Molecular Properties Determining Unbound ... - ACS Publications

Article pubs.acs.org/molecularpharmaceutics

Molecular Properties Determining Unbound Intracellular and Extracellular Brain Exposure of CNS Drug Candidates Irena Loryan,† Vikash Sinha,‡ Claire Mackie,§ Achiel Van Peer,‡ Wilhelmus H. Drinkenburg,∥ An Vermeulen,⊥ Donald Heald,# Margareta Hammarlund-Udenaes,*,† and Carola M. Wassvik∇,○ †

Translational PKPD Group, Department of Pharmaceutical Biosciences, Associate Member of SciLife Lab, Uppsala University, Uppsala, Sweden ‡ Clinical Pharmacology, §Pharmaceutical Development and Manufacturing Science, ∥Neuroscience Discovery, and ⊥Model Based Drug Development, Janssen Research and Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium # Clinical Pharmacology, Janssen Research and Development, LLC, Titusville, New Jersey 08560, United States ∇ Computational Chemistry, Discovery Sciences, Janssen Research and Development, Toledo, Spain S Supporting Information *

ABSTRACT: In the present work we sought to gain a mechanistic understanding of the physicochemical properties that influence the transport of unbound drug across the blood−brain barrier (BBB) as well as the intra- and extracellular drug exposure in the brain. Interpretable molecular descriptors that significantly contribute to the three key neuropharmacokinetic properties related to BBB drug transport (Kp,uu,brain), intracellular accumulation (Kp,uu,cell), and binding and distribution in the brain (Vu,brain) for a set of 40 compounds were identified using partial least-squares (PLS) analysis. The tailoring of drug properties for improved brain exposure includes decreasing the polarity and/or hydrogen bonding capacity. The design of CNS drug candidates with intracellular targets may benefit from an increase in basicity and/or the number of hydrogen bond donors. Applying this knowledge in drug discovery chemistry programs will allow designing compounds with more desirable CNS pharmacokinetic properties. KEYWORDS: blood−brain barrier (BBB), brain drug delivery, neuropharmacokinetics, in silico modeling, PLS analysis, BBB drug transport, intracellular accumulation, binding and distribution in the brain

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INTRODUCTION

the ratio of total brain to total plasma concentrations, Kp,brain (often termed logBB), have been established.8−10 The majority of the logBB computational models identified high lipophilicity and low hydrogen-bonding potential as favorable characteristics for BBB penetrants.8,11 However, there has been a broad implementation of the “free drug hypothesis” in CNS drug discovery as it is more closely related to clinically relevant pharmacodynamic measures.1,5,12 This makes the use of logBB as a critical parameter for the assessment of the BBB drug transport less suitable, as logBB is a composite of several properties, both binding in plasma, binding and distribution in the brain, and the BBB transport properties.2 This has emphasized the role of Kp,uu,brain as a measure of BBB transport, to map the influence of active efflux and influx transport.1,13,14

A pharmacologically relevant unbound drug brain exposure is a key prerequisite for neurotherapeutics and new chemical entities (NCEs) designed to act on CNS intra- and extracellular targets.1,2 The design of potent NCEs capable of crossing the blood−brain barrier (BBB) at an adequate level is central to medicinal chemists working in the neuroscience area, as the BBB in many instances impedes access to brain targets. Presently, unbound drug brain to plasma and cell partitioning coefficients (Kp,uu,brain and Kp,uu,cell, respectively) are acknowledged as core neuropharmacokinetic (neuroPK) parameters for quantitative assessment of the BBB and cellular barrier penetration properties of CNS drug candidates.2−6 An important question is which physicochemical properties of the drug candidate can be used to optimize these parameters of brain penetration and intrabrain distribution? When addressing physicochemical properties and how they influence CNS drug exposure, there are several possible relationships.7 Numerous computational approaches to predict © XXXX American Chemical Society

Received: September 4, 2014 Revised: October 21, 2014 Accepted: December 14, 2014

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DOI: 10.1021/mp5005965 Mol. Pharmaceutics XXXX, XXX, XXX−XXX

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Molecular Pharmaceutics Up until now, only very few in silico models related to unbound drug CNS exposure are available.13,15 The first modeling of the physicochemical determinants of Kp,uu,brain was developed by Fridén et al. using a chemically diverse data set of 41 marketed drugs.13 It revealed that out of 16 molecular descriptors studied, the number of hydrogen bond acceptors (a_acc) was the most significant variable having a crossvalidated coefficient of determination, Q2, of 0.43 for Kp,uu,brain.13 The authors concluded that a 2-fold increase in Kp,uu,brain can be achieved by removal of two hydrogen bond acceptors. Later, Chen and colleagues built quantitative Kp,uu,brain predictions using machine learning algorithms by applying both a direct and an indirect approach. In the latter they combined predictive models for Kp,brain, the volume of distribution of unbound drug in the brain (Vu,brain) and the fraction of unbound drug in plasma ( f u,plasma) in one consensus model.15 Yet again, hydrogen bonding descriptors such as topology index kappa 2, sum of positive electrostatic potentials on van der Waals (VDW) surface, and the number of hydrogen bond donor atoms (a_don) were found to be the most significant predictors for Kp,uu,brain. To date, no in silico approach has been used to correlate chemical structure to the unbound drug cell partitioning coefficient between the intra- and extracellular fluid, Kp,uu,cell, likely due to this being a very recent parameter with limited experimental data available. A prediction of Kp,uu,cell and Vu,brain was presented by Fridén and colleagues.3 The relationship is based on drug pKa values as well as physiological volumes and pH values of plasma, ISF, and cytosolic and lysosomal compartments. Recently, Spreafico and Jacobson enriched this pH partitioning physical model by the addition of a lipid binding component.16 The number of computational models specifically designed to predict Vu,brain are also limited and based on Kp,uu,cell estimations.4,16 A simple LogD7.4 based in silico relationship of Vu,brain was presented by Fridén et al.4 A more elaborate in silico Vu,brain model was developed as a part of an indirect consensus Kp,uu,brain model by Chen et al.;15 however, the details on the performance of the model and contributing molecular descriptors were not further described. Thus, in spite of some progress achieved on the understanding of the relationship between physicochemical properties of drugs and their unbound drug CNS exposure, it is still rather limited. The main purpose of the current study was to identify interpretable molecular descriptors that significantly contribute to all three key neuroPK properties related to BBB transport (Kp,uu,brain), intracellular accumulation (Kp,uu,cell), and overall binding and distribution in brain (V u,brain). A pharmacologically and structurally diverse data set comprising 40 molecules was used and included compounds from late preclinical and clinical development phases as well as marketed ones, all having sufficient information about the parameters to be studied.17 These compounds represent chemistry from eight different neuroscience projects and one non-CNS target. Twenty-nine were used in the training set and 11 were used in the test set.

