Structure–pharmacokinetic relationship of in vivo rat biliary excretion

BIOPHARMACEUTICS & DRUG DISPOSITIONBiopharm. Drug Dispos. 31: 82–90 (2010)

Published online 11 November 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/bdd.692

Structure–Pharmacokinetic Relationship of In Vivo RatBiliary Excretion

Yue Chen, Kimberly Cameron, Angel Guzman-Perez, David Perry, Dong Li, and Hua Gao�

Pfizer Global Research and Development, Groton Laboratories, 558 Eastern Point Road, Groton, CT 06340, USA

ABSTRACT: Accurately measuring and predicting biliary excretion would be extremely valuablein evaluating the contribution of biliary excretion to the total systemic clearance, understandingpotential mechanisms of hepatobiliary toxicity as well as potentials for drug–drug interactions indrug discovery. In this study, in vivo rat biliary excretion of drug-like molecules was measuredusing bile duct cannulated rats. Literature biliary excretion data with similar experimentalconditions were collected. A predictive quantitative structure–pharmacokinetic relationship (QSPR)model was developed using genetic algorithm guided principal component regression analysis and2D molecular descriptors. In the derived model, hydrophobicity expressed with calculateddistribution coefficients (cLogD) is the most important molecular property correlating biliaryexcretion. The derived model has been validated using literature data, and should be useful inestimating biliary excretion potentials of molecules in drug discovery. Copyright r 2009 John Wiley& Sons, Ltd.

Key words: biliary excretion; QSAR (quantitative structure–activity relationship); QSPR (quanti-tative structure–pharmacokinetic relationship); in silico modeling; ADMET (absorp-tion; disposition; excretion and toxicity)

Introduction

Clearance of drug molecules mainly occurs in theliver (hepatic clearance and biliary excretion) andkidneys (renal excretion). In general, drug mole-cules are capable of going through three majorclearance pathways: (1) clearance by P450s andother metabolic enzymes in the liver; (2) excretionof compounds to the bile; (3) glomerular filtrationand renal tubular secretion of compounds by thekidneys. Biliary excretion is a very important hepa-tic elimination pathway for many drug moleculesand xenobiotics and has significant impacts onexposure, pharmacological and toxicological ef-

fects of drug molecules. Accurately measuringand predicting biliary excretion would be ex-tremely valuable in evaluating the contributionof biliary excretion to the total systemic clear-ance, understanding potential mechanisms ofhepatobiliary toxicity as well as potentials fordrug–drug interactions.

Important physicochemical properties of com-pounds affecting the extent of biliary excretioninclude molecular size, charge, polarity/lipophi-licity. Small polar molecules are mainly elimi-nated through renal filtration. Polar drugmolecules with intermediate and high molecularweights tend to have higher biliary excretionthan renal elimination. There have been efforts totry to establish a correlation between the mole-cular weight of a compound and the extent of itsbiliary excretion in animals [1–3].

*Correspondence to: Pfizer Global Research and Develop-ment, MS 8220-3065, Pfizer Inc., Groton, CT 06340, USA.

E-mail: [email protected]

Received 12 March 2009Revised 30 September 2009

Accepted 5 October 2009Copyright r 2009 John Wiley & Sons, Ltd.

There is limited human information availableregarding the biliary excretion of drug moleculesdue to the difficulty in obtaining human in vivodata. In recent years, in vitro methods have beendeveloped using hepatocytes to estimate thebiliary excretion potential of molecules [4].Unfortunately, the results obtained with in vitromodels are difficult to compare with human invivo data. This is primarily due to the fact thatdata generated in vitro are used to determinevalues for the intrinsic biliary clearance of a drug,which is difficult to scale to the in vivo percentageof the total dose excreted into the bile. Analternative approach for estimating in vivo biliaryexcretion is to use bile duct cannulated rats.Despite the species differences and potentialdifficulties in extrapolating biliary excretion fromone species to another, this method is still usedroutinely as a screening tool in early drugdiscovery. However, due to the complexitiesand cost of the procedure required to measurerat in vivo biliary excretion, it is far less amenablefor generating large amounts of data to derive astructure–pharmacokinetic relationship, therebylimiting the structure–activity relationship (SAR)information available to support drug designefforts. In silico models, however, have proven tobe very useful tools in predicting biologicalactivities, important biopharmaceutical proper-ties and ADMET (absorption, metabolism, elim-ination and toxicity) parameters [5,6]. The aim ofthis study was to investigate the structure–phar-macokinetic relationship of rat in vivo biliaryexcretion of drug-like molecules and to derive apredictive quantitative structure–pharmacoki-netic relationship (QSPR) model for estimatingbiliary excretion in drug discovery.

