Structure-Based Virtual Screening Identifies Novobiocin, Montelukast, and Cinnarizine as TRPV1 Modulators with Anticonvulsant Activity In Vivo

Mitigation of Acute Seizures and Neuropathology after Traumatic Brain Injury by Structure-Based Discovery-Identified Drugs

Our objective was to test the hypothesis that structure-based identified or designed compounds exhibiting neuroprotective, antioxidant, and anti-inflammatory properties in vitro will mitigate early seizures and neuropathology after traumatic brain injury (TBI) in vivo. The neuroprotective and anti-inflammatory effects of 11 compounds identified by computer-assisted approximations were tested in vitro in neuronal microglial co-cultures. Among these, compound FBA exhibited the best neuroprotective (MAP-2, microtubule-associated-protein 2, a neuronal damage biomarker), antioxidative (nitrite production), and anti-inflammatory effects in vitro (all p < 0.01). Consequently, its neuroprotective and antiseizure effects were assessed in vivo in adult male Sprague-Dawley rats exposed to severe lateral fluid-percussion-induced TBI. Rats under continuous video-electroencephalogram monitoring received prophylactic treatment with an intraperitoneal (i.p.) injection of FBA (FBApro, 30 mg/kg) or vehicle (VEH, 48% PEG in 0.9% saline, 3 mL/kg) at 2 and 24 h post-TBI. Rats that developed status epilepticus received 1-2 additional on-demand FBA doses (FBApro+, 100 mg/kg, i.p.) to stop epileptiform activity. FBApro treatment reduced the cortical lesion area (18.9 ± 4.1 mm2, n = 7) compared with VEH treatment (24.8 ± 5.7 mm2, n = 10, p < 0.05). FBApro treatment also showed a favorable effect on the white matter by reducing plasma levels of pNF-H, a biomarker of axonal injury, compared with VEH treatment (Cohen's delta 0.657). Both FBApro (368 ± 407 s) and FBApro+ (256 ± 327 s) treatments reduced the average cumulative seizure duration compared with VEH (896 ± 703 s, both p < 0.05). The FBApro+ treatment regimen also reduced the mean relative theta and alpha power and increased the mean relative gamma power in the electroencephalogram (p < 0.05). Our data identified FBA as a novel structure-based discovered compound with promising favorable effects on structural and functional recovery after TBI.

Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance

Recent advances in structural biology have led to the publication of a wealth of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule structures, including many complexes with small molecules of interest for drug design. While it is common to incorporate information from the atomic coordinates of these complexes into docking (e.g., pharmacophore models or scaffold hopping), there are limited methods to directly leverage the underlying density information. This is desirable because it does not rely on the determination of relevant coordinates, which may require expert intervention, but instead interprets all density as indicative of regions to which a ligand may be bound. To do so, we have developed CryoXKit, a tool to incorporate experimental densities from either cryo-EM or XRC as a biasing potential on heavy atoms during docking. Using this structural density guidance with AutoDock-GPU, we found significant improvements in re-docking and cross-docking, important pose prediction tasks, compared with the unmodified AutoDock4 force field. Failures in cross-docking tasks are additionally reflective of changes in the positioning of pharmacophores in the site, suggesting it is a fundamental limitation of transferring information between complexes. We additionally found, against a set of targets selected from the LIT-PCBA dataset, that rescoring of these improved poses leads to better discriminatory power in virtual screenings for selected targets. Overall, CryoXKit provides a user-friendly method for improving docking performance with experimental data while requiring no a priori pharmacophore definition and at virtually no computational expense. Map-modification code available at: https://github.com/forlilab/CryoXKit.

TRPV1 channel in the pathophysiology of epilepsy and its potential as a molecular target for the development of new antiseizure drug candidates

Identification of transient receptor potential cation channel, subfamily V member 1 (TRPV1), also known as capsaicin receptor, in 1997 was a milestone achievement in the research on temperature sensation and pain signalling. Very soon after it became evident that TRPV1 is implicated in a wide array of physiological processes in different peripheral tissues, as well as in the central nervous system, and thereby could be involved in the pathophysiology of numerous diseases. Increasing evidence suggests that modulation of TRPV1 may also affect seizure susceptibility and epilepsy. This channel is localized in brain regions associated with seizures and epilepsy, and its overexpression was found both in animal models of seizures and in brain samples from epileptic patients. Moreover, modulation of TRPV1 on non-neuronal cells (microglia, astrocytes, and/or peripheral immune cells) may have an impact on the neuroinflammatory processes that play a role in epilepsy and epileptogenesis. In this paper, we provide a comprehensive and critical overview of currently available data on TRPV1 as a possible molecular target for epilepsy management, trying to identify research gaps and future directions. Overall, several converging lines of evidence implicate TRPV1 channel as a potentially attractive target in epilepsy research but more studies are needed to exploit the possible role of TRPV1 in seizures/epilepsy and to evaluate the value of TRPV1 ligands as candidates for new antiseizure drugs.

In Silico Screening Identification of Fatty Acids and Fatty Acid Derivatives with Antiseizure Activity: In Vitro and In Vivo Validation

High fat diets have been used as complementary treatments for seizure disorders for more than a century. Moreover, many fatty acids and derivatives, including the broad-spectrum antiseizure medication valproic acid, have been explored and used as pharmacological agents to treat epilepsy. In this work, we have explored the anticonvulsant potential of a large library of fatty acids and fatty acid derivatives, the LIPID MAPS Structure Database, using structure-based virtual screening to assess their ability to block the voltage-gated sodium channel 1.2 (NaV1.2), a validated target for antiseizure medications. Four of the resulting in silico hits were submitted for experimental confirmation using in vitro patch clamp experiments, and their protective role was evaluated in an acute mice seizure model, the Maximal Electroshock seizure model. These four compounds were found to protect mice against seizures. Two of them exhibited blocking effects on NaV1.2, CaV2.2, and CaV3.1.

