Advances in Bioscience and Biotechnology, 2013, 4, 872-895 ABB
http://dx.doi.org/10.4236/abb.2013.49116 Published Online September 2013 (http://www.scirp.org/journal/abb/)
Some substrates of P-glycoprotein targeting β-amyloid
clearance by quantitative structure-activity relationship
(QSAR)/membrane-interaction (MI)-QSAR analysis
Tongyang Zhu, Jie Chen, Jie Yang*
State Key Laboratory of Pharmaceutical Biotechnology, Life College, Nanjing University, Nanjing, China
Received 28 April 2013; revised 29 May 2013; accepted 15 June 2013
Copyright © 2013 Tongyang Zhu et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The pathogenesis of Alzheimer’s disease (AD) puta-
tively involves a compromised blood-brain barrier
(BBB). In particular, the importance of bra in -t o- blo od
transport of brain-derived metabolites across the BBB
has gained increasing attention as a potential mecha-
nism in the pathogenesis of neurodegenerative disor-
ders such as AD, which is characterized by the aber-
rant polymerization and accumulation of specific mis-
folded proteins, particularly β-amyloid (Aβ), a neu-
ropathological hallmark of AD. P-glycoprotein (P-gp),
a major component of the BBB, plays a role in the
etiology of AD through Aβ clearance from the brain.
Our QSAR models on a series of purine-type and
propafenone-type substrates of P-gp showed that the
interaction between P-gp and its modulators dep end ed
on Molar Refractivity, LogP, and Shape Attribute of
drugs it transports. Meanwhile, another model on
BBB partitioning of some compounds revealed that
BBB partitioning relied upon the polar surface area,
LogP, Balaban Index, the strength of a molecule com-
bined with the membrane-water complex, and the
changeability of the structure of a solute-membrane-
water complex. The predictive model on BBB parti-
tioning contributes to the discovery of some molecules
through BBB as potential AD therapeutic drugs.
Moreover, the interaction model of P-gp and modu-
lators for treatment of multidrug resistance (MDR)
indicates the discovery of some molecules to increase
Aβ clearance from the brain and reduce Aβ brain
accumulation by regulating BBB P-gp in the early
stages of AD. The mechanism provides a new insight
into the therapeutic strategy for AD.
Keywords: P-Glycoproteins; Quantitative
Structure-Activity Relationship; ATP-Binding Cassette
Transporters; Multidrug Resistance; Blood-Brain Barrier
1. INTRODUCTION
Therapy for central nervous system (CNS) diseases re-
quires drugs that can cross the blood-brain barrier (BBB)
[1]. BBB not only maintains the homeostasis of the CNS,
but also refuses many potentially important diagnostic
and therapeutic agents from entering into the brain [2].
The pathological hallmarks of Alzheimer’s disease (AD)
are progressive brain atrophy and the accumulation of
cortical senile plaques, formed by the aggregation of amy-
loid beta peptide (Aβ) [3], and neurofibrillary tangles
(NFT), namely the self-assembly of hyperphosphorylated
forms of the microtubule associated protein tau into fi-
bers termed “paired helical filaments (PHFs)” [4,5]. The
pathogenesis of AD’s senile plaque and NFT lesions
putatively involves a compromised BBB [6], which pro-
tects the brain against endogenous and exogenous com-
pounds and plays an important part in the maintenance of
the microenvironment of the brain [7]. The ability of
drug permeating across BBB becomes critical in the de-
velopment of new medicines, especially in the design of
new drugs active in brain tissue. In particular, the impor-
tance of brain-to-blood transport of brain-derived me-
tabolites across the BBB has gained increasing attention
as a potential mechanism in the pathogenesis of neu-
rodegenerative disorders such as Parkinson’s disease (PD)
[8] and AD characterized by the aberrant polymerization
and accumulation of specific misfolded proteins, par-
ticularly Aβ. P-glycoprotein (P-gp or MDR1/ABCB1) is
a 170-kDa transmembrane (TM) protein widely expres sed
from the epithelial cells of the intestine, liver, kidney,
placenta, uterus, and testis to endothelial cells of the B BB
[9]. It belongs to the ABC (ATP-binding cassette) trans-
porter family and serves to pump exogenous substances
*Corresponding a uthor.

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 873
out of the cells. The domain topology of P-gp con sists of
two homologous halves each consisting a TM domain
preceding a cytosolic nucleotide binding domain. Each
TM domain is composed of six TM α-helix segments in-
volved in efflux as well as in drug binding [10]. The
ABC transport protein P-gp, a major component of the
BBB, mediates the efflux of Aβ from the brain as well as
is a major factor in mediating resistance to brain entry by
numerous exogenous chemicals, including therapeutic phar-
maceuticals [11]. P-gp plays a role in the etiology of AD
through the clearance of Aβ fro m the br ain. S ome drugs,
such as rifampicin, dexamethasone, caffeine, verapamil,
hyperforin, β-estradiol and pentylenetetrazole, were able
to improve the efflux of Aβ from the cells via P-gp up-
regulation [12]. Meanwhile, some compounds have been
shown to reverse the P-gp mediated multidru g resistance
(MDR), including verapamil, adriamycin, cyclosporin,
and dexverapamil [13]. Hartz et al. have shown that up-
regulating P-gp in the early stages of AD has the poten-
tial to increase Aβ clearance from the brain and reduce
Aβ brain accumulation by a transgenic mouse model of
AD (hAPP-overexpressing mice) [14]. Abuznait et al.
have also elucidated the impact of P-gp up-regulation on
the clearance of Aβ [12], which indicated that targeting
Aβ clearance via P-gp up-regulation was effective in sl ow-
ing or halting the progression of AD and there was the
possibility of P-gp as a potent ial therapeutic target for AD.
P-gp at the BBB functions as an active efflux pump by
extruding a substrate from the brain, which is important
for maintaining loco-regional homeostasis in the brain
and protection against toxic compounds [8]. P-gp is also
discovered in various resistant tumor cells and expressed
widely in many normal tissues and plays a very impor-
tant role in drug ADME-Tox (absorption, distribution,
metabolism, excretion, and toxicity). MDR is a matter of
growing concern in chemotherapy. Cells which express
the MDR phenotype can over-express efflux transporters
after exposure to a single agent. As a result, these cells
become resistant to the selective agent and cross-resistant
to a broad spectrum of structurally and functionally dis-
similar drugs. The drug efflux pump P-gp has been show n
to promote MDR in tumo rs as well as to influ en ce AD M E
properties of drug candidates [15]. P-gp is expressed at
the BBB, the blood-cerebrospinal fluid barrier, and the
intestinal barrier, thus modulating the absorption and ex-
cretion of xenobiotics across these barriers. P-gp and its
ligands (substrates and inhibitors) are therefore exten-
sively studied both with respect to reversing MDR in
tumors and for modifying ADME-Tox properties of drug
candidates, such as CNS active agents [15]. P-gp pos-
sesses broad substrate specificity and the substrates in-
clude members of many clinically important therapeutic
drug classes, including anti-HIV protease inhibitors, cal-
cium channel blockers used in the treatment of angina,
hypertension, antibiotics and cancer chemotherapeutics
[16]. In this active efflux process, energy originating
from ATP hydrolysis is directly consumed. Because of
such a wide distribution of P-gp, if a drug such as quini-
dine or verapamil inhibits the function of P-gp, it will
also inhibit the excretion of digoxin by P-gp’s leading to
increased plasma levels and toxicity due to digoxin. It is
believed to be an important protective mechanism aga inst
environmental toxins. Since the function of P-gp always
results in the lack of intracellular levels of the drug ne-
cessary for effective therapy, the overexpression of P-gp
in certain malignant cells is always associated with MDR
phenotype [17]. Although a low resolution structure of
P-gp is obtained, its physiological function and mecha-
nisms of MDR modulation are still not very clear [18]. It
is well known that a large number of structurally and
functionally diverse compounds act as substrates or mod u-
lators of P-gp, including calcium and sodium channel
blockers, calmodulin antagonists and structural analogues,
protein kinase C inhibitors, steroidal and structurally re-
lated compounds, indole alkaloids, cyclic peptides and
macrolide compounds, flayanoids and miscellaneous com-
pounds [19], which mostly share common structural fea-
tures, such as aromatic ring structures and high lipo-
philicity. Some of them possess MDR reversing activity
while only a small number of them have entered clinical
study. Classification of candidate drugs as substrates or
inhibitors of the carrier protein is crucial in drug devel-
opment [20].
On the other hand, the prerequisite to cure neurologi-
cal disorders is that the drug distribution in CNS can
reach effectively therapeutic concentrations [2]. Usually,
the high BBB penetration is needed for drugs that acti-
vate in brain. The molecule negotiating the BBB must g o
through cellular membranes comprised of a lipid bilayer.
Until now, it is widely accepted that interaction of com-
pounds with P-gp is a complex process and at this time
the details of its mechanism of action are still the sub ject
of hot debate. Although the experimental analysis of drug
permeability is essential, the procedure of experiment is
time consuming and complicated, a theoretical model of
drug permeability is effective to give predictions. Mem-
brane-interaction (MI)-QSAR (quantitative structure-ac-
tivity relationship) method is a structur e-based d esi gn me-
thodology combined with classic intramolecular QSAR
analysis to model chemically and structurally diverse
compounds interacting with cellular membranes. Our
modified MI-QSAR method that combines QSAR with
solute-membrane-water complex simulating the BBB en-
vironment is more close to the body condition than MI-
QSAR and possesses higher ability to predict organic
compounds across BBB [21]. There are several critical
assumptions considered that can influence validity and
correctness of any QSAR study as follows: the same

