradients, and active transport systems are all conserved (114).
IN VIVO APPROACHES
Microdialysis can be considered as a key technique to time-dependent information regarding unbound drug concentrations. With microdialysis, both the rate and extent of drug transport and distribution processes can be determined (126,127). Thus, it can be used to obtain Kp,uu,brain in conjunction with the rate of transport processes. Moreover, this can be done at multiple locations, and this feature has shown that even for a drug like acetaminophen that is not subjected to any active transport, substantial differences in pharmacokinetic profiles exist in different brain compartments (6). While there is some limit to use this water-based technique for the highly lipophilic drugs, lots of microdialysis experiments have contributed to a boost in the understanding on drug exchange across the BBB (126,128,129). Especially the use of microdialysis at multiple brain locations has provided insight into the relative contribution of CNS distribution and elimination processes to the local (differences in) pharmacokinetics of a compound (6, 7, 130). It has paved the way to the development of a generic multi-compartmental CNS distribution model (Fig. 3), with some validated human CNS predictions that will be discussed later in this review.
A generic multi-compartmental CNS distribution model. (a) Multi-compartmental CNS distribution model structure. (b) and (c) Observations and model prediction (light orange solid lines) for the 9 compounds in rat for each dose and without and with co-administration of active transport blockers. The x-axis represents the time in minutes and the y-axis represents the concentration of the 9 compounds in ng/ml. The panel is stratified by brain compartments (colors) and by active transport blockers
Then, positron emission tomography (PET) is a valuable non-invasive in vivo monitoring technique that can be used to visualize drug CNS distribution in living animals and human. However, the PET technique cannot distinguish parent compounds from their metabolites or bound and unbound drug. Furthermore, it may also encounter difficulties in obtaining useful data when a very high non-specific binding (NSB) to non-target proteins and phospholipid membranes occurs (131). Recently, a novel lipid membrane binding assay (LIMBA) was established as a fast and reliable tool for identifying compounds with unfavorably high NSB in the brain tissue (132).
COMBINATORY MAPPING APPROACH
Combinatory mapping is an approach that combines three compound-specific parameters obtained from in vitro, ex vivo, and in vivo data: Kp,brain, Vu,brain, and Fu,plasma, for calculation of Kp,uu,brain (133). This approach also can be used to obtain not only Kp,uu,brain but also to understand unbound drug disposition in the cell cytosol and the lysosomes. Recently, this approach has been extended to predict drug exposure in different brain regions such as frontal cortex, striatum, hippocampus, brainstem, cerebellum, and hypothalamus, in which also the impact of transporters and receptors in each region was taken into account (134). Although this approach is useful to support the selection of potential CNS drugs in drug discovery, it has two limitations. The first limitation is that it can only predict the parameters at steady-state. The second limitation is that the approach cannot be translated to predict the parameters, for instance, inter-species or inter-disease conditions because the processes to obtain the parameters in this approach are not connected with system properties which will be changed in these conditions.
CONDITION DEPENDENCY AND PBPK MODELING
Condition Dependency
Drug distribution into and within the brain depends on the interaction between system and drug properties. Drug properties remain the same, whatever the species and conditions are in which the drug has been administered. This indicates that interspecies variability in drug distribution into and within the brain is the result of differences in physiological and biochemical parameters. Factors which cause variation in drug pharmacokinetics include genetic background, species differences, gender, age, diet, disease states, and drug treatment (4). Factors which cause variation in drug pharmacodynamics include seasonal effect (135), age (136), gender (137), and species (138). Influences of these conditions on CNS system properties are summarized in Table V.
(Semi-) PBPK Modeling
PBPK models need to be informed on system and on drug properties to model the interaction and predict the PK in different compartments. Especially as obtaining pharmacokinetic data from the human brain is highly restricted, working in the PBPK model framework is valuable as it can be translated to predict the target site concentrations in inter-species and inter-disease situations (4). Some translational studied have been reported by using an animal (semi-) PBPK model for CNS drugs but they are relatively sparse and range from simple to more advanced (Table VI).
Recently, a generic multi-compartmental CNS distribution model structure has been proposed, that could successfully describe the pharmacokinetics in plasma and different CNS compartments (brainECF, CSF in the lateral ventricle (CSFLV) and CSF in the cisterna magna (CSFCM)), using microdialysis data for 9 paradigm compounds with substantial differences in physicochemical properties (9) (Table VI, Fig. 3). These compounds are acetaminophen, atenolol, methotrexate, morphine, paliperidone, phenytoin, quinidine, remoxipride, and risperidone. This is the first model that can nicely predict human brainECF and CSF time concentration profiles which were obtained from physiologically “close to normal” brain for morphine and acetaminophen (9).
For remoxipride, Stevens et al. have shown that brainECF pharmacokinetics, as measured with microdialysis, represented the target site concentrations, because these concentrations could be directly linked to the effect of remoxipride on plasma prolactin levels in an advanced mechanism-based model (185). After scaling to human, this indeed could also be concluded for human CNS remoxipride effects on human plasma prolactin levels. This underscores the importance of having information on pharmacokinetics at the CNS target region.
