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QSAR model of unbound brain-to-plasma partition coefficient, K : Incorporating P-glycoprotein efflux as a variable. p,uu,brain

Elena Dolgikh, Ian A. Watson, Prashant V. Desai, Geri A. Sawada, Stuart Morton, Timothy M. Jones, and Thomas J. Raub J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00229 • Publication Date (Web): 29 Sep 2016 Downloaded from http://pubs.acs.org on September 30, 2016

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Journal of Chemical Information and Modeling

QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable. Elena Dolgikh†*, Ian A. Watson‡, Prashant V. Desaiº, Geri A. Sawada§, Stuart Morton¶, Timothy M. Jones§, Thomas J Raubº, §. †

Global Scientific Informatics, ‡Advanced Analytics, ºComputational ADME, ¶IT Informatics

and §Drug Disposition, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, 46285, United States. Keywords: In silico unbound brain partition coefficient, Kp,uu,brain, in silico ADME, ADME QSAR, P-glycoprotein efflux, Blood-Brain Barrier, Brain Target Engagement Ratio, bTER, experimental variability.

ABSTRACT We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, Kp,uu,brain based on the in-house dataset of ~1000 compounds. We discuss effects of experimental variability; explore applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux

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information significantly improves the model performance resulting in in R2 of 0.53, RMSE of 0.57, Spearman’s Rho correlation coefficient of 0.73 and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in-vitro, higher throughput dataset that is 4 times larger than the in vivo dataset.

INTRODUCTION In the early stages of neuroscience drug discovery projects, quickly assessing molecules for their potential to achieve brain exposure sufficient for effective target engagement is both a necessity and a challenge. There are many medium- to high-throughput in vitro assays and in silico tools that are routinely used to estimate different properties of prospective leads, such as potency and ADME (absorption, distribution, metabolism and excretion), but evaluating a compound’s ability to penetrate the blood-brain barrier (BBB) remains a rather costly and time–consuming endeavor involving non-trivial in vivo measurements. One of the reasons is the physical and biochemical complexity of the barrier itself. Formed primarily by the endothelial cells of brain capillaries with highly restrictive tight junctions and high expression of efflux transporters, most notably P-glycoprotein (P-gp), it is an intricate and delicate system that has been difficult to reproduce in its entirety in vitro.1 Another reason is the multiplicity of factors influencing the balance between plasma and brain distribution of a given compound, including passive permeability, active efflux, and unbound plasma and brain clearances.2 Thus, while in silico and in vitro methods have been developed to model various separate components that influence brain penetration, as reviewed recently by Summerfield et al.3, the main approach for estimating overall brain penetration potential, at least for now, remain the non-trivial in vivo measurements.


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There has been a slow, but growing, acceptance that brain target engagement (bTER) is dependent upon the unbound (and not the total) brain concentration4-9; however, measuring this directly by microdialysis is labor intensive, time consuming, and not always amenable to compounds of interest.10 Effective unbound concentration of a compound in the brain, i.e., concentration necessary to elicit necessary pharmacodynamic effect, is a function of many factors, not the least of which are compound’s potency and sufficient unbound plasma concentration, dictated, in turn, by clearance and dose. Aside from these two factors however, one of the more popular views in the field is that the most important parameter for evaluating CNS exposure potential of a compound is extent, not rate, represented by the unbound brain-toplasma concentration partition coefficient of Kp,uu,brain.2, 4, 11-12 Physiologically, the parameter represents a steady-state balance between clearance rates into and out of the brain and depends on several processes4: , =

(1)

where PS is passive permeability rate, Cluptake is active uptake by transporters, Clefflux is active efflux by transporters and Clbulk and Clmet are clearances due to brain interstitial fluid bulk flow and brain metabolism, respectively. The two latter terms are often assumed to be insignificant by comparison to active and passive uptake and efflux terms leading to a categorical interpretation of Kp,uu,brain where value of ~1 indicates solely passive membrane transport across the blood-brain barrier, a ratio of more than 1 points to active uptake, and a value of less than 1 indicates presence of active efflux. In practice, Kp,uu,brain is often calculated from the expression below where individual components are derived from a combination of in vitro and in vivo measurements:


