In vivo, in vitro and in silico methods for small molecule transfer across the BBB

REVIEWS In Vivo, In Vitro and In Silico Methods for Small Molecule Transfer Across the BBB JURGEN MENSCH,1 JULEN OYARZABAL,2 CLAIRE MACKIE,3 PATRICK AUGUSTIJNS4 1

ChemPharm development, Johnson & Johnson Pharmaceutical Research & Development, a Division of Janssen Pharmaceutica N.V., Beerse, Belgium 2 Medicinal Chemistry Department, Drug Discovery Informatics Section, Spanish National Cancer Research Centre, Madrid, Spain 3

ADME-Tox, Johnson & Johnson Pharmaceutical Research & Development, a division of Janssen Pharmaceutica N.V., Beerse, Belgium 4

Laboratory for Pharmacotechnology and Biopharmacy, Katholieke Universiteit Leuven, O&N, Gasthuisberg, 3000 Leuven, Belgium

Received 24 October 2008; revised 5 February 2009; accepted 10 February 2009 Published online 30 April 2009 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jps.21745

ABSTRACT: The inability of molecules to permeate the BBB is a significant source of attrition in Central Nervous System (CNS) drug discovery. Given the increasing medical drivers for new and improved CNS drugs, small molecule transfer across the BBB is attracting a heightened awareness within pharmaceutical industry and medical fields. In order to assess the potential for small CNS molecules to permeate the BBB, a variety of methods and models, from in silico to in vivo going through in vitro models are developed as predictive tools in drug discovery. This review gives a comprehensive overview of different approaches currently considered in drug discovery to circumvent the lack of small molecule transfer through the BBB, together with their inherent advantages and disadvantages. Particularly, special attention is drawn to in silico models, with a detailed and contemporary point of view on prediction tools and guidelines for rational design. ß 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4429–4468, 2009

Keywords: blood–brain barrier; in vitro model; in vivo model; in silico modeling; CNS; permeability; passive diffusion; active transport; drug discovery; molecular descriptors

INTRODUCTION The blood–brain barrier (BBB) is a specialized system of capillary endothelial cells that protects the brain from harmful substances such as toxins Correspondence to: Jurgen Mensch (Telephone: 32-14-606320; Fax: 32-14-60-5838; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 98, 4429–4468 (2009) ß 2009 Wiley-Liss, Inc. and the American Pharmacists Association

and viruses circulating in the blood stream (Fig. 1). Moreover, this barrier also keeps out many would-be central nervous system (CNS) therapeutic agents,1 while supplying the brain with the required nutrients for proper function. Unlike endothelial cells in the systemic (peripheral) circulation, a minimal level of pinocytosis and lack of membrane fenestrations characterizes the endothelial cells forming the BBB.2,3 Due to this restriction in the paracellular (tight junctional)

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Figure 1. Schematic representation of the blood– brain barrier (modified from Francis et al.316).

pathway, exchange between the blood and the brain is dominated by the transcellular route, making the endothelial cells the ‘‘gatekeepers’’ of the brain. Therefore passive diffusion through the BBB is the primary process of translocation from the blood stream to the brain for the large majority of therapeutic compounds. Furthermore, the BBB includes an enzymatic barrier at the cerebral endothelia, capable of metabolizing drug and nutrients.4–6 Enzymes such as glutamyl transpeptidase (GTP), alkaline phosphatase, and aromatic acid decarboxylase are present at elevated concentration in cerebral microvessels, yet often in low concentration or absent in non-neuronal capillaries. The BBB also expresses various efflux transporters that are involved in another significant transport mechanism: carrier mediated efflux. By this mechanism, drugs are extruded from the brain with the ABC transporter Pglycoprotein (P-gp) being the principle efflux mechanism.7–12 Other efflux transporters at the level of the BBB are the multidrug resistance proteins (MRP)13,14 and breast cancer resistant proteins (BCRP).15–17 Although it is assumed that

passive diffusion through the BBB is the most important permeability process, more and more scientists support the idea that carrier-mediated transport and active influx/efflux of drugs may be more important then is generally assumed.18–20 However much work is still needed to fully characterize the drug transporters at the BBB before a reconsideration of the present view on transport to the brain can be suggested. CNS-targeting drugs need to cross the BBB in order to reach their therapeutic receptors inside the brain. Over 98% of small molecules intended for therapeutic use in the CNS never reach the market because of their inherent inability to cross the BBB.21,22 The BBB effectively restricts delivery of valuable pharmaceuticals to the brain thereby presenting major therapeutic limitations toward the treatment of CNS diseases.23,24 It is reported that 12% of all drugs are active in the CNS, but only 1% of all drugs are active in the brain for diseases other than affective disorders.25 However, diagnostic imaging agents, anti-infective, antiviral and anticancer drugs targeting the brain are, nowadays, an unmet medical need. For example, cerebral metastases are clinical significant in 10–30% of patients with neoplasia.26 Successful treatment has been limited by difficulties in delivering therapeutic agents to the central nervous system. Specifically, drug penetration of the blood–brain barrier (BBB) poses a unique and challenging problem in brain tumors therapy.27 In addition, the trend toward increasing life expectancy emphasizes the need to develop new drugs for age-related neurodegenerative conditions such as Alzheimer and Parkinson’s disease. The entry of a CNS drug candidate from the blood stream into the brain depends on many physicochemical factors, including lipophilicity, total polar surface area (TPSA); charge state, molecular size, flexibility and hydrogen-bonding potential. Further confounding factors include plasma protein binding, active uptake into the CNS and efflux out of the CNS. In today’s drug discovery research, many models are used to assess the transport characteristics of drug candidates across the BBB, including in silico, in vitro, and in vivo methods. The recent developments of combinatorial chemistry call for systems that can be used for high throughput screening. Given the increasing number of compounds to test in early discovery, costly and labor-intensive in vivo measurements and traditional, low throughput, in vitro assays of

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CNS pharmacokinetic properties are impossible. For this reason, there has been an increasing interest in in silico and high throughput in vitro methods for predicting in vivo properties early in the drug discovery process. Unfortunately, reliable brain uptake data generated using a consistent in vivo methodology for modeling these in silico and in vitro processes are relatively scarce in literature and so far no reliable high throughput in vitro model is available in drug discovery yet. This article starts with a comprehensive summary of different in vivo and in vitro methods, applied in drug discovery, together with their inherent pros and cons. The data obtained by these models are used to build in silico models for the prediction of blood–brain barrier permeability. The latter are presented in the last part of the review.

IN VIVO METHODS In vivo brain uptake experiments provide the most reliable reference information for testing and validating other models.28 A number of noninvasive and invasive techniques are available for in vivo measurement of brain uptake and can be broadly classified according to two methodological approaches: (I) methods based on equilibrium studies between brain and blood (extent of brain penetration, Brain/Plasma ratio, Kp or log BB), and (II) methods based on kinetic parameters (rate of brain penetration, Permeability ( P)
surface area (S) product). The PS parameter is an indicator for the initial uptake into the CNS. log PS can be determined by for example brain uptake index (BUI) techniques, in vivo intravenous administration and in situ brain perfusion. Noninvasive techniques are external detection methods that can consequently be applied in humans and permit to measure individually the time course of uptake together with plasma pharmacokinetics. The objective of this part of the document is to review reported BBB in vivo models in order to have a clear idea about their strengths and limitations together with their application scope in the drug discovery process.

Invasive In Vivo Methods Brain/Plasma Ratio, Kp (log BB) The equilibrium distribution between blood and brain is defined as the ratio of concentrations in DOI 10.1002/jps

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brain and blood (log BB). log BB is the logarithm of the ratio of the steady-state total concentration of a compound in the brain to that in the blood/ plasma, log(BB) ¼ log(Cbrain/Cplasma). This parameter depends upon the passive diffusion characteristics, the transporters (uptake and efflux) at the BBB, metabolism and the relative drug binding affinity differences between the plasma proteins and brain tissue.20,29 Most pharmaceutical companies generate log BB data in animals (often rat or mouse) as part of routine biopharmaceutical profiling of compounds.30 Currently, there is no uniform standard protocol for determination of the brain/plasma ratio, resulting in variation in the number of sampling points, dose size, route of administration (PO, IV, SC, IP), and species investigated.31 Single time-point log BB determinations are of limited value as they depend on the time chosen and the relationship between the concentration in plasma and brain at that timepoint. More often, the brain/plasma ratio is determined at multiple time-points after oral, intravenous or subcutaneous administration to eliminate the time dependence of the resulting brain/plasma ratio. The brain/plasma ratio is then calculated from the areas under the curve (AUCbrain in h ng/g; AUCplasma in h ng/mL) for brain and plasma concentrations. Generally, compounds with a brain/plasma ratio of greater than 0.3–0.5 are considered to have sufficient access to the central nervous system (CNS), compounds with a value greater than 1 freely cross the BBB, whereas compounds with a brain/ plasma ratio smaller than 0.1 may be unable to enter the CNS. However, some exceptions to this simplistic rule have been reported.32 log BB measurements (Fig. 2A) usually require several animals per time-point and are therefore costly and labor intensive. In addition the log BB parameter does not give any indications of free, unbound drug that is responsible for the pharmacological effect.33 In fact, the log BB essentially represents the inert partitioning into lipid matter.34 For these reasons, brain/plasma ratios of compounds cannot be directly linked to their CNS activity. Moreover, the interpretation of brain/ plasma ratios in terms of BBB permeability should be done with care as this question implies a link between extent and rate of CNS penetration, the former being a distribution parameter which depends not only on the rate of permeation across the BBB but also on the binding affinity, transporters and metabolism processes. Recent

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Kp;u ¼

Cbrain ðwith Cu ; plasma Cu;plasma

¼ the unbound concentration in the plasmaÞ (2) Kp;uu ¼

Cu;brain ðwithCu;brain Cu;plasma

¼ the unbound concentration in the

(3)

brain interstitial fluidÞ where Kp,u eliminates the influence of plasma protein binding, while Kp,uu factors out both the influence of plasma protein binding and the influence of brain tissue binding. Hence the Kp,uu is directly related to the BBB’s passive transport for drugs as well as the effects of efflux and influx. Brain Uptake Index (BUI)

Figure 2. Schematic overview of the experimental setup of different invasive in vivo brain uptake methods. A: The intravenous injection PS technique as well as the log BB method with an intravenous injection of the compound into the tail vein; (B) the setup of the BUI method; (C) the experimental setup of the in situ brain perfusion technique characterized by the ligated branches of the internal and external carotid artery; (D) the intracerebral microdialysis technique with dialysis.

studies demonstrated the need for an integrated approach in which permeability, efflux/influx, plasma protein and tissue binding were used for improved prediction of the CNS penetration.35–42 Bostro¨ m et al. obtained more clarity into the dominant factors influencing CNS penetration by transformation of the Kp value (Cbrain/Cplasma) into unbound partition ratios, Kp,u and Kp,uu as shown in Eqs. (1)–(3):38

BUI ¼

ð½Hdpm=½14 CdpmÞðbrainÞ ð½Hdpm=½14 CdpmÞðinjected solutionÞ
100 with dpm

¼ disintegrations per minute

Cbrain Kp ¼ ðwith Cbrain Cplasma ¼ AUCbrain and Cplasma ¼ AUCplasma Þ

The BUI or carotid artery single injection with brain tissue sampling was originally described by Oldendorf43 and is one of the oldest techniques to estimate the uptake of drugs into the brain.44 The BUI is a single pass technique and, as applied to adult, anesthetized rats, comprises the rapid (<0.5 s) carotid intra arterial administration of a buffered solution (0.2 mL bolus) containing a test compound (3H labeled) and a reference compound (14C labeled) or vice versa45 (Fig. 2B). The reference compound can easily penetrate the brain and is included as an internal standard to define the amount of injected material that actually distributes into the brain. The bolus passes through the brain within 2 s after single injection and the animal is decapitated 15 s after injection. The short experimental period minimizes potential efflux46 from the brain and excludes systemic recirculation. Following decapitation, the brain concentrations of test and reference compounds are measured and related to the plasma concentrations to calculate the BUI as described by Pardridge47 in Eq. (4):

(1)

(4)

Applying the Kety–Renkin–Crone48–50 equation of capillary physiology the BUI method is a relative fast technology and therefore very suitable for

