scholarly journals Biochemical Targets and Molecular Mechanism of Ginsenoside Compound K for Treating Osteoporosis Based on Network Pharmacology

2021 ◽  
Author(s):  
Sen Zhang ◽  
Shihong Shen ◽  
Pei Ma ◽  
Daidi Fan
Keyword(s):  
Osteoclast Differentiation ◽  
Interaction Network ◽  
Gene Clusters ◽  
Network Pharmacology ◽  
Compound K ◽  
Downstream Signaling ◽  
Therapy Targets ◽  
Treatment And Prevention ◽  
Prevention Of Osteoporosis ◽  
Ginsenoside Compound K

Abstract Ginsenosides have been proven to be potential beneficial in treatment of osteoporosis. To investigate the potential of ginsenosides in osteoporosis, ginsenoside compound K (GCK) was selected to explore the potential therapy targets and mechanism based on network pharmacology (NP). 206 and 6590 targets were obtained for GCK and osteoporosis, respectively, in which 138 targets were identified as co-targets of GCK and osteoporosis based on intersection analysis. Five central gene clusters and hub genes (STAT3, PIK3R1, VEGFA, JAK2 and MAP2K1) were identified through protein-protein interaction network analysis. Gene Ontology (GO) enrichment implied that phosphatidylinositol-related biological process, molecular modification and function may play an important role for GCK in treatment and prevention of osteoporosis. Functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that 16 targets were enriched in the osteoclast differentiation. Also, except for being identified as hub targets, MAPK and phosphatidylinositol-related proteins were enriched in the downstream signaling of c-Fms in the osteoclast differentiation pathway. Molecular docking further confirmed that GCK could interact with active cavity on the surface of c-Fms (osteoclast differentiation-related membrane receptor), and their complex could be stabilized by three H-bonds with residues including Glu 664 (3.19 Å), Glu 664 (2.62 Å) and Cys 666 (2.78 Å). Summarily, GCK could interfere the occurrence and progress of osteoporosis through c-Fms-mediated MAPK and phosphatidylinositol-related signaling regulating osteoclast differentiation.

scholarly journals Systemic Elucidate The Potential Bioactive Compounds and Hypoglycemic Mechanism of Polygonum Multiflorum Based on Network Pharmacology

2020 ◽  
Author(s):  
Yunfei Song ◽  
Jianbo Yang ◽  
Wenguang Jing ◽  
Qi Wang ◽  
Yue Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Bioactive Compounds ◽  
Acid Metabolism ◽  
Osteoclast Differentiation ◽  
Interaction Network ◽  
Arachidonic Acid Metabolism ◽  
Hypoglycemic Effect ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Polygonum Multiflorum

Abstract Background: Diabetes is a complex metabolic disease characterized by hyperglycemia, plaguing the whole world. However, the action mode of multi-component and multi-target for traditional Chinese medicine (TCM) could be a promising treatment of diabetes mellitus. According to the previous research, the TCM of polygonum multiflorum (PM) showed noteworthy hypoglycemic effect. Up to now, its hypoglycemic active ingredients and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum (PM).Methods: First, the compounds with good pharmacokinetic properties were screened from the self-established library of PM, and the targets of these compounds were predicted and collected through database. Relevant targets of diabetes were summarized by searching database. The intersection targets of compound-targets and disease-targets were obtained soon. Secondly, the interaction net between the compounds and the filtered targets was established. These key targets were enriched and analyzed by PPI analysis, molecular docking verification. Finally, the key genes were used to find the biologic pathway and explain the therapeutic mechanism by GO and KEGG analysis. Results: In this study, 29 hypoglycemic components and 63 hypoglycemic targets of PM were filtrated based on online network database. Then the component-target interaction network was constructed and five key components resveratrol, apigenin, kaempferol, quercetin and luteolin were further obtained. Sequential studies turned out, AKT1, EGFR, ESR1, PTGS2, MMP9, MAPK14, and KDR were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. There were 38 metabolic pathways, including regulation of lipolysis in adipocytes, prolactin signaling pathway, TNF signaling pathway, VEGF signaling pathway, FoxO signaling pathway, estrogen signaling pathway, linoleic acid metabolism, Rap1 signaling pathway, arachidonic acid metabolism, and osteoclast differentiation closely connected with the hypoglycemic mechanism of PM.Conclusion: In summary, the study used systems pharmacology to elucidate the main hypoglycemic components and mechanism of PM. The work provided a scientific basis for the further hypoglycemic effect research of PM and its monomer components, but also provided a reference for the secondary development of PM.

scholarly journals Systemic elucidation on the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology

2020 ◽  
Author(s):  
Yunfei Song ◽  
Jianbo Yang ◽  
Wenguang Jing ◽  
Qi Wang ◽  
Yue Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Bioactive Compounds ◽  
Acid Metabolism ◽  
Osteoclast Differentiation ◽  
Interaction Network ◽  
Arachidonic Acid Metabolism ◽  
Hypoglycemic Effect ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Polygonum Multiflorum

Abstract Background: Diabetes is a complex metabolic disease characterized by hyperglycemia, plaguing the whole world. However, the action mode of multi-component and multi-target for traditional Chinese medicine (TCM) could be a promising treatment of diabetes mellitus. According to the previous research, the TCM of polygonum multiflorum (PM) showed noteworthy hypoglycemic effect. Up to now, its hypoglycemic active ingredients and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum (PM).Methods: First, the compounds with good pharmacokinetic properties were screened from the self-established library of PM, and the targets of these compounds were predicted and collected through database. Relevant targets of diabetes were summarized by searching database. The intersection targets of compound-targets and disease-targets were obtained soon. Secondly, the interaction net between the compounds and the filtered targets was established. These key targets were enriched and analyzed by PPI analysis, molecular docking verification. Thirdly, the key genes were used to find the biologic pathway and explain the therapeutic mechanism by GO and KEGG analysis. Lastly, the part of potential bioactive compounds were under enzyme activity inhibition tests.Results: In this study, 29 hypoglycemic components and 63 hypoglycemic targets of PM were filtrated based on online network database. Then the component-target interaction network was constructed and five key components resveratrol, apigenin, kaempferol, quercetin and luteolin were further obtained. Sequential studies turned out, AKT1, EGFR, ESR1, PTGS2, MMP9, MAPK14, and KDR were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. There were 38 metabolic pathways, including regulation of lipolysis in adipocytes, prolactin signaling pathway, TNF signaling pathway, VEGF signaling pathway, FoxO signaling pathway, estrogen signaling pathway, linoleic acid metabolism, Rap1 signaling pathway, arachidonic acid metabolism, and osteoclast differentiation closely connected with the hypoglycemic mechanism of PM.Conclusion: In summary, the study used systems pharmacology to elucidate the main hypoglycemic components and mechanism of PM. The work provided a scientific basis for the further hypoglycemic effect research of PM and its monomer components, but also provided a reference for the secondary development of PM.

scholarly journals Systemic elucidate the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum based on network pharmacology

2020 ◽  
Author(s):  
Yunfei Song ◽  
Jianbo Yang ◽  
Wenguang Jing ◽  
Qi Wang ◽  
Yue Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Bioactive Compounds ◽  
Acid Metabolism ◽  
Osteoclast Differentiation ◽  
Interaction Network ◽  
Arachidonic Acid Metabolism ◽  
Hypoglycemic Effect ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Polygonum Multiflorum

Abstract Background: Diabetes is a complex metabolic disease characterized by hyperglycemia, plaguing the whole world. However, the action mode of multi-component and multi-target for traditional Chinese medicine (TCM) could be a promising treatment of diabetes mellitus. According to the previous research, the TCM of polygonum multiflorum (PM) showed noteworthy hypoglycemic effect. Up to now, its hypoglycemic active ingredients and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential bioactive compounds and hypoglycemic mechanism of Polygonum multiflorum (PM).Methods: First, the compounds with good pharmacokinetic properties were screened from the self-established library of PM, and the targets of these compounds were predicted and collected through database. Relevant targets of diabetes were summarized by searching database. The intersection targets of compound-targets and disease-targets were obtained soon. Secondly, the interaction net between the compounds and the filtered targets was established. These key targets were enriched and analyzed by PPI analysis, molecular docking verification. Finally, the key genes were used to find the biologic pathway and explain the therapeutic mechanism by GO and KEGG analysis. Results: In this study, 29 hypoglycemic components and 63 hypoglycemic targets of PM were filtrated based on online network database. Then the component-target interaction network was constructed and five key components resveratrol, apigenin, kaempferol, quercetin and luteolin were further obtained. Sequential studies turned out, AKT1, EGFR, ESR1, PTGS2, MMP9, MAPK14, and KDR were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. There were 38 metabolic pathways, including regulation of lipolysis in adipocytes, prolactin signaling pathway, TNF signaling pathway, VEGF signaling pathway, FoxO signaling pathway, estrogen signaling pathway, linoleic acid metabolism, Rap1 signaling pathway, arachidonic acid metabolism, and osteoclast differentiation closely connected with the hypoglycemic mechanism of PM.Conclusion: In summary, the study used systems pharmacology to elucidate the main hypoglycemic components and mechanism of PM. The work provided a scientific basis for the further hypoglycemic effect research of PM and its monomer components, but also provided a reference for the secondary development of PM.

