Emerging analytical techniques for tamoxifen and metabolite quantification in diverse matrices.

Authors

Hemn A H Barzani, Rebaz Anwar Omer, Nergz Bayiz Abdulrahman, Hazha Omar Othman, Ali Sedeeq Noaman, Seerwan Hamadameen Sulaiman

Published in

Analytical methods : advancing methods and applications. Aug 18, 2026. Epub Aug 18, 2026.

Abstract

Tamoxifen (TMX), a key selective estrogen receptor (ER) modulator used in hormone-dependent breast cancer therapy, undergoes extensive metabolism to produce active metabolites, including endoxifen (END), 4-hydroxytamoxifen (4HT), and N-desmethyltamoxifen (NDT). The simultaneous determination of these compounds across pharmaceutical, biological, and environmental matrices remains analytically challenging due to their structural similarity, wide concentration range, strong protein binding, and significant matrix effects. This review critically evaluates emerging analytical techniques for TMX and its metabolites, focusing on chromatographic, electrochemical, and spectroscopic methods, and comparing their performance in terms of sensitivity, selectivity, reproducibility, cost, and matrix applicability. Among these, LC-MS/MS and UPLC-MS/MS methods demonstrate superior sensitivity (LOD as low as 0.03 ng mL^-1) and high selectivity, making them well-suited for complex biological matrices and pharmacokinetic studies. In contrast, HPLC-UV and fluorescence methods are more applicable for pharmaceutical formulations due to their simplicity and cost-effectiveness, despite lower sensitivity. Electrochemical techniques offer rapid, low-cost analysis with enhanced sensitivity when using modified electrodes, although their selectivity and stability remain limited. Spectroscopic methods provide economical alternatives but are mainly restricted to high-concentration samples due to interference. Overall, method selection is highly matrix-dependent, and future developments should focus on improving sensitivity and selectivity, integrating green analytical chemistry, and advancing nanomaterial-based and AI-assisted analytical approaches.

PMID:
42610522
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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