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 874
mechanism of action of all studied analogs; a comparable
manner of their binding to the receptor; correlation of
binding to the interaction energies; correlation of meas-
ured biological activities to the binding affinities [22].
All the accuracy answer and research based on the above
questions above may guarantee that proper and reliable
relationships are obtained. However, different mechanisms
and different binding sites may be involved in the case of
MDR modulators. Several screening assays can help in
the identification of substrates and inhibitors although
they have both advantages and drawbacks, such as cyto-
toxity assays [23], inhibition of efflux assays [16], P-gp-
ATPase activation assays, and drug transport assays [24].
The goal of a QSAR study is to find a means of pre-
dicting the activity of a new compound. If possible, a
desirable goal is the understanding of the biology and
chemistry that give rise to that activity and the conse-
quential possibility of reengineering the compound to re-
move or enhance that activity. One successful example is
the transformation of nalidixic acid with the help of
QSAR into an important family of drug: the quinolone
carboxylates, such as norfloxacin, fleroxacin, ciproflox-
acin, and levofloxacin [25]. Since the method was estab-
lished in the 1960s, QSAR equations have been used to
describe the biological activities of thousan ds o f different
drugs and drug candidates [26]. The method definitely
provides a more accurate way to synthesize or filtrate the
new chemical compounds. At last, the final destination is
to degrade the cost of research and manufacture. To date,
so many methods have been used in QSAR study and
some of them have got successful results. There are gen-
eral methods used in the literatures these years, such as
multiple linear regression (MLR) method, partial least
square regression (PLSR) [18], MI-QSAR analysis [21],
3D QSAR [27], and artificial neural network (ANN) [28].
In order to get more accurate results and QSAR models,
we have used two different analyses: MLR and PLSR.
Moreover, we focus on constructing theoretical models
of the interaction between organic compounds and P-gp
as well as the predictive models of BBB partitioning of
organic compounds on the basis of QSAR analysis and
MI-QSAR analysis.
2. MATERIALS AND METHODS
2.1. P-Glycoprotein Ligands
Building of some compounds. 36 purine derivatives
were selected and used in QSAR analysis (Table 1) [29].
These compounds were divided into two sets: the train-
ing set and the test set. The study of the MDR-reversing
properties of these derivatives was carried out in vitro on
P388/VCR-20 cells, a murine leukemia cell line whose
resistance was induced by vincristine (VCR), and KB-A1
cells, a human epidermoid carcinoma cell line whose
resistance was induced by adriamycin (ADR). The com-
pounds were tested at four concentrations (0.5 - 5 μM) in
association with VCR (P388/VCR-20 cells) or ADR (KB -
A1 cells). In this test, MDR ratio in P388/VDR-20 and
KB-A1 in vitro was used as biological activity for the
whole dataset, namely



50
ratio 50
IC CD
MDR ICCD mod .
Here “CD” is the abbreviation for cytotoxic drug (such
as VCR and ADR) in cytotoxity assays, and “mod” means
modulators. It is defined as ratio between the IC50 values
(concentration that inhibits the growth of MDR cells by
50%) of the cytotoxic agent in absence and presence of
relatively nontoxic concentration of the modifier [23].
Most often the IC50 for several concentration of a cyto-
toxic drug is evaluated in the presence and absence of a
nontoxic concentration of a P-gp modifier. In this assay
modulators that interacted with P-gp and thus reduced
the efflux of the cytotoxic compounds would increase the
apparent toxicity of the cytotoxic compound [16]. The
IC50 data were based on a general assessment of cytotox-
icity and thus might account for more then one acting
mechanism in the resistant cells used [16]. Furthermore,
it is well known that the MDR ratio for any given com-
pound can vary greatly depending on the cell type used
for the assay as well as the intrinsic cytotoxicity of the
compounds used. The data is also dependent on the con-
centration of the P-gp substrates or modulators used in
the studies [30].
Similarly, another 21 propafenone analogs were se-
lected from the literature of Diethart Schmid et al. and
used in QSAR analysis (Table 2) [31]. In this test Ka of
P-gp ATPase in the adriamycin-resistant subline CCRF
ADR5000 was used as biological activity for the whole
dataset [31]. The assays were performed based on the
colorimetric determination of inorganic phosphate released
by the hydrolysis of ATP. Table 2 shows all the struc-
tures and the experimental biological activity value.
Finally, all two-dimensional (2D) structures of these
compounds mentioned above were constructed using the
chemical drawing software ChemDraw 8.0 and prepared
for the next calculation.
Calculation of some descriptors. Molecular descrip-
tors are “numbers that characterize a specific aspect of
the molecular structure” [32]. There are some molecular
descriptors used in QSAR studies as follows: physico-
chemical properties (i.e. hydrophobicity, aqueous solu-
bility, molecular electronegativity, and molecular refrac-
tivity), quantum chemical parameters (e.g. atomic charges,
energies of HOMO (highest occupied molecular orbital)
and LUMO (lowest unoccupied molecular orbital)) [33],
topological indexes (such as molecular connectivity in-
dexes) [34], and other 3D descriptors. Molecular de-
scriptors were mostly calculated by the commercial soft-

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 875
Table 1. The structures and MDR ratios of 35 purine derivatives in the training/test sets.
In vitro reversal fold
reversion (MDR ratio) In vitro reversal fold reversion (MDR ratio)
No. Structure P388/VCR-20KB-A1 No. Structure P388/VCR-20 KB-A1
A1
N
N
N
N
HN
N NH CH
2
50 171
A19
N
N
N
N
N
H
NH NCH
2
F F
36 49
A2
N
N
N
N
HN
N NH CH
2
78 278
A20
N
N N
N
N
H
NH NCH
2
O
2
SN
CH
3
Cl
70 214
A3
N
N
N
N
HN
N
NH CH
2
O
2
SN
CH
3
75 238
A21
N
N N
N
N
H
NH
NCH 2
35 113
A4
N
N
N
N
NH
N
H
2
CCH
2
53 236
A22
N
N
N
N
N
H
NH NCH2
O
CH3
133 200
A5
N
N
N
N
HN
N NH CH
2
O
CH
3
236 160
A23
N
N N
N
N
H
NH
NCH2
193 189
A6
N
N
N
N
HN
N NH CH2
93 208 A24
N
N N
N
N
H
NNH
24 142
A7
N
N N
N
HN
N NH
SO
2
N
CH
3
Cl
124 102
A25
N
N N
N
N
H
N
N
CH
F
F
13 6

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 876
Continued
A8
N
N
N
N
NH
N NH CH
2
30 120 A26
N
N N
N
HN
NNCH
F
F
24 9
A9
N
N
N
N
HN
N NH CH
2
O
2
SN
CH
3
57 75
A27
N
N N
N
HN
N NH CH
2
O
84 406
A10
N
N N
N
HN
N NH CH
2
OCH
3
CH
3
CH
3
108 136
A28
N
N N
N
HN
N
NH
SO
2
N
CH
3
Cl
57 68
A11
N
N
N
N
HN
N NH
O
37 44
A29
N
N
N
HN
N
NH CH
2
108 723
A12
N
N N
N
HN
N NH
15 83
A30
N
N N
N
HN
N NH H
2
C C
27 370
A13
N
N N
N
HN
N NH CH
2
78 272
A31
N
N N
N
N
H
NH
NCH
2
288 210
A14
N
N
N
N
HN
N
NH CH
2
O
CH
3
56 147 A32
N
N N
N
NH
N NH CH
2
FF
59 121
A15
N
N
N
N
HN
N NH CH
2
O
CH
3
75 152
A33
N
N N
N
HN
N
NH H
2
C
NH
O
71 499
A16
N
N
N
N
HN
N NH CH
2
51 209
A34
N
N N
N
N
HN
N
NH CH
2
13 264

