Inserting Ensartinib Into the Starting Lineup for <i>ALK</i>-Rearranged Lung Cancer—A Likely Limited Role on a Deep Bench

Dagogo‐Jack, Ibiayi

doi:10.1001/jamaoncol.2021.3369

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Ensartinib acts by inhibiting of ALK whose aberration causes cancers. [190][191][192][193][194] Aumolertinib is a small molecule drug that is used as a targeted therapy for non-small cell lung cancer (NSCLC) patients with Increasing sensitivity epidermal growth factor receptor (EGFR) mutations. This EGFR tyrosine kinase inhibitor (EGFR-TKI) belongs to the third generation and demonstrates selectivity for mutant EGFR compared to wild-type EGFR.…”

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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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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“…Ensartinib is a modification of crizotinib that retains the benzyloxy group and replaces aminopyridine with aminopyridazine as the pharmacodynamic group, further increasing its lipid solubility and affinity while enhancing its capacity for blood-brain barrier penetration. 38 , 39 Ensartinib is a potent TKI against ALK fusions with remarkable intracranial activity both in crizotinib-resistant patients and the first-line setting. 40 , 41 Type II MET TKIs, such as cabozantinib, merestinib, glesatinib, and foretinib, are also ATP-competitive but bind to the inactive MET conformation.…”

Section: Updated Landscapes Of Met Tkismentioning

confidence: 99%