Structural requirements of the ASBT by 3D-QSAR analysis using aminopyridine conjugates of chenodeoxycholic acid

Article abstract of DOI:10.1021/bc100273w

The human apical sodium-dependent bile acid transporter (ASBT) is a validated drug target and can be employed to increase oral bioavailability of various drug conjugates. The aim of the present study was to investigate the chemical space around the 24-position of bile acids that influences both inhibition and uptake by the transporter. A series of 27 aminopyridine and aminophenol conjugates of glutamyl-chenodeoxycholate were synthesized and their ASBT inhibition and transport kinetics (parametrized as K_i, K _t, and J_max) measured using stably transfected ASBT-MDCK cells. All conjugates were potent ASBT inhibitors. Monoanionic conjugates exhibited higher inhibition potency than neutral conjugates. However, neutral conjugates and chloro-substituted monoanionic conjugates were not substrates, or at least not apparent substrates. Kinetic analysis of substrates indicated that similar values for K_i and K_t implicate substrate binding to ASBT as the rate-limiting step. Using 3D-QSAR, four inhibition models and one transport efficiency model were developed. Steric fields dominated in CoMFA models, whereas hydrophobic fields dominated CoMSIA models. The inhibition models showed that a hydrophobic or bulky substitute on the 2 or 6 position of a 3-aminopyridine ring enhanced activity, while a hydrophobic group on the 5 position was detrimental. Overall, steric and hydrophobic features around the 24 position of the sterol nucleus strongly influenced bile acid conjugate interaction with ASBT. The relative location of the pyridine nitrogen and substituent groups also modulated binding.

Full text of DOI:10.1021/bc100273w

2038
Bioconjugate Chem. 2010, 21, 2038–2048
Structural Requirements of the ASBT by 3D-QSAR Analysis Using
Aminopyridine Conjugates of Chenodeoxycholic Acid
Xiaowan Zheng,‡ Yongmei Pan,‡ Chayan Acharya,† Peter W. Swaan, and James E. Polli*
Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore,
Maryland 21201, United States. Received June 17, 2010; Revised Manuscript Received August 26, 2010
The human apical sodium-dependent bile acid transporter (ASBT) is a validated drug target and can be employed
to increase oral bioavailability of various drug conjugates. The aim of the present study was to investigate the
chemical space around the 24-position of bile acids that influences both inhibition and uptake by the transporter.
A series of 27 aminopyridine and aminophenol conjugates of glutamyl-chenodeoxycholate were synthesized and
their ASBT inhibition and transport kinetics (parametrized as Ki, Kt, and Jmax) measured using stably transfected
ASBT-MDCK cells. All conjugates were potent ASBT inhibitors. Monoanionic conjugates exhibited higher
inhibition potency than neutral conjugates. However, neutral conjugates and chloro-substituted monoanionic
conjugates were not substrates, or at least not apparent substrates. Kinetic analysis of substrates indicated that
similar values for Ki and Kt implicate substrate binding to ASBT as the rate-limiting step. Using 3D-QSAR, four
inhibition models and one transport efficiency model were developed. Steric fields dominated in CoMFA models,
whereas hydrophobic fields dominated CoMSIA models. The inhibition models showed that a hydrophobic or
bulky substitute on the 2 or 6 position of a 3-aminopyridine ring enhanced activity, while a hydrophobic group
on the 5 position was detrimental. Overall, steric and hydrophobic features around the 24 position of the sterol
nucleus strongly influenced bile acid conjugate interaction with ASBT. The relative location of the pyridine nitrogen
and substituent groups also modulated binding.
oral bioavailability by 2-fold, compared to acyclovir in rats (8).
Overall, ASBT permits bile acid conjugates with substitutions
at the C-24 side chain region, but the chemistry space extending
beyond the C-24 region needs to be further explored, especially
for ASBT substrates. In fact, several quantitative structure-activity
relationships studies have been reported for general ASBT
inhibition. Thus, we sought to apply 3D-QSAR approaches to
elucidate the substrate requirements in the space extending
beyond the C-24 region using comparative molecular field
analysis (CoMFA) (9) and comparative molecular similarity
indices analysis (CoMSIA) (10). These algorithms are particu-
larly useful when the structure of a target protein is unknown
and may be applied for lead compound optimization (11).
The objective of this study was to synthesize and characterize
substrate uptake and inhibition properties of C-24 bile acid probe
moiety conjugates, and subsequently develop comprehensive
3D-QSAR models to elucidate the substrate requirements on
positions beyond C-24 region. Pyridine and phenol are common
chemical scaffolds in drug structures (12). Therefore, C-24
aminopyridine and aminophenol conjugates of glutamyl-CDCA
were synthesized and their inhibition and uptake kinetics were
evaluated. Pyridine and phenol probes were functionalized using
different ring substitutions in order to study chemical substituent
effect on ASBT inhibition and uptake. Using a combination of
inhibition and uptake studies, we differentiate ASBT inhibitors
from substrates and developed 3D-QSAR models for both
classes of bile analogues. These studies provide new insight
into the substrate requirements for ASBT uptake and shed light
on the design of bile acid conjugates that can increase intestinal
drug permeability and bioavailability.
INTRODUCTION
Bile acids are primarily absorbed in the terminal ileum via
the apical sodium-dependent bile acid transporter (ASBT;
SLC10A2) (1, 2), which displays high transport capacity and
efficiency (3). This transporter may also be employed as a
prodrug target for enhancing oral drug bioavailability of poorly
permeating compounds, where drug is conjugated to bile acid
and bile acid allows for the conjugate to be taken up by ASBT
(3). However, the lack of knowledge of ASBT substrate
requirements has impeded progress in rational prodrug design
to target ASBT. Structure-activity studies have largely em-
ployed ASBT inhibition rather than substrate uptake, perhaps
due to inhibition studies being more facile. Various transport
studies have been performed over 30 years using animal tissues
and organs, but the interpretability of the bile acid transport by
ASBT has been confounded by other intestinal and hepatic
transporters (4).
Our laboratory previously developed a stably transfected
ASBT-MDCK monolayer to evaluate ASBT-mediated transport
of solutes (5). Using a series of native bile acids to evaluate
ASBT binding properties, we found that chenodeoxycholate
(CDCA) was the most potent inhibitor among native bile acids
and conjugation through C-24 of CDCA with taurine or glycine
enhanced binding affinity (6). A series of C-24 CDCA conju-
gates were then synthesized using glutamic acid or lysine as a
linker, and the results showed that only conjugates with one
negative charge and a single bulky ester substituent proximal
or distal to the C-24 region were ASBT substrates (7).
Furthermore, a C-24 bile acid prodrug of valacyclovir enhanced
* To whom correspondence should be addressed. E-mail: jpolli@
rx.umaryland.edu. Tel. +1410-706-8292; Fax: +1 410-706-5017.
‡ Xiaowan Zheng and Yongmei Pan contributed equally to this work.
