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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Advancing precision oncology with AI-powered genomic analysis.

Ruby Srivastava¹

¹Bioinformatics, Centre for Cellular and Molecular Biology-CSIR, Hyderabad, India.

Frontiers in Pharmacology

|May 15, 2025

Summary

Artificial intelligence (AI) and multiomics data integration are revolutionizing precision medicine and cancer therapeutics by enabling deeper insights into disease mechanisms. These approaches facilitate personalized treatments, biomarker discovery, and drug development, despite computational and ethical challenges.

Keywords:

artificial intelligence bioinformatics genomics precision oncology therapeutics and analysis resource

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Area of Science:

Bioinformatics and Computational Biology
Genomics and Precision Medicine
Oncology and Drug Development

Background:

Multiomics data integration offers a holistic view of biological systems for disease therapeutics.
Quantitative integration of diverse omics data presents significant computational challenges.
Precision medicine relies on individual omics profiles for early detection, biomarker discovery, and targeted therapies.

Purpose of the Study:

To review the potential of AI and multiomics data integration in understanding diseases and advancing therapeutics.
To highlight challenges in quantitative omics data integration and AI-driven clinical workflows in oncology.
To explore AI-powered bioinformatics strategies for drug selection, genome profiling, and tumor clonality analysis.

Main Methods:

Review of AI-driven bioinformatics tools for multiomics data analysis.
Exploration of integrative multiomics strategies for drug prioritization and selection.
Discussion of AI applications in cancer genomics, including genome profiling and tumor clonality analysis.

Main Results:

AI and multiomics integration facilitate personalized treatment selection and drug discovery.
These approaches aid in early disease detection, biomarker identification, and treatment monitoring.
AI assists clinicians by computing scores to prioritize drugs for optimal patient treatment.

Conclusions:

AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology.
Addressing technical, ethical, and privacy concerns is crucial for deploying AI in genomics.
The integration of multiomics data with AI holds significant promise for future disease understanding and therapeutic interventions.