DMD #26260dmd.aspetjournals.org/content/dmd/early/2009/12/07/dmd.108.0262… · DMD # 26260 3...

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IN SILICO PREDICTION OF BILIARY EXCRETION OF DRUGS IN RATS

BASED ON PHYSICOCHEMICAL PROPERTIES

Gang Luo*, Stephen Johnson, Mei-Mann Hsueh, Joanna Zheng, Hong Cai, Baomin Xin,

Saeho Chong, Kan He, Timothy W. Harper

Pharmaceutical Candidate Optimization-Metabolism and Pharmacokinetics (G. L., T. H., M.-

M. H., J. Z., H. C., B. X., S. C., K. H.), Computer Assistant Drug Design (S. J.) Bristol-

Myers Squibb Company, Pennington, New Jersey.

DMD Fast Forward. Published on December 7, 2009 as doi:10.1124/dmd.108.026260

Copyright 2009 by the American Society for Pharmacology and Experimental Therapeutics.

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on December 7, 2009 as DOI: 10.1124/dmd.108.026260

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Running Title

IN SILICO PREDICTION OF RAT BILIARY EXCRETION

Corresponding author: Timothy W. Harper

Email: [email protected]

Phone: 609-818-5180

Fax: 609-818-3675

The number of text pages: 44

The number of tables: 6

The number of figures: 9

The number of references: 40

The number of words in abstract: 242

The number of words in introduction: 625

The number of words in discussion: 1350

ABBREVIATIONS

BDC, bile duct-cannulated; BCRP, breast cancer resistance protein; BSEP, bile salt export

pump; ΔGsolv aq, free energy of aqueous solvation; ΔGsolv DMSO, free energy of solvation in

dimethyl sulfoxide; LC/MS/MS, liquid chromatography with tandem mass spectrometry;

MRP, multidrug resistance-associated protein; P-gp, P-glycoprotein; PSA, polar surface

area.


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ABSTRACT

Evaluating biliary excretion, a major elimination pathway for many compounds, is

important in drug discovery. The bile duct-cannulated (BDC) rat model is commonly

employed to determine the percent of dose excreted as intact parent into bile. However, a

study using BDC rats is time-consuming and cost-ineffective. The present report describes

a computational model that has been established to predict biliary excretion of intact parent

in rats as a percent of dose. The model was based on biliary excretion data of 50 BMS

compounds with diverse chemical structures. The compounds were given intravenously at

<10 mg/kg to BDC rats and bile was collected for at least 8 hours after dosing. Recoveries

of intact parents in bile were determined by liquid chromatography with tandem mass

spectrometry. Biliary excretion was found to have a fairly good correlation with polar

surface area (PSA) (r = 0.76), and with free energy of aqueous solvation (ΔGsolv aq) (r = -

0.67). In addition, biliary excretion was also highly corrected with the presence of a

carboxylic acid moiety in the test compounds (r = 0.87). An equation to calculate biliary

excretion in rats was then established based on physiochemical properties via a multiple

linear regression. This model successfully predicted rat biliary excretion for 50 BMS

compounds (R = 0.94) and for 25 previously reported compounds (R =0.86) whose

structures are markedly different from those 50 BMS compounds. Additional calculations

were conducted to verify the reliability of this computation model.


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Introduction

Biliary excretion is a major elimination pathway for many drugs and discovery compounds

both in humans and in preclinical animals. For example, pravastatin and losoxantrone were

found to be mainly eliminated as intact parent through biliary excretion in humans

(Hatanaka 2000; Joshi et al. 2001). In rats, pravastatin and methotrexate were minimally

metabolized and primarily excreted intact into bile (Masuda et al. 1997; Kurihara et al.

2005). Extensive biliary excretion can be linked to a high clearance (Arimori et al. 2003),

enterohepatic recirculation (Caldwell and Cline 1976; Rollins and Klaassen 1979), toxic

gastrointestinal side effects (Kato et al. 2002) and potential drug-drug interactions (Luo et

al. 2007). As a result, most lead discovery compounds are assessed for biliary excretion in

selected preclinical animals early in the drug discovery and development process.

Among the preclinical animal models, rats are the most commonly used model species for

pharmacology, pharmacokinetics and toxicology. The existing experimental models for

determining rat biliary excretion include bile duct-cannulated rats and isolated perfused rat

liver. However, these models are very time-consuming and cost-ineffective because of

complicated preparation of test models and difficulty in bile sample analyses.

Undoubtedly, a computational model for predicting rat biliary excretion could significantly

reduce laboratory efforts and, consequently, cost. Furthermore, a computational model

could enable scientists to determine the potential for biliary excretion of virtual compounds,

thereby helping to prioritize synthetic efforts in drug discovery programs. However, such a

model has not yet been reported, despite efforts to identify factors that critically influence

rat biliary excretion. In previous work, molecular weight was commonly identified as a


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dominant factor influencing biliary excretion (Millburn et al. 1967; Abou-El-Makarem et al.

1967a; Abou-El-Makarem et al. 1967b; Hirom et al. 1972a; Hirom et al. 1972b; Hughes et

al. 1973a; Hughes et al. 1973b; Wright and Line 1980; Proost et al. 1997; Han et al. 2001)

and a hypothesis of “molecular weight threshold” was proposed. For example, Wright and

Line demonstrated in their study with 18 cephalosporin derivatives that molecular weight

450 was the threshold for rat biliary excretion; above that molecular weight threshold,

biliary excretion increased in a generally progressive way and became the principal route of

excretion of the higher-molecular-weight derivatives (Wright and Line 1980). Nevertheless,

molecular weight alone cannot predict rat biliary excretion though it may indicate a trend

towards increased biliary excretion. For example, the carboxylate and lactone forms of

irinotecan have little difference in molecular weights but the carboxylate exhibits much

more biliary excretion than does the latter (Arimori et al. 2003; Itoh et al. 2004).

