Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays

Approaching Pharmacological Space: Events and Components

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

In Silico Study in MPO and Molecular Docking of the Synthetic Drynaran Analogues Against the Chronic Tinnitus: Modulation of the M1 Muscarinic Acetylcholine Receptor

Tinnitus is a syndrome that affects the human auditory system and is characterized by a perception of sounds in the absence of acoustic stimuli, or in total silence. Research indicates that muscarinic acetylcholine receptors (mAChRs), especially the M1 type, have a fundamental role in the alterations of auditory perceptions of tinnitus. Here, a series of computer-aided tools were used, from molecular surface analysis software to services available on the web for estimating pharmacokinetics and pharmacodynamics. The results infer that the low lipophilicity ligands, that is, the 1a-d alkyl furans, present the best pharmacokinetic profile, as compounds with an optimal alignment between permeability and clearance. However, only ligands 1a and 1b have properties that are safe for the central nervous system, the site of cholinergic modulation. These ligands showed similarity with compounds deposited in the European Molecular Biology Laboratory chemical (ChEMBL) database acting on the mAChRs M1 type, the target selected for the molecular docking test. The simulations suggest that the 1 g ligand can form the ligand-receptor complex with the best affinity energy order and that, together with the 1b ligand, they are competitive agonists in relation to the antagonist Tiotropium, in addition to acting in synergism with the drug Bromazepam in the treatment of chronic tinnitus.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Drug repositioning: A bibliometric analysis

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

Dequalinium chloride is an antagonists of α7 nicotinic acetylcholine receptors

Dequalinium chloride has been used primarily as antiseptic compounds, but recently has been investigated for its effects on specific targets, including muscarinic acetylcholine receptors. Here we investigated dequalinium chloride as an antagonist to α7 nicotinic acetylcholine receptors. The pharmacological properties of dequalinium were established using cell lines stably co-transfected with the calcium-permeable human α7 nicotinic acetylcholine receptors and its chaperone NACHO, calcium dye fluorescent measurements or a calcium-sensitive protein reporter, and patch clamp recording of ionic currents. Using calcium dye fluorescence plate reader measurements, we find dequalinium chloride is an antagonist of α7 nicotinic acetylcholine receptors with an IC₅₀ of 672 nM in response to activation with 500 μM acetylcholine chloride and positive allosteric modulator PNU-120596. However, using a membrane-tethered GCAMP7s calcium reporter allowed detection of α7-mediated calcium flux in the absence of PNU-120596. Using this approach revealed an IC₅₀ of 157 nM for dequalinium on 300 μM acetylcholine-evoked currents. Using patch clamp recordings with 300 μM acetylcholine chloride and 10 μM PNU-120596, we find lower concentrations are sufficient to block ionic currents, with IC₅₀ of 120 nM for dequalinium chloride and 54 nM for the related UCL 1684 compound. In summary, we find that dequalinium chloride and UCL1684, which are generally used to block SK-type potassium channels, are also highly effective antagonists of α7 nicotinic acetylcholine receptors. This finding, in combination with previous studies of muscarinic acetylcholine receptors, clearly establishes dequalinium compounds within the class of general anti-cholinergic antagonists.

Effects of Sublethal Organophosphate Toxicity and Anti-cholinergics on Electroencephalogram and Respiratory Mechanics in Mice

Organophosphates are used in agriculture as insecticides but are potentially toxic to humans when exposed at high concentrations. The mechanism of toxicity is through antagonism of acetylcholinesterase, which secondarily causes excess activation of cholinergic receptors leading to seizures, tremors, respiratory depression, and other physiological consequences. Here we investigated two of the major pathophysiological effects, seizures and respiratory depression, using subcutaneous injection into mice of the organophosphate diisopropylfluorophosphate (DFP) at sublethal concentrations (2.1 mg/Kg) alone and co-injected with current therapeutics atropine (50 mg/Kg) or acetylcholinesterase reactivator HI6 (3 mg/Kg). We also tested a non-specific cholinergic antagonist dequalinium chloride (2 mg/Kg) as a novel treatment for organophosphate toxicity. Electroencephalogram (EEG) recordings revealed that DFP causes focal delta frequency (average 1.4 Hz) tonic spikes in the parietal region that occur transiently (lasting an average of 171 ± 33 min) and a more sustained generalized theta frequency depression in both parietal and frontal electrode that did not recover the following 24 h. DFP also caused behavioral tremors that partially recovered the following 24 h. Using whole body plethysmography, DFP revealed acute respiratory depression, including reduced breathing rates and tidal volumes, that partially recover the following day. Among therapeutic treatments, dequalinium chloride had the most potent effect on all physiological parameters by reducing acute EEG abnormalities and promoting a full recovery after 24 h from tremors and respiratory depression. Atropine and HI6 had distinct effects on EEGs. Co-treatment with atropine converted the acute 1.4 Hz tonic spikes to 3 Hz tonic spikes in the parietal electrode and promoted a partial recovery after 24 h from theta frequency and respiratory depression. HI6 fully removed the parietal delta spike increase and promoted a full recovery in theta frequency and respiratory depression. In summary, while all anticholinergic treatments promoted survival and moderated symptoms of DFP toxicity, the non-selective anti-cholinergic dequalinium chloride had the most potent therapeutic effects in reducing EEG abnormalities, moderating tremors and reducing respiratory depression.

Medicinal applications and molecular targets of dequalinium chloride

For more than 60 years dequalinium chloride (DQ) has been used as anti-infective drug, mainly to treat local infections. It is a standard drug to treat bacterial vaginosis and an active ingredient of sore-throat lozenges. As a lipophilic bis-quaternary ammonium molecule, the drug displays membrane effects and selectively targets mitochondria to deplete DNA and to block energy production in cells. But beyond its mitochondriotropic property, DQ can interfere with the correct functioning of diverse proteins. A dozen of DQ protein targets have been identified and their implication in the antibacterial, antiviral, antifungal, antiparasitic and anticancer properties of the drug is discussed here. The anticancer effects of DQ combine a mitochondrial action, a selective inhibition of kinases (PKC-α/β, Cdc7/Dbf4), and a modulation of Ca²⁺-activated K⁺ channels. At the bacterial level, DQ interacts with different multidrug transporters (QacR, AcrB, EmrE) and with the transcriptional regulator RamR. Other proteins implicated in the antiviral (MPER domain of gp41 HIV-1) and antiparasitic (chitinase A from Vibrio harveyi) activities have been identified. DQ also targets α -synuclein oligomers to restrict protofibrils formation implicated in some neurodegenerative disorders. In addition, DQ is a typical bolaamphiphile molecule, well suited to form liposomes and nanoparticules useful for drug entrapment and delivery (DQAsomes and others). Altogether, the review highlights the many pharmacological properties and therapeutic benefits of this old 'multi-talented' drug, which may be exploited further. Its multiple sites of actions in cells should be kept in mind when using DQ in experimental research.

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.