Paroxetine Hydrochloride

近日，北陆药业（300016）发布《2024年年度报告》。2024年度，北陆药业继续稳健经营，实现营业收入98,355.45万元，同比增长10.42%；归属于上市公司股东的净利润1,365.17万元，同比增加119.02%，其中母公司净利润5,635.84万元，同比增加838.39%；归属于上市公司股东的扣除非经常性损益的净利润1,409.44万元，同比增加120.27%。为完成“二次创业”、实现“中国医药制造百强企业”的中长期目标，北陆药业在2024年继续落实“化药+中药”双轮驱动战略布局，主营业务依然专注于对比剂、降糖药以及中枢神经类中成药的生产与销售，同时通过收购承德天原药业进一步扩大中成药战略版图。稳健经营  化药板块收入持续增长公司经过近两年的持续改革，逐步克服了化学药主打产品碘对比剂及降糖药管线受集采价格下降带来的巨大挑战，在首次中标执标及后续接续中标工作的推动下，实现了集采产品销量的增长。同时，公司也积极拥抱集采政策，将其作为公司研发、营销等工作的重要考量因素，加大对拟参与集中带量采购产品的研发及申报，力争抓住集采执标的契机，扩大产品的市场份额及品牌影响力。以上举措为公司提供稳定的利润支撑，确保良好的现金流状况，为公司的长期发展奠定坚实的基础。2024年度，公司对比剂产品实现销售收入58,804.95万元，同比增长9.87%，公司降糖类产品实现销售收入12,598.34万元，同比增长33.24%。原料药批文增加  持续开拓海外市场原料药是公司化药板块的重要组成部分，北陆药业原料药产品涵盖钆类及碘类的原料药及辅料、中间体等，在保障制剂生产之余，各产品也积极开拓国内及国外市场。控股子公司海昌药业是国内为数不多的具有碘类对比剂原料药生产资质和产能的企业之一，其年产1000吨的碘造影剂项目设有碘海醇、碘克沙醇、碘普罗胺、碘佛醇、碘帕醇、碘比醇及碘美普尔等共计7个品种生产线。除了碘海醇，海昌药业已陆续获批碘克沙醇原料药、碘帕醇原料药等批件，为公司“原料药+制剂”一体化战略进一步夯实基础。同时海昌相关原料药产品也在积极申报海外注册。海昌药业在原料药方面的优势和产业布局的增强，有利于提升公司产品的多样性及公司的综合竞争力。深化中成药战略布局  中药收入同比增长19.65%加大中成药板块布局是公司近几年的发展战略之一，公司通过并购中成药企业拓展中成药板块业务，逐步构建丰富的产品矩阵及核心竞争力，努力提升公司的市场份额及影响力，进一步增强公司在中成药领域的品牌形象。九味镇心颗粒是国内第一个通过国家食品药品监督管理总局批准治疗广泛性焦虑症的纯中药制剂，也是公司确立“抗焦虑中药第一品牌”的“黄金单品”。报告期内，公司收购天原药业80%的股权，其拥有的58个中药批文成为公司中成药板块重要组成部分。截至报告期末，天原药业生产、销售的品种共计20种，涵盖清热解毒类、感冒类、补肾强身类、脾胃肠消化类、活血止痛类、肝胆类，其中，核心产品金莲花颗粒系全国独家品种、国家医保乙类、OTC 乙类，系公司全力打造的另一“黄金单品”。公司金莲花颗粒、感冒清热颗粒等中药品种已中标多个地方集采，公司将借助集采执标，协同基层布局，提升金莲花颗粒等产品的市场份额及品牌影响力。报告期内，公司中药制剂实现销售收入16,844.68万元，同比增长19.65%。对比剂全线出海  海外收入同比增长15.02%公司海外市场前期的拓展工作成效凸显。公司出口的产品主要为对比剂制剂及原料药，业务覆盖范围为南美洲、非洲、亚洲等市场，公司没有产品出口美国。报告期内，公司对比剂全线产品成功出海并实现营收大幅增长。原料药方面，不仅海昌药业的碘类原料药继续拓展海外市场，沧州分公司生产钆类原料药也逐步实现了国际化。随着欧盟EU GMP认证和巴西ANVISA  GMP合规认证的完成，公司对比剂产品的国际化有望进一步提速。九味镇心颗粒在香港和泰国的注册工作继续按计划推进，为出口东南亚等地创造条件。报告期内，公司实现海外营业收入13,216.14万元，同比增长15.02%。储备产品丰富  产品梯队建设成型经过2年多的研发投入和收购工作，公司的产品矩阵相对丰富，管线也实现了多元化。报告期内，公司获批钆特酸葡胺制剂及原料药、盐酸帕罗西汀肠溶缓释片、西甲硅油乳剂、复方聚乙二醇电解质散（Ⅲ）、钆布醇注射液新增规格的注册证书，完成了钆喷酸葡胺注射液、钆贝葡胺注射液一致性评价。研发团队不断提升研发效率，为扩大公司在相关领域的战略布局积极储备具有市场竞争潜力的品种。截至目前，公司审评中的项目12项，研发立项项目23项，共计35项。通过并购，公司中药领域批文数量达到59个，其中医保品种45个、基药品种18个、OTC品种41个。九味镇心颗粒、金莲花颗粒、五子衍宗丸、连翘败毒丸、参苓白术丸、消栓通络片、利肝隆片等21个品种在产在销，其余品种皆作为储备品种，等待适当时机开发上市。2025年，公司管理层将在董事会的带领下继续落实各项发展目标。包括但不限于各部门统筹协作，全面落实公司年度目标；加强各生产基地建设及管理，全面提升公司产能；继续推进天原药业的整合与赋能工作，提升中成药业务盈利水平；继续深化营销体系改革，强化营销管理中心职能，狠抓关键政策和重点项目的落地；坚持创新驱动，培养核心团队，提升研发能效；加速公司国际化进程，提升海外市场营收贡献；继续加强内控管理，有效控制风险。关于北陆药业年报更多信息可登录巨潮资讯网www.cninfo.com.cn查询。往期推荐01 【喜报】北陆药业碘海醇注射液欧盟获批上市02北陆药业控股子公司海昌药业碘帕醇化学原料药获批上市03 控股子公司海昌药业碘克沙醇原料药取得韩国注册证书识别二维码关注我们

shiota/ iStock Researchers in pharma and beyond have historically glommed onto a limited number of disease targets, limiting innovation. AI could change that. Proteins that fight tumor growth, gene exons that can be modified, and receptors on immune cells that control their cancer-killing powers. These are the types of mechanisms and bodily processes that could be key to curing or treating disease, and which form the targets of modern drug development. While the number of such targets is nearly infinite, a relatively small number have emerged as the most popular among pharmaceutical companies. In a phenomenon termed “herding,” numerous pharmaceutical firms, research organizations and even academic scientists are targeting not just the same narrow band of conditions, but the same limited set of drug targets. In 2020, 68% of targets pursued by the top 10 pharmaceutical firms were the basis of five or more R&D programs. Some firms were dedicating 80% or more of their research time and dollars to those popular targets. Chief among these targets are those associated with cancer; in 2022, there were nine assets per actively investigated oncology