Please wait a minute...

中国生物工程杂志

China Biotechnology
China Biotechnology
China Biotechnology  2024, Vol. 44 Issue (1): 8-18    DOI: 10.13523/j.cb.2308100
    
Development Trends of the Industries of the Future Biopharmaceuticals
Huiqing QIU1,Zijie YANG2,Fang GUO3,Yutao ZHAN1,**()
1 Research Center for Industries of the Future, Westlake University, Hangzhou 310024, China
2 School of Life Sciences, Westlake University, Hangzhou 310024, China
3 School of Engineering, Westlake University, Hangzhou 310024, China
Download: HTML   PDF(2104KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

The industries of the future are the primary arena of the transformative productive forces. The industries of the future biopharmaceuticals primarily refer to the industries that are currently in the incubation stage, driven by cutting-edge biopharmaceutical technologies, and have the potential to have a wide range of applications in disease prevention, diagnosis, and treatment in the future. However, the development of the industries of the future biopharmaceuticals is subject to great uncertainty due to factors such as the difficulty of technological breakthroughs and the prospects for industry development. To cultivate China’s system of the industries of the future biopharmaceuticals in a more scientific, precise, and efficient manner, this study innovatively uses artificial intelligence text analysis technology to analyze biopharmaceutical technology research projects from renowned research institutions in major developed countries over the past five years. Combined with expert research, we have identified the key technologies that are currently the focus of global biopharmaceutical technology research and development. Based on this, and in consideration of China’s national conditions, we propose targeted policy recommendations.

Key words： Industries of the future biopharmaceuticals      Transformative productive forces      Industries of the future      Biopharmaceutical technology     
Received: 07 August 2023      Published: 04 February 2024
ZTFLH:  Q81  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Huiqing QIU
Zijie YANG
Fang GUO
Yutao ZHAN
Cite this article:

Huiqing QIU, Zijie YANG, Fang GUO, Yutao ZHAN. Development Trends of the Industries of the Future Biopharmaceuticals. China Biotechnology, 2024, 44(1): 8-18.

URL:

https://manu60.magtech.com.cn/biotech/10.13523/j.cb.2308100     OR     https://manu60.magtech.com.cn/biotech/Y2024/V44/I1/8