† Present Address: Institut Europe´en de Chimie et Biologie, Uni-
versite´ Bordeaux 1, 2 rue Robert Escarpit, 33607 Pessac Cedex, France.
EXPERIMENTAL PROCEDURES
Materials. [3H]-Taurocholic acid was purchased from Perkin-
Elmer (Waltham, MA). Taurocholic acid was purchased from
Sigma (St. Louis, MO). CDCA was obtained from TCI America
10.1021/bc100273w  2010 American Chemical Society
Published on Web 10/22/2010

Conjugate QSAR for ASBT Transporter
Bioconjugate Chem., Vol. 21, No. 11, 2010 2039
Scheme 1. General Synthetic Approaches to Obtain CDCA-Glutamyl-Pyridine or CDCA-Glytamyl-Phenol Conjugatesa
a X ) N, H; Y ) H, CH3, OCH3, NH2, OH, Cl, F, Br; Z ) H, CH3.
(Portland, OR). Protected glutamic acid analogues were from
Novabiochem (Gibbstown, NJ). Aminopyridine analogues were
purchased from Sigma Aldrich (St. Louis, MO) and Matrix
Scientific (Columbia, SC). Fetal bovine serum (FBS), trypsin, and
Dulbecco’s modified Eagle’s medium (DMEM) were purchased
from Invitrogen Corporation (Carlsbad, CA). All other reagents
and chemicals were of the highest purity commercially available.
Synthetic Procedures. A series of aminopyridines were
conjugated to CDCA using glutamic acid as a linker. Two
synthetic approaches were employed (Scheme 1). Among the
native bile acids, CDCA was selected on the basis of its
favorable transport across ASBT-MDCK monolayers (6).
N-Hydroxybenzotriazole (HOBT) Esters of CDCA (II). CDCA
I (5 g) was activated with HOBT (0.5 equiv) and HBTU (1.2
mmol) in 20 mL of anhydrous dimethylformamide (DMF) along
with 1.2 equiv of triethylamine (TEA) at RT for 4 h to form
CDCA-OBt ester II. The reaction was terminated by adding
50 mL of water. Subsequently, the reaction mixture was
extracted with ethyl acetate (EtOAc) (3×). The combined
organic layer was washed with brine (1×), dried with anhydrous
sodium sulfate, filtered, and evaporated under vacuum to yield
a white fluffy solid CDCA-OBt ester II (90% yield). Mass
spectrometry (MS) showed appropriate peaks: [M+H]+ 510.81,
[M+Na]+ 532.81. II was used in approaches A and B.
followed by addition of appropriate aminopyridine or ami-
nophenol probes at 60 °C overnight. Reaction was terminated
by adding 30 mL of water. The reaction mixture was extracted
with EtOAc (3×). The combined organic layer was washed with
1 N HCl (3×), 1 N NaOH (3×), and brine (1×). Then, the
organic extract was dried with anhydrous sodium sulfate and
filtered. The EtOAc extract was evaporated under vacuum to
yield thick, yellow oil. The crude product was further purified
by silica gel column chromatography using a mobile phase of
EtOAc and hexane (95:5). Fractions were collected and solvent
evaporated to yield the neutral conjugate V. Purity of the
conjugate was characterized by TLC and MS techniques.
Approach 2 was applied as shown in Scheme 1. First, 2.0 g
of N-boc-R-benzyl-glutamic acid VII was coupled to aminopy-
ridine or aminophenol probes (1.1 equiv) using HBTU (1.2
equiv) as coupling reagents in 10 mL of anhydrous DMF along
with of 2.2 equiv of TEA at 70 °C overnight. Reaction was
terminated by adding 30 mL of water. The reaction mixture
was extracted with EtOAc (3×). The combined organic layer
was washed with 1 N HCl (3×), 1 N NaOH (3×), and brine
(1×). Then, the organic extract was dried with anhydrous
sodium sulfate, filtered, and evaporated under vacuum to yield
orange oil. The crude product was further purified by silica gel
column chromatography using a mobile phase of EtOAc and
hexane (90:10). Fractions were collected and solvent evaporated
to give a solid, white intermediate. Purity of the intermediate
conjugates was characterized by TLC and MS techniques. The
intermediate was then selectively N-deprotected using 25%
trifluoroacetic acid (TFA) in anhydrous dichloromethane (DCM)
at RT for 1-2 h. Solvent was evaporated under vacuum to yield
off-white solid VIII.
Synthesis of CDCA-R-Benzyl-Glutamyl Probe (V). Approach
1 was applied as shown in Scheme 1. II (5.0 g) was reacted
with to 0.8 equiv of L-R-benzyl glutamic acid III in 30 mL of
anhydrous DMF along with 1 equiv of TEA at 60 °C overnight.
Reaction was terminated by adding 50 mL of water. The reaction
mixture was extracted with EtOAc (3×). The combined organic
layer was washed with 1 N HCl (3×) and brine (1×). Then,
the organic extract was dried with anhydrous sodium sulfate,
filtered, and evaporated under vacuum to yield off-white solid
glutamyl-CDCA conjugates IV (75% yield). MS showed
appropriate peaks: [M+H]+ 612.81, [M+Na]+ 634.81.
VIII (1.0 g) was reacted with 1.2 equiv of CDCA-OBt ester
II, along with 1 equiv of TEA in 15 mL of anhydrous DMF at
RT overnight. Reaction was terminated by adding 50 mL of
water. The reaction mixture was purified as described above
(see synthetic approach I) to yield the neutral conjugate V.
IV (1.0 g) was activated with 1.2 equiv of HBTU in 15 mL
of anhydrous DMF along with 1.2 equiv of TEA for 10 min

2040 Bioconjugate Chem., Vol. 21, No. 11, 2010
Zheng et al.
Synthesis of CDCD-Glutamyl Probe (VI). The neutral con-
jugates V were subjected to catalytic hydrogenation for the
removal of the benzyl ester using 10% Pd/charcoal in ethanol
at 50 psi overnight. Suspension was filtered through Celite and
solvent evaporated under vacuum to yield the final conjugates
VI. For chloro- and bromo-substituted neutral conjugates V,
the benzyl ester was removed in 10 mL 33% hydrobromic acid
in acetic acid at RT for 2 h. Reaction was terminated by adding
30 mL of EtOAc. The reaction mixture was extracted with water
(3×). The organic layer was washed with brine (1×), dried with
anhydrous sodium sulfate, filtered, and evaporated under vacuum
to yield the final conjugates VI.
Figure 1. General structure of CDCA-glutamyl-probe (pyridines,
phenol) conjugates. Database alignment employed the sub-structure
CDCA-glutamate.
Characterization of Conjugates. Identity and purity of bile
acid conjugates was characterized by TLC, MS, and H NMR
1
trometry using [M+H]+ peak for all conjugates. The mobile
phase and retention time for each conjugate are detailed in
Supporting Information Table S1. The limit of quantification
for all conjugates was between 1 and 2.5 nM.
Kinetic Analysis. Inhibition data were analyzed in terms of
inhibition constant Ki and used a modified Michaelis-Menten
competitive inhibition model, as previously described, taking
into account aqueous boundary layer (ABL) resistance (17).
(proton, COSY). MS was performed on a LCQ ESI-MS (Thermo
1
Fisher Scientific Inc., Waltham, MA). H NMR spectra were
recorded on a Varian Inova 500 MHz (Varian Inc., Palo Alto, CA).
The mass spectra in all cases gave a molecular ion in agreement
with the calculated mass of the conjugates. No CDCA impurity
was detected in any final conjugate (13). LC-MS/MS and 1H NMR
results are compiled in Supporting Information Table S1. The
chemical shifts are reported in parts per million (ppm) relative to
tetramethylsilane (TMS) (δ ) 0.0 ppm).
Cell Culture. Stably transfected ASBT-MDCK cells were
grown as previously described (14). Briefly, cells were grown
at 37 °C, 90% relative humidity, 5% CO2 atmosphere and fed
every two days. Media comprised DMEM supplemented with
10% FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin.
Cells were passaged after reaching 90% confluency.
Inhibition Study. To characterize compound binding affini-
ties to ASBT, cis-inhibition studies of taurocholate uptake were
conducted as previously described (15). Briefly, cells were
seeded in 12 well cluster plates (Corning; NY) at a density of
1.5 million cells/well. Inhibition studies were performed on the
fifth day in Hank’s Balance Salts Solution (HBSS). Cells were
exposed to donor solution containing 2.5 µM taurocholate
(spiked with 0.5 µCi/mL [3H]-taurocholate) in the presence of
a conjugate at eight concentrations (0-100 µM) for 10 min.
Cells were lysed using NaOH. Cell lysate was counted for
taurocholate radioactivity using a liquid scintillation counter
(Beckmann Instruments, Inc., Fullerton, CA). For neutral
conjugates, 2.5% DMSO was included in transport buffer, which
has been shown to not affect ASBT kinetics (16).
Uptake Study. The uptake of conjugates by ASBT, repre-
senting cellular absorption mediated by the transporter, was
evaluated. Cells were seeded as above for inhibition studies.
Uptake studies employed HBSS buffer or modified HBSS buffer
(i.e., sodium-free). Since ASBT is a sodium-dependent trans-
porter, studies using sodium-free buffer enabled passive perme-
ability measurement of conjugate. Cells were exposed to donor
solution contain eight different conjugate concentrations (1-500
µM). Subsequently, cells were lysed as previous described (16)
by adding 0.25 mL acetonitrile to each well, following evapora-
tion at RT. A solution (1 mL) containing 50% water and 50%
acetonitrile was added to each well for 10 min. Sample was
stored in -80 °C until analysis by LC-MS/MS. Taurocholate
uptake was measured on each study occasion as a control.
Uptake data were analyzed in terms of Kt, Jmax, normJmax
,
and Pp, where Jmax is the transport capacity; normJmax is
normalized conjugate Jmax against taurocholate Jmax from the
same occasion; Kt is the Michaelis-Menten coefficient for
ASBT-mediated transport; and Pp is the conjugate passive
permeability coefficient. For 18, estimated Kt was less than 0.1
µM, such that Kt was assigned a value of 0.1 µM, due to the
limited ability to accurately estimate potent Kt values (17). Data
were analyzed using WinNonlin Professional (Pharsight Cor-
poration; Mountain View, CA).
3D-QSAR Analysis. 3D-QSAR models were developed for
Ki and normJmax/Kt. The inhibition constant Ki reflects compound
binding affinity to ASBT, with smaller values denoting greater
binding. normJmax/Kt reflects the efficiency of conjugate trans-
location by ASBT.
The implementation of the CoMFA and CoMSIA techniques
was performed as described previously (18, 19). Briefly,
conjugates were sketched with Sybyl 7.2 (Tripos Inc., St. Louis,
MO). Each structure was minimized using the Powell method
and Tripos force field (20) with maximum iterations of 2000
steps and termination gradient of 0.001 kcal/mol. Partial atomic
charges were computed by the Gasteiger-Hu¨ckel method (21).
Conjugates were aligned via “database alignment” based on a
template structure in Figure 1. The reference compound during
alignment was 12 for Ki models and 15 for the normJmax/Kt
model. A regularly placed grid of 2.0 Å was created around
aligned molecules. An sp3 carbon atom with charge +1.00 was
used as a probe atom. The steric and electrostatic fields were
truncated at +30.00 kcal/mol. In contour maps of models, areas
with above 80% favorability among the fields being viewed were
regarded as beneficial for the biological activity, while those
with field values below 20% were regarded as detrimental. The
attenuation factor was 0.3. Models were obtained using the
partial-least-squares (PLS) analysis (22). Leave-one-out cross
validation (LOO) was performed to determine the optimum
number of components, and nonvalidation PLS was applied to
obtain the final model. Models were evaluated by the cross-
validated standard coefficient q2 and correlation coefficient r2.
A model with high q2 (above 0.5), high r2, low standard error
of prediction (SEP), and low standard error of estimate (SEE)
was considered to possess good predictability. Models were also
evaluated using a test set of four randomly selected conjugates.
Analytical Methods. The conjugate was quantified by LC-
MS on a Thermo Finnigan Surveyor HPLC system, equipped
with a Thermo Finnigan Surveyor autosampler and a Thermo
TSQ quantum mass spectrometer (Thermo Fisher Scientific Inc.,
Waltham, MA). The column used was a Phenomenex Luna C8
(50 × 2.0 mm, 3 µm; Phenomenex; Torrance, CA, USA) heated
to 45 °C. The mobile phase was A, 0.1% formic acid in water;
and B, 0.1% formic acid in acetonitrile, with a flow rate of 0.4
mL/min. The injection volume was 10 µL. Detection was
achieved under positive ion electrospray tandem mass spec-
RESULTS AND DISCUSSION
Synthesis. A series of aminopyridine and aminophenol
conjugates of glutamyl-CDCA were synthesized. The general

Conjugate QSAR for ASBT Transporter
Bioconjugate Chem., Vol. 21, No. 11, 2010 2041
Table 1. Structures and the Biological Activities of the Bile Acid Conjugatesa

2042 Bioconjugate Chem., Vol. 21, No. 11, 2010
Zheng et al.
Table 1. Continued
a Data were summarized as mean ((SEM) of three measurements. b To correct for variation in ASBT expression levels across studies, Jmax of each
bile acid was normalized against taurocholate Jmax from the same occasion. c 2, 18, 22, and 25 were used as the test set. The other 23 compounds were
used as the training set. d Not determinable owing to high passive permeability. e Not measurable due to lack of transportation by ASBT.
structure is depicted in Figure 1, and all conjugate structures
are drawn in Table 1. The synthetic strategy was based on
coupling the carboxyl group at the C-24 position of CDCA to
the amino group of the common drug scaffolds pyridine or
phenol, using glutamic acid as a linker (Scheme 1). A negative
charge around the C-24 region was designed to promote ASBT
translocation (7, 23). The corresponding neutral analogues with
R-benzyl protection on the glutamic acid linker were also
synthesized. Unsubstituted, monosubstituted, and disubstituted
pyridine and phenol were employed as functioned probe
moieties that yielded conjugates with varying chemistry in the
region beyond C-24.
