Biomedical Engineering Reference
In-Depth Information
negative surface area, polarizability (Volsurf W2), and hydrophobicity (Volsurf
D3). It shows a good performance with an
r
of 0.97. The five descriptors indicate
that lipophilic, polarizable compounds with a basic moiety and a large size might
interact with the hERG channel. The model was evaluated with a test set of 13
compounds, and gained an
r
value of 0.75. In a second test, the model was evaluated
in the ability to classify 82 active and nonactive compounds selected from the
World Drug Index. The molecules of the dataset with an IC
50
<
m
m were
considered as actives. The model showed a good performance classifying correctly
83% of the blockers and 82% of the nonblockers. Also, a hologram QSAR model
was generated using the same data set. The model shows an excellent performance
with an
r
value of 0.98. The test set achieved an r value of 0.90, indicating that the
model has also a high predictive power. For the active compounds of the World
Drug Index, the model was able to correctly classify 81% of the molecules. The
HQSAR model was further tested against 743 compounds approved in the World
Drug Index, where it achieved a low rate of false positive (18%).
Five QSAR models with a
q
2
ranging from 0.65 to 0.90 were developed by
Fioravanzo et al. [
45
], where 29 compounds from a training set and 30 compounds
from a test set were analyzed through PLS and PCA techniques with EVA and
DRAGON descriptors.
Song et al. [
46
] used a combination of fragment-based descriptors, support
vector regression (SVR), partial least squares (PLS) and random forest (RF) to
develop a QSAR model to predict the activity of hERG blockers. Seventy-one
compounds were initially used to calculate and identify fragment descriptors
correlated with hERG inhibition. Subsequently, they were used to build SVR,
PLS, and RF models. Nineteen compounds served as test set. The best performance
was achieved by the SVR model with an
r
2
of 0.91 and a
q
2
of 0.64. The model
showed also a good performance for the test set with an
r
2
of 0.85. The analysis of
the fragment descriptors highlights that lipophilic fragments have a positive impact
on the hERG activity, while hydrophilic moieties generally decrease the binding
affinity. Fluorine and methane sulfonamide are two exceptions on this general rule.
The two moieties, despite their hydrophilic nature, are positively related with the
hERG activity. The model also reveals that tertiary amines are important for drug
binding.
The role of the nitrogen atom on the hERG activity of compounds was studied by
Zolotoy et al. [
47
]. They found that a tertiary amino group is present in 84% of the
hERG channel blockers with IC
50
<
1
1
m
M. In 73% of the inhibitors with 1
m
M
<
IC
50
<
M the charged nitrogen is located at the periphery of the compounds.
In 84% of the weak blockers, the amine is primary, secondary, or neutral.
The CODESSA program was used by Coi et al. [
48
] to study a series of hERG
blockers. Two experiments were performed. In the first experiment, the compounds
of the dataset were divided into 55 and 27 molecules for the training and test set,
respectively. The first model showed an
r
2
of 0.77 using 12 descriptors. The training
and the test set used to generate the second model contained 64 and 18 compounds,
respectively. The best model selected for this sets showed an
r
2
of 0.74 using nine
descriptors. The analysis of the descriptors selected in the two models highlighted
10
m
