研究中的目标识别和验证

Keerthana N, Koteeswaran K
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引用次数: 0

摘要

目标识别是生物医学研究的关键一步,因为它为开发新的疗法和药物奠定了基础。基因研究,包括全基因组关联研究(GWAS)、基因组测序、功能基因组学和数据整合,对于了解疾病遗传学和潜在治疗靶点至关重要。转录组学和蛋白质组学提供了基因和蛋白质表达的数据,从而更容易确定调节失调疾病的靶点。靶点识别对于药物发现、精准医疗、降低药物损耗、提高疗效以及最终改变患者护理和药物开发至关重要。靶点验证是药物开发的一个关键阶段,因为它可以验证所揭示的分子靶点是否在疾病进展中发挥重要作用,因而是否适合治疗。它采用了一系列方法,包括基因验证、药理学验证和动物模型验证。靶点验证可确保所发现的靶点与生理相关、可用于药物治疗,并对疾病进程产生直接影响,从而减少药物损耗、促进精准医疗并加快疗法开发。一直以来,靶点识别依赖于有限的知识,通常是通过基于假设或先前观察的候选技术。靶点验证实验研究基因敲除或 RNA 干扰如何影响疾病症状。近年来,基因组学、蛋白质组学和功能基因组学都取得了进展,高通量筛选和数据整合也是如此。基于 CRISPR 的技术和高通量测序有助于验证靶点。单细胞验证、机器学习和人工智能、先进的体外模型(如有机体)以及患者衍生模型都将有助于使未来的靶点相关性评估和治疗反应更加精确和个体化。这些发展有可能为研究目标的确定和验证带来巨大的变革。
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Target identification and validation in research
Target identification is a critical step in biomedical research because it lays the groundwork for the development of new therapies and drugs. Genetic research, including genome-wide association studies (GWAS), genomic sequencing, functional genomics, and data integration, is crucial for understanding disease genetics and potential treatment targets. Transcriptomics and proteomics give data on gene and protein expression, making it easier to identify targets in dysregulated diseases. Target identification is essential for drug discovery, precision medicine, lowering medication attrition, increasing therapeutic efficacy, and, eventually, transforming patient care and drug development. Target validation is a critical stage in drug development because it verifies that revealed molecular targets play a substantial role in disease progression and are therefore suitable for treatment. It employs a range of approaches, including genetic validation, pharmacological validation, and animal model validation. Target validation assures that discovered targets are physiologically relevant, druggable, and have a direct impact on disease processes, thereby reducing pharmaceutical attrition, promoting precision medicine, and hastening therapeutic development. Historically, target identification relied on limited knowledge, typically through candidate-based techniques based on assumptions or prior observations. Target validation experiments looked into how gene knockdown or RNA interference affected illness symptoms. Genomics, proteomics, and functional genomics have all made advances in recent years, as have high-throughput screening and data integration. CRISPR-based technologies and high-throughput sequencing have assisted in the validation of targets. Single-cell validation, machine learning and artificial intelligence, advanced in vitro models like organoids, and patient-derived models will all help to make future assessments of target relevance and treatment responses more precise and individualized. These developments have the potential to dramatically revolutionize research target identification and validation.
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