Trends in global clinical trial registration for MSC-based therapeutic products

S. Kusakawa, R. Sawada,S. Yasuda, T. Kuroda,Y. Sato

CYTOTHERAPY(2020)

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摘要
Background \u0026 Aim Mesenchymal stromal cells (MSCs) are a type of somatic cells that exist in the various tissues including bone marrow, adipose, and umbilical cord of the human body, and have been widely studied as cells involved in the maintenance of homeostasis and repair of living tissues. Their use as cell sources for regenerative medicine is attracting attention. Several MSC-based therapeutic products have already been developed and are available in the market. However, despite the growing field of regenerative medicine, few cell-based products have grown as blockbusters worldwide yet. To examine trends in global clinical trial registration for MSC-based therapeutic products, we analyzed the number and type of clinical trials that were registered in different parts of the world based on the registration dataset in ClinicalTrial.gov. Methods, Results \u0026 Conclusion We extracted more than 1000 intervention studies using MSCs or MSC-like cells derived from bone marrow, adipose, fetal tissues, and others from the database and categorized all of them. We have successfully used a public database to collect useful data to understand trends in MSC-based clinical studies. Trial registrations could be tracked since 2005, increasing yearly and peaked in 2016. In the distribution analysis of tissue origin, MSCs derived from bone marrow were the most common, followed by those derived from fat, and those derived from fetal tissues such as placenta and cord blood. In clinical trials, the usage of MSC in orthopedic surgery was the most popular. Since only 2% of the submitted study results in the extracted studies were posted, it was hard to predict the growth of the research field. Surveillance of massive data in ClinicalTrial.gov will provide further insights into scientific or clinical decision-making in the development of MSC-based products. We will further analyze the extracted studies to identify relevance between registered data items and will discuss it.
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