Extract
Note: Please read the complete
full text with Figures and Tables at
Introduction
A majority of drugs used today are taken orally,
so the drugs must traverse several semipermeable cell membranes
before reaching the systemic
circulation[1]. These membranes are biological barriers that selectively inhibit the passage of
drug molecules and are composed primarily of a bimolecular
lipid matrix, containing mostly cholesterol and phospholipids.
The lipids provide stability to the membrane and determine
its permeability characteristics. Drugs can cross a biological
barrier by passive diffusion, facilitate passive diffusion,
active transport, and pinocytosis, but passive diffusion is the
most common method[1]. In the small intestine, in which the
major part of oral drug absorption occurs, the barrier to drug
absorption is a membrane comprising intestinal columnar
epithelial cells. For any novel drug that is developed, it is
necessary to examine how it is absorbed in the small intestine.
In the absorption process, transport across a cell membrane
depends on the concentration gradient of the solute. Most
drug molecules are transported across a membrane by simple
diffusion from a region of high concentration (eg
gastrointestinal fluids) to one of low concentration (eg
blood)[2]. Because drug molecules are rapidly removed by the systemic
circulation and distributed into a large volume of body fluids
and tissues, drug concentration in the blood is initially low
compared with that at the administration site, producing a
large gradient. The diffusion rate is directly proportional to
the gradient, but also depends on the molecule¡¯s lipid
solubility, degree of ionization, and the size and the area of
the absorptive surface[3]; that is, the
diffusion rate depends on the characteristics and area of the intestinal membrane
and the drug¡¯s properties, as well as the drug concentration
gradient on both sides of the membrane.
Given the limitations of biomembranes, artificial
membranes are usually used instead of biomembranes to study
the absorption of drugs in the intestine. In vitro
studies show that artificial membranes have some properties that are
similar to those of biomembranes in vivo; that is, artificial
membranes can be good mimics of
biomembranes[3-5]. The Caco-2 cell monolayer is commonly used as an artificial
membrane. When cultured on semipermeable membranes,
Caco-2 cells, which are derived from a human colon
adenocarcinoma, differentiate into a highly functionalized
epithelial barrier with remarkable morphological and
biochemical similarity to small intestinal columnar epithelium. The
Papp values obtained from Caco-2 transport studies have been
proven to correlate with those of human intestinal absorption,
and the Caco-2 monolayer model has been proven to be
extremely useful as a tool for mechanistic studies of drug absorption[3]. However, although the Caco-2 model is well
characterized and has been proven to be useful, assays using
the system are not perfect. The system requires a 3-week
growth period and regular maintenance feeding, so it remains
a relatively low throughput method. Irvine and co-workers
tried another system using Madin-Darby canine kidney
(MDCK) cells and found that it was possibly a useful tool
for testing rapid membrane
permeability[3]. MDCK cells are commonly used for studying cell growth regulation,
metabolism, and transport mechanisms in distal renal
epithelia. They can also differentiate into columnar epithelia
and form tight junctions when cultured on semipermeable
membranes. Irvine and co-workers studied a large number of
compounds using both MDCK and Caco-2 assays to
evaluate the suitability of MDCK cells as a possible tool for
assessing membrane permeability and found that they were
well suited. Others have also pointed out that MDCK cells
are good candidates for modeling simple
epithelia[6].
Based on the study carried out by Irvine and coworkers,
we built membrane-interaction quantitative
structure-activity relationship analysis (MI-QSAR) models to predict drug
permeability through MDCK cells. The QSAR approach
involves statistical analysis of various molecular descriptors
for a series of biologically active molecules. The result of a
QSAR study provides useful clues regarding the type of
substituents that should be tested to improve the activity
further. QSAR can play a vital role in lead exploitation. One
successful example is the transformation of nalidixic acid
with the help of QSAR into an important family of drugs: the
quinolone carboxylates[7]. Since the method was established
in the 1960s, QSAR equations have been used to describe
the biological activities of thousands of different drugs and
drug candidates[8,9]. Some new approaches have been used
in QSAR since its initial development, for example, principal
component analysis (PCA), partial least squares (PLS ),
artificial neural network (ANN)[10]. MI-QSAR is an advanced
form of QSAR, in which membrane-interaction properties are
added to the descriptor pool. MI-QSAR models predicting
drug permeability through Caco-2 cells have been built by
Kulkarni and co-workers[11]. In the present study, we built
MDCK cell models for MI-QSAR analysis.
