Prediction of hERG Channel Inhibition Using In Silico Techniques - Ion Channels and Their Inhibitors - page 197

With the aim to subdivide specific and nonspecific hERG blockers in a dataset of
113 compounds, Kramer et al. [ 36 ] developed a method where pharmacophore and
QSAR techniques are combined. SA15 (sertindole analogue), clemastine,
tolterodine, and haloperidol were used to build the first pharmacophore model.
Similar to other published models, it contains a positive ionizable nitrogen feature
connected to two aromatic/hydrophobic features, one of which is in close proximity
with a hydrophobic spot. The model was tested against the entire dataset and 51
compounds matched the pharmacophore. The potent hERG blockers astemizole,
cisapride, flunarizine, and sertindole, which match the first pharmacophore model,
were used to develop a more specific model. Differently from the first pharma-
cophore, in this model the hydrophobic queries are directly connected with
the charged nitrogen. The second model is similar to the one obtained by Cavalli
et al. [ 30 ].
Through the analysis of 56 compounds using Catalyst Garg et al. [ 37 ] generated
seven models. The most predictive pharmacophore model consists of one hydro-
phobic group (HP), one aromatic ring (RA) and one hydrogen bond acceptor lipid
group (HBAl), which are three important features for potent hERG blockers. This
model was able to find 22 of the 25 potent blockers in the dataset, showing to be
capable to distinguish between potent and nonpotent blockers.
With the aim to avoid the potentially lethal hERG blockage of chemokine
receptor antagonists, Shamovsky et al. [ 38 ] analyzed the influence of four classes
of fragments on the hERG inhibition. They obtained two pharmacophore models.
The first pharmacophore model consists of one aromatic ring, one basic center, two
hydrogen bond donors, and one hydrogen bond acceptor. The fragments that match
this pharmacophore increase the hERG blocking potency even if they decrease the
lipophilicity of the compounds. In the second pharmacophore model there are two
aromatic rings connected with a basic nitrogen. Here, the fragments that match this
pharmacophore increase the hERG potency by increasing the lipophilicity of the
compounds.
Coi et al. [ 39 ] generated a “toxicophore” using the docking poses of compounds
docked in the hERG channel in the closed state. The analysis of the interactions of
the lowest-energy poses with the hERG channel shows that there are several hot
spots in the binding site: Ser624 (E), Gly657 (I), the region around Phe656 (H),
and four cavities in the region around Tyr652 (C). The docking pose of the potent
hERG blocker astemizole was used as “template” to generate the toxicophore. The
analysis of the poses of astemizole, haloperidol, ritanserine, R59022, cisapride,
spiperone, 8-hydroxy-DPAT, sotalol, quinidine, trifluperidol, and tetracaine, allowed
the identification of the features needed to interact with the hERG channel: (a)
the optimal distance between the protonated nitrogen and an hydrogen-bond accep-
tor is 4.5 ˚ ; (b) aromatic rings located in I or in E. Moreover, the authors suggest
that also hydrophobic interactions with the amino acids located in C and/or H
should be avoided. To evaluate the toxicophore, 18 known hERG blockers/
nonblockers were docked into the homology model of the hERG channel in the
closed state. The results confirmed that the toxicophore is able to distinguish
between hERG inhibitors and noninhibitors.