How to find the right drug for each patient? Advances and challenges in pharmacogenomics

Area of Science:

• Oncology
• Pharmacogenomics
• Computational Biology

Background:

• Cancer is a complex and heterogeneous disease, making effective treatment a significant challenge.
• One-size-fits-all cancer therapies are ineffective; thus, treatments tailored to individual molecular profiles are essential.
• Pharmacogenomics investigates the link between an individual's genetic makeup and their response to drugs.

Purpose of the Study:

• To review advancements in experimental techniques and computational approaches for predicting drug efficacy in cancer.
• To highlight the role of -omics technologies in identifying predictive biomarkers for drug response.
• To bridge the gap between preclinical cancer models and direct patient treatment prediction.

Main Methods:

• Utilizing diverse biological models (cell lines, mouse models) as patient proxies for preclinical studies.
• Leveraging high-throughput -omics technologies to generate extensive molecular data.
• Employing computational approaches, including machine learning and dynamic modeling, to analyze complex datasets.

Main Results:

• The integration of -omics data with computational methods offers potential for discovering predictive biomarkers.
• Current methods still face limitations in precisely predicting drug efficacy in cancer models and patients.
• Ongoing research in experimental and computational techniques is progressively advancing prediction capabilities.

Conclusions:

• Predicting drug efficacy in cancer requires personalized approaches based on molecular characteristics.
• Pharmacogenomics, coupled with advanced '-omics' technologies and computational analysis, is key to developing tailored cancer therapies.
• Further development of experimental and computational strategies is necessary to achieve precise prediction of drug response in clinical settings.