into nine groups based on the major pharmacological target (s) (Table S1, Supporting Information). Thirty-three compounds were taken from the Janssen R&D neuroscience discovery and development portfolio (Beerse, Belgium and La Jolla, USA). Four compounds are well-known reference compounds for their respective targets developed at Bayer AG (B5-Bay 60-7550, [26]), Novartis International AG (G10-5-cyano-pyridine-2-carboxylic acid [3-(5-amino-3-difluoromethyl-3,6-dihydro-2H-[1,4]oxazin-3-yl)-phenyl]-amide), and Eli Lilly and Co. (G6-LY2811376, [27], G9-LY2886721 with Clinical Trials Identifier NCT01561430). The antipsychotics risperidone (F4), paliperidone (F5), and olanzapine (F6) were also included in the data set. Experimental Data. The experimental procedures are fully ̈ male Sprague− described in Loryan et al.17 In short, drug-naive Dawley 250−300 g rats (Taconic, Lille Skensved, Denmark) were used for the preparation of fresh brain slices following the protocol approved by the Animal Ethics Committee of Uppsala, Sweden (Ethical Approval N C329/10 and C351/11). Male Sprague- Dawley rats and Swiss SPF mice obtained from Charles River Laboratories, Inc. (Germany) were used for in vivo pharmacokinetic studies. All animals were housed in groups at 18 to 22 °C under a 12 h light/dark cycle with ad libitum access to food and water. Binding and Distribution in Brain (Vu,brain, f u,brain). Vu,brain was estimated using the brain slice method according to previously published protocols.18,19 In brief, six 300 μm brain slices were incubated with a cassette of compounds (i.e., a mixture of five pharmacological agents) in a HEPES-buffered artificial extracellular fluid (aECF) with an initial concentration of 200 nM of each compound (n = 5 rats per cassette). A 5 h incubation was maintained at 37 °C in a benchtop shaker (MaxQ4450 Thermo Fisher Scientific, NinoLab, Sweden). The viability of the brain slices was assessed using a cytotoxicity detection kit (Roche Diagnostics GmbH, Germany). At equilibrium, the concentration of the compounds in aECF was assumed to be equal to interstitial fluid concentration in the brain slice (Cu,brainISF) The unbound fraction of drug in rat brain homogenate f u,brain was assessed using published protocols with minor modifications.14,20 Briefly, compounds (5 μM) were added to brain homogenate diluted 10-fold with phosphate saline buffer (PBS). The brain homogenate was dialyzed against PBS at pH 7.4 during 5 h in a Pierce Rapid Equilibrium Dialysis Device (RED) (Thermo Scientific, Rockford, IL, USA). Following incubation, samples from both the buffer and brain homogenate sides were analyzed. Corrected for the brain homogenate dilution (D) factor (hD) f u,brain was calculated.17 BBB Transport (Kp,uu,brain). The assessment of steady-state Kp,uu,brain was achieved through the combinatory mapping approach2,17,21,22 K p,uu,brain ≈

K p,brain Vu,brainfu,plasma

(1)

where Kp,brain is the ratio of total brain to total plasma drug concentrations at steady state. Kp,brain was obtained at multiple time points (minimum four) after oral or subcutaneous administration. Generally, samples were taken at 30 min, 1, 2, 4, 7, and 24 h after administration; the dose varied among the different study protocols between 5 and 30 mg/kg.17 At the designated time points, the rats or mice (n = 3 per time point) were anesthetized and blood samples were immediately

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EXPERIMENTAL SECTION Data Set. The data set used in the current study comprised 40 compounds covering a wide range of physicochemical properties (Table 1 and Figure 2) and active on various pharmacological CNS targets.17 Compounds were stratified B


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Molecular Pharmaceutics

spite of its high skewness (3.6) since it had proved important in previous in-house models of brain exposure (data not shown); moreover, a negative correlation to Kp,brain was established.28 Statistical Modeling. PLS modeling was used to relate Kp,uu,brain, Kp,uu,cell, and Vu,brain to the calculated molecular descriptors in SIMCA 13.0 (Umetrics, Sweden).29 Experimental data of Kp,uu,brain, Kp,uu,cell, and Vu,brain were log-transformed prior to PLS modeling, in order to cope with the large differences in numerical values. The frequency distributions of the log-transformed parameters are presented in Figure 1.

collected into 10 mL BD K3EDTA vacutainer vials (BD Biosciences, Plymouth, U.K.). Afterward, animals were sacrificed, and the brain was quickly removed and homogenized in demineralized water (1/9 w/v). Plasma and brain homogenate samples were stored at −20 °C pending analysis. Kp,brain was calculated from the areas under the curve for total brain and plasma concentrations from zero to the last time point with measurable concentration for an individual dose (AUC0‑t). Intracellular Accumulation (Kp,uu,cell). Kp,uu,cell was assessed as an average ratio for all cell types within the brain by a combination of Vu,brain and f u,brain.4 K p,uu,cell = Vu,brainfu,brain

(2)

