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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5865_Библиотеки_им_академика_М_И_Перельмана.pdf
QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Y
or small group of compounds at a time is removed from the training set, the model is recalibrated on the
reduced data set, and the biological response value is predicted for the removed compound using the
modified model. The procedure is repeated for each compound in the training data set. The result from
2
R
the cross-validation procedure is validated through
, predicted residual sum of squares (PRESS),
LOO
and root mean square error (RMSE), which is used as a criterion of both robustness and predictive ability of the QSAR models (Baumann, 2003). The maximum number of models produced by the LOO
procedure is equal to the number of available examples ‘n’. The standard deviation of the prediction
) statistics can also be calculated. Cross-validated correlation coefficient R
(S
PRESS
2
LOO
, and S
PRESS
, is
calculated according to the formula defined below:
2
R
LOO
S
PRESS
RMSE
n
Y Y
−
( )
∑
1= −
∑
PRESS
=
N d
PRESS
= . (11)
i i LOO
1
i
=
n
Y Y
−
( )
i Training
1
i
=
− −( )1
2
(9)
2
(10)
N
In the above Equations (9-11),
Y
,
i
i =1,...., n are the measured and predicted values of the dependent variable over the available
i LOO
training set. Often, a high
R
2
value (R
LOO
Y
Training
( )
is the mean average activity values of the training set, while
2
> 0.5) is considered as a proof of high predictive ability of
LOO
the QSAR model (Todeschini, Consonni, Mauri, & Pavan, 2004).
Furthermore, the developed QSAR models were subjected to a randomization test for validation
purpose. The Y-randomization test is used to assess whether a model is obtained by a chance correlation
(Rücker, Rücker, & Meringer, 2007; Golbraikh, & Tropsha, 2002). The dependent variable vector is
randomly shuffled and a new QSAR model is developed using the original independent variable matrix.
2
The new QSAR models are expected to have low R
values after several iterations. If the opposite hap-
pens, then an acceptable QSAR model cannot be obtained for the specific modeling method and data.
c
The randomization experiment was further validated by computing the
c
2 2 2
R R R R
=
Saha, and Roy (2010)
– . Finally, the predictive accuracy of the developed QSAR
p r
( )
R2metric proposed by Mitra,
p
models was evaluated more rigorously by test set compounds.
Thus, among the available in silico techniques QSAR is one of the most important areas in chemoinformatics, and its advances have widened the scope of rational drug design and the search for the
mechanism of drug action (Gupta, 2007). Over the past few decades, a variety of QSAR models for
AChEIs have been developed by different research groups to improve selectivity and ADMET proper-
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
ties (Garg, Gandhi, & Mohan, 2008). The developed models should be able to identify and describe
important physico-chemical features of the compounds that underpin variations in molecular activity
for designing potent and safe AChEIs.
COMPUTATIONAL APPROACHES ON DRUG DESIGN
In the past few decades, drug discovery efforts have shifted toward the design of more potent and selective AChEIs with fewer side effects. The interest in the discovery of novel potent AChEIs is expected
to continue in the future, as current AChEIs lack perfection. In current scenario, direct- and indirectcomputer-aided drug design (CADD) techniques are of utmost importance in terms of being time-saving
and cost-effective techniques. One of the rationale starting points for the design of novel scaffolds is
the development of a ligand-based predictive model to derive different structural features essential for
receptor binding, using ligand-based molecular modeling tools (Ooms, 2000, John, 2013). CADD is also
known as computer-assisted molecular design (CAMD) has undergone a paradigm shift in the past few
years. Modeling and informatics have become indispensable components of rational drug design (RDD).
Previously, chemical analysis through molecular modeling has been very prominent in CADD. However,
currently modeling and informatics are contributing in tandem toward CADD. Although, CADD is a topic
of medicinal chemistry and before venturing into this exercise one must employ computational chemistry
methods to understand the properties of chemical species, on the one hand, and employ computational
biology techniques to understand the properties of biomolecules on the other.