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compounds that are labile or are rapidly metabolized. The disadvantages of the technique are that exposure to the brain is very short (seconds) and that BUI cannot easily be related to cerebrovascular permeability as it depends on multiple factors including blood flow, brain region and time between injection and decapitation. Therefore the BUI method is only suitable to study compounds with moderate to high BBB permeability and with radiolabel available. Another disadvantage is that the total injected dose does not reach the whole brain as only about 20% of the drugs enter the internal carotid artery while 80% reaches the external carotid artery. In Situ Brain Perfusion The in situ brain perfusion technique (or internal carotid artery perfusion technique), as shown in Figure 2C, is an extension51–53 of the BUI technique to overcome the limitations of the latter. The in situ brain perfusion was first developed by Takasato et al.51 to avoid metabolism of the test compound because there is no systemic exposure of the test compound prior to transport through the BBB. The method also involves a longer experimental period, comprising a 15–60 s perfusion of the carotid artery (compared to the carotid artery single injection) followed by sampling of drug levels within the brain. In the Takasato method, all branches of the external and internal carotid artery are ligated. After ligating the common carotid artery, the reference and test compounds are perfused retrograde via the external into the internal carotid artery in anesthetized rats. Following perfusion, the animal is decapitated, the brain is collected and analyzed for reference and test compounds to quantify the BBB PS product:   VD  V0 PS ¼ (5) t where t is the duration of the perfusion period (min), VD or V0 is defined as brain volume of distribution for the test and the reference compound respectively and calculated as the ratio of brain/perfusate concentration at time t. More recent simplifications of the technique54 include direct catheterization of the common carotid artery, and stopping endogenous blood flow by severing the heart ventricles before start of the perfusion to minimize mixing of perfusate and endogenous blood. In addition to the manipulation DOI 10.1002/jps

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of perfusate composition, control of flow rate and duration is possible with the perfusion technique. The in situ brain perfusion techniques use an internal carotid artery perfusion at a rate of approximately 3.5–4 mL/min for 0.25–1 min. A perfusion flow rate of 3.5–4 mL/min induces an arterial pressure equal to the ambient systolic pressure that prevents mixing of the perfusate with circulating rat plasma within the cerebral circulation. The perfusion time can be extended up to 10 min with reduced flow rate to 1.25 mL/ min for compounds with low PS values.55 The internal carotid artery perfusion technique is more sensitive than the BUI because the experimental time period is prolonged to 15–60 s. This technique allows estimating the PS product of fast and poor brain penetrating compounds. The main advantage of the in situ brain perfusion technique is that there is no systemic exposure of the compound, thus avoiding metabolism. However metabolism that occurs within the brain microcirculation cannot be avoided.47 Another major advantage is that there is total control over the perfusate solute concentration, and that the other constituents of the perfusion fluid can easily be varied, allowing the characterization of saturable transport systems, plasma protein binding, and the effects of regulatory modifiers, hormones and neurotransmitters that can be presented to the brain at defined concentrations.54 Also, the effect of pH, ionic content, and flow rate can be monitored.45 Murakami et al. compared the BBB PS products of drugs in rats and mice. A good correlation between the BBB PS values was obtained in the two species indicating that this technique is amenable for use in mice.56 Consequently, the transport of drugs into the brain of genetically modified animals, such as P-gp knockout mice, can be assessed using this in situ perfusion technique.53,57 Recently, the relationship between in situ brain permeability and brain free fraction was investigated using the in situ brain perfusion technique.58,59 The obtained results showed that either BBB permeability, brain tissue binding, efflux and/or plasma protein binding play an important role in the CNS penetration. A problem with the in situ brain perfusion technique is that, in rats, the common carotid artery has branches between the neck region and the brain. Therefore, to ensure the delivery of compounds to the brain, these branches have to be ligated which increases the experimental difficulty and labor intensity of the method. Another

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disadvantage of this technique is that a complete kinetic analysis requires a high number of animals and the in situ brain perfusion technique requires radiolabeled compound or reference compound as well. Intravenous Injection PS Technique The intravenous injection technique (Fig. 2A) to determine the BBB PS product of test compounds does not require access to a carotid artery; the experimental setup is similar to the intravenous injection technique to determine the brain/plasma ratio as described above. The IV injection PS technique involves the cannulation of a femoral or tail-vein of rats or mice for injection of the compound. At one or various time points (0.25– 60 min or longer) after injection, arterial blood is collected either by cannulation of a femoral or other vein or by sacrificing the animals. Brain levels can be determined at the end of the experiments (single time point)60 or alternatively at all predetermined time points (multiple time point). Multiple time point experiments require the decapitation of several animals per time point while the single time point experiments can be performed without sacrificing many animals. In the case of initial rate studies (single time point), the goal is to capture the unidirectional uptake phase while no assumptions are required about distribution phenomena taking place after a test compound has entered the brain. The single time point technique involves the arterial blood sampling at suitable time intervals to construct blood or plasma concentration-time curve (AUCplasma(0–t)); at the end of the experiment the animal is sacrificed and the brain concentration is measured for the computation of the BBB PS product:   Cbraintest  Cbrainref PS ¼ : (6) AUCplasmað0tÞ This results in the estimation of the unidirectional influx from blood or plasma to brain. The brain concentration should represent tissue concentration after correction for intravascular content. The intravascular volume can be determined by coadministration of a vascular marker (e.g., albumin, inulin) that is not significantly accumulating in the brain tissue during the experiment. Alternatively, a washing procedure via the ascending aorta can be performed to clear the intravascular content before decapitation of the animal.

Multiple time point uptake techniques have been originally designed by Brodie et al.61 and provide information about the time course of brain uptake. The exposure is much longer and measurable brain concentrations of poorly permeating compounds can be obtained. For brain tissue concentrations measured at different sampling times BBB uptake can be estimated when the concentration difference between brain and plasma relative to plasma concentration is plotted versus time62 and BBB PS products can be calculated using the Renkin–Crone equation.48–50 One of the main advantages of this technique is that plasma and brain pharmacokinetics can be obtained. Additionally, due to longer exposure to cerebral microvessels the sensitivity of the technique increases. Another advantage is that the degree of experimental difficulty is much lower compared to the BUI method as well as the in situ brain perfusion. The major disadvantage of the single intravenous injection technique is that there may be extensive metabolism of the test compound by, and distribution into, peripheral tissues, which may confound the interpretation of the brain uptake data and prevent accurate computation of the BBB PS product. In addition, at later time points, there is the possibility of back diffusion from brain to plasma resulting in inaccurate estimation of the BBB PS product. The need of radiolabeled test compound and/or coadministration of a reference compound increases the experimental difficulty as well. Quantitative Auto Radiography (QAR) QAR involves the administration (IV, SC, PO) of a radioactive tracer (typically 14C labeled compound) into an experimental animal. Blood samples are collected periodically and radioactivity measured to determine the concentration time course. The animal is sacrificed and the brain is frozen immediately. The frozen brain is sectioned into 20 mm thick sections and placed inside an X-ray cassette with an X-ray film and autoradiographed. The film is developed and the distribution and quantitation of the radioactivity is determined by an image analysis method.63–65 To calculate PS values of a test compound, separate measurements of regional blood flow must be performed.66 The QAR technique is cheap and easy to perform. Another advantage of QAR is the high spatial resolution however the time resolution is rather slow since one animal has to be used for one time point. Furthermore QAR

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cannot make the distinction between parent compound and metabolites or differentiate protein bound and unbound compounds. Intracerebral Microdialysis Intracerebral microdialysis involves the implantation of a microdialysis probe, mostly cylindrical,67 into the extracellular space of the brain. The microdialysis probe consists of a semi-permeable hollow fiber membrane (Fig. 2D), which is perfused with a physiological solution, the perfusate. The perfusate is an aqueous solution that, preferably, closely matches the composition of the ionic composition of the extracellular cerebral fluid surrounding the probe,68 to prevent unwanted changes in composition of the extracellular fluid. Molecules that penetrate into the brain following oral, intravenous or subcutaneous administration can diffuse over the membrane into the perfusate along their concentration gradient. The solution that exits the dialysis probe, the dialysate, can be continuously collected and the concentration of the molecule of choice can be determined by an appropriate analytical technique. Microdialysis has a number of advantages. Due to the continuous collection of the dialysate, the free, unbound concentration of one or more compounds and their metabolites in the extracellular fluid of the brain can be measured at multiple time points within an individual freely moving animal.69 This reduces the number of animals needed for pharmacokinetic investigations and avoids the problems associated with inter-animal variability. With the dialysis principle, providing protein free dialysate, sample clean up procedures for analysis are not required. In addition, as both plasma and brain levels of compound can be determined over time; it is possible to determine the kinetics of influx and efflux from the brain.70 More interestingly, the probe can be placed in any region of the brain, which may be useful when targeting a compound to a specific area of the brain (such as in a brain tumor). However, if one is not interested in localized concentrations, this raises the issue of where to place the probe and whether multiple probes should be used in order to get an appropriate representation of drug levels throughout the brain.45 Additionally, the insertion of the microdialysis probe into the brain can damage the BBB and consequently affect the BBB functionality. Another limitation of this technique is that it DOI 10.1002/jps

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greatly depends on the sensitivity of the assay method.71 The dialysis procedure leads to lower concentration samples that require sensitive analytical methods. Presently, HPLC combined with mass-spectroscopy methods result in high selectivity and sensitivity whereby the latter limitation looses importance. Finally, the most important limitation of the microdialysis technique is the determination of the relationship between the concentrations of the drug of interest, in the dialysate, to that in the extracellular fluid (recovery). The recovery of a particular substance is defined as the ratio, expressed as a percentage, of the concentration in the dialysate to the concentration in the extracellular fluid. The recovery can be experimentally determined in vitro but the extraction across the fiber wall measured in vitro is generally greater than that occurring in vivo;72 consequently, complicated mathematical models73–76 and time-consuming, more complicated methods are required.71 In particular, the zero-net flux (ZNF)67,77,78 and the retrodialysis67,78–80 method are often used in practice. Even though in vitro calibration of the probes is considered sufficient, in vivo recovery may still deviate from in vitro recovery.

Noninvasive In Vivo External Detection Methods Positron Emission Tomography (PET) Positron emission tomography (PET) has been shown to be a noninvasive, quantitative approach to measure the BBB PS product in animals81,82 and humans.83–85 The method involves a bolus injection (or infusion) into the body of a positron emitting radionuclide or a test compound labeled with an isotope that emits positrons. Three main types of PET tracers are in use: (1) nutrient analogs like 18F-deoxy-glucose and several amino acid analogs (e.g., 11C-methionine, 18F-DOPA) for measurement of metabolism, (2) ligands for neurotransmitter receptors or transporters, and (3) tracers for detection of BBB damage (e.g., 68 Ga-EDTA).86 After injection, the subject is placed in a counter that detects the emission of gamma photons emitted by the tracer in the brain as a result of destruction of the positrons. Also a plasma tracer curve is generated from periodic blood sampling. With the use of computerized imaging techniques, two-dimensional images of the brain can be determined in real time, allowing for a kinetic evaluation of brain uptake.87–89

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PET is today the most advanced technology to obtain biochemical information, such as glucose metabolism, blood flow, distribution of receptors, P-gp transport activity,90 enzymes and neurotransmitters directly in vivo, because it is noninvasive, rapid, repeatable, and offers very high sensitivity. Thus, it allows monitoring of the whole pharmacokinetic time course in physiological conditions in the same animal because the transport of the tracers can be visualized in whole brain over time. Brain kinetics can be analyzed by compartmental modeling or graphical evaluation, which allows the calculation of the BBB PS permeability.85,91 However, PET is expensive and the preparation and stability of the tracers are matters of concern. Another disadvantage of PET is that no distinction can be made between parent compound, metabolites, and protein bound or unbound tracer. Magnetic Resonance Imaging (MRI) Contrast-enhanced magnetic resonance imaging is a noninvasive, sensitive technique for the detection and progress-monitoring of BBB defects in a number of clinical pathologies like multiple sclerosis, stroke, and brain tumors. While the MRI technology has also been applied to qualitatively assess BBB permeability in both animals and humans, this technique offers also the possibility of quantitative measurements of BBB permeability. Recent technological advancements in the field of MRI, and the availability of dedicated high-field research scanners have allowed the development of sophisticated noninvasive techniques with high spatial resolution for evaluating BBB function using MRI.92 The MRI technique for estimating the BBB PS product is based on the graphical analysis method developed by Patlak et al.93 This technique involves quantifying the tracer, gadoliniumdiethylenetriaminepentaacetic acid (Gd-DTPA), distribution in brain tissue after an intravenous bolus injection and was successfully demonstrated in a rat ischemic stroke brain model.94 On the other hand MRI is very costly and therefore it is applicability for routine screening in drug discovery is limited.

