Prediction of hERG Channel Inhibition Using In Silico Techniques - Ion Channels and Their Inhibitors - page 201

Ermondi et al. [ 42 ] reported a GRIND-based 3D-QSAR model generated by the
ALMOND software. They used the 31 hERG blockers that Cavalli et al. [ 30 ] used to
develop the CoMFA model, and six compounds as a test set. The descriptors were
calculated using probes that represent the hydrophobic interaction (DRY), hydrogen
bond acceptor properties (sp 2 carbonyl oxygen), hydrogen bond donor properties
(neutral flat amide NH), and molecular shape (TIP probe). The PLS analysis resulted
in a model with three latent variables, which shows an r 2 of 0.93 and a q 2 of 0.69.
The analysis of the model suggests that the presence of aromatic rings on the edges
of the molecule, hydrogen bond donor moieties not related to the basic nitrogen,
hydrogen bond acceptors far from the aromatic rings and placed at the same distance
of the hydrogen bond donors from the aromatic group, increase the potency of the
hERG blockers. The 37 molecules were also analyzed using the DRY-DRY GRIND
descriptors. Interestingly, the PC1 discriminates between the potent blockers and the
weak blockers. In particular, it highlighted that the potent blockers have two or more
hydrophobic regions far away from each other. The ALMOND model was com-
pared with the CoMFA model obtained by Cavalli et al. [ 30 ]. Both models show a
comparable predictivity and have problems to predict the activity of lipophilic
hERG blockers. In the ALMOND model, there is a second hydrophobic feature,
which is missing in the CoMFA model, probably due to the different calculation
methods for hydrophobic interactions.
The 3D-QSAR techniques, such as Catalyst, CoMFA, CoMSiA, and ALMOND
have proven to be a powerful tool for hERG potency prediction. Together with the
pharmacophore models, 3D-QSAR shed light on the molecular determinants that
characterize hERG inhibitors, and provided insights for the potential binding mode
of blockers. The 3D-QSAR models further underline the importance of a basic
nitrogen, of a hydophobic/aromatic moieties, and of a hydrogen bond donor and
acceptor groups.

4.3 2D-QSAR

Although 3D-QSAR techniques are a powerful resource in the drug discovery
process and 3D-models can be very useful to discover compounds, which poten-
tially block the hERG channel, they suffer from limitations due to the requirement
of conformational sampling alignment (CoMFA, CoMSiA).
Aptula et al. [ 43 ] developed a 2D-QSAR model based on the stepwise regression
analysis using the hydrophobicity corrected for ionization (logD) and the maximum
diameter for the molecules (Dmax) as descriptors. The model, based on 19
molecules, shows a good internal and external validation with an r 2 of 0.87 and a
q 2 of 0.73. The analysis of 81 hERG blockers highlights that the most active
compounds have a Dmax 18 ˚ , indicating that the cavity of the hERG channel

is big enough to accommodate large compounds.
Keser

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u et al. [ 44 ] developed a QSAR model for a series of 55 hERG inhibitors.
The model consists of five descriptors, such as ClogP, molar refractivity, partial

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