Information technology is playing a major role in decision making in pharmaceutical sciences. Storage,
retrieval, and analysis of data of chemicals/biochemicals of therapeutic interest are major components
of pharmacoinformatics. We know that the new drug discovery is a comprehensive, expensive and time
consuming process. It is estimated that a drug from concept to market would take around 12-14 years
and cost more than US$1.3 billion on an average (Malik, 2008). Traditional way of new drug discovery involves the identification of a lead compound (natural or synthetic) through random screening for
some desired biological activity and building a library around the scaffold to find the most potent and
efficacious analog. This is an iterative process and generates the structure-activity relationship (SAR)
that may further help to design more molecules until the desired need is met. In recent years, RDD has
emerged as a widely used approach for the drug design and discovery in order to reduce the time and
expenses (Gomeni, Bani, D’Angeli, Corsi, & Bye, 2001). CADD or in silico drug design is one of the
RDD technologies that is now widely used in industrial and academic drug discovery and development
programs. It is also estimated that computer modeling and simulations account for about 10% of pharmaceutical R&D expenditure, and that they give rise to 20% by 2016 (Kapetanovic, 2008). In most current
applications of CADD, attempts are made to find a ligand that will interact favorably with a receptor
that represents the target site. Binding of ligand to the receptor may include hydrophobic, electrostatic,
and hydrogen-bonding interactions. In addition, solvation energies of the ligand and receptor site also
are important because partial to complete desolvation must occur prior to binding (Wang, Chiu, & He,
2005). This approach to CADD optimizes the fit of a ligand in a receptor site. However, optimum fit in
a target site does not guarantee that the desired activity of the drug will be enhanced or that undesired
side effects will be diminished. CADD is dependent upon the amount of information that is available
about the ligand and receptor. Ideally, one would have 3D structural information for the receptor and the
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377
QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
ligand-receptor complex either from X-ray diffraction or NMR but in the opposite extreme, one may have
no experimental data to assist in building models of the ligand and receptor, in which case computational
methods must be applied without the constraints that the experimental data would provide.
CADD approaches can be broadly divided into two categories:
1. Ligand based drug design (LBDD).
2. Structure based drug design (SBDD).
LBDD methods are applied when the structure of the macromolecule - target is unknown, and there is
unable to design its reliable model. The main reason behind is many important receptors are membranebound proteins, which are notoriously difficult to crystallize. So, in that situation, a lead compound
or active ligand must be found, and then the structure of the ligand guides the drug design process.
These methods are based on analysis of sets of ligands with known biological activity (Taft, da Silva,
& da Silva, 2008). They include: design of pharmacophore models, which represents a set of points in
space with the certain properties and distances between them, which define binding on the given group
of ligands with a target. Furthermore, quantitative structure-activity relationship (“classic” QSAR) is
another effective LBDD method for the development of close analogues of known compounds and its
modification. The 3D-QSAR methods are somewhat capable of predicting adequately pharmacological activity of compounds from different chemical classes. Where there is sufficient information, these
methods can be used in conjunction to increase the accuracy of simulation and enhance the drug design
process (Hillisch, Pineda, & Hilgenfeld, 2004).
SBDD is one of the several methods in the rational drug design and pharmaceutical research. The
main aim in SBDD is to identify chemical compounds or peptides that bind strongly to key regions of
biologically relevant molecules, e.g. enzymes or receptors, for which three-dimensional structures are
known (Song, Lim, & Tong, 2009). The 3D structure of protein targets is most often derived from X-ray
crystallography or nuclear magnetic resonance (NMR) techniques or even homology modeling. Designed
chemical compounds should be able to inhibit or stimulate the biological activity of their target molecules
(Anderson, 2003). Pang et al. has performed docking studies for a series of benzylamino AChEIs, which
was mainly focused on obtaining correct binding modes for the bound ligands (Pang, & Kozikowski,
1994). Further, Pang and Kozikowski have performed docking simulations for huperzine A and E2020
(aricept) into a series of 69 AChE conformers chosen from molecular dynamics (MD) studies so as to
approximate the receptor flexibility space available to an inhibiting ligand (Pang, & Kozikowski, 1994).