IN VITRO METHODS BBB permeability properties of CNS drug candidates in pharmaceutical research are important

for their applicability and should be determined at an early stage of the drug discovery process. In addition, the increased throughput of combinatorial chemistry calls for systems that can be used for high throughput screening. In this context, animal models and associated in vivo studies are not appropriate. Therefore, over the past decade many scientists have been searching for an efficient in vitro model to assess the in vivo BBB drug permeability. In order to appropriately estimate the BBB-permeability in vivo or to categorize drug candidates with respect to their transport potency, the in vitro model must possess some basic characteristics and requirements. These basic properties and requirements were described by Reichel et al.95 and Gumbleton and Audus96 and some common advantages of in vitro BBB permeability models were summarized by Lundquist and Renftel.97 A number of cell-based and noncell-based in vitro models have been developed for in vivo measurement of brain uptake. A brief overview of these in vitro models is described in this section. For more detail on specific systems the reader is referred to the corresponding publications.

Cell-Based In Vitro Models from Cerebral Origin Isolated Brain Capillaries (Isolated Brain Microvessels) Brain capillaries have been used since the start of BBB research.98 Brain capillaries can be isolated from various sources, animals and human, using nonenzymatic mechanical-, combined mechanical-enzymatic- or solely enzymatic isolation procedures.99–105 Isolated brain capillaries are metabolically active but, during the isolation procedure, a significant loss of adenosine triphosphate (ATP) and hence activity has been reported.99,106 Although isolated brain capillaries reflect the in vivo situation well, they are not well suited for BBB permeability screening because the luminal surface of the microvessels is difficult to access. Hence only drug transport experiments can be performed from the abluminal (brain) side to the blood. Primary or Low Passage Brain Capillary Endothelial Cells (BCECs) Primary or low passage brain capillary endothelial cells are, apart from isolated brain capillaries, the in vitro models closest to the

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in vivo situation.95,97,100 Primary as well as low passage BCECs maintain many of the endothelial and BBB-specific in vivo properties although some of the features, such as BBB-transporters and enzymes, are down regulated or even lost during preparation.95,107,108 The preparation of the in vitro BCECs from primary isolated cells occurs in two different steps. The first step involves the isolation of brain capillaries from human or animal brains. In the second step isolated brain capillaries can be plated and cultured in culture flasks. The BCECs, which grow out of the capillaries, may be separated and cultured alone or in combination with astrocytes or astrocyteconditioned medium.100,109,110 Although human BCECs would be most ideal from a scientific point of view, there are ethical and tissue access constraints. Therefore most researchers use BCECs from animal sources. The yield of BCECs from porcine and bovine species (200 million cells/brain) is much higher compared to the yield of BCECs from rat (1–2 million cells/brain). In addition, the use of rat BCEC’s for transendothelial permeability is limited because pericytes and other contaminants cause small cracks in the monolayer resulting in variable monolayer integrity.111 The latter disadvantage is less important with bovine and porcine BCECs. For those reasons and the ease of availability, bovine and porcine species are the most popular source for in vitro primary or low passage cell culture models of BBB permeability. Bovine BCEC cultures have been widely used in several variants and applications by many researchers.112–123 Originally, the use of bovine BCECs for BBB permeability measurements has been described by Bowman et al.112 Later on Audus and coworkers104,105,113,114 developed a monoculture system of bovine BCECs (Fig. 3A). These cells form monolayers retaining many morphological and biochemical properties similar to the BBB in vivo.87–88,95–102 In order for the bovine BCEC culture in vitro permeability model to be considered representative of the in vivo situation, Pardridge et al.124 compared the in vitro and in vivo permeability results of several compounds. An acceptable in vitro–in vivo correlation was found, however the in vitro effective permeability (Pe) was about 150-fold greater than the in vivo permeability while the BCEC culture in vitro method underestimated the Pe for L-dopa and D-glucose, both substrates for carriermediated transport. These findings of overestimation of BBB Pe values for lipid-mediated transport DOI 10.1002/jps

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Figure 3. Comprehensive representation of different in vitro endothelial monolayer experimental setups. A shows the traditional Transwell monoculture model (mono-dimensional); B1 and B2 are bidimensional (astrocytes þ endothelial cell) cell culture models of which B1 is showing the no-contact setup and B1 the close-contact setup; (C) is a snapshot of the tri-dimensional hollow-fiber BBB in vitro model in which endothelial cells grow at the inside of a fiber membrane and astrocytes can grow at the outside of the membrane.

and underestimation of Pe values for carriermediated transport by the in vitro model is attributed to the loss of expression of BBB-specific proteins, like P-gp, in BCEC cultures. One intervention to improve barrier tightness and BBB specific gene expression is to mimic the astroglial influence present in vivo by adding astrocyte conditioned culture medium124–132 or by coculture100–103 with astrocytes.86 Astrocytes can either be grown on the bottom of the culture well

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plate (no contact, Fig. 3B1) or on the underside of the filter membrane (close contact, Fig. 3B2). Although it was proven that pretreatment of the BCEC monolayers with astrocyte conditioned medium resulted in an increase in number and size of intercellular tight junctions, the most common approach to improve the barrier properties of bovine BCEC cultures is to coculture the endothelial cells with primary astrocytes isolated from neonatal rats. This approach has been shown to modulate the BCEC monolayer properties, such as up-regulation of P-gp function and increased TEER (700 V cm2) and to retain the characteristics that are normally associated with the BBB in vivo. Galla and coworkers133–136 pioneered the use of primary or low passage porcine brain capillary endothelial cells as an in vitro permeability model of the BBB. The porcine BCEC cultures have been studied to less extent then the bovine BCECs but research to date has shown that porcine BCECs may also serve as an appropriate permeability model of the BBB.110,133–137 Although primary or low passage brain capillary endothelial cells provide a close phenotypic resemblance to the in vivo BBB cells, the major disadvantage associated with this cell system is the time and resources required to isolate, seed and incubate the primary cells as well as the astrocytes. Furthermore, the intra- and interbatch reproducibility of the primary or low passage brain capillary endothelial cells regarding phenotypic and permeability properties is another important disadvantage.

Immortalized Brain Endothelial Cells To overcome the disadvantages of primary culture systems with respect to labor intensity and batchto-batch reproducibility, a number of immortalized brain cell lines from many species have been established. Reichel et al.,95 Gumbleton and Audus,96 and De Boer and Gaillard100 have given an overview of the most frequently used cell lines of which the immortalized rat brain endothelial cell line (RBE4) is the most extensively characterized.138,139 Although these cell lines do form monolayers (Fig. 3A), none of them appear to generate completely tight junctions. This property of paracellular leakiness limits their use in studying BBB transport140 but the immortalized brain endothelial cell line have proven to be useful for mechanistic and biochemical studies.

Tridimensional Hollow-Fiber BBB Model Most of all cell-based permeability experiments are performed using the Transwell experimental setup (Fig. 3A and B) in which cell monolayers are formed on a microporous membrane. Nevertheless, Transwell models ignore the presence of intraluminal blood cells and blood flow, lacking the presence of shear stress141 that has been demonstrated to determine further differentiation of endothelial cells, to inhibit cell growth and to induce metabolic changes.142–147 Only few research groups have reported on the use of flow based hollow-fiber models (Fig. 3C) and applied their models both on primary endothelial cells and on immortalized cell lines to investigate BBB properties. Janigro and coworkers148–150 have developed an in vitro model of the BBB characterized by a tridimensional architecture. In the hollow fiber apparatus, the endothelial cells are seeded intraluminally and are exposed to flow conditions, whereas glia cells are cultured on the extraluminal surface of the hollow fiber tube. This model is able to generate a restrictive paracellular pathway with reported TEER at 5 weeks of 580 to 1100 V cm2. Cucullo et al.151 have reported further advantages of using a hollow-fiber flow system in comparison to Transwell models such as significantly increased TEER, decreased permeability for sucrose and inulin and remarkably longer lifetime. Neuhaus et al.141 established a flow based hollow-fiber in vitro model of immortalized porcine brain endothelial cells cocultured with glia cells. The usability of this model was increased up to 4 months and it was shown that the layer tightness of the model was significantly enhanced. The reported hollow-fiber in vitro models represent an innovative development in in vitro BBB models with increased BBB properties. Unfortunately, such models may not be exploited as a high throughput in vitro BBB permeability screen, especially due to the technical demands of the approach.

Cell-Based In Vitro Models from Noncerebral Origin One of the greatest limitations of currently available immortalized BEC lines is their insufficient barrier property, rendering these systems unsuitable for use as BBB permeability screens. Therefore some research groups have turned to other cell lines, although of nonbrain origin, such as ECV304/C6, Madin-Darby Canine Kidney

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(MDCK) and Caco-2. Noncerebral peripheral endothelial cell lines are easily accessible but fail as BBB screens mainly due to poor paracellular properties. Madin-Darby Canine Kidney (MDCK) In contrast to most noncerebral endothelial cell lines, the commonly used MDCK cell line achieves a reproducible TEER (>2000 V cm2), shows low permeability for sucrose96 and is easy to grow. Moreover, the MDCK cell line can be transfected with the multidrug resistance gene (MDR1)152 resulting in the polarized expression of P-gp, which has proven to be one of the most important BBB efflux mechanism. All these characteristics have led to their proposed use as a BBB screen152–158 and are summarized by Feng.69 Garberg et al.109 recently found that the MDR1MDCK cell line gave the best representation of in vivo BBB permeability for passive and effluxed compounds compared to other in vitro models, including a primary bovine and human BEC line cocultured with astrocytes, a rat and mouse immortalized BEC line and the noncerebral Caco-2 and ECV304/C6 cell lines. Also Wang et al.157 showed that the MDR1 transfected MDCK cell line could be used as a BBB model to aid drug discovery. Despite these positive findings one should be careful not to disregard the potential effect of other efflux transporters present in the in vivo BBB. Efflux proteins such as MRP and BCRP may also play an important role in overall BBB permeability. ECV304/C6 Cell Line Another epithelial cell line that has received some attention as a potential in vitro BBB model is the ECV304 cell line which is a bladder carcinoma cell line with epithelial and endothelial properties. The use of ECV304, cocultured with C6 glioma cells or in C6 conditioned media was proposed by Hurst and Fritz159 Although the ECV304/C6 cell line demonstrated many of the key features of the blood–brain barrier,159–163 a number of factors including low TEER values and a lack of P-gp expression show that the ECV304/C6 cells have limited applicability for assessing the permeability of compounds across the BBB. Caco-2 Cell Line The permeability of monolayers of the human colon adenoma derived cell line, Caco-2, has been DOI 10.1002/jps

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shown to correlate well with in vivo intestinal absorption in man and has become a well established in vitro method for prediction of intestinal absorption.164–167 Caco-2 cells have also been used to estimate BBB permeability, however recent comparisons between bovine brain endothelial cells cocultured with rat astrocytes and the Caco-2 cell line indicate that the differences between both cell lines are significant.97,168 As a result, the caco-2 cell line may not be as useful for predictions of transport across the blood–brain barrier.

Noncell-Based In Vitro Models Immobilized Artificial Membrane Chromatography (IAM) The IAM stationary phase consists of a monolayer of phosphatidylcholine covalently bound to an inert silica support. The resulting IAM surface is a chemically stable chromatographic material that simulates the lipid phase of a biological cell membrane169 and thereby affects the retention of compounds on the basis of solute-IAM partitioning. The retention times of solute molecules on IAM columns are used to calculate the solute IAM capacity factors (k’IAM) that are linearly related to the equilibrium IAM partition coefficient (KIAM) and, ultimately, with their membrane partition coefficient (Km). Diffusion through biological membranes, which is often a rate-limiting step in drug absorption, can be characterized by a membrane partition coefficient (Km).170 The greater the retention time and subsequently kIAM0 is, the greater the membrane permeability for the drug candidate. Drug partitioning into IAMs has shown promise for predicting drug permeability across biological membranes such as human skin, caco-2 cells and intestinal tissue.171,172 Hence, several researchers have proposed the immobilized artificial membrane chromatography technique as a model for predicting drug permeability of the blood–brain barrier.173–177 However, this methodology has several limitations: the IAM does not mimic the dynamics of fluid membranes, in particular lateral diffusion, the IAM does not mimic the diffusion across a membrane bilayer and the IAM can have a poor predictive power when brain uptake is affected by plasma protein binding, active transport or metabolism.