Materials and methods
MDCK cell permeation coefficients The dependent
variable used in MI-QSAR analysis is the MDCK cell
permeability coefficient, Papp. Irvine and co-workers performed
permeability experiments on a data set of 55 structurally and
chemically diverse drugs ranging in molecular weight from 130 to
470 and varying in net charge at pH
7.4[3]. MDCK cells were obtained from the American Type Culture Collection (ATCC;
Rockville, MD, USA). MDCK cells were maintained in
minimal essential medium containing 10% fetal bovine serum and
fresh L-glutamine 2 mmol/L. Culture inserts were preincubated
with culture medium at 37 °C for 1 h and then seeded with
6.64×105 cells per cm2. MDCK monolayers were washed and
fed with fresh medium 1 h post-seeding and again 24 h post-
seeding. After 3 d of culturing, MDCK monolayers could be
used to test the permeability of compounds. All compounds
were tested in 6 replicate monolayers. Monolayers were
incubated with donor and acceptor solutions for 60 min at
37 °C, 95% humidity, with 30 r/min reciprocal
shaking[3,12]. The permeability coefficient was calculated according to the
following equation[3]:
In this equation dQ/dt is the permeability rate,
C0 is the initial concentration in the donor compartment, and
A is the surface area of the filter.
Table 1 contains the Papp values for 22 structurally
diverse drugs used as a training set of compounds and 8 drug
compounds as a test set. The 22 training set compounds
and 8 test compounds were selected according to the
criterion that members of the test set were to be representative of
all members of the training set in terms of the range of
Papp values, molecular weights, and structural and chemical
diversities, to achieve a composite representative subset.
Table 1 also contains a composite summary of the human
percentage absorption values of many of the drugs in the
table. These data were obtained from published values.
Compared with the Papp value and the human percentage
absorption value, it is obvious that the
Papp values are indicative of
in vivo drug uptake. Figure 1 shows the structures of these
thirty molecules.
Building solute molecules and a
dimyristoyl-phosphatidyl-choline (DMPC) monolayer
All the solute molecules of the training set and test set were built using HyperChem
software[13]. A single DMPC molecule was built using
HyperChem from the published crystal structure
data[14]. The DMPC molecule was selected as the model phospholipid in
this study. The structure of the DMPC molecule is shown in
Figure 2. An assembly of 25 DMPC molecules, 5×5×1 in
x, y, and z directions, respectively, was used as the model
membrane monolayer. The size of the monolayer simulation
system was selected based on the work done by van der Ploeg
and Berendsen[15]. These workers built 2 decanoate bilayers,
with 2×8×2 and 2×16×2 phospholipid molecules, respectively,
and performed a molecular dynamic simulation for each of
them. It was found that the estimated order parameters for
these 2 model bilayers agreed with one another, which sug
gests that the smaller assembly is adequate for modeling
short-range properties. Other researchers have obtained
similar geometric and energetic equilibrium property values with
regard to the size of the simulation system that permits a
minimum effective size (phospholipids) of the monolayer to
be defined[16].