Correa-Basurto et al. has performed molecular docking studies and density functional theory (DFT)
of 88 N-aryl derivatives as AChEIs (Correa-Basurto, Flores-Sandoval, Marin-Cruz, Rojo-Dominguez,
Espinoza-Fonseca, & Trujillo-Ferrara, 2007). Vasilyev et al. published a very effective QM/MM study
by utilizing semiempirical PM3 theory and the OPLS force field, demonstrating the strong influence of
hydrogen bonding within the catalytic triad on the configuration of the bare enzyme, as well as on the
acylation and phosphorylation reactions (Vasilyev, 1994). Fuxreiter and Warshel studied AChE acylation
through calculation of activation free energies using the empirical valence bond (EVB) and an all-atom
free energy perturbation (FEP) approach, in conjunction with the PDLD/S-LRA method (Fuxreiter,
& Warshel, 1998). More explicit quantum detail on the acylation reaction was provided by Zhang et
al. who used a pseudobond ab initio QM/MM approach with HF/3-21G for the quantum layer and the
AMBER95 forcefield for the remainder of the enzyme (Zhang, Kua, & McCammon, 2002). Bencsura
et al. carried out quantum work on reactions within the active site related to enzyme phosphorylation
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
(Bencsura, Enyedy, & Kovach, 1996). Further, quantum mechanical and QM/MM exploration of the
phosphorylation reaction was performed by Hurley et al (Hurley, Wright, Lushington, & White, 2003).
Recently, Zhou et al. determined the catalytic reaction mechanism of AChE by Born-Oppenheimer ab
initio QM/MM molecular dynamics simulations (Zhou, Wang, & Zhang, 2010).
Classical QSAR analysis has been proven reasonably effective to explain variations in AChE inhibitory activity and assessing, predicting AChE inhibition of new molecules. QSAR studies explored the
function of various ligand substituent groups via simple charge, physicochemical, structural, and shapebased descriptors, achieving reasonable reproduction of experimental in vitro AChE inhibition results.
Numerous descriptors are available to describe these physicochemical properties such as the steric, hydrophobic and electronic features of molecule which have been shown to be important for enzyme-ligand
interactions. Over the past few decades, a variety of QSAR models for AChEIs have been developed by
different research groups. Here we review the developed QSAR models and qualitative SAR data for
different chemical series of AChEIs. AChEIs from various chemical classes had different interactions
with the AChE enzyme, which affect the biological response and structure-activity relationships (SAR).
QSAR can be employed to guide the designing of further potent AChEIs.
Tacrine (9-amino-1,2,3,4-tetrahydroacridine, THA) as the first anti-Alzheimer’s drug approved by the
FDA in 1993, which has beneficial effects on cognition in patients with AD (Knapp, Knopman, Solomon, Pendlebury, Davis, & Gracon, 1994). Tacrine was described as a reversible inhibitor of AChE and
BuChE in 1961. However, soon afterwards it was found that it suffers from serious side effects such as
slow pharmacokinetics and hepatotoxicity. However, the structure seems to be very attractive to introduce
many substitutions on Tacrine, for improvement of its activity and removal of toxicity. Very few SAR
studies have been reported for Tacrine derivatives as shown in Figure 16. QSAR model of amino group
substituted Tacrine derivatives have been generated. The bilinear correlation was established between
the inhibition constant K
with structural length parameter L (L = Verloop length parameter for R sub-
i
stituent) (Recanatini, Cavalli, & Hansch, 1997).
R = H, Me, (CH
Log l/Ki = - 0.296(0.136) L + 0.569(0.220) log(β x10
n = 11, r
2
= 0.844, q2 = 0.842, s = 0.142, F
)nCH3 n=1-5, (CH2)nR’ [n=1-2 R’=Ph,NH2,CH=CH2]
2
L
+ 1) + 7.921(0.539) (1)
= 12.59, L
3,7
= 4.903, log β = -4.867
opt
Figure 16. N-monosubstituted 1,2,3,4-tetrahydro-9- aminoacridines
(Recanatini, et al., 1997).
379
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
where n is the number of molecules, r2 is the squared correlation coefficient, q2 is the cross-validated
correlation coefficient, s is the standard deviation, F is the variance ratio. This a typical bilinear Equation (1) leads to L
(optimized length parameter), which corresponds to a minimum of activity rather
opt
than maximum in activity. However, the QSAR model suggests that compounds with extreme length
parameter L values showed better activity.
th
Another series of Tacrine analogues with hydroxyl group at 6
position along with amino group
substitution as shown in Figure 17 were synthesized (Shutske et al. 1989).
R = H, CH3, C3H7, CH2C6H4R’ [R’ =Me, F,CF3 etc] and R
Log l/IC
n = 30, r
= - 0.560(0.132) L + 0.877(0.263) log(β x10L + 1 + 1.509(0.386)I-Cl+ 7.141(0.528) (2)
50
2
= 0.861, q2 = 0.857, s = 0.305, F
= 38.83, L
4, 25
= H, Cl, F, OCH3, CF3
1
= 5.116, log β = -4.869.
opt
Similar to previous QSAR model for tacrine analogues (Equation 1), this QSAR equation also possess an inverse bilinear term in L. With the L value about 5 Å, minimum AChE activity is observed.
The indicator descriptor I-Cl has a value of 1 when a 6-Cl substituent is present and 0 when 6-Cl is not
th
present, as seen from Equation (2). The substitution of –Cl group at 6
position/ R1 position is favorable
for higher AChE inhibitory activity.
The earliest application of comparative molecular field analysis (CoMFA) methodology to the AChE
inhibition problem, appears to be the work of Cho et al. who employed templates obtained from crystal
structures of AChE inhibited by edrophonium, physostigmine and 9-amino-, 2,3,4-tetrahydroacridine
(THA) to align a set of 60 structurally diverse known inhibitors (Cho, Garsia, Bier, & Tropsha, 1996).
On the other hand, Tong et al. developed a CoMFA model for a series of 1-benzyl-4[2-(N-benzoylamino) ethyl] piperidine derivatives and N-benzylpiperidine benzisoxazoles, finding a strong correlation
between the inhibitory activity of those N-benzylpiperidines and two separate alignment considering
both steric and electronic aspects of the ligand set (Tong, Collantes, Chen, & Welsh, 1996). They also
included neutral and N-piperidine-protonated species for developing 3D-QSAR model. The developed
model shows a strong correlation between the AChE inhibitory activity of N-benzylpiperidines and its
steric and electronic factors.
Figure 17. 9-amino-1,2,3,4-tetrahydroacridin-1-ol analogues
(Shutske et al. 1989).
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Also, Hasegawa and coworkers applied a novel genetic algorithm (GA) based variable selection
approach in CoMFA. They called this approach as GA-based region selection (GARGS) scheme to
refine the CoMFA model. GARGS approach was tested on the polychlorinated dibenzofurans (PCDF)
dataset to obtain a CoMFA model with improved predictivity (Hasegawa, Kimura, & Funatsu, 1999). A
comprehensive SAR picture for 9-amino-1,2,3,4-tetrahydroacridine-based AChEIs a tacrine derivative
designed on the basis of both the classical and the 3D QSAR equations was synthesized and kinetically
evaluated, to test the predictive ability of the 2D and 3D-QSAR models (Recanatini, Cavalli, Belluti,
Piazzi, Rampa, Bisi, Gobbi, Valenti, Andrisano, Bartolini, Cavrini, 2000).