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Parallel Artificial Permeability Assay (PAMPA) A more promising technology is the Parallel Artificial Permeability Assay first developed as a surrogate for gastrointestinal (GI) absorption. PAMPA is suitable for passively permeating compounds, showing moderately good correlation with data from Caco-2 cells and in vivo studies. By modifying the lipid composition of the artificial membranes, the system appears capable of predicting CNS permeability with reasonable accuracy. Di et al.178 developed this PAMPA lipid model specifically for BBB application. They used porcine brain lipid extract dissolved in n-dodecane (2% w/v) as their PAMPA membrane barrier and demonstrated that with this method molecules can be binned into CNSþ and CNS classes. Carrara et al.179 showed that PAMPA BBB permeability is mostly dependent on the percentage of dodecane and the effect of phospholipids is relevant only for medium permeable compounds. Wexler et al.180 developed a combined solubility/ permeability assay for determining permeability properties (GI and BBB) of chemical entities. Firstly, solubility was determined at different pH values by comparing the concentration of a saturated compound solution to its dilute, known concentration. The filtered, saturated solution from the solubility assay was then used as the input material for the BBB permeability determination. The advantages of the combination method are: (1) reduction of sample usage and preparation time, (2) elimination of interference from compound precipitation in the membrane permeability determination, (3) maximization of input concentration to the permeability assay for improved reproducibility, and (4) optimization of sample tracking by streamlining data entry and calculations. The obtained BBB permeability ranking of compounds correlated well with literature BBB permeability. Despite the above-mentioned research, PAMPA only shows a relationship with brain penetration for compounds that are not subject to active transport. PAMPA may, therefore, be used as an early screen for passive BBB permeation. Other cell based permeability assays, transporter assays or in silico calculations that provide information about the relevant active transporters can then be used as an additional screen to improve the PAMPA result. Such a combined approach enables a better differentiation between paracellular, active (absorptive and secretory) and the passive transcellular components of transport.181,182

IN SILICO MODELS FOR BLOOD–BRAIN BARRIER PERMEABILITY: PREDICTION TOOLS In silico prediction methods have gained popularity in drug discovery processes, as they are cheaper and less time-consuming than obtaining experimental data through in vivo and in vitro methods. Modeling the BBB permeability of compounds is a challenge in drug design both because of the quality and quantity of experimental data available and the difficult task of establishing a useful relationship between the molecular structure and the measured blood– brain partitioning.183 The objective of this part of the document is to review reported BBB permeation models in order to have a clear idea about their strengths and limitations together with their application and scope in the drug discovery process. According to some authors, the best index of BBB penetration is the BBB permeability-surface area product, which has units of mL min1 g1 as the measure of unidirectional clearance from blood to brain across the BBB. Therefore, they suggest replacing log BB by BBB PS product to predict BBB penetration.184,185 However, mainly due to the lack of publicly available data for BBB PS,186 the majority of in silico models of drug brain penetration attempt to predict blood–brain barrier permeability using log(BB) as the index of BBB permeability. There are three key points that play an essential role during the model construction process and therefore have a remarkable impact on models performance and applicability; including (1) data (quality and quantity of training and test sets), (2) descriptors, and (3) modeling approaches.

Data: Quality and Quantity of Training and Test Sets With some notable exceptions, careful data selection and analysis has often become a casualty in the development and validation of novel computational models.187 In silico models are built from data fed into them. Modeling algorithms will unquestioningly process the most nonsensical of input data and produce its corresponding output: a model providing nonsensical estimations. Therefore, if noisy data is utilized to build a model, then its corresponding estimations

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will contain these uncertainties clearly underlined: ‘‘garbage in, garbage out.’’ Data to Build Quantitative log BB Models Although many log BB models have been developed, the number of compounds used to train log BB models is very limited.188,189 The available log BB data are not only limited but also generated from different experimental protocols190 and therefore often uncertain and contradictory. Furthermore, various other factors can influence the BBB penetration, for example, plasma protein binding, active efflux/influx by transporters and metabolism. Nevertheless, passive diffusion is for most drugs the primary process of translocation from the blood stream to the brain191 and therefore all in silico BBB models that have been built so far refer to passive transport.192 Hence, active transport and efflux mechanisms will result in compounds appearing to be outliers to a passive transport algorithm.189 The largest publicly available data set of log BB values, which contains 328 data points for 302 compounds, has been recently published.189 Literature values from in vivo and in vitro BBB distribution for passive transport from blood, plasma, or serum to rat brain have been assembled. In vitro distribution data were combined with in vivo log BB values for 207 compounds, after a detailed analysis where the systematic difference between in vivo and in vitro distributions was identified and taken into account.189 For this data set, the experimental error in log BB is around 0.3 log units as standard deviation (SD). It is reported that the SD value on the corresponding in vitro data is 0.21 log units, appreciably smaller than the in vivo SD, 0.33 log units. This is perhaps not surprising because the indirect method to determine the in vivo values precludes any active transport and efflux mechanisms.189 This is in accordance with previously reported studies where experimental errors from log BB data were estimated to be up to 0.3 log units too.193 Hence, all models derived from this data can never estimate log BB values with RMSEP (root of the averaged mean squared error of prediction) significantly better than 0.3 log units unless the corresponding log BB model exhibits overfitting.194 Therefore, this is the limitation of any Quantitative Structure-Activity Relationships (QSAR) model, that is, its predictive power is constrained by the quality of data utilized to build the model, as it was mentioned previously. DOI 10.1002/jps

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The use of this large database has been proposed for consistent comparisons of the predictive accuracy of current and future log BB quantitative models.194 However, most data sets that have been used to build models for blood– brain barrier penetration so far are much smaller.188,189 Hence, log BB models developed from these limited data sets and used for highthroughput screening of drug compounds, may result in large prediction errors since many groups of compounds will be outside the chemical space of the compounds used to set up the models.195 Data to Build Qualitative Discriminant BBB Models An interesting alternative approach to the quantitative predictive models is building a qualitative discriminant model to classify chemical structures based on their ability to penetrate into the brain. Compounds that are able to pass the BBB are denoted as BBBþ, and those compounds that have little ability to cross the blood–brain barrier are denoted as BBB. As shown in Table 1, there are different criteria to define the threshold for log BB values and to classify compounds as able or unable to cross the BBB (BBBþ or BBB). Interpreting the meaning of log BB values is not a trivial task. The aim is to have a guideline to determine if a compound crosses the blood–brain barrier with enough concentration to get the desired therapeutic effect in the brain or to determine the permeability of the BBB for a compound. BBBþ drugs can cross the BBB by different mechanisms but BBB drugs show an even more complex situation. Some compounds simply do not penetrate at all whereas others are rapidly metabolized, expelled by active efflux processes or simply do not show activity because they do not interact with any CNS targets.191,196 Quantifying log BB to define BBBþ drugs is dependent on experimental conditions, IC50 values, etc. Therefore, this difference in the criteria described in Table 1 makes sense and is driven by each research group’s experience. Optimal definition of log BB threshold values to classify compounds as BBBþ or BBB has a clear impact on the performance for classification models.197

Molecular Descriptors The early attempts to understand the relationships between the physicochemical properties of

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Table 1. Reported criteria to classify compounds as BBBþ or BBB

Study Cruciani232 Crivori196 Li229 Deconinck230 Rose183 Klon235 Lobell239 Trotter265 Doniger264 Luco247 Subramanian255 Subramanian255 Subramanian255 Cabrera243 Ajay274 Engkvist313 Engkvist313 Adenot194 Adenot194

BBBþ

BBB

BBB

log BB > 0 log BB  0 log BB  0.1 log BB  0 log BB  0 log BB  0.477 log BB  0.40 Cross the BBB CNS active compound log BB  1.00 log BB  1.00 log BB  0.75 log BB  0.40 log BB  0 CNS active compound CNS active compound log BB  1.00 log BB  0.63 CNS active compound

log BB < 0.3 log BB < 0 log BB < 0.1 log BB < 0 log BB < 0 log BB < 0.477 log BB < 0.40 Not cross the BBB CNS inactive compound log BB < 1.00 log BB < 1.00 log BB < 0.75 log BB < 0.40 log BB < 0 CNS inactive compound CNS inactive compound log BB < 1.00 log BB < 0.63 CNS inactive compound

0.3 < log BB < 0

N (number of compounds) 97a 229b 415a 147a 28b 178a 150b 476a 324a 27b 181b 181b 181b 28b >65000a 8678a 24a 82b 1692a

a

To define training and/or test sets. As threshold to classify compounds based on their estimated BBBpred values.

b

compounds and BBB permeability focused on the role of lipophilicity.198 As early as 1899, the first observations linking hydrophobic character and CNS penetration were reported in a study of the narcotic activity of neutral organic hypnotic agents.199 Years later, in 1958, the influence of the addition of polar and nonpolar groups to arylboronic acids described partitioning between benzene and water as a prognosticator of a compound’s BBB permeability.200 A landmark series of studies, starting in the late 1960s, by Corwin Hansch, the pioneer of Quantitative Structure-Activity Relationships, demonstrated experimentally a parabolic relationship between log P and CNS-activity in rodents and set the stage for the modern era.201,202 Hansch, in attempting to quantify the role of lipophilicity, argued that compounds with low log P values favor CNS exclusion and, conversely, within homologous series, optimal CNS penetration occurred with compounds possessing a log P around 2.198,203 This seems to have led to the well known ‘‘rule-of-two.’’204 Although lipophilicity clearly plays an important role in BBB permeability, there has perhaps been an over-reliance on optimizing this single attribute at the expense for other important considerations. For example, it is well recognized

that compound lipophilicity has a profound effect on pharmacokinetic properties.205–207 High lipophilicity can contribute to excessive volumes of distribution, increased metabolic liability and lower unbound drug concentration in the plasma and/or brain and may negatively affect pharmaceutical properties, particularly solubility.198 An important study was published in 1988 describing a linear relationship between log BB and Dlog P for a series of histamine H2 antagonists,208 where Dlog P is defined as the difference between the octanol/water and cyclohexane/water partition coefficients.209 Dlog P ¼ log Poct=water  log Pcycl=water

(7)

This parameter is strongly correlated to the hydrogen bonding capacity of a compound.210 Hence, the relationship between log BB and Dlog P highlights the growing understanding of the important influence of descriptors, lipophilicity and hydrogen-bonding capability in the BBB permeation. Hydrogen-bonding capability is implicitly measured through the polar surface area (PSA). In fact, PSA is defined as the surface sum over all polar atoms, (usually oxygen and nitrogen), also including attached hydrogens. PSA is closely correlated (R ¼ 0.962) with the computed

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free energy of solvation of a compound in water. This explains the value of the correlation coefficient between calculated-log P (Clog P) and PSA of 0.15, and why these two orthogonal descriptors generate a good model for log BB prediction, Eq. (1) reported in Linear Relationships between Descriptors and log BB Section.211 log Pcycl/water is a very important descriptor, alone or in combination with log Poct/water (as D log P), to investigate those inter- and intramolecular hydrogen-bonding properties that determine many pharmacokinetic processes.212 In particular, D log P plays an important role in BBB passive diffusion since it is an implicit measurement of intramolecular hydrogen bonds.208 There is only one method available to obtain the prediction of Log Pcycl/water: Absol calculation (Pharma Algorithms, Inc., Toronto, Canada). Therefore, some new strategies have been recently published or/and are under development due to its potential key role in log BB estimation 212 as well as in other pharmacokinetic properties, such as human intestinal absorption. From the initial findings of the role of lipophilicity and hydrogen bonding on BBB permeability, several studies, most notably from the groups of Abraham,203 Gratton et al.,213 Clark and coworkers193,211, and van de Waterbeemd and coworkers,214,215 have attempted to capture the key physicochemical properties that influence BBB penetration. The following properties have been identified as generally having a strong influence: lipophilicity, number of hydrogen bond donors (HBD), polar surface area (PSA), molecular size and shape, with fewer contributions from hydrogen bond acceptors (HBA). The physicochemical measures are often inter-dependent and it is clearly important to consider the composite of these individual characteristics rather than each one separately. In certain instances, molecular weight can operate as a crude amalgamation of these collective properties. Although this type of rule-based approach is clearly a simplification of the complex underlying biochemical process and physicochemical features involved in BBB permeability, it provides useful and simple guidelines for medicinal chemists concerned with designing individual compounds or libraries with improved probability of CNS penetration. A compound will be expected to be readily BBBpermeable if it meets the following parameters: Sum of nitrogen and oxygen (N þ O) atoms in a molecule is five or less.216 DOI 10.1002/jps

