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精准医学与哮喘生物制剂的选择:了解反应预测因子和临床缓解的当前知识状况

Precision medicine and choosing a biologic in asthma: understanding the current state of knowledge for predictors of response and clinical remission.

作者信息

Balasubramanyam Sadhana, George Elizabeth K, Wang Eileen

机构信息

Houston Methodist Hospital, Houston, Texas.

University of Alabama at Birmingham, Birmingham, Alabama.

出版信息

Curr Opin Allergy Clin Immunol. 2025 Feb 1;25(1):66-74. doi: 10.1097/ACI.0000000000001044. Epub 2024 Nov 29.

Abstract

PURPOSE OF REVIEW

We review updated key literature on comparative meta-analyses and real-world effectiveness of asthma biologics, with a focus on predictors of response and clinical remission while highlighting ongoing knowledge gaps. We aim to provide insight into the many factors to consider when choosing a biologic to treat uncontrolled moderate to severe asthma.

RECENT FINDINGS

Predictors of response included higher type 2 (T2) biomarkers, shorter duration of asthma, and presence of key T2-related comorbidities. There were outcome-related variations in predictors. Predictors of clinical remission included better controlled asthma, better lung function, and higher T2 biomarkers. Few real-world studies included those treated with tezepelumab, a clear knowledge gap.

SUMMARY

Asthma biologics demonstrate clear real-world effectiveness. There have been significant strides in better understanding predictors of response or clinical remission to guide management, yet ongoing knowledge gaps and the heterogeneity of asthma preclude a simple algorithmic approach. Our tools for precision medicine include consideration of clinical phenotypes and shared decision making while striving to achieve clinical remission in all our patients with asthma.

摘要

综述目的

我们回顾了关于哮喘生物制剂的比较荟萃分析和真实世界有效性的最新关键文献,重点关注反应预测因素和临床缓解情况,同时强调了当前存在的知识空白。我们旨在深入探讨在选择生物制剂治疗未控制的中度至重度哮喘时需要考虑的诸多因素。

最新发现

反应预测因素包括较高的2型(T2)生物标志物、哮喘病程较短以及存在关键的T2相关合并症。预测因素存在与结局相关的差异。临床缓解的预测因素包括哮喘控制较好、肺功能较好以及T2生物标志物较高。很少有真实世界研究纳入接受tezepelumab治疗的患者,这是一个明显的知识空白。

总结

哮喘生物制剂在真实世界中显示出明确的有效性。在更好地理解反应或临床缓解的预测因素以指导管理方面已经取得了重大进展,但目前存在的知识空白以及哮喘的异质性使得无法采用简单的算法方法。我们的精准医学工具包括考虑临床表型和共同决策,同时努力使所有哮喘患者实现临床缓解。

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