Abstract

During the drug development process, almost 35% of the compounds fail due to poor absorption, distribution, metabolism, excretion and toxicity (ADMET). An important role on these failures is played by improper interactions with antitarget proteins, such as cytocrome P450, P-glycoprotein and the hERG potassium channel. The hERG potassium channel is expressed in various cells and tissues, such as heart, neurons and smooth muscle. In the heart, the hERG channel plays an important role in the third phase of heart repolarization, due to the conduction of the rapid delayed rectifier K+ current (Ikr). A delay of this phase of repolarization leads to a syndrome called Long QT syndrome (LQTs) which might cause a potentially fatal arrhythmia called Torsade de Pointes (TdP). Many different classes of compounds were withdrawn from the market in the past decade due to their interaction with the hERG channel. Similar to other antitarget proteins, the hERG channel is polyspecific in the ligand recognition, hence it can interact with many classes of compounds, such as psychiatric, antihistaminic, antiarrhytmic and antimicrobial drugs. Several studies show that some molecules do not dissociate during the channel gating and are trapped in the closed state of the hERG channel. In this study, propafenone and derivatives were docked into homology models of the hERG channel in the closed and open states to shed more light on hERG inhibition and on drug trapping. With the aim to investigate the interactions between the hERG channel in the closed state and the compounds investigated, a series of trapped propafenone derivatives were docked into the homology model of the hERG channel in the closed conformation using Dock, the docking tool of MOE, and Glide, the docking tool of Schrödinger. A svl script called ROTALI was used to generate RMSD matrices with which the duplicate poses lying in different directions of the central cavity were detected and deleted, thus allowing to identify possible binding modes through agglomerative hierarchical clustering. This analysis led to the identification of two possible binding modes. The same process was applied to the poses obtained by docking the propafenones into a homology model of the hERG channel in the open state. Three possible binding modes were selected through agglomerative cluster analysis of the RMSD matrix generated taking into account the propafenone derivatives’ common scaffold and the amino acids that might interact. Finally, in order to take into account protein flexibility, nine propafenone derivatives were docked into eight models of the hERG channel in the open state obtained from snapshots of molecular dynamics simulations. Clustering both according to the common scaffold RMSD and the RMSD matrix of the amino acids interacting with the poses, two binding modes were selected. Biological studies suggest that non-trapped propafenones hinder the hERG channel gating with a mechanism called “foot in the door”. In four out of the five selected clusters, it is possible to explain the “foot in the door” mechanism. Interestingly, ranking the poses of the five clusters above-mentioned according to the potential energy values of the R1 substituent, and according to this value divided by the number of heavy atoms, it is possible to distinguish between trapped and non-trapped propafenones. In the nontrapped compounds, this value is always higher than in the trapped ones. The fact that it works also in cluster five, where the R1 substituents are placed under the ring formed by the four Phe656, might indicate that drug trapping phenomena depend more on intrinsic properties of the R1 susbstituent rather than on its conformation when it interacts with the hERG channel. Hence, this might indicate that the rigidity and the bulkyness of the substituent determines whether a propafenone derivatives is trapped or not independently of the binding mode in the hERG channel.