In the present study 50 discovery compounds from Bristol-Myers Squibb Company (BMS)

with diverse chemical structures were evaluated in BDC rats after intravenous

administration. Predictions of rat biliary excretion were then made based on polar surface

area (PSA), free energy of aqueous solvation (ΔGsolv aq) and presence, or absence, of

carboxylic acid moieties in these molecules. The prediction was highly correlated with the

observed in vivo biliary excretion. When the same prediction method was applied to 25

compounds whose biliary excretion was published previously (Hirom et al. 1972b; Russell

and Klaassen 1973; Fahrig et al. 1989; Monsarrat et al. 1990; Masuda et al. 1997; Hinchman

et al. 1998; Payan et al. 1999; Song et al. 1999; Arimori et al. 2003; Chong et al. 2003;

Funakoshi et al. 2003; Moriwaki et al. 2003; Kurihara et al. 2005; Takayanagi et al. 2005;

Kamath et al. 2005a; Kamath et al. 2005b; Akashi et al. 2006; Beconi et al. 2007; Kamath et

al. 2008), a high correlation was also seen between the predicted and observed values.


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Following the validation work, the two sets of compounds were combined into a single

combined dataset that was then used to generate a final model equation to predict biliary

elimination.


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Methods

Chemicals

Fifty (50) discovery compounds were prepared by Bristol-Myers Squibb Company

(Pennington and Lawrenceville, NJ and Wallingford, CT).

Preparation of Bile Duct-Cannulated Rats

Male Harlan Sprague-Dawley (SD) rats (250-300 g) were purchase from Harlan

(Indianapolis, IN). Rats were anesthetized by inhalation of isoflurane (administered at 2-3%

using oxygen as the carrier) prior to surgery. While the rats were under isoflurane

anesthesia, the abdominal and dorsal neck areas were shaved, rinsed with isopropanol and

then scrubbed with Nolvasan scrub. The sites were then swabbed with betadine. The rats

were covered with sterile drapes and sterile instruments were used. The abdominal cavity

was opened, and a small polyethylene catheter inserted into the common bile duct. Another

catheter was inserted into the duodenum at the level of the common bile duct for the

recirculation and infusion of bile. Both cannulae were passed through the abdominal

musculature and then routed subcutaneously to the dorsal neck area and exteriorized through

a small incision. The abdominal cavity was sutured shut, and the skin was closed with

wound clips. All rats received 25 ml of warm sterile saline during surgery (applied directly

into the abdominal cavity), and another 10 ml of saline or 5% dextrose sc, post-operatively.

Post-surgical analgesia buprenorphine (0.1 mg/kg, sc) was administered. The rats were then

allowed to recover. This surgery was done two days before study. Control bile was

collected the day after surgery and used the following day during the course of the

experiment to infuse as a replacement for the bile collected after dosing. A jugular vein and

femoral vein were also cannulated to allow intravenously administration of dose solution


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and blood sample collection. Upon completion of the study the rats were euthanatized by

CO2 inhalation overdose.

Studies in Bile Duct-Cannulated Rats

Test compounds were dosed intravenously to two or three rats, either in a cassette dosing

format (maximum of four compounds per dose group with each compound dosed at less

than 2 mg/kg to minimize potential drug-drug interactions) or in a discrete dosing format

(dosed at less than 10 mg/kg to avoid saturation of transporter(s) involved in biliary

excretion). In general, bile samples were collected at 0-1, 1-2, 2-4 and 4-8 hr intervals after

dose administration.

Determination of Parent Compounds in Bile Samples

Bile samples were diluted with plasma (10- to 50-fold) and analyzed against a plasma

standard curve. A Packard MultiPROBE liquid handler (Perkin Elmer, Shelton, CT) was

used to transfer 25 to 50 μL of each standard, quality control, or bile sample diluted in

plasma to a 96-well plate for protein precipitation extraction. After the addition of 120 μL

of acetonitrile containing the internal standard, the samples were vortex mixed and the

resulting supernatant was separated from the precipitated proteins by centrifugation for 5

minutes. An aliquot of the supernatant was transferred using a Tomtec automated liquid

handler to a second clean 96-well plate. The mixture was diluted five times with 0.1%

formic acid in water, and the plate was capped and vortex mixed. An aliquot of 5 to 10 μL

of supernatant was injected into liquid chromatography with tandem mass spectrometric

detection (LC/MS/MS) for analysis. The high pressure liquid chromatography (HPLC)

system consisted of a Shimadzu LC10ADvp pumps (Columbia, MD) and an HTC PAL


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autosampler (Leap Technologies, Cary, NC) linked to a Phenomenex Synergi Hydro-RP

analytical column (2.0 x 50 mm, 4 μm; Torrance, CA). Mobile phase A consisted of 0.1%

formic acid in water. Mobile phase B was 100% acetonitrile. LC flow rate was 0.3

mL/min. The initial mobile phase composition was 20% B for 1 min, followed by a linear

gradient to 80% B over 1 min. Mobile phase composition was held at 80% B for 1 min,

then returned to initial conditions over the next 0.1 min, and re-equilibrated for 0.5 min.

Total analysis time for most compounds was 3.5 min. The HPLC system was interfaced to a

Sciex API3000 or Sciex API4000 Q trap (Toronto, Canada) equipped with the

Turboionspray source. The source temperature was set at 450°C and the ionspray voltage

set to 4.8 kV. UHP nitrogen was used as nebulizer and auxiliary gas.

Cumulative biliary excretion of parent compound was calculated from the bile volume and

corresponding concentrations at each time interval. The biliary excretion was expressed as

percent of dose excreted into bile as parent.

Calculation of Physicochemical Properties

Various physicochemical properties, including molecular weight (MW), ACD log D at pH

6.5, ACD log D at pH 7.4, ACD log P, clog P, free energy of aqueous solvation (ΔGsolv aq),

free energy of solvation in dimethyl sulfoxide (ΔGsolv DMSO) and number of rotational bonds,

were generated with Abacus, an in-house application containing various property calculators

from a number of vendors. Polar surface area (PSA) was computed using the TPSA

algorithm in SciTegic Pipeline Pilot v.51 (Accelrys, Inc. San Diego, CA). For ΔGsolv aq, a

conformational search was performed using MacroModel (Schrodinger, Inc. New York,

NY) using the OPLS2005 forcefield with implicit solvation. The 10 lowest energy

conformers were each submitted to Jaguar (Schrodinger, Inc. New York, NY) for single


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point energy calculations with B3LYP/6-311+g**/water. The calculated ΔGsolv aq of the

lowest energy conformation from this self-consistent reaction field calculation was the value

utilized in regression studies.

