Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

Journal: European review for medical and pharmacological sciences

PMID: 39749373

Abstract

OBJECTIVE: CYP2D6 plays a critical role in metabolizing tamoxifen into its active metabolite, endoxifen, which is crucial for its therapeutic effect in estrogen receptor-positive breast cancer. Single nucleotide polymorphisms (SNPs) in the CYP2D6 gene can affect enzyme activity and thus impact tamoxifen efficacy. This study aimed to use machine learning algorithms (MLAs) to identify significant predictors of Breast Cancer-Free Interval (BCFI) and to apply bioinformatics tools to investigate the structural and functional implications of CYP2D6 SNPs.

Authors

K Sridharan

Department of Pharmacology and Therapeutics, College of Medicine & Health Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain. kannans@agu.edu.bh.
K Sekaran
C George Priya Doss

School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.

Keywords

Algorithms Antineoplastic Agents, Hormonal Breast Neoplasms Cytochrome P-450 CYP2D6 Female Humans Machine Learning Middle Aged Polymorphism, Single Nucleotide Tamoxifen

External Resources

View on PubMed Access via DOI PubMed (39749373)

Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

Abstract

Authors

Keywords

External Resources

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