General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

Abstract

The research and development of new traditional Chinese medicine (TCM) drugs have progressively established a novel system founded on the integration of TCM theory, human experience, and clinical trials (termed the “Three Combinations”). However, considering TCM’s distinctive features of “syndrome differentiation and treatment” and “multicomponent formulations and complex mechanisms”, current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system. Moreover, significant obstacles persist in gathering human experience data, evaluating clinical efficacy, and controlling the quality of active ingredients, which impede the innovation process in TCM drug development. Network pharmacology, centered on the “network targets” theory, transcends the limitations of the conventional “single target” reductionist research model. It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions. This approach aligns with the holistic perspective of TCM, offering a novel method consistent with TCM’s holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs. It is internationally recognized as a “next-generation drug research model”. To advance the research of new tools, methods, and standards for TCM evaluation and to overcome fundamental, critical, and cutting-edge technical challenges in TCM regulation, this consensus aims to explore the characteristics, progress, challenges, applicable pathways, and specific applications of network pharmacology as a new theory, method, and tool in TCM drug development. The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

Keywords

Network pharmacology / Research and development of new traditional Chinese medicine drugs / Expert consensus

References

[1] Opinions of the Central Committee of the CPC and the State Council on Promoting the Development of Traditional Chinese Medicine Inheritance and Innovation. (2019-10-02) [2024-07-01]. (2019-10-02) [2024-07-01].https://www.gov.cn/gongbao/content/2019/content5449644.htm.

[2] General Office of the State Council.Several Policy Measures on Accelerating the Characteristic Development of Chinese Medicine. General Office of the State Council (Guo Ban Fa [2021] No. 3). (2021-01-22)[2024-07-01].https://www.gov.cn/gongbao/content/2021/content_5588816.htm.

[3] Xinhua News Agency. The Central Committee of the Communist Party of China and the State Council have issued the “Healthy China 2030” Plan Outline. State Council Bulletin of China. 2016;32:5-20.

[4] The Standing Committee of the National People’s Congress. Traditional Chinese Medicine Law of China. Beijing: China Traditional Chinese Medicine Press, 2017.

[5] National Medical Products Administration. Implementing Opinions on Promoting the Development of Traditional Chinese Medicine Inheritance and Innovation.(2020-12-25) [2024-07-01].https://www.nmpa.gov.cn/directory/web/nmpa/xxgk/fgwj/gzwj/zwjyp/20201225163906151.html.

[6] National Medical Products Administration.National Plan for Drug Safety and Promotion of Quality Development in the 14^th Five-Year Plan. (2021-12-30) [2024-07-01].https://www.nmpa.gov.cn/directory/web/nmpa/zwgk/ghcw/ghjh/20211230192314164.html.

[7] National Medical Products Administration. Several Measures on Further Strengthening the Scientific Supervision of Traditional Chinese Medicine and Promoting the Development of Traditional Chinese Medicine Inheritance and Innovation. (2023-01-04) [2024-07-01].https://www.nmpa.gov.cn/xxgk/fgwj/gzwj/gzwjyp/20230103172324162.html.

[8] Special Provisions on the Administration of Registration of Chinese Medicines. (2023-02-10) [2024-07-01].

[9] Chen P, Wu BW, Zhang P, et al. Biological network-based exploration of the principles of traditional Chinese medicine. Chin Sci Bull. 2024; 69(1):17-29. https://doi.org/10.1016/j.scib.2023.11.054.

[10] Tang JN, Sun Y. Application of computational biology in pharmaceutical research. Acta Pharm Sin. 2024; 59(8):2192-2198. https://doi.org/10.16438/j.0513-4870.2023-1256.

[11] Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008; 4(11):682-690. https://doi.org/10.1038/nchembio.118.

[12] Han L, Cheng XR, Bo XC, et al. New situation and new strategy: network pharmacology and research and development of new drugs of traditonal Chinese medicine. Chin J Pharmacol Toxicol. 2018; 32(11):827-828. https://doi.org/10.3867/j.issn.1000-3002.2018.11.001.

[13] Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013; 11(2):110-120. https://doi.org/10.1016/S1875-5364(13)60037-0.

[14] Lai X, Wang X, Hu Y, et al. Editorial: network pharmacology and traditional medicine. Front Pharmacol. 2020;11:1194. https://doi.org/10.3389/fphar.2020.01194.

[15] Zhang P, Zhang DF, Zhou W, et al. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform. 2024; 25(1):bbad518. https://doi.org/10.1093/bib/bbad518.

[16] Han L, Zhou WX.Artificial intelligence accelerates network pharmacology. Chin J Pharmacol Toxicol. 2023; 37(S1):2-3.