target among the top pharma firms, up from 1.8 in 2000, with most of the research dedicated to breast, hematological and brain cancer. While there are understandable reasons for herding and even advantages in certain situations, the overall effect of this “me-too” style of innovation is to set limits in drug development. But how can we break away from the crowd and forge our own path? As vice president of business development at Evogene, which offers an AI-driven computational chemistry platform, I believe artificial intelligence is the answer. AI has the power to transform drug development by identifying novel targets and compounds that can interact with them. The Drawbacks of Herding Well-known examples of “me-too” drugs include many of the selective serotonin reuptake inhibitors (SSRIs) used in treating depression—such as Prozac, Zoloft and Paxil—and various statins—think Lipitor and Crestor. One of the most comprehensive studies of drug targets curated 1,578 FDA-approved drugs acting on 893 unique molecular targets, meaning nearly 50% of FDA-approved drugs share common targets. Similarly, about 60% of drugs on the World Health Organization’s list of essential treatments fit into this “me-too” category, and more than 50% of the 50 novel drugs approved by the FDA in 2024 relied on pre-existing targets. In many ways, herding makes sense, and it has benefitted some patients and pharma companies. Many of the targets that experience herding, especially in the oncology space, are highly efficacious, and pharma firms want to follow what works and, ideally, create best-in-class treatments in these drug classes. Of course, there is a great demand—and a large need—for these improvements. In addition, when it comes to single-gene diseases such as thalassemia, Duchenne muscular dystrophy and Huntington’s, herding has emerged alongside progress in developing gene therapies. For example, instead of focusing solely on the mutant gene, companies might look at targeting cellular pathways disrupted by the mutant protein. Such approaches make up only 10%–30% of attempts to develop new therapies, while the majority still focus on the mutant gene itself. Such concentrated research efforts, exemplified by the focus on the CFTR gene in cystic fibrosis leading to therapies like Ivacaftor, can indeed accelerate progress in understanding and treating rare genetic diseases. But the focus on the gene itself and a small set of cellular pathways leaves other pathways under explored. This over-concentration diverts critical resources, exacerbating challenges in resource allocation and clinical trial design and ultimately hindering efforts to pursue novel targets and more innovative therapies. This is especially problematic for rare diseases, where the pool of trial participants is naturally low and standards limit patients to one trial at a time. If all or most of the patients are enrolled in trials testing drugs with the same target or few targets, there is little room to open a trial with a new target, further discouraging innovation. Innovation Through AI Pursuing new targets and therapies is essential to reversing the herding trend, and one of the tools for doing this is AI. AI offers the ability to navigate the vast chemical and biological landscape, uncovering hidden patterns and relationships that traditional methods overlook. By integrating data from genomics, proteomics and cheminformatics, AI can identify novel targets that have been underexplored, evaluating their potential with remarkable speed