Doxazosin Mesylate

精彩内容 第十一批国采将于10月21日开标，6个心脑血管系统药物在列，11亿大品种领跑。米内网数据显示，近年来中国公立医疗机构终端心脑血管系统化药市场承压，2023年至今已“三连跌”。在2025年上半年TOP20集团中，信立泰、扬子江、新和成首次登榜；销售额TOP20品牌中原研药霸榜，“一哥”易主，独家品种大涨128.6%；销售量TOP20品牌中国采中标品种霸榜，京新药业、南京正大天晴等有多个品牌上榜。 市场3连跌！信立泰、扬子江首次登榜 近年来，随着国家集采及省级集采的持续推进，心脑血管系统化药在医疗机构的市场格局发生着巨大变化。 米内网数据显示，2024年中国城市公立医院、县级公立医院、城市社区中心以及乡镇卫生院（简称中国公立医疗机构）终端心脑血管系统化药销售规模跌破千亿后，2025年上半年以约8%的幅度继续下跌，2023年至今已“三连跌”，且下跌幅度呈逐年递增态势。 近年来中国公立医疗机构终端心脑血管系统化药销售情况（单位：万元） 来源：米内网中国公立医疗机构药品终端竞争格局 从治疗亚类看，心脑血管系统化药以高血压用药为主，2025年上半年占比超过50%；心脏病治疗用药以约18%的占比紧接其后，销售额同比下滑接近23%；血脂调节剂有“回暖”迹象，以约18%的占比排位第三；排位第四的脑血管治疗用药“承压”，销售额同比下滑超过30%。 在已落地执行的九批十轮化药集采中，共有63个心脑血管系统药物（不含落标品种）被纳入，其中高血压用药首当其冲，占比超过58%。 随着国家集采的常态化推进，已纳入国采的心脑血管系统化药销售额下滑明显，由2019年超810亿元下滑至2024年的约540亿元，预计2025年或跌破500亿元，占比上基本与非国采品种“平分秋色”。 近年来中国公立医疗机构终端心脑血管系统化药国采品种与非国采品种销售额占比 来源：米内网中国公立医疗机构药品终端竞争格局 在一级集团TOP20中，晖致、诺华、阿斯利康稳居前三，2025年上半年销售额分别超过35亿元、31亿元、18亿元；与2024年相比，浙江海正药业、欧加农制药均提升7个位次，正大制药提升4个位次，信立泰、扬子江药业、新和成首次登榜。 2025H1中国公立医疗机构终端心脑血管系统化药TOP20集团 注：标红为销售额增速达两位数 来源：米内网中国公立医疗机构药品终端竞争格局 2025年上半年，信立泰、扬子江药业、新和成分别有6个、19个、18个心脑血管系统化药在销。其中，信立泰的国产1类创新药阿利沙坦酯片销售额超过5亿元，同比增长超过17%；扬子江药业的苯磺酸左氨氯地平片、氨氯地平贝那普利片(Ⅰ)销售额均超过2亿元，注射用尼可地尔、盐酸法舒地尔注射液、奥美沙坦酯氨氯地平片等同比增长均超300%；新和成的奥美沙坦酯片、替米沙坦片、依折麦布片均销售过亿，奥美沙坦酯氢氯噻嗪片同比增长超300%等。 “一哥”易主！独家品种大涨128.6% 从销售额排名看，2025年上半年心脑血管系统化药TOP20品牌中，诺华的沙库巴曲缬沙坦钠片、晖致的阿托伐他汀钙片、阿斯利康的瑞舒伐他汀钙片依次位列前三，销售额均超过10亿元，其中诺华的沙库巴曲缬沙坦钠片首次“称王”；拜耳的硝苯地平控释片、晖致的苯磺酸氨氯地平片位列第四、第五，销售额均超过9亿元。 2025H1中国公立医疗机构终端心脑血管系统化药销售额TOP20品牌 注：标*为增速低于5% 来源：米内网中国公立医疗机构药品终端竞争格局 20个品牌中有13个为进口品牌，其中有12个为原研药（含原研地产化），7个已被纳入国家集采；国产品牌中，4个为国采品种，1个为国产1类新药等；从企业层面上看，诺华、晖致、阿斯利康、拜耳均有2个品种上榜，悦康药业、信立泰、南京正大天晴、齐鲁制药等国内企业亦有品种上榜。 13个品牌销售额实现正增长，其中有7个涨逾10%，拜耳的独家品种非奈利酮片暴涨128.63%、辉瑞的甲磺酸多沙唑嗪缓释片大涨23.88%；受第十批国采执行影响，远大医药(中国)的重酒石酸去甲肾上腺素注射液、齐鲁制药的盐酸艾司洛尔注射液分别大跌56.15%、39.77%。 从销售量排名看，2025年上半年心脑血管系统化药TOP20品牌中，齐鲁制药及乐普制药的阿托伐他汀钙片、浙江京新药业的苯磺酸氨氯地平片依次位列前三，销售量均超过10亿片；浙江华海药业的厄贝沙坦片以超9亿片的销售量排位第四，卫材的甲磺酸倍他司汀片以超8亿片的销售量排位第五。 2025H1中国公立医疗机构终端心脑血管系统化药销售量TOP20品牌 注：标*为增速低于5% 来源：米内网中国公立医疗机构药品终端竞争格局 国采中标品种霸榜，20个品牌中有15个为国采中标品种，4个为进口原研药（2个未纳入国采），1个为非国采国产品种；从企业层面上看，浙江京新药业、南京正大天晴制药、东瑞制药均有2个品种上榜。 从销售量增长情况看，8个品牌涨逾10%，其中重庆药友制药的苯磺酸氨氯地平片暴涨111.33%、福建东瑞制药的阿托伐他汀钙片大涨42.08%、浙江京新药业的苯磺酸氨氯地平片大涨31.84%等。 11亿明星药承压！6大品种迎战第十一批国采 截至9月19日，190个心脑血管系统化药已有企业过评或视同过评。21个品种遭“哄抢”，过评企业数达20家及以上，其中苯磺酸氨氯地平片以64家企业领跑，己酮可可碱注射液、盐酸多巴胺注射液、盐酸乌拉地尔注射液、缬沙坦氨氯地平片(Ⅰ)、瑞舒伐他汀钙片、注射用尼可地尔、阿托伐他汀钙片、盐酸多巴酚丁胺注射液均达30家以上。 从企业过评情况看，上海医药、石家庄四药、华海药业、福元医药、远大健康、倍特药业、复星医药、华润医药、鲁南制药等药企（以集团计）已过评品种数均达20个及以上，其中上海医药以35个品种领跑，石家庄四药以31个品种紧接其后。 近日，第十一批国采标书正式出炉，将于10月21日开标。6个为心脑血管系统药物在列，包括3个心脏病治疗用药、1个血脂调节剂、1个高血压用药、1个脑血管治疗用药。 纳入第十一批国采的心脑血管疾病药物 来源：米内网综合数据库 从市场规模看，6个药品2024年在中国公立医疗机构终端的销售额合计超过36亿元，其中普伐他汀口服常释剂型以超11亿元领跑，尼可地尔口服常释剂型、贝尼地平口服常释剂型销售额均超过7亿元。 从竞争情况看，5个品种符合申报资格企业数超过10家，其中尼可地尔口服常释剂型、去氧肾上腺素注射剂（1ml:10mg）、贝尼地平口服常释剂型均达15家及以上。 