Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA.
BMC Biol. 2020 Oct 13;18(1):138. doi: 10.1186/s12915-020-00868-3.
Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited.
We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development.
We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.
越来越多的证据表明，科学合作在生命科学的变革性创新中起着至关重要的作用。例如，当代药物发现和开发反映了来自学术中心、制药行业、监管科学界、医疗保健提供者和患者的个人团队的工作。然而，公众对于学术界和产业界之间的合作如何促进新靶点的识别和首创药物发现的理解有限。
我们对大量合作和引文的科学语料库（来自 1.7 亿个科学记录的 97688 篇论文和 1862500 次引用）进行了全面的网络分析，以量化创新药物开发的成功轨迹。通过关注四类心血管药物，我们展示了知识如何在机构之间流动，突出了许多不同机构在新药开发中的潜在贡献。我们强调了这种网络分析如何有助于增加工业和政府的支持，并提高药物发现和开发的效率或加速决策。
我们证明，对大型公共数据库的网络分析可以识别和量化药物发现和开发中的研究人员和机构关系。如果广泛应用，这种类型的网络分析可能有助于增强公众对生物医学研究的理解和支持，并可以确定在学术界、制药行业和医疗保健系统中促进首创药物发现决策的因素。
