Network pharmacology combined with molecular docking and experimental verification to elucidate the effect of flavan-3-ols and aromatic resin on anxiety

Abstract

This study investigated the potential anxiolytic properties of flavan-3-ols and aromatic resins through a combined computational and experimental approach. Network pharmacology techniques were utilized to identify potential anxiolytic targets and compounds by analyzing protein–protein interactions and KEGG pathway data. Molecular docking and simulation studies were conducted to evaluate the binding interactions and stability of the identified targets. Behavioral tests, including the elevated plus maze test, open field test, light–dark test, actophotometer, and holeboard test, were used to assess anxiolytic activity. The compound-target network analysis revealed complex interactions involving 306 nodes and 526 edges, with significant interactions observed and an average node degree of 1.94. KEGG pathway analysis highlighted pathways such as neuroactive ligand-receptor interactions, dopaminergic synapses, and serotonergic synapses as being involved in anxiety modulation. Docking studies on EGCG (Epigallocatechin gallate) showed binding energies of −9.5 kcal/mol for MAOA, −9.2 kcal/mol for SLC6A4, and −7.4 kcal/mol for COMT. Molecular dynamic simulations indicated minimal fluctuations, suggesting the formation of stable complexes between small molecules and proteins. Behavioral tests demonstrated a significant reduction in anxiety-like behavior, as evidenced by an increased number of entries into and time spent in the open arm of the elevated plus maze test, light–dark test, open field center activity, hole board head dips, and actophotometer beam interruptions (p < 0.05 or p < 0.01). This research provides a comprehensive understanding of the multi-component, multi-target, and multi-pathway intervention mechanisms of flavan-3-ols and aromatic resins in anxiety treatment. Integrated network and behavioral analyses collectively support the anxiolytic potential of these compounds and offer valuable insights for future research in this area.

Introduction

Anxiety disorders are complex mental health conditions characterized by recurrent and sudden episodes of unexplainable panic, fear, tension, and/or anxiety. These episodes often manifest alongside noticeable physiological symptoms such as palpitations, sweating, and disturbances in the autonomic nervous system. Its global incidence ranges from 3.8 to 25%, with approximately 70% of reported cases being chronic1. As one of the most prevalent mental health issues, anxiety disorders significantly impact individuals’ quality of life and societal balance2. In Western medicine, common treatment approaches include selective serotonin reuptake inhibitors (SSRIs), benzodiazepines such as diazepam, and other pharmacological agents3,4.

Benzodiazepines function by interacting with γ-aminobutyric acid (GABA) receptors. This interaction enhances GABAergic activity, leading to increased permeability of chloride ion channels. Consequently, there is a substantial influx of chloride ions into cells. This mechanism promotes neuronal cell hyperpolarization, inducing a central inhibitory effect crucial for alleviating symptoms of anxiety5. In contrast, selective serotonin reuptake inhibitors (SSRIs) operate by inhibiting presynaptic 5-HT reuptake. This action increases the concentration of serotonin (5-HT) in the synaptic cleft, facilitating enhanced transmission of 5-HT neurons and ultimately producing anxiolytic effects6. However, prolonged use of these medications is frequently linked to the development of drug dependence, cognitive impairment, and increased susceptibility to motor dysfunction7.

