肢端肥大症的个性化医疗:来自ACROFAST临床试验的见解。

IF 2.8 Q3 ENDOCRINOLOGY & METABOLISM
Marta Araujo-Castro, Betina Biagetti, Víctor Navas-Moreno, María Bernarda-Iriarte, Rebeca Martínez-Hernández, Elena Valassi, Manel Puig-Domingo, Mónica Marazuela
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引用次数: 0

摘要

个性化医疗在肢端肥大症的治疗中变得越来越重要,这是由于肿瘤分类、分子谱分析和成像方式的进步。与使用第一代生长抑素受体配体(fgsrl)作为一线药物的常规试错方法相比,这种方法可以在更短的时间内更频繁地实现生化控制,目前大多数临床指南仍推荐使用第一代生长抑素受体配体作为一线药物。涵盖领域:在这篇综述中,我们总结了肢端肥大症个体化治疗的最新进展,特别关注手术和药物治疗反应的预测标志物。此外,我们强调了从ACROFAST临床试验中获得的观点,这为个体化治疗策略的临床实施提供了有价值的见解。专家意见:我们认为肢端肥大症研究的未来在于个性化医疗方法的不断进步和完善。ACROFAST通过展示预测性生物标志物的组合如何提高治疗效果,提高生化缓解率和缩短随访时间,奠定了关键的基础。此外,在多组学和临床数据集上训练的人工智能和机器学习算法的应用可能有助于改善结果,减少当前肢端肥大症药物管理的反复试验性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized medicine in acromegaly: insights from the ACROFAST clinical trial.

Introduction: Personalized medicine has gained importance in the management of acromegaly, driven by advances in tumor classification, molecular profiling, and imaging modalities. This approach leads to achieved biochemical control more frequently and in a shorter time frame when compared to the usual trial-and-error approach using first-generation somatostatin receptor ligands (fgSRLs) as first-line drug, which is still currently recommended in most clinical guidelines.

Areas covered: In this review, we summarize recent advances in personalized medicine for acromegaly, with a particular focus on predictive markers of surgical and medical treatment response. Moreover, we highlight the perspectives gained from the ACROFAST clinical trial, which has contributed with valuable insights into the clinical implementation of individualized therapeutic strategies.

Expert opinion: We consider that the future of research in acromegaly lies in the continued advancement and refinement of personalized medicine approaches. ACROFAST has laid critical groundwork by demonstrating how the combination of predictive biomarkers enhances therapeutic outcomes, improving the rate of biochemical remission and in a shorter follow-up period. In addition, the application of artificial intelligence and machine learning algorithms trained on multiomic and clinical datasets may be useful to improve outcomes and reduce the trial-and-error nature of current pharmacologic management of acromegaly.

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来源期刊
Expert Review of Endocrinology & Metabolism
Expert Review of Endocrinology & Metabolism ENDOCRINOLOGY & METABOLISM-
CiteScore
4.80
自引率
0.00%
发文量
44
期刊介绍: Implicated in a plethora of regulatory dysfunctions involving growth and development, metabolism, electrolyte balances and reproduction, endocrine disruption is one of the highest priority research topics in the world. As a result, we are now in a position to better detect, characterize and overcome the damage mediated by adverse interaction with the endocrine system. Expert Review of Endocrinology and Metabolism (ISSN 1744-6651), provides extensive coverage of state-of-the-art research and clinical advancements in the field of endocrine control and metabolism, with a focus on screening, prevention, diagnostics, existing and novel therapeutics, as well as related molecular genetics, pathophysiology and epidemiology.
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