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190 J.N. Singh and S.S. Sharma
Prediction of hERG Channel Inhibition Using
In Silico Techniques
Andrea Schiesaro and Gerhard F. Ecker
Contents
1 Introduction ... . .. . .. . ... ... . .. . .. . ... ... . .. . .. . . .. ... . .. . .. . . .. . .. . .. . .. . . .. . .. . .. . .. . . .. . .. 193
2 Long QT Syndrome ......................................................................... 193
3 Structure of the hERG Channel .. ... . .. . .. . .. . .. . ... .. . . .. ... . .. . .. . .. . .. . ... ... . .. . .. . .. . . 194
4 Ligand-Based Approaches .................................................................. 195
4.1 Pharmacophore Models .............................................................. 195
4.2 3D-QSAR ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . .. . .. . ... ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . 198
4.3 2D-QSAR ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . .. . .. . ... ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . 201
4.4 1D-QSAR ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . .. . .. . ... ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . 206
4.5 Classification Models ................................................................. 207
4.6 Matched Molecular Pairs .. ........................................................... 216
5 Structure-Based Approaches . ... .. . . . . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. ... ... .. . .. . 217
5.1 Homology Models of the hERG Channel . . .. . .. . .. . .. . .. . . . . . . . . . .. . .. . .. . .. . .. . .. 217
5.2 hERG Inhibition and Drug Trapping ... ... ... . .. . .. ... . .. . .. . .. . .. . .. . .. . .. . .. . .. . . 218
5.3 Amino Acids Involved in hERG Inhibition . . .. . ................................... 220
5.4 Hydrogen Bonds with Ser624, Thr623 or Val625? ................................ 222
5.5 Role of Gly648 . . .. . .. . ... . .. . .. . .. . ... . .. . .. . .. . ... . .. . .. . ... ... . .. . .. . . .. ... . .. . .. . 223
5.6 Which Subunits Are Involved in Drug Binding? .. .. . .. ........................... 223
5.7 Influence of Para-Substituents on the Phenyl Ring .. .............................. 224
5.8 Two or Three Binding Interactions? ................................................ 225
5.9 Docking Studies and Prediction of hERG Binding Affinity . . . . . . ................. 225
5.10 Case Studies: Docking Studies and Improvement of the Selectivity ... . .. . .. . .. . . 227
5.11 Orthogonal Binding Site? .. . .. . ... . .. . .. . .. . ... . .. . .. . .. . ... . .. . .. . .. . ... . .. . .. . .. . . 229
5.12 Docking Results . . .. . ... . .. . .. . ... . .. . .. . ... . .. . .. . .. . ... . .. . .. . . .. . .. . ... ... . .. . .. . . 229
6 Conclusions ... . .. . .. . .. . . .. ... . .. . .. . ... ... . .. . .. . .. . ... . .. . .. . .. . . .. . .. . .. . .. . . .. ... . .. . .. . 233
References .. . .. . . .. . .. . ... ... . .. . .. . ... . .. . .. . ... ... . .. . .. . . .. . .. . ... ... . .. . .. . ... . .. . .. . . .. . .. . . 233
Abstract Drug -induced long QT syndrome still represents a major risk for late-
stage clinical failure of drug candidates. One of the main factors inducing long
QT is inhibition of cardiac hERG channels. Early prediction of potential hERG
G.F. Ecker (*)
Pharmacoinformatics Research Group, Department of Medicinal Chemistry, University of Vienna,
Althanstraße 14, Vienna, 1090, Austria
e-mail: gerhard.f.ecker@univie.ac.at
S.P. Gupta (ed.), Ion Channels and Their Inhibitors,
DOI 10.1007/978-3-642-19922-6_7,
#
Springer-Verlag Berlin Heidelberg 2011
191
inhibitory activity of hits and lead compounds thus is of major importance in the
drug discovery and development process. Both structure- and ligand-based
approaches were used to develop models, which shed light on the molecular basis
of hERG channel inhibition. In this article, an overview on recent approaches for
prediction of hERG channel blockers has been provided.