The theory behind Kp,uu,cell is based on the divergences in the nature of the measurements obtained from the brain slice and brain homogenate methods. On the one hand, f u,brain primarily represents nonspecific binding of compound to various intracellular lipids and proteins. On the other hand, Vu,brain provides evidence on overall uptake of the compound by brain parenchymal cells accounting for nonspecific and specific binding, active cellular membrane transport, pH partitioning, etc. The bioanalysis of all studied matrices was performed using reverse-phase liquid chromatography followed by detection with a tandem mass spectrometer (LC−MS/MS) Quattro Ultima, (Micromass, Manchester, U.K.). The LC system consisted of an LC-10AD pump Shimadzu, Kyoto, Japan) and a SIL-HTc autosampler (Shimadzu, Kyoto, Japan). LC− MS/MS measurement conditions for all sample processing and compound-specific bioanalytical parameters are summarized in Loryan et al.17 The spectra analysis was performed using the MassLynx software, version 4.0 (Micromass, Manchester, U.K.). PLS Modeling of neuroPK Parameters. Molecular Descriptors. A wide range of molecular descriptors was included. The criteria for inclusion were 2-fold: first, to select those descriptors that had previously shown to be important for Kp,uu,brain and brain penetration in general,13,15,23 and second, covering as many different properties as possible, including those based on the three-dimensional (3D) molecular structure. In total, 188 molecular descriptors were calculated with MOE 2011.10.24 ClogP was calculated with BioByte,25 TPSA was calculated using the method by Ertl et al.26 The logD7.4 and the pKa_MB were both calculated with Advanced Chemistry Development, Inc. (ACD/Laboratories), v.12.01.27 For the descriptors that required a 3D structure as input, compounds were first submitted to a stochastic conformational search in MOE using the Generalized Born solvation model and an energy cutoff of 7 kcal/mol. The conformer with the smallest value of the globularity descriptor and with a total energy difference to that of the global minimum not larger than 1 kcal/mol was selected as input for the descriptor calculation. This was done with the aim of using the most extended low energy conformer for each compound. In the case where a racemic mixture was used in the experiments, molecular descriptors were calculated separately for the each enantiomer and the average value was used for modeling. The removal of skewed descriptors (skewness higher than 1.5 or lower than −1.5) and descriptors with zero variance was done in SIMCA and a matrix consisting of 175 molecular descriptors was submitted for principal component (PCA) and PLS analyses. The globularity descriptor was kept in the set in

Figure 1. Frequency distribution of the logarithms of (A) the ratio of brain interstitial to plasma unbound drug concentrations, logKp,uu,brain, (B) the ratio of brain intracellular to extracellular unbound drug concentrations, logKp,uu,cell, and (C) the volume of distribution of unbound drug in brain, logVu,brain for the set of 40 compounds.

D(determinant)-optimal design in MODDE 9.1 (Umetrics, Sweden) was applied to select a subset of molecules (the test set) that best represented the properties of the candidate set of 40 compounds. Accordingly, the set was divided into a training set (29 compounds) and a test set (11 compounds). The PLS models were developed based on the training set and challenged by the test set. The predictive power of the model was evaluated by the coefficient of determination (R2) and the cross-validated R2 (Q2). The leave-many-out cross validation method using 7 groups, i.e., every seventh observation was excluded during each cross validation round, was applied. The root mean squared error of the estimate (RMSEE) was used to assess the performance of the models, according to C


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Table 1. Pharmacological Target(s), Ion Class, Dissociation Constant/s (pKa), Molecular Weight (MW, g·mol−1), Octanol− Water Partitioning Coefficient (logP), Topological Surface Area (TPSA, Å2), Number of Hydrogen Bond Acceptor Atoms (a_acc), and Number of Hydrogen Bond Donor Atoms (a_don) of 40 Structurally Diverse Compounds IDf

target

ion class

pKag,h

MW

logPg,h

TPSA

a_acci

a_donj

A1 A2 A3 B1 B2 B3 B4 B5a C1 C2 C3 D1 D2 D3 D4 E1 E2 F1 F2 F3 F4e F5e F6e G1 G2 G3 G4 G5 G6b G7 G8 G9c G10d H1 H2 I1 I2 I3 I4 I5

PDE10 PDE10 PDE10 PDE2 PDE2 PDE2 PDE2 PDE2 GS GS GS mGlu2 mGlu2 mGlu2 mGlu2 mGlu5 mGlu5 alpha-7 fast D2 D2/D3/5-HT6 D2/5-HT2 D2/5-HT2 D2/5-HT2A BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 BACE-1 H4 H4 P2X7 P2X7 P2X7 P2X7 P2X7

weak base weak base weak base weak base weak base neutral weak base base weak base base weak base neutral weak base base neutral neutral neutral weak base base base base base base base base base base base base base base base base base base neutral neutral neutral neutral neutral

4.4/2.6 4.4/3.1 3.9/2.3 4.0 5.6/1.9 NMIG 2.8 9.4/3.4 5.7 6.0 5.6 NMIG 5.0 6.3 NMIG NMIG NMIG 3.5 7.8/2.1 8.2 8.2/3.1 8.2/2.6 8/5.6 7.8 7.9 7.5 8.2 9.2 8.5 7.8 7.9 7.7 7.4 8.6/5.9 8.7/6.3 NMIG NMIG NMIG NMIG NMIG

353.4 379.5 369.4 362.3 432.5 397.4 412.4 476.6 447.4 454.5 417.3 344.9 451.4 454.4 380.9 338.3 352.4 416.4 372.3 308.3 410.5 426.5 312.4 377.4 427.4 445.4 389.4 378.9 320.4 421.4 371.3 390.4 389.3 263.3 233.3 375.2 405.8 374.2 388.3 421.8

2.4 2.6 1.6 3.9 2.6 1.8 1.4 N.A. 4.2 4.0 3.3 4.6 4.2 4.3 >5 2.8 3.1 3.1 4.0 2.6 3.0 2.4 2.8 1.9 2.4 2.8 1.0 1.6 1.0 2.7 2.2 2.4 1.9 −0.83 0.3 1.4 2.4 2.4 2.0 2.8

64.8 64.8 85.0 55.6 77.7 76.5 79.8 98.0 82.7 70.7 58.4 23.5 36.7 42.7 23.5 60.3 55.6 94.4 41.0 41.0 61.9 82.2 30.9 112 112 120 122 80.4 64.2 113 113 89.6 113 76.3 67.1 76.8 49.0 74.8 63.9 74.8