Exacerbated by delayed therapeutic effects, substantial rates of nonresponse, and the emergence of adverse effects such as nausea and headache, patients face significant challenges linked to the administration of the mentioned pharmacotherapies8,9,10. Thus, there is a need for the development of antianxiety medications with enhanced tolerability profiles and a decreased likelihood of adverse effects. Current treatments for anxiety disorders, like therapy and medication, show effectiveness but also have drawbacks such as partial effectiveness, side effects, and potential dependency. Moreover, not all patients respond well to these treatments, highlighting the need for personalized approaches. Existing treatments often focus on symptom management rather than addressing underlying mechanisms. Therefore, there's a need for new therapies that are more effective, tolerable, and tailored to the diverse nature of anxiety disorders, aiming for better outcomes. The worldwide emergence of the COVID-19 pandemic, first identified in December 2019, has been linked to a significant increase in psychological issues, such as heightened anxiety and despondency. This has led to notable public concern regarding mental health11. Subsequent investigations have revealed that sleep disturbances, anxiety, and depressive symptoms persist in individuals even six months after hospital discharge and subsequent recovery12. Traditional antianxiety medications often target specific molecules, requiring prolonged administration and increasing susceptibility to a range of side effects and potential dependency issues13. Given these challenges, there has been growing interest in exploring alternative medicinal approaches to address anxiety. The aim is to mitigate the adverse effects and unfavorable reactions associated with conventional Western medicine treatments. Despite the variety of anxiolytic agents available on the market, their effectiveness is limited by various inherent constraints and drawbacks14,15. Network pharmacology is a robust framework in today's biomedical landscape that effectively integrates and coordinates intricate networks involving drugs, targets, and diseases. This process facilitates a comprehensive understanding of complex pharmacological interactions16,17,18. This approach places significant importance on high-throughput screening, advanced network visualization, and thorough analysis, making it a crucial tool in advancing research in traditional medicine. In contemporary drug discovery, molecular docking, a widely used computational technique, plays a key role in elucidating drug functionality and mechanism. The tool accurately predicts the binding modality and corresponding binding free energy between target proteins and investigated compounds19. Significant progress has been made in investigating therapeutic interventions for various central nervous system disorders, such as Alzheimer's disease, anxiety, and depression as pregabalin was identified as a potential anxiolytic through molecular docking and pharmacophore modeling studies20. This underscores the crucial role of these studies in modern neuropsychopharmacological research17,21,22. The use of virtual screening, which heavily relies on molecular docking methodologies, has become indispensable in contemporary drug development. This approach facilitates the efficient and strategic identification of potential lead compounds23. Additionally, molecular dynamics (MD), a computational simulation method that integrates principles from physics, mathematics, and chemistry, has proven to be a powerful tool for in-depth exploration of protein dynamics. This can be achieved by tracking intricate changes in protein conformation over time24,25. This integrated approach provides a comprehensive understanding of the potential mechanisms underlying their effects on anxiety. Network pharmacology analyzes interactions between bioactive compounds and pathways related to anxiety, while molecular docking predicts their binding affinity to target proteins. Experimental validation confirms these predictions, enhancing the reliability of the findings. This multi-faceted approach not only highlights the therapeutic potential of flavan-3-ols and aromatic resin for anxiety but also advances our understanding of the molecular mechanisms involved, contributing to the development of novel anxiolytic agents with improved efficacy and safety profiles.

In this study, we aim to explore the potential effects of flavan-3-ols and aromatic resin in anxiety using in silico and in vivo techniques. The active compound targets were predicted and a drug-target interaction network was constructed. Additionally, molecular docking and dynamic simulations were used to validate the predicted targets and assess the binding affinity and stability of compound-target interactions, and in vivo, anxiety models were used to know the potential anti-anxiety effects. Understanding the molecular mechanisms of flavan-3-ols and aromatic resin in anxiety can help in the development of new treatments and target mechanisms for the prevention and treatment of this severe disease.

Materials and methods

Materials and reagents

Flavan-3-ols, including catechin and epigallocatechin gallate, were procured from Yucca Enterprises, which is located in Mumbai, Maharashtra, India. An aromatic resin, specifically Oudh, was obtained from Shabbar Dawasaz, located in Aurangabad, Maharashtra, India. The reference standard drug clonazepam was acquired from Abbott, India.

Protein–protein interaction (PPI) network

Analysis of protein–protein interactions (PPIs) is a crucial tool for understanding the complex involvement of proteins in various biochemical cascades. This approach aids in obtaining a comprehensive understanding of cellular architecture, biological processes, and functional modalities. The investigation involved the use of the advanced virtual screening platform STRING 11.0 (https://string-db.org/)26, to explore the intricate network of interconnected proteins. The relevant genetic components were carefully uploaded to the STRING database, providing essential insights into multifaceted PPIs. The construction of the PPI network was meticulously tailored to the context of 'Homo sapiens', and criteria for assessing the confidence level in interactions among target proteins were rigorously calibrated for the highest reliability, exceeding the threshold of data confidence set at 0.9. In this intricate network visualization, individual nodes represent distinct proteins, while the connecting edges intricately illustrate associations among diverse protein entities.

Compound and disease-target genes

The initial crucial step in constructing the compound-target network involves identifying genes linked to the disease. Relevant data about anxiety-associated target genes were methodically collected from credible sources, including GeneCards27 (https://www.genecards.org) and the Disgenet Database (https://www.disgenet.org/search). Moreover, comprehensive information on the protein targets of flavan-3-ols and aromatic resin was obtained from SwissTargetPrediction (http://www.swisstargetprediction.ch/) and the Stitch database (http://stitch.embl.de/).

Construction of a compound-target network

After conducting the protein–protein interaction analysis, the next step was to clarify the complex molecular mechanisms involved. This was achieved by building a detailed compound‒target network using Cytoscape28, visualization software version 3.7.1. This network structure is essential for understanding and analyzing the interactions between bioactive components and their specific targets. This approach helps to reveal the pathways involved in this complex biological network.