Methods

In vivo biliary excretion

The study was conducted in accordance with theInstitute of Laboratory Animal Resources andwas approved by the Institutional Animal Careand Use Committee. Eight-week-old jugular veinand bile duct cannulated Sprague-Dawley rats(220–250 g) were obtained from Charles RiverBreeding Laboratories, Inc., Wilmington, MA,

USA. An i.v. bolus dose (1 mg/kg) was adminis-tered via a jugular vein cannula. A bile samplewas collected from the bile duct cannula for 24 h.All samples were quantified using a PE-SciexAPI-3000 (Turbo Ionspray source, 5001C) quad-ruple mass spectrometer. An aliquot of bilesample (10 ml) was transferred to a HPLC vial,and was precipitated with 20 volumes of metha-nol containing an appropriate internal standard.The sample was vortex-mixed and centrifuged.The supernatant was analysed using HPLC/MS/MS. An aliquot of 10 ml of the sample was injectedonto a 2.0� 30 mm, 4 mm Synergi Max-RP columnmaintained at room temperature and eluted witha linear gradient consisting of ammonium acetatebuffer (pH 6.8, 10 mM), methanol and acetonitrile.The percentage of biliary excretion was calcu-lated based on the intravenous dose, drugconcentration in the bile and the volume of thebile collected over 24 h. Equation (1) was used tocalculate the cumulative biliary excretion (CEb)as a percentage of the total dose administered.

CEb ¼Cb � Vb � 100

Dose � Body weightð1Þ

where CEb is the cumulative excretion as thepercentage of total parent compound excretedinto the bile over 24 h, Cb is the compoundconcentration in the bile, Vb is the total bilevolume in 24 h and dose is expressed as mg/kg.

Literature in vivo biliary excretion data

In obtaining literature data, the study focused onin vivo rat bililary excretion data which wereobtained using similar experimental proceduresand conditions. Sixteen literature data points[1–3] were obtained and included in the modeldevelopment process. Other literature data [7–28]were collected and used to further validate thedeveloped QSPR model.

A total of 56 biliary excretion data points for 40proprietary compounds and 16 literature com-pounds was used in deriving a QSPR model.A logit transformation of all the data used fordeveloping the model was performed accordingto Equation (2), which will enable predictions ofa derived model within a boundary of 0 to 100%.So, A is the natural logarithm of the ratio of thepercentages of dose excreted into the bile over

IN SILICO BILIARY EXCRETION MODEL 83

Copyright r 2009 John Wiley & Sons, Ltd. Biopharm. Drug Dispos. 31: 82–90 (2010)DOI: 10.1002/bdd

that not excreted into the bile.

A ¼ LogCEb

100� CEb

� �ð2Þ

The whole set of 56 compounds used in devel-oping the QSPR model was randomly dividedinto two subsets. A subset of 46 compounds wasselected as a training set to develop a model, anda subset of 10 compounds was used as avalidation set to validate the developed model.Other literature data listed in Tables 1 and 2 wereused as external validation sets to further test themodel performance.

This study used two-dimensional (2D) mole-cular descriptors including cLogP (BioByteCorp.), cLogD and pKa calculated using ACDsoftware version 9.03 (ACD/Labs), Abrahamdescriptors calculated using group contributionapproach [29], Moriguchi descriptors [30] and asubset of MOE (Molecular Operating Environ-ment) descriptors [31]. Explicit 3D moleculardescriptors were not used to avoid bias of theanalysis due to predicted conformational effectsand speed of calculation for fast prediction.

A genetic algorithm (GA)-guided principal com-ponent regression (PCR) analysis was used todevelop the QSPR model [32].

Results

QSPR analysis

The cumulative biliary excretion of the parentcompound of the 56 compounds ranged from0.003% to 84% of the total dose administered. Thedistribution of CEb is plotted in Figure 1. Theuniqueness of this data set is that nearly 30% ofCEb data is below 0.1%. It is uncommon to findbiliary excretion data in this range from theliterature. The wide dynamic range of theexperimental data provided an excellent oppor-tunity for quantitative structure–pharmacoki-netic relationship analysis. There are 20 acidiccompounds with a calculated pKa ranging from1.48 to 4.78 and 36 neutral compounds. Inaddition to the wide dynamic range of CEb data,this set of compounds also covers a broad rangeof physicochemical properties such as cLogP(�3.00–9.05), cLogD (�4.82–7.82) and molecularweight (320–708).

