Biomedical Engineering Reference
In-Depth Information
that the relative number of double bonds (RNDBs), the factorized molecular
volume (MV/XYZB), the relative number of carbon atoms (RNCAs) and the
relative negative charge (RNCG) are the most important descriptors. The first two
descriptors are negatively correlated with the pIC
50
, highlighting that the
hydrophobicity and the proprieties related to the volume of the molecules increase
the potency of the hERG blockers. These two descriptors might explain why
the ideal hERG blocker candidates are hydrophobic molecules with a large size,
as in the case of some potent hERG blockers such as MK-499 and astemizole. On
the contrary, small molecules with high globularity lacking hydrophobic moieties
normally show a lower ability to block the hERG channel.
Yoshida et al. [
49
] analyzed 104 compounds collected from the literature. The
model included the topological polar surface area (TPSA), the octanol/water parti-
tion coefficient (ClogP), the largest value in the distance matrix (diameter) and the
summed surface area of atoms with partial charges from
0.25 to 0.20
(PEOE_VSA-4). They also introduced an indicator variable (Cell) indicating
whether the drugs were tested in the hERG channels expressed in HEK cells
(value of 0), or in CHO cells (value of 1). In the most significant model the
statistical analysis showed an
r
2
of 0.70 and a
q
2
of 0.67. The interactions between
the hERG channel and the blockers occur in the inner cavity. Many studies show
that the size of the cavity is big enough to accommodate large compounds, and this
is fully in agreement with the positive coefficient of the diameter descriptor. The
Positive coefficient for the PEOE_VSA-4 descriptor might be explained by the
interactions with amino acids such as Thr623, Ser624, Ser649, and Tyr652, which
are capable to form hydrogen bond interactions. The pore region has a hydrophobic
nature due to the high number of hydrophobic amino acids, thus the potency of the
drugs increase with the ClogP of the molecules. In contrast, increasing the hydro-
philicity of the compounds, represented by the TPSA descriptor, decrease hERG
potency.
Seierstad et al. [
50
] reported a combination of models based on neural network
ensembles with different representations of the structural properties. For 439
compounds, six sets of descriptors were calculated using DirectedDiversity, com-
prising 117 Kier-Hall (KH) topological indices, 142 Ghose-Crippen atom types,
166 Isis keys, 150 atom pair descriptors, 49 electrotopologic state descriptors, six
common medicinal chemistry descriptors, and 146 2D descriptors calculated with
the Molecular Operating Environment (MOE) package. To select the descriptors,
different feature selection algorithms were employed comprising four filter-based
ones such as PCA, correlation with response variable, difference in distribution
between actives and inactives, training error of single-feature models, and two
wrapper-based such as Forward stepwise selection and Simulated annealing. The
neural network and neural network ensemble models were obtained from tenfold
cross-validation procedures. The authors found that the “neural network ensembles
have greater generalization ability and are less susceptible to the particular choice
of training and test sets” showing that this methodology is a powerful tool for hERG
prediction. In the filter-type technique, the PCA showed the best performance,
although the simulated annealing reached the highest
r
2
value. The 2D model,