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, =

,

,

=

,

,

∙

,

,

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(2)

where Ctotal,brain, Cu,brain, Ctotal,plasma and Cu,plasma are total and unbound brain and plasma concentrations, and fu,brain and fu,plasma are fractions unbound in brain and plasma, respectively. Relatively large datasets can be acquired for measured Ctotal,plasma and Ctotal,brain in rodents and for fu,plasma and fu,brain in various species, and there have been a few QSAR models of Kp,uu,brain published by AstraZeneca group13-15 using such datasets. Thus, Friden et al.13 used a training set comprised of Kp,uu,brain for 43 drugs measured directly by microdialysis in rat brain to build a model using multivariate PLS method. Later, Chen and co-workers14 extended the dataset to 173 compounds including proprietary molecules and investigated two machine learning algorithms, support vector machine (SVM) and random forest (RF), as well as direct and indirect modelling approaches to predict Kp,uu,brain. Their best model, a consensus model combining SVM and RF models, showed 0.85 accuracy on an independent test set and also correctly predicted 89% of 111 marketed drugs with CNS indication as having “high” exposure. More recently, AZ group further tested the Chen et al. model with additional data15 and explored a new set of descriptors to improve performance for the new test set. A smaller dataset of 40 compounds mostly from the Janssen neuroscience portfolio was recently used to develop and validate another Kp,uu,brain model using PLS approach by Loryan et al.16 The model performed very well on the given test set, but showed poor performance when applied to the more diverse Friden et al. dataset prompting authors to suggest that local Kp,uu,brain models built on specific datasets might have an advantage over global QSAR models. In this paper, we describe development and validation of an in-house QSAR Kp,uu,brain model based on a large, internal dataset of ~ 1000 compounds. In this model, we implement a model-


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within-a-model approach by incorporating prediction of a key in vitro assay - P-gp substrate assay - as an additional descriptor. P-glycoprotein is the predominant transmembrane multidrug efflux pump that is expressed at the apical membrane of BBB endothelium.17 It possesses a large and flexible binding cavity18 that enables it to bind and extrude molecules of a variety of physical-chemical characteristics. Impact of this efflux transporter on brain exposure of drugs that are P-gp substrates had been demonstrated in many studies. Thus, Mahar Doan et al. showed that marketed CNS-active drugs have a 3-fold lower incidence of P-gp-mediated efflux than nonCNS drugs.19 Wang et al.20 concluded that most of the molecules in a dataset enriched in CNS actives showed no significant efflux. A more thorough quantitative analysis by Doran et al.21 of 34 CNS drugs demonstrated for the majority of them only a slight increase in Kp,brain in P-gp knockout mice versus wild-type mice indicating their penetration into the brain is not significantly hindered by P-gp transport. Herein, we discuss performance of our Kp,uu,brain model in the context of experimental variability, assess its applicability in terms of both continuous and categorical prediction, evaluate effect of including P-gp efflux prediction as a variable, and discuss its performance on both internal, independent test set and two public datasets of diverse compounds. MATERIALS AND METHODS In vivo Mouse Brain Uptake Assay (MBUA). The MBUA was originally developed at Pharmacia Corporation, Kalamazoo, USA and has been routinely used at Eli Lilly since 2004 to assess the ability of compounds to cross BBB and distribute into the brain.22 The method involves administration of a single, intravenous dose of 2.17 µmol/kg of a solute dosed via tail vein injection, followed by sampling of blood and brain at 5 and 60 minutes after administration.23 Three animals are used for each time point for each compound


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and the reported values of the plasma and brain concentrations as well as brain-to-plasma ratios are calculated as averages of the three measurements. In vivo Infusion Male CF-1 mice at 6-10 weeks of age (~25 g body weight) were instrumented with a jugular vein catheter for dosing. Each compound was administered as a 24-hour intravenous infusion via the jugular vein catheter at an infusion rate of 4 mL/kg/h using a harness and tether system that permitted free movement of the animal in its cage during the dosing period. Compound was dosed as a solution in either 20% (w/v) Captisol, pH 2 or 8, or 10% (v/v) DMSO and 90% (v/v) propylene glycol. A 20-µL aliquot of whole blood was collected via tail vein using K2EDTA-coated capillary tubes at 1, 4, 8, 12, 16, and 20 h after the start of infusion and compound levels quantified using dried blood spot (DBS) analysis.24 At 24 hours, terminal blood was collected into K2EDTA blood tubes via intracardiac puncture following anesthetization with CO2 and plasma was also generated. Brain was removed and each hemisphere was weighed, transferred to separate polypropylene tubes, and snap-frozen in liquid nitrogen prior to storage at -70°C until analysis. All samples were analyzed using LC with MS/MS detection. In vitro Apparent Passive Permeability and P-glycoprotein Efflux Mechanistic passive permeability measurements were performed in MDR1-transfected MDCK cells plated on Transwell 0.4 µM pore polycarbonate filters. Detailed account of the assay has been published previously.25 Briefly, apical-to-basolateral (AB) and basolateral-to-apical (BA) transport of 5 µM test compound was assessed in the presence and absence of P-gp specific inhibitor, 2.5 µM LSN335984 (Eli Lilly and Company, Indianapolis, IN). Apparent passive permeability (Papp) was calculated as the average of AB and BA rates in the presence of the inhibitor and net efflux ratio (NER) was calculated as the ratio of the two rates (BA/AB) in the absence of the inhibitor over