To prevent unfavorable van der Waals¡¯ interactions
between solute molecules and the membrane DMPC molecules,
the single DMPC molecule in the center of the monolayer,
which was located at position x, y=3,3 of the 5×5 DMPC
monolayer model, was taken out[11], creating a space for the
test solute molecules to insert into. Each of the test solute
molecules of the permeation data set was inserted at 3
different positions or depths in the DMPC monolayer, with the
most polar group of the solute facing toward the head group
region of the monolayer. Three corresponding MDS(molecular dynamic simulation) models were generated for
each solute molecule with regard to the trial positions of the
solute molecules in the monolayer. The 3 trial positions
were as follows[4,5,17]: solute molecule in the head group
region, solute molecule between the head group region and
the aliphatic chains, and solute molecule in the tail region of
the aliphatic chains.
The energetically favorable geometry of the solute
molecule in the monolayer was sought after using each of these
trial positions. The 3 different positions of AZT, one of the
training set solute molecules, are shown in Figure 3A to
illustrate this modeling procedure. The most energetically
favorable geometry of this solute molecule in the model DMPC
monolayer is shown in Figure 3B.
Molecular dynamic simulations MDS were carried out
using the Discover module in Material
Studio[18] with compass force field and NVT ensemble(an ensemble in which
the dynamics are modified to allow the system to exchange
heat with the environment at a controlled temperature). The
selection of the simulation temperature was based on the
phase transition temperature for DMPC, which was 297
K[11]. A simulation temperature of 311 K was selected because it is
body temperature, and it is also above the DMPC phase
transition temperature. Temperature was held constant in
the MDS by coupling the system to an external fixed
temperature bath. The trajectory step size was 0.001 ps over a
total simulation time of 10 ps for each solute of the training
set and test set. After 10 ps of simulation,
molecule-membrane interactions arrived at an energetically stable
phase(Figure 4). Two-dimensional periodic boundary conditions
were used (a=32 Å, b=32 Å, c=80 Å, and
g=96.0º) for the DMPC molecules of the monolayer model, but not the test
solute molecule. Only a single solute molecule was explicitly
considered in each MDS. The angle g is the angle that an
extended DMPC molecule makes with the "planar surface"
of the monolayer. Each of the solute molecules was placed
at each of the 3 different positions in the monolayer, as
described earlier, with the most polar portion of the solute
"facing" toward the head group region. A snapshot of molecule
AZT and the membrane around it was taken after MDS, as
shown in Figure 3b.
Calculation of descriptors Both intramolecular
physicochemical properties and features of the solute molecules
and intermolecular solute-membrane interaction properties
were calculated. These properties and features will both be
referred to as descriptors from this point forward because
they constitute the trial pool of independent variables used
to build the QSAR models. The descriptors used in the
MI-QSAR analysis can also be divided into the following 3 kinds:
1) solute aqueous dissolution and solvation descriptors; 2)
solute-membrane interaction descriptors; and 3) general
intramolecular solute descriptors. Table 2 and Table 3
reporting the trial pool of descriptors used in the MDCK cell
permeation MI-QSAR modeling use both classifications of the
descriptors.
The general intramolecular solute descriptors included
as part of the trial descriptor pool are defined in Table 2. The
term "general" is used because solute descriptors in this
class may be useful in describing different aspects of the
bioavailability (in this case the MDCK cell permeation
process) of a solute. There are other intermolecular
properties computed using intramolecular computational methods,
which are not included in Table 2, for example
ClogP (the logarithm of 1-octanol/water partition coefficient), MP
(melting point), Tc (critical tempeture), Sol (water solubility).
All of these descriptors are intermolecular properties. They
are classified as solvation and dissolution intermolecular
descriptors and are reported in Table 3b. In Table 3a, there are
solute-membrane interaction descriptors extracted directly
from the MDS trajectories. These intermolecular descriptors
were calculated using the most stable (lowest total potential
energy) solute-membrane geometry from the 3 positions
sampled for each of the solutes. For example, Figure 3b
shows the lowest potential energy state of AZT in the
membrane monolayer, which was used to estimate the
solute-membrane interaction descriptors. Other solute-membrane
interaction descriptors used in the QSAR descriptor trial set
were determined using data from the MDS trajectories.