ASBT Inhibition. Table 1 lists Ki values of the 27 conjugates,
which included 24 pyridine-based conjugates and 3 phenol-based
conjugates. Neutral conjugates with R-benzyl protection on C-24
were poorly water-soluble. Therefore, among the 24 neutral
conjugates that were synthesized, only 11, 13, and 17 allowed
Ki to be measured.
All 24 monoanionic conjugates were potent inhibitors with
Ki ranging from 1.51 to 23.6 µM with CDCA-glutamyl-3-
aminopyridine 2 being the most potent inhibitor. They are in
the range of native bile acids. The three neutral conjugates were
less potent than their corresponding monoanionic conjugates
by approximately 10-20-fold.
ASBT Uptake. While all 27 conjugates exhibited high
inhibition potency, only 22 showed to be substrates. Table 1
lists kinetic parameters Kt, normJmax, normJmax/Kt, and Pp. Kt
represents transporter affinity. To accommodate variation in
ASBT expression levels across studies, Jmax of each bile acid
was normalized against taurocholate Jmax from the same occasion
yielding normJmax. The normJmax/Kt value represents the ASBT
mediated transport efficiency. Neutral compound 17 exhibited
a high passive permeability, such that it was not able to be
assessed as a substrate or not. The other two neutral compounds,
11 and 13, were not further evaluated. Results from 17 were
consistent with previous findings that a negative charge around
the C-24 region is required for translocation (7).
Kt values ranged from 0.1 µM for 18 to 92.2 µM for 12.
normJmax ranged from 0.0791 for 5 to 2.67 for 18. normJmax/Kt
ranged from 0.00388 µM-1 for 6 to 26.7 µM-1 for 18. Pp ranged
from 1.19 × 10-7 cm/s for 5 to 100 × 10-7 cm/s for 22. Figure
2A shows the uptake profile of 18, which was the most potent
substrate in terms of both Kt and normJmax; Figure 2B shows
the nonsubstrate 23.
Ring substituent position had an important effect on transport
efficiency. For example, for 3-aminopyridine conjugates, sub-
stitution at position 2 led to more potent substrate compared to
other positions, e.g., 5 (11.2 µM) vs 7 (21.2 µM), and 18 (0.1
µM) vs 19 (4.30 µM) when substitution is moved from 2 to 5
position. Substitution at position 6 significantly decreased
activity, when 8 (24.6 µM) is compared with 5 and 7; 20 (11.7
µM) is compared with 18 and 19; 12 (92.2 µM) is compared
with 5; 22 (83.6 µM) is compared with 21 (33.0 µM). The type
of substituent group also influenced Kt. For example, all fluoro-
substituted conjugates were potent substrates, bromo-substituted
conjugates were weak substrates, while chloro-substitution
abolished uptake.
Comparison of Inhibition Kinetics vs Transporter Kinet-
ics. Transporter-mediated translocation minimally involves
substrate binding to the transporter, transporter conformation
changes involving substrate translocation, dissociation of sub-
strate from transporter, and transporter conformation restoration.

Conjugate QSAR for ASBT Transporter
Bioconjugate Chem., Vol. 21, No. 11, 2010 2043
Figure 2. Concentration-dependent uptake of (A) CDCA-glutamyl-3-
amino-2-fluoropyridine 18 and (B) CDCA-glutamy-3-amino-6-chloro-
pyridine 23 into ASBT-MDCK monolayers. The conjugate was
measured at varying concentrations (2.5-500 µM) in the presence and
absence of sodium to delineate ASBT-mediated and passive uptake
components. Data indicate total uptake in the presence of sodium (•)
and passive uptake in the absence of sodium (O). Uptake of 18 was
several-fold higher in the presence of sodium than in the absence of
sodium, reflecting ASBT-mediated uptake. Uptake of 23 was the same
in the presence and absence of sodium; passive uptake was high, such
that the compound was either not a substrate or not measurable as a
substrate due to high passive permeability.
Figure 3. Relationship of Kt and Ki. (A) For all 22 bile acid conjugates
that were substrates, Kt vs Ki are plotted. 6, 12, and 22 exhibited several-
fold higher Kt values than Ki values. (B) With 6, 12, and 22 removed,
most conjugates yielded similar Kt and Ki. Linear regression showed
slope ) 0.995, r2 ) 0.403, and p ) 0.003. 14 and 18 yielded Kt , Ki,
although all were <10 µM.
The initial binding step is assessed by Ki. Results show that
ASBT inhibition requirements were different from the ASBT-
mediated transport requirements. A potent inhibitor may be a
weak substrate, such as 6, 12, and 22. Figure 3A illustrates the
relationship of transporter affinity Kt and inhibition potency Ki
for all 22 substrates. Figure 3B excludes 6, 12, and 22, where
Kt was notably weaker than Ki. For these 19 conjugates in Figure
3B, there was a modest association between Kt and Ki with linear
slope ) 0.995, r2 ) 0.403, and p ) 0.003. Qualitatively, most
conjugates yielded similar potency of Kt and Ki, suggesting that
conjugate binding was broadly the slow kinetic step for
conjugate translocation. These results agree with previous native
bile acid findings (6). It should be noted that 14 and 18 yielded
Kt values that were over 7-fold more potent than Ki although
they both were potent inhibitors and substrates (e.g., Ki and Kt
< 10 µM). For 6, 12, and 22, where Kt was far less potent than
Ki, postbinding events were rate-limiting.
Figure 4. Relationship between normJmax and Ki. Transporting capacity
was not associated with inhibition potency. Linear regression showed
a slope of -0.008 µM-1 and r2 ) 0.003. 15, 18, 22, and 25 yielded
Jmax higher than taurocholate. All other conjugates exhibited Jmax less
than taurocholate Jmax, and ranged from 0.0791 to 0.716.
Figure 4 shows that transport capacity was not associated
with inhibitory potency. Capacities of most conjugates spanned
a narrow range and were less than taurocholate. However,
normJmax of 15, 18, 22, and 25 were higher than that of
taurocholate. Supporting Information Figures S1 and S2 show
that transport capacity and transport efficiency were not related
to Kt or Ki, respectively.
ASBT Inhibition 3D-QSAR Models. Figure 5 shows the
superimposition of all 27 compounds for inhibition model
development. Twenty-three compounds were included in the
training set. The remaining, randomly selected four compounds
(i.e., 2, 18, 22, and 25) were used as the test set. Four 3D-
QSAR models, including one CoMFA and three CoMSIA
models, were developed using pKi data (Table 2). CoMSIA-1
included electrostatic and steric fields, whereas CoMSIA-2
included an additional hydrophobic field. CoMSIA-3 included
the three molecular descriptors from models 1 and 2, as well as
hydrogen bond donor and acceptor fields.