[17] Li S. Possible Association Between Chinese Medicine Evidence and Molecular Network Regulation. Scientific and Technological Progress and Socio-Economic Development for the 21 ^st Century (Upper Volume), Beijing: China Science and Technology Press, 1999:442.

[18] Li S, Wang YY, Ji L, et al. A discussion and case study of complexities in traditional Chinese medicine. J Syst Simul. 2002; 11:1429-1431. https://doi.org/10.16182/j.cnki.joss.2002.11.007.

[19] Li S, Wu L, Zhang Z. Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach. Bioinformatics. 2006; 22(17):2143-2150. https://doi.org/10.1093/bioinformatics/btl363.

[20] Li S. Computational systems biology-based TCM research: a case study of cold/hot zheng and associated formula. Modern Tradit Chin Med Mater Med World Sci Technol. 2007; 9(1):105-111.

[21] Li S, Zhang ZQ, Wu LJ, et al. Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol. 2007; 1(1):51-60. https://doi.org/10.1049/iet-syb:20060032.

[22] Hopkins AL. Network pharmacology. Nat Biotechnol. 2007; 25(10):1110-1111. https://doi.org/10.1038/nbt1007-1110.

[23] Li S. Concept and research on biological molecular network markers of traditional Chinese medicine syndrome. J Tradit Chin Med. 2009; 50(9):773-776. https://doi.org/10.13288/j.11-2166/r.2009.09.002.

[24] Li S. Network target: a starting point for traditional Chinese medicine network pharmacology. Chin J Chin Mater Med. 2011; 36(15):2017-2020. https://doi.org/10.4268/cjcmm20111502.

[25] Liu AL, Du GH. Network pharmacology: new guidelines for drug discovery. Acta Pharm Sin. 2010; 45(12):1472-1477. https://doi.org/10.16438/j.0513-4870.2010.12.010.

[26] Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011; 12(1):56-68. https://doi.org/10.1038/nrg2918.

[27] Xiao W, Cao L, Fan QR, et al. Action mechanism studies of Guizhi Fuling Capsule: based on a bio-network analysis. Comput Appl Chem. 2012; 29(12):1455-1459. https://doi.org/10.16866/j.com.app.chem2012.12.011.

[28] Zhang XZ, Xiao W, Xu XX, et al. Study on mechanism of the Reduning Injection on the influenza virus using network pharmacology method. Acta Phys Chim Sin. 2013; 29(7):1415-1420.

[29] Zhou WX, Cheng XR, Zhang YX. Network pharmacology: a new philosophy for understanding of drug action and discovery of new drugs. Chin J Pharmacol Toxicol. 2012; 26(1):4-9. https://doi.org/10.3867/j.issn.1000-3002.2012.01.002.

[30] Liu ZH, Sun XB. Network pharmacology: new opportunity for the modernization of traditional Chinese medicine. Acta Pharm Sin. 2012; 47(6):696-703. https://doi.org/10.16438/j.0513-4870.2012.06.001.

[31] Zhang BL, Fan XH, Liu Y, et al. Strategy and core technologies for the secondary development of Chinese patent medicine. Chin J Chin Mater Med. 2013; 38(22):3797-3800. https://doi.org/10.4268/cjcmm2013220. doi: 10.4268/cjcmm2013220.

[32] Harrold JM, Ramanathan M, Mager DE. Network-based approaches in drug discovery and early development. Clin Pharmacol Ther. 2013; 94(6):651-658. https://doi.org/10.1038/clpt.2013.176.

[33] Ding W, Gu J, Cao L, et al. Traditional Chinese herbs as chemical resource library for drug discovery of anti-infective and anti-inflammatory. J Ethnopharmacol. 2014; 155(1):589-598. https://doi.org/10.1016/j.jep.2014.05.066.

[34] Xiao W, Wu Y, Zhao BJ, et al. Development and industrialisation of a patent Chinese medicine Sanjie ZhenTong Capsule for the treatment of endometriosis//Chinese Society of Traditional Chinese Medicine. Proceedings of the Fourth Forum on Scientific and Technological Achievements of Chinese Traditional Medicine. Jiangsu Kang Yuan Pharmaceutical Co., [201401-04 ZY-02].

[35] Zhang X, Gu J, Cao L, et al. Insights into the inhibition and mechanism of compounds against LPS-induced PGE2 production: a pathway network-based approach and molecular dynamics simulations. Integr Biol (Camb). 2014; 6(12):1162-1169. https://doi.org/10.1039/C4IB00141A.

[36] Li S. Mapping ancient remedies: applying a network approach to traditional Chinese medicine. Science. 2015; 350(6262):S72-S74.

[37] Menche J, Sharma A, Kitsