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公私合作伙伴关系:药物发现和开发中的化合物和数据共享。

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development.

机构信息

Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.

出版信息

SLAS Discov. 2021 Jun;26(5):604-619. doi: 10.1177/2472555220982268. Epub 2021 Feb 13.

Abstract

Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.

摘要

公共和私营实体(如学术机构、政府和制药公司)之间的合作是科学研究的一个组成部分,生命科学界已经形成了许多此类倡议的典范。存在着几个具有广泛目标的联盟实例,即合作推动科学进步和改善公共福利。这些合作对于促进高风险或全球公共卫生战略中的突破性科学领域可能是至关重要的,否则这些领域可能不会取得进展。一个用来描述这些联盟的常见术语是公私合作伙伴关系 (PPP)。这篇综述讨论了药物发现/开发中这些伙伴关系的不同方面,并提供了应用实例和成功案例研究。涵盖的具体领域包括在药物发现过程的各个阶段(从化合物库的命中鉴定到临床候选药物的共享)共享化合物的 PPP;还讨论了支持更好的数据整合和构建更好的机器学习模型的 PPP 实例。该综述还提供了 PPP 的实例,这些 PPP 解决了参与方之间知识或资源的差距,并推进了药物发现,特别是在具有未满足和/或社会需求的疾病领域,如神经紊乱、癌症和被忽视的罕见疾病。

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