CoMFA and CoMSIA models were comparable in terms of
their predictability of pKi, with CoMFA-1 (q2 ) 0.675, r2 )
0.965) and CoMSIA-3 (q2 ) 0.612, r2 ) 0.958) being the best
inhibition models (Table 2). Figure 6 shows the relationship of
observed versus predicted pKi for training set and test set of
CoMFA-1 and CoMSIA-3. The corresponding data are listed
in Supporting Information Table S2. Both models accurately
predicted activities of three out of the four test compounds. 2
was underpredicted by both models, where the observed Ki was
1.51 µM and predicted Ki were 8.13 µM and 11.7 µM by
CoMFA-1 and CoMSIA-3, respectively.
Table 2 also shows the relative contributions of specific fields
to the inhibition activities. For CoMFA-1, the steric feature

2044 Bioconjugate Chem., Vol. 21, No. 11, 2010
Zheng et al.
and electrostatic features in CoMSIA-1 were 37.7% and 62.3%,
respectively. The different effects of steric and electrostatic fields
between CoMFA and CoMSIA are attributed to their energy
function forms. The contribution of the steric feature in CoMSIA
is further reduced when the hydrophobic field was added, with
12.0% and 8.4% for CoMSIA-2 and CoMSIA-3, respectively.
For these two models, the hydrophobic was the most determin-
ing feature that contributed 50.2% and 40.8%, respectively.
Considering the improved q2 in CoMSIA-2 and CoMSIA-3 vs
CoMSIA-1, the hydrophobic feature was the major contributor
to improve pKi predictability. Hydrogen bond donor and
acceptor in CoMSIA-3 contributed 13.1% and 17.3%, respec-
tively, and account for the slight q2 increase from 0.572 in
CoMSIA-2 to 0.612 in CoMSIA-3.
3D-QSAR Contour Maps for Inhibition. Figures 7 and 8
illustrate features of the CoMFA-1 and CoMSIA-3 models,
respectively. Contour maps were drawn relative to CDCA-
glutamyl-3-amino-2,6-dimethylpyridine 12.
Figure 5. Superimposition of training set and test set compounds for
CoMFA Contour Maps. The steric contours from CoMFA-1
(Figure 7A) reveal that bulky groups at the 2 or 6 position (green
region) of 3 aminopyridine afford beneficial steric interactions
for inhibition, whereas bulky substitutes on position 5 (yellow
region) are detrimental. The contours explain the higher activity
of 2,6-dimethyl-substituted 12 (Ki ) 1.94 µM) over 2-methyl
substituted 8 (7.87 µM) and 6-methyl substituted 5 (4.66 µM).
The two green regions also accounted for the higher activity of
5 and 8 than 6 (11.1 µM) and 7 (23.64 µM), where the methyl
group is moved from 2 or 6 to 4 or 5. The same pattern is
evident for bromo substitution. When bromine is moved from
position 6 in 22 to 5 in 21, the activity decreased from 4.96 to
21.1 µM. The electrostatic contour from CoMFA-1 (Figure 7B)
indicates that an electropositive group near the -COO (blue
region) will be favorable. Considering that a hydroxyl hydrogen
on a carboxylic group bears a positive charge, this blue area
reflects that a carboxylic acid near C-24 improved inhibition
more than the corresponding conjugates with R-benzyl protec-
tion. Inhibition potency decreased from 6.61 to 122 µM
comparing 10 to 11, from 1.94 to 28.1 µM comparing 12 to
13, and from 3.07 to 17.78 µM comparing 16 to 17. These
findings agree with previous observations that a negative charge
near C24 region is preferred (7). In Figure 7B, the red contour
region behind the 2 position and a blue contour region near the
6 position of the 3-aminopyridine ring indicate that an electro-
negative or electropositive group enhanced inhibition.
inhibition model development.
Table 2. Result from CoMFA and CoMSIA Analysis
CoMFA-1a CoMSIA-1a CoMSIA-2a CoMSIA-3a CoMFA-2b
q2
0.675
0.255
0.965
0.004
5
0.388
0.349
0.879
0.155
5
0.572
0.292
0.959
0.091
5
0.612
0.278
0.958
0.091
5
0.538
0.550
0.999
0.025
9
SEPc
r2
SEEc
NOCc
Fraction
steric
0.731
0.269
0.377
0.623
0.120
0.378
0.502
0.084
0.202
0.408
0.131
0.173
0.612
0.388
electronic
hydrophobic
donor
acceptor
a Models for pKi. b Model for log(normJmax/Kt). c SEP: standard error
of prediction; SEE: standard error of estimate; NOC: number of
components.
CoMSIA Contour Maps. Steric contour maps from CoMSIA-3
(Figure 8A) reveal that the green regions are patterned similarly
to those from CoMFA-1 (Figure 7A). However, CoMSIA-3
provides two additional yellow contours above the carboxylate
group indicating that a steric interaction near -COO is
unfavorable. The yellow areas are consistent with the blue
electrostatic contour of CoMFA-1 (Figure 7B), where R-benzyl
protection of the carboxylate decreased the inhibition potency
(i.e., 10 vs 11, 12 vs 13, and 16 vs 17). Electrostatic contours
(Figure 8B) are consistent with the patterns observed in
CoMFA-1 (Figure 7B), e.g., red and blue regions around 2 and
6 position of the 3-aminipyridine ring. Hydrophobic contour
plots (Figure 8C) indicate that hydrophobic substitution on the
2 or 6 position enhances inhibition (orange regions), whereas
such substitution on the 5 position reduces potency (white
contour). This result is consistent with steric contours of
CoMFA-1 (Figure 7A). Hydrogen bond donor contours (Figure
8D) suggest that a -OH in the carboxyl group can be regarded
as a hydrogen bond donor. The cyan area near the -COO group
agrees with the steric (Figure 8A) and hydrophobic contours
(Figure 8C), indicating that the R-benzyl group on -COO
remarkably reduced the inhibition activities. The purple area
near the pyridine ring indicates that a hydrogen-bond donor
Figure 6. Observed versus predicted pKi plot of the CoMFA-1 (A)
and CoMSIA-3 (B) using 23 training set conjugates (•) and 4 test set
conjugates (O). The solid line is the regression for the training set.