5 5 6 3 7 4 5 6 4 4 3 1 3 4 1 4 3 6 3 3 4 5 2 5 5 5 5 3 3 5 5 4 5 4 3 5 3 5 4 5

0 0 1 1 0 0 0 2 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 2 2 2 2 2 1 2 2 2 2 2 2 0 2 2 0 2

a

Bayer AG (BAY60−7550). bEli Lilly (LY2811376). cEli Lilly (LY2886721). dNovartis International AG (5-cyano-pyridine-2-carboxylic acid [3-(5amino-3-difluoromethyl-3,6-dihydro-2H-[1,4]oxazin-3-yl)-phenyl]-amide). eF4,risperidone; F5, paliperidone; F6, olanzapine. fGroup A, inhibitors of PDE10 (phosphodiesterase 10); group B, inhibitors of PDE2 (phosphodiesterase 2); group C, modulators of GS (gamma secretase); group D, positive allosteric modulators of mGlu2 (metabotropic glutamate receptor 2); group E, positive allosteric modulators of mGlu5 (metabotropic glutamate receptor 5); group F, antagonists of alpha-7 (alpha-7 nicotinic receptor), fast dissociating antagonist of D2 (dopamine D2 receptor), antagonists of D2, D3, and 5-HT6 (dopamine D2, D3, and 5-hydroxytryptamine 5-HT6 receptors), antagonists of D2 and 5-HT2 receptors, and antagonists of D2 and 5-HT2A receptors; group G, inhibitors of BACE-1 (beta-site amyloid precursor protein cleaving enzyme 1); group H:, antagonists of H4 (histamine H4 receptor); group I, antagonists of P2X7 (P2X purinoceptor 7). gN.A., not available; NMIG, no measurable ionizable group (pKa < 2 for bases and >12 for acids). hThe dissociation constant and the logP were determined at 25 °C by potentiometric titration of a solution of the compounds using Sirius T3 instrument (Sirius Analytical Ltd., U.K.). iNumber of hydrogen bond acceptor atoms not counting acidic atoms but counting atoms that are both hydrogen bond donors and acceptors such as −OH. jNumber of hydrogen bond donor atoms not counting basic atoms but counting atoms that are both hydrogen bond donors and acceptors such as −OH.

RMSEE =

⎛ ∑ (Y − Y )2 ⎞ obs pred ⎜ ⎟ ⎜ N−1−A ⎟ ⎝ ⎠

number of observations, and A is a number of components in the model. Selection of the most significant X-variables was performed in a stepwise manner by exclusion of the variables having small contributions based on their variable importance for projection (VIP)-scores using the principle of parsimony. The final models

(3)

where (Yobs − Ypred) refers to the fitted residuals for the observations in the training set in logarithmic units, N is a D


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Figure 2. Frequency distribution of (A) calculated n-octanol−water partitioning coefficient ClogP, (B) molecular weight MW (g·mol−1), (C) topological surface area TPSA (Å2), and (D) most basic dissociation constant MB_pKa for the set of 40 compounds.

Table 2. PLS Model Statistics training set a

models

A

logKp,uu,brain logKp,uu,cell logVu,brain

1 1 2

R

2b

0.76 0.69 0.69

Q

Fridén set13

test set 2c

RMSEE

0.75 0.67 0.65

0.30 0.21 0.33

d

R2

RMSEP

0.82 0.66 0.53

0.31 0.33 0.63

e

RMSEP 0.77 0.38 0.63

a

A, number of components in the model. bR2, coefficient of determination. cQ2, cross-validated coefficient of determination. dRMSEE, root mean square error of estimation. eRMSEP, root mean square error of prediction.

were selected based on the highest R2 and Q2 and the lowest RMSEE. Further validation was achieved by model reestimation after data randomization, i.e., permutation tests with 200 iterations with subsequent evaluation of R2 and Q2. If R2 and Q2 random models are significantly lower than the selected ones, then the risk of chance correlation is small. External validation of the models was performed by letting the derived models predict values of the studied neuroPK properties for the test set and compare those with experimentally observed ones. Determination coefficient R2 and root-mean-square error of prediction (RMSEP) were used for the assessment of the PLS models. RMSEP =

⎛ ∑ (Y − Y )2 ⎞ obs pred ⎜ ⎟ ⎜ ⎟ N ⎝ ⎠

Comparison to established models was done using the data set of the experimentally derived Kp,uu,brain, Kp,uu,cell, and Vu,brain values presented by Fridén and colleagues.13

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RESULTS Experimental values of the main neuroPK parameters Kp,bu,brain, Kp,bu,cell, and Vu,brain for the 40 compounds, as well as data on the fractions of unbound drug in brain homogenate f u,brain and plasma f u,plasma are listed in the Supporting Information Table S1.17 The BBB transport assessed by Kp,uu,brain varied from 0.02 to 2.0. Kp,uu,cell as a measure of intracellular accumulation ranged from 0.15 to 24. Binding and distribution in brain estimated by Vu,brain varied from 2.8 to 624 mL·g brain−1. The log transformed neuroPK variables were approximately normally distributed for this data set, which was a requirement for the analysis (Figure 1). LogKp,uu,brain for the entire data set (n = 40) had a mean value ± standard deviation of 0.62 ± 0.61, logKp,uu,cell 0.036 ± 0.48, and logVu,brain 1.5 ± 0.61. To capture as wide a range as possible of molecular properties related to the chemical structure of the studied compounds, 188 1D, 2D, and 3D molecular descriptors were calculated with MOE, ACD/Laboratories, and Biobyte among other software (see Experimental Section for more detail). All

(4)

where (Yobs − Ypred) refers to the fitted residuals for the observations in the test set in logarithmic units. The response contour plots display the response surface contour for the best PLS model of the selected neuroPK parameter with the two designated most significant molecular descriptors on the axes. E