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 877
Continued
A17
N
N N
N
HN
N
NH CH
2
70 171 A35
N
N
N
N
HN
NN
CH
F
F
3 5
A18
N
N N
N
HN
N
NH CH
2
129 156
Note: Ratio of IC50 (cytotoxic alone (VCR for P388/VCR-20, ADR for KB-A1 cells))/IC50 (cytotoxic + modulator) (1 μM in associatio n with VCR or 2 .5 μM in
association with ADR) [29].
Table 2. The structures and Ka values and LogP of 18 propafenone analogs in the training/test sets.
No. Structure Ka (μM/L)LogPNo.Structure Ka (μM/L) LogP
A36
O
OH
O
CH
3
3.34 3.39A45
O N
OH
OCH
3
1.53 4.3
A37
O
OH
O
N
CH
3
CH
3
5.3 3.62A46
O
O
OH
N
N F
1.47 4.93
A38
O
OH
O
N
2.59 3.67A47
OH
O
OH
N
N F
0.55 5.2
A39
O N
OH
O
CH
3
122 1.42A48
O
ON
OH
7.64 4.25

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 878
Continued
A40
O N
OH
O
N F
0.36 4.93A49
OH
ON
OH
12.2 4.52
A41
O N
OH
O
CH
3
N F
6.13 2.67A50
OCH
3
ON
OH
2.26 4.88
A42
O N
OH
O
CH
3
O
120 0.94A51
O N
OH
O
CH
3
OH
10.5 2.38
A43
O N
OH
O
O
18.5 2.54A52
O N
OH
OH
12.8 3.94
A44
O N
OH
O
OH
1.01 3.98A53
O
O
OH
N
N F
4.15 4.93
ware packages Chemoffice Chem3D Ultra 8.0, involv ing
molecular mechanism parameters (Stretch-Bend Energy
(Estretch), Bending Ener gy (Ebend), Torsion Energy (Etorsion),
Total Energy (Etotal), van der Waals Energy (EVDW), etc),
quantum chemistry parameters (i.e. Electronic Energy
(Eelectronic), HOMO Energy (EHOMO) and LUMO Energy
(ELUMO)), hydrophobic parameters (such as ClogP), ste-
reo parameters (eg. Es, Balaban Index (BI), Connolly
Accessible Area (CAA), Molecular Weight (MW), Shape
Attribute (ShA), Total Connectivity (Tcon), and Wiener

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 879
Index (WI)), thermodynamic parameters, including Henry’s
Law Constant (H), Hydration Energy (Ehyd), Logarithm
of partition coefficient in n-octanol/water (LogP), Molar
Refractivity (MR), and molecular polar surface area (P SA)
that defined as the surface area (Å2) occupied by polar
atoms, usually oxygen, nitrogen and hydrogen attached
to them, which will restrict molecule penetration into the
membranes [2]. The other properties involved in number
of hydrogen bond acceptor (NBA) and number of hy-
drogen bond donor (NBD).
The energy parameters root in the results of molecular
mechanism and molecular dynamics. The total energy of
a system expressed as follows [35]: Etotal = Evalence +
Ecrossterm + Enonbone. Here, the valence interactions includes
bond stretching (bond), valence angle bending (angle),
dihedral angle torsion (torsion), and inversion, also called
out-of-plane interactions (oop) terms, which are part of
nearly all forcefields for covalent systems. A Urey-Brad-
ley term (UB) may be used to account for interactions
between atom pairs involved in 1 - 3 configurations (i.e.,
atoms bound to a common atom): Evalence = Ebond + Eangle
+ Etorsion + Eoo p + EUP. Modern (second-generation) force-
fields generally achieve higher accuracy by including
cross terms to account for such factors as bond or angle
distortions caused by nearby atoms. Crossterms can in-
clude the following terms: stretch-stretch, stretch-bend-
stretch, bend-bend, torsion-stretch, torsion-bend-bend,
bend-torsion-bend, stretch-torsion-stretch. The interaction
energy between non-bonded atoms is accounted by van
der Waals (VDW), electrostatic (Coulomb), and hydro-
gen bond (hbond) terms in some older forcefields.
Enon-bond = EVDW + ECoulomb + Ehbond. Restraints that can be
added to an energy expression include distance, angle,
torsion, and inversion restraints. Restraints are useful for
information on restraints and their implementation and
use if you are interested in only part of a structure, and so
is the documentation for the particular simulation engine.
These descriptors were calculated using Chemoffice
Chem3D Ultra 8.0 and Hyperchem 7.5 as follows: 1) the
structures of the compounds were drawn in ChemDraw
8.0 and sequentially changed to 3D structures by Che m3D;
2) the chosen compounds were minimized by molecular
mechanism using MM2 force field with RMS (root mean
square) gradient of 0.100; and 3) under the menu of
Analyze-compute properties, the properties were selected
to calculate and finally every descriptor value of each
compound was gotten.
QSAR models. QSAR models of some purine deriva-
tives (Table 1) were achieved by partial sum of squares
for regression with software SPSS 10.0. A training set of
26 structurally diverse purine derivatives are measured is
used to construct QSAR models. The QSAR models are
optimized using MLR fitt i ng and stepwi se method (Eqs.1-
5). A test set of five compounds is evaluated using the
QSAR models as part of a validation process. Take MDR
ratio in vitro in P388/VDR cell lines as dependent vari-
able and molecule descriptors as independent variable.
With the aid of Virtual Computational Chemistry Labora-
tory software [20], QSAR modeling was constructed by
PLSR (Eq.6).
Similarly, a training set of 18 structurally diverse
propafenone analogs (Table 2) are measured is used to
construct QSAR models. The QSAR models are opti-
mized using MLR fitting and stepwise method (Eqs.7-
11). Another QSAR modeling was constructed by PLSR
(Eq.12). A test set of five compounds is evaluated using
the QSAR models as part of a validation process.
2.2. Blood-Brain-Barrier
Data. 37 organic compounds [36,37] were elected to
compose a train set while another 8 organic compounds
were acted as a test set (Table 3). The dependent vari-
able is the logarithm of the BBB partition coefficient, log
BB = log(Cbrain/Cblood), where Cbrain is the concentration
of the test compound in the brain, and Cblood is the con-
centration of the test compound in blood. Experimental
values of logBB published to date lie approximately be-
tween −2.00 to +1.04. Compound s with logBB values of
>0.30 are readily distributed to the brain whereas com-
pounds with values <−1.00 are poorly distributed to the
brain. Building of all these compounds was performed
using the Build modules of Hyperchem 7.5. The geome-
try of these compounds was opitimized using the Amber
94 force field in gas state and sequentially placed at a
periodic solvent box with a volume of 16 × 10 × 18 Å3,
which included 96 water molecules. Here, temperature is
300˚K and pressure is 1 standard atmosphere. Then, the
compounds in water were minimized by the above method
and simulated by Monte Carlo method.
Molecular modeling of a dimyristoylphosphatidyl-
choline (DMPC) monolayer membrane complex with
a layer of water. A model of DMPC monolayer mem-
brane compo sed of 25 DMPC molecules (5 × 5 × 1) was
constructed using Material Studio and minimized for 200
steps with the smart minimizer. Here, the parameter of
the single crystal of DMPC with a = 8 Å, b = 8 Å, and γ
= 96.0˚ resulted in each lipid molecule with an average
area of 64Å2 similar to Stouch’s research results [38].
Moreover, a layer of water (40 × 40 × 10 Å3) including
529 water molecules was added to the polar side of the
DMPC monolayer membrane (Figure 1).
Molecular dynamic s imulation of compo u nd -D M PC -
water complex models. A compound displaced a DMPC
molecule in the DMPC monolayer membrane at three
different positions (upper, center or lower) to form each
solute-membrane-water complex. Molecular dynamic

