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DMD #26260
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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.
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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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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
Rat biliary excretion of 25 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
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 6 Correlation between predicted and observed rat biliary excretion of fifty BMS
compounds using Equation 2
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
corresponding physicochemical properties (Table 1). A correlation between the predicted
and observed rat biliary excretion was calculated using a simple linear regression of least
squares.
Figure 7 Correlation between predicted and observed rat biliary excretion of seventy five
compounds using Equation 3
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Rat biliary excretion of 75 compounds was predicted using the equation
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
the dose.
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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
study is 0.70.
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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
20
40
60
80
100
0 2 4 6 8 10
XXXVIIIXLXLIVXXXIV
Time (h)
Bil
iary
Exc
reti
on (
% o
f do
se)
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Fig. 2a
-20
0
20
40
60
80
100
200 300 400 500 600 700 800
N = 50
r = 0.60
Molecular Weight
Bil
iary
Exc
reti
on (
% o
f do
se)
r2 = 0.36
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Fig. 2b
-20
0
20
40
60
80
100
40 80 120 160 200
Polar Surface Area (sq. Ang)
Bil
iary
Exc
reti
on (
% o
f do
se) N = 50
r = 0.76
r2 = 0.58
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Fig. 2c
-20
0
20
40
60
80
100
-60 -50 -40 -30 -20 -10
ΔG Solv aq (kcal/mol)
Bil
iary
Exc
reti
on (
% o
f do
se) N = 50
r = -0.67
r2 = 0.45
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Fig. 3
-20
0
20
40
60
80
100
-20 0 20 40 60 80 100
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
0
20
40
60
80
100
-20 0 20 40 60 80 100 120
Calculated Biliary Excretion (% of dose)
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
0
20
40
60
80
100
-20 0 20 40 60 80 100
Calculated Biliary Excretion (% of dose)
Obs
erve
d B
ilia
ry E
xcre
tion
(%
of
dose
)
N = 25
R = 0.86
R2 = 0.74
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-20
0
20
40
60
80
100
-20 0 20 40 60 80 100
Fig. 6
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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-20
0
20
40
60
80
100
-20 0 20 40 60 80 100
Fig. 7
Calculated Biliary Excretion (% of dose)
Obs
erve
d B
ilia
ry E
xcre
tion
(%
of
dose
)
N = 75
R = 0.92
R2 = 0.84
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