Abstract

This study investigated the potential anxiolytic properties of flavan-3-ols and aromatic resins through a combined computational and experimental approach. Network pharmacology techniques were utilized to identify potential anxiolytic targets and compounds by analyzing protein–protein interactions and KEGG pathway data. Molecular docking and simulation studies were conducted to evaluate the binding interactions and stability of the identified targets. Behavioral tests, including the elevated plus maze test, open field test, light–dark test, actophotometer, and holeboard test, were used to assess anxiolytic activity. The compound-target network analysis revealed complex interactions involving 306 nodes and 526 edges, with significant interactions observed and an average node degree of 1.94. KEGG pathway analysis highlighted pathways such as neuroactive ligand-receptor interactions, dopaminergic synapses, and serotonergic synapses as being involved in anxiety modulation. Docking studies on EGCG (Epigallocatechin gallate) showed binding energies of −9.5 kcal/mol for MAOA, −9.2 kcal/mol for SLC6A4, and −7.4 kcal/mol for COMT. Molecular dynamic simulations indicated minimal fluctuations, suggesting the formation of stable complexes between small molecules and proteins. Behavioral tests demonstrated a significant reduction in anxiety-like behavior, as evidenced by an increased number of entries into and time spent in the open arm of the elevated plus maze test, light–dark test, open field center activity, hole board head dips, and actophotometer beam interruptions (p < 0.05 or p < 0.01). This research provides a comprehensive understanding of the multi-component, multi-target, and multi-pathway intervention mechanisms of flavan-3-ols and aromatic resins in anxiety treatment. Integrated network and behavioral analyses collectively support the anxiolytic potential of these compounds and offer valuable insights for future research in this area.

ntroduction

Anxiety disorders are complex mental health conditions characterized by recurrent and sudden episodes of unexplainable panic, fear, tension, and/or anxiety. These episodes often manifest alongside noticeable physiological symptoms such as palpitations, sweating, and disturbances in the autonomic nervous system. Its global incidence ranges from 3.8 to 25%, with approximately 70% of reported cases being chronic1. As one of the most prevalent mental health issues, anxiety disorders significantly impact individuals’ quality of life and societal balance2. In Western medicine, common treatment approaches include selective serotonin reuptake inhibitors (SSRIs), benzodiazepines such as diazepam, and other pharmacological agents3,4.

Benzodiazepines function by interacting with γ-aminobutyric acid (GABA) receptors. This interaction enhances GABAergic activity, leading to increased permeability of chloride ion channels. Consequently, there is a substantial influx of chloride ions into cells. This mechanism promotes neuronal cell hyperpolarization, inducing a central inhibitory effect crucial for alleviating symptoms of anxiety5. In contrast, selective serotonin reuptake inhibitors (SSRIs) operate by inhibiting presynaptic 5-HT reuptake. This action increases the concentration of serotonin (5-HT) in the synaptic cleft, facilitating enhanced transmission of 5-HT neurons and ultimately producing anxiolytic effects6. However, prolonged use of these medications is frequently linked to the development of drug dependence, cognitive impairment, and increased susceptibility to motor dysfunction7.

