Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Boush, Mahnaz; Kiaei, Ali Akbar; Safaei, Danial; Abadijou, Sadegh; Salari, Nader; Mohammadi, Masoud

doi:10.1101/2023.04.23.23289008

Cited by 6 publications

(5 citation statements)

References 79 publications

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“…The usual practice involves conducting a network meta-analysis to evaluate comparative effectiveness. [205], [206], [207], [208], [209], [210], [211].…”

Section: Other Treatment Strategymentioning

confidence: 99%

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Parichehreh,

Kiaei,

Boush

et al. 2024

Preprint

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BackgroundMalignant neoplasm of the pancreas (MNP), a highly lethal illness with bleak outlook and few therapeutic avenues, entails numerous cellular transformations. These include irregular proliferation of ductal cells, activation of stellate cells, initiation of epithelial-to-mesenchymal transition, and changes in cell shape, movement, and attachment. Discovering potent drug cocktails capable of addressing the genetic and protein factors underlying pancreatic cancer’s development is formidable due to the disease’s intricate and varied nature.MethodIn this study, we introduce a fresh model utilizing Graph Attention Networks (GATs) to pinpoint potential drug pairings with synergistic effects for MNP, following the RAIN protocol. This protocol comprises three primary stages: Initially, employing Graph Neural Network (GNN) to suggest drug combinations for disease management by acquiring embedding vectors of drugs and proteins from a diverse knowledge graph encompassing various biomedical data types, such as drug-protein interactions, gene expression, and drug-target interactions. Subsequently, leveraging natural language processing to gather pertinent articles from clinical trials incorporating the previously recommended drugs. Finally, conducting network meta-analysis to assess the relative effectiveness of these drug combinations.ResultWe implemented our approach on a network dataset featuring drugs and genes as nodes, connected by edges representing their respective p-values. Our GAT model identified Gemcitabine, Pancrelipase Amylase, and Octreotide as the optimal drug combination for targeting the human genes/proteins associated with this cancer. Subsequent scrutiny of clinical trials and literature confirmed the validity of our findings. Additionally, network meta-analysis confirmed the efficacy of these medications concerning the pertinent genes.ConclusionBy employing GAT within the RAIN protocol, our approach represents a novel and efficient method for recommending prominent drug combinations to target proteins/genes associated with pancreatic cancer. This technique has the potential to aid healthcare professionals and researchers in identifying optimal treatments for patients while also unveiling underlying disease mechanisms.HighlightsGraph Attention Networks (GATs) used to recommend drug combinations for pancreatic cancerRAIN protocol applied to extract relevant information from clinical trials and literatureGemcitabine, Pancrelipase Amylase, and Octreotide identified as optimal drug combinationNetwork meta-analysis confirmed the effectiveness of the drug combination on gene targetsNovel and efficient method for drug discovery and disease mechanism elucidationAbstract Figure

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“…The usual practice involves conducting a network meta-analysis to evaluate comparative effectiveness. [205], [206], [207], [208], [209], [210], [211].…”

Section: Other Treatment Strategymentioning

confidence: 99%

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Parichehreh,

Kiaei,

Boush

et al. 2024

Preprint

Self Cite

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“…Typically, They conduct a network meta-analysis to evaluate the efficacy in relation to effectiveness. [280][281][282][283][284][285][286][287][288][289] Figure 1: The impact of suggested drug combinations on the management of Lung adenocarcinoma cases…”