The anti-seizure potential of cysteine leukotriene receptor antagonists: A systematic review of animal studies

BACKGROUND

Emerging literature has suggested the antiepileptic activity of cysteine leukotriene receptor (CysLTR) antagonists in experimental animals of epilepsy. Leukotrienes are substances that cause inflammation and affect brain activity, blood flow, oxidation, and inflammation in the brain. These processes are related to epilepsy and its complications. CysLTR antagonists are drugs that prevent leukotrienes from working. They may be useful for treating epilepsy, especially for people who do not respond to other drugs. Therefore, the current study aims to systematically review the potential anti-seizure effect of CysLTR antagonists in experimental studies.

METHOD

We systematically reviewed the online databases using online databases such as Google Scholar, science direct, and PubMed until December 2022 to identify experimental studies assessing the anti-seizure activity of CysLTR antagonists. The Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) was used to evaluate the risk of bias (RoB) of the included studies.

RESULTS

Initially we identified 3823 studies. After screening using inclusion and exclusion criteria, 8 studies were finally included in the current study. All included studies, reported that CysLTR antagonists reduced the intensity of seizures in animal models of epilepsy.

CONCLUSION

In conclusion, CysLTR antagonists could be a potential therapeutic approach for the treatment of epilepsy. However, further preclinical and clinical studies are required to confirm their efficacy, safety, and mechanism of anti-seizure activity.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

A Combined Ligand- and Structure-Based Virtual Screening To Identify Novel NaV1.2 Blockers: In Vitro Patch Clamp Validation and In Vivo Anticonvulsant Activity

Epilepsy is a neurological disorder characterized by recurrent seizures that arise from abnormal electrical activity in the brain. Voltage-gated sodium channels (NaVs), responsible for the initiation and propagation of action potentials in neurons, play a critical role in the pathogenesis of epilepsy. This study sought to discover potential anticonvulsant compounds that interact with NaVs, specifically, the brain subtype hNaV1.2. A ligand-based QSAR model and a docking model were constructed, validated, and applied in a parallel virtual screening over the DrugBank database. Montelukast, Novobiocin, and Cinnarizine were selected for in vitro testing, using the patch-clamp technique, and all of them proved to inhibit hNaV1.2 channels heterologously expressed in HEK293 cells. Two hits were evaluated in the GASH/Sal model of audiogenic seizures and demonstrated promising activity, reducing the severity of sound-induced seizures at the doses tested. The combination of ligand- and structure-based models presents a valuable approach for identifying potential NaV inhibitors. These findings may provide a basis for further research into the development of new antiseizure drugs for the treatment of epilepsy.

Discovery of novel TRPV1 modulators through machine learning-based molecular docking and molecular similarity searching

The transient receptor potential vanilloid 1 (TRPV1) channel belongs to the transient receptor potential channel superfamily and participates in many physiological processes. TRPV1 modulators (both agonists and antagonists) can effectively inhibit pain caused by various factors and have curative effects in various diseases, such as itch, cancer, and cardiovascular diseases. Therefore, the development of TRPV1 channel modulators is of great importance. In this study, the structure-based virtual screening and ligand-based virtual screening methods were used to screen compound databases respectively. In the structure-based virtual screening route, a full-length human TRPV1 protein was first constructed, three molecular docking methods with different precisions were performed based on the hTRPV1 structure, and a machine learning-based rescoring model by the XGBoost algorithm was constructed to enrich active compounds. In the ligand-based virtual screening route, the ROCS program was used for 3D shape similarity searching and the EON program was used for electrostatic similarity searching. Final 77 compounds were selected from two routes for in vitro assays. The results showed that 8 of them were identified as active compounds, including three hits with IC50 values close to capsazepine. In addition, one hit is a partial agonist with both agonistic and antagonistic activity. The mechanisms of some active compounds were investigated by molecular dynamics simulation, which explained their agonism or antagonism.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

Transient receptor potential (TRP) channels are cation channels that regulate key physiological and pathological processes in response to a broad range of stimuli. Moreover, they systemically regulate the release of hormones, metabolic homeostasis, and complications of diabetes, which positions them as promising therapeutic targets to combat metabolic disorders. Nevertheless, there are significant challenges in the design of TRP ligands with high potency and durability. Herein we summarize the four challenges as hydrophobicity, selectivity, mono-target therapy, and interspecies discrepancy. We present 1134 TRP ligands with diversified modes of TRP-ligand interaction and provide a detailed discussion of the latest strategies, especially cryogenic electron microscopy (cryo-EM) and computational methods. We propose solutions to address the challenges with a critical analysis of advances in membrane partitioning, polypharmacology, biased agonism, and biochemical screening of transcriptional modulators. They are fueled by the breakthrough from cryo-EM, chemoinformatics and bioinformatics. The discussion is aimed to shed new light on designing next-generation drugs to treat obesity, diabetes and its complications, with optimal hydrophobicity, higher mode selectivity, multi-targeting and consistent activities between human and rodents.

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.