Construction and testing of MI-QSAR models
Independent and useful descriptors can be extracted from all
descriptors calculated earlier. The methods in common use are
forward regression, backward regression and stepwise
regression. Stepwise regression is the method used most
frequently. The resulting regression equation from the
stepwise regression method is not the best, rather it is an
optimized result. SPSS was used to obtain the regression
equation; it selected proper variables according to the
partial sum of squares of regression in every step. The partial
sum of squares for regression means that the sum of squares
for regression increased or decreased after one variable was
added to or deducted from the present regression equation.
A parameter, F, is defined to check whether to introduce a
variable or reject it when partial sum of squares for
regression is of some value.
The stepwise regression method was used in this study,
which was the combination of forward regression and
backward regression methods. The single worst variable was
picked out after each new variable was added. Two
threshold constants, Fentry and
Fremoval
(Fentry<Fremoval
), were given before the regression. If the
F value of one variable, which had the largest partial sum of squares for regression of all
the variables not included in the regression equation, was
larger than or equal to Fentry, the variable could be
introduced into the equation. Conversely, if the
F value of one variable, which had the smallest partial sum of squares for
regression among all the variables included in the
regression equation, was less than or equal to
Fremoval, the variable should be removed from the regression equation. These steps
were carried out alternately until there was no variable to be
introduced into the regression equation and none to be
removed from it. The adjustment of the threshold values
Fentry and
Fremoval could affect the result of the selection of variables.
If the prepared variables were few, it was appropriate to
increase Fentry in order to introduce variables to the regression
equation as much as possible. If the prepared variables were
too many, Fremoval could be decreased to cut down the
number of variables introduced into the regression equation.
SPSS was used to carry out the regressions and
construct the models[19].
Results and Discussion
The best MI-QSAR models for MDCK cell permeability
were realized by considering the combination of general
intramolecular solute, intermolecular dissolution/solvation
solute, and intermolecular membrane-solute descriptors
presented as a function of the number of terms, that is,
descriptors, included in a given MI-QSAR model:
Papp=115.657+319.687ClogP
n=22 R2=0.646 S=0.396 (2)
Papp=3113.84+374.691Clog
P+338.881EHOMO
n=22 R2=0.719 S=0.362 (3)
Papp=3115.743+399.894Clog
P+374.586EHOMO
+35.07Es
n=22 R2=0.764 S=0.341 (4)
Papp=3453.482+409.333Clog
P+391.596EHOMO
+48.403Es-
0.0971PMY
n=22 R2=0.813 S=0.312 (5)
Papp=3609.933+387.817ClogP
+356.922EHOMO+
32.256Es-0.163PMY
-16594Ct
n=22 R2=0.866 S=0.272 (6)
Papp=1029.094+398.972Clog
P+459.781EHOMO+24.048E
s-
0.174PMY-18164.4Ct
+2.594Enb
n=22 R2=0.896 S=0.248 (7)
where n is the number of compounds,
R2 is the coefficient of determination, and S is the standard error of the estimate
(and its value could be different if the unit of the variables
changed). The descriptors found in the best MI-QSAR
models are as follows: 1) Clog P is the logarithm of the
l-octanol/water partition coefficient; 2)
EHOMO is the highest occupied molecular orbital energy; 3)
Es is stretch energy, the energy
contribution associated with the deformation of a bond from
its equilibrium bond length; 4) PMY is principal moment of
inertia Y, the inertia along the y axis in the rectangular
coordinates; 5) Ct is total connectivity, which is a kind of
index defined according to the number of atoms and bonds
and their connecting sequence
(Ct is a structural parameter; molecules with different structures have different
connectivity indices according to a given definition); and 6)
Enb is the energy of interactions between all of the non-bonded
atoms.
The values of the 6 descriptors found in the 1-6 term
MI-QSAR models for each compound in the training set and test
set are given in Table 4.