The regression line has a slope ) 0.965 and r2 ) 0.965 for CoMFA-1,
and 0.959 and 0.958 for CoMSIA-3, respectively.
dominated (73.1% contribution) the electrostatic feature. For
all CoMSIA models, the steric feature was less important than
the electrostatic feature. For example, the contributions of steric

Conjugate QSAR for ASBT Transporter
Bioconjugate Chem., Vol. 21, No. 11, 2010 2045
Figure 7. Steric (A) and eletrostatic (B) contour maps around compound CDCA-glutamyl-3-amino-2,6-dimethylpyridine 12 for CoMFA-1 inhibition
model. In panel A, green contours show where bulky substitute favored inhibition, while yellow contours were disfavored. In panel B, blue region
represents favorable electropositive interaction for inhibition, while red shows disfavored electropositive character.
Figure 8. Steric (A), electrostatic (B), hydrophobic (C), hydrogen bond donor (D), and hydrogen bond acceptor (E) contour maps around CDCA-
glutamyl-3-amino-2,6-dimethylpyridine 12 for CoMSIA-3 inhibition model. In panel A, green and yellow contours represent favorable and unfavorable
bulky substituent interaction regions for inhibition. In panel B, blue and red contours represent regions that favor electropositive and electronegative
groups, respectively. In panel C, orange and white contours are areas where hydrophobic groups enhance and diminish activity. In panel D, cyan
and purple contours indicate regions where an inhibitor’s hydrogen bond donor is favored and disfavored, respectively. In panel E, magenta and red
contours represent favorable and unfavorable hydrogen-bond acceptor regions, respectively.

2046 Bioconjugate Chem., Vol. 21, No. 11, 2010
Zheng et al.
ASBT Transport Efficiency Model. As with the inhibition
models, 2, 18, 22, and 25 were reserved as test compounds for
transport efficiency modeling. Figure 9 shows the superimposi-
tion of all 22 compounds for transport efficiency model
development. No QSAR model was derived from Kt data with
significant q2.
A comprehensive CoMFA model (CoMFA-2) was derived
from log(normJmax/Kt) (Table 2). The q2 and r2 values were 0.538
and 0.999, respectively. Similar to CoMFA-1, steric fields
dominated the electrostatic fields for transport efficiency with
contributions of 61.2% and 38.8%, respectively. Figure 10 shows
the relationship between observed and predicted uptake ef-
ficiencies of the training and test set (corresponding data in
Supporting Information Table S2). The uptake efficiencies of
test sets 22 and 25 were underestimated by 2- and 6-fold,
respectively. 2 and 18 were significantly underestimated by
approximately 9- and 400-fold, respectively. A potential con-
tributing factor to the prediction error of 18 is that its uptake
efficiency was over 100-fold greater than that of all other
conjugates. The underestimated activity of compound 2 might
be due to chemical or physical features beyond the predictive
capacity of CoMFA and CoMSIA models.
Figure 9. Superimposition of training set and test compounds for
transport efficiency model development.
CoMFA Model Contours for Transport Efficiency. Figure
11 shows steric and electrostatic contours of CoMFA-2 relative
to CDCA-glutamyl-2,6-diaminopyridine 15, the most efficient
ASBT substrate within the training set (normJmax/Kt ) 0.324
µM-1). As above, a large green contour near positions 5 and 6
on the pyridine ring in Figure 11A indicates that a bulky group
is beneficial. Compared to other position 6 substituents, transport
efficiency of 14 with a methoxy group at position 6 on the
3-aminopyridine ring (normJmax/Kt ) 0.202 µM-1) was high
compared to 8 (methyl substituted, 0.0119 µM-1), 20 (fluoro,
0.0262 µM-1), and 22 (bromo, 0.0415 µM-1). The other green
region near 1 and 6 positions on the pyridine ring of 15 indicates
that steric interaction is favorable. A yellow contour between 4
and 5 positions indicates that a steric substituent will decrease
efficiency. For example, fluoro substitution exhibited higher
efficiencies than corresponding bromo substitution (19 vs 21).
Figure 11B shows the electrostatic contour. The large blue
region near the 4 and 5 positions on the 2,6-aminopyridine ring
reflects the lower efficiency of the para hydroxyl in 27 compared
to the ortho hydroxyl in 25 or the meta hydroxyl in 26. The
small blue region under the 6 position of 15 agrees with its
high efficiency, compared to compounds lacking a 6-amino
substitute such as 1. The red region near the 4 position of 15
indicates that an electronegative substitute at the 4 position on
2-aminopyridine could enhance the transport efficiency.
Figure 10. Observed vs predicted log(normJmax/Kt) plot of the
CoMFA-2 using 18 training set conjugates (•) and 4 test set conjugates
(O). The solid line is the regression for the training set. The regression
line has slope ) 1.00 and r2 ) 0.999.
behind the 2 and 6 positions of the 3-aminopyridine ring is
detrimental. Hydrogen bond acceptor contours (Figure 8E)
determine favorable (magenta) areas near the pyridine nitrogen
and a red (disfavored) area around the 6 position on 3-ami-
nopyridine ring, reflecting the decreased activity from 2.43 to
7.18 µM when a hydroxyl group is moved from the meta
position in 26 to the para in 27 on the phenol ring.
Comparison of CoMFA and CoMSIA Models for Inhibi-
tion. CoMFA-1 possessed slightly greater predictability than
CoMSIA-3 in terms of q2 (0.675 and 0.612, respectively).
However, the advantage of CoMSIA is the employment of a
Gaussian-type energy calculation and additional molecular
descriptors, including hydrophobic fields, hydrogen bond ac-
ceptor, and hydrogen bond donor. For example, the influence
of the R-benzyl group is not accommodated by the steric contour
of CoMFA-1 (Figure 7A), but is accommodated by the steric
and hydrophobic contours of CoMSIA-3 (Figure 8A,B). The
hydrophobic contour of CoMSIA-3 had a similar pattern to the
steric contour of CoMFA-1, indicating that the hydrophobic field
of CoMSIA-3 may share common features with the steric field
of CoMFA-1. The similarity is not surprising, because a large
hydrophobic substitution may have significant steric effects.
LogP Effects on ASBT Inhibition and Transport. Partition
coefficient (LogP) is a measurement of compound solubility and
lipophilicity (24). Since CoMFA and CoMSIA models indicated
that steric and hydrophobic features strongly influenced con-
jugate interaction with ASBT, LogP may explain inhibition or
transport efficiency. Calculated LogP (CLogP) was computed
Figure 11. Steric (A) and eletrostatic (B) contour maps around CDCA-glutamyl-2,6-diaminopyridine 15 for CoMFA-2 transport efficiency model.
In panel A, green contours show where bulky substitute favored inhibition, while yellow contours were disfavored. In panel B, the blue region
represents favorable electropositive interaction for inhibition, while red shows disfavored electropositive character.

Conjugate QSAR for ASBT Transporter
Bioconjugate Chem., Vol. 21, No. 11, 2010 2047
ments of ASBT inhibition. Baringhaus and co-workers (26)
explored the chemical features of ASBT inhibitors with 3D
pharmacophore using structurally diverse molecules. 3D phar-
macophore and Bayesian models were derived and applied by
our lab to identify novel ASBT inhibitors among commercial
drugs (15). However, the above studies do not systemically
involve C-24 conjugated bile acid analogues, nor do they address
the more complex process of ASBT-mediated uptake. Swaan
and colleagues (27) applied CoMFA analysis on a series of bile
acid-peptide analogues and found that inhibition was favored
by an electron donor group between C-24 and C-27 and steric
bulk at the end of the side chain. These results corroborate our
present findings.