Sulea et al. derived a structure-activity relationship as a function of an ensemble of different ligand
conformers (Sulea, Kurunczi, Oprea, & Simon, 1998). The resulting topological pharmacophore maps
corresponded well with the spatial constraints apparent in crystallographic receptor structures. Recently,
Bernard and coworkers used a hybrid method to estimate the binding affinity of 82 N-benzylpiperidine
derivatives by flexible docking them into the mouse AChE active site and establishing a 3D-QSAR model
based on this ensemble via CoMFA analysis (Bernard, Kireev, Chrétien, Fortier, & Coppet, 1999). Sippl
et al. also used a docking strategy for aligning ligand structures as a precursor to the CoMFA model
generation (Sippl, Contreras, Parrot, Rival, & Wermuth, 2001). Chaudhary et al. have developed 3DQSAR models using both CoMFA and CoMSIA models through pharmacophore-based alignment, were
in good agreement with each other and demonstrated significant superiority over MCS-based alignment
2
in terms of leave one- out (LOO) cross-validated q
values of 0.573 and 0.723 and the r2 values of 0.972
and 0.950, respectively (Chaudhary, Roy, & Saxena, 2009). Developed model shows the importance of
steric, electrostatic, and hydrophobicity for AChE inhibition activity. While, hydrophobic factor plays a
major contribution to the AChE inhibitory activity, which is in strong agreement with the fact that the
AChE is having a wide active site gorge (∼20 Å) occupied by a large number of hydrophobic amino
acid residues. Further, comparative 2D-QSAR analysis of three different classes of AChEIs namely,
physostigmine analogues, 1,2,3,4-tetrahydroacridines and benzylamines was performed by Recanatini
et al. (1997). Akula et al. developed 3D-QSAR model using CoMFA based on molecular docking on
a series of 19 bis-tacrine compounds (Akula, Lecanu, Greeson, & Papadopoulos, 2006). In this study,
the author has predicted the inhibitory activities against AChE using 3D-QSAR model and molecular
2
docking technique with q
= 0.714, No. of components= 2, r2 = 0.97, SEE = 0.082 and F= 328.4. The
developed 3D-QSAR model suggests that mono-chloro heterodimeric substituents better occupy and
fit in the binding sites.
In another study, Jung et al. has used a variable selection method to derive QSAR models for tacrine
derivatives (Jung, Tak, Lee, & Jung, 2007). Authors have used variable selections of stepwise multiple
linear regression (MLR), genetic algorithm (GA)-MLR, and simulated annealing (SA)-MLR approaches
to develop the QSAR of tacrine derivatives against AChE inhibitory activity.
The best QSAR equation was developed from simulated annealing (SA) MLR, which suggest that
the hydrophobicity and rigidity of compounds increased inhibitory activity of tacrine derivatives. The
QSAR models have greater explanatory capability and better prediction, with a smaller standard error
than other methods. The resulting models describing the significant roles of hydrophobic and electrostatic interaction on increasing AChE inhibitory activity, while hydrophilic and topological feature of
molecules was shown to decrease AChE inhibitory activity. Geldenhuys et al. has performed 3D-QSAR
study on bis-aza aromatic quaternary ammonium analogs at the blood–brain barrier choline transporter
(Geldenhuys, Lockman, Nguyen, Van der Schyf, Crooks, Dwoskin, & Allen, 2005). Choudhary et al.
developed CoMFA model on some natural compounds acting as AChEIs (Choudhary, & Rode, 2003).
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381
QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Shen et al. performed CoMFA and CoMSIA analysis along with a docking study to explore the binding mode of 2-substituted 1-indanone derivatives with AChE (Shen, Liu, & Tang, 2007). Both CoMFA
2
and CoMSIA models were developed successfully with a good correlation coefficient (r
2
cross-validated coefficient (q
= 0.7). From this study authors have suggested that the benzene ring
=0.9) and
of donepezil could form a π-π interaction with the side chain of Trp84. Also a more hydrophobic and
bulky group with a highly positive charge to replace the small protonated nitrogen moiety could increase
the binding affinity through hydrophobic, van der Waals, and cation–π forces with Trp84, Tyr330, and
His440 of AChE respectively.
Martin-Santamaria et al. has used the comparative binding energy (COMBINE) methodology to
identify the key residues that modulate the inhibitory potencies of three structurally different classes of
AChEIs (tacrines, huprines, and dihydroquinazolines) targeting the catalytic active site of this enzyme
(Martin-Santamaria, Munoz-Muriedas, Luque, & Gago, 2004). In another study, Roy et al. performed
3D-QSAR analysis on 78 molecules (52 and 26 molecules in training and test set, respectively) diverse
carbamates analogs using CoMFA and CoMSIA methods (Roy, Dixit & Saxena, 2008). The highly pre-
2
dictive models (CoMFA q
2
predictive r
= 0.812), well explained the variance in binding affinities both for the training and the test
= 0.733, r2 = 0.967, predictive r2 = 0.732, CoMSIA q2 = 0.641, r2 = 0.936,
set compounds. In this study, the author suggests that steric, electrostatic and hydrophobic interactions
play an important role in describing the variation in binding affinity. Specially, the carbamoyl nitrogen
should be more electropositive; substitutions on this nitrogen should have the high steric bulk and hydrophobicity, while the amino nitrogen should be electronegative in order to have better activity.