The observed and predicted (using the 3-6 term
MI-QSAR models) MDCK cell permeation coefficients of the
training and test set compounds are listed in Table 5 and
plotted in Figure 5. Corticosterone, ondansetron, phenytoin,
progesterone, propranolol hydrochloride, and testosterone
are observed to permeate better than predicted by each of
the MI-QSAR models, whereas acetylsalicylic acid, bupropion hydrochloride, methylprednisolone, and nadolol
have a lower permeation coefficient than the one predicted
by any of the models. Nevertheless, none of the compounds
in either the training or test sets are outliers for the 3-6 term
MI-QSAR models. Figure 6 contains plots of
R2 and S for the training set.
R2 increases with increasing numbers of
descriptor terms, whereas the value of S decreases when the
number of descriptor terms increases.
Analyzing Equations 2-7, it appears that
ClogP in the 1-term model accounts for much of the variance of
Papp across
the training set. Both principal moment of inertia
Y and total connectivity have a negative effect on the permeation
coefficient. In 1-term to 5-term models, the descriptors are all
intramolecular descriptors, but in the 6-term model, one
membrane-solute interaction descriptor,
Enb, is added in.
Eight compounds from the parent MDCK cell permeation
coefficient data were selected to construct a test set to
validate the MI-QSAR models. The molecules of the test set
were selected so as to span the entire range of MDCK cell
permeability for the composite training set. At the bottom of
Table 5 are the observed and predicted
Papp values for this test set. Figure 5 plots the test set as the last 8 compounds.
There are no outliers, but antipyrine and guanabenz,
compounds 1 and 3 of the test set, are predicted to have lower
permeability coefficients than observed. Conversely,
dexamethasone, hydrocortisone, propylthiouracil and AZT
have higher predicted Papp values in all the models than the
observed value.
The most important descriptor in the models is
ClogP. ClogP is the computed logarithm of the l-octanol/water
partition coefficient, and it has been ubiquitously used as a
quantitative measure of hydrophobicity/lipophilicity since
the 1960s[20,21]. A high ClogP value of a molecule implies that
the molecule dissolves easily in hydrophobic materials and
dissolves poorly in water. In the MI-QSAR models we built,
ClogP had a positive effect on
Papp, which implies that chemicals with high lipophilicity have better permeability through
membranes than hydrophilic structures. It is easy to see why
this is the case, because biomembranes are mainly composed
of double phospholipid layers, so small hydrophobic
molecules can pass through the layer with little obstruction.
Commonly, lipophilic drugs permeate the small intestinal
columnar epithelium quicker and easier than hydrophilic
drugs.
In Equation 3, another descriptor,
EHOMO appears, which represents HOMO energy. HOMO energy is the energy of
the highest occupied molecular orbit, which is the opposite
of LUMO energy, the energy of the lowest unoccupied
molecular orbit[22]. The greater
EHOMO is, the greater the electron-donating capability; conversely, the smaller
ELUMO is, the smaller the resistance to accept electrons. Compounds
that present larger values of
EHOMO are more electron donor and the compounds that present smaller values of
ELUMO are more electron acceptor. These variables are interpreted as
measures of molecular reactivity and stability. As
EHOMO increases (relative to other molecules), the molecule is less
stable and more reactive. For
ELUMO, the situation is the opposite. HOMO energy is relevant because Equation 3
measures the electron-donating character of the drug
molecules. Papp has a positive relationship with HOMO
energy, which means that drug molecules with higher
electron-donating capacities can permeate membranes better.
As seen in the literature, energy is often discussed in
many research programs. There is another energy descriptor,
stretch energy, which influences Papp
as EHOMO does in our models. Stretch energy is the energy contribution
associated with the deformation of a bond from its equilibrium bond
length. It is a kind of vibration energy, which is composed of
bend energy and stretch energy. From Models 3-6, we can
see that stretch energy is positively correlated with the value
of Papp. Increasingly positive stretch energy values
correspond to increasing Papp. Drug molecules with higher stretch
energies metamorphose better and can permeate membranes
more easily.