Using our generic multi-compartmental CNS distribution model, we can provide predictions of human CNS pharmacokinetics for all the nine compounds. For a direct comparison of rat and human pharmacokinetics in the different CNS compartments in response to plasma pharmacokinetics, the same plasma exposure was used for individual compound. In Fig. 4, it can be seen that, in general, human CNS pharmacokinetics, especially that in the CSF in the subarachnoid space (CSFSAS), which is including the lumbar CSF is typically slower than that in the rat. This provides important information on the relationship between brainECF (which often is the target site) pharmacokinetics and the lumbar CSF concentrations that are often used as biomarker of brain target site concentrations. Also, it can be seen that the differences in the pharmacokinetics at the more early time points of the different CNS compartments is larger in human than in the rat. With time, these differences fade out. The consequences for drug-target interaction kinetics (186) and further processes towards CNS drug effects remain to be determined.
Simulation of time concentration profiles of brainECF, CSFLV, CSFCM and CSFSAS in rat and human for the 9 compounds. Differences in the time concentration between rat and human reflected the differences of the rate and extent of drug distribution in brain between in rat and in human since identical plasma exposure was used as an input
Remaining Gaps and Challenges on PBPK Modeling, Towards a Generic PBPK Model
The ultimate aim is to have a CNS PBPK model that can predict human brain compartment concentrations on the basis of the physicochemical properties of a compound, which can be determined by in vitro measurements, or in silico prediction. Thus, in the overview in Table VI, it can be seen that we still have a number of gaps in the currently available (semi-) PBPK models of CNS drugs. Most of the models require in vivo data on the compound(s), and most of the predictions have not been validated on human data. Even the most comprehensive model (9), with validated prediction of human CNS drug distribution (for acetaminophen and morphine), still requires in vivo data for individual compound predictions. Thus, it can be seen that there is a need for further development of a generic, fully PBPK model for CNS drug distribution (187–189).
To have a PBPK model that would predict CNS drug distribution, based the physicochemical properties of an individual drug, for different species and in different conditions, a number of challenges remain:
-
Having a PBPK model structure with all relevant compartment/parameters, as physiological parameter values reported are sparse and variable (see Table I).
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Having drug physicochemical parameter values from in vitro, and/or in silico, or even some in vivo measurements, which may not necessarily be correct. For example, in vitro or in vivo data may depend on the experimental setting, while in silico information really depends on the data availability, used to obtain the equation.
-
Having human data sets for validation of prediction by the model, with typically limited availability.
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Having information on pathophysiological changes in human CNS properties in (the many) disease conditions. For example, BBB characteristics may change in Alzheimer’s disease, multiple sclerosis, and pharmacoresistant epilepsies (190).
DISCUSSION AND CONCLUSION
Pharmacokinetics of drugs in the CNS is governed by a combination of CNS system physiology and drug properties. This means that variability in CNS system physiological parameters (condition dependency) may lead to variability of CNS pharmacokinetics. Therefore, it is important to explicitly distinguish between system physiology and drug properties, either by changing conditions and investigating the pharmacokinetics of one drug, or investigating the pharmacokinetics of different drugs in the same condition.
PBPK models make this distinction; however, being based on total drug plasma and total tissue concentrations at equilibrium (SS), while more recent PBPK models include, at best, unbound plasma SS concentrations. However, as body processes are based on the interaction with the unbound drug and are time-dependent, it is crucial to include measuring the unbound drug in each compartment as a function of time (Mastermind Research Approach (MRA)) (4), for which microdialysis has been proven the key technique. Using the MRA, microdialysis has provided lots of valuable data that pave the way towards a semi-physiological generic CNS drug distribution model, yet applicable for nine compounds with highly different physicochemical properties with excellent description of the rat data for all these compounds, and adequate prediction of human CNS data that were available for acetaminophen and morphine (9).
One microdialysis experiment in a single freely moving animal can provide a lot of data points, obtained under the same experimental condition of the animal, and thereby revealing the interrelationships of processes. With this microdialysis has already contributed to reduction and refinement in the use of animals. Furthermore, all this information can further be “condensed” into a generic PBPK model and will thereby help in the reduction in the future use of animals (replacement) (191).
So, in order to be able to predict CNS drug effects in human, next steps would be a development of a full PBPK CNS drug distribution model, and combine it with target binding kinetics, receptor occupancy, and signal transduction (186,192), and include system changes by human disease condition.
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This research article was prepared within the framework of project no. D2-501 of the former Dutch Top Institute Pharma, currently Lygature (Leiden, the Netherlands; www.lygature.org).
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Guest Editors: Robert E. Stratford, Nimita Dave, and Richard F. Bergstrom
The original version of this article was revised: Figure 3 in the PDF and electronic versions of the published article contains formatting errors caused by the typesetter.
An erratum to this article is available at http://dx.doi.org/10.1208/s12248-017-0080-x.
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Yamamoto, Y., Danhof, M. & de Lange, E.C.M. Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations. AAPS J 19, 891–909 (2017). https://doi.org/10.1208/s12248-017-0050-3
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DOI: https://doi.org/10.1208/s12248-017-0050-3