Fig.1 Market size and growth rate of the Chinese biopharmaceutical industry from 2016 to 2022 Data source: AskCI Consulting
Fig.2 Research and development scale and growth rate of the Chinese biopharmaceutical industry from 2016 to 2022 Data source: Frost & Sullivan, Huajing Industry Research Institute
地区 态势
长三角 产业创新能力和国际交流水平较高,拥有最多的跨国生物医药企业
全链条政策支撑体系持续完善,进一步完善生物医药产业空间布局
十三届全国人大常委会第二十九次会议表决通过了关于授权上海市人民代表大会及其常务委员会制定浦东新区法规的决定,为未来浦东生物医药产业高质量创新发展奠定了良好的政策环境
京津冀 人力资源储备充足,拥有丰富的临床资源和教育资源,产业链互补优势较强
协同创新与特色化发展并行
北京市发布实施《北京市加快医药健康协同创新行动计划(2021-2023年)》,在提升原始创新策源能力、推动临床溢出效应显现、推动产业国际化高质量发展、完善产业发展生态等方面聚焦建设
珠三角 市场经济体系成熟,医药流通体系发达,毗邻港澳,对外辐射能力强,民营资本比较活跃
政策引导前沿领域与技术快速发展
深圳市人大常委会运用经济特区立法权,发布《深圳经济特区细胞和基因产业促进条例(征求意见稿)》;深圳市推出《深圳市光明区关于支持合成生物创新链产业链融合发展的若干措施》,为全国首个合成生物专项扶持政策
Table 1 Development trends of the biopharmaceutical industry in the Yangtze River Delta, Beijing-Tianjin-Hebei Region, and Pearl River Delta
Fig.3 The number of listed biopharmaceutical companies in China by region Data source: Compiled from publicly available information, as of 2022
序号 关键技术点 项目数 总金额/美元 项目平均金额/美元
1 基因编辑(gene editing) 620 583 499 984 941 129
2 CRISPR-Cas(CRISPR-Cas) 1 478 835 656 289 565 397
3 基因递送(gene delivery) 220 223 349 613 1 015 226
4 表观遗传疗法(epigenetic therapy) 91 59 208 661 650 645
5 免疫检查点抑制剂(immune checkpoint inhibitor) 267 128 979 717 483 070
6 嵌合抗原受体(chimeric antigen receptor) 482 342 213 722 709 987
7 重组抗体(recombinant antibody) 174 175 865 009 1 010 718
8 抗体药物偶联物(antibody drug conjugate) 109 72 481 417 664 967
9 靶向蛋白质降解(targeted protein degradation) 114 55 974 261 491 002
10 小分子抑制剂(small molecule inhibitor) 328 336 834 823 1 026 935
11 药物递送(drug delivery) 607 408 357 578 672 747
12 疫苗设计(vaccine design) 193 451 131 791 2 337 470
13 mRNA疫苗(mRNA vaccine) 47 57 828 014 1 230 383
14 诱导型人工多能干细胞(induced pluripotent stem cell) 491 454 274 103 925 202
15 细胞重编程(cell reprogramming) 114 92 159 499 808 417
16 蛋白质结构(protein structure) 351 299 333 783 852 803
17 蛋白质设计(protein design) 67 69 649 413 1 039 543
18 蛋白质组学(proteomics) 1 400 1 266 466 418 904 619
19 全基因组测序(whole-genome sequencing) 613 678 372 744 1 106 644
20 全转录组测序(whole transcriptome) 315 305 331 014 969 305
21 空间转录组学(spatial transcriptomics) 114 158 813 931 1 393 105
22 单细胞测序(single cell sequencing) 846 617 718 599 730 164
23 高通量测序(high throughput sequencing) 237 165 610 173 698 777
24 代谢组学(metabolomics) 352 422 170 208 1 199 347
25 抗逆转录病毒疗法(anti-retroviral therapy) 46 53 207 055 1 156 675
26 异种移植(xenotransplantation) 49 34 864 963 711 530
27 类器官(organoid) 921 775 986 882 842 548
28 相分离(phase separation) 100 74 377 166 743 772
29 3D打印(3D print) 91 59 793 758 657 074
30 人工智能+生物医药(artificial intelligence + biophamaceuticals) 821 699 078 464 851 496
合计 11 658 9 958 589 052 854 228
Table 2 30 Key technological points in global biopharmaceutical research
Fig.4 Proportional distribution of funding amounts for 30 key technological points The size of the rectangle for each key technological point is directly proportional to the amount of funding
Fig.5 The linear relationship between the number of funded projects and the total amount of funding for 30 key technological points