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the ratio of the two rates in the presence of the inhibitor. Based on the Papp values, compounds with < 2 x 10-6 cm/s rates are categorized as low permeable, those between 2 and 15 x 10-6 cm/s as moderately permeable and those > 15 x 10-6 cm/s as highly permeable. Likewise, with respect to efflux, compounds with NER < 3 are classified as nonsubstrates of P-gp, whereas those with NER > 3 are assigned P-gp substrate class. In vitro Brain and Plasma Protein Fraction Unbound All fraction unbound (fu) measurements were performed using an equilibrium dialysis method, details of which have been reported previously.26 In brief, 1 µM test compound was incubated for 4.5 h with brain homogenate or plasma in the donor compartment of the dialysis chamber plate. Following that, all samples were analyzed using LC with MS/MS detection and fu values calculated as the ratio of the receiver chamber concentration and the donor chamber concentration. Model Datasets Before building models, each dataset was curated and their experimental variability evaluated to provide baseline for the optimum performance that could be expected of each model. For the Kp,brain dataset, molecules with measurements at the detection limit of the assay or high intra-assay variability were removed, including those with plasma concentration at 5 minutes of < 100 nM, brain concentration at 5 minutes of < 20 nM and Kp,brain values < 0.01 or with %CV missing or > 80%. Remaining Kp,brain values were converted into log10 scale and RMSE was calculated for measurements repeated twice or more using least square fit in JMP statistical discovery software.27 The RMSE was then converted to minimum significant ratio (MSR)28-29 using equation 3, providing estimate of the fold-variability for experimental measurements. = 10"∙√"∙$%& (3)


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For compounds with repeated measurements, when the ratio of the largest to the smallest measurement was outside of MSR, they were excluded from the model building, whereas for the rest, the values were averaged. Table 1 summarizes size, range of values and experimental variability of each dataset. For the fu datasets, the following criteria were used to accept compounds for the models: recovery between 70-130%, %CV of triplicate measurements < 50%, initial concentration in matrix (brain homogenate or plasma) prior to dialysis = 1.0 ± 0.3 µM, and dialysis plate positive control (imipramine) values within ± 3-fold of historical mean. Compounds with fu values outside of the detection limits of the assay (reported with qualifiers) were also excluded. Based on our internal analysis indicating no major difference in fu values of between different species, as well as supported by conclusions by Di et al.30 and Summerfield et al.31 data from multiple species were combined for the model building with the exception where the fu values across multiple species were outside of the assay MSR, calculated using equations above. Those values were excluded. Table 1. Kp,brain, fu,plasma, and fu,brain dataset description. N

Range

RMSE (log scale)

MSR

Kp,brain

1381

22-0.01

0.20

3.9

fu,plasma

7365

0.001-1

0.13

2.4

fu,brain

3816

0.001-1

0.08

1.7

Finally, experimental variability of Kp,uu,brain value was calculated using the equation 4 below resulting in final Kp,uu,brain MSR of 5-fold. "

"

"

'(, ) = *+'( ) + '(-,./012 ) + '(-, 0340 ) 5

(4)