The structural similarity among compounds was analyzed using the AtomPair fingerprints

with the Tanimoto similarity coefficient. Pairs of compounds with a Tanimoto similarity

greater than 0.7 were considered to be very similar in structure.

Prediction of Biliary Excretion

Correlations were calculated using a simple linear least squares regression between in vivo

biliary excretion and individual physicochemical properties. The individual

physicochemical properties included ACD log D at pH 6.5, ACD log D at pH 7.4, ACD log

P, clog P, MW, number of rotational bonds, ΔGsolv aq, ΔGsolv DMSO, PSA and

presence/absence of carboxylic acid moieties. Based on these correlations, a multiple linear

regression model was generated for the prediction of biliary excretion using three

physicochemical properties (PSA, ΔGsolv aq, and presence/absence of carboxylic acid

moieties). Each of these parameters was highly correlated with the observed extent of rat

biliary excretion.


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Results

Rat Biliary Excretion of Fifty BMS Compounds Biliary excretion, expressed as % of

intravenous dose, of 50 BMS compounds was obtained experimentally in BDC rats (Table

1). The biliary excretion of this set of compounds ranged from 0.1% to 100% of the

intravenous dose. Notably, biliary excretion of the test compounds in BDC rats was

observed to occur mostly during the first two hours and was almost complete within the first

four hours after drug administration regardless of total cumulative biliary excretion (Figure

1). This is consistent with findings from other laboratories (Itoh et al. 2004). This

observation suggests that biliary excretion of small molecules in rats is an efficient process

and high biliary excretion may result in high clearance, at least for small molecules such as

those tested in the present study.

Correlation between Rat Biliary Excretion and Individual Physicochemical Properties

Various physicochemical properties, including ACD log D at pH 6.5 and ACD log D at pH

7.4, ACD log P, clog P, MW, number of rotational bonds, ΔGsolv aq, ΔGsolv DMSO), and PSA

were generated for the fifty compounds investigated (Table 1). Simple linear regression

analyses were performed to evaluate the relationships between the observed rat biliary

excretion (expressed as % of dose) and the individual physicochemical properties to identify

those physicochemical properties which significantly influenced rat biliary excretion of the

test compounds. As shown in Table 1 and Figure 2a, molecular weight correlated with rat

biliary excretion (correlation coefficient r = 0.60). This moderate correlation agrees with

previous findings from other laboratories (Millburn et al. 1967; Abou-El-Makarem et al.

1967a; Hirom et al. 1972a; Hirom et al. 1972b; Hughes et al. 1973a; Hughes et al. 1973b;

Wright and Line 1980; Proost et al. 1997; Han et al. 2001). However, PSA and ΔGsolv aq


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exhibited even greater degrees of correlation (r = 0.76 and r = -0.67, respectively), as shown

in Table1, and Figure 2b and 2c. ΔGsolv DMSO also correlated with biliary excretion, but to a

slightly lower degree than ΔGsolv aq (data not shown). In addition, the number of rotational

bonds also exhibited a moderate correlation (r = 0.42). Interestingly, the presence of a

carboxylic acid moiety in the test compounds appears to play a marked role in rat biliary

excretion (r = 0.87), as shown in Table 1. None of the other physicochemical properties

examined exhibited significant correlations.

Prediction of Rat Biliary Excretion for Fifty BMS Compounds A multiple linear

regression analysis was performed between rat biliary excretion and the three

physicochemical properties (PSA, ΔGsolv aq and presence/absence of carboxylic acid

moieties) that appeared to influence rat biliary excretion significantly. An equation was

then established for predicting rat biliary excretion (% of dose) from the regression as

follows:

395.29616.0289.50245.0 . −Δ×−×+×= aqSolvGAcidPSAretionBiliaryExc (Eq. 1)

where Acid = 1 indicates the presence of an acid moiety and Acid = 0 indicates the absence

of acidic functionality.

Based on this equation, predicted rat biliary excretion was calculated for each of test

compounds (Table 2). Comparisons of observed and calculated values indicate that this

computational model fits the training data well (Table 2 and Figure 3). The correlation was

0.94, while the mean absolute difference between the predicted and observed values was

only 7.8% of dose. There were no obvious outliers in this dataset.


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Prediction of Rat Biliary Excretion for Compounds in Literature The computational

model was also applied to compounds whose rat biliary excretion was published previously.

The criteria for inclusion of these data were: MW < 1,000 and relatively low intravenous

bolus dose (< 50 mg/kg). Table 3 summarizes results for 25 compounds described in

literature reports (Hirom et al. 1972b; Russell and Klaassen 1973; Fahrig et al. 1989;

Monsarrat et al. 1990; Masuda et al. 1997; Hinchman et al. 1998; Payan et al. 1999; Song et

al. 1999; Arimori et al. 2003; Chong et al. 2003; Funakoshi et al. 2003; Moriwaki et al.

2003; Kurihara et al. 2005; Takayanagi et al. 2005; Kamath et al. 2005a; Kamath et al.

2005b; Akashi et al. 2006; Beconi et al. 2007; Kamath et al. 2008). The reported rat biliary

excretion values ranged from 0.9% to 90% of the intravenous dose. PSA and ΔGsolv aq were

calculated as described above. Rat biliary excretion was predicted from Equation 1 and the

predicted values are listed in Table 4. As shown in Table 4 and Figure 4, the biliary

excretion values predicted from Equation 1 agreed well with the reported in vivo results.

The only obvious deviations were cephradine and taxol for which the predictions (66% and

44%, respectively) were much higher than observed values (27% and 12%, respectively).

Overall, there was a very good correlation between the predicted and observed values (r =

0.86, r2 = 0.73), with a mean absolute difference between the predicted and observed values

of only 13% of dose.

Analysis of Structural Diversity for Compounds The 50 BMS compounds used in

training mode were from 14 different discovery programs (Table 1). The physiochemical

properties shown in Table 1 also indicate significant structure diversity. These compounds

differ in MW (278 to 738), number of rotational bonds (1 to 16), PSA (46.9 to 183.4 sq.