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 880
Table 3. The structures and LogBB values of some compounds in the training/test sets.
No Structure LogBBNoStructure LogBB
Training set
B1 NH2
NH2NS
N
−0.04 B20
N
H
3
C
H3C
0.85
B2
N
O
CH
3
H
NNO
HN
NC
3
H
−2.00 B21 N
N
0.03
B3
H3C
N
H3C
OS
NH
N
HN O
−1.30 B22 H
HH
H
HH
0.37
B4
N
H
N
N
H
N
C
H
3
O
H3CN
CH3
OS−1.06 B23
CH3
CH3 HO
−0.15
B5
H
N
Cl
N
HN
C
l
0.11 B24
H
3
C
H3COH
−0.17
B6
NN
O
H3C
N
CH3
H3C
0.49 B25
CH3
CH
3
H3C
0.97
B7 CH3
N
CH3
N
N
N
HN
H2N
−1.17 B26
CH3
CH
3
H3C
H3C
1.04

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 881
Continued
B8
NH2
H2N
N
S
N
−0.18 B27 F
F
F
Cl
0.08
B9 H2N
NH2
N
S
N
NH2
−1.15 B28
H3CCl
Cl
Cl
0.40
B10
H2N
NH2
N
S
N
HN
CH
3
O
−1.57 B29 Cl
F
O
F
F
FF
0.24
B11
N
OH N
CH3
O
−0.46 B30 H3C
OH −0.16
B12
N
OH NO
−0.24 B31
F
F
FO
CH2
0.13
B13
N
OOH
−0.02 B32
F
F
F
Br
Cl
0.35

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 882
Continued
B14
N
OH NN
S
0.44 B33
H3C
0.93
B15
N
ON
HN
S
0.14 B34 H3C
O
H
−0.16
B16
N
ON
HN
O
0.22 B35
F
B
r
F
FF
0.27
B17 NN
H3C
H3C
−0.06 B36 CH3 0.37
B18
N
NNH2
−1.40 B37
ClCl
C
l
0.34
B19
H3C
N
H
3
C
NH
O
0.25
Test set
T1
CH3
H
3
C
O
−0.08 T5 H
3
CCH
3
0.81

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 883
H
Continued
T2 H3CC
3
CH3
1.01 T6
H
H
HH
0.04
T3
CH3
H3C
CH3
0.90 T7 CH3
H3C 0.76
T4 CH3
OH3C 0.00 T8
H
3
CCH
3
O
−0.15
Figure 1. Compound B1 colored by atom-type in water and the solvent box defined in Monte
Carlo simulation. 3. RESULTS
simulation of the complex was performed for 1000 steps
by Discover module with Materials Studios, using Com-
pass force field. Here, the 3D volume was restricted to a
border of X = 40 Å, Y = 40 Å, Z = 91.76 Å, and γ =
96.0˚.
3.1. QSAR Analysis Based on MDR Ratio in
P388/VDR-20 and KB-A1 in Vitro
MDR ratio of compounds in vitro in KB-A1/ADR cell
lines was taken as the dependent variable. A training set
of 26 co mpounds (Ta ble 4) was used to construct QSAR
models. The QSAR models were optimized using MLR
fitting and stepwise method by SPSS (Eqs.1-5). A test
set of 5 compounds (A27-A31) was evaluated using the
models as part of a validation process (Figure 2 upper,
Table 5).
QSAR model of BBB partitioning of some com-
pounds. MI-QSAR model of a training set of 37 com-
pounds through BBB were achieved by partial sum of
squares for regression with SPSS. Molecular dynamics
simulations were used to determine the explicit interac-
tion of each compound with a model of DMPC monolayer
membrane complexed with a layer of water. An addi-
tional set of intramolecular solute descriptors were com-
puted and considered in the trial pool of descriptors for
building MI-QSAR models. The MI-QSAR models were
optimized using multidimensional linear regression fit-
ting and stepwise method. The MI-QSAR models were
then evaluated by a test set of 8 compounds.
Similarly, MDR ratio of compounds in vitro in P388/
VDR cell lines acted as the dependent variable. With the
aid of Virtual Computational Chemistry Laboratory soft-
ware (http://vcclab.org) [20], some QSAR models were
constructed by PLSR (Eq.6, Figure 2 down). Table 6
shows the calculated descriptors mentioned above and

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 884
Table 4. The molecular descriptors of some compounds related to MDR ratios in the training/test sets.
No. LogMR ShA BI LogP Ehyd (kcal/mol)No. LogMRShA BI LogP Ehyd (kcal/mol)
Training set
A1 1.20 37.03 2,662,570 3.33 −3.56 A14 1.22 39.02 3,358,755 1.48 −19.64
A2 1.18 35.03 2,440,928 2.59 −13.71 A15 1.22 39.02 3,358,755 1.48 −19.71
A3 1.22 40.02 3,649,082 1.14 −16.23 A16 1.20 37.03 2,662,570 2.13 −15.99
A4 1.14 32.03 1,669,953 2.21 −13.54 A17 1.202 37.03 2,662,570 2.13 −16.23
A5 1.22 39.02 3,358,755 1.33 −19.71 A18 1.202 37.03 2,662,570 2.13 −15.95
A6 1.20 37.03 2,662,570 2.13 −16.22 A19 1.18 37.03 3,091,919 1.61 −15.9
A7 1.22 40.02 3,491,392 0.76 −17.67 A20 1.23 41.02 4,008,723 0.76 −17.36
A8 1.20 37.03 2,662,570 2.28 −16.3 A21 1.14 32.03 1,651,352 1.9 −13.79
A9 1.24 42.02 4,491,514 1.29 −16.34 A22 1.22 39.02 3,324,212 1.33 −20.06
A10 1.24 41.02 4,055,919 0.99 −19.77 A23 1.20 37.03 2,634,052 2.13 −15.77
A11 1.18 36.03 2,271,976 1.41 −17.73 A24 1.19 36.03 2,246,188 2.13 −16.15
A12 1.19 36.03 2,271,976 2.13 −16.17 A25 1.15 35.03 2,244,801 1.71 −14.88
A13 1.15 33.03 1,900,460 2.28 −13.57 A26 1.15 35.03 2,271,261 1.71 −15.03
Test set
A27 1.19 37.03 2,662,570 1.41 −18.09 A30 1.25 41.02 3,977,672 2.98 −13.52
A28 1.22 40.02 3,491,392 0.76 −17.61 A31 1.20 37.03 2,634,052 2.13 −15.95
A29 1.21 37.03 2,662,570 2.75 −15.55
Table 5. The experimental values and the predictive values of MDR ratio of these compounds.
Predictive values of MDR ratio Predictive values of MDR ratio
No. MDR
ratio
(KB-A1) Eq.1 Eq.2 Eq.3 Eq.4 Eq.5
No. MDR
ratio
(KB-A1) Eq.1 Eq.2 Eq.3 Eq.4 Eq.5
Training set
A1 171 114.32 215.61 200.33 123.15 180.87 A14 147 151.94151.04 171.02 173.75144.79
A2 278 80.79 200.54 305.22 260.52 228.39 A15 152 150.27140.09 157.75 159.16130.87
A3 238 161.08 71.37 78.37 79.12 92.55 A16 209 115.64233.15 217.87 209.32197.83
A4 236 44.84 113.11 178.94 196.99 213.61 A17 171 115.64233.15 217.87 209.32193.94
A5 160 150.27 140.09 157.75 168.14 148.66 A18 156 116.97252.09 236.91 229.26219.10
A6 208 113.02 199.36 184.18 174.42 159.21 A19 49 81.98 22.31 33.79 29.47 25.56
A7 102 163.01 77.39 67.24 80.67 105.63 A20 214 198.4493.91 119.73 132.46165.76
A8 120 114.32 215.61 200.33 180.88 153.75 A21 113 42.69 80.90 121.41 145.60174.56
A9 75 237.62 101.82 179.28 149.94 146.81 A22 200 150.27140.09 149.67 160.44139.02
A10 136 222.49 204.96 297.19 322.30 315.82 A23 189 113.02199.36 176.36 167.80160.14
A11 44 79.86 58.80 41.66 49.61 53.08 A24 142 92.42 159.30 116.68 117.52112.54
A12 83 92.42 159.30 121.35 121.70 115.60 A25 6 52.12 10.08 9.16 8.45 8.02
A13 272 53.80 124.41 185.32 190.50 197.66 A26 9 52.12 10.08 9.53 8.76 8.16
Testset
A27 406 99.21 81.97 70.98 80.61 81.67 A30 370 243.10375.08 504.47 282.78192.47
A28 68 163.01 77.39 67.24 80.67 106.16 A31 210 114.32215.61 191.82 183.83174.21
A29 723 125.69 411.51 400.76 323.36 248.59