从过评企业看，石家庄四药以4个过评品种领跑，北京福元医药以3个过评品种紧接其后，百诚医药、倍特药业、成都瑞尔医药、浙江诺得药业、远大健康等8家企业均有2个过评品种在列。 此外，4个心脑血管系统药物满足充分竞争条件但未纳入第十一批国采，其中有2个因各省医药集中采购平台统计的2024年采购金额小于1亿元而被排除；1个因存在专利侵权高风险而被排除，1个品种因“跨医保目录”品种区分医保性质，各品规均不满足充分竞争格局而被排除。 满足条件但未纳入第十一批国采的心脑血管系统药 注：销售额低于1亿元用*代替 4个药品中，沙库巴曲缬沙坦口服常释剂型2024年在中国公立医疗机构终端的销售额超过38亿元，其中原研厂家主导市场，占比超过98%，该药目前符合申报资格企业数已达28家。 资料来源：米内网数据库、上海阳光医药采购网等 注：米内网《中国公立医疗机构药品终端竞争格局》，统计范围：中国城市公立医院、县级公立医院、城市社区中心以及乡镇卫生院，不含民营医院、私人诊所、村卫生室；上述销售额以产品在终端的平均零售价计算。数据统计截至9月19日，如有疏漏，欢迎指正！ 免责声明：本文仅作医药信息传播分享，并不构成投资或决策建议。本文为原创稿件，转载文章或引用数据请注明来源和作者，否则将追究侵权责任。投稿及报料请发邮件到872470254@qq.com稿件要求详询米内微信首页菜单栏商务及内容合作可联系QQ：412539092 【分享、点赞、在看】点一点不失联哦

If scientists run enough variations of a lab experiment, perhaps AI models could, someday, predict the results of future versions of that study. A new Seattle-based startup called Synthesize Bio believes it may be able to achieve that sooner than later, starting with gene-expression studies. Synthesize has already generated some promising results in predicting these types of lab experiments, according to a preprint published Tuesday. Synthesize will officially launch on Wednesday, its co-CEOs , Jeff Leek and Robert Bradley, exclusively told Endpoints News, as it releases its AI model, called GEM-1. The 15-employee biotech was founded in 2023 and raised a previously undisclosed $10 million seed round in October 2023, led by Madrona Ventures. Other investors include Sahsen Ventures, AI2 Incubator, Inner Loop Capital, and MD Venture Investments. Leek and Bradley came up with the idea behind Synthesize in the hallways of the Fred Hutchinson Cancer Center. Leek is the cancer center’s chief data officer, and Bradley is the scientific director of a translational data science research center. They wanted to see if biotech could follow a similar trajectory in building large AI models, similar to how companies like OpenAI and Anthropic grew large language models trained on nearly all of the internet’s data. “We felt like this is an idea where we believe it can transform how scientists go about their work,” Bradley said in an interview. The Fred Hutch duo built a model called GEM-1 over the last two years. The model is trained on tons of different gene-expression experiments that they curated and annotated from public datasets. Synthesize is already at work on the next-generation version of the model called GEM-2. “This is the worst version of the GEM model you will ever see,” Leek said. “It’s only going to get better over time.” Gene-expression experiments and single-cell studies have been a hot space within the AI bio field. Large organizations, like the Chan Zuckerberg Initiative and the Arc Institute , are championing