Ang), and ΔGsolv aq (-53.0 to -15.7 kcal/mol) as well as in ACD log P, cLogP, and LogD


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(pH 6.5). Furthermore, structural similarity was analyzed against each other among 50

BMS compounds using AtomPair fingerprints with the Tanimoto similarity coefficient.

Using a threshold of Tanimoto similarity of 0.70, 28 similarity pairs (including 4 single

similarity pairs from 8 compounds and 5 similarity clusters from 20 compounds) were

identified out of 1,225 total pairs (Table 5). Structure similarity was only observed between

or among compounds from same discovery program. The remaining 22 compounds were

found not to have significant similarity with any other compounds. This analysis indicated

that the 50 BMS compounds used in the study are diverse in chemical structure, representing

31 different structural clusters as described by AtomPair fingerprints.

Structure similarity among the 25 compounds from literature reports was also examined as

described above. Seven similarity pairs (including 4 single similarity pairs from 8

compounds and a similarity cluster from 3 compounds) were identified out of 300 total pairs

(Table 6). The remaining 14 compounds were found not to have significant similarity with

any other compounds. Therefore, the twenty five compounds from literature reports should

represent 19 diverse chemotypes. Furthermore, structure similarity was examined

comparing the 25 literature compounds against the 50 BMS compounds. The highest

Tanimoto Coefficient observed was only 0.36, indicating very low structural similarity

between the BMS compounds and the literature compounds.

Establishment of Similar Prediction Equations A second prediction equation was

generated using multiple linear regression as described above, but applied to the data

(observed biliary excretion, PSA, ΔGsolv aq, and presence/absence of a carboxylic acid

moiety) obtained from the 25 literature compounds.


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674.12529.0572.41148.0 . −Δ×−×+×= aqsolvGAcidPSAretionBiliaryExc (Eq. 2)

where Acid is defined in the same way as for Equation 1. The predicted biliary excretion of

25 literature compounds based on Equation 2 showed a reasonably good correlation with the

reported in vivo biliary excretion (Figure 5, r = 0.86 and r2 = 0.74). The predicted biliary

excretion values obtained by applying Equation 2 to the 50 BMS compounds also agreed

well with the observed biliary excretion values (Figure 6, r = 0.94 and r2 = 0.89), similar to

the agreement observed with predictions from Equation 1.

A third equation was generated based on data from the combined dataset of the 50 BMS

compounds and the 25 literature compounds.

527.23701.0382.47169.0 . −Δ×−×+×= AqSolvGAcidPSAretionBiliaryExc (Eq. 3)

Predictions based on the model developed using the combined 75-compound dataset

exhibited a high correlation with the observed biliary excretion values (Figure 7, R = 0.92

and R2 = 0.84).

Impact of A Different PSA Calculation on Prediction Model PSA appears to be

critical physiochemical property in the current prediction models as demonstrated above.

To explore if PSA data generated from a different calculation method have significant

impact on prediction of biliary excretion, a second set of PSA data was generated for all

compounds using the TPSA algorithm in the OEChem Toolkit (OpenEye Scientific

Software, Inc., Santa Fe, NM). The two sets of PSA data were highly correlated (r = 0.97

and r2 = 0.94), indicating that the prediction model should not be limited by the software

used to calculate the physicochemical properties.


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Discussion

In drug discovery, a computational model for predicting rat biliary excretion based on

physicochemical properties would allow the estimation of biliary excretion for both existing

and virtual compounds. A reliable model could significantly reduce the number of high-cost

BDC rat studies that are currently needed. Previous efforts to explore the factors

influencing rat biliary excretion have met with limited success (Millburn et al. 1967; Abou-

El-Makarem et al. 1967a; Hirom et al. 1972a; Hirom et al. 1972b; Hughes et al. 1973a;

Hughes et al. 1973b; Wright and Line 1980; Proost et al. 1997; Han et al. 2001). Previous

efforts likely failed to establish a highly predictive computational model due to the small

sets of test compounds and lack of diverse chemical structures in each of those studies. In

addition those efforts tended to focus on single physicochemical properties (for example,

molecular weight) instead of a combination of multiple physicochemical factors (Millburn et

al. 1967; Abou-El-Makarem et al. 1967a; Hirom et al. 1972a; Hirom et al. 1972b; Hughes et

al. 1973b; Wright and Line 1980).

In the present study, 50 BMS compounds were evaluated in BDC rats. Observed biliary

excretion, expressed as percent of the intravenous dose, ranged from 0.1 to 100%. These

compounds were assessed under the same experimental conditions, including identical strain

and gender as well as comparable body weights, a standard surgical procedure for

preparation of BDC rats, an adequate duration (at least eight hours) of bile collection,

relatively low intravenous doses of test compounds (to avoid saturation of biliary transport),

and similar LC/MS/MS methods for quantitation of intact parent in bile samples.

Additionally, this study included a relatively large set of test compounds covering a wide

diversity of chemical structures. The BMS compounds were from 14 different discovery


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programs. The compounds covered a range of physicochemical properties including,

molecular weights (278 to 739), numbers of rotational bonds (1 to 16), PSA (46.9 to 183.4

sq. Ang), and ΔGsolv aq. (-53.0 to -15.7 kcal/mol) as well as differences in ACD log P,

cLogP, and LogD (pH 6.5). Analysis of structural similarity confirmed diversity of

structure and indicated the compounds represent 31 different chemotypes. As a result, the

test compounds and the results of biliary excretion generated in the present study should be

adequately qualified for establishing a computational model.

Using a multiple linear regression method to correlate rat biliary excretion and three

physicochemical properties (PSA, ΔGsolv aq and presence/absence of carboxylic acid

moieties), an equation for predicting rat biliary excretion was established. The observed and

calculated rat biliary excretion of 50 internal BMS training compounds showed good

correlation (r = 0.94, r2 = 0.89). The mean of the absolute differences between the observed

and the predicted biliary excretion values was only 7.8% with no obvious outliers.

When the same computational model was applied to 25 structurally diverse compounds

whose rat biliary excretion data were published by different laboratories (Hirom et al.

1972b; Russell and Klaassen 1973; Fahrig et al. 1989; Monsarrat et al. 1990; Masuda et al.