Keywords hERG • Pharmacophore • QSAR • Classification • Homology
model • Docking
Abbreviations
ADMET Absorption, distribution, metabolism, elimination and toxicity
AUC Area under the curve
CCR Chemokine receptor
CoMFA Comparative molecular field analysis
CoMSIA Comparative molecular similarity indices analysis
CPG-NN Counter-propagation neural network
CSD Cambridge structural database
eag Ether-a-go-go
ECG Electrocardiogram
hERG Human ether-a-go-go-related-gene K
þ
channel
ISA Iterative simulated annealing
KNN k Nearest neighbor
LDA Linear discriminant analysis
LIE Linear interaction energy
LMS Least median squares
LQTs Long QT syndrome
LSER Linear solvation energy relationship
MD Molecular dynamics
PCA Principal component analysis
PLS Partial least squares
PLS-DA Partial least squares discriminant analysis
PNN Probabilistic neural network
QA Quaternary ammonium
QSAR Quantitative structure-activity relationship
RF Random forest
ROC Receiver operating characteristic
S Transmembrane domain
SA Simulated annealing
SAR Structure-activity relationship
SOM Self-organizing maps
SVM Support vector machine
SVR Support vector regression
TdP Torsade de Pointes
192 A. Schiesaro and G.F. Ecker
1 Introduction
The ensemble of properties, which describe absorption, distribution, metabolism,
elimination and toxicity (ADMET) are of utmost importance for the drug discovery
process. In light of the studies on antitarget proteins responsible for poor ADMET
properties of drug candidates, many high-throughput methods have been developed
for early identification of compounds with a bad ADMET profile. In the past
decade, many strategies were developed to screen libraries of compounds and to
assess the risk of a bad ADMET profile. There is overwhelming evidence that the
inhibition of the hERG potassium channel is directly correlate d with a lethal
arrhythmia called Torsade de Points. Thus, the discovery of unwanted interactions
between compounds under development and the hERG channel is a killing criteria.
One of the main challenges of antitarget proteins such as hERG, cytochrome P-450,
P-glycoprotein and serum albumin is their polyspecificity. Even though the binding
sites of many hERG blockers were indentified through docking into homology
models, the molecular basis of the hERG polyspecificity is not clear yet. Due to the
lack of crystal structures, ligand-based approaches were developed to predict the
hERG activity of candidate compounds. Recent studies show that a combination of
different methodologies, such as 2D- and 3D-QSAR with pharmacophore modeling
and classification algorithms was quite successful and might be a powerful tool in
the in silico screening of compound libraries. In this chapter, we will present
selected in silico models developed to predict hERG channel blockers.
2 Long QT Syndrome
The cardiac action potential is composed of five phases. Phase 0 is characterized by
rapid depolarization due to an inward Na
þ
current. This phase is followed first by an
initial short repolarization (phase 1) due to an outward K
þ
current and then by a
plateau (phase 2), which is characterized by a slow repolarization rate. During the
plateau phase, an inward Ca
2+
current is balanced by an outward K
þ
current. In
phase 3 of the cardiac action potential, the rapid and the slow delayed rectifier K
þ
(I
Kr
and I
Ks
, respectively) determine the repolarization of the myocyte with its
membrane potential to the resting state (phase 4). A long cardiac action potential
is a safeguard mechanism that prevents the onset of potentially dangerous
arrhythmias. However, this mechanism is not perfect, as a delay of cardiac repolar-
ization prolongs the QT time.
Through the electrocardiogram is possible to register the electrical activity of the
heart. The P wave registers the atrial depolarization, followed by the QRS complex,
which registers the ventricular depolarization. The T wave finally corresponds
to the ventricular repolarization. During the interval between the Q and T waves
(QT interval), which is a measure of repolarization duration, the heart is refractory
to new excitations. A delay in ventricular repolarization is registered in the ECG as
Prediction of hERG Channel Inhibition Using In Silico Techniques 193
a prolonged QT interval. The Long QT syndrome (LQTs) is associated with
increased risk of occurrence of potentially lethal arrhythmias called Torsade de
Pointes (TdP). The LQTs is estimated to affect 1 person every 5,000–10 ,000 people
[1]. The LQTs is mostly due to mutations of the hERG channel, a potassium-
selective voltage-gated channel that conducts the I
Kr
current, and to the KCNQ1
channel that encodes the Kv7.1 channel, which is responsible for the I
Ks
current. Up
to now, there are almost 300 mutations known to affect the hERG potassium
channel.
Many classes of compounds known to block the hERG pota ssium channel
prolong the QT interval and thus also might be involved in potentially dangerous
arrhythmias such as TdP [2–21].
3 Structure of the hERG Channel
The name of the hERG potassium channel origins from the human homologous
gene ether-a-go-go (eag) found in Drosophila melanogaster. The human-related
ether-a-go-go-related gene is also called KCNH2 according to the new nomencla-
ture, and the protein encoded is often referred to as hERG, even if the official
annotation according to the new nomenclature name is Kv11.1.
The hERG potassium channel has a tetrame ric architecture, where each subunit
is formed by six transmembrane domains (S1–S 6). Up to date two forms of hERG
are known, hERG1a and hERG1b, which can form a homo- or hetero-tetrameric
structure with different kinetic proprieties. Each subunit is formed by a voltage-
sensing transmembrane domain (S1–S4), and the K
þ
-selective pore (S5–S6), which
is responsible for the K
þ
conduction.
The K
þ
channel can be divided into three regions: the K
þ
-selectivity filter, the
central cavity and the inner pore [22]. The K
þ
-selectivity filter is a narrow cylinder
built to mimic perfectly an aqueous environment and to conduct specifically K
þ
ions. The highly conserved signature sequence in the Kv channels (Thr/Ser-Val-
Gly-Tyr/Phe-Gly) at the C-terminal end of the selectivity filter is responsible for the
creation of the aqueous environment. The hydroxyl groups of Thr as well as the
carbonyl groups of the other four amino acids form several octahedral binding sites,
which coordinate the K
þ
ions analogous to a water filled environment. The diame-
ter of the cylinder is too large for the Na
þ
ions and thus they cannot be well
coordinated, so they stay in the aqueous environment where the water molecules
coordinate the ions.
The central cavity is a wide water-filled pore located below the selectivity filter,
whose size changes in the open and in the closed conformation.
The inner pore is located at the cytoplasmatic side of the membrane and connects
the central cavity with the cytoplasm of the cell. It consists of two concentric rings
made by four Tyr652 and four Phe656. The four Phe656 form the first ring located at
the cytoplasmatic side of the hERG channel. The four Tyr652 build the second ring,
which faces the central cavity. These two concentric rings are considered the main
194 A. Schiesaro and G.F. Ecker
feature responsible for the polyspecificity of the hERG channel, mainly due to the
possibility to make multiple and compound specific interactions. In the closed
conformation, the S6 domains intersect at the cytoplasmatic side of the channel,
closing the inner pore and preventing the flow of K
þ
ions. During channel activa-
tion, the S6 domains rotate outward, opening the inner pore and allowing ions to
flow.
The hERG channel and other K
+
-channels contain a PAS domain on the cyto-
plasmic N-terminus, [23] which is involved in protein-protein interactions.
Although the function of the PAS domain in the hERG channel is not understood,
mutagenesis studies show that disruption of this domain reduces the outward
current and prolongs the QT interval [24].
Also, the role of the C terminus domain is not yet completely clarified, although
a cAMP-binding domain is located there. cAMP accelerates the deactivation
kinetics of the hERG channel [25].
4 Ligand-Based Approaches
4.1 Pharmacophore Models
Several pharmacophore models have been published up to date. They provide
a powerful tool for the identification of potential hERG blockers by virtual screen-
ing of compound databases. Some pharmacophore models were already reviewed
elsewhere [26, 27].
Morgan and Sullivan [28] analyzed a set of class III antiarrhythmic drugs. In the
pharmacophore model, the charged nitrogen is linked by 1–3 atoms to two features,
which might be an aromatic ring or an alkyl moiety. A third feature, consisting of
a para-substituted ring, is linked with the charged nitrogen through a chain of 1–4
atoms. The model derived by Ekins et al. [29] was generated from a training set of
15 compounds taken from the literature. The pharmacophore model contains one
positive ionizable feature connected with four hydrophobic moieties. A “construc-
tionist approach” was used by Cavalli et al. [30] to develop a pharmacophore
model. The crystal structure of astemizole was used as template on which
molecules with similar geometry were aligned, leading to the addition of new
pharmacophoric features, which were not present in the previous pharmacophore.
To the initial pharmacophore consisting of a basic nitrogen (N) and two aromatic
features (C0 and C1), a fourth feature defined by the aromatic ring (C2) of an
astemizole derivative was added. Thus, the charged nitroge n group (N) is connected
to three aromatic groups (C0–C2). The authors noted that all the hERG blockers
tested have the C0 feature, while some molecules lack the C1 or C2 moiet ies. They
also found that a polarizable function in C0, such as a carboxylic or a sulfonamidic
group, may also influence the activity.
Prediction of hERG Channel Inhibition Using In Silico Techniques 195