Transforming IBD care: the future of personalized therapy through multi-omics and pharmacogenomics.

Niloofar Khoshnam Rad, Ghazal Roostaei, Shekoufeh Nikfar, Mohammad Abdollahi
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Abstract

Introduction: Inflammatory bowel disease (IBD) treatment remains challenging, with many patients experiencing suboptimal responses despite advances in therapies. This necessitates more precise and personalized approaches.

Areas covered: A literature search was conducted using Embase, Scopus, and PubMed (January 2000-March 2024) with keywords such as 'inflammatory bowel disease,' 'pharmacogenomics,' 'multi-omics,' and 'multi-omics.' This review discusses (1) key pharmacogenomic markers influencing drug metabolism, efficacy, and toxicity; (2) the role of multi-omics (genomics, proteomics, metabolomics) in elucidating IBD pathogenesis and predicting therapeutic outcomes; and (3) emerging technologies such as AI-driven predictive models and organoids. Challenges in translating these tools into clinical practice - including cost, data standardization, and workflow integration - are critically examined.

Expert opinion: Integrating pharmacogenomics with multi-omics holds transformative potential for IBD care. While TPMT genotyping exemplifies current clinical utility, future frameworks will require harmonized multi-omic data to guide therapy selection. Key barriers include high costs of omics profiling, interpretative complexity, and clinician training gaps. Collaborative efforts among researchers, clinicians, and policymakers are essential to validate biomarkers, standardize methodologies, and implement cost-effective assays. Prioritizing real-world studies and AI-powered decision-support tools will accelerate the shift from trial-and-error to personalized IBD management.

改变IBD治疗:通过多组学和药物基因组学实现个性化治疗的未来。
炎症性肠病(IBD)的治疗仍然具有挑战性,尽管治疗取得了进展,但许多患者的反应并不理想。这就需要更精确和个性化的方法。涉及领域:使用Embase、Scopus和PubMed进行文献检索(2000年1月- 2024年3月),关键词为“炎症性肠病”、“药物基因组学”、“多组学”和“多组学”。本文综述了(1)影响药物代谢、疗效和毒性的关键药物基因组学标志物;(2)多组学(基因组学、蛋白质组学、代谢组学)在阐明IBD发病机制和预测治疗结果中的作用;(3)人工智能驱动的预测模型和类器官等新兴技术。将这些工具转化为临床实践的挑战-包括成本,数据标准化和工作流程集成-被严格审查。专家意见:整合药物基因组学与多组学对IBD治疗具有变革性潜力。虽然TPMT基因分型是当前临床应用的例证,但未来的框架将需要统一的多组学数据来指导治疗选择。主要障碍包括组学分析的高成本、解释的复杂性和临床医生培训的差距。研究人员、临床医生和政策制定者之间的合作对于验证生物标志物、标准化方法和实施具有成本效益的分析至关重要。优先考虑现实世界的研究和人工智能驱动的决策支持工具,将加速从试错法到个性化IBD管理的转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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