Network Pharmacology Approach uncovering Pathways involved in targeting Hsp90 through Curcumin and Epigallocatechin to control Inflammation.

2019 ◽  
Vol 16 ◽  
Author(s):  
Umme Hani ◽  
Shivananda Kandagalla ◽  
B.S. Sharath ◽  
K Jyothsna. ◽  
H Manjunatha.
Keyword(s):  
Gene Ontology ◽  
Molecular Chaperones ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Heme Oxygenase 1 ◽  
Nrf2 Pathway ◽  
Functional Behavior ◽  
Inflammatory Proteins ◽  
Prime Target

: Hsp90 are molecular chaperones of chronic inflammatory proteins and have emerged as prime target for treatment of inflammation. Principal components from Curcuma longa and Camellia sinensis, Curcumin and EGC respectively possesses anti-inflammatory properties inhibiting cytokines responsible for inflammation. Both act on common pathways in upregulation of heme oxygenase 1 through Pkcδ-Nrf2 pathway and downregulation of Tlr4, which in turn suppress expression of Hsp90. Curcumin and EGC were also found to bind -N and -C terminal domain of Hsp90 respectively. Based on this, work was designed with network pharmacological approach. Hsp90 associated gene targets of Curcumin and EGC were collected from databases, and gene ontology studies were done. PPI were obtained from string database for specific genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Protein interaction network was constructed by cytoscape, and networks of Hsp90, Curcumin and EGC were merged to get common genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Cluego analysis was done for obtained common genes to identify functional behavior in human diseases. Main proteins involved were identified as key regulators in Pkcδ-Nrf2 and Tlr4 pathway for controlling expression of Hsp90 from Curcumin and EGC in inflammation. Docking was performed on main proteins, Hsp90, Pkcδ and Tlr4 with Curcumin and EGC, significant binding energy was obtained for docked complexes. Combinatorial effects of Curcumin and EGC were observed in Pkcδ-Nrf2 and Tlr4pathway. Present study is an attempt to unravel common pathways mediated in intervention of Curcumin and EGC for suppression of Hsp90 associated with inflammation.

scholarly journals Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (5) ◽  
pp. 1934578X2110167
Author(s):  
Xing-Pan Wu ◽  
Tian-Shun Wang ◽  
Zi-Xin Yuan ◽  
Yan-Fang Yang ◽  
He-Zhen Wu
Keyword(s):  
Molecular Docking ◽  
Target Prediction ◽  
Interaction Network ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio ◽  
The Core ◽  
Pathway Annotation

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

scholarly journals Bacteriocins of Non-aureus Staphylococci Isolated from Bovine Milk

2017 ◽  
Vol 83 (17) ◽  
Author(s):  
Domonique A. Carson ◽  
Herman W. Barkema ◽  
Sohail Naushad ◽  
Jeroen De Buck
Keyword(s):  
Staphylococcus Aureus ◽  
Bacterial Infections ◽  
Genome Mining ◽  
Bovine Milk ◽  
Gene Clusters ◽  
Antibiotic Use ◽  
Content Type ◽  
Treatment And Prevention ◽  
Whole Genomes

ABSTRACT Non-aureus staphylococci (NAS), the bacteria most commonly isolated from the bovine udder, potentially protect the udder against infection by major mastitis pathogens due to bacteriocin production. In this study, we determined the inhibitory capability of 441 bovine NAS isolates (comprising 26 species) against bovine Staphylococcus aureus. Furthermore, inhibiting isolates were tested against a human methicillin-resistant S. aureus (MRSA) isolate using a cross-streaking method. We determined the presence of bacteriocin clusters in NAS whole genomes using genome mining tools, BLAST, and comparison of genomes of closely related inhibiting and noninhibiting isolates and determined the genetic organization of any identified bacteriocin biosynthetic gene clusters. Forty isolates from 9 species (S. capitis, S. chromogenes, S. epidermidis, S. pasteuri, S. saprophyticus, S. sciuri, S. simulans, S. warneri, and S. xylosus) inhibited growth of S. aureus in vitro, 23 isolates of which, from S. capitis, S. chromogenes, S. epidermidis, S. pasteuri, S. simulans, and S. xylosus, also inhibited MRSA. One hundred five putative bacteriocin gene clusters encompassing 6 different classes (lanthipeptides, sactipeptides, lasso peptides, class IIa, class IIc, and class IId) in 95 whole genomes from 16 species were identified. A total of 25 novel bacteriocin precursors were described. In conclusion, NAS from bovine mammary glands are a source of potential bacteriocins, with >21% being possible producers, representing potential for future characterization and prospective clinical applications. IMPORTANCE Mastitis (particularly infections caused by Staphylococcus aureus) costs Canadian dairy producers $400 million/year and is the leading cause of antibiotic use on dairy farms. With increasing antibiotic resistance and regulations regarding use, there is impetus to explore bacteriocins (bacterially produced antimicrobial peptides) for treatment and prevention of bacterial infections. We examined the ability of 441 NAS bacteria from Canadian bovine milk samples to inhibit growth of S. aureus in the laboratory. Overall, 9% inhibited growth of S. aureus and 58% of those also inhibited MRSA. In NAS whole-genome sequences, we identified >21% of NAS as having bacteriocin genes. Our study represents a foundation to further explore NAS bacteriocins for clinical use.

