和元生物：上半年营业收入同比增长6% 多措并举筑牢发展根基

Core Insights - The company reported a revenue of 120 million yuan for the first half of 2025, representing a year-on-year growth of 6%, while the net profit attributable to shareholders was a loss of 105 million yuan, reducing the loss by 8.78 million yuan compared to the same period last year [1] Group 1: Business Performance - The cell and gene therapy CRO business generated revenue of 40.70 million yuan, an increase of 3.79% year-on-year, with over 14,000 research laboratory clients served, indicating a continuous improvement in market coverage [1] - The CDMO business revenue was 65.62 million yuan, remaining stable compared to the previous year, with 52 IND approvals obtained for clients and over 540 CDMO projects undertaken, including 5 Phase III clinical projects [1] - The company secured over 90 million yuan in new CDMO orders during the year [1] Group 2: Regenerative Medicine and Capacity Expansion - The regenerative medicine segment made significant progress, including the development of processes for stem cells and immune cells, and collaboration with leading companies and research institutions [2] - The company’s subsidiary successfully registered human dermal fibroblast exosomes in the global cosmetic ingredient directory, expanding opportunities in anti-aging and health management [2] - The first phase of the Lingang industrial base has been fully operational, with production capacity and line numbers ranking among the top in the international industry, enhancing delivery efficiency and customer retention [2] Group 3: Technological Innovation - The company is focusing on technological innovation, with R&D investment of 23.21 million yuan, accounting for 19.37% of revenue, and filing for 2 invention patents and 8 utility model patents [2] - The company has developed an intelligent R&D system leveraging AI technology, including a virus yield prediction model based on BERT, which optimizes production efficiency [3] - AI models have been created for adenoviral vector optimization, utilizing deep learning frameworks to enhance research and development efficiency [3]