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3
Rare diseases 2030: how augmented AI will support diagnosis and treatment of rare diseases in the future.2030年的罕见病:增强人工智能将如何在未来支持罕见病的诊断和治疗
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4
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数据孤岛正在破坏药物研发,使罕见病患者的处境雪上加霜。

Data silos are undermining drug development and failing rare disease patients.

机构信息

Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Department of Medicine, Orphan Disease Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

出版信息

Orphanet J Rare Dis. 2021 Apr 7;16(1):161. doi: 10.1186/s13023-021-01806-4.

DOI:10.1186/s13023-021-01806-4
PMID:33827602
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8025897/
Abstract

Data silos are proliferating while research and development activity explode following genetic and immunological advances for many clinically described disorders with previously unknown etiologies. The latter event has inspired optimism in the patient, clinical, and research communities that disease-specific treatments are on the way. However, we fear the tendency of various stakeholders to balkanize databases in proprietary formats, driven by current economic and academic incentives, will inevitably fragment the expanding knowledge base and undermine current and future research efforts to develop much-needed treatments. The proliferation of proprietary databases, compounded by a paucity of meaningful outcome measures and/or good natural history data, slows our ability to generate scalable solutions to benefit chronically underserved patient populations in ways that would translate to more common diseases. The current research and development landscape sets too many projects up for unnecessary failure, particularly in the rare disease sphere, and does a grave disservice to highly vulnerable patients. This system also encourages the collection of redundant data in uncoordinated parallel studies and registries to ultimately delay or deny potential treatments for ostensibly tractable diseases; it also promotes the waste of precious time, energy, and resources. Groups at the National Institutes of Health and Food and Drug Administration have started programs to address these issues. However, we and many others feel there should be significantly more discussion of how to coordinate and scale registry efforts. Such discourse aims to reduce needless complexity and duplication of efforts, as well as promote a pre-competitive knowledge ecosystem for rare disease drug development that cultivates and accelerates innovation.

摘要

随着遗传和免疫学的进步,许多以前病因不明的临床描述疾病的研究和开发活动呈爆炸式增长,数据孤岛也在不断增加。这一事件激发了患者、临床医生和研究界的乐观情绪,他们相信针对特定疾病的治疗方法即将问世。然而,我们担心各种利益相关者出于当前经济和学术激励的驱动,将数据库以专有格式分割的趋势不可避免地会使不断扩大的知识库碎片化,并破坏当前和未来开发急需治疗方法的研究工作。专有数据库的大量增加,加上缺乏有意义的结果衡量标准和/或良好的自然病史数据,减缓了我们生成可扩展解决方案的能力,这些解决方案本可以使慢性服务不足的患者群体受益,但目前还无法应用于更常见的疾病。当前的研发格局使太多的项目面临不必要的失败,尤其是在罕见病领域,这对高度脆弱的患者造成了严重的伤害。这种系统还鼓励在不协调的平行研究和注册中收集冗余数据,最终延迟或否认对表面上可治疗的疾病的潜在治疗方法;它还浪费了宝贵的时间、精力和资源。美国国立卫生研究院和食品和药物管理局的一些团体已经开始着手解决这些问题。然而,我们和许多其他人认为,应该就如何协调和扩大注册工作进行更多的讨论。这种讨论旨在减少不必要的复杂性和重复工作,同时为罕见病药物开发培养和加速创新的竞争前知识生态系统。