3D-QSAR models using a conformationally sampled phar-
macophore (CSP) approach were developed for inhibition
requirements using C-24 bile acid conjugates that employed
piperidines as probe moieties (28). Results here using pyridines
and phenols as probe moieties in part agree with these piperidine
findings, where hydrophobicity favored inhibition activity. In
contrast to piperidine findings, the results showed a position
effect, where the relative location of the pyridine nitrogen and
substituent groups modulated the conjugate ASBT inhibition
and translocation.
C-24 bile acid conjugates have previously been shown to be
ASBT substrates (7, 29-31). One of these studies was a SAR
study involving a series of C-24 cholic acid conjugates and
found that the C-24 side chain can be 14 Å in length or longer
for translocation and that large hydrophobic moieties increase
binding to ASBT (29). In another study, C-24 conjugation with
glutamic acid and lysine also found that charge and hydropho-
bicity around the 24 position influenced translocation (7). Results
of this study are in agreement with these prior findings that a
negative change near C-24 promotes substrate translocation and
that steric bulk at certain positions of the aminopyridine ring
beyond C-24 also favors translocation.
Figure 12. Influence of LogP on ASBT inhibition (A) and transport
efficiency (B). (A) Observed pKi vs ClogP shows a fairly negative
correlation between pKi and CLogP (slope ) -0.331, r2 ) 0.367, and
p ) 0.0008). However, there was no simple correlation (r2 ) 0.021)
among the 24 monoanionic conjugates when the three neutral conjugates
(i.e., the R-benzyl substituted conjugates 11, 13, and 17) were
eliminated. (B) Observed log(normJmax/Kt) vs ClogP shows no relation-
ship between log(normJmax/Kt) and CLogP with r2 ) 0.015 and p )
0.586.
CONCLUSION
With the view that bile acid conjugates can serve as prodrugs
that target ASBT to increase oral drug permeability and
bioavailability, a series of aminopyridine or aminophenol
conjugates of glutamyl-bile acids were synthesized. Their ASBT
inhibition and transport efficiency were evaluated to investigate
their interaction beyond the C-24 region of CDCA with ASBT.
Similar values for Ki and Kt indicate that substrate binding to
ASBT was generally the rate-limiting step in overall substrate
transport. 3D-QSAR models were developed for ASBT inhibi-
tion. The steric field dominated in the CoMFA model, whereas
the hydrophobic field dominated other features in the CoMSIA
models. Monoanionic conjugate exhibited higher inhibition
potency than neutral conjugate. A hydrophobic or bulky
substituent on the 2 or 6 position of a 3-aminopyridine ring
enhanced inhibition, while a hydrophobic group on the 5 position
was detrimental. Both inhibition models predicted test set
inhibition. Recently, the value of inhibition-only QSAR models
for drug transporters has been questioned (32), which prompted
us to also develop ASBT models for transport efficiency. 3D-
QSAR has not previously been applied to ASBT substrate
studies. Similar to the inhibition models, the steric field was
the predominant contributor; however, we demonstrate that steric
contributions to ASBT transport cannot be explained simply
by lipophilicity. Overall, these 3D-QSAR models provide new
insight into the substrate requirements for ASBT uptake and
shed light on the design of bile acid conjugates that can increase
intestinal drug permeability and bioavailability.
using SYBYL according to Hansch and colleagues (24, 25)
(Table 1). Plots of observed pKi vs CLogP (Figure 12A) shows
a fairly negative correlation between pKi and CLogP with r2 )
0.367 and p ) 0.0008 (i.e., lipophilicity reduced binding
potency). Comparison of R-benzyl-substituted conjugates to their
nonsubstituted counterparts (11 vs 10, 13 vs 12, and 17 vs 16)
essentially accounted for this trend. However, elimination of
these three neutral conjugates (open circles in Figure 12A) leads
to no relationship between pKi and CLogP (r2 ) 0.021) among
the 24 monoanionic conjugates (filled circles in Figure 12A).
This lack of simple correlation between inhibition and CLogP
is supportive of COMFA and COMSIA findings that hydro-
phobicity played differing roles in conjugate binding (i.e.,
hydrophobic or bulky substitute on the 2 or 6 position of a
3-aminopyridine ring enhanced activity, while a hydrophobic
group on the 5 position was detrimental).
There was no correlation between Log(normJmax/Kt) and
CLogP, with r2 ) 0.015 (Figure 12B). Additionally, no
correlation was observed between Kt vs CLogP and normJmax
vs CLogP as well with r2 ) 0.037 and 0.005, respectively. The
lack of correlation between transport parameters and CLogP
indicates that other physicochemical and structural variables
impact transport beyond hydrophobicity, and further suggests
a greater complexity in conjugate translocation compared to
conjugate binding.
ACKNOWLEDGMENT
Comparison to Previous ASBT Models. Several computa-
tional models have been developed to study structural require-
This work was support in part by National Institutes of Health
grants DK67530 (to J.E.P.) and DK61425 (to P.W.S.). The

2048 Bioconjugate Chem., Vol. 21, No. 11, 2010
Zheng et al.
(17) Balakrishnan, A., Hussainzada, N., Gonzalez, P., Bermejo,
M., Swaan, P. W., and Polli, J. E. (2007) Bias in estimation of
transporter kinetic parameters from overexpression systems:
Interplay of transporter expression level and substrate affinity.
J. Pharmacol. Exp. Ther. 320, 133–144.
(18) McKay, D. B., Chang, C., Gonzalez-Cestari, T. F., McKay,
S. B., El-Hajj, R. A., Bryant, D. L., Zhu, M. X., Swaan, P. W.,
Arason, K. M., Pulipaka, A. B., Orac, C. M., and Bergmeier,
S. C. (2007) Analogs of methyllycaconitine as novel noncom-
petitive inhibitors of nicotinic receptors: pharmacological char-
acterization, computational modeling, and pharmacophore de-
velopment. Mol. Pharmacol. 71, 1288–1297.
(19) Kaler, G., Truong, D. M., Khandelwal, A., Nagle, M., Eraly,
S. A., Swaan, P. W., and Nigam, S. K. (2007) Structural variation
governs substrate specificity for organic anion transporter (OAT)
homologs. Potential remote sensing by OAT family members.
J. Biol. Chem. 282, 23841–23853.
(20) Clark, M., Cramer, R. D., and Vanopdenbosch, N. (1989)
Validation of the general-purpose Tripos 5.2 force-field. J. Com-
put. Chem. 10, 982–1012.
(21) Gasteiger, J., and Marsili, M. (1980) Iterative partial equaliza-
tion of orbital electronegativity: A rapid access to atomic charges.