PMY is the principal moment of inertia along the
y axis. It gives information about how the product of force and
distance influence the value of
Papp. Papp decreases as the value
of PMY increases. The coefficients of
PMY in Models 4-6 are very small when contrasted with the coefficients of other
variables. This indicates that a change in
PMY value has comparatively little effect on the value of
Papp and the permeability of the drug molecules.
Compared with the descriptors discussed earlier,
connectivity is an element that rarely appeared in QSAR models
constructed by other researchers. Connectivity is a
parameter defined according to the conformation of a molecule,
and presumes that there is some functional relationship
between molecule properties and connectivity. One standard
definition of connectivity is as
follows[23,24]:
Figure 7 is the structure of isopentanol without
hydrogens. The figures are the bond numbers of each
carbon connected with other carbons.
From the foregoing equation, we can see that
connectivity reflects the connection and ramification conditions of the
molecules. Connectivity is the quantitative description of
the molecular structure. Molecules with different structures
have different connectivity values. The connectivity method
has been widely used in structure-activity analyses. In the
models we constructed, connectivity is included in Model 5
and Model 6 and it has a negative effect on the
Papp value. The larger the connectivity value, the more complicated the
structure will be, and there will be more branch chains.
Therefore an increase in connectivity will decrease the
permeability of a molecule.
Enb is the energy of interactions between non-bonded
atoms. It includes van der Waals¡¯ energy, electrostatic
energy and hydrogen bond terms in some older forcefields, as
the following equation describes[25],
Enon-bond=EvdW+
Ecoulomb+Ehbond
(8)
The non-bond energy terms in Equations 6 and 7
suggest that permeability increases with increasing binding of
the solute to the phospholipid regions of the membranes.
Kulkarni and coworkers carried out MI-QSAR analysis
using the permeability coefficients of some drug molecules
tested by Caco-2 cells. Thirty molecules were included in
their training set, and significant MI-QSAR models were built.
Twenty-three intramolecular descriptors, and 11
membrane-solute interaction descriptors were calculated. In our study,
we calculated more than 70 descriptors for every drug
molecule in the training set, including electronic, steric, and
thermodynamic properties. MI-QSAR models were built based
on the analysis of these descriptors. In addition, the present
study confirmed the conclusion of Irvine and coworkers,
that MDCK cells are suitable for studying drug permeability
through biomembranes.
Acknowledgements
We are very grateful to the administrators in the
Computer Laboratory of College of life, Nanjing University, for
their support in carrying out this study.
References
1 Moo JC, David PT, Clay TC, Thomas JV, Jeffrey FS. The Madin
Darby Canine Kidney (MDCK) epithelial cell monolayer as a
model cellular transport barrier. Pharm Res 1989; 6:
71-7.
2 Magali T, Peter R, Daniel HS. Pharmacology. Churehill
Livingstone: Harcourt Asia; 1998.
3 Irvine JD, Takahashi L, Lockhart K, Cheong J, Tolan JW, Selick
HE, et al. MDCK (Madin-Darby Canine Kidney) cells: a tool for
membrane permeability screening. J Pharm Sci 1999; 88: 28-33.
4 Kulkarni A, Hopfinger AJ, Osbrone R, Bruner LH, Thompson
ED. Prediction of eye irritation from organic chemicals using
membrane-interaction QSAR analysis. Toxicol Sci 2001; 59:
335-45.
5 Kulkarni A, Hopfinger AJ. Membrane-interaction QSAR analysis:
application to the estimation of eye irritation by organic
compounds. Pharm Res 1999; 16: 1244-52.
6 Rothen-Rutishauser B, Kraemer SD, Braun A, Guenthert M,
Wunderli-Allenspach H. MDCK cell cultures as an epithelial
in vitro model: cytoskeleton and tight junctions as indicators for
the definition of age-related stages by confocal microscopy.
