Advanced High-Content Phenotypic Screening to Identify Drugs That Ameliorate the Inhibition of Skeletal Muscle Cell Differentiation Induced by Cancer Cachexia Serum.

先进的高内涵表型筛选,用于鉴定能够改善癌症恶病质血清诱导的骨骼肌细胞分化抑制的药物

阅读:5
作者:Nakane Atsushi, Nakagawa Hiroyuki, Nagata Hidetaka
Background/Objectives: Cancer cachexia (CC) is a prevalent and debilitating syndrome in cancer patients, characterized by severe muscle and weight loss, leading to increased mortality and reduced quality of life. Despite the significant impact, effective treatments are lacking due to an incomplete understanding of its underlying mechanisms. In this study, we aim to develop drugs that ameliorate the inhibition of muscle differentiation induced by CC. We established an advanced, high-content phenotypic screening system using the serum of cancer patients and identified potential compounds. Methods: We used cancer patients' sera as pathophysiological stimuli in our screening system to evaluate their effects on muscle atrophy and differentiation. Various histone deacetylase (HDAC) inhibitors were tested for their efficacy. The system's translational relevance was validated by comparing results with clinical data and in vivo cachexia models. Results: Using our screening system, we evaluated several cancer patients' sera and found that they reflect clinical features of cancer cachexia. In addition, HDAC inhibitors, particularly those with broad-spectrum inhibition, showed promise as agents to ameliorate the inhibition of muscle differentiation induced by CC sera. This system's findings were consistent with clinical and in vivo data, highlighting its potential for identifying new drugs. Conclusions: The high-content phenotypic screening system effectively mimics some key aspects of CC pathophysiology on skeletal muscle, providing a valuable tool for drug discovery and understanding CC mechanisms. The translational relevance of our system offers a promising avenue for therapeutic advancements in the management of cancer cachexia, with the potential to improve patient outcomes and quality of life.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。