Exacerbated by delayed therapeutic effects, substantial rates of nonresponse, and the emergence of adverse effects such as nausea and headache, patients face significant challenges linked to the administration of the mentioned pharmacotherapies8,9,10. Thus, there is a need for the development of antianxiety medications with enhanced tolerability profiles and a decreased likelihood of adverse effects. Current treatments for anxiety disorders, like therapy and medication, show effectiveness but also have drawbacks such as partial effectiveness, side effects, and potential dependency. Moreover, not all patients respond well to these treatments, highlighting the need for personalized approaches. Existing treatments often focus on symptom management rather than addressing underlying mechanisms. Therefore, there's a need for new therapies that are more effective, tolerable, and tailored to the diverse nature of anxiety disorders, aiming for better outcomes. The worldwide emergence of the COVID-19 pandemic, first identified in December 2019, has been linked to a significant increase in psychological issues, such as heightened anxiety and despondency. This has led to notable public concern regarding mental health11. Subsequent investigations have revealed that sleep disturbances, anxiety, and depressive symptoms persist in individuals even six months after hospital discharge and subsequent recovery12. Traditional antianxiety medications often target specific molecules, requiring prolonged administration and increasing susceptibility to a range of side effects and potential dependency issues13. Given these challenges, there has been growing interest in exploring alternative medicinal approaches to address anxiety. The aim is to mitigate the adverse effects and unfavorable reactions associated with conventional Western medicine treatments. Despite the variety of anxiolytic agents available on the market, their effectiveness is limited by various inherent constraints and drawbacks14,15. Network pharmacology is a robust framework in today's biomedical landscape that effectively integrates and coordinates intricate networks involving drugs, targets, and diseases. This process facilitates a comprehensive understanding of complex pharmacological interactions16,17,18. This approach places significant importance on high-throughput screening, advanced network visualization, and thorough analysis, making it a crucial tool in advancing research in traditional medicine. In contemporary drug discovery, molecular docking, a widely used computational technique, plays a key role in elucidating drug functionality and mechanism. The tool accurately predicts the binding modality and corresponding binding free energy between target proteins and investigated compounds19. Significant progress has been made in investigating therapeutic interventions for various central nervous system disorders, such as Alzheimer's disease, anxiety, and depression as pregabalin was identified as a potential anxiolytic through molecular docking and pharmacophore modeling studies20. This underscores the crucial role of these studies in modern neuropsychopharmacological research17,21,22. The use of virtual screening, which heavily relies on molecular docking methodologies, has become indispensable in contemporary drug development. This approach facilitates the efficient and strategic identification of potential lead compounds23. Additionally, molecular dynamics (MD), a computational simulation method that integrates principles from physics, mathematics, and chemistry, has proven to be a powerful tool for in-depth exploration of protein dynamics. This can be achieved by tracking intricate changes in protein conformation over time24,25. This integrated approach provides a comprehensive understanding of the potential mechanisms underlying their effects on anxiety. Network pharmacology analyzes interactions between bioactive compounds and pathways related to anxiety, while molecular docking predicts their binding affinity to target proteins. Experimental validation confirms these predictions, enhancing the reliability of the findings. This multi-faceted approach not only highlights the therapeutic potential of flavan-3-ols and aromatic resin for anxiety but also advances our understanding of the molecular mechanisms involved, contributing to the development of novel anxiolytic agents with improved efficacy and safety profiles.

In this study, we aim to explore the potential effects of flavan-3-ols and aromatic resin in anxiety using in silico and in vivo techniques. The active compound targets were predicted and a drug-target interaction network was constructed. Additionally, molecular docking and dynamic simulations were used to validate the predicted targets and assess the binding affinity and stability of compound-target interactions, and in vivo, anxiety models were used to know the potential anti-anxiety effects. Understanding the molecular mechanisms of flavan-3-ols and aromatic resin in anxiety can help in the development of new treatments and target mechanisms for the prevention and treatment of this severe disease.

Materials and methods

Materials and reagents

Flavan-3-ols, including catechin and epigallocatechin gallate, were procured from Yucca Enterprises, which is located in Mumbai, Maharashtra, India. An aromatic resin, specifically Oudh, was obtained from Shabbar Dawasaz, located in Aurangabad, Maharashtra, India. The reference standard drug clonazepam was acquired from Abbott, India.

Protein–protein interaction (PPI) network

Analysis of protein–protein interactions (PPIs) is a crucial tool for understanding the complex involvement of proteins in various biochemical cascades. This approach aids in obtaining a comprehensive understanding of cellular architecture, biological processes, and functional modalities. The investigation involved the use of the advanced virtual screening platform STRING 11.0 (https://string-db.org/)26, to explore the intricate network of interconnected proteins. The relevant genetic components were carefully uploaded to the STRING database, providing essential insights into multifaceted PPIs. The construction of the PPI network was meticulously tailored to the context of 'Homo sapiens', and criteria for assessing the confidence level in interactions among target proteins were rigorously calibrated for the highest reliability, exceeding the threshold of data confidence set at 0.9. In this intricate network visualization, individual nodes represent distinct proteins, while the connecting edges intricately illustrate associations among diverse protein entities.

Compound and disease-target genes

The initial crucial step in constructing the compound-target network involves identifying genes linked to the disease. Relevant data about anxiety-associated target genes were methodically collected from credible sources, including GeneCards27 (https://www.genecards.org) and the Disgenet Database (https://www.disgenet.org/search). Moreover, comprehensive information on the protein targets of flavan-3-ols and aromatic resin was obtained from SwissTargetPrediction (http://www.swisstargetprediction.ch/) and the Stitch database (http://stitch.embl.de/).

Construction of a compound-target network

After conducting the protein–protein interaction analysis, the next step was to clarify the complex molecular mechanisms involved. This was achieved by building a detailed compound‒target network using Cytoscape28, visualization software version 3.7.1. This network structure is essential for understanding and analyzing the interactions between bioactive components and their specific targets. This approach helps to reveal the pathways involved in this complex biological network.null