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Clog P  (N þ O) > 0.216 Polar surface area (PSA) of the compound should be below a certain limit. Two different limits have been proposed: ˚ 2.217 PSA 60–70 A 2 215 ˚ . PSA 90 A Molecular weight should be kept below 450.215 log D7.4 value in the range between 1 and 3 is recommended.215 The rules-of-thumb listed above provide reliable guidelines given the properties of known efficacious CNS agents and would appear to require consideration of the contribution of freely rotatable bonds.218 In the context of oral bioavailability, reduced molecular flexibility has a positive influence; ideally, total number of rotatable bonds should be lower than 11.219 Although the role of molecular shape is not well understood, cross-sectional area (AD) has been used as surrogate for shape198 indicating ˚ 2. that CNSþ compounds generally have AD < 80 A These studies have shown that the cross-sectional area, AD, of a compound oriented in an amphiphilic gradient such as air–water or lipid–water interface is crucial for membrane permeation. In fact, they have revealed that BBB permeation is only possible if a compound exhibits a cross˚ 2, an intermediate airsectional area AD < 80 A water partition and an ionization constant pKa < 10 for bases and pKa > 4 for acids.220 Recently, an algorithm has been developed to orient a molecule in an amphiphilic gradient such as the lipid–water interface and to calculate the cross-sectional area of the molecule perpendicular to the axis of amphiphilicity, taking the conformational ensemble of a molecule into account. Thus, cross-sectional areas relevant for membrane permeation are estimated, ADcalcM. The calculated ADcalcM of compounds with known ability to cross the BBB have been used together with their corresponding estimated octanol-water distribution coefficients log D7.4, combining log P and the ionization constant. This yields a limiting crosssectional area and an optimal range for octanolwater distribution to predict BBB permeation. These two descriptors fulfill the conditions required for membrane partitioning and based on that a reliable classification model has been built to categorize compounds as BBB penetrating agents:221 ˚2 ADcalcM < 70 A 0.6 log D7.4 < 7.0

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An additional set of descriptors related, although nontrivially, to the physicochemical principles involved in passive diffusion described above, have been utilized to build log BB models yielding good correlation coefficients for training set, internal validation by leave-one-out (LOO) cross validation and external test set: r2, q2 and predicted-r2, respectively, from 0.70. These descriptors are, (i) COSMO s-moments obtained from quantum mechanical calculations using the continuum solvation model COSMO and a subsequent statistical decomposition of the resulting polarization charge densities, these descriptors have been already introduced as a general descriptor set for partition coefficients;222 (ii) properties like the solvent-accessible surface area (SASA), and amphiphilic components of the SASA as determined from Monte Carlo simulations of the compounds in water;223 (iii) solvation free energies, Gsolv;224,225 (iv) Abraham or Linear Free Energy Relationship descriptors (LFER).180 These five LFER descriptors (E, S, A, B, and V) are based on empirically determined values for simple molecules and ‘‘fragments’’ and can be regarded as a point in a five-dimensional space. The Euclidean distance between two such points, comparing two different models coming from two different data sets, indicate how close the sets of coefficients are and hence, how close the two systems are chemically.226 Most of these descriptors (all of them except E) are highly correlated (in all cases, r > 0.85) to those physicochemical principles involved in passive diffusion described above: S is correlated to the number of H bond acceptors, A is correlated to the number of H-bond donors, B is correlated to the number of H-bond acceptors and PSA and V are correlated to MW.195 They can be calculated by the ADME Boxes program (Pharma Algorithms, Inc., Toronto, Canada). Several log BB prediction methods are heuristic approaches and start with large number of descriptors (more than 3000) having initially no explicit relationship with physicochemical principles involved in passive diffusion, and then filter out the most relevant descriptors. These large sets of descriptors come from different sources. Some of them are derived from semi-empirical AM1 calculations,227 others from graph similarity measurements,228 simple molecular properties,229,230 molecular connectivity and shape,191,229,230 electropological state218,229,231 quantum chemical properties,191,229 VolSurf descriptors196,232,233 VSA descriptors,234 geometrical properties,229 chemical fingerprints such as the extended con-

nectivity fingerprints and functional connectivity fingerprints (FCFP),235 BCUT descriptors,230 and MACCS structural keys.235 An additional set of descriptors is extracted from trajectories obtained through Molecular Dynamics Simulations where intermolecular solute-membrane interactions are modelled.236,237

Methods to Construct Blood–Brain Barrier Permeability Models In this section, different methods to construct log BB prediction models are reviewed. Once an optimal experimental data set in terms of quality and quantity is available, and descriptors capitalizing information about those assayed compounds included in the data set are calculated, then the best method to correlate the experimental data and descriptors should be identified and applied to construct the most reliable QSAR model according to the input data.

Linear Relationships between Descriptors and log BB The most common statistical method applied in the prediction of BBB permeation is Multiple Linear regression (MLR).183,189,194,208,211,217,222– 224,227,231,238–243 Hansch introduced this classical approach to regression problems in QSARs, assuming that the predictor variables, normally called X (chemical descriptor space), are orthogonal (mathematically independent). A limitation of MLR is the sensitivity to correlated descriptors. MLR is satisfactorily applied in QSAR studies if the main problem of the selection of variables is faced and solved. It is assumed that the descriptors are exact and completely relevant (100%) for the modeling of the biological response under study. Through these Hansch-analyses, where the descriptors capturing the key physicochemical properties that influence BBB penetration have been identified, successful log BB models have been built, for example, Eq. (8).211 log BB ¼ 0:152 log P  0:0148 PSA þ 0:139

(8)

where n ¼ 55, r2 ¼ 0.787, s ¼ 0.354 (s ¼ standard deviation). Recently a MLR log BB model based on the five LFER descriptors, described above, was trained

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on what is currently the largest log BB data set (Eq. 9).189 log BB ¼ 0:526 þ 0:185E  0:596S  0:623A  0:630B þ 0:630V  1:210Ic  0:438Iv

(9)

where n ¼ 328, r2 ¼ 0.753, s ¼ 0.30, and E, S, A, B, and V the LFER parameters. Iv and Ic are indicator variables. Ic is an indicator for carboxylic acids (Ic ¼ 1); and, Iv ¼ 0 for the in vivo data and Iv ¼ 1 for the in vitro data. In vitro BB values, for volatile organic and inorganic compounds, come from the combination of two partition coefficients: gas to blood and gas to brain244,245 which were determined separately using blood and brain samples from either humans or rats.189 Besides MLR, other statistical methods such as Principal Component Analysis (PCA), Principal Component Regression (PCR) and especially Partial Least-Squares (PLS) are increasingly used in QSAR analysis and have been applied in the prediction of BBB. These multivariate projection methods are particularly suitable when the number of variables equals or exceeds the number of compounds. PLS and these similar approaches, do not assume that the descriptors are exact and 100% relevant for modeling Y (log BB in this case). In fact, PLS has been successfully applied in the prediction of BBB permeation.228,233,246–252 In addition, a PLS-Discriminant Analysis (PLSDA) is a practical way to transform a class-model into a pseudocontinuous variable-model. The derivation of a PLS equation from an initial X-block and Y-block leads to a new quantitative univariate Y-variable, varying between two indices of class (0 for BBB and 1 for BBBþ). The predicted variable can be thought of as a predictor of blood–brain barrier permeation on a scale from 0 to 1. Discriminant analysis can be used to determine the threshold of a PLSpredicted variable that discriminates optimally between the two original classes. A simple and sometimes very useful approach is the Linear Discriminant Analysis (LDA), since trough this technique different classes of compounds can be separated by using a pool of different explanatory variables and may be used to predict class membership, in this case BBBþ versus BBB. It can also be used as a way to highlight the most discriminant variables (‘‘key descriptors’’) in the context of a variable selection problem. DOI 10.1002/jps

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For those cases where a large set of descriptors (larger than compounds) is available, an additional approach, alternative to PLS, has been proposed. This involves a systematic variable selection method, Variable Selection and Modeling method based on the Prediction (VSMP), along with MLR.229,231,253,254 Another method utilized in these cases is a multivariate genetic partial least squares (G/PLS) to derive the corresponding log BB model.255 Both Genetic Algorithm (GA) and PLS have been shown to be valuable analysis tools in cases where the data set has more descriptors than samples. G/PLS combines the best features of GA and PLS and retains the ease of interpretation of GA by back transforming the PLS components to the original variables. Finally, CODESSA (comprehensive descriptors for structural and statistical analysis)-PRO256 approach, which includes diverse statistical structure–property correlation techniques that can be used for the analysis in combination with the calculated molecular descriptors, is described. The best multilinear regression (BMLR) procedure257–260 available in the framework of the CODESSA-PRO was used to find the best correlation models from selected noncollinear descriptors. A log BB model has been built and reported using this approach, CODESSA-PRO.261

Nonlinear Relationships between Descriptors and log BB MLR, PLS and the other methods discussed above are usually applied to data sets where a linear relationship between descriptors and biological response under analysis, log BB in this case, is anticipated. However, there are also many other methods that are used in the analysis of nonlinear QSAR data, for example: Neural Networks (NN): A feed-forward Neural Network is defined as an Artificial Neural Network (ANN). Additionally, a general regression neural network (GRNN) has been used for construction of QSPkR models.262 GRNN was designed for regression through the use of Bayes’ optimal decision rule and is defined in this case as a Bayesian Neural Network. Thus, software based on Neural Network models was developed for predicting blood–brain barrier permeability through quantitative estimations of log BB values.192,238,242,262 If our final aim is a classification method (BBBþ or BBB), instead of log BB

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quantitative estimations, a probabilistic neural network method can be applied. The training process of this probabilistic neural network is usually orders of magnitude faster than those of the traditional neural networks.229 Support Vector Machine (SVM): In linearly separable cases, SVM constructs a hyperplane that separates two different classes of vectors with a maximum margin.263 This statistical learning method has been applied to predict BBB-penetrating and BBB-nonpenetrating agents achieving a high prediction overall accuracy, in the range of 76–83.7%229,264,265 (Tab. 2). Gaussian Processes: On the basis of a Bayesian probabilistic approach, this method is widely used in the field of machine learning but has rarely been applied in quantitative structure-activity relationship and ADME modeling. Main disadvantage of the Gaussian Processes technique is that it generates ‘‘black box’’ models, which are difficult to interpret.266 k Nearest Neighbor method:229 This method is very similar, if not identical, to the local lazy regression (LLR).194 Local lazy regression is a modeling approach that utilizes linear models for individual neighborhoods and obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set. The neighborhood of a query molecule in the data set is determined on the fly.267 This approach is also an example of an instance-based learning technique.268–270 k-NN has been utilized as a classification method, to predict penetrating and nonpenetrating BBB structures,229 as well as to estimate log BB values.194 Naı¨ve Bayesian modeling: Naı¨ve Bayesian categorization provides an attractive solution to the problem of predicting biological activity because of the algorithm’s robustness when dealing with the extremely noisy data sets271 faced by researchers in the Pharmaceutical industry. A Gaussian naı¨ve Bayesian algorithm220 and two Laplacian-modified naı¨ve Bayesian classifiers (Pipeline Pilot, 5.1; SciTegic, Inc., San Diego, CA, 2005) have been implemented and utilized to predict compounds as BBB penetrant or nonpenetrant. The Gaussian Bayesian classifier, models continuous numerical data using a Gaussian distribution.235 Bayesian classifiers perform well with chemical fingerprints and their tolerance of noisy data provides an advantage over logistic regression models. An additional advantage of using these classifiers is their inherent extensi-

bility. Compounds can be easily added to the training set resulting in more robust predictions.235 In this case, the main drawback is that generated models are difficult to interpret. They are not very useful guidelines for rational design of new compounds, mainly when several descriptors are utilized (Fig. 4).235 Recursive Partitioning (RP): In recursive partitioning, classification trees were built using the classification and regression trees (CART) method for categorical variables. CART tries to build a decision tree, which describes a response variable as a function of different explanatory variables (Fig. 5).230 This recursive partitioning approach, tree-based method, can easily handle nonlinear stepwise variable selection and complexity reduction and is extremely robust with respect to outliers. In the RP method, the splitting decision was made according to the t-test value. This approach, a decision tree-based classification model which suggests that it is very suited as part of fast screening methods in drug design, is easy to use and interpret.195 Substructure analysis: This is an important method in drug discovery. It has been used to understand and predict molecular properties.272 To classify new molecules with unknown BBB permeability, data sets with known BBBþ and BBB-molecules are fragmented into all possible fragments up to a user defined fragment size, and the frequency profiles of the two data sets are calculated and compared to each other.