A QSPR model correlating in vivo rat cumula-tive biliary excretion with molecular propertieshas been derived. Equation (3) is the estimated

0

2

4

6

8

10

12

14

16

18

20

4

16

11

4

14

7

Binned CEb

No.

of c

ompo

unds

x ≤

0.01

0.01

< x

≤ 0

.1

0.1

< x

≤ 1

1 <

x ≤

10

10 <

x ≤

50

50 <

x

Figure 1. Distribution of cumulative biliary excretion dataanalysed

Table 1. Reported and calculated cumulative biliary excretionof literature compounds.

Compound Cumulative biliaryexcretion (% total

dose)

Totaldose(mg/kg)

Reference

Reported Predicted

Amphetamine 0.9 0.02 7.5 8Flurbiprofen o0.5 0.06 20 12Flurbiprofen-glucuronide

8.3 2.50 20 12

Fluvastatin 13–26 8.00 0.82 27Glycyrrhizin 80.6 99.99 100 9HSR-903 6.02 6.96 5 7Itraconazole 0.23 13.66 30 23J-104132 88 88.23 1 22Methamphetamine 0.5 0.02 7.5 8Methotrexate 70 99.53 10 11Piperaquine o1 0.32 13 26Salicylate �0 0.02 0.5 10Saquinavir 2.5 1.01 5 25Sulfamethoxazole 1.2 0.01 5 19Taurocholate 100 93.67 4 10TBuMA 33.3 14.89 1.32 21TEMA 0.71 0.07 0.77 21Temazepam 0.5 0.01 2 16Temocaprilat 78 95.59 1 24Terfenadine �0 2.39 10 13Theophylline 2.29 0.17 5 18TR-14035 29.4 64.76 3 14Valproic acid 1.8 0.01 150 20

Y. CHEN ET AL.84


linear equation with seven molecular descriptors.

A ¼ 1:79CX � 1:69X

bH2 11:20 log L

� 2:91pH2 � 1:75cLogD� 0:04PEOE VSA 0

� 0:09PEOE VSA 3� 18:00

n ¼46; r2 ¼ 0:85; q2 ¼ 0:79; RMSE ¼ 1:36

ð3Þ

In this equation, CX is the Moriguchi descriptorrepresenting the summation of numbers of carbonand halogen atoms weighted by C:1.0, F:0.5, Cl:1.0,Br:1.5, and I:2.0 in a molecule, �bH

2 is the overall orsummation of solute hydrogen bond basicity, logLis the solute gas–hexadecane partition coefficient,pH

2 is a combined dipolarity/polarizability descrip-tor, clogD is the distribution coefficient at pH 7.4,and PEOE_VSA-0 and PEOE_VSA-3 [31] are thedescriptors representing the summation of Van derWaals surface area (VSA) with partial atomiccharges in a range of �0.05 to 0.00 and a range of�0.20 to�0.15, respectively, n is the number of datapoints in the training set, r is the correlationcoefficient, q is the cross-validated correlationcoefficient with leave-one-out method, and RMSEis the root mean square error. The relativeimportance of molecular descriptors used in thismodel is: clogD: 1.00, logL: 0.81, pH

2 : 0.44, CX: 0.41,�bH

2 : 0.23, PEOE_VSA-0: 0.15, and PEOE_VSA-3:0.08. clogD is the most important moleculardescriptor. The observed and calculated cumulativebiliary excretion data for the training and test sets ofcompounds are plotted in Figure 2. Eighty-fivepercent (r2 5 0.85) of the variance in the training setcan be accounted for with Equation (3). For thetraining set of compounds, 74% of compoundswere predicted within 3 folds of experimental CEb,85% of compounds within 5 folds. A QSAR analysisusing a partial least square (PLS) regression methodwith the same set of molecular descriptors yieldedan equivalent model.

Validation of the QSPR model

To evaluate the predictive ability of the derivedQSPR model, the cumulative biliary excretion datafor the validation set of 10 compounds not includedin the training set were calculated using Equation (3)and are plotted in Figure 2. The experimental andpredicted CEb for the 10 compounds has an r2 of0.84. Sixty percent of the compounds were