and accuracy. Over the past three years the market for AI platforms for the entire healthcare industry has tripled . As more companies— and regulators —embrace AI-based research, companies will be better equipped to break free from the constraints of herding and the redundancy of structurally similar drugs, shifting toward more innovative and diverse therapeutic strategies. McKinsey predicts that AI could increase the number of actionable drug targets by at least 30% . There are already signs of progress in de-herding as novel biological targets and molecules for drug development emerge. For example, AstraZeneca, in collaboration with BenevolentAI, has identified novel targets in chronic kidney disease (CKD) beyond the well-known renin-angiotensin system, opening pathways to treatments focused on kidney fibrosis and inflammation. AI-based research by Verve Therapeutics, meanwhile, has shifted from traditional statin targets to gene editing therapies for inherited cardiovascular conditions. And for rare diseases, including amyotrophic lateral sclerosis (ALS) and Duchenne muscular dystrophy, companies like Insilico have utilized AI-driven systems to identify specific genetic targets. In addition to finding new targets, deep learning enables the de novo design of novel ligands, the active molecules that are the basis of many potential new drugs. Generative architectures such as GANs (generative adversarial networks), VAEs (variational autoencoders) and GPTs (generative pre-trained models) allow researchers to create unique molecular structures that are specifically tailored to their targets. This capability is transformative because it avoids the redundancy of relying on existing compound libraries, which are often biased toward known targets. AI-driven de novo ligand generation explores new chemical spaces, ensuring structural diversity and reducing the risk of producing yet another version of an existing drug. Companies like Atomwise , Insilico and Recursion are using AI for structure-based drug design, and have discovered unique ligands for various indications. Evogene recently began to leverage its AI-driven computational chemistry platform, first developed for agricultural applications, to break into the world of novel ligand discovery and drug development. Despite its promise, AI-driven drug discovery faces challenges such as variable data quality and the “black box” nature of some models, which can complicate validation and regulatory approval. While the promise of AI-first drugs is yet to be fully realized—none have progressed beyond Phase II clinical trials due to long development timelines—it’s important to note that the most advanced drugs in the pipeline were created using technology that has since matured rapidly. These issues are recognized as part of an evolving field, with ongoing innovation addressing concerns like data standardization and model transparency. I have strong confidence that these hurdles will be overcome in time, ultimately enabling AI to revolutionize drug development and improve patient outcomes. AI use in the pharmaceutical industry is far from universal, but by collaborating with AI-first companies, pharmas can remain at the forefront of technological advancements, ensuring their pipelines are fueled by cutting-edge breakthroughs rather than incremental advances. As AI-driven research becomes the norm, fuelled by these strategic collaborations, we can anticipate transformative progress that will benefit pharmaceutical companies and patients alike.