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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
R
2
S
Table 2. Leave one out cross validation parameter of QSAR models obtained from GFA and G/PLS,
leave five out cross validation parameter of QSAR models obtained from SVM and ANN methodology
QSAR Models R2 (Training)
GFA 0.887
G/PLS 0.889
SVM 0.885
ANN 0.906
a
Leave one out method (LOO),bLeave five out method (L5O).
(Gupta et al. 2011, 2012).
LOO
a
0.868 1.068 0.211 0.186
a
0.849 1.221 0.256 0.198
b
0.787 1.815 0.275 0.242
b
0.795 1.697 0.266 0.234
PRESS
PRESS
RMSE
LUMO (lowest unoccupied molecular orbital energy), is an electronic descriptor, and the LUMO
energy is the inverse measure of the electron accepting ability of a compound. In other words,
higher the LUMO energy the lower will be the electron accepting ability of the compound. The
above mentioned thermodynamic descriptors indicates that hydrophobicity of the atoms in different structural environment was essential for the better AChE inhibitory activity and which in turn
enhances the brain uptake of drugs.
The robustness and significance of QSAR models were critically assessed using different crossvalidation techniques on the test data set as presented in Table 2. The validation data were not involved by any means in the process of selecting the most appropriate descriptors or in the develop-
2
R
ment of the QSAR models. Predictive ability (
) of the selected models was evaluated by a test
pred
set of 11 compounds, and which are not included in the training data set.
The generated QSAR models using thermodynamic, electrotopological and electronic descriptors
showed that non-linear methods are more robust than linear methods, and provide insight into the
structural features of the compounds that are important for AChE inhibition.
Bolognesi et al. has done pioneering work on synthesis and molecular modeling for designing
multi-potent anti-Alzheimer drugs (Bolognesi, Cavalli, Valgimigli, Bartolini, Rosini, Andrisano,
Recanatini, & Melchiorre, 2007). Molecular modeling, synthesis and kinetic evaluation of 11HIndeno-[1,2-b]-quinolin-10-ylamine derivatives were reported by Rampa et al. (Rampa, Bisi, Belluti,
Gobbi, Valenti, Andrisano, Cavrini, Cavalli, & Recanatini, 2000). The virtual screening study has
been performed for lead identification of AChEIs–Histamine H
receptor antagonists from phar-
3
macophore mapping, and further verified by QM/MM and docking methodology by Bembenek et
al. (2008). Taft et al. have designed novel hybrid AChEIs by using molecular modeling, docking
and ADMET studies (da Silva, Campo, Carvalho, & Taft, 2006). Mizutani et al. performed highthroughput virtual screening through docking analysis, for the discovery of novel AChEIs (Mizutani, & Itai, 2004). Recently, Ambure et al. has performed pharmacophore mapping-based virtual
screening followed by molecular docking studies in search of potential AChEIs for the treatment
of AD (Ambure, Kar & Roy, 2014).
386
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QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease
CONCLUSION AND FUTURE PROSPECTIVE
There has been a huge effort invested towards the discovery of new AChEIs during the past few decades.
Several chemical classes of AChEIs were discovered and among them few have undergone for clinical
trials during this period. The search of human AChEIs continues with the aim of identifying drugs with
a better biological/chemical and low toxicity profile to currently marketed drugs. The examination of
the quantitative models for the structure-activity relationships of several series of compounds belonging
to diverse classes of AChEIs allowed us to understand the physico-chemical properties involved in the
QSAR analysis. The influences of molecular descriptors are very promising in identifying the strong and
weak inhibitors of diverse structural classes required for AChE enzyme inhibition. The development of
QSAR models and computational prediction of biological activity will continue to be one of the most
exciting areas of QSAR studies.
Here, we presented an overview of different QSAR methods and recent developments in AChEIs by
different research groups. This is an attempt of consolidation of known data, with detailed knowledge
about AChE inhibitor-enzyme binding modes, and structural insights gained from QSAR models, which
might offer a starting point for the design of new more potent and safer AChEIs. The QSAR methodology can greatly help in this effort, being firmly founded on physico-chemical and statistical bases. In
rational drug design, these are the requirements that must be met even more strictly in fields like that of
the agent for the treatment of AD.
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