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通过人工智能和现实世界的证据增强免疫性工作流:故障点,工具和闭环操作蓝图

Ahmad Z Al Meslamani¹, Anan S Jarab², Eman Merghani Ali Mohammed³

¹College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates.

|January 13, 2026

概括

人工智能 (AI) 和现实世界的证据 (RWE) 可以克服生物中的免疫性挑战. 这些技术提供了切实可行的解决方案,以提高新兴疗法的安全性,有效性和市场后监测.

科学领域:

生物制药的发展.
免疫学免疫学免疫学
计算生物学是一种计算生物学.

背景情况:

免疫性对生物药物的安全性和有效性构成重大风险.
目前的免疫性评估方法面临的局限性包括测试干扰,偏差和风险转化方面的困难.
新兴的治疗方式需要先进的评估策略.

研究的目的:

审查免疫性评估工作流程中常见的故障点.
通过人工智能 (AI) 和现实世界的证据 (RWE) 识别和链接实际解决方案.
在免疫性评估中概述人工智能增强RWE的框架.

主要方法:

从2022-2025年对文献进行叙述性审查,包括原始研究,评论和监管数据.
在免疫原性测试中分析常见的故障位置.
人工智能和RWE应用程序的综合,以解决这些故障.

主要成果:

人工智能和RWE为药物/标干扰,矩阵/稀释偏差和脆弱的基于细胞的测试提供解决方案.
发展包括T细胞和B细胞表位预测,新抗原优先级和NLP用于药物监测.
在免疫性评估中提出了人工智能增强RWE的闭环蓝图.

结论:

人工智能增强的RWE可以通过专注于临床显著效应来提高免疫性评估.
这种方法优化了实验室资源的利用,并加快了信号验证.
它改善了市场后的警,并为生物制剂提供了持续学习系统.

关键词:

在这里,我们可以看到AIAIAI.抗药抗体 (ADA) 是一种抗药性抗体.抗体人性化的人性化人工智能的人工智能是人工智能.标本预测预测的预测

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