Section: -110mentioning

confidence: 99%

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

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Background: Adenocarcinoma of the lung is the most common type of lung cancer, and it is characterized by distinct cellular and molecular features. It occurs when abnormal lung cells multiply out of control and form a tumor in the outer region of the lungs. Adenocarcinoma of the lung is a serious and life-threatening condition that requires effective and timely management to improve the survival and quality of life of the patients. One of the challenges in this cancer treatment is finding the optimal combination of drugs that can target the genes or proteins that are involved in the disease process. Method: In this article, we propose a novel method to recommend combinations of trending drugs to target its associated proteins/genes, using a Graph Neural Network (GNN) under the RAIN protocol. The RAIN protocol is a three-step framework that consists of: 1) Applying graph neural networks to recommend drug combinations by passing messages between trending drugs for managing disease and genes that act as potential targets for disease; 2) Retrieving relevant articles with clinical trials that include those proposed drugs in previous step using Natural Language Processing (NLP); 3) Analyzing the network meta-analysis to measure the comparative efficacy of the drug combinations. Result: We applied our method to a dataset of nodes and edges that represent the network, where each node is a drug or a gene, and each edge is a p-value between them. We found that the graph neural network recommends combining Gefitinib, Paclitaxel, and Icotinib as the most effective drug combination to target this cancer associated proteins/genes. We reviewed the clinical trials and expert opinions on these medications and found that they support our claim. The network meta-analysis also confirmed the effectiveness of these drugs on associated genes. Conclusion: Our method is a novel and promising approach to recommend trending drugs combination to target cancer associated proteins/genes, using graph neural networks under the RAIN protocol. It can help clinicians and researchers to find the best treatment options for patients, and also provide insights into the underlying mechanisms of the disease.

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“…As seen in several recently published medical AI papers, the RAIN protocol stands out by employing AI to tackle a particular medical query. [63][64][65][66][67][68][69][70][71][72]…”

Section: Objectivementioning

confidence: 99%

Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol

Askari,

Kiaei,

Boush

et al. 2024

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BackgroundTongue Neoplasms is a common form of malignancy, with squamous cell carcinoma of the tongue being the most frequently diagnosed type due to regular mechanical stimulation. Its prevalence remains on the rise among neoplastic cancer cases. Finding effective combinations of drugs to target the genetic and protein elements contributing to the development of Managing Tongue Neoplasms poses a difficulty owing to the intricate and varied nature of the ailment.MethodIn this research, we introduce a novel approach using Deep Modularity Networks (DMoN) to identify potential synergistic drug combinations for the condition, following the RAIN protocol. This procedure comprises three primary phases: First, employing Graph Neural Network (GNN) to propose drug combinations for treating the ailment by extracting embedding vectors of drugs and proteins from an extensive knowledge graph containing various biomedical data types, such as drug-protein interactions, gene expression, and drug-target interactions. Second, utilizing natural language processing to gather pertinent articles from clinical trials involving the previously recommended drugs. Finally, conducting network meta-analysis to evaluate the comparative efficacy of these drug combinations.ResultWe utilized our approach on a dataset containing drugs and genes as nodes, connected by edges indicating their associated p-values. Our DMoN model identified Cisplatin, Bleomycin, and Fluorouracil as the optimal drug combination for targeting the human genes/proteins associated with this cancer. Subsequent scrutiny of clinical trials and literature confirmed the validity of our findings. Additionally, network meta-analysis substantiated the efficacy of these medications concerning the pertinent genes.ConclusionThrough the utilization of DMoN as part of the RAIN protocol, our method introduces a fresh and effective way to suggest notable drug combinations for addressing proteins/genes linked to Tongue Neoplasms. This approach holds promise in assisting healthcare practitioners and researchers in pinpointing the best treatments for patients, as well as uncovering the fundamental mechanisms of the disease.HighlightsA new method using Deep Modularity Networks and the RAIN protocol can find the best drug combinations for treating Tongue Neoplasms, a common and deadly form of cancer.The method uses a Graph Neural Network to suggest drug pairings from a large knowledge graph of biomedical data, then searches for clinical trials and performs network meta-analysis to compare their effectiveness.The method discovered that Cisplatin, Bleomycin, and Fluorouracil are suitable drugs for targeting the genes/proteins involved in this cancer, and confirmed this finding with literature review and statistical analysis.The method offers a novel and powerful way to assist doctors and researchers in finding the optimal treatments for patients with Tongue Neoplasms, and to understand the underlying causes of the disease.Abstract Figure

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Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis

Cited by 6 publications

References 79 publications

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol

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