8

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Details of P-gp efflux dataset curation have previously been reported by Desai et al.25 Compounds were classified as substrates or non-substrates based on the net efflux ratio cut-off of 3. Compounds likely to be false negative were excluded. The resultant training set included 3424 compounds with ~50/50 distribution of substrates and nonsubstrates. QSAR Details Each of the models described in this paper has been built and validated using similar strategies.. Because Kp,brain model includes the results of a separate QSAR model for Pgp, these particular models were developed in sequence – first the P-gp model was calibrated and built using available P-gp data. Then predictions from the resulting P-gp model were used in building the final Kp,brain model. All of the models are Support Vector Fingerprint32 models, built using SVMlight.33 For input, SVMlight requires class memberships, or continuous activity values, together with a set of features and associated counts. For our models, the features employed include sets of molecular fingerprints used for molecular similarity determinations such as locally developed linear path based fingerprints, atom pair fingerprints, ring substitution pattern fingerprints, fingerprints derived from molecular properties such as clogP (BioByte Corp., Claremont, CA) and PSA.34 However, in principle, any well behaved set of molecular descriptors/fingerprints could be used for SVM model building. In addition, for Kp,brain model, Random Forest approach35, using a different set of molecular features, were compared with the SVM Fingerprint models. Overall, over 1800 2D descriptors were considered, including common physiochemical properties like logP and molecular weight, together with descriptors based on molecular connectivity indices, pharmacaphoric features, hydrophobicity related features, etc. Minimally varying and highly correlated descriptors were eliminated before further analysis. In our experience, Random Forest can deal poorly with the


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very sparse fingerprint data used by the SVM models, so different, and a greater number of predictors were used. The precise means by which we incorporated the P-gp model into the Kp,brain model is as follows. Although the P-gp model is a classification model, SVMlight outputs a class score together with the class assignment. That class score will generally be in the range -1 to 1. That range is subdivided into 10, equally sized, buckets, which enables any score in the range -1 to 1 to be translated into a bucket number between 1 and 10. Because SVMlight requires feature number and count data, we used the bucket value as the count for the P-gp feature(s) in the input to SVMlight. Reasonable numbers of P-gp features to incorporate into the Kp,brain model were evaluated during model calibration, and in the final model we used 10 replicates. This is also the means by which other “models” (clogP, PSA, etc) are incorporated into SVM Fingerprint models. This scheme has the very desirable effect of maintaining the ordinal nature of the input data, whereas if different buckets were assigned to different bits, values not in the same bucket would simply be different – with no notion of how different. Given a wide range of predictor variables (kinds of fingerprints), a calibration process was used to identify the best set of fingerprints to use in each model. Between 30 and 50 stratified random samples, divided into 60/40 train/test splits were used to determine which sets of fingerprints most often produced the most accurate models. Since these molecules are all registered in the Lilly structure database, and therefore given an accession time, we also created a separate 80%/20% train/test chronological split of the data where the test set was left out of the calibration process. Such chronological splits have been observed to present particular challenges to QSAR models – presumably because the test set can contain molecules that are significantly different from molecules in the training set – new chemical series being explored for example.


10

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Different measures were used in determining the best regression or classification model. In the case of regression models, the model that provided the best combination of high R2 and low RMSE was selected for the final model. For the P-gp classification model, the model with the highest average sum of positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity was selected. These are standard parameters used to evaluate performance of classification QSAR models and are defined below by equations 5 through 8, where TP, FP, TN, and FN are true positive, false positive, true negative and false negative, respectively. 667 = 86 :6

86

(5)

;67 = 8; :;

8;

(6) 86

?@A@B@AC = 86:;

(7)

8;

0.3 in vivo and 32% of compounds fell within quadrant IV with high P-gp efflux in vitro (NER > 3) corresponding to low Kp,uu,brain of < 0.2. Out of the remaining 27% of compounds, 18% fell within quadrant III exhibiting low efflux ratio in vitro yet low Kp,uu of < 0.2. Closer examination of this subset revealed that 50% of the compounds were either acidic or zwitterionic in nature at neutral pH consistent with known poor brain permeability of compounds with low acidic pKa.4546

A small part of the dataset showed no correlation between the two parameters suggesting

possible false negatives in vitro due to P-gp inhibition, possible P-gp species difference47-48, or involvement of alternative endogenous active transporters. Given this overall significant correlation between the P-gp efflux and in vivo Kp,uu,brain, we decided to incorporate our internal QSAR P-glycoprotein model prediction as an additional fingerprint in the Kp,uu,brain model following model-within-a-model approach.49 One of the immediate advantages of such approach being larger chemical space coverage of the resulting compounded model since P-gp model is trained on ~4000 molecules vs. ~ 1000 available for the Kp,brain model alone. Internal Datasets. Performances of each continuous response model on the test set of 91 compounds are shown in Figure 3 and Table 2. One can see a clear improvement in all the statistical metrics - R2, Rho and RMSE - upon incorporation of the P-gp fingerprint (P-gp fp) into the model. Percent of compounds predicted within the experimental variability range of their measured Kp,uu,brain value also improves upon incorporation of the P-gp assay information.