1997; Hinchman et al. 1998; Payan et al. 1999; Song et al. 1999; Arimori et al. 2003; Chong

et al. 2003; Funakoshi et al. 2003; Moriwaki et al. 2003; Kurihara et al. 2005; Takayanagi et

al. 2005; Kamath et al. 2005a; Kamath et al. 2005b; Akashi et al. 2006; Beconi et al. 2007;

Kamath et al. 2008), the predicted and observed biliary excretion values were again very

close for most compounds. The clear exceptions were cephradine and taxol (Monsarrat et

al. 1990; Moriwaki et al. 2003). The correlation coefficient was 0.88, and the mean of the


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absolute differences between the observed and predicted values was only 13%. Such a

successful prediction is significant because the twenty five literature compounds are

structurally distinct from the fifty BMS compounds, and the rat biliary excretion data were

generated in approximately twenty different laboratories using various experimental

conditions.

A similar prediction model was established using PSA, ΔGsolv aq, presence/absence of

carboxylic acid, and the reported rat biliary excretion (percent of intravenous bolus dose)

from 25 literature compounds alone or from the combined 75-compound dataset that

includes both the literature and in-house datasets. Those models successfully predicted the

in vivo rat biliary excretion, verifying the reliability of prediction model reported in the

present study. In addition, variations in PSA values calculated by a different method should

not impact the biliary elimination prediction model.

In the present study, the top three critical physicochemical properties influencing biliary

excretion in rats were found to be PSA, ΔGsolv aq and presence/absence of carboxylic acid

moieties. Interestingly, molecular weight was found to play an important, but less critical

role in biliary excretion. The correlation coefficient between molecular weight and biliary

excretion was 0.60, less than that between PSA (or ΔGsolv aq) and biliary excretion. For

example, irinotecan exists stably in lactone and carboxylate forms. Although the molecular

weights of the two forms differ by only 16 amu, the percent of dose excreted in bile was

quite different (7% and 60%, respectively) (Arimori et al. 2003). Based on Equation 1

described above, biliary elimination is predicted to be 15% and 79% of dose respectively,

close to the observed values.


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In the present study, the presence or absence of a carboxylic acid was noted as a key factor

for biliary excretion. This is in agreement with many well-know examples of carboxylate-

containing compounds that exhibit high levels of biliary elimination such as irinotecan

(carboxylate form), methotrexate, bile acids (such as taurocholic acid), glucuronide-,

glutathione-, and sulfation-conjugates derived from the parent compounds.

Active transport by specific canalicular membrane transporters such as p-glycoprotein (P-

gp, ABCB2), MRP2, breast cancer resistance protein (BCRP, ABCG2) and BSEP, (Chandra

and Brouwer 2004) is generally believed to contribute significantly to biliary elimination.

While the predictive model for biliary excretion generated in the present study cannot

distinguish contributions from specific transporters, it may reflect a generalized

physicochemical space encompassing common structural characteristics recognized by the

canalicular membrane transporters that are involved in biliary elimination.

Biliary excretion of a compound may also be related to its hepatic metabolism. The

relationship between biliary excretion and metabolism seems to be inversely correlated.

Compounds with high biliary excretion are often poor metabolized (such as digoxin

(Caldwell and Cline 1976; Funakoshi et al. 2003), methotrexate (Henderson et al. 1965a;

Henderson et al. 1965b; Fahrig et al. 1989) and pravastatin (Hatanaka 2000), while those

that are highly metabolized generally show poor biliary excretion (such as dasatinib and

BMS-182874, (Chong et al. 2003; Kamath et al. 2008)). Furthermore, many carboxylic

acid-containing compounds in the present study were poorly metabolized by rat liver

microsomes (data not shown) and showed a high biliary excretion. Though the relationship

between biliary excretion and hepatic metabolism is beyond the scope of the present study,

the predictive model of rat biliary excretion described here may be of use in efforts to


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estimate the relative contributions of rat biliary excretion and hepatic metabolism to the

overall elimination of test compounds in rats.

The predictive model generated from the present study likely applies only to rat biliary

excretion since significant species differences in biliary excretion have been observed

(Henderson et al. 1965a; Henderson et al. 1965b; Abou-El-Makarem et al. 1967b; Bertagni

et al. 1972; Gregson et al. 1972; Hirom et al. 1972b; Hughes et al. 1973a). For instance,

biliary excretion of methotrexate was 57-72% in rats, 7% in dogs, 16% in monkeys, and

10% or less in humans (Henderson et al. 1965a; Henderson et al. 1965b; Masuda et al. 1997;

Luo et al. 2007). In general, biliary excretion often plays a more significant role in the

elimination of xenobiotics in rats than in other species, possibly due to high expression of

transporters on the canalicular membrane of rats coupled with high bile flow relative to

body weight. The relationship of biliary elimination trends across species remains to be

investigated adequately.

This predictive model estimates the percent of intravenous dose excreted into bile but not

the biliary clearance as in unit of mL/min/kg. To calculate biliary clearance, corresponding

pharmacokinetic data are needed. For most compounds employed in the present study, the

pharmacokinetic data from rats were not available.


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Acknowledgements

We thank the following colleagues at Bristol-Myers Squibb Company for providing rat

biliary excretion data for the current study: Laishun Chen, Aberra Fura, Christine Huang,

Hongjian Zhang, Ming Zheng, and Yang Zheng. We also thank colleagues in Technical

Support Unit at Bristol-Myers Squibb Company for preparing and dosing BDC rats and

collecting bile and urine samples. We also thank Dr. Wen Chyi Shyu for her critical review

and comments on the manuscript. Finally, we would like to thank the anonymous reviewers

that made helpful suggestions to improve this manuscript.


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Moriwaki, T., H. Yasui and A. Yamamoto (2003). A recirculatory model with enterohepatic

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Footnotes

Send reprint request to:

Dr. Timothy W. Harper, Bristol-Myers Squibb Company, 311 Pennington Rockyhill Road,

Pennington, New Jersey 08534. Email: [email protected]

Dr. Gang Luo’s current mail address: Covance Inc., 3301 Kinsman Boulevard, Madison, WI

53704.


