Thirty years from FDA approval of pegylated liposomal doxorubicin (Doxil/Caelyx): an updated analysis and future perspective.

BMJ oncology Pub Date : 2025-01-09 eCollection Date: 2025-01-01 DOI:10.1136/bmjonc-2024-000573
Alberto A Gabizon, Shira Gabizon-Peretz, Shadan Modaresahmadi, Ninh M La-Beck
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Abstract

In 2025, it will be 30 years since the initial clinical approval of pegylated liposomal doxorubicin (PLD) by the Food and Drug Administration. PLD predated the field of nanomedicine and became a model nanomedicine setting key pharmacological principles (prolonged circulation, slow drug release and the enhanced permeability and retention (EPR) effect) for clinical application of other nano-drugs in cancer therapy. The impressive reduction of cardiotoxicity conferred by PLD is the most valuable clinical asset. While PLD has gained a strong foothold in relapsed ovarian cancer and metastatic breast cancer, it has not been extensively tested in primary (neoadjuvant) and adjuvant therapy and has not fulfilled the expectations from the results in animal models efficacy-wise. This discrepancy may be due to the large dose gap between mice and humans and the apparent variability of the EPR effect in human cancer. PLD is a complex product and we are still in a learning curve regarding a number of factors such as its interaction with the complement system and its immune modulatory properties, as well as its integration in multimodality therapy that may potentiate its value and role in cancer therapy.

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Molecular and immune landscape of tumours in geriatric patients with non-small cell lung cancer, melanoma and renal cell carcinoma. Thirty years from FDA approval of pegylated liposomal doxorubicin (Doxil/Caelyx): an updated analysis and future perspective. Cancer incidence and competing mortality risk following 15 presenting symptoms in primary care: a population-based cohort study using electronic healthcare records. Which patients with symptoms should be referred urgently for investigation of suspected cancer? From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.
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