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This performance is very similar to the prospective validation of the Chen et al.14 model reported by Varadharajan et al.15 where R2 of 0.53 and RMSE of 0.58 were obtained for a temporal, independent, validation set. However, whereas their Kp,brain model was a combination of 3 separate Kp,brain models, in our case we only used one to achieve the same performance. Varadharajan et al.15 also reported a new set of consensus models using a new set of descriptors resulting in R2 of 0.63-0.65 and RMSE of 0.45-0.46, however the model selection process for the best consensus model included the temporal test set thus making comparison of the prospective performances of the models challenging. Finally, a Kp,uu,brain model published by Loryan et al.16 was reported to perform with R2 of 0.82 and RMSE of 0.3. The model, however, was built on a much small training set of 30 compounds and validated on 10. Its poor performance on the more diverse Friden et al.13 dataset prompted the authors to suggest that local Kp,uu,brain models might be a better approach than global QSAR models. Considering the complexity and variability of the experimental data used to build the model, overall performance of the continuous response model is reasonable and has a potential to be used to rank-order compounds. Yet, an alternative approach can be to utilize the prediction in a qualitative manner to separate desirable compounds, i.e., those with higher potential for brain penetration from undesirable ones, i.e., those with low potential for brain penetration.


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Figure 3. Predicted versus measured values for the validation set of 91 compounds for each individual model - A) fu,plasma, B) fu,brain, and C) Kp,brain (with P-gp variable included), as well as for the composite model, Kp,uu,brain (D). Solid line is line of unity, whereas dotted and dashed lines represent 3-fold and 5-fold margins, respectively.

Table 2. Continuous Response Model Performance


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Model

Training Set

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Test Set

N

N

R2

Rho

RMSE

within 3-fold

within 5-fold

fu,brain

2292

91

0.64

0.78

0.42

78%

93%

fu,plasma

3774

91

0.7

0.84

0.38

84%

95%

Kp (w/o P-gp fp)

1030

91

0.44

0.67

0.64

63%

67%

Kp (w/ P-gp fp)

1030

91

0.55

0.75

0.56

70%

75%

Kp,uu (w/o P-gp fp)

1030

91

0.39

0.63

0.63

57%

68%

Kp,uu (w P-gp fp)

1030

91

0.53

0.73

0.57

63%

78%

To evaluate the model from this perspective, we post-processed continuous response prediction into 2 classes taking into account previously estimated intra-assay (~3-fold) and inter-assay (~5fold) variability of the experimental data. Thus Kp,uu,brain values > 0.3 (i.e., within 3-fold of Kp,uu = 1) were assigned to “high” Kp,uu class, whereas values less than 0.2 were assigned to “low” Kp,uu class. Compounds with predicted values between 0.2 and 0.3 were classified as indeterminate and were excluded from the class-based analysis. Again, we saw an improvement in overall accuracy when P-glycoprotein prediction was included as a descriptor resulting in an overall accuracy of 80% – slightly better than 76% reported for temporal validation set of Chen et al14 model by Varadharajan et al.15 - and kappa and Matthews coefficient (MCC) of 0.60, as shown in Table 3. Examining results at the individual compounds level, we saw, for example, that 6 compounds - all known P-gp substrates and with low measured Kp,uu,brain - were previously incorrectly predicted to have high Kp,uu,brain of > 0.3, but, upon incorporation of P-gp fingerprint, were correctly assigned to the low Kp,uu,brain class. Table 3. Classification Model Performance N/% coverage Kp,uu w/o P- 79/87% gp fp