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Figure Legends

Figure 1 Typical examples of biliary excretion time-courses in BDC rats

Test compounds were given intravenously to two BDC rats in cassette dosing format at a

dose of approximately 0.9 mg/kg for each compound. The bile samples were continually

collected up to 9 hrs after dosing and the concentrations of intact parent in bile samples were

determined using LC/MS/MS.

Figure 2 Correlation between percent of dose excreted in rat bile and individual

physicochemical properties of test compounds

Correlation between rat biliary excretion expressed as percent of intravenous dose and

molecular weight (2a), PSA (2b) and ΔGsolv aq (2c) of fifty BMS compounds was calculated

using simple linear regression of least squares.

Figure 3 Correlation between predicted and observed rat biliary excretion of fifty BMS

compounds using Equation 1

Rat biliary excretion of 50 BMS compounds was predicted (Table 2) using the equation

biliary excretion = 0.245*PSA + 50.289*Acid (1 or 0) – 0.616*ΔGsolv aq -29.395 and the

corresponding physicochemical properties of BMS compounds (Table 1). A correlation

between the predicted and observed rat biliary excretion was calculated using a simple linear

regression of least squares.

Figure 4 Correlation between predicted and reported rat biliary excretion of twenty five

literature compounds using Equation 1


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Rat biliary excretion of 25 compounds was predicted using the equation

biliary excretion = 0.245*PSA + 50.289*Acid (1 or 0) – 0.616*ΔGsolv aq – 29.395 and the

corresponding physicochemical properties (Table 4). A correlation between the predicted

and observed rat biliary excretion was calculated using a simple linear regression of least

squares.

Figure 5 Correlation between predicted and reported rat biliary excretion of twenty five

literature compounds using Equation 2


biliary excretion = 0.148*PSA + 41.572*Acid (1 or 0) – 0.529*ΔGsolv aq – 12.674 and the



squares.

Figure 6 Correlation between predicted and observed rat biliary excretion of fifty BMS


Rat biliary excretion of 50 BMS compounds was predicted using the equation biliary

excretion = 0.148*PSA + 41.572*Acid (1 or 0) – 0.529*ΔGsolv aq – 12.674 and the



squares.

Figure 7 Correlation between predicted and observed rat biliary excretion of seventy five



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biliary excretion = 0.169*PSA + 47.382*Acid (1 or 0) – 0.701*ΔGsolv aq - 23.527) and the

corresponding physicochemical properties (Tables 1 and 4). A correlation between the

predicted and observed rat biliary excretion was calculated using a simple linear regression

of least squares.


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Table 1. Biliary excretion in BDC rats and physicochemical properties of fifty BMS compounds

Compound Program Code

Biliary Excretion

(% of dose)

ACD Log P

cLog P Log D pH 6.5

MW Rotation bonds

(number)

PSA (sq. Ang)

ΔG solv aq (kcal/mol)

Acid (presence)

I A 3.0 4.19 6.81 4.19 471.4 1 46.9 -18.7 -

II B 1.3 1.14 3.06 NA 278.3 1 52.1 -15.7 -

III C 1.5 1.08 3.24 1.08 406.5 10 91.7 -33.3 -

IV D 67.8 3.88 1.91 1.48 555.4 4 101.1 -30.2 +

V C 0.7 2.80 2.30 2.80 374.4 6 70.2 -28.0 -

VI E 80.0 3.30 5.47 3.24 713.8 15 163.4 -30.0 +

VII C 8.6 1.88 2.03 1.88 443.4 7 72.6 -30.8 -

VIII F 4.0 0.53 1.39 -1.39 371.5 8 77.8 -25.4 -

IX D 83.0 4.02 4.16 1.87 573.4 3 116.6 -34.1 +

X G 5.2 -0.52 1.53 -3.44 368.4 5 96.2 -25.1 -

XI B 0.5 -0.65 1.89 -0.78 306.4 6 86.4 -23.1 -

XII B 0.7 0.12 1.92 -2.35 308.3 2 85.9 -20.5 -

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XIII B 11.5 1.17 3.85 -0.93 305.3 2 66.8 -23.2 -

XIV H 1.0 -0.37 2.39 -2.36 347.3 4 85.6 -25.3 -

XV I 83.9 3.37 3.33 0.87 539.6 11 144.3 -42.6 +

XVI I 80.2 3.70 3.67 1.20 560.7 9 134.5 -39.1 +

XVII I 32.4 2.70 3.09 -0.32 492.0 7 127.0 -33.4 -

XVIII I 77.0 2.59 2.80 0.09 551.6 10 164.8 -47.6 +

XIX I 81.8 4.22 3.97 1.72 581.1 9 134.5 -37.7 +

XX I 41.4 3.33 2.61 0.31 480.0 8 121.4 -34.0 -

XXI I 60.0 2.65 2.15 0.15 577.6 10 156.9 -42.7 +

XXII I 11.9 3.51 4.48 0.49 467.6 7 109.2 -29.4 -

XXIII I 29.7 2.12 2.99 0.90 468.6 7 119.0 -31.3 -

XXIV J 2.7 3.45 4.20 1.56 602.8 13 128.1 -31.0 -

XXV I 63.2 2.55 3.06 2.54 517.9 7 151.5 -42.8 -

XXVI J 33.0 4.15 4.62 2.26 663.8 12 142.0 -36.0 -

XXVII I 43.9 2.76 3.41 2.76 524.0 6 136.9 -38.9 -

XXVIII J 0.1 5.27 4.76 3.41 434.5 5 74.2 -21.3 -




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XXIX J 9.4 3.63 4.03 1.74 619.7 10 132.8 -37.7 -