In Silico Models for Blood–Brain Barrier Permeability: Model Performance Considering these three key factors described above for the model construction process, that is: (1) data quality and quantity, for training and test sets, (2) descriptors utilized, and (3) modeling method, many log BB prediction models may be developed combining different possibilities among those three factors. To have a comparative picture about their impact on model performance and applicability, several reported log BB models for passive diffusion, qualitative and quantitative, are summarized in Tables 2 and 3. For each of these models, a concise description about those three factors, covering many combinations, is reported. The two tables present a good summary about log BB models reported to date: from technical details to degree of reliability.

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Bayesian Neural Network

Recursive Partitioning

Support Vector Machine

Trotter265

PCA

Crivori196

25 descriptorsj

Recursive Partitioning

Backpropagation Neural Network (ANN)

Backpropagation Neural Network (ANN)

Substructure Analysis

Substructure Analysis

LDA

PLS-DAi

Andres314

Engkvist313

Engkvist313

Engkvist313

Engkvist313

Adenot194

Adenot194

7417 fragments for CNSþ and 10779 fragments for CNS

7417 fragments for CNSþ and 10779 fragments for CNS

92 atom types as molecular descriptors

92 atom types as molecular descriptors

117 descriptors

2 descriptorso

1605

1696

8678

7810

8678

7810

186

55

55

229

229

110

>65000

>65000

>65000

274

274

172

129

102

102

332

332

332

332

1093

1093

Training Set

76

74.78

82

97.0

b

91.0

NDl

70.8

77.25q NDl

92.0

70.8 77.25g

NAg

868q 24

79.5q

NAg 79.5q

868q

84.0

83.3

97.7

>75

NDl

81.5

24

96.0

93.0

93.0

NDn

38

42

43

35

ND

>90.0

NAg l

120

83.75

81.0

80c

65.0 81.0

275b

81.45h

NAg 70.0

50h 275b

50

75.7h

NAg NAg

91.1 95.9

NAg

84.4

83.7d

77.1d

74.3d

76.5d

95.8

96.8

Test Set Overall Accuracy

94.0

80.6

h

304

49

45

45

81.8

83d e

73.5

71.6

73.2

91.0

90.6

Training Set Overall Accuracy

83d

83

d

83

d

500

500

Test Set

Number of hydrogen bonding donors, PSA, indicator variable for carboxylic acid, positively charged form fraction at pH ¼ 7.4 and number of rotatable bonds. Compounds with known CNS activity, as: CNSþ or CNS. c Classify from experimental log BB data, no threshold is provided to assess as BBBþ/BBB. d From 415 agents, 5-fold cross-validation study—average values for overall accuracy. e After 10 repetitions of the cross-validation procedure—average value. f Including the use of boosting. g NA, no data available. h 30 different test sets of 50 compounds randomly selected (25 BBBþ and 25 BBB)—average value for overall accuracy. i Using BBBpred as the discriminant variable, a threshold value of 0.63 was fixed to delineate BBBþ and BBB compounds. j After discarding electronic parameters, since they were unable to calculate them for 201 compounds, just 25 descriptors were considered. k Using BBBpred as the discriminant variable, a threshold value of 0.00 was fixed to delineate BBBþ and BBB compounds. l ND, corresponding analysis was not done. m Using BBBpred as the discriminant variable, a thresholds: BBB < 0.3, BBBþ > 0.0, and 0.3 < BBB < 0.0. n ND, Corresponding analysis was not done using this threshold reported in m; but it is reported in reference 217, k, using as threshold 0.00. o ˚ 2 and 0.6 < log D 7.4 < 7.0. Ranges for those two key descriptors: ADcalcM < 70A p Just considering the Number of Heteroatoms. q From 8678 agents, 10-fold cross-validation study—average values for overall accuracy.

a

1 descriptorp

LDA

Gerebtzoff221

2 descriptorso

LDA

Gerebtzoff221

72 VolSurf descriptors

PLS-DAm

72 VolSurf descriptors

7 descriptors, 1D, and 166 based on ISIS fingerprints, 2D 7 descriptors, 1D, and 166 based on ISIS fingerprints, 2D 72 VolSurf descriptors

7 descriptors 1D

9 descriptors

9 descriptors

72 descriptors

Cruciani232

PLS-DA

Bayesian Neural Network

Ajay274

Crivori

Bayesian Neural Network

Ajay274

k

Bayesian Neural Network

Ajay274

196

Backpropagation Neural Network (ANN)

Doniger264

Doniger

264

Support Vector Machine

Naı¨ve Bayesian

Klon235 6 descriptors

1630 descriptors 1630 descriptors

Recursive Partitioningf

Deconinck

Recursive Partitioning

35 descriptors selected by RFE, out of 199

35 descriptors selected by RFE, out of 199

35 descriptors selected by RFE, out of 199

5 descriptors explicetly involved in passive diffusiona 35 descriptors selected by RFE, out of 199

Deconinck230

Support Vector Machine

Li229

Number Descriptors 5 Abraham Descriptors

230

k-Nearest Neighbour

Li229

Li

Li

229

Recursive Partitioning

229

Recursive Partitioning

Abraham195

Method

Abraham195

Qualitative Study: BBBþ or BBB

Table 2. Details about Modelling Approaches Utilized, Data Sets and Classification Accuracy of Models for BBBþ and BBB

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obtained by using a rationally reduced selection of descriptors, are comparable to those obtained by using larger number of descriptors. From Table 2, a remarkably interesting model, developed by Abraham and coworkers195 should be highlighted due to four important reasons: Overall accuracies, for training and test sets, are bigger than 90%. Test set is quite large, 500 compounds (50%of the training set). Although, as a drawback it is skewed towards BBBþ compounds (90.2% of the total) while the compound distribution for the training set was slightly more balanced: 76.1% as BBBþ and 23.9% as BBB. Descriptors: Only five descriptors explicitly involved in passive diffusion are used: number of hydrogen bond donors, PSA, indicator for carboxylic acid, positively charged form fraction at pH 7.4 and number of rotatable bonds. Interpretability: The recursive partitioning method was utilized to build this classification model. This decision tree, as shown in Figure 6, provides a very useful guideline not only to prioritize compounds but also to design new compounds meeting the criteria of those key descriptors identified by this recursive partitioning.

Figure 4. Distribution of BBBþ compounds (in gray) and BBB compounds (in white) as a function of the number of heteroatoms in the molecule. A large majority of BBBþ compounds have no more than 9 heteroatoms in their structure.

Qualitative Discriminant BBB In Silico Models To categorize compounds as penetrating or nonpenetrating agents through the blood–brain barrier (BBBþ or BBB), several models have been developed as shown in Table 2. These models have utilized different sets of descriptors, and data sets, as well as a wide range of modeling techniques achieving, in general, acceptable prediction accuracies for compounds classifications independently of the combination of the three variables. The lowest limit in overall accuracy from where a classification tool should be considered as reliable and useful (better than random) is 70%. Among those statistical methods employed to construct models, optimal performances (overall accuracies greater than 75%) are obtained from recursive partitioning, bayesian or back propagation neural network, k-nearest neighbor, naı¨ve Bayesian, PCA and support vector machine even when test sets contain reasonable number of compounds (more than 50).195,196,229,230,235,264 Table 2 shows as well that models performances,

Figure 5. General structure of a CART model. (Xi) Selected split variable, (ai) selected split value.

Quantitative In Silico log BB Models As Table 3 summarizes, several quantitative log BB models have been developed for passive diffusion covering many combinations of the three key factors. Table 3 reports which descriptors and methods were used to develop each of these models as well as their predictive power. Assessing model performance is not quite a straightforward exercise as data sets, utilized as training and test sets, are not identical among all of them, although a good overview about their applicability could be achieved. Table 3 indicates some important points that should be highlighted: Regarding the Data Set. Descriptors reflecting the nature of the data (e.g., in vivo or in vitro), and providing different weights to the final in silico model depending on that, add a real value to its predictive power.189,194 Developing a predictive model with a root-mean-squared error of prediction (RMSEP) significantly better than experimental errors is not possible.189,193,194

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MLR MLR MLR MLR PLS MLR MLR MLR MLR MLR MLR PLS PLS PLS PLS PLS PLS PLS MLR MLR MLR MLR

Rose183

Hou273 Kaznessis223 Ooms233 Keseru¨ 225 Lombardo224 Hutter227 Hutter227 Lobell239 Lobell239 ¨ sterberg249 O ¨ sterberg249 O ¨ sterberg249 O ¨ sterberg249 O Norinder250 Norinder250 Norinder246,250 Young208 Kaliszan240 Platts241 Platts241

MLR PLS PLS MLR kNN & MLR MLR MLR MLR MLR MLR

Wichmann222 Urbano-Cuadrado228 Urbano-Cuadrado228 Konovalov194 Konovalov194 Konovalov194 Narayanan231 Narayanan231 Narayanan231 Rose183

Rose183

Method

Quantitative Study: log BB Estimation 3 descriptors. 3 COSMO-RS s-moments as descriptors 136 descriptors. Approximate Similarity (AS) Matricesc 136 descriptors. Approximate Similarity (AS) Matricesc 7 descriptors. LFER and Ic and Ivm 7 descriptors. LFER and Ic and Ivm 5 descriptors. PSA, SsssN and Alog P and Ic and Ivf 3 descriptors, V1, out of 324 identified by VSMP 4 descriptors, V4, out of 324 identified by VSMP 4 descriptors, V4, out of 324 identified by VSMP 3 descriptors, squared terms included, identified by RSQUARE 3 descriptors, squared terms included, identified by RSQUARE 3 descriptors, squared terms included, identified by RSQUARE 3 descriptorsn 5 descriptorso 31 VolSurf descriptors 1 descriptor, Gsolv 1 descriptor, Gsolv 12 descriptors, some from AM1 calculations 12 descriptors, some from AM1 calculations 5 descriptors, out of 34 calculated descriptors 5 descriptors, out of 34 calculated descriptors 4 descriptors, 3 hydrogen bonding related and log P 4 descriptors, 3 hydrogen bonding related and log P 4 descriptors, 3 hydrogen bonding related and log P 4 descriptors, 3 hydrogen bonding related and log P Eletrotopological indices (E-state descriptors) Eletrotopological indices (E-state descriptors) MolSurf descriptors 1 descriptor, Dlog P as (log Poct  log Pcycl) 2 descriptors, MW and log Pcycl 6 descriptors, LFER &Iir 6 descriptors, LFER & Iir

Number Descriptors

78 76 79 55 55 90 90 48 48 69 45 35 23 58 28 28 20 20 148 74

102

86

103 136 108 291 291 291 88 88 88 102

Training Set

Table 3. Details About Modelling Approaches Utilized, Data Sets and Performance of Quantitative log BB Models

NAb 0.66

20k 28i

0.77 0.94 0.76 0.72 0.67 0.87 0.87 0.84 0.84 0.76 0.72 0.71 0.72 0.80 0.75 0.86 0.69 0.85 0.75 0.76

0.71 NAb NAb NAb NAb NAb 0.84 0.86 0.86 0.66

NAb NAb 22d NAg NAg NAg 28 28 92i NAb

35 NAb NAb NAb NAb 22 61i 17 150p NAb NAb 34 22 NAb 30 30 NAb NAb NAb 74s

r2

Test Set

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0.70 NAb NAb NAb NAb 0.67u 77j 0.68 88.0j NAb NAb 0.37q 0.50q NAb 0.37q 0.35q NAb NAb NAb 0.72s

