AIMC Topic: Gefitinib

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Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

IUBMB life Nov 29, 2024
Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategie...
Gene Expression Profiling Transcriptome Lung Neoplasms Biomarkers, Tumor Antineoplastic Agents Drug Resistance, Neoplasm Gefitinib Adenocarcinoma of Lung Multiomics Gene Expression Regulation, Neoplastic Humans Prognosis Tumor Microenvironment Machine Learning
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Identification of new potential candidates to inhibit EGF via machine learning algorithm.

European journal of pharmacology Nov 23, 2023
One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerat...
Machine Learning Gefitinib Drug Repositioning Algorithms Epidermal Growth Factor Molecular Docking Simulation
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Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

International journal of molecular sciences Sep 8, 2022
In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a dee...
Gene Expression Regulation, Neoplastic Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Deep Learning Systems Biology Metformin Gefitinib NF-kappa B Mouth Neoplasms Biomarkers Carcinoma, Squamous Cell Drug Design Drug Discovery Biomarkers, Tumor Humans
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Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 20, 2020
PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Prognosis Gefitinib Docetaxel Clinical Trials, Phase III as Topic Antineoplastic Combined Chemotherapy Protocols Carcinoma, Non-Small-Cell Lung Clinical Trials, Phase II as Topic Nivolumab Randomized Controlled Trials as Topic Male Female Survival Rate Disease Progression Lung Neoplasms
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Efficacy according to blind independent central review: Post-hoc analyses from the phase III, randomized, multicenter, IPASS study of first-line gefitinib versus carboplatin/paclitaxel in Asian patients with EGFR mutation-positive advanced NSCLC.

Lung cancer (Amsterdam, Netherlands) Nov 30, 2016
OBJECTIVE: The Phase III, randomized, open-label IPASS study (NCT00322452) of first-line epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) gefitinib versus carboplatin/paclitaxel for Asian patients with advanced non-small-cell lun...
Gefitinib Quinazolines ErbB Receptors Protein Kinase Inhibitors Paclitaxel Male Randomized Controlled Trials as Topic Adenocarcinoma Aged Middle Aged Disease-Free Survival Aged, 80 and over Neoplasm Staging Carcinoma, Non-Small-Cell Lung Lung Neoplasms Smoking Antineoplastic Agents Mutation Humans Asian People Antineoplastic Combined Chemotherapy Protocols Female Carboplatin
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