一级指标 二级指标 选项
熟悉度指标 对该技术的熟悉程度 A非常熟悉 B比较熟悉 C一般 D不太熟悉 E不熟悉
技术性指标 对我国人民普遍的生命健康安全的重要程度 A很重要 B较重要 C一般重要 D不太重要 E完全不重要
该技术在我国的研发基础 A很好 B较好 C一般 D较差 E差
我国通过自主研发或联合开发实现该技术突破的难度 A难度大 B难度较大 C难度一般 D难度较小 E难度很低
产业化指标 对拉动经济增长的重要程度 A很重要 B较重要 C一般重要 D不太重要 E完全不重要
预期实现产业化或投入商业应用所需的时间 A 5年 B 6~10年 C 11~15年 D 15年以上
产业化综合成本 生物医药技术从最初研发到最终转化为市场商品整个过程中的投入1)
Table 3 Expert evaluation indicator system for key technologies in the industries of the future biopharmaceuticals
序号 关键技术点 技术性得分 技术性得分排序 产业化得分 产业化得分排序
1 基因编辑 0.564 6 12 0.557 6 17
2 CRISPR-Cas 0.611 2 9 0.742 3 9
3 基因替代疗法 0.248 5 28 0.417 4 27
4 表观遗传疗法 0.104 6 30 0.349 9 29
5 免疫检查点抑制剂 0.587 8 10 0.866 9 2
6 嵌合抗原受体 0.574 7 11 0.725 6 10
7 重组抗体 0.829 7 2 0.814 4 5
8 抗体药物偶联物 0.752 1 3 0.806 8 7
9 靶向蛋白质降解 0.449 3 17 0.701 1 12
10 小分子抑制剂 0.642 9 7 0.843 0 3
11 药物递送 0.532 3 15 0.815 5 4
12 疫苗设计 0.409 5 21 0.518 7 24
13 mRNA疫苗 0.550 8 13 0.750 3 8
14 诱导型人工多能干细胞 0.667 7 4 0.551 9 18
15 细胞重编程 0.261 2 27 0.435 7 26
16 蛋白质结构 0.540 1 14 0.540 0 20
17 蛋白质设计 0.342 6 24 0.529 3 21
18 蛋白质组学 0.643 2 6 0.700 7 13
19 全基因组测序 0.444 7 18 0.610 2 14
20 全转录组测序 0.426 8 19 0.570 4 15
21 空间转录组学 0.339 8 25 0.408 6 28
22 单细胞测序 0.417 2 20 0.560 7 16
23 高通量测序 0.851 7 1 0.714 9 11
24 代谢组学 0.386 5 22 0.545 0 19
25 抗逆转录病毒疗法 0.280 5 26 0.519 9 23
26 异种移植 0.355 4 23 0.489 3 25
27 类器官 0.459 2 16 0.523 3 22
28 相分离 0.181 1 29 0.293 4 30
29 3D打印 0.614 1 8 0.814 4 5
30 人工智能+生物医药 0.663 8 5 0.933 8 1
Table 4 Technical and industrialization scores and rankings for 30 key technological points
Fig.6 The comprehensive scores for 30 key technological points
[1]   李晓华, 王怡帆. 未来产业的演化机制与产业政策选择. 改革, 2021(2): 54-68.
[1]   Li X H, Wang Y F. The evolution mechanism of future industry and choice of industrial policy. Reform, 2021(2): 54-68.
[2]   DiMasi J A, Grabowski H G, Hansen R W. Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of Health Economics, 2016, 47: 20-33.
doi: 10.1016/j.jhealeco.2016.01.012
[3]   Hay M, Thomas D W, Craighead J L, et al. Clinical development success rates for investigational drugs. Nature Biotechnology, 2014, 32(1): 40-51.
doi: 10.1038/nbt.2786 pmid: 24406927
[4]   Kennedy D, Norman C. What don’t we know? Science, 2005, 309(5731): 75.
[5]   国家自然科学基金委员会科学传播与成果转化中心. Science发布:全世界最前沿的125个科学问题. [2023-08-06]. https://www.nsfc.gov.cn/csc/20340/20289/22023/index.html.
[5]   Center for Science Communication and Transformation, National Natural Science Foundation of China. Science release: 125 of the world’s most cutting-edge scientific questions. [2023-08-06]. https://www.nsfc.gov.cn/csc/20340/20289/22023/index.html.
[6]   周泉. 2021年全球及中国生物医药行业现状分析. [2023-08-06]. https://m.huaon.com/detail/787854.html.
[6]   Zhou Q. Analysis of global and Chinese biopharmaceutical industry status in 2021. [2023-08-06]. https://m.huaon.com/detail/787854.html.
[7]   IQVIA. 2023年全球医药研发全景展望. [2023-08-06]. https://www.iqvia.com/zh-cn/locations/china/library/brochures/global-trends-in-r-and-d-2023.
[7]   IQVIA. Global trends in biopharmaceutical R&D 2023. [2023-08-06]. https://www.iqvia.com/zh-cn/locations/china/library/brochures/global-trends-in-r-and-d-2023.
