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  • Еволюція моделей клінічного аутсорсингу на світовому фармацевтичному ринку
  • https://doi.org/10.32983/2222-4459-2026-2-40-51Copy DOI Icon

Еволюція моделей клінічного аутсорсингу на світовому фармацевтичному ринку

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Abstract

Today, pharmaceutical companies aim to speed up the time it takes for drugs to reach the market at minimal cost, increasingly collaborating with contract research organizations (CROs) in all areas, from study design and patient recruitment to regulatory submissions and data management. The aim of this article is to summarize and systematize the stages of development of clinical outsourcing models based on a study of the characteristics of the global CRO market. The article explains the essence of CROs and clinical research sites (CRSs). The factors driving the growth of the global CRO market have been identified: increased research and development funding, a rise in the number of clinical trials, and the expiration of patents for well-known drugs. The phases of clinical trials are analyzed in terms of content, number of participants, duration, cost, and success rate. The risks of clinical trials have been identified, including: significant cost, high dropout rate, considerable duration, and complexity of regulatory acts. Regional features of conducting clinical trials have been considered. The top 10 leaders in the global CRO market in 2024 have been characterized. Models of cooperation between CROs and sponsors have been summarized. The advantages, disadvantages, and examples of applying existing clinical outsourcing models have been determined. The stages of development of clinical outsourcing models have been systematized: from targeted outsourcing to strategic partnership and transformational outsourcing. Current directions for improving CRO activities have been determined. The goal of further research in this direction should be to explore ways to expand cooperation between CROs and leading IT companies regarding the implementation of digital health technologies in working with clinical trial participants and artificial intelligence for managing data during its course.

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