0.74a NAb 0.65a NAb NAb NAb NAb 0.79a 0.79a 0.75a 0.71a 0.68a 0.68a 0.77a 0.70a 0.78a NAb NAb 0.71a NAb

(Continued)

96.4j

0.38l

NAb NAb 0.82e 0.31g 0.30g 0.39g 0.67 0.69 80.4j NAb

pred-r2

0.62a

NAb

0.68a 0.80a NAb 0.73a 0.75a 0.58a 0.82a 0.85a 0.85a 0.62a

q2

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Genetic Algorithm & MLR

Genetic Algorithm & MLR

Genetic Algorithm & MLR

Abraham189 Abraham189 Obrezanova266 Obrezanova266 Iyer236

Iyer236

Pan237

Pan237

MLR PLS PLS PLS MLR MLR MLR Backpropagation Neural Network (ANN) Genetic Algorithm & MLR Genetic Algorithm & MLR Genetic Algorithm & PLS Genetic Algorithm & PLS Genetic Algorithm & PLS Genetic Algorithm & PLS PLS Bayesian Neural Network

Method

MLR MLR MLR Bayesian Neural Network Backpropagation Neural Network (ANN) MLR MLR Gaussian Processac Gaussian Processae Genetic Algorithm & MLR

Cabrera243 Cabrera243 Cabrera243 Yap262 Garg224

Hou315 Hou315 Subramanian255 Subramanian255 Subramanian255 Subramanian255 Stanton251 Winkler238

Platts241 Luco247 Luco247 Luco247 Clark211 Clark211 Liu.242 Liu242

Quantitative Study: log BB Estimation

Table 3. (Continued )

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7 7 7 7 5

descriptors. LFER and Ic and Ivm descriptors. LFER and Ic and Ivm descriptorsad descriptorsad descriptors, out of multiple molecular descriptorsaf 5 descriptors, out of multiple molecular descriptorsaf 2 descriptors (Clog P & PSA), out of multiple molecular descriptorsaf 2 descriptors (Clog P & PSA), out of multiple molecular descriptorsaf

descriptors, out of 27, identified by this method descriptors, out of 27, identified by this method descriptors descriptors descriptors descriptors descriptors (including one of new HSA), out of 161 descriptors, out of multiple property-based descriptors, identified by ARD 3 TOPS-MODE descriptors 3 TOPS-MODE descriptors 3 TOPS-MODE descriptors 7 descriptors, out of 1497 descriptorsab 18 descriptors

4 4 7 7 7 7 4 7

6 descriptors, LFER & Iir 18 descriptors 18 descriptors 18 descriptors 2 descriptors, Clog P and PSA 2 descriptors, Clog P and PSA 2 descriptors, MW and LA (molecular lipoaffinity) 3 descriptors (TPSA, LA, and MW)

Number Descriptors

104

150

46

328 164 85 85 56

114 86 114 129 132

59 59 58 58 58 58 97 85

118 58 58 58 55 55 55 55

Training Set

0.69

NAb

0.69

NAb

10ag

46

0.75 0.71 0.61 0.69 0.85

0.70 0.33z 0.70 NAb 0.81

NAb 28k 28i 30 50 NAb 164 21 21 7

0.76 0.76 0.85 0.85 0.85 0.85 0.78 0.81

0.75 0.85 0.85 0.85 0.79 0.79 0.79 0.81

r2

12 23 39 181w 181y 181p NAb 21

30t 14 25 27w 7 5 11 11

Test Set

NAb 0.53ai

0.64a

0.70ah

NAb 0.25u 0.74 0.81 0.68

0.60a

NAb

NAb NAb NAb NAb 0.80a

NAb 0.33l 85.7j 0.70 0.79

0.88 0.64 0.61 66.2j 64.5j 63.3j NAb 0.65

0.71a 0.71a 0.82a 0.82a 0.82a 0.82a 0.75a NAb 0.66a NAb 0.66a NAb NAb

0.73t 0.24q 0.54q 92.3j 0.40x 0.23x 0.84 0.81

pred-r2

0.71a 0.75v 0.75v 0.75v NAb NAb 0.76a NAb

q2

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Genetic Algorithm & MLR Genetic Algorithm & MLR PCR PLS MLR

Pan237 Pan237 Labute234 Sun252 Abraham195 h

2 descriptors, out of multiple molecular descriptorsaf 4 descriptors, out of multiple molecular descriptorsaf 3 descriptors, out of multiple molecular descriptorsaf 15 descriptors, out of 32 VSA Atom types as molecular descriptors 5 descriptors. LFER

Number Descriptors 13aj 25aj 8aj NAb 13 NAb

63aj 17aj 75 57 30

Test Set

24aj

Training Set

0.69 0.80 0.83 0.91 0.87

0.85

r2

0.66a 0.72a 0.73a 0.50al NAb

0.83a

q2

0.79ak 0.92ak NAb 0.33am NAb

0.76ak

pred-r2

c

b

Internal validation by leave-one-out (LOO) cross-validation. NA, no data available. AS fuse information of both the isomorphic similarity and nonisomorphic dissimilarity. d 5 random independent test sets were extracted from initial set of 130 compounds. e Considering the prediction for all the compounds which have participated once in test stages (3
22 þ 2
21)—not involved in their corresponding training sets. f Three first descriptors identify by other studies as key.216 Ic and Iv linked to the nature of the data set.174 g Pred-r2 was not reported, but the root-mean-squared error of prediction: RMSEP. Monte Carlo Crossvalidation Method, leave-group-out approach; with N ¼ 30 000 and nv ¼ n/ 218 was used. h log PS estimation. i Qualitative Test Set: BBBþ/BBB. Using BBBpred as the discriminant variable, a threshold value of 0.00 was fixed to delineate BBBþ and BBB compounds. j Test Set Overall Accuracy. k Five random independent test sets were extracted from initial set. l Pred-r2 was not reported, but the overall mean absolute error for the five groups. m Ic and Iv linked to the nature of the data set.174 n Rational selection. Physchem properties that may be involved in passive diffusion: log P, PSA, and MW. o Rational selection. Physchem properties that may involved in passive diffusion including amphipilic components of the SASA, determined from MC simulations. p Qualitative Test Set: BBBþ/BBB. Using BBBpred as the discriminant variable, a threshold value of 0.4 was fixed to delineate BBBþ and BBB compounds. q Pred-r2 was not reported, but the root-mean-square error for the dependent variable: RMSE. r Ii an indicator variable that is set to 1 for a compound containing a carboxylic acid fragment and 0 otherwise. s Two selected test sets and rebuilt models; reported result as average for those two test sets. t Five randomly selected test sets, each of 30 compounds (20% of the full set) and rebuilt models; reported result as average of those 5 test sets. u Pred-r2 was not reported, but the standard deviation of the error for the estimations. v Three cross-validation groups were used, this is the highest obtained value. w Qualitative Test Set: BBBþ/BBB. Using BBBpred as the discriminant variable, a threshold value of 1.0 was fixed to delineate BBBþ and BBB compounds. x Mean Absolute Error (MAE) in the log BB predictions. y Qualitative Test Set: BBBþ/BBB. Using BBBpred as the discriminant variable, a threshold value of 0.75 was fixed to delineate BBBþ and BBB compounds. z Mean Absolute Error (MAE) is reported instead of r2. ab Genetic Algorithm was used in the descriptors selection process. ac Basic Gaussian Process, with fixed hyperparameters. ad Rational selection. Physchem properties that may involved in passive diffusion: Number of Hbond donors and acceptors, rotatable bonds, hydrophobes, log P, MW, and PSA. ae Gaussian Process with hyperparamenters obtained by nested sampling. af Membrane-Interactions QSAR: descriptors come from solute-membrane interactions (from Molecular Dynamics Simulations), solute aqueous dissolution and solvation descriptors, and general intramolecular solute descriptors. ag 6 sets of 10 compounds randomly selected from initial set. ah Range in pred-r2 over the six test sets. ai Average pred-r2 over three independent test sets (13 þ 25 þ 8). aj Data sets, training and test, were divided into subsets based on 4D molecular similarity measures using cluster analysis; models are constructed for each cluster subset: Local Models compared versus a General Model. ak Pred-r2 over each independent test set using its particular Local Model. al RMSEE: root-mean-square error of estimation. am RMSEP: root-mean-square error of prediction.

a

Genetic Algorithm & MLR

Method

Pan237

Quantitative Study: log BB Estimation

Table 3. (Continued )

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Figure 6. Decision tree from five descriptors: PSA, IV, NHD, Fþ and NRB; and 1093 compounds as training set. Flow direction: BBBþ is to left hand side; BBB is to right hand side. The number at the end of the tree is the number of BBBþ/BBB.19.

Using the largest available database of 328 log BB values,189 a quantitative benchmark was proposed for a consistent comparison of the predictive accuracy of quantitave models for BBB permeation.194 Local Models Versus General Models. The k-nearest neighbours (kNN)-MLR method, similar to the local lazy regression (LLR),267 achieved slightly better predictive results compared to the global MLR method.194 Poor q2 suggests that the possibility of compounds in the data set, interacting differently with the BBB membrane, should be explored. Then, by using a combination of 4D-molecular similarity measures and cluster analysis, optimum local QSAR models are constructed. These local models lead to a remarkable improvement in the predictive power compared to the general log BB model.237 Descriptors. Models based on the 5 Abraham descriptors obtained from the general linear free energy relationship (LFER), have shown a very good performance; for example, Eq. (2).189,194 Descriptors, such as log P and PSA that, from a physicochemical point of view, may be involved in the passive diffusion process, contribute to the development of good log BB models.194,211,237,242,249 In cases where the number of tertiary amines is considered together with log P and PSA, the predictive power of the corresponding log BB model, for the same data set, is just slightly worse than the model obtained using LFER descriptors (increment in RMSEP is 0.08).194 The number of tertiary amines, titratable nitrogens at physiological pH, may not be directly linked to passive diffusion. The key role of this descriptor might only be a consequence of the nature of the utilized data sets, biased towards CNS active compounds, which are mainly targeting biogenic amine binding GPCRs. It can be

argued that this descriptor is more a reflection of the history of CNS drug targeting rather than any intrinsic preference of the BBB for basic moieties.187 Quantitative Models Performance. There are several models with an acceptable number of compounds as the test set, greater than 25, where predicted-r2 is bigger than 0.70 or RMSEP is lower than 0.40.189,192,231,237,241,243,249,250,262,273 Models with optimal performance are achieved just by using simple statistical methods and a reduced number of descriptors, the most relevant.189,231,237,241,249,273 These simple models are very useful guidelines, key for medicinal chemists, to prioritize and design new compounds. Quantitative log BB Models as Classification Tools. In most of reported cases, relative large test sets have been utilized to validate the predictive power of log BB models when they are used as a qualitative estimation, BBBþ or BBB. These tests show quantitative models as good prioritization tools where overall accuracies are, in general, larger than 70%. This is the lower limit from where a classification tool may be considered as reliable. Optimal definition of log BB estimated threshold values to classify compounds as CNS active or CNS inactive has a clear impact on the performance for classification models.197,255 Table 2 is only focused on classification BBB models and shows the good performance of these models. In this case training sets are based on binary data: BBBþ and BBB; therefore, no quantitative log BB estimation is performed just a qualitative assessment. Independently of the method applied to develop these models, overall accuracies are, in most of the cases, larger than 70%. Several reported examples show quite high performances, greater than 80%, even using large test sets, more than 75 compounds and different types of descriptors and methods, for example, Recursive Partitioning, Support Vector Machine, Bayesian Neural Network or PLS-DA.191,195,229,274 A remarkable example has been recently reported by Abraham and coworkers195 where recursive partitioning was utilized to build a predictive classification model using 5 physicochemical descriptors explicitly involved in passive diffusion: PSA (Polar Surface Area), Iv (Indicator variable for carboxylic acid), NHD (number of hydrogen bonding donors), Fþ (positively charged fraction at physiological pH, 7.4) and NRB (number of rotatable bonds). This decision tree has a high predictive power,

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IN VIVO, IN VITRO, AND IN SILICO METHODS

overall accuracy over 95%, against a large test set of 500 compounds. Model interpretability provided by this recursive partitioning decisiontree based classification, Figure 6, is a key added value to understand which are the most important factors to discriminate compounds as BBBþ or BBB and therefore, to design new chemicals. Reliable classification models are very useful prioritization tools with a broad application scope: for example, to select among proposed chemically feasible compounds within focused virtual libraries, to select which compounds will be assayed in expensive in vivo experiments.