Pharm Res 1998; 15: 964-71.
7 Alka K. C-QSAR: a database of 18000 QSARs and associated
biological and physical data. J Comput Aided Mol Des 2003; 17:
187-96.
8 Hugo K. QSAR and 3D-QSAR in drug design. Part 1: methodology.
Drug Discov Today 1997; 2: 457-67.
9 Hugo K. QSAR and 3D-QSAR in drug design. Part 2:
applications and problems. Drug Discov Today 1997; 2: 538-46.
10 Lennart E, Erik J. Multivariate design and modeling in QSAR.
Chemometrics Intell Lab Syst 1996; 34: 1-19.
11 Kulkarni A, Han Y, Hopfinger AJ. Predicting Caco-2 cell
permeation coefficients of organic molecules using
membrane-interaction QSAR analysis. J Chem Inf Comput Sci 2002; 42: 331-42.
12 Braun A, Hammerle S, Suda K, Rothen-Rutishauser B, Gunthert
M, Kramer SD, et al. Cell cultures as tools in biopharmacy. Eur
J Pharm Sci 2000; 11 Suppl 2: S51-60.
13 HyperChem. HyperChem, Release 6.0 for MS Windows.
Waterloo (Ontario, Canada): Hypercube; 2001.
14 Hauser H, Pascher I, Pearson RH, Sundell S. Preferred
conformation and molecular packing of phosphatidylethanolamine and
phosphatidylcholine. Biochim Biophys Acta 1981; 650: 21-51.
15 van der Ploeg P, Berendsen HJC. Molecular dynamic simulation
of a bilayer membrane. J Chem Phys 1982; 76: 3271-6.
16 Stouch TR. Lipid membrane structure and dynamics studied by
all atom molecular dynamics simulations of hydrated
phosphatidylcholine vesicles. Mol Simulation 1993; 1: 335-62.
17 Gurtovenko AA, Patra M, Karttunen M, Vattulainen I. Cationic
DMPC/DMTAP lipid bilayers: molecular dynamics study.
Biophys J 2004; 86: 3461-72.
18 Accelrys: Materials Studios. San Diego (USA): Accelrys; 2001.
19 SPSS. SPSS for Windows. Chicago (USA): SPSS Inc; 2001.
20 Butina D, Segall MD, Frankcombe K. Predicting ADME
properties in silico: methods and models. Drug Discov Today 2002; 7
Suppl 11: S83-8.
21 Albert PL. Screening for human ADME/Tox drug properties in
drug discovery. Drug Discov Today 2001; 6: 357-66.
22 Honorio KM, Da Siliva ABF. An AM1 study on the
electron-donating and electron-accepting character of biomolecules. Int
J Quant Chem 2003; 95: 126-32.
23 Kier LB, Hall LH. Molecular connectivity in chemistry and drug
research. New York: Academic Press; 1976.
24 Kier LB, Hall LH. Molecular connectivity in structure-activity
analysis. London: John Wiley; 1986.
25 Iyer M, Mishra R, Han Y, Hopfinger AJ. Predicting blood-brain
barrier partitioning of organic molecules using
membrane-interaction QSAR analysis. Pharm Res 2002; 19: 1611-21.
26 Deretey E, Feher M, Schmidt JM. Rapid prediction of human
intestinal absorption. Quant Struct-Act Relat 2002; 21:
493-506.
27 Levet-Trafit B, Gruyer MS, Marjanovic M, Chou RC.
Estimation of oral drug absorption in man based on intestine
permeability in rats. Life Sci 1996; 58: 359-63.
28 Zhao YH, Abraham MH, Hersey A, Luscombe CN. Quantitative
relationship between rat intestinal absorption and Abraham
descriptors. Eur J Med Chem 2003; 38: 939-47.
29 Peter B. Modeling liquid properties, solvation, and
hydrophobi-city: A molecular size-based perspective. Perspect Drug Discov
Des 2000; 19: 19-45.
|