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compound H2) with excellent results (R2 = 0.82 and RMSEP = 0.31, Table 2). The final PLS model describing Kp,uu,brain comprised only two molecular descriptors: the capacity factor at energy level 8 (vsurf_Cw8) and TPSA (Tables 3 and 4). Both descriptors are related to polarity and hydrogen bonding capacity in different ways: vsurf_Cw8 represents the hydrogen bond donor and acceptor regions per surface unit of a molecule based on the 3D structure, and TPSA is the approximated surface area that belongs to polar atoms according to Ertl et al.26 Judging from the negative regression coefficients, these molecular descriptors have an inverse relationship to Kp,uu,brain. According to the analysis, increasing the polarity and/or hydrogen bonding capacity of a compound will lead to a decrease in the Kp,uu,brain. Modeling Intracellular Accumulation. The best model of logKp,uu,cell consisted of one single significant latent variable with R2 of 0.69 and Q2 of 0.67 (Table 2 and Figure 4). The three molecular descriptors that made up the final model for intracellular partitioning were the polar volume at energy level 4 (vsurf_Wp4), the most basic pKa (pKa_MB), and the fractional partial positive surface area (Jurs_FPSA_3) (Tables 3 and 4). These descriptors can be interpreted as being related to basicity and hydrogen bond donor capacity. The vsurf_Wp4 is the resulting volume from the favorable interaction with a carbonyl probe at −2.0 kcal/mol. This represents polar interactions with hydrogen bond donating character and is dependent on the 3D structure of the input molecule. The Jurs_FPSA_3 is the charged-weighted partial positive surface area over the total molecular surface area and should be related to hydrogen bond donor regions. Validation of the model using the test set showed good performance with R2 = 0.66 and RMSEP = 0.33 (Table 2). In this case the number of compounds in the test set equaled 10. The experimental value of f u,brain for observation F3 was below the limit of quantification, which resulted in a missing value for Kp,uu,cell, and therefore, it was not included in the model validation. From this analysis we conclude that the more basic a compound is and the larger molecular surface area related to hydrogen bond donor atoms it has, the more likely the compound will partition into and accumulate inside the brain cells. Modeling Tissue Binding and Distribution in Brain. The final model of logVu,brain had two latent variables and four molecular descriptors: ClogP, a_acc, vsurf_HB5, and vsurf_Iw7 (Tables 4 and 5). The model exhibited a good fit to the observed logVu,brain values with R2 of 0.69 and Q2 of 0.65 (Table 2 and Figure 5). The external validation using the test set revealed slightly inferior results when compared to those of the training set with R2 of 0.53 and RMSEP = 0.63. This is mainly due to the underprediction of observations in groups D, G, and H. One observation, D10, was removed before prediction since it fell outside of the model space as defined by the Hotelling’s T2 range at 95% critical level. The molecular descriptors selected by the model are related to lipophilicity (ClogP), hydrogen bond acceptor properties (a_acc and vsurf_HB5) and the distribution of polar regions in the molecule (vsurf_Iw7). The VolSurf descriptor vsurf_HB5 represents the difference between the hydrophilic volume using a water probe and the polar volume using a carbonyl oxygen probe and should be interpreted as polar interactions with hydrogen bond acceptor character. The vsurf_Iw7 is the distance between the center of mass to the barycenter of the polar groups in the molecule at

molecular descriptors are listed in the Supporting Information, Tables S2 and S3. The chemical diversity and the physicochemical property space covered by the compounds in the data set was assessed by the frequency distribution of representative molecular descriptors ClogP, molecular weight (MW, g·mol−1), topological surface area (TPSA, Å2), and most basic pKa (MB_pKa) (Table 1 and Figure 2). PLS Models. Models of BBB transport, intracellular accumulation, binding, and distribution within the brain were established using a multivariate partial least-squares (PLS). The PLS model summary statistics and an overview of loading weights and regression coefficients for each of the model components are presented in Tables 2−4. Back-transformed Table 3. Loadings in Each Component for Scaled and Centered Variables of the logKp,uu,brain, logKp,uu,cell, and logVu,brain PLS Models model

vsurf_CW8 TPSA

logKp,uu,cell

vsurf_Wp4 Jurs_FPSA_3

logVu,brain

variable description

wc[1]

capacity factor 8 topological polar surface area (Å2) polar (donor) volume at energy level 4 fractional positively charged (partial) surface area most basic pKa calculated octanol−water partition coefficient (P) number of hydrogen bond acceptor atoms hydrogen bond acceptor capacity 5 hydrophilic integy moment 7

−0.73 −0.68

variable ID

logKp,uu,brain

pKa_MB CLogP a_acc vsurf_HB5 vsurf_IW7

wc[2]

0.65 0.56 0.51 0.59

0.37

−0.55

0.20

−0.52

0.32

−0.28

−0.87

Table 4. Regression Coefficients and Standard Error for the logKp,uu,brain, logKp,uu,cell, and logVu,brain PLS Models model

variable ID

CoeffCSa

CoeffCScvSEb

logKp,uu,brain

vsurf_CW8 TPSA vsurf_Wp4 Jurs_FPSA_3 pKa_MB CLogP a_acc vsurf_HB5 vsurf_IW7

−0.49 −0.46 0.40 0.34 0.31 0.40 −0.26 −0.22 −0.34

0.13 0.13 0.09 0.08 0.14 0.19 0.14 0.08 0.22

logKp,uu,cell

logVu,brain

a

Regression coefficients corresponding to centered and scaled Xvariables and scaled (but uncentered) Y-variables. bJack-knife standard error of the coefficients CoeffCS computed from all rounds of cross validation.

observed and predicted values of Kp,uu,brain, Kp,uu,cell, and Vu,brain for the training and test sets together with their respective error of prediction are presented in Supporting Information, Tables S4−S6. Modeling BBB Transport. The best performing logKp,uu,brain model had one significant latent variable and exhibited a good fit to experimental values with a coefficient of determination (R2) of 0.76 and a cross-validated R2 (Q2) of 0.75 for the 29 compounds in the training set (Table 2 and Figure 3). The model was challenged using the preselected test set of 10 compounds (due to a missing value for Kp,uu,brain of F


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Figure 3. Predicted versus observed logKp,uu,brain for the training set and test set. In panel a, the training set (n = 29) is represented as filled blue circles and the test set (n = 10) as filled orange circles. In panel b, both training set and test set (n = 39) are colored by vsurf_Cw8 (low to high values as blue to red) and sized by TPSA (low to high values as small to large circles).

