Biomedical Engineering Reference
In-Depth Information
Based on a dataset of 67 compounds, 50 in the training set and 17 in the test set,
Roy et al. [
55
] combined the extended topochemical atom (ETA) indices for diverse
hERG blockers and non-ETA-descriptors with factor analysis followed by multiple
linear regression, stepwise regression, and PLS, to develop QSTR models. The best
model was obtained combining ETA and non-ETA descriptors (
r
2
of 0.619 and a
q
2
of 0.546). The model indicates that the hERG affinity is increased by enlarging the
size of the molecule and increasing the electron richness, while the presence of a
carboxylic group and an aliphatic tertiary nitrogen is detrimental for the hERG
activity, which, under the light of all models discussed so far, seems quite unlikely.
Due to their simplicity, the 2D-QSARs were extensively applied to develop
models able to predict hERG potency. Interestingly, some 2D-descriptors provided
structural information of the central cavity of the hERG channel, suggesting that it
can accommodate large molecules. The 2D-descriptors also provided some inter-
esting insight into the characteristics of hERG blockers, indicating that the nature
and the charge of the nitrogen atom might have a strong influence on the potency.
4.4
1D-QSAR
A new QSAR technique was proposed by Diller et al. [
56
,
57
] to analyze 230
compounds collected from the literature. To minimize the variation on the IC
50
values due to the use of different cell lines, the data were corrected by introducing
a correction factor for each cell type to obtain a match of the data measured in HEK
cells. Through the projection of the atoms of the molecules onto one dimension
using multidimensional scaling, the structures were described as a 1D string
of atoms. Six descriptors were employed to generate the model: Size (number of
heavy atoms), C-Aliph-Estate (the electro-topological state of the atom if the atom
is a carbon not in an aromatic ring), C-Arom-Estate (the electro-topological state of
the atom if the atom is a carbon in an aromatic ring ), N-Acc-Estate (10 minus the
Estate of the atom if the atom is a nitrogen with a free lone pair), N-Don-Estate
(the electro-topological state of a nitrogen with an attached hydrogen), and
O-Estate (the electro-topological state of the atom if the atom is an oxygen). The
statistical analysis of the model showed a correlation coefficient of 0.68 for
the training (189 compounds) and of 0.76 for the test (41 compounds) set, respec-
tively. The descriptors that mainly contribute to hERG inhibition were the N-Acc
E-state and the C-Arom-Estate, revealing the importance of the basic nitrogen and
the aromatic ring.
The same QSAR technique based on 1D-descriptors was used by Diller et al.
[
58
]. The one dimensional representation of the molecules was achieved by multi-
dimensional scaling from 2D topological descriptors. To generate the model, six
descriptors were used: the number of heavy atoms, E-state key for aliphatic
carbons, E-state key for aromatic carbons, E-state key for nitrogen atoms with
a free lone pair, E-state key for nitrogen atom with an attached hydrogen and E-state
key for oxygen atoms. The IC
50
values of 230 compounds collected from the
