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药物代谢组学在泌尿系统癌症个性化医疗中的应用

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers.

作者信息

Amaro Filipa, Carvalho Márcia, Bastos Maria de Lourdes, Guedes de Pinho Paula, Pinto Joana

机构信息

Associate Laboratory i4HB-Institute for Health and Bioeconomy, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.

UCIBIO/REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.

出版信息

Pharmaceuticals (Basel). 2022 Feb 28;15(3):295. doi: 10.3390/ph15030295.

Abstract

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

摘要

前列腺癌(PCa)、膀胱癌(BCa)和肾细胞癌(RCC)是最常见的泌尿系统癌症,并且其发病率一直呈上升趋势。手术是这些癌症的标准治疗方法,但该手术仅在疾病局限时有效。对于转移性疾病,PCa通常采用雄激素剥夺疗法治疗,而BCa采用化疗治疗,RCC主要采用靶向治疗。然而,由于耐药性的产生和治疗相关毒性,这些治疗选择的反应率仍然不尽人意。因此,需要发现具有预后和预测价值的生物标志物,以便将患者分层到不同风险组,最大限度地减少过度治疗和耐药性发展的风险。药物代谢组学是代谢组学的一个分支,是一种基于个体自身代谢特征预测其药物反应的有吸引力的工具,该代谢特征可在药物暴露前、期间和之后收集。因此,本综述重点关注药物代谢组学方法在识别最常见泌尿系统癌症对激素治疗、靶向治疗、免疫治疗和化疗的代谢反应中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03ef/8952371/364742041315/pharmaceuticals-15-00295-g001.jpg

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