XXX J 0.5 4.87 5.61 2.91 437.5 5 73.4 -21.3 -

XXXI J 10.4 4.62 5.06 2.71 647.8 11 133.2 -33.7 -

XXXII J 19.7 4.11 4.15 1.97 413.5 6 86.2 -25.9 -

XXXIII J 27.4 3.24 4.19 1.48 524.6 8 124.2 -34.5 -

XXXIV I 6.4 3.78 3.49 0.75 486.6 10 102.2 -29.3 -

XXXV J 30.3 2.20 3.40 0.44 566.6 8 135.1 -38.5 -

XXXVI J 1.9 5.27 6.00 3.34 481.6 7 81.7 -22.4 -

XXXVII B 3.7 0.003 1.47 -1.70 379.4 6 101.5 -28.9 -

XXXVIII J 92.1 2.34 2.86 -0.16 636.7 10 154.8 -39.0 +

XXXIX K 0.1 3.93 4.82 3.93 456.9 4 67.8 -16.7 -

XL J 47.0 1.65 2.77 -0.24 641.8 16 139.6 -47.7 -

XLI L 4.4 1.44 3.04 0.32 444.3 5 93.5 -25.3 -

XLII L 0.2 0.23 1.65 -0.97 420.3 3 79.8 -33.0 -

XLIII L 8.0 0.91 2.51 -0.21 430.3 4 94.6 -26.6 -

XLIV J 25.5 3.00 3.76 1.11 570.7 14 127.9 -37.7 -




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XLV J 21.7 2.30 3.18 0.40 512.6 12 111.9 -37.8 -

XLVI L 2.0 0.54 1.70 -0.87 407.3 4 75.0 -25.0 -

XLVII J 20.7 2.82 2.97 2.82 644.7 12 148.3 -39.6 -

XLVIII J 100 1.93 2.06 -0.48 674.7 13 183.4 -44.7 +

IL M 29.5 3.56 4.72 3.07 738.9 8 146.5 -53.0 -

L N 0.1 3.45 4.58 3.43 457.5 7 90.2 -18.7 -

r 0.2418 -0.0426 0.0410 0.5991 0.4189 0.7624 -0.6736 0.8710




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Table 2. Observed and predicted biliary excretion (% of dose) of fifty BMS compounds

Compound Observation Prediction Difference Absolute Difference

I 3.0 -6.4 -9.4 9.4

II 1.3 -6.9 -8.2 8.2

III 1.5 13.6 12.1 12.1

IV 67.8 64.3 -3.5 3.5

V 0.7 5.1 4.4 4.4

VI 80.0 79.4 -0.6 0.6

VII 8.6 7.4 -1.2 1.2

VIII 4.0 5.3 1.3 1.2

IX 83.0 70.5 -12.5 12.5

X 5.2 9.6 4.4 4.4

XI 0.5 5.6 5.1 5.5

XII 0.7 4.3 3.6 3.6

XIII 11.5 1.2 -10.3 10.3

XIV 1.0 7.2 6.2 6.2

XV 83.9 82.5 -1.4 1.4

XVI 80.2 78.0 -2.2 2.2

XVII 32.4 22.3 -10.1 10.1

XVIII 77.0 90.6 13.6 13.6

XIX 81.8 77.1 4.7 4.7

XX 41.4 21.3 -20.1 20.1

XXI 60.0 85.6 25.6 25.6

XXII 11.9 15.5 3.6 3.6


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XXIII 29.7 19.1 -10.6 10.6

XXIV 2.7 21.1 18.4 18.4

XXV 63.2 34.1 -29.1 29.1

XXVI 33.0 27.5 -5.5 5.5

XXVII 43.9 28.1 -15.8 15.8

XXVIII 0.1 1.9 1.8 1.8

XXIX 9.4 26.4 17.0 17.0

XXX 0.5 1.7 1.2 1.2

XXXI 10.4 24.0 13.6 13.6

XXXII 19.7 7.8 -11.9 11.9

XXXIII 27.4 22.3 -5.1 5.1

XXXIV 6.4 13.7 7.3 7.3

XXXV 30.3 27.4 -2.9 2.9

XXXVI 1.9 4.4 2.5 2.5

XXXVII 3.7 13.3 9.6 9.6

XXXVIII 92.1 82.8 -9.3 9.3

XXXIX 0.1 -2.5 -2.6 2.6

XL 47.0 34.2 -12.8 12.8

XLI 4.4 9.1 4.7 4.7

XLII 0.2 10.5 10.3 10.3

XLIII 8.0 10.2 2.2 2.2

XLIV 25.5 25.2 -0.3 0.3

XLV 21.7 21.4 -0.3 0.3

XLVI 2.0 4.4 2.4 2.4


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XLVII 20.7 31.4 10.7 10.7

XLVIII 100 95.0 -5.0 5.0

IL 29.5 39.2 9.7 9.7

L 0.1 4.2 4.1 4.1

Note: r = 0.94 between observation and prediction;

The mean of the differences is zero, while the mean of the absolute differences is 7.8% of

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Table 3. Dose, duration of bile collection and observed biliary excretion of twenty-five literature compounds in BDC rats

Compound Dose

(mg/kg)

Bile Collection

(h)

Observation

(% of dose)

Reference

BMS-182874 48 12 0.9 (Chong et al. 2003)

BMS-187345 45 12 4.5 (Chong et al. 2003)

BMS-387032 8.9 9 11.0 (Kamath et al. 2005a)

Cephradine 25 6 27.3 (Moriwaki et al. 2003)

Dasatinib 10 9 9.6 (Kamath et al. 2008)

Digitoxin 0.01 4 53.7 (Funakoshi et al. 2003)

Digoxin 0.08 12 59.0 (Russell and Klaassen 1973)

DNP-NAC 0.05 1 42.0 (Hinchman et al. 1998)

EPE-glucuronide 1 24 64.5 (Payan et al. 1999)

Erythromycin <1 1 34.0 (Akashi et al. 2006)

Estradiol-17β-glucuronide <1 1 87.0 (Akashi et al. 2006)

Fexofenadine 11.8 9 69.8 (Kamath et al. 2005b)




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Irinotecan (lactone form) 10 4 7.0 (Arimori et al. 2003)

Irinotecan (carboxylate form) 10 4 59.8 (Arimori et al. 2003)

Lissamine Fast Yellow 25.25 3 90.0 (Hirom et al. 1972b)

Methotrexate 10 2.5 72.0 (Masuda et al. 1997)

7-OH Methotrexate 4.0 3.3 72.0 (Fahrig et al. 1989)

Olmesartan <1 3 83.0 (Takayanagi et al. 2005)

Ouabain 0.08 12 55.0 (Russell and Klaassen 1973)

Phenolphthalein glucuronide 24.8 3 82.0 (Hirom et al. 1972b)

Pravastatin (carboxylate form) <1 1 80.0 (Kurihara et al. 2005)

Sitagliptin 2.0 72 16.4 (Beconi et al. 2007)

Taxol 10 24 11.5 (Monsarrat et al. 1990)

Vinblastine <1 1.5 30.0 (Kurihara et al. 2005)

Vincristine 0.5 2 42.6 (Song et al. 1999)

DNP-NAC = S-2,4-dinitrophenyl-N-acetylcystein;

EPE, 1-(2'-ethylphenyl)ethanol;

The mean of the differences is 3.9%, while the mean of the absolute differences is 13% of dose.