moonshot projects that view these types of experiments as key building blocks in eventually making models of virtual cells. Virtual cells could allow more experiments traditionally done in the lab to be accurately simulated by computers. Other startups, like Noetik , Tahoe Therapeutics , and Xaira Therapeutics , are also taking their own twists on building large or rich datasets using some of these experiments. Synthesize believes its model can tackle a wider range of gene-expression experiments than the rest. To test that belief, they looked at how GEM-1’s predictions compared to real-world results. They set a cut-off date of mid-2024, training their model on experimental data in public databases before that date, and then using new experiments added after that date as tests. Overall, Synthesize’s team found GEM-1 “yielded accuracy comparable to the best-possible performance estimated by comparing the results of matched lab experiments,” according to the preprint. The preprint also found that about 95% of these new experiments were similar to those in GEM-1’s training data, meaning the model had already seen experiments that studied similar types of cells or similar perturbations. GEM-1’s predictions varied in accuracy. The model nailed the effect on gene expression of certain molecules, like the hair-loss drug finasteride and an alpha blocker called doxazosin. But predictions for other compounds were less than 50% accurate, like the HIV treatment dolutegravir or cancer drug afatinib, according to the preprint. An area of active research is working on confidence intervals, which could give users a sense of the quality of a prediction. Bradley said that’s something the broader AI field is also working on, as it’s an important problem. That could be critical in telling the finasteride and afatinib predictions apart. “Our goal is not to replace experiments,” Bradley said. “Our goal is to have a seamless blend of wet-lab work and AI experimentation. Experiments are critical. They’re always going to be the gold standard.” Instead, GEM-1 could allow scientists on finite budgets to consider more data for experiments they may not have the time or resources to run. It could also enable scientists to get a sense of results on experiments that are impossible or impractical today, Bradley said. For instance, the model could help predict a drug’s activity in tissue where you can’t take a patient’s biopsy, he said. Leek said there are blind spots, where GEM-1’s performance is wanting. That’s why they are eager to release the model openly, including free access on its website , to gain a sense of how researchers use it and its real-world performance.