Tetrahedron 36, 3219–3228.
(22) Wold, S., Ruhe, A., Wold, H., and Dunn, W. (1984)
Collinearity problem in linear regression. The partial least squares
(pls)> approach to generalized inverses. SIAM J. Sci. Stat.
Comput. 5, 735–743.
(23) Lack, L., and Weiner, I. M. (1966) Intestinal bile salt transport:
structure-activity relationships and other properties. Am. J.
Physiol. 210, 1142–1152.
(24) Leo, A., Hoekman, D. H., and Hansch, C. (1995) Exploring
QSAR, hydrophobic, electronic, and steric constants, American
Chemical Society, Washington, DC.
(25) Leo, A., Hansch, C., and Ekins, D. (1971) Partition coefficients
and their uses. Chem. ReV. 71, 525–616.
(26) Baringhaus, K. H., Matter, H., Stengelin, S., and Kramer, W.
(1999) Substrate specificity of the ileal and the hepatic Na(+)/
bile acid cotransporters of the rabbit. II. A reliable 3D QSAR
pharmacophore model for the ileal Na(+)/bile acid cotransporter.
J. Lipid Res. 40, 2158–2168.
(27) Swaan, P. W., Szoka, F. C., Jr., and Oie, S. (1997) Molecular
modeling of the intestinal bile acid carrier: a comparative
molecular field analysis study. J. Comput.-Aided Mol. Des. 11,
581–588.
(28) Gonzalez, P. M., Acharya, C., Mackerell, A. D., Jr., and Polli,
J. E. (2009) Inhibition requirements of the human apical sodium-
dependent bile acid transporter (hASBT) using aminopiperidine
conjugates of glutamyl-bile acids. Pharm. Res. 26, 1665–1678.
(29) Kagedahl, M., Swaan, P. W., Redemann, C. T., Tang, M.,
Craik, C. S., Szoka, F. C., Jr., and Oie, S. (1997) Use of the
intestinal bile acid transporter for the uptake of cholic acid
conjugates with HIV-1 protease inhibitory activity. Pharm. Res.
14, 176–180.
(30) Mills, C. O., Milkiewicz, P., Saraswat, V., and Elias, E. (1997)
Cholyllysyl fluroscein and related lysyl fluorescein conjugated
bile acid analogues. Yale J. Biol. Med. 70, 447–457.
(31) Swaan, P. W., Hillgren, K. M., Szoka, F. C., Jr., and Oie, S.
(1997) Enhanced transepithelial transport of peptides by conjuga-
tion to cholic acid. Bioconjugate Chem. 8, 520–525.
(32) Giacomini, K. M., Huang, S. M., Tweedie, D. J., Benet, L. Z.,
Brouwer, K. L., Chu, X., Dahlin, A., Evers, R., Fischer, V.,
Hillgren, K. M., Hoffmaster, K. A., Ishikawa, T., Keppler, D.,
Kim, R. B., Lee, C. A., Niemi, M., Polli, J. W., Sugiyama, Y.,
Swaan, P. W., Ware, J. A., Wright, S. H., Yee, S. W., Zamek-
Gliszczynski, M. J., and Zhang, L. (2010) Membrane transporters
in drug development. Nat. ReV. Drug DiscoVery 9, 215–236.
author gratefully acknowledges Dr. Gasirat Tririya and Rana
Rais for their assistance in chemical synthesis and Dr. Guoyun
Bai for NMR assignment assistance.
Supporting Information Available: Additional related tables
and figures. This material is available free of charge via the
Internet at http://pubs.acs.org.
LITERATURE CITED
(1) Dawson, P. A., and Oelkers, P. (1995) Bile acid transporters.
Curr. Opin. Lipidol. 6, 109–114.
(2) Dawson, P. A., Lan, T., and Rao, A. (2009) Bile acid
transporters. J. Lipid Res.
(3) Balakrishnan, A., and Polli, J. E. (2006) Apical sodium
dependent bile acid transporter (ASBT, SLC10A2): a potential
prodrug target. Mol. Pharmaceutics 3, 223–230.
(4) Lack, L. (1979) Properties and biological significance of the
ileal bile salt transport system. EnViron. Health Perspect. 33,
79–89.
(5) Balakrishnan, A., Sussman, D. J., and Polli, J. E. (2005)
Development of stably transfected monolayer overexpressing the
human apical sodium-dependent bile acid transporter (hASBT).
Pharm. Res. 22, 1269–1280.
(6) Balakrishnan, A., Wring, S. A., and Polli, J. E. (2006)
Interaction of native bile acids with human apical sodium-
dependent bile acid transporter (hASBT): influence of steroidal
hydroxylation pattern and C-24 conjugation. Pharm. Res. 23,
1451–1459.
(7) Balakrishnan, A., Wring, S. A., Coop, A., and Polli, J. E. (2006)
Influence of charge and steric bulk in the C-24 region on the
interaction of bile acids with human apical sodium-dependent
bile acid transporter. Mol. Pharmaceutics 3, 282–292.
(8) Tolle-Sander, S., Lentz, K. A., Maeda, D. Y., Coop, A., and
Polli, J. E. (2004) Increased acyclovir oral bioavailability via a
bile acid conjugate. Mol. Pharmaceutics 1, 40–48.
(9) Cramer, R. D., Patterson, D. E., and Bunce, J. D. (1988)
Comparative molecular-field analysis (Comfa) 0.1. Effect of
shape on binding of steroids to carrier proteins. J. Am. Chem.
Soc. 110, 5959–5967.
(10) Klebe, G., Abraham, U., and Mietzner, T. (1994) Molecular
similarity indexes in a comparative-analysis (Comsia) of drug
molecules to correlate and predict their biological-activity.
J. Med. Chem. 37, 4130–4146.
(11) Martin, Y. C. (1998) 3D QSAR: current state, scope, and
limitations. Drug DiscoVery Des. 12, 3–23.
(12) Bemis, G. W., and Murcko, M. A. (1996) The properties of
known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–
2893.
(13) Gonzalez, P., and Polli, J. E. (2008) Impact of impurity on
kinetic estimates from transport and inhibition studies. J. Phar-
macol. Exp. Ther. 326, 296–305.
(14) Balakrishnan, A., Sussman, D. J., and Polli, J. E. (2005)
Development of stably transfected monolayer overexpressing the
human apical sodium-dependent bile acid transporter (hASBT).
Pharm. Res. 22, 1269–1280.
(15) Zheng, X., Ekins, S., Raufman, J. P., and Polli, J. E. (2009)
Computational models for drug inhibition of the human apical
sodium-dependent bile acid transporter. Mol. Pharmaceutics 6,
1591–1603.
(16) Rais, R., Gonzalez, P. M., Zheng, X., Wring, S. A., and Polli,
J. E. (2008) Method to screen substrates of apical sodium-
dependent bile acid transporter. AAPS J. 10, 596–605.
BC100273W