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 885
Figure 2. Comparison of the experimental MDR values with the corresponding predicted MDR values. Upper: MDR value in
KB-A1/ADR cell lines (blue rhombic dots); MDR as predicted by Eq.4 MLR model (red square dots) and by Eq.5 MLR model
(yellow triangle dots) for all the molecules of the training and test set. Down: MDR value in P388/VDR cell lines (blue rhombic dots);
MDR as predicted by the method of PLSR (Eq.6) (red square dots) for all the molecules of the training and test set. The rhombic dots
represented the experimental values (P388) and the predicted values of MDR, respectively.
Table 6. Comparison of experimental value of MDR ratio with predicted value of MDR ratio by PLSR.
No. MDR
(P388) Pred MDR LogPMR EVDW ShA WI No.MDR (P388) Pred MDR LogP MR EVDW ShA WI
A1 50 43.66 3.3315.85 32.21 37.03 5476 A18 129 62.872.13 15.90 27.08 37.035476
A2 78 40.71 2.5915.10 21.84 35.08 4872 A19 36 29.481.61 15.13 21.63 37.035585
A3 75 53.15 1.1416.63 24.83 40.02 6522 A20 70 73.870.76 17.12 25.52 41.026855
A4 53 55.39 2.2113.91 20.16 32.03 3916 A21 35 54.411.9 13.82 20.17 32.033874
A6 93 86.84 2.1315.83 32.75 37.03 5476 A24 24 24.572.13 15.39 23.42 36.034855
A8 30 47.64 2.2815.85 25.21 37.03 5476 A25 13 11.321.71 14.21 22.20 35.034487
A9 57 79.51 1.2917.56 26.03 42.02 7353 A26 24 11.441.71 14.21 20.92 35.034538
A10 108 138.69 0.9917.40 29.44 41.02 6935 A27 84 58.011.41 15.54 26.05 37.035476
A11 37 30.21 1.4115.08 24.04 36.03 4909 A28 57 35.880.76 16.66 23.78 40.026244
A12 15 27.61 2.1315.39 23.512
36.03 4909
A29 108 49.032.75 16.06 25.95 37.035476
A13 78 87.23 2.2814.27 29.12 33.03 4216 A30 27 35.792.98 17.61 26.49 41.026804
A14 56 96.24 1.4816.50 28.19 39.02 6288 A32 59 30.181.83 15.13 22.08 37.035642
A15 75 89.20 1.4816.47 27.54 39.02 6288 A33 71 73.840.86 15.79 27.62 38.035822
A16 51 54.90 2.1315.88 25.61 37.03 5476 A34 13 31.952.58 15.64 25.36 37.035476
A17 70 54.43 2.1315.88 25.49 37.03 5476 A35 3 12.221.71 14.21 20.40 35.034589

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 886
the result of predicted value was in Table 5. LogKa2.4240.484 LogP


LogMDR = −6.537 + 7. 16 2 LogMR N = 16; R = 0.860; F = 39.748 (7)
N = 27; R = 0.445; F = 6.187 (1) LogKa3.6120.285 LogP0.0732ShA


LogMDR = −37.830 + 48.862 LogMR − 0.499 ShA N = 16; R = 0.900; F = 27.676 (.8)
N = 27; R = 0.889; F = 45.415 (2) LogKa2.5730.480 LogP0.285 ShA0.651 MR


LogMDR = −35.816 + 52.416 LogMR − 0.717 ShA +
6.612 × 10−7 BI N = 16; R = 0.914; F = 20.251 (9)
LogKa = 7.313 − 0.752 LogP − 0.647 ShA + 1.642
MR + 0.605 EHOMO
N = 27; R = 0.919; F = 41.442 (3)
LogMDR = −38.791 + 56.923 LogMR − 0.769 ShA +
5.897 × 10−7 BI − 0.159 LogP N = 16; R = 0.928; F = 17.111 (10)
LogKa = 10.021 − 0.875 LogP − 1.044 ShA + 2.263
MR + 0.673 EHOMO + 6.734 × 10−4 WI
N = 27; R = 0.927; F = 33.504 (4)
LogMDE = −42.192 + 61.818 LogMR − 0.801 ShA +
4.791 × 10−7 BI − 0.369 LogP + 3.595 × 10−2 Ehyd N = 16; R = 0.945; F = 16.832 (11)
LogKa = 3.662 − 0.279 LogP − 4.71 × 10−3 MW +
1.223 × 10−2 EHOMO
N = 27; R = 0.936; F = 29.749 (5)
LogMDR = 7.611 + 3.138 × 10−2 LogP − 0.245 MR +
0.495 EVDW − 0.509 ShA + 8.802 × 10−4 WI N = 18, Q2 = 0.7100 (12)
3.3. QSAR Analysis Based on BBB Partitioning
of Organic Compounds
N = 30; Q2 = 0.4650 (6)
3.2. QSAR Analysis Based on Ka of ATPase in
CCRF ADR5000 Cell Lines On the other hand, 37 organic compounds of training set
and 8 compounds of test set were built and minimized,
dissolved in liquid, and optimized by Monte Carlo meth od.
Molecular modeling of the compound-membrane-water
complex model revealed that the energy of an organic
compound inserted at the middle position in the DMPC
model with a layer of water was lower than that of the
other two positions. Molecular descriptor s of compounds
in a training set and a test set are listed in Table 9. Six
QSAR equations were constructed based on Table 9 and
were listed as follows.
Meanwhile, took Ka of ATPase of compounds in CCRF
ADR5000 cell lines as dependent variable. Some QSAR
models of a training set of 16 compounds were built us-
ing MLD method (Eqs.7-11) and PLSR method (Eq.12)
(see Figure 3). All the molecular descriptors were listed
in Table 7. A test set of 2 compounds was evaluated
using the models as part of a validation process. Table 8
displays the comparison of the experiment Ka and pre-
diction Ka va lues of ATP as e.
Figure 3. Comparison of the experimental Ka value (blue rhombic dots) with the corresponding predicted Ka as predicted
by Eq.11 MLR model (red square dats) and by Eq.12 PLSR model (yellow triangle dots) for all the molecules of the
training and test.