Osteoporosis and its treatment

1984 ◽  
Vol 22 (1) ◽  
pp. 1-4
Keyword(s):  
Young Adults ◽  
Unit Volume ◽  
Mineral Content ◽  
Bone Volume ◽  
Economic Consequences ◽  
Minor Trauma ◽  
Fractured Neck Of Femur ◽  
Neck Of Femur ◽  
Treatment And Prevention ◽  
Prevention Of Osteoporosis

The mineral content of bone is greatest in young adults and thereafter decreases with age. Osteoporosis can be defined as a reduction in the amount of bone mineral per unit volume of anatomical bone or skeleton, or more simply as a reduction in bone volume, exceeding the natural decline with age. The process diminishes bone strength but produces no symptoms until a fracture occurs in response to minor trauma - typically of a vertebral body, the proximal end of the femur or the lower forearm. Osteoporosis has major economic consequences: 10% of all orthopaedic beds in Britain are occupied by patients with fractured neck of femur, most of which are associated with osteoporosis.1 This article discusses the diagnosis, causes, treatment and prevention of osteoporosis.

scholarly journals Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

2021 ◽  
Author(s):  
Xiting Wang ◽  
Tao Lu
Keyword(s):  
Molecular Docking ◽  
Blood Circulation ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Detection Algorithm ◽  
Network Pharmacology ◽  
Systems Pharmacology ◽  
Protein Protein Interaction ◽  
Further Development ◽  
Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

scholarly journals Investigating drug–target interactions in frontotemporal dementia using a network pharmacology approach

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Archana Balasubramanian ◽  
Raksha Sudarshan ◽  
Jhinuk Chatterjee
Keyword(s):  
Substance Abuse ◽  
Targeted Therapy ◽  
Drug Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Specific Drug ◽  
Protein Protein Interaction ◽  
App Gene ◽  
Protein Protein Interaction Network

Abstract Background Frontotemporal dementia (FTD) is the second most common type of dementia in individuals aged below 65 years with no current cure. Current treatment plan is the administration of multiple medications. This has the issue of causing adverse effects due to unintentional drug–drug interactions. Therefore, there exists an urgent need to propose a novel targeted therapy that can maximize the benefits of FTD-specific drugs while minimizing its associated adverse side effects. In this study, we implemented the concept of network pharmacology to understand the mechanism underlying FTD and highlight specific drug–gene and drug–drug interactions that can provide an interesting perspective in proposing a targeted therapy against FTD. Results We constructed protein–protein, drug–gene and drug–drug interaction networks to identify highly connected nodes and analysed their importance in associated enriched pathways. We also performed a historeceptomics analysis to determine tissue-specific drug interactions. Through this study, we were able to shed light on the APP gene involved in FTD. The APP gene which was previously known to cause FTD cases in a small percentage is now being extensively studied owing to new reports claiming its participation in neurodegeneration. Our findings strengthen this hypothesis as the APP gene was found to have the highest node degree and betweenness centrality in our protein–protein interaction network and formed an essential hub node between disease susceptibility genes and neuroactive ligand–receptors. Our findings also support the study of FTD being presented as a case of substance abuse. Our protein–protein interaction network highlights the target genes common to substance abuse (nicotine, morphine and cocaine addiction) and neuroactive ligand–receptor interaction pathways, therefore validating the cognitive impairment caused by substance abuse as a symptom of FTD. Conclusions Our study abandons the one-target one-drug approach and uses networks to define the disease mechanism underlying FTD. We were able to highlight important genes and pathways involved in FTD and analyse their relation with existing drugs that can provide an insight into effective medication management.

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