对话市政协委员刘伟：从“研”到“用”，北京创新药“全链条”发力

Core Viewpoint - Beijing's "New 32 Measures" significantly supports the high-quality development of innovative pharmaceuticals by streamlining clinical trial approvals and enhancing market access, insurance payments, financing support, and manufacturing upgrades [1][2]. Group 1: Policy Measures - The "New 32 Measures" cover critical areas such as clinical trials, approval processes, production and distribution, clinical usage, AI empowerment, and financing, addressing industry pain points with a combination of international best practices and local needs [1][2]. - Key measures include encouraging local production of innovative drugs, promoting digital transformation in pharmaceutical manufacturing, and establishing a 500 billion yuan industry fund and a 100 billion yuan merger fund to alleviate funding challenges [2]. - The approval efficiency has improved, with clinical trial approval times reduced from 60 working days to 30, and supplementary application review times cut from 200 working days to 60 [2]. Group 2: Advantages of Beijing - Beijing's unique advantages in the pharmaceutical sector include its status as the capital and an international innovation center, housing regulatory bodies that facilitate policy trends [3]. - The city boasts over 90 universities, the highest number of national-level pharmaceutical research institutes, and nearly 30% of the country's biomedical key laboratories, providing robust support for research and development [3]. - A concentration of high-end pharmaceutical talent and professional service institutions enhances the ecosystem for innovative drug development and financing, with a leading number of patents and new drugs [3]. Group 3: AI Integration - The integration of AI and big data in the pharmaceutical sector is accelerating medical research and drug development, transitioning from exploratory phases to practical applications that enhance decision-making and efficiency [4]. - In pathology, AI is improving digital image analysis and standardization, addressing resource shortages and subjective discrepancies among pathologists [4]. - In drug development, AI is expected to reduce costs and timeframes significantly, moving from a trial-and-error approach to a more predictable model driven by data and algorithms [4]. Group 4: Recommendations for Policy Optimization - Recommendations include focusing on all stages of innovative drug development, enhancing collaboration among hospitals, universities, and enterprises, and improving the technology transfer system [6]. - Strengthening support for resource allocation and policy backing is crucial, leveraging investment funds to attract social capital for innovative drug research [6]. - Suggestions also include streamlining access to innovative drugs, enhancing hospital procurement support, and exploring the integration of AI software into equipment upgrade policies [6][7].