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Table 4. Physiochemical properties, observed and predicted biliary excretion of twenty-five literature compounds in BDC rats

Compound PSA

(sq. Ang)

ΔG solv aq

(kcal/mol)

Acid

(presence)

Observation

(% of dose)

Prediction

(% of dose)

BMS-182874 76.7 -16.7 - 0.9 -1.2

BMS-187345 87.5 -20.4 - 4.5 3.5

BMS-387032 75.6 -22.1 - 11.0 2.7 (2.8)

Cephradine 115.68 -29.5 + 27.3 65.7

Dasatinib 100.6 -35.5 - 9.6 15.9

Digitoxin 185.4 -45.2 - 53.7 43.9

Digoxin 205.9 -50.9 - 59.0 52.4

DNP-NAC 147.8 -19.8 + 42.0 69.3

EPE-glucuronide 120.2 -25.2 + 64.5 65.9

Erythromycin 179.9 -40.4 - 34.0 39.6

Estradiol-17β-glucuronide 141.9 -32.2 + 87.0 75.7

Fexofenadine 80.4 -27.8 + 69.8 57.7




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Irinotecan (lactone form) 106.9 -29.4 - 7.0 14.9

Irinotecan (carboxylate form) 134.6 -41.3 + 59.8 79.3

Lissamine Fast Yellow 169.6 -47.4 + 90.0 91.6

Methotrexate 199.2 -47.4 + 72.0 98.9

7-OH Methotrexate 220.2 -53.0 + 72.0 107.5

Olmesartan 143.1 -23.0 + 83.0 70.1

Ouabain 202.9 -58.2 - 55.0 56.7

Phenolphthalein glucuronide 174.7 -38.9 + 82.0 87.7

Pravastatin (carboxylate form) 130.3 -28.2 + 80.0 68.2

Sitagliptin 65.0 -28.3 - 16.4 4.0

Taxol 190.6 -44.1 - 11.5 44.5

Vinblastine 130.7 -26.4 - 30.0 18.9

Vincristine 144.5 -36.0 - 42.6 27.2




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Table 5 Structure similarity among fifty BMS compounds

Program Code

Similarity Pair

Similarity Cluster

Similarity Coefficient

D IV and IX 0.72

I XV and XVI

XV and XVIII

XV and XIX

XVI and XVIII

XVI and XIX

XVIII and XIX

0.77

0.72

0.72

0.74

0.93

0.72

I XXII and XXIII 0.72

J XXIV and XLIV

XL and XLIV

XL and XLV

XLIV and XLV

0.72

0.83

0.71

0.91

I XXV and XXVII 0.79

J XXVI and XXIX

XXVI and XXXI

XXVI and XXXVIII

XXIX and XXXI

XXIX and XXXVIII

0.92

0.88

0.76

0.95

0.71

J XXX and XXXII

XXX and XXXIII

XXX and XXXVI

XXXIII and XXXV

0.72

0.72

0.81

0.89


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XXXIII and XXXVI

XXXV and XXXVI

0.89

0.79

L XLI and XLII

XLI and XLIII

XLII and XLIII

0.71

0.97

0.73

J XLVII and XLVIII 0.88

The threshold of Tanimoto similarity coefficient for structure similarity used in the present

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Table 6 Structure similarity among 25 compounds from literature reports

Similarity Pairs Similarity Cluster Similarity Coefficient

BMS-182874 and BMS-187345 0.78

7-OH Methotrexate and Methotrexate 0.87

Digitoxin and Digoxin

Digitoxin and Ouabain

Digoxin and Ouabain

0.99

0.70

0.70

Irinotecan (carboxylate) and Irinotecan (lactone) 0.87

Vinblastine and Vincristine 0.96

The threshold of Tanimoto similarity coefficient used in the present study is 0.70




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Fig. 1

0

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40

60

80

100

0 2 4 6 8 10

XXXVIIIXLXLIVXXXIV

Time (h)

Bil

iary

Exc

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% o

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Fig. 2a

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20

40

60

80

100

200 300 400 500 600 700 800

N = 50

r = 0.60

Molecular Weight

Bil

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Exc

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% o

f do

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r2 = 0.36




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Fig. 2b

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100

40 80 120 160 200

Polar Surface Area (sq. Ang)

Bil

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Exc

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% o

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r = 0.76

r2 = 0.58




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Fig. 2c

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-60 -50 -40 -30 -20 -10

ΔG Solv aq (kcal/mol)

Bil

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Exc

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% o

f do

se) N = 50

r = -0.67

r2 = 0.45




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Fig. 3

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Calculated Biliary Excretion (% of dose)

Obs

erve

d B

ilia

ry E

xcre

tion

(%

of

dose

)

N = 50

R = 0.94

R2 = 0.89




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Fig. 4

-20

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80

100

-20 0 20 40 60 80 100 120


Obs

erve

d B

ilia

ry E

xcre

tion

(%

of

dose

)

N = 25

R = 0.86

R2 = 0.73




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Fig. 5

-20

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-20 0 20 40 60 80 100


Obs

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d B

ilia

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xcre

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(%

of

dose

)

N = 25

R = 0.86

R2 = 0.74




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-20

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-20 0 20 40 60 80 100

Fig. 6


Obs

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d B

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xcre

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(%

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)

N = 50

R = 0.94

R2 = 0.89




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-20

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-20 0 20 40 60 80 100

Fig. 7


Obs

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d B

ilia

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xcre

tion

(%

of

dose

)

N = 75

R = 0.92

R2 = 0.84




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