第11批集采结果公布：从降价导向转向质量导向
2 1 Shi Ji Jing Ji Bao Dao·2025-11-12 04:10

Core Insights - The 11th batch of national organized drug centralized procurement results has been officially announced, set to be implemented in February 2026, involving 55 drugs across various therapeutic areas [1][2] - The procurement aims to stabilize clinical needs, ensure quality, prevent excessive competition, and avoid collusion, marking a shift from a price-oriented approach to a quality-oriented one [1][6] Summary by Sections Procurement Results - A total of 55 drugs were included in the 11th batch, with 4.6 million medical institutions participating and 272 companies winning bids for 453 products [1][2] - The average selection rate increased from 49% in the 10th batch to 57% in the 11th batch, attributed to the introduction of a revival mechanism [2][3] Pricing and Competition - The average price difference for selected drugs narrowed to 1.7 times, with 13 products triggering a price correction mechanism to maintain orderly pricing [2][3] - The competitive landscape saw an increase in the number of participating companies, with an average of 14 companies per product, doubling from previous batches [2][3] Quality Control and Regulatory Changes - New requirements for bidders include production experience and compliance with Good Manufacturing Practices (GMP), enhancing quality control [2][7] - The procurement process emphasizes transparency and fairness, aiming to reduce hidden costs and improve market dynamics [5][7] Industry Impact and Future Trends - The centralized procurement system is evolving towards a more transparent and quality-focused model, which is expected to drive innovation and compliance among pharmaceutical companies [6][8] - The market is witnessing a shift as companies adapt to new competitive dynamics, with some focusing on generic drugs while others invest in innovative drug development [7][8] Overall Development Direction - The drug procurement system has matured, maintaining stable core principles while continuously optimizing implementation measures based on feedback [9]