In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7

https://doi.org/10.1016/j.compbiomed.2021.104346Get rights and content

Highlights

  • QSAR models for voltage-gated sodium channel 1.7 inhibition were developed.

  • Monte Carlo method with SMILES notation and molecular graph descriptors was used.

  • Different methods were applied for the determination of the model goodness.

  • Molecular fragments with influence on inhibitory action were determined.

  • Presented study can be useful in the search for novel analgesics.

Abstract

The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results.

Introduction

In the 21st century, chronic pain emerged as a serious global health problem since currently, approximately 10–55% of the world's population is suffering from chronic pain (20% are adults) [1,2]. Currently, for chronic pain treatment various therapeutics are used including anticonvulsants, non-steroidal anti-inflammatory drugs (NSAIDs), antidepressants, and opioids, and such treatment has a vast impact on finances [[3], [4], [5], [6], [7]]. The financial impact is not the only side effect of these therapeutics since they exhibit low tolerability and safety, lack of robust efficacy and they have significant abuse potential [3,7,8]. With all stated facts in mind, the most important drawback is related to low effect since existing therapies are brought to only 50% of patients [9]. All stated drawbacks of current therapeutics lead to the need to develop new, more safe, and effective drugs for chronic pain treatment.

One of the key targets for analgesia excitable cells such as nerve cells, muscles, and heart tissues has been identified. In these cells, voltage-gated sodium channels (Navs or VGSCs), an important class of transmembrane proteins, have a crucial role in the generation and transmission of action potentials (AP), therefore analgesia can be achieved by targeting it [[10], [11], [12]]. The structure of Navs is determined by four domains, all having a functional voltage sensor domain (VSD) and a pore module (PM). The (LOF) mutations in Nav1.7 (one Nav isoform) can lead to disturbances in the pain in perception, whereas paroxysmal extreme pain and erythromelalgia are obtained from gain-of-function (GOF) mutations and these findings made Nav1.7 an important target for the pain treatment [13].

In the design of potential therapeutics process methodologies developed by chemoinformatics and computational chemistry found a very important place. The search for novel lead compounds or the optimization of therapeutic activity (or pharmacokinetic properties) of the series of chemical compounds with already determined biological activity becomes the main contribution of these in silico methods in drug development research [14,15]. Among many developed methods, quantitative structure-activity relationship (QSAR) and molecular docking are the most used ones. In most cases, the QSAR model had to be represented as the mathematical equation that links biological activities of studied molecules with their chemical characteristics, defined as molecular descriptors [16]. Current QSAR models are developed based on various molecular descriptors, calculated from defined molecule structure, all possessing their own strengths and weakness [17,18]. For example, QSAR models based on molecular topology are relatively easily computed but the connection between topological descriptors and their physical interpretation is not easily established. Different from this approach, 3D QSAR modeling is based on well-defined chemical structures with appropriate physical interpretation, but model development is time-consuming since molecules have to be prepared, aligned and different computation approaches are used for model development. Recently novel approach has emerged in QSAR modeling, where conformation-independent optimal descriptors based on constitutional and topological molecular features and descriptors based on the Simplified Molecular Input Line Entry System (SMILES) notation are used to develop QSAR model with the application of the Monte Carlo optimization process. SMILES notation descriptors have proven to be a great addition to the already used ones since they can be associated with molecular fragments and therefore possess physical meaning [[19], [20], [21]].

In the presented research, several in silico methods were applied for the search of novel compounds that have potential voltage-gated sodium channel 1.7 inhibition activities. For model development, both conformation-independent and 3D modeling were used. Developed conformation-independent QSAR models were based on SMILES notation and local graph invariants, while for 3D modeling field contribution approach was used. Further, a comparison between these approaches was made. One of the main aims of this study was the determination of molecular fragments or structural requirements responsible for the voltage-gated sodium channel 1.7 inhibition effects, and this research defined structural attributes in small molecules related to ligand-receptor interactions, which could be used for the design and development of therapeutics for chronic pain treatment. Since the most of currently used chemical databases use SMILES notation for chemical representation, one of the main aims of this modeling was to develop QSAR that can be used by other scientists for screening or molecular design purposes. As the final validator of the established QSAR models, molecular docking studies were used.

Section snippets

Materials and methods

A group of 45 active aryl sulfonamide Nav1.7 inhibitors, with activities defined as IC50 values (range from 0.3 to 2300 nM and they were converted to the corresponding pIC50 (-log IC50)) obtained with the same biological assay method were taken from literature and used for QSAR model development [[22], [23], [24], [25]]. General chemical structure for all studied molecules is presented in Fig. 1. SMILES notation obtained from the above-stated database were canonized using Open Babel and their

Results and discussion

To consider any QSAR model for predicting, its applicability domain (AD) should be defined prior to use [39,40]. To define AD for all developed QSAR models in this study, the methodology described in the literature was used [29,30]. According to obtained results, all molecules fall within defined AD and no outliers were determined. Numerical values for all metrics used for the determination of developed QSAR models goodness are presented in Table 1 and according to the presented results; the

Conclusion

In summary, the main aim of this research was to develop robust QSAR models with good predictability, determined with various statistical parameters, for human Nav1.7-VSD4-NavAb inhibitors with the Monte Carlo optimization method as the main conformation independent model developed based on optimal descriptors derived both from a local graph and SMILES notation invariants. The assessment of developed QSAR model robustness and predictive potential was achieved with the application of various

Declaration of competing interest

We have no conflicts of interest to disclose.

Acknowledgments

This study was supported by the Faculty of Medicine, University of Niš, Republic of Serbia under project “Development and design of novel therapeutics based on in silico methods” (Number 70), and by the Ministry of Education and Science, the Republic of Serbia.

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