Asadabadi et al. used a combinatorial feature selection method to describe the QSAR of dual site
inhibitors of AChE that involved constitutional, topological, Galvez topological charge indices, empirical,
molecular walk counts, atom-centered fragments, 2D autocorrelations, BCUT properties and functional
group descriptors(Asadabadi, Abdolmaleki, Barkooie, Jahandideh, & Rezaei, 2009). In 2008 Saracoglu
et al. established the structure-activity relationships (SAR) through the Electron- Topological Method
(ETM) for a class of AChE inhibitors related to tacrine (9- amino-1,2,3,4-tetrahydroacridine) and 11
H-Indeno-[1,2-b]- quinolin-10-ylamine that tetracyclic tacrine analogues (Saracoglu, Kandemirli, 2008).
Recently, Chen et al. performed 3D-QSAR analysis on multi-target-directed AChEIs of tacrine–nimodipine dihydropyridine derivatives by using both CoMFA and CoMSIA methods (Chen, Liu, Zhao,
& Zhang, 2012). The developed models were statistically significant and highly predictable (receptor
2
based research CoMFA model, q
= 0.686, r2 = 0.948; CoMSIA model, q2 = 0.756, r2 = 0.907 in all of
the models no. of components = 6. Comparison of the methods for the predictabilities of QSAR models
based on both aligns database and autodocking alignment for same test set (12 compounds).
Yan et al. developed the QSAR models using MLR analysis and SVM methods for predicting the
inhibitory activity of 404 AChEIs (Yan, & Wang, 2012). The statistical analysis of Multi linear Regression Models and Support Vector Machine Models were analysed and suggested key chemical properties:
molecule charge, electronegativity and H-bond donors for modeling and bioactivity of AChEIs. Recently,
Jiang et al. has carried out CoMFA, CoMSIA, and hologram quantitative structure-activity relationship
(HQSAR) analysis on AChEIs (Jiang, Yang, Chen, & Liang, 2013). Three different alignment rules were
used to generate CoMFA and CoMSIA models to explore the effect of alignment methods on models.
2
The developed models were statistically significant and highly predictable (CoMFA q
2
=0.996, predicted r
2
= 0.973, predicted r2 = 0.734) and revealed a considerable predictive ability.
r
=0.789; CoMSIA q2 =0.755, r2 =0.973, predicted r2 = 0.706; HQSAR q2 = 0.884,
= 0.748, r2
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Prado-Prado et al. used 3D MARCH-INSIDE (MI)-DRAGON, software to calculate 3D structural
parameters (Prado-Prado García-Mera, Escobar, Alonso, Caamaño, Yañez, & González-Díaz, 2012).
Both the classes of 3D parameters were used as input to train Artificial Neural Network (ANN) algorithms. This method was successfully used for the prediction of 22 different rasagiline derivatives with
possible AChE inhibitory activity. Korabecny et al. have presented the synthesis and QSAR study of
some novel series of 7-methoxytacrine (7-MEOTA)-donepezil like compounds for their ability to inhibit
AChE and BuChE. The 7-MEOTA unit has less toxicity than tacrine (THA) derivative, connected with
the analogues of N-benzylpiperazine moieties mimicking N-benzylpiperidine fragment from donepezil.
7-MEOTA-donepezil like compounds exerted mostly non-selective profile in inhibiting cholinesterases of
different origin and is capable to simultaneously bind CS as well as PAS of AChE (Korabecny, Dolezal,
Cabelova, Horova, Hruba, Ricny, Sedlacek, Nepovimova, Spilovska, Andrs, Musilek, Opletalova,
Sepsova, Ripova, & Kuca, 2014).
