Construction and Practical Verification of a "Risk-Collaboration-Digitalization" Trinity Project Management Model for Cardiovascular and Cerebrovascular Innovative Drug R&D
Abstract
Cardiovascular and cerebrovascular diseases (CCVDs) have become a major global public health challenge. The research and development (R&D) of innovative drugs for CCVDs faces dilemmas including long R&D cycles (12-15 years on average), high costs (USD 2.5 billion per drug on average), and high clinical failure rates (over 40% for Phase III clinical trials) due to characteristics such as target complexity, strict requirements for hard clinical endpoints, and strong demand for multidisciplinary collaboration. Traditional project management models, which focus on "schedule-cost" dual control, are insufficient to adapt to the scientific uncertainties and cross-domain collaboration needs in CCVD innovative drug R&D. Based on the full R&D process of CCVD innovative drugs (drug discovery → preclinical research → Phase I/II/III clinical trials → New Drug Application, NDA), this study proposes a "Risk-Collaboration-Digitalization" trinity project management model through literature analysis, risk map construction, cross-functional team (CFT) mechanism design, and digital tool integration. Taking a new PCSK9 inhibitor (a lipid-lowering innovative drug for CCVDs) R&D project of a pharmaceutical enterprise as a verification case, key indicators before and after the model application were compared: the R&D cycle was shortened by 15.2%, the success rate of Phase III clinical trials increased by 21.3%, the one-time approval rate of NDA rose from 72% to 89%, and R&D costs decreased by 12.5%. This study fills the theoretical gap in specialized project management for CCVD innovative drugs and provides an operable practical framework for pharmaceutical enterprises to improve the efficiency and success rate of innovative drug R&D.
References
[2] Project Management Institute (PMI). (2022). Pharmaceutical Industry Project Management Guide (2022 Edition). Publishing House of Electronics Industry.
[3] Zhang, X., Li, M., & Wang, H. (2023). Construction of a "Clinical-R&D" Cross-Functional Collaboration Model for Oncology Innovative Drug R&D. Chinese Journal of New Drugs, 32(15), 1501-1507.
[4] Center for Drug Evaluation (CDE), NMPA. (2024). Guidelines for Clinical Trial Design of Cardiovascular and Cerebrovascular Innovative Drugs (No. 12, 2024).
[5] Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Landmesser, O., & Susekov, A. V. (2022). 2022 ESC/EAS Guidelines for the management of dyslipidaemias. European Heart Journal, 43(34), 3207-3291.
[6] Li, N., Liu, C., & Zhang, W. (2022). Application Status and Prospects of AI in Clinical Trial Data Management of Innovative Drugs. China Pharmacy, 33(20), 2449-2454.
[7] World Health Organization (WHO). (2024). Global Health Estimates 2024: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2024. WHO.
[8] Clinical Data Interchange Standards Consortium (CDISC). CDISC Standards for Clinical Data Exchange. Retrieved 2024, March 15 from https://www.cdisc.org/standards

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