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Related Experiment Video

Updated: May 19, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Utilizing ¹⁸F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

Clinical Trials and Translational Advances in Metabolic Targeting.

Hariharan Thirumalai Vengateswaran¹, Mohammad Habeeb¹, Huay Woon You²

¹Department of Pharmaceutics, Crescent School of Pharmacy, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.

Cancer Treatment and Research

|May 17, 2026

Summary

Metabolic reprogramming offers new therapeutic strategies for diseases. Advanced analysis and AI integration are key to overcoming challenges and personalizing treatments for better patient outcomes.

Keywords:

Artificial intelligence Cancer Clinical trials Metabolomics

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Last Updated: May 19, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

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Published on: July 21, 2018

Advancements in the Metabolic Profiling of Three-Dimensional Brain Tumor Spheroids for Drug Screening

Advancements in the Metabolic Profiling of Three-Dimensional Brain Tumor Spheroids for Drug Screening

Published on: September 5, 2025

Area of Science:

Biomedical research
Metabolic pathways
Disease therapeutics

Background:

Metabolic reprogramming is a hallmark of various disorders.
Metabolic therapies show promise but face challenges due to complex cellular networks.
Variability and resistance hinder the advancement of metabolic treatments.

Purpose of the Study:

To provide a comprehensive overview of clinical trials and advancements in metabolic targeting.
To highlight the role of small-molecule inhibitors and multi-omics technologies.
To discuss the integration of AI and machine learning in metabolic therapies.

Main Methods:

Review of recent clinical trials and translational advancements.
Focus on small-molecule inhibitors and multi-omics technologies.
Integration of artificial intelligence (AI) and machine learning (ML) for personalized medicine.

Main Results:

Metabolism-based treatments offer potential for individualized and accurate therapies.
Advanced single-cell analysis methods are crucial for overcoming variability and resistance.
AI/ML, metabolomics, and advanced imaging enable patient stratification and treatment optimization.

Conclusions:

Metabolic targeting is transforming biomedical treatment, offering novel disease management opportunities.
Personalized metabolic therapies are becoming feasible through advanced analytical methods and AI.
Continued research in metabolic pathways promises more effective and targeted disease interventions.