Multi-Pathway Study for Oxaliplatin Resistance Reduction
Abstract
:1. Introduction
2. Information and Methodology
2.1. Sources of Information
2.1.1. Searchers and Time of the Search
2.1.2. Timeframe for Searching the Literature
2.1.3. Search Databases
2.1.4. Type of Literature Searched
2.1.5. Search Terms
2.1.6. Search Strategy
2.2. Criteria for Inclusion and Exclusion
2.2.1. Criteria for Inclusion
2.2.2. Criteria for Exclusion
2.2.3. Evaluation of the Literature Quality and Data Extraction
3. Development of First-Line Pt Drugs
4. Molecular Mechanism of Oxaliplatin
5. Strategies to Enhance Platinum-Based Anticancer Efficiency and Reduce Systemic Resistance
5.1. Glutathione-Based Strategies to Reduce Oxaliplatin Resistance
5.1.1. Improving Plasma Half-Life and Tumor Selection
- (1)
- (2)
- Open-window vascularization and decreased lymphatic drainage are caused by enhanced permeability and retention (EPR) effects, which enrich macromolecules in malignant tissues as a result of disarray [51].
5.1.2. Inhibition of Glutathione Synthesis in Malignant Tissues
5.1.3. Reduced Drug Intake or Increased Drug Exportation
5.1.4. Support for GSH Conversion Enhancements
5.2. In Vivo Anticancer Activity
5.3. Discovery of Drug Resistance Genes and Prediction of Drug Resistance
5.3.1. Discovery of Drug Resistance Genes
Platinum Prodrug Design and Its Compounds
Application of CRISPR Screening Technology in Drug Resistance Gene Discovery
5.3.2. Prediction of Drug Resistance
Application Scope of Drug Resistance Prediction Technology Based on Artificial Intelligence Technology
- (1)
- Deep research into genomic data: AI can forecast pathogen resistance to drugs using whole genome sequencing (WGS) and machine learning models. As an example, one study predicted antibiotic resistance in multidrug-resistant Acinetobacter baumannii-utilizing deep neural network (DNN) models in conjunction with WGS and gene expression data, with a prediction accuracy of 98.64% [68].
- (2)
- Analysis of mass spectrometry data: AI techniques can also be used to study mass spectrometry data to predict antimicrobial resistance. For instance, by employing machine learning algorithms to evaluate clinical strains’ mass spectrometry data and combining them with data on drug resistance, helpful classifiers like gradient-enhanced decision trees (LightGBMs) and deep neural network classifiers (MLPs) have been developed, which have greatly boosted the accuracy of drug resistance prediction [69].
- (3)
- Integration of multi-omics data: AI models could more accurately forecast drug resistance by integrating data from genomes, transcriptomics, and metabolomics. In this regard, AI models may predict resistance phenotypes and minimum inhibitory concentrations (MICs) by investigating the relationship between bacterial DNA sequences and antimicrobial resistance phenotypes [69].
Specific Applications of Artificial Intelligence in Drug Resistance Prediction
5.4. Utilizing Biomarkers and Therapeutic Targets to Diminish Oxaliplatin Resistance
5.4.1. Targeting Kinases to Conquer Oxaliplatin Resistance
5.4.2. Targeting MiRNA to Minimize Oxaliplatin Resistance
5.5. Decreasing Oxaliplatin Resistance by Inducing Anti-Apoptotic Pathway
5.5.1. Inhibition of PLK4
5.5.2. Inhibition of Signaling Pathways
5.6. Autophagy
5.6.1. Oxidative Stress-Induced DNA Damage Repair Response
5.6.2. Autophagy Inhibition
5.7. Impact of DNA Damage Pathways on Increasing Anticancer Efficiency and Reducing Systemic Drug Resistance
5.7.1. DNA Damage Repair
5.7.2. Blocking Homologous Recombination Repair
5.7.3. Nucleus Destruction
6. Combination Therapy
6.1. XL413, a Novel CDC7 Inhibitor
6.2. Oxaliplatin and XL413 Combination Therapy in Cancer
7. Relationship Between Strategies for Enhancing Platinum Medicines’ Anticancer Activity
7.1. Synergies Between the Various Pathways
7.2. Pathways in Antagonistic Relationship
8. Conclusions and Prospects
- (1)
- Glutathione-based
- (2)
- Using biomarkers and therapeutic targets
- (3)
- DNA damage pathway
- (4)
- Combination therapy
Author Contributions
Funding
Conflicts of Interest
Abbreviations
References
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PubMed database |
#1 Oxaliplatin [Title/Abstract] #2 XL413 [Title/Abstract] #3 CDC7 inhibitor [Title/Abstract] #4 Drug resistance [MeSH Major Topic] #5 Cancer treatment [Title/Abstract] #6 Combination therapy [MeSH Major Topic] #7 #1 OR #2 OR #3 AND #4 #8 #1 OR #2 OR #3 AND #5 #9 #1 OR #2 OR #3 AND #6 |
Generation | Pt Drug | Molecular Structure | Market Time | Listed Country |
---|---|---|---|---|
First | Cisplatin | 1978 | Japan/Italy | |
Second | Carboplatin | 1986 | America | |
Third | Oxaliplatin | 1996 | France |
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Ye, T.; Wu, C.; Na, J.; Liu, X.; Huang, Y. Multi-Pathway Study for Oxaliplatin Resistance Reduction. Curr. Issues Mol. Biol. 2025, 47, 172. https://doi.org/10.3390/cimb47030172
Ye T, Wu C, Na J, Liu X, Huang Y. Multi-Pathway Study for Oxaliplatin Resistance Reduction. Current Issues in Molecular Biology. 2025; 47(3):172. https://doi.org/10.3390/cimb47030172
Chicago/Turabian StyleYe, Tong, Chen Wu, Jintong Na, Xiyu Liu, and Yong Huang. 2025. "Multi-Pathway Study for Oxaliplatin Resistance Reduction" Current Issues in Molecular Biology 47, no. 3: 172. https://doi.org/10.3390/cimb47030172
APA StyleYe, T., Wu, C., Na, J., Liu, X., & Huang, Y. (2025). Multi-Pathway Study for Oxaliplatin Resistance Reduction. Current Issues in Molecular Biology, 47(3), 172. https://doi.org/10.3390/cimb47030172