Figure 4. Predicted versus observed logKp,uu,cell for the training set and test set. In panel a, the training set (n = 29) is represented as filled blue circles and the test set (n = 11) as filled orange circles. In panel b, both training set and test set (n = 40) are colored by vsurf_Wp4 (low to high values as blue to red) and sized by Jurs_FPSA_3 (low to high values as small to large circles).

data set is currently the only authentic, clean, and unbiased data set compatible to the present one. The Fridén et al. data set was collected according to (1) availability of clinical data on drug concentrations in the CSF and (2) coverage of the chemical space represented by drugs on the Swedish market. Compared to our CNS candidate drug data set, the Fridén data set represents a much wider range of chemical structures. This is easily appreciated in Figure 6c where the complete set of the 41 drugs is projected onto the logVu,brain model space as determined by our data set. Almost one-third (32%) of the drugs in the Fridén data set were outliers in this model as judged by the Hotelling’s T2 range at the 95% confidence limit (ten compounds) and the probability of belonging to the model, DModX, of 5% in SIMCA (three compounds). The same analysis to remove observations that fall outside of the model applicability domain was performed for the logKp,uu,brain and logKp,uu,cell models. For the logKp,uu,brain model 11 compounds were removed. The overall accuracy of the

energy level 7 (−5 kcal/mol). If all polar groups are located in only one part of the molecule, this descriptor will assume a large value (unless this coincides with the center of mass). Descriptors vsurf_HB5 and vsurf_Iw7 both take a 3D molecular structure as input and are dependent upon the conformation used. According to this analysis, highly lipophilic compounds containing few hydrogen bond acceptor regions and exhibiting an even distribution of polarity with respect to the center of mass are more likely to show a high degree of binding to brain tissue and to distribute outside of the ISF. Comparison to Established Models. The experimental data of Kp,uu,brain obtained via the combinatory mapping approach that is available in the literature is scarce.2,17 However, we wanted to compare our analysis of the relationship between chemical structure and brain exposure to what has been found by others. For this we chose the set of 41 marketed drugs with available experimental values for Kp,uu,brain studied by Fridén et al.13 To our knowledge, Fridén’s G


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prediction of logKp,uu,brain for the remaining 30 drugs was poor, RMSEP = 0.77, as compared to that of our test set, RMSEP = 0.31. An overview of the statistics for the Fridén data set can be found in Table 2. Observed versus predicted values are displayed in Figure 6a and Table S7, Supporting Information. Similarly, seven compounds were removed from the Fridén data set before prediction with the logKp,uu,cell model. Also for this parameter, the model performed poorly with RMSEP = 0.38 as compared to 0.21 for our internal set (Table 2 and Figure 6b). For the last parameter, logVu,brain, 26 compounds were judged to fall within the model space, and the performance was slightly better than for the two previous parameters with RMSEP = 0.63, the same as for our test set, but inferior to our training set (RMSEP = 0.33), Table 2 and Figure 6d. We found that the molecular descriptors of polarity and hydrogen bonding capacity were identified as being important for the drug transport across the BBB. Decreasing the polarity or hydrogen bond capacity of a compound will lead to an increase in the Kp,uu,brain and vice versa. The specific descriptors selected as most important for our data set, vsurf_Cw8 and TPSA, were not able to explain the variance in Kp,uu,brain for the remaining 30 compounds in the Fridén data set. Hence our model was not predictive of the drugs in the Fridén data set. Instead we found that vsurf_W1, and the a_don are the descriptors from our data set that best correlate with Kp,uu,brain for these compounds (results not shown). Although vsurf_W1, the hydrophilic volume resulting from the interaction of the compound with a water probe at −0.2 kcal/mol, and the a_don are not the same as vsurf_Cw8 and TPSA, they describe the same general relationship between chemical structure and Kp,uu,brain. All four are related to polarity and hydrogen bonding capacity and when heteroatoms are removed from a molecule or the size of polar substituents is decreased, the unbound brain-to-plasma ratio increases. The fact that our models performed poorly for the Fridén data set suggests that it would be beneficial to build local models for specific data sets. Although a topic of constant debate, it was recently noted that especially when using property descriptors (as the ones in this study) local models tend to give slightly superior results when compared to global models.30 To the best of our knowledge, no QSAR models have previously been reported for logKp,uu,cell and logVu,brain, so a direct comparison of the descriptors that were selected to be important for intracellular accumulation and brain tissue binding and distribution was not possible.

ISF = interstitial fluid (here used interchangeably with extracellular fluid).

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DISCUSSION In the present work we sought to gain a mechanistic understanding of which molecular properties determine the unbound drug BBB transport as well as the intra- vs extracellular exposure in the brain. For this we used a set of 40 potential neurotherapeutic compounds that are structurally diverse and together represent a modern biological target landscape (e.g BACE-1, GSM, PDE2, and PDE10).17 Applying this knowledge in drug discovery chemistry projects will allow designing compounds with more desirable CNS pharmacokinetic properties. BBB Transport. Our model for logKp,uu,brain featured two molecular descriptors, vsurf_Cw8 and TPSA. It highlights the existence of an inverse correlation between polarity and hydrogen bonding capacity, on the one hand, and drug transport across the BBB, on the other hand. To achieve an increase in the Kp,uu,brain for a certain compound, the overall

a

where Abrain (nM·g brain−1) is the amount of drug in the brain slice to the measured final buffer after reaching equilibrium, Cbuffer (μM·L−1). Vu,brain is estimated using the fresh brain slice method.

By decreasing the lipophilicity of a compound and/or reducing the number and size of hydrogen bond acceptor regions in the same, it is likely to show a decreased degree of binding to brain tissue and to distribute less outside of the ISF. This could be beneficial when evaluating candidates suitable as PET tracers. Please keep in mind that changes in Vu,brain do not affect transport across the BBB.

Abrain C buffer Vu,brain = Vu,brain: the volume of distribution of unbound drug in the brain (mL·g brain−1) defines the overall uptake of drug by brain tissue.

where f u,brain is the fraction of unbound drug determined from equilibrium dialysis using brain homogenate. For Vu,brain, see below.

For an intracellular target, increasing the basicity of a compound or increasing the molecular surface area related to hydrogen bond donor atoms may be beneficial as this is likely to increase the Kp,uu,cell (Kp,uu,cell greater than unity). This includes the potential accumulation in acidic cellular subcompartments. For an extracellular target the inverse relationship is true.