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 887
Table 7. The molecular descriptors of some compounds related to ATPase in the training/test sets.
No. LogP ShA MR EHOMO (eV)WI MW No.LogPShAMR EHOMO (eV) WI MW
A36 3.39 21.04 9.254 −9.14 1366 312.41A45 4.3 26.0411.42 −9.17 2345383.53
A37 3.62 24.04 10.55 −9.20 1949 355.48A46 4.93 32.03 13.27 −8.24 4689462.57
A38 3.67 25.04 10.84 −9.16 2172 367.49A47 5.2 32.0313.38 −8.19 4329464.58
A39 1.42 18.05 7.86 −9.12 920 277.37A48 4.25 26.04 11.45 −9.24 2607383.53
A40 4.93 32.03 13.27 −8.16 4329 462.57A49 4.52 26.04 11.59 −8.94 2367385.55
A41 2.67 25.04 10.29 −8.15 2244 372.44A50 4.88 27.03 12.06 −8.94 2550399.58
A42 0.94 19.05 8.01 −9.15 1050 293.37A51 2.38 26.04 10.99 −9.09 2400383.49
A43 2.54 25.04 10.52 −9.20 2172 369.46A52 3.94 25.04 10.95 −9.05 2172369.51
A44 3.98 32.03 13.50 −9.16 4227 459.59A53 4.93 32.03 13.27 −8.19 4509462.57
Table 8. Comparison the experimental values with the predictiv e v alue s of Ka of these compounds.
Predictive values of Ka Predictive values of Ka
No. Ka
(μM/L) Eq.7 Eq.8 Eq.9 Eq.10 Eq.11Eq.12No. Ka
(μM/L)Eq.7 Eq.8 Eq.9 Eq.10 Eq.11Eq.12
A36 3.34 6.07 12.75 9.37 6.43 6.14 13.57A45 1.532.20 3.02 3.32 2.69 2.08 3.50
A37 5.30 4.70 6.62 7.10 6.19 5.60 7.33 A46 1.471.09 0.73 0.52 0.48 0.81 1.02
A38 2.59 4.44 5.41 5.36 3.98 3.06 6.24 A47 0.550.81 0.61 0.46 0.50 0.53 0.84
A39 122 54.54 76.92 73.09 90.64 160.46 70.37A48 7.642.33 3.13 3.83 3.33 4.22 3.60
A40 0.36 1.09 0.73 0.52 0.53 0.51 1.02 A49 12.201.72 2.62 3.39 4.98 5.00 2.99
A41 6.13 13.54 10.43 7.17 11.79 7.23 11.56A50 2.261.15 1.75 2.37 3.50 3.28 2.04
A42 120.00 93.12 89.09 81.21 80.47 99.3780.44A51 10.5018.71 10.66 14.57 16.6 13.2012.02
A43 18.50 15.65 11.36 11.73 8.26 5.56 12.59A52 12.803.29 4.53 4.74 4.57 3.91 5.15
A44 1.01 3.15 1.36 2.10 1.66 2.13 1.88 A53 4.151.09 0.73 0.52 0.51 0.65 1.02
Table 9. The molecular descriptors of the compounds related to BBB in the training/test sets.
No PSA (Å2) ClogP BI (Å) Estretch (Kcal/mol)Etotala (Kcal/mol)Etorsiona (Kcal/mol)ΔEtotalb (Kcal/mol) ΔEtorsionb (Kcal/mol)
Training set
B1 78.90 1.20 12378 −1.35503 −298.2972 −1713.1146 42.46 11.30
B2 94.00 1.99 1101758 −0.15595 −406.0803 −1789.8084 −65.32 −65.39
B3 73.00 3.80 1738650 −1.48472 −256.3021 −1703.1425 84.46 21.27
B4 87.00 1.63 1346396 −1.39112 −302.7543 −1841.5635 38.00 −117.15
B5 39.00 1.02 41807 0.58131 −226.3773 −1734.7452 114.38 −10.33
B6 26.80 3.23 305770 −0.09264 −228.2923 −1679.4604 112.47 44.96
B7 88.80 1.01 58510 0.71038 −279.0781 −1671.3414 61.68 53.07
B8 76.60 2.80 62216 −0.38334 −309.2981 −1654.6730 31.46 69.74
B9 104.40 1.77 83798 −0.35599 −313.4237 −1639.9898 27.34 84.43
B10 108.80 2.00 193593 −0.52172 −548.5593 −1640.9214 −207.80 83.49
B11 47.90 2.51 352512 −0.09496 −312.1226 −1656.7465 28.64 67.67
B12 45.20 4.27 779210 0.00479 −163.8011 −1716.3101 176.96 8.11
B13 38.50 2.61 158640 −0.09491 −170.3338 −1716.7159 170.43 7.70
B14 40.00 4.28 431722 −1.30506 −247.0951 −1748.0241 93.66 −23.61
B15 39.20 5.88 766256 0.09911 −289.2825 −1735.4004 51.48 −10.98
B16 54.90 5.14 766256 −0.14215 −181.0636 −1743.6068 159.70 −19.19
B17 18.80 0.62 20863 0.18071 −331.7044 −1695.6999 9.05 28.72

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 888
Continued
B18 46.70 0.27 20264 −1.36843 −209.4697 −1644.6752 131.29 79.74
B19 44.10 2.80 190375 −2.97778 −311.9182 −1713.8942 28.84 10.52
B20 5.40 4.85 210631 −0.06079 −235.7250 −1704.3399 105.03 20.08
B21 0.00 −0.47 4 0.00000 −407.3194 −1729.3793 −66.56 −4.96
B22 0.00 2.14 972 −0.00009 −239.8807 −1675.1827 100.88 49.23
B23 23.40 0.07 213 0.00000 −160.1278 −1672.3898 180.63 52.03
B24 22.60 0.69 712 0.00000 −319.0674 −1742.6968 21.69 −18.28
B25 0.00 3.74 1899 0.00067 −282.3721 −1751.6193 58.39 −27.20
B26 0.00 3.61 1661 0.00000 −285.7132 −1731.9518 55.05 −7.54
B27 0.00 1.43 1661 −0.00008 −238.7249 −1731.3090 102.03 −6.89
B28 0.00 2.48 633 0.00003 −291.5583 −1725.7370 49.20 −1.32
B29 11.60 2.46 21380 −0.00005 −418.0323 −1682.7138 −77.27 41.70
B30 24.40 −0.24 47 0.00000 −329.3150 −1704.6187 11.44 19.80
B31 10.70 1.27 7864 −0.00002 −253.3453 −1747.7044 87.41 −23.29
B32 0.00 2.37 7322 −0.00003 −268.8335 −1714.2486 71.93 10.17
B33 0.00 3.31 931 0.02567 −353.8395 −1739.7672 −13.08 −15.35
B34 24.40 −0.24 47 0.00000 −187.4520 −1720.5500 153.31 3.87
B35 0.00 1.93 7322 −0.00003 −177.4875 −1728.8621 163.27 −4.45
B36 0.00 2.64 2050 −0.02344 −220.3940 −1681.1548 120.36 43.26
B37 0.00 2.63 712 −0.00002 −231.5752 −1722.2582 109.18 2.16
Test set
T1 22.70 0.321 712 0.00000 −274.7201 −1713.7409 66.04 10.68
T2 0.00 3.738 1838 0.00000 −225.6308 −1716.6234 115.13 7.79
T3 0.00 4.267 4150 0.00000 −331.3754 −1700.6397 9.38 23.78
T4 11.30 0.870 791 0.00000 −181.5954 −1700.8447 159.16 23.57
T5 0.00 4.397 4650 0.00000 −404.2903 −1741.2420 −63.53 −16.83
T6 0.00 1.103 0 0.00000 −282.9386 −1746.1889 57.82 −21.77
T7 0.00 3.339 791 0.00063 −271.9174 −1681.9440 68.84 42.47
T8 22.70 −0.208 213 0.00000 −364.8884 −1695.3605 −24.13 29.06
Note: aEtotal and Etorsion mean the t otal e nergy and the tors ion energ y of the co mpound- DMPC-water co mplex; bΔEtotal and ΔEtorsion ar e the res idues between t he
compound-DMPC-water complex and the DM PC-water complex. n = 37 R = 0.947 S = 0.248 (17)
2
logBB0.5521.73 10PSA


LogBB = 8.730 × 10−2 − 1.04 × 10−2 PSA + 0.222
ClogP − 9.60 × 10−7 BI − 0.183 Estretch + 1.364 × 10−3
ΔEtotal − 2.68 × 10−3 ΔEtorsion
n = 37 R = 0.835 S = 0.398 (13)
2
logBB0.2291.70 10PSA0.131ClogP

 
n = 37 R = 0.878 S = 0.352 (14) n = 37 R = 0.955 S = 0.232 (18)
logBB = 4.965 × 10−2 − 1.28 × 10−2 PSA + 0.211
ClogP − 6.40 × 10−7 BI Here, n means the number of compounds in a training
set, R means the correlative coefficient, and S means the
standard residual error. LogBB = log(Cbrain/Cblood). PSA
means the total polar surface area of a molecule. CLogP
and BI display calculated LogP and connective index of
molecular average total distance (relative covalent ra-
dius), respectively. They come from CS calcul at i on. ΔEtotal
and ΔEtorsion are related to interaction between a com-
pound and the membrane-water model. The total energy
n = 37 R = 0.924 S = 0.285 (15)
LogBB = 6.262 × 10−2 − 1.36 × 10−2 PSA + 0.205
ClogP − 7.11 × 10−7 BI − 0.185 Estretch
n = 37 R = 0.938 S = 0.264 (16)
LogBB = 6.580 × 10−2 − 1.21 × 10−2 PSA + 0.206
ClogP − 7.77 × 10−7 BI − 0.197 Estretch + 1.330 × 10−3
ΔEtotal