[8]   Giglio P, Micklus A. Biopharma dealmaking in 2022. Nature Reviews Drug Discovery, 2023, 22(2): 92-93.
doi: 10.1038/d41573-023-00012-0
[9]   Micklus A, Giglio P. Biopharma dealmaking in 2021. Nature Reviews Drug Discovery, 2022, 21(2): 93-94.
doi: 10.1038/d41573-022-00017-1 pmid: 35043002
[10]   Food and Drug Administration. Novel drug approvals for 2022. [2023-08-06]. https://www.fda.gov/drugs/new-drugs-fda-cders-new-molecular-entities-and-new-therapeutic-biological-products/novel-drug-approvals-2022.
[11]   European Medicines Agency.Human medicines: highlights of 2022. [2023-08-06]. ttps://www.ema.europa.eu/en/news/human-medicines-highlights-2022.
[12]   Pharmaceuticals and Medical Devices Agency. List of approved products. [2023-08-06]. https://www.pmda.go.jp/english/review-services/reviews/approved-information/drugs/0002.html.
[13]   医药魔方. 2022年中国批准上市的新药. [2023-08-06]. https://xueqiu.com/8965749698/239470918.
[13]   Pharmcube. New drugs approved by China in 2022. [2023-08-06]. https://xueqiu.com/8965749698/239470918.
[14]   王美华. 中国医药, 迈向创新. 人民日报海外版, 2021-02-02( 11).
[14]   Wang M H. Chinese medicine, towards innovation. People’s Daily Overseas Edition, 2021-02-02(11).
[15]   中商产业研究院. 2022年中国生物医药行业产业链上中下游市场剖析. [2023-08-06]. https://www.askci.com/news/chanye/20220510/1635011851682_4.shtml.
[15]   Zhongshang Industrial Research Institute. Analysis of the middle and downstream markets of China’s biopharmaceutical industry chain in 2022. [2023-08-06]. https://www.askci.com/news/chanye/20220510/1635011851682_4.shtml.
[16]   Shang J B, Liu J L, Jiang M, et al. Automated phrase mining from massive text corpora. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(10): 1825-1837.
doi: 10.1109/TKDE.2018.2812203 pmid: 31105412
[17]   Gu X T, Wang Z H, Bi Z Y, et al. UCPhrase: unsupervised context-aware quality phrase tagging. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, DOI:10.1145/3447548.3467397.
doi: 10.1145/3447548.3467397
[18]   Cohan A, Feldman S, Beltagy I, et al. Specter: document-level representation learning using citation-informed transformers. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 2270-2282.
[19]   Han J W, Pei J, Yin Y W, et al. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 2004, 8: 53-87.
doi: 10.1023/B:DAMI.0000005258.31418.83
[20]   Lu J H, Yang L Y, Mac Namee B, et al. A rationale-centric framework for human-in-the-loop machine learning. Annual Meeting of the Association for Computational Linguistics, 2022, DOI:10.48550/arXiv.2203.12918.
doi: 10.48550/arXiv.2203.12918
[21]   Guo F, Luo Y, Yang L Y, et al. SciMine: an efficient systematic prioritization model based on richer semantic information. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information, 2023, DOI: 10.1145/3539618.3591764.
doi: 10.1145/3539618.3591764
[22]   Yang Z J, Wang Y K, Zhang L J. AI becomes a masterbrain scientist. bioRxiv, 2023, DOI: 10.1101/2023.04.19.537579.
doi: 10.1101/2023.04.19.537579
No related articles found!
主管：中国科学院 　主办：中国科学院文献情报中心 　中国生物技术发展中心 　中国生物工程学会