K p,uu,cell = Vu,brainfu,brain

Kp,uu,cell: the steady state relationship of intracellular to extracellular unbound drug concentrations describes the drug transport across the cellular membrane (average for all cell types within the brain).

Vu,brainfu,plasma

K p,brain

K p,uu,brain =

where Kp,brain is obtained as the in vivo ratio of total brain to total plasma drug concentrations at steady state and f u,plasma is the fraction of unbound drug in plasma, obtained with equilibrium dialysis technique. For Vu,brain, see below.

Decreasing the polarity and/or hydrogen bonding capacity of a compound is likely to result in an increase of Kp,uu,brain.

assessment definition

Kp,uu,brain: the ratio of brain ISFa to plasma unbound drug concentrations at steady state provides a direct quantitative description of the net BBB drug transport, including passive transport and active influx/efflux.

Table 5. Tailoring of Neuropharmacokinetic Parameters Based on Physicochemical Properties of Compound

optimization


H


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Figure 5. Predicted versus observed logVu,brain for the training set and test set. In panel a, the training set (n = 29) is represented as filled blue circles and the test set (n = 10) as filled orange circles. In panel b, both training set and test set (n = 39) are colored by ClogP (low to high values as blue to red) and sized by a_acc (low to high values as small to large circles).

Figure 6. Applying our PLS models to a data set of 41 drugs published by Friden et al.13 The observed versus predicted plots for all compounds within the model space as determined by the Hotelling’s T2 range and DModXPS for each one of the three neuroPK parameter models: a, logKp,uu,brain (n = 30); b, logKp,uu,cell (n = 34); and d, logVu,brain (n = 28). Compounds are colored and sized by the two most important molecular descriptors in each model (compare Figures 3−5). Panel c shows all 41 drugs projected on to the logVu,brain model variable space. The ellipse represents the Hotelling’s T2 range at the 0.95 critical level. All compounds residing outside of this limit and compounds with a low probability of belonging to the model according to DModXPS ( 95 Å2 (independently of the value for vsurf_Cw8), panel A Figure 7. This is well in line with a recent analysis by Hitchcock.31 A large data set composed of 4176 compounds from Amgen was investigated with respect to physicochemical properties and ER measured in an MDR1LLC-PK cell assay.31 The author found that 52% of the molecules with a TPSA > 90 Å2 were categorized as P-gp efflux substrates, while only 14% of the compounds with TPSA < 70 Å2 were considered P-gp efflux substrates. In comparison to our results, the fact that only half of their compounds with a TPSA > 90 Å2 were identified as P-gp substrates likely indicates the in vivo importance of other efflux transporters. In an analysis of more than 2000 Eli Lilly in-house compounds, Desai et al. showed that, for molecules with TPSA < 60 Å2 and an MB_pKa < 8, only 10% were classified as P-gp substrates, while that number was 75% for compounds with TPSA > 60 Å2 and MB_pKa > 8.34 The same authors also stated that ClogP seems of little importance for classifying molecules into P-gp substrates or nonsubstrates. This also holds true for 11,303 Pfizer compounds, when Wager et al. conclude that there is little correlation between lowering the ClogP and the efflux liability for compounds with ClogP values ≤5.23 For our data set, ClogP was not found to be related to the Kp,uu,brain which is compatible with the results from both Desai et al.34 and Wager et al.23 Another well-studied efflux transporter present at the BBB is the breast cancer resistance protein (BCRP). Although several computational models discriminating BCRP substrates from nonsubstrates based on their chemical structure have recently been published,35−37 we have found no study discussing the

Figure 7. Contour plots for Kp,uu,brain, Kp,uu,cell and Vu,brain PLS models. (A) Effect of topological surface area TPSA (Å2) and capacity factor 8 (CW8) on the brain interstitial to plasma unbound drug concentrations ratio, Kp,uu,brain. Contour labels denote Kp,uu,brain. (B) Effect of the polar (donor) volume at energy level 4 (vsurf_Wp4) and most basic pKa (pKa_MB) on the brain intracellular to extracellular unbound drug concentrations ratio, Kp,uu,cell. Contour labels denote Kp,uu,cell. (C) Effect of the number of hydrogen bond acceptor atoms (a_acc) and n-octanol−water partition coefficient (cLogP) on the volume of distribution of unbound drug in brain, Vu,brain (mL·g brain−1). Contour labels denote Vu,brain.

physicochemical properties or easily interpreted molecular descriptors of BCRP substrates. Intracellular Accumulation. The Kp,uu,cell concept is innovative, as it provides an exclusive opportunity to evaluate the cellular barrier function, often referred to as a secondary barrier, with regard to drug transport into the brain parenchymal cells, without specification of the cell type.4 It is noteworthy that Kp,uu,cell is a composite parameter determined by various mechanisms that are important for the cellular barrier net flux such as active uptake into the cells or efflux at the cellular barrier level, passive transport, lysosomal trapping, J