predicted within 3 folds of experimental CEb,and 80% of the compounds within 5 folds. Tofurther test the predictive power of the derivedmodel, the biliary excretion for a set of 23 drugsor drug-like molecules from the literature waspredicted using this model, the reported and thecalculated cumulative biliary excretion data aresummarized in Table 1. For compounds with adefinitive CEb value, the reported vs calculatedCEb data with an r2 of 0.91 are plotted in Figure 3.In general, the model both correctly predicted thebiliary excretion for all these compounds withthe exception of itraconazole and binned thecompounds into high and low biliary excretion.The over-estimation of the biliary excretion foritraconazole could be due to a couple of factors:First, itraconazole has been reported to be aninhibitor of P-glycoprotein (P-gp) [33] and breastcancer resistance protein (BCRP) [34], althoughitraconazole itself is not a substrate of P-gp [35].Whether itraconazole can inhibit other effluxtransporters involved in biliary excretion and thecontribution to the low biliary excretion ofitraconazole itself is not clear. Second, thetransport of drug molecules from the liver tothe bile by transporters has been reported to be asaturable process [9]. The reported i.v. dose foritraconazole for the biliary excretion studywas 30 mg/kg, while the dose in our study was

0.001

0.01

0.1

1

10

100

0.001

experimental CEb

pre

dic

ted

CE

b

0.01 0.1 1 10 100

Figure 2. Plot of experimental vs calculated CEb data usingEquation (3) (~ training set of compounds; m test set ofcompounds)



1 mg/kg. It was indicated in the literature thatitraconazole had a dose-dependent pharmacoki-netics [23]. For all the literature compoundscollected in this study, only two other compounds,glycyrrhizin [9] and valproic acid [20], were studiedusing higher doses (see Table 1). In the cases ofglycyrrhizin and valproic acid, dose-dependentpharmacokinetics were not indicated in the originalreports. Considering that these literature com-pounds are not in the chemistry space of thetraining set of compounds and the experimentaldata came from different laboratories, the perfor-mance of this model is quite satisfactory for thisdiverse set of compounds. Sturino et al. [28] recentlypublished a set of biliary excretion data expressed asthe compound maximum concentration in the bile(Cmax) for prostaglandin D2 receptor antagonists.We predicted the biliary excretion for these com-pounds (see Table 2). Although we are unable to doa direct comparison between the reported bile Cmax

and the calculated cumulative biliary excretion,there is, nonetheless, a nice correlation between thebiliary Cmax and the predicted cumulative biliaryexcretion for this set of compounds. The result againdemonstrated the good performance of the derivedmodel against various drug-like compound sets.

Discussion

The biliary excretion of drugs and their metabo-lites occurs predominantly by ATP-dependent

drug efflux pumps that transfer substrate acrossthe canalicular membrane into the bile [7]. It isgenerally considered to be a complex processinvolving a number of different types of transpor-ters [14,20,22,24]. Careful examination of thederived QSPR model reveals that several mole-cular descriptors such as clogD, �bH

2 ; pH2 and

PEOE_VSAs are all important molecular proper-ties underlying ligand–transporter interactions. Ingeneral, biliary excretion decreases with increas-ing hydrophilicity as shown in Figure 4. Hydro-phobicity is a principal molecular propertyunderlying many structure–activity and struc-ture–pharmacokinetic relationships. As the rela-tive importance of the molecular descriptors usedreveals, hydrophobicity is the most importantdescriptor in this QSPR model. A significantcorrelation (r2 5 0.64) was found between hydro-phobicity expressed with clogD and the biliaryexcretion of the data used in this study, suggestingthat biliary excretion decreases with increasinglipophilicity. It is also obvious from Figure 4 thatacidic compounds in general have higher biliaryexcretion than neutral compounds. Since manyorganic anion transporters are involved in biliaryexcretion, the ionization state (expressed by pKa)of a molecule should play an important role.However, pKa and clogD are highly correlated inthe data set analysed (Figure 5). With the currentdata set, we were unable to dissect the contribu-tions to biliary excretion from hydrophobicity andionization state. Moreover, clogD is a function ofhydrophobicity and pKa of a molecule. clogD waschosen as the molecular descriptor in the QSPRequation. Another important parameter affectingbiliary excretion is logL (solute gas–hexadecanepartition coefficient). clogD and logL are highlycorrelated for compounds with low hydrogenbinding abilities. For compounds with highhydrogen bonding abilities, logL is higher thanclogD, since hydrogen bonding is not involved inthe gas–hexadecane partitioning process whilehydrogen bonding plays an important role inbuffer/octanol partitioning. The coefficient forlogL is a combination of exoergic dispersion forcesthat make a positive contribution to the coeffi-cient, and an endoergic cavity term that makes anegative contribution [29]. This could indicate thata similar mechanism is involved in the biliaryexcretion process.

0.01

0.1

1

10

100

0.1 1 10 100

reported CEb

pre

dic

ted

CE

b

Itraconazole

Figure 3. Plot of reported and calculated CEb data usingEquation (3) for literature compounds

Y. CHEN ET AL.86


It had been reported that biliary excretionincreases with increasing molecular weights [1–3].For the data set analysed in this study, there is noclear correlation between cumulative biliary excre-tion and molecular weight as shown in Figure 6. Itmight be due to the fact that in the previousliterature reports, only a congeneric series ofcompounds was analysed. Due to the lack ofdiversity of chemistry covered in those reports,those analyses have very limited applications inthe perspective of prediction of biliary excretionin drug discovery. Although there is no clear

correlation with molecular weight, the Moriguchiparameter CX does play a role in Equation (3)(r2 5 0.40 between CEb and CX). As stated in theprevious paragraph, CX is a summation ofweighted hydrophobic heavy atoms (carbon andhalogen atoms). It is correlated with molecularweight (r2 5 0.89) for this data set. PEOE_VSA-0also has correlation with molecular weight(r2 5 0.77). Since this is a multi-parameter principalcomponent regression (PCR) equation, it is diffi-cult, if not impossible, to figure out the contribu-tion of molecular weight to the final equation from

Table 2. Biliary concentrations of D2 antagonists and predicted cumulative biliary excretion [28].

Compoundnumber

Structure Cmax (mM) Predictedbiliaryexcretion

7

S

OO

N

OH

O

OH

Cl

3900 55.35

6S

OO

N

O

OH

O

Cl

1100 23.51

13F

S

N

O O

OH

O

Cl

103 7.33

12 F

N

O

O

OH

Cl

81 1.15

11 F

Br

N OH

O

Cl

29 0.12



molecular weight contributions to individual de-scriptors. For compounds with a molecular weightlarger than 600, cumulative biliary excretion ishighly correlated (r2 5 0.60) with �bH

2 , a parameterdescribing the summation of solute hydrogen bondbasicity. The second most important moleculardescriptor for compounds with a molecular weightlarger than 600 is clogD (r2 5 0.40). IncreasingclogD decreases biliary excretion.

Undoubtedly, the ability to estimate the biliaryexcretion of drug molecules is important from apharmacokinetic and toxicological perspective.Considering the complex nature of the in vivobiliary excretion, the performance of the derived

QSPR model for the compounds analysed, espe-cially for the literature compounds, is quiteimpressive. Another important feature of thismodel is that it predicts low levels of biliaryexcretion (o1%) reasonably well as shown inFigure 2. The application of this QSPR model couldprovide possible insight regarding the possiblehepatobiliary disposition of drugs and theirmetabolites. The derived QSPR model could be avery useful tool in designing compounds withbetter hepatobiliary disposition profiles. For exam-ple, the model has been successfully used inseveral of our in-house research projects to mini-mize hepatobiliary liabilities of compounds.Cumulative biliary excretion as a percentage ofdose administered is influenced not only by biliaryclearance but also other clearance pathways ofdrug molecules. Therefore, the derived model doesnot always reveal the underlying ligand–transpor-ter interactions. Although elucidating eliminationmechanisms is not the intention of this study,many of the used descriptors in the QSPR modelare also important molecular descriptors for otherelimination pathways, such as increasing clogDincreases metabolism through the P450 pathway(contrary to the contribution to biliary excretion). Itis also important to note, however, that themajority of the compounds used in this study areneutral and acidic molecules. The uncertainty ofusing this model to predict the biliary excretion forbasic compounds could be high. Further analysiswith the inclusion of basic compounds is needed toincrease the coverage of chemistry space.

0.001

0.01

0.1

1

10

100

-6

cLogD

CEb

r2=0.64

-4 -2 0 2 4 6 8 10

Figure 4. Plot of CEb vs clogD (~ neutral compounds;& acids)

0

2

4

6

8

10

12

14

-6

pK

a

cLogD

-4 -2 20 4 6 8 10

Figure 5. Plot of pKa vs clogD

0.001

0.01

0.1

1

10

100

200

CE

b

MW

300 400 500 600 700 800

Figure 6. Plot of CEb vs molecular weight

Y. CHEN ET AL.88


Conclusion

In conclusion, cumulative biliary excretion in bileduct cannulated rats correlates well with mole-cular hydrophobicity, polarity and size of amolecule. Hydrophobicity is the most importantmolecular property underlying the relationship.The derived QSPR model provides a useful toolin understanding the molecular properties un-derlying the biliary excretion, and in estimatingbiliary excretion potentials of drug or drug-likemolecules.

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Structure–pharmacokinetic relationship of in vivo rat biliary excretion

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