PPV

NPV Sensitivity Specificity Accuracy

Kappa

MCC

0.78

0.73

0.51

0.51

0.80

0.71

0.76


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Kp,uu w P- 81/89% gp fp

0.83

0.77

0.80

0.81

0.80

0.60

0.60

External Datasets. Wagger dataset. While chronological, prospective validation of our model using in-house compounds is most relevant to the ongoing Eli Lilly projects, we also decided to see how well the model performed using an external dataset of diverse compounds. Thus, we applied Kp,uu,brain prediction to the Wager et al.50 dataset of marketed CNS drugs after excluding 11 compounds that were part of our model training set (Table S1). Out of 108 compounds, a majority or 78 compounds (72%) were predicted to have Kp,uu,brain > 0.3, 13 compounds (12%) fell in the indeterminate 0.2-0.3 range, and 17 compounds (16%) were predicted to have Kp,uu,brain < 0.2. All of the compounds predicted to have Kp,uu,brain > 0.3, were also predicted to be either ‘nonsubstrates’ or ‘indeterminate’ by the P-gp substrate model, Figure 4. On the other hand, out of the 17 compounds predicted to have low Kp,uu,brain, 5 were predicted to also be P-gp substrates and a subsequent search of the literature revealed that cabergoline51, bromocriptine52, eletriptan6, and metoclopramide21 are indeed substrates of the efflux transporter. Another two compounds, gabapentin13 and topiramate13 have been previously reported to have low Kp,uu,brain of 0.14 and 0.33, respectively. The latter result underlines an important point that for a compound to be a successful CNS candidate, Kp,uu,brain ~1 is desirable but not absolutely necessary if, Pglycoprotein efflux notwithstanding, the overall unbound brain exposure is above the protein target IC50, i.e. has a bTER > 1.


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Figure 4. Categorical Kp,uu,brain prediction for Wager et al.50 dataset colored by P-gp substrate prediction: green – nonsubstrates, pink – substrates, and yellow – indeterminate. Friden dataset. We have also included Friden et al. dataset13 as an additional test set (Table S2), excluding three compounds that were part of our Kp,brain training set. Results of the continuous response and categorical prediction are shown in Figure 5. Although this data set was obtained in rats whereas our model was trained on mouse data, overall performance of the Kp,uu,brain continuous model was reasonable with R2 of 0.44 and 51% and 68% of compounds predicted within 3- and 5-fold, respectively. Excluding indeterminate values with Kp,uu,brain between 0.2 and 0.3, categorical prediction performed even better with the overall accuracy of 82% and kappa coefficient of 0.64.


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Figure 5. Kp,uu,brain prediction for Friden et al. dataset. A) Predicted vs. measured Kp,uu,brain values. Solid line is line of unity, dotted lines and dashed lines are 3- and 5-fold margins. B) Concordance between predicted and measured Kp,uu categories. CONCLUSIONS In many drug discovery projects, quickly estimating compounds’ brain exposure potential – whether to subsequently enhance it or to minimize it - is an important yet difficult task. Here, we demonstrate the reported Kp,uu,brain model provides good qualitative prediction of low vs. high brain penetration potential for both internal and external independent test sets. Incorporation of the P-gp assay data as a fingerprint allows for indirect coverage of a wider chemical space than that afforded by the in vivo dataset alone. P-gp fingerprint takes place of the more simple polar surface area descriptor in the final model and improves model performance as evaluated by any of the standard statistical measures. As such, this Kp,uu,brain model is a useful in silico tool that can be used to rank-order compounds to focus on more favorable molecules, or to qualitatively distinguish more promising scaffolds from the less promising ones in the early stages of hit-tolead optimization process. When viewed together with the P-gp substrate model, it can also serve as a starting point for further, more mechanistic studies of SAR related to the transporter,


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including evaluation of hydrogen bond acceptor and donor strengths44 or transporter-ligand interactions.53 ASSOCIATED CONTENT Supporting Information. Supporting Information Available: Kp,uu,brain prediction results for Wagger et al. and Friden et al. datasets. This material is available free of charge via the Internet at: http://pubs.acs.org.

ACKNOWLEDGEMENTS The authors would like to thank Dr. Suntara Cahya for his valuable insight and suggestions. AUTHOR INFORMATION Corresponding Author * Elena Dolgikh Email: [email protected]. ABBREVIATIONS P-gp, P-glycoprotein; ADME, Absorption, Distribution, Metabolism and Excretion; BBB, Blood-brain Barrier; NER, Net Efflux Ratio; MSR, Minimum Significant Ratio; SVM, support vector machine; RF, Random Forest REFERENCES 1. Pardridge, W. M., Blood-Brain Barrier Biology and Methodology. J Neurovirol 1999, 5 (6), 556-569. 2. Liu, X.; Chen, C.; Smith, B. J., Progress in Brain Penetration Evaluation in Drug Discovery and Development. J Pharmacol Exp Ther 2008, 325 (2), 349-356. 3. Summerfield, S. G.; Dong, K. C., In Vitro, in Vivo and in Silico Models of Drug Distribution into the Brain. J Pharmacokinet Pharmacodyn 2013, 40 (3), 301-314. 4. Liu, X.; Chen, C., Strategies to Optimize Brain Penetration in Drug Discovery. Curr Opin Drug Discov Devel 2005, 8 (4), 505-512.


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