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Molecular Pharmaceutics and other types of intracellular sequestration of the drugs.3 Our logKp,uu,cell model revealed the importance of the three molecular descriptors vsurf_Wp4, pKa_MB, and Jurs_FPSA_3 (Tables 3 and 4). Compounds that are more basic and contain more hydrogen bond donors have higher probability for intracellular accumulation (Table 5). This could be beneficial for compounds with their site of action in the intracellular space, such as BACE-1, GSM, and PDE10 enzymes.38 However, as emphasized above, the value of Kp,uu,cell does not provide insight regarding the mechanism driving this concentration disequilibrium, and therefore, it is important to determine whether the increased intracellular accumulation is due to cytosolic accumulation or due to trapping of basic compounds inside of acidic organelles.9,23 Although our data set span both neutral and basic compounds (Figure 2), the majority of compounds that show measured values for Kp,uu,cell greater than unity are from groups G and H, which are all basic molecules with pKa >7. It seems reasonable to assume that the high value of Kp,uu,cell for these compounds could be due to lysosomal trapping and hence our model could be biased toward this mechanism of intracellular accumulation. Binding and Distribution in the Brain. The octanol− water partitioning coefficient, ClogP, came out as the most important descriptor for logVu,brain, thus being the main driver for binding and distribution in the brain in our model. A higher ClogP results in a larger Vu,brain (Figure 5 and Table 5). As outlined already by Wang and Welty in 1995, Vu,brain values that are higher than 1 mL·g brain −1 indicate intracellular accumulation and/or excessive brain tissue binding because it exceeds the total volume of water in the brain, which is 0.8 mL· g brain−1.39 However, the optimization and fine-tuning of CNS compounds based on predicted or measured Vu,brain values as it was proposed by Spreafico and Jacobson16 is misleading since Vu,brain does not account for BBB transport as the most important driver for sufficiently high brain exposure of unbound drug.17 Thus, the extent of brain tissue binding and distribution is not relevant for brain exposure. With increasing binding, there will possibly be a longer half-life in brain, given that the permeability across the BBB is not high enough to cause a more rapid equilibration across the BBB.40,41 When selecting positron emission tomography tracers low Vu,brain values, i.e., less nonspecific binding, are likely more beneficial. Optimizing Brain Exposure. In the field of neuroscience, an increased confidence that the compound of interest is present at a sufficient unbound concentration at the target site in the brain could help mitigate the risk of late stage failure early in the drug discovery process and reduce development costs. The novel combinatory mapping approach focusing on assessment of unbound drug CNS exposure by means of the main neuroPK parameters Kp,uu,brain, Kp,uu,cell, and Vu,brain can contribute to establishing reliable PKPD relationships already in the early drug discovery and development stages.2,17,40 However, classifying compounds based on only Kp,uu,brain could be strategically wrong when decisions are made in isolation and not combined with the pharmacological potency of the NCEs. Thus, the optimization of neuroPK properties must be done in parallel with tuning of the PD characteristics. Overall, these neuroPK parameters are presently gaining acceptance as quantitative measures to value CNS drug candidates regarding their BBB and cellular barrier penetration properties and intracerebral distribution capability. By relating each one of these three neuroPK parameters to interpretable molecular descriptors, we can provide medicinal chemists with

a set of simple rules describing how physicochemical properties of a molecule dictate its fate within the CNS. This information can easily be translated into changes in the chemical structure of a compound in a lead series aimed at improving its overall brain exposure or to change its intra- and extracellular distribution. We chose to work with well-known property descriptors and linear regression modeling techniques for their transparency and ease of interpretation. The number of compounds and the chemical space covered is limited to 40 neurotherapeutic candidates. This is reflected by the poor performance of our models when applied to an external test set with 41 marketed drugs representing a much wider range of chemical structures. In the future, as more experimental data of Kp,uu,brain become available, the scope of such models could turn out to be more generally applicable to any drug-like compound. Predictive models like those available for many physicochemical properties such as logP could then be established, and while that would be useful, we do believe that the intuitive guidelines for medicinal chemists provided here contribute to the design of compounds with improved CNS properties. Present findings support the view that the key to future advances in estimating the unbound drug exposure in the CNS from chemical structure alone lies in a better understanding of the function and substrate specificity of the numerous transporters located at the BBB. In summary, the present article shows that tailoring of drug properties for better brain penetration includes decreasing the polarity and/or hydrogen bonding capacity of a compound. Designing CNS drug candidates directed at intracellular targets may require optimization to overcome the cellular barrier. This could potentially be achieved by increasing the basicity and/or number of hydrogen bond donors of the compounds. Optimizing the molecular structure already in the early stages of the discovery process may decrease failure later on due to poor brain and cellular penetration.

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ASSOCIATED CONTENT

S Supporting Information *

Supporting Tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +46 18 471 4300 or +46 70 425 0485. E-mail: mhu@ farmbio.uu.se. Present Address ○

(For C.M.W.) CVMD Medicinal Chemistry, AstraZeneca R&D, Mölndal, Sweden. Author Contributions

I.L., writing of manuscript, assessment of neuroPK parameters, development of PLS models; V.S., feedback on manuscript; C.M., provision of initial PK parameters, feedback on manuscript; A.V.P., feedback on manuscript; W.D., feedback on manuscript; A.V., feedback on manuscript; D.H., feedback on manuscript; M.H.U., writing of manuscript, feedback on manuscript, proofreading; C.W., writing of manuscript, calculation of molecular descriptors, development of PLS models, feedback on manuscript, proofreading. All authors have given approval to the final version of the manuscript Notes

The authors declare no competing financial interest. K


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ACKNOWLEDGMENTS We express our sincere gratitude to Koen Wuyts and the Early Drug Developability in vivo Group, Janssen Pharmaceutica, for providing the Kp,brain values. We also gratefully acknowledge the excellent assistance of Britt Jansson (Uppsala University) and Lieve Dillen, Dirk Roelant, and Suzy Geerinckx (BA/DMPK, Janssen Pharmaceutica) with the bioanalysis. During the project, I.L. was funded by Janssen Pharmaceutica NV.

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ABBREVIATIONS Abrain, amount of drug in brain tissue; BBB, blood−brain barrier; BCRP, breast cancer resistance-associated protein; BCSFB, blood−CSF barrier; CB, cellular barrier; Cbuffer, concentration of compound in the buffer; CSF, cerebrospinal fluid; Ctot,plasma, total drug concentration in plasma; Ctot,brain, total drug concentration in brain; Cu,brainISF, unbound drug concentration in brain interstitial fluid; Cu,plasma, unbound drug concentration in plasma; ECF, extracellular fluid (same as ISF); ER, efflux ratio; f u,brain, unbound fraction of drug in brain homogenate; f u,hD, unbound fraction of drug in diluted brain homogenate; f u,plasma, unbound fraction of drug in plasma; ICF, intracellular fluid in the brain; ISF, interstitial fluid in the brain; Kp,brain, ratio of total brain to total plasma drug concentrations; Kp,uu,brain, ratio of brain ISF to plasma unbound drug concentrations; Kp,uu,cell, ratio of brain ICF to ISF unbound drug concentrations; LC−MS/MS, liquid chromatography tandem mass spectrometry; NCE, new chemical entity; neuroPK, neuropharmacokinetics; P-gp, P-glycoprotein; RMSECV, root mean square error from cross-validation; RMSEE, root mean square error of estimation; RMSEP, root mean square error of prediction; Vu,brain, volume of distribution of unbound drug in brain (mL·g brain−1)

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