Recently, Wong et al. reviewed the application of multi-criteria QSAR on AChEIs. It includes QSAR
models for different series of compounds, comparative studies, and advances in methodologies (Wong,
Duchowicz, Mercader, & Castro, 2012). SVM method was used for predicting AChEIs potential of
unknown sets of compounds and for exhibiting the molecular descriptors associated with AChEIs (Lv
and Xue, 2010).
Our research group has developed molecular docking guided comparative GFA, G/PLS, SVM and
ANN QSAR models of structurally diverse 42 dual binding site AChEIs (Gupta et al. 2011). The binding
conformations of 42 AChEIs were identified on the basis of the molecular docking approach as presented
in Figure 18. These 42 dual binding site AChEIs were used in developing four different chemometric
models using thermodynamic, electrotopological and electronic descriptors, in order to understand its
structure-activity relationships, and thereby utilize in rational drug design approach. These compounds
were partitioned into a training set of 31 compounds, and a test set of 11 compounds (75%: 25% ratio),
in accordance with the HCA-Ward’s algorithm.
The GFA and G/PLS methods were used for variable selection as presented in model 6-7. Best mod-
2
els were selected by the statistical measures, such as, squared correlation coefficient (R
ed correlation coefficient (q
2
), and predictive correlation coefficient R
2
, R2 is a relative measure of
pred
), cross-validat-
the fit by the regression equation.
GFA Model
pIC50 = 5.793 + 0.443Atype_C_3 + 0.301Atype_C_32 – 0.965Atype_N_70
– 1.057S_ssS + 0.071Atype_H_46 – 0.215LUMO (6)
N = 31, R
n
Test
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2
= 11, R
= 0.887, LOF = 0.079, R
2
= 0.756, r2 = 0.788, R
pred
2
= 0.859, q2 = 0.838, F = 31.44,
a
2
= 0.705, R
0
2
= 0.561.
m
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Figure 18. Docked conformation of 42 dual binding site (CS and PAS) AChEIs
(Gupta et al. 2011, 2012).
G/PLS Model
pIC50 = 5.808 + 0.469Atype_C_3 + 0.200Atype_N_75 – 1.446Atype_C_30
– 1.059S_ssS + 0.077Atype_H_46 – 0.209LUMO (7)
N = 31, R
n
Test
2
= 11, R
= 0.889, LSE = 0.029, R
2
= 0.706, r2 = 0.757, R
pred
The values between the predicted versus experimental pIC
set compounds based on GFA, G/PLS, SVM and ANN QSAR models are presented in the scatter plot
as shown in Figure 19. The positive coefficient of atom-type thermodynamic descriptors, Atype_C_3,
Atype_C_32 and Atype_H_46 in QSAR model obtained by GFA, indicated that this arrangement of
carbon and hydrogen atoms in the compound increases the AChE inhibitory activity. The negative
slope Atype_N_70 descriptor in GFA derived QSAR model revealed that AChE inhibitory activity
decreases with an increase in the hydrophobicity associated with this nitrogen. The positive slope of the
2
= 0.827, q2 = 0.739, F = 40.04,
a
2
= 0.659, R
0
2
= 0.520.
m
of the training data set and the test data
50
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
Figure 19. Scatter plot of actual vs. predicted activity of the training set (diamonds) and test set (triangles) compounds of QSAR model obtained using GFA method; (a), QSAR model obtained using the
G/PLS method (b), QSAR model obtained using the SVM method (c), and QSAR model obtained using
the ANN method (d)
(Gupta et al. 2011, 2012).
Atype_N_75 descriptor in QSAR model obtained from G/PLS revealed that AChE inhibitory activity
increases with an increase in hydrophobicity associated with this nitrogen. The negative correlation
of the Atype_C_30 descriptor in building QSAR model from G/PLS revealed that, for most potent
compounds, its contribution (or descriptor value) should be less, and for the least potent compounds its
contribution should be more. Both GFA and G/PLS derived QSAR models showed negative correlation
to S_ssS and LUMO descriptors. The negative coefficient of S_ssS (E-state index of the fragment of
sulphur atom bonded with two single bonds) indicates that compounds having high S_ssS value have
lower AChE inhibitory activity.
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