Beyond Passive Diffusion: Influencing Active Transport For many years, the main approach utilized in CNS discovery programs to achieve small molecules transfer across the BBB is the classical ‘‘chemistry driven platform’’ where the strategy to go through the BBB is via passive diffusion. The chemistry-driven approach is focused solely on lipid-mediated small-molecule transport, and the usual compound properties such as MW, hydrogen bonding, lipid-solubility and plasma-protein binding. Thus, small molecules can generally cross the BBB in pharmacologically significant amounts if they meet certain criteria, described above, such as molecular weight or number of hydrogen bondforming functional groups (PSA), etc.281–289 Circulating molecules can only gain access to the brain interstitium, from blood, via a transcelullar route through the brain capillary endothelial membranes. If a molecule is lipid soluble and has a molecular mass less than 400 and is not avidly bound by plasma proteins or is a substrate for an active efflux transport system at the BBB, then the circulating molecule may gain access to brain by lipid-mediated free diffusion. In the absence of the lipid-mediated pathway, circulating molecules may gain access to brain only via transport of certain endogenous transport systems within the brain capillary endothelium. These endogenous transporters have an affinity for both small molecules and large molecules and can be broadly classified into three categories:281 Carrier-mediated transporters (CMT) Active efflux transport (AET) Receptor-mediated transport (RMT) DOI 10.1002/jps

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Carrier-Mediated Transporters BBB Carrier-Mediated Transport systems (CMTs) and/or brain delivery vectors, are a feasible option for small molecules to cross the BBB and achieving CNS-active drug candidates by hitting two targets: a CMT and the corresponding therapeutic target—via prodrug, drug, etc. Nowadays in vivo studies indicate that several compounds may be transported by some CMTs at the BBB.23,24,198,275–288 Discovery of novel transporters could lead to new pathways for drug targeting across the BBB. The challenges of CNS drug discovery and development could be made easier with the discovery of novel BBB transporters, from a BBB genomics program,289,290 which could be used as drug delivery vectors. Therefore, in addition to binding to the target placed at the CNS, the small molecule of interest must interact effectively with the BBB carrier: quite a challenge for medicinal chemists. Active Efflux Transport P-glycoprotein, encoded by the multidrug resistance gene MDR1,291 is the prototypic AET system at the BBB, and accounts for the active efflux of molecules in the brain to blood direction. Active efflux in the brain to blood direction requires the concerted actions of two different types of transporters: an energy requiring transporter at one membrane of the endothelium, and an energyindependent transporter, or exchanger, at the opposite membrane of the endothelium. Certain drugs are excluded from penetration in brain because these drugs are substrates for BBB AET systems.281 Due to their relative promiscuity, active efflux transporters often complicate the development of treatments for CNS disorders by limiting the penetration of drugs into the brain.198,292–296 Whereas most efflux transporters have not been well characterized, P-gp is believed to be the most ubiquitous and is the best characterized so far. Reported P-gp substrates include a structurally diverse set of compounds such as antineoplastic agents anthracyclines, vinca alkaloids, and taxanes. Other examples include immunosuppressive agents, protease inhibitors, antibiotics, and cardiac glycosides.291 Due to its original discovery as a main contributor to multiple resistance in tumors, research on P-gp focused on the development of

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inhibitors rather than the prediction of substrate properties.282–299 Nowadays, the role of P-gp in ADME and especially brain uptake300 is well understood and thus P-gp is increasingly considered as an anti-target.301,302 Within the past few years, several models have been published for the prediction of P-gp substrate properties. Based on a set of 220 compounds with data from polarized transport across MDR1 transfected cell monolayers, a classification system based on simple descriptors was developed using a stepwise classification structure-activity relationship (C-SAR) method. A ‘‘Rule of fours’’ for a crude estimation of P-gp substrates was derived from such classification system:303 Substrates: (N þ O)  8, MW > 400 and acid pKa > 4. Nonsubstrates: (N þ O) 4, MW < 400 and base pKa < 8. These rules are based on the compound size, Hbond accepting properties and ionization; and they support the view that P-gp functioning can be compared to a complex system with fuzzy specificity. In addition, an ensemble of two-to-four point pharmacophores, which discriminates between Pgp substrates and nonsubstrates, was built based on a training set of 144 structurally diverse compounds. Overall accuracy for training set is 80%. This multiple-pharmacophore model, that highlights the amphiphilic nature of P-gp substrates, was validated against an external test set of 51 compounds and its overall accuracy is 63%.304 Then, in order to extend the prediction range beyond compounds covered by these pharmacophoric models, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including substrates and nonsubstrates of P-gp, was used to train and test a SVM classification system together with a set of 159 molecular descriptors. This SVM system gave a prediction accuracy of P-gp substrates and nonsubstrates of 79.4% by using fivefold cross validation. This accuracy is slightly better, using the same data set and a fivefold cross validation, than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), 70.8%, Bayesian neural networks, 74.4%, and the decision tree approach, 71.5%. This study indicates the potential of SVM in facilitating the prediction of P-gp substrates.305 Simultaneously, a PLS-DA classification model, using 67 molecular

descriptors, was developed. This model is able to classify correctly 81% of the 33 compounds from the test set. In this case the threshold definition to bin compounds is playing an important role197,306 in fact, by just lowering the threshold from 0.16 to 0.12 the rate of substrates well classified can be improved up to 80%.191 Recently a new strategy, combining two different approaches, has been reported. To differentiate nonsubstrates from substrates of P-gp, a robust predictive pharmacophore model was targeted in a supervised analysis of three-dimensional (3D) pharmacophores from 163 published compounds. A comprehensive set of pharmacophores was generated from conformers of whole molecules of both substrates and nonsubstrates of P-glycoprotein. Four-point 3D pharmacophores were employed to increase the amount of shape information and resolution, including the ability to distinguish chirality. Subsequently, a simple classification tree using nine distinct pharmacophores was constructed to distinguish nonsubstrates from substrates of P-gp. An overall accuracy of 87.7% was achieved for the training set and 87.6% for the external independent test set, consisting of 97 compounds. Furthermore, each of nine pharmacophores can be independently utilized as an accurate marker for potential P-gp substrates. The analysis revealed that hydrogen-bond acceptors, aromatic rings, and hydrophobic groups are essential features for substrate activity and that a positive ionizable feature can also play a distinct role. Furthermore, the P-gp model has also demonstrated success against a few hundred compounds from more than 10 internal projects at Amgen. These evaluated by the model and 82% of the compounds flagging for P-gp activity were confirmed in either in vitro or in vivo assays.307 Receptor-Mediated Transport Certain large-molecule peptides or proteins undergo transport from brain to blood via RMT across the BBB. Neither the CMT system nor the AET system is a portal of entry to the brain for large-molecule drugs. However, large-molecule drugs, such as recombinant proteins, antisense drugs, monoclonal antibodies or gene medicines, can be targeted to the brain using RMT systems. Some endogenous ligands or peptidomimetic mAbs (monoclonal antibodies) that bind exofacial epitopes on BBB RMT systems and that are endocytosing antibodies can act as molecular

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Trojan horses to ferry drugs, proteins and nonviral gene medicines across the BBB using endogenous RMT systems.281,308 This strategy is currently implemented in some real cases driving to promising results; for example, provides compositions and methods for increasing transport of agents across the BBB, while allowing their activity once across the barrier to remain substantially intact.309 More details about RMT can be obtained from recent reviews.310–312 Outlook and Future Directions for In Silico Models There is no doubt about the need for reliable in silico models predicting small molecule transfer across the blood–brain barrier. In fact, in silico prediction methods have gained popularity in drug discovery processes, as they are cheaper and less time-consuming than obtaining experimental data through in vivo and in vitro methods. Several in silico log BB models for passive diffusion have been included in this review. Tables 2 and 3, provide a detailed overview of current progress in this field, from initial steps by Hansch et al. to nowadays, as well as the caveats. Following analysis highlights some important points related to limitations and warnings, application scope and reliability for up to date reported log BB models: Data sets: As for any in silico model, data sets must be as uniform as possible. Ideally, all data should be generated from identical experimental conditions and come from the same laboratory. If not, the risk for ‘‘garbage in, garbage out’’ in the models increases, resulting in estimations with high uncertainty. In addition, knowing the experimental error in the data set is quite useful since this may help to identify overfitting models as well as to know where the expectations for the model should be placed. Descriptors: Rational selection of descriptors involved in passive diffusion, for example, those considered in rules-of-thumb for log BB, as well as considering these 5 Abraham descriptors (LFER), which are highly correlated with the previous, provides models with a very good performance. This is a very important point for models interpretability and utility: not just as prediction tools but also as guidelines in the design of new compounds. On the other hand, there are algorithms to select the most relevant DOI 10.1002/jps

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descriptors, such as GA, VSMP, RSQUARE, and RFE, providing validated results. Although plenty of descriptors may be calculated for each chemical structure there are still some caveats, as for example, log Pcycl/ water. There are not many in silico models to estimate this partition coefficient that have an impact, together with log Poct/water, on passive diffusion estimation.208 Statistical Methods: MLR and PLS are applied to data sets with a linear relationship between descriptors and log BB. For this reason, derived models are quite straightforward to interpret and to use as guidelines in the design process. However, sometimes nonlinear methods such as Neural Networks (NN), Support Vector Machine (SVM), etc. are needed to get reliable models. Such ‘‘black boxes’’ provide very useful prediction tools in the prioritization process of compounds, to be synthesized (from Virtual Library), to be assayed (from in-stock compounds), etc. Other methods utilized for classification purposes, such as recursive partitioning, are quite powerful not only due to their predictive power but also as guidelines for medicinal chemists. Local models versus General models: (i) Knowledge about the chemical space for the training set is very important information as this will help to define the application scope of the model. Otherwise, estimations will be based on extrapolations to other chemical spaces and therefore dramatically decrease the performance of the model. (ii) Building up specific log BB models for each independent chemical series results in an improvement of their performance and reliability. Quantitative and Qualitative models: As Tables 2 and 3 describe, classification models together with those quantitative models utilized as a categorization tool, provide in general quite good performances with overall accuracies above 70%. Therefore, these are pretty useful prioritization tools in the drug discovery process. But, one key factor that should be properly defined to get optimal performance for quantitative log BB estimations as classification tools, is the threshold separating BBB penetrating from

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nonpenetrating. Application Scope for quantitative and qualitative models within the Drug Discovery process. (i) Compound prioritization should be applied in different phases of the drug discovery process: from compounds to be synthesized to compounds to be assayed. In this scenario, qualitative linear models and quantitative models together with nonlinear statistical methods (‘‘black boxes’’) fit pretty well. (ii) Rational design. In this case a qualitative linear model and some quantitative model(s) such as recursive partitioning, are mandatory to identify the key descriptors. This knowledge is very useful to select compounds for screening in order to refine the model, as well as to design novel chemical structures and focused analogues based on optimal values for the descriptors.

ACKNOWLEDGMENTS The authors would like to acknowledge Dr. Roy De Maesschalck from Johnson & Johnson Pharmaceutical Research & Development, a division of Janssen Pharmaceutica N.V., Beerse, Belgium for proofreading and his scientific input to this review.

REFERENCES

With the increasing knowledge of the numerous active transport processes involved in the blood– brain barrier, it is becoming evident that models based on passive diffusion alone, reported in Tables 2 and 3, are not suitable to cover most of the options. Therefore, a sequential screening approach based on reliable models for passive diffusion in combination with models predicting P-gp substrate properties may be a good strategy to cover a broad range of drug entry and efflux mechanisms.

CONCLUSIONS Various In Vivo, In Vitro, and In Silico methods, for estimating small molecule transfer across the BBB, have been developed in pharmaceutical industry and by academia. All methods have their inherent benefits and drawbacks highlighted in this review. The more reliable in vivo methodologies are less feasible in the current high throughput environment of combinatorial chemistry. Therefore scientists are focusing on the development of fast in vitro and in silico models. In there efforts to understand in a simple way a complex organ, these scientists are facing probably the major disadvantage of BBB permeability research: the lack of quantity and quality of in vivo BBB permeability datasets. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 98, NO. 12, DECEMBER 2009

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