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 889
and the torsion energy of the membrane-water complex
are −340.7589 and −1724.4164 (Kcal/mol), respectively.
ΔEtotal is the change in the total potential energy of the
solute-membrane-water complex comparing with that of
the membrane-water model and so is ΔEtorsion.
With the increase of the independent variable, the rela-
tivity of QSAR model was also improved an d its predic-
tive ability was enhanced. The most significant Eq.18
displayed that the capability of a compound through
BBB was directly proportional to ClogP and ΔEtotal, but
inversely proportional to PSA, BI, Estretch, and ΔEtorsion.
Figure 4 showed the comparison of the experimental
logBB with the corresponding predicted logBB of the
molecules in the training set based on Eqs.17 and 18
models (see Table 10). Compound B18 was predicted
with a higher logBB than observed, supported by the
result of Iyer et al. [ 35].
The test set of 8 compounds to span almost the entire
range in BBB partitioning was selected for validation of
the QSAR models mentioned above. The observed and
predicted logBB values for this test set were given in
Table 10 and plotted in Figure 4 (right). It seemed to
suggest that Eqs.17 and 18 models could predict logBB
for other compounds in drug design.
4. DISCUSSION
Some predictive models of MDR, Ka and BBB parti-
tioning of organic compounds were built by simulating
the interaction between modulators or drugs and P-gp
and/or the interreaction of the organic compound with
the phospholipide-rich regions of cellular membranes.
We have constructed theoretical models of the interac-
tion between org anic compounds and P-gp and comp o u nd s
with the affinity for and simulation of the P-gp ATPase.
On one hand, the interaction between compounds and
P-gp (P-gp binding or MDR-reversal activity of com-
pounds) is found to depend on LogP, LogMR, and ShA
of compounds it transports, which proportional to Log
MR whereas inversely proportional to LogP and ShA
(see Eqs.1-5). Moreover, modulators or drugs interacting
with P-gp and thus reducing the efflux of the cytotoxic
compounds would increase the apparent toxicity of the
cytotoxic compounds, which might account for more
than one mechanism of action in the resistant cells used.
There were many uncertainty factors in the MDR ratio
assay method which was convinced by our linear
Figure 4. Comparison of the experimental logBB values (blue rhombic dots) for all the molecules of the training sets (upper) or the
test set (down) to the corresponding predicted logBB as predicted by Eq.17 MI-QSAR model (red square dots) and by Eq.18
MI-QSAR model (yellow triangle dots).

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 890
Table 10. The experimental values and the predictive values of Log BB of these compounds.

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 891
regression models. Our research results using two dif-
ferent statistic methods, MLR and PLSR, have revealed
that the QSAR equation was also improved and the pre-
dictive ability of the models was enhanced with the in-
crease of the variable. Eq.5 was built on KB-A1 cell line
with a cytotoxic compound of 2.5 μM ADR while Eq.6
was based on P38 8/VDR-20 cell line with 1.5 μM VCR.
Here, most of the models gave satisfactory cross-vali-
dated Q2 above 0.500, conventional R above 0.800 and
less SE values, indicating their proper predictive ability.
Significant differences between values were examined
using two-tailed paired T test provided by SPSS. All the
results were considered not significant if P < 0.05. Eq.5
model was the most significant and indicated that the
potential of P-gp modulators interacted with P-gp de-
pended upon MR, BI, Ehyd, ShA, and LogP. The former
three displayed positive contributions to the MDR activ-
ity of P-gp, suggesting that the MDR activity increased
accordingly with the increase of MR. The latter two dis-
played negative contribution to the MDR activity of
P-gp.
On the other hand, our bu ilt models for Ka o f ATPase
based on the analogies of purine and propafenone ana-
logs suggested that the enzyme hydrolysis of these com-
pounds largely depended on LogP, MR, ShA, MW and
EHOMO, especially positive related to MR but negative to
LogP and ShA (see Eqs.7 to 11). Both models, Eq.11 by
MLR and Eq.12 by PLSR, pointed out that EHOMO, posi-
tive related with the activity of P-gp ATPase, was an
important parameter for the compound stimulated AT-
Pase activity with high affinity, whereas another LogP
was negative related with the activity of P-gp ATPase.
Figure 3 showed that molecular A39 and A42 with
higher Ka value of ATPase were depart from other
compounds. This may be because they have lower lipo-
philicity, which is supported by the research results of
Diethart Schmid et al. [31]. The results above showed
that the P-gp binding capacity of these compounds shares
common characteristics with their ATPase hydrolysis,
namely their hydrophobic parameters (such as logP) and
steric parameters (e.g. MW, ShA, and MR).
In another aspect, our MI-QSAR models indicated that
the distribution of organic molecules through BBB was
not only influenced by organic solutes themselves, but
also related to the properties of the solute-membrane-
water complex, namely interactions of the mo lecule with
the phospholipide-rich regions of cellular membranes.
The QSAR model, especially Eq.18 most significant,
revealed that the capability of BBB partitioning of an
organic compound focused on six significant features.
Obviously, two descriptors, ClogP and ΔEtotal, had posi-
tive regression coefficients and the other four descriptors,
PSA, BI, Estretch, and ΔEtorsion, had negative regression
coefficients. Moreover, PSA descriptor was found as a
dominant descriptor in these QSAR models, which was
related to the aqueous solubility of the solute compound
along with a direct lipophilicity descriptor. When the
value of PSA of a molecule lessened within the range
from 0 to 108.80 Å2, its value of LogBB would increase.
This was consistent with the experimental results that the
more polarity it possessed, the more difficultly a mole-
cule entered the hydrophobic environment of BBB [38].
BI as the connective index of molecular average total
distance pertained to the volume parameter. Our research
result showed that a molecule more and more difficultly
acrossed through BBB by diffusion with the addition of
its bulk. However, the value of LogBB of a molecule
increased with the increase of ClogP. It meaned that the
hydrophobic molecule could pass through BBB more
easily than the hydrophilic molecule does. The presence
of Estretch descriptor suggested that with the decrease of
the stretch-bend energy of a molecule, its value of
LogBB increased. Two of the descriptors, ΔEtotal and
ΔEtorsion, found in the logBB QSAR models (Eqs.17 and
18), reflected the behavior of the solutes in the mem-
brane and the entire membrane-solute complex. Along
with the meaning mentioned, ΔEtotal was equivalent to
the change in the average total potential energy between
the ternary complex of solute-membrane-water and the
binary complex of membrane-water. Similarly, ΔEtorsion
was the difference between the dihedral torsion energy of
the ternary complex and that of the binary complex. Here,
the more the change value of ΔEtotal was, the more its
value of LogBB increased. This may be because small
molecules across BBB membrane could lead to the change
of the complex structure. The more changeability of the
structure resulted in greater change of the total potential
energy, while the addition of the energy change could be
the important cause of the increase of the capability of a
small molecule through BBB. On the contrary, the less
the difference of the torsion energy was, the larger its
LogBB value was. It displayed that a small molecule
tight combined with the membrane-water complex could
lead to the increase of its LogBB. Moreover, the rela-
tionship would suggest that the solute became more
flex ible with in the memb rane-wa ter compl ex, which wo u l d
possess the greater logBB value, in agreement with the
research results of Iyer M et al. [35]. Furthermore, BBB
partitioning was mainly found to depend upon two pa-
rameters, namely PSA and ClogP, where the ability of
organic molecules permeating across BBB was directly
proportional to LogP but inversely proportional to PSA
(see Eqs.13-18), which was consistent with the research
results of Chen and co-worker [2], namely the increasing
PSA decreased LogBB rapidly while LogP was positively
related to LogBB. It indicated that molecul es w ith h igher
lipophilic would be partitioned into the lipid bilayer
more easily with more chances to penetrate BBB, sup-

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 892
ported by the research result of Wang et al., namely a
large number of structurally and functionally diverse
compounds as substrates or modulators of P-gp mostly
sharing common structural features, such as aromatic
ring structures and high lipophilicity [19]. PSA of CNS
active drug should be lower than 90 Å2 [2], while the
penetration through the BBB is optimal for LogP value
in the range 1.5 - 2.7 (Norinder & Haeberlein, 2002).
In addition, several non-MI-QSAR computational mo d-
els to describe and predict BBB partitioning have been
reported that includes other descriptors besides PSA and
ClogP [39]. An alternative, complementary approach to
BBB partitioning prediction uses MI-QSAR analysis de-
veloped by Iyer M et al. [35]. Their research results
showed that BBB partitioning of an organic compound
depended upon PSA, CLogP, and the conformational
flexibility of the compounds as well as the strength of
their “binding” to the model biologic membrane. The
MI-QSAR models indicated that BBB partitioning proc-
ess could be reliably described for structurally diverse
molecules and provided interaction s of the molecule with
the phospholipide-rich regions of cellular membranes.
An extension of these approaches that combined QSAR
with solute-membrane-water complex had been devel-
oped by us, which was addition of a layer of water on the
hydrophilic side of DMPC monolayer membrane in order
to simulate the truth BBB environment. Our results re-
vealed that the distribution of organic molecules through
BBB was not only influenced by the properties of or-
ganic solutes, but also related to the property of the sol-
ute-membrane-water complex. The former involved the
polarity, hydrophobic, size, and conformational freedom
degree of organic molecules, while the latter dealt with
the strength of an organic molecule combined with BBB
membrane and the structural changeability of a solute-
membrane-water complex. Furthermore, the capability of
a small molecule across BBB was mainly related to four
physicochemical factors, which depended on the relative
polarity of a small molecule (namely PSA and ClogP),
the molecular volume (i.e. BI), the strength of a small
molecule combined with DMPC-water model (viz.
ΔEtorsion), and the changeability of the structure of a sol-
ute-membrane-water complex (scilicent ΔEtotal). The QSAR
model showed that the less polarity and more hydropho-
bic molecules relatively easily passed through BBB and
entered brain to cure. The reason for the change of the
total energy was that small molecules across BBB mem-
brane caused the structural change of the solute-mem-
brane-water complex. The more the changeability of the
complex structure was, the more the change value of its
total energy was, and the more easily a small molecule
penetrated BBB.
In particular, cerebral clearance of Aβ was considered
to occur via elimination across BBB, as well as prote-
olytic degradation. Attenuation of its elimination was
likely to result in the increase of cerebral Aβ deposition,
which may facilitate progression of AD [40]. P-gp de-
toxified cells by exporting hundreds of chemically unre-
lated toxins but had been implicated in MDR in the
treatment of cancers. Substrate promiscuity was a hall-
mark of P-gp activity, thus a structural description of
poly-specific drug-binding was important for the rational
design of anti-amyloid accumulation drugs, anticancer
drugs and MDR inhibitors. The x-ray structure of apo
P-gp at 3.8 angstroms revealed an internal cavity of ap-
proximately 6000 Å3 with a 30 Å separation of the two
nucleotide-binding domains. Two additional P-gp struc-
tures with cyclic peptide inhibitors demonstrated distinct
drug-binding sites in the internal cavity capable of ster eo-
selectivity that was based on hydrophobic and aromatic
interactions. Apo and drug-bound P-gp structures had
portals open to the cytoplasm and the inner leaflet of the
lipid bilayer for drug entry. The inward-facing confor-
mation represented an initial stage of the transport cycle
that was competent for drug binding [41]. Currently,
P-gp was identificated as an energy-dependent pump,
whereas ATPase activity as an assay in itself was possi-
bly problematical because the assay was based upon one
assumption that drug-induced ATP hydrolysis reflects
transport by the transporter [16]. There may be many
ways in which this activity could be altered, including
direct action on the ATP binding domain. Scientists once
observed some compounds such as daunomycin and vin-
blastine inhibit ATPase activity, but increase in others,
suggesting that modulation of ATPase activity was
highly dependent on experimental conditions and may
not correlate well with the ab ility of P-gp to transp ort the
drug [42-44]. The work of Litman et al. was one of the
few studies suggesting that affinity between drugs and
ATPase activity has no correlation to LogP, but Surface
Area [45]. Because of the less comparability of molecu-
lar structures in a training set, our QSAR model pos-
sessed more universal significance. However, the preci-
sion of the QSAR models was so low that there was still
a distance to its application. So a series of organic com-
pounds with similar structures are chosen and consist of
a training set, thus the precision of QSAR simulation will
be largely increased, while the prediction of the ana-
logues through BBB will be greatly improved.
5. CONCLUSIONS
The pa thogenesis of AD is c haracterized by the aggrega-
tion of Aβ into neurotoxic plaques. P-gp is involved in
MDR and in neurodegenerative disorders such as PD,
AD and epilepsy. P-gp mediates the efflux of Aβ from
the brain as well as mediates MDR, while P-gp trans-
ports neutral or positively-charged hydrophobic sub-

T. Y. Zhu et al. / Advances in Bioscience and Biotechnology 4 (2013) 872-895 893
strates with consuming energy from ATP hydrolysis. In
comparison with the ability of organic molecules perme-
ating across BBB, P-gp binding or MDR-reversal activ-
ity of compounds has a negative correlation with LogP.
Moreover, P-gp binding or MDR-reversal activity of
compounds is main ly pr opo rtio nal t o Log MR (Eqs.1 to 5)
but inversely proportional to LogP (Eqs.4 and 5). Simi-
larly, ATPase activity of these compounds was largely
negatively related to LogP (Eqs.7 to 12) but positively
related to MR (Eqs.9 to 11), where most compounds are
with logP value more than 2.7. This show ed that the P-gp
binding capacity of these compounds shared common
characteristics with their ATPase hydrolysis, namely
their hydrophobic parameters (i.e. logP) and steric pa-
rameters (e.g. MR). Additionally, the distribution of or-
ganic molecules through BBB was not only influenced
by organic solutes themselves, but also related to the
properties of the solute-membrane water complex,
namely interactions of the molecule with the phosphol-
ipide-rich regions of cellular membranes. The ability of
organic molecules permeating across BBB was mostly
proportional to LogP (Eqs.14 to 18) but inversely pro-
portional to PSA (Eqs.13 to 18), which is con sistent with
the research results of Chen and co-workers [2], namely
the increasing PSA decreased LogBB rapidly while LogP
positively related to LogBB. Chen et al. have indicated
that the optimum logP for designing CNS active drug
was about 2.9 and the compound with LogP lower than
2.9 had a positive correlation with logBB, but the com-
pound with logP bigger than 2.9 made an unfavorable
contribution [2]. It is disclosed that molecules with
higher lipophilic would be partitioned into the lipid bi-
layer more easily with more chances to penetrate BBB,
supported by the research result of Wang et al., namely a
large number of structurally and functionally diverse
compounds as substrates or modulators of P-gp mostly
share common structural features, such as aromatic ring
structures and high lipophilicity [1 9]. The LogP not only
offered opportunity to p enetrate the lip id bilayer, bu t also
gave favorable contribution to binding with P-gp or P450.
There may be two reasons for this phenomenon. Firstly,
the compounds with higher liposolubility are more vul-
nerable to cytochrome P450 metabolism, leading to
faster clearance [46]. P450 enzymes catalyze the me-
tabolism of a wide variety of endogenous and exogenous
compounds including xenobiotics, drugs, environmental
toxins, steroids, and fatty acids. Aminated thioxanthones
have recently been reported as P-gp inhibitors as well as
its interaction with cytochrome P450 3A4 (CYP3A4), as
many substrates of P-gp and CYP3A4 are common [47],
which could be a major cause of P-gp binding or MDR-
reversal activity of compounds inversely proportional to
LogP. The second reason was related to the mechanism
of P-gp action. According to the model proposed by
Higgins and Gottesman [48], after entering into the
phospholipid bilayer, compound may interact with P-gp
in the inner leaflet of the lipid bilayer. Upon interaction
with P-gp, the compound was flipped from the inner
leaflet to the outer leaflet of the lipid bilayer. The lipo-
philic compounds with high LogP entered into cellular
membrane easily and intended to retain there, so its op-
portunity to interact with P- gp increased and then its op-
portunity to be pumped out of cells enhanced.
In conclusion, the predictive model of BBB partition-
ing of organic compounds contributed to the discovery of
potential AD therapeutic drugs. Moreover, the interac-
tion model of P-gp and modulators for the treatment of
multidrug resistance indicates the discovery of some
molecules to increase Aβ clearance from the brain and
reduce Aβ brain accumulation by regulating BBB P-gp
in the early stages of AD. Because P-gp is a transporter
whose ligands are almost exclusively small molecules, it
is not surprising that the pump itself is unable to trans-
port Aβ. Nazer and co-worker have indicated the non-
proteolytic clearance of Aβ via receptor-mediated trans-
port across the BBB and investigated P-gp and the low-
density lipoprotein receptor-related protein (LRP) in-
volving Aβ efflux across the BBB [49]. Nevertheless,
LRP or P-gp alone was insufficient for non-proteolytic
transcytosis of intact Aβ. LRP in transcytosing intact Aβ
across the BBB may require a co-transporter, such as
P-gp [49]. Elucidation of the molecular mechanisms of
the potential of LRP and P-gp to efflux cortical Aβ
across BBB should help to promote rational therapeutic
strategy in AD.
6. ACKNOWLEDGEMENTS
This work was supported by a grant from Basic Scientific Research
Expenses of Central University (020814360012), National Key Tech-
nology R&D Program (2008BAI51B01) and Specialized Research
Fund for the Doctoral Program of Higher Education (2012009111
0038).
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