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60年药物创新的经验教训。

Lessons from 60 years of pharmaceutical innovation.

作者信息

Munos Bernard

机构信息

Lilly Corporate Center, Indianapolis, Indiana 46285, USA.

出版信息

Nat Rev Drug Discov. 2009 Dec;8(12):959-68. doi: 10.1038/nrd2961.

DOI:10.1038/nrd2961
PMID:19949401
Abstract

Despite unprecedented investment in pharmaceutical research and development (R&D), the number of new drugs approved by the US Food and Drug Administration (FDA) remains low. To help understand this conundrum, this article investigates the record of pharmaceutical innovation by analysing data on the companies that introduced the approximately 1,200 new drugs that have been approved by the FDA since 1950. This analysis shows that the new-drug output from pharmaceutical companies in this period has essentially been constant, and remains so despite the attempts to increase it. This suggests that, contrary to common perception, the new-drug output is not depressed, but may simply reflect the limitations of the current R&D model. The implications of these findings and options to achieve sustainability for the pharmaceutical industry are discussed.

摘要

尽管在制药研发(R&D)方面进行了前所未有的投资,但美国食品药品监督管理局(FDA)批准的新药数量仍然很少。为了帮助理解这一难题,本文通过分析自1950年以来推出了约1200种已获FDA批准新药的公司的数据,来研究制药创新的记录。该分析表明,这一时期制药公司的新药产出基本保持不变,尽管有增加新药产出的尝试,但目前依然如此。这表明,与普遍看法相反,新药产出并非受到抑制,而可能只是反映了当前研发模式的局限性。本文还讨论了这些发现的意义以及制药行业实现可持续发展的选择。

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J Comb Chem. 2009 Jan-Feb;11(1):3-13. doi: 10.1021/cc800183m.
2
Rebuilding the R&D engine in big pharma.重塑大型制药公司的研发引擎。
Harv Bus Rev. 2008 May;86(5):68-70, 72-6, 128.
3
India takes an open source approach to drug discovery.印度在药物研发方面采取开源方法。
指数的六个D与药物发现:一条扭转埃罗定律的道路。
Drug Discov Today. 2025 Apr;30(4):104341. doi: 10.1016/j.drudis.2025.104341. Epub 2025 Mar 22.
4
Drug pricing models, no 'one-size-fits-all' approach: a systematic review and critical evaluation of pricing models in an evolving pharmaceutical landscape.药品定价模式,不存在“一刀切”的方法:对不断演变的制药格局中定价模式的系统评价与批判性评估
Eur J Health Econ. 2024 Nov 4. doi: 10.1007/s10198-024-01731-w.
5
Advances and Challenges of Bioassembly Strategies in Neurovascular In Vitro Modeling: An Overview of Current Technologies with a Focus on Three-Dimensional Bioprinting.神经血管体外建模中生物组装策略的进展与挑战:当前技术综述,重点介绍三维生物打印。
Int J Mol Sci. 2024 Oct 12;25(20):11000. doi: 10.3390/ijms252011000.
6
State of the Art in Sub-Phenotyping Midbrain Dopamine Neurons.中脑多巴胺能神经元亚分型的研究现状
Biology (Basel). 2024 Sep 3;13(9):690. doi: 10.3390/biology13090690.
7
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BMC Pulm Med. 2024 Aug 29;24(1):425. doi: 10.1186/s12890-024-03193-5.
8
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BMC Cancer. 2024 Jul 26;24(1):900. doi: 10.1186/s12885-024-12609-8.
9
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Front Mol Biosci. 2024 Jun 11;11:1413214. doi: 10.3389/fmolb.2024.1413214. eCollection 2024.
10
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Medicine (Baltimore). 2024 Jun 21;103(25):e38142. doi: 10.1097/MD.0000000000038142.
Cell. 2008 Apr 18;133(2):201-3. doi: 10.1016/j.cell.2008.04.003.
4
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Harv Bus Rev. 2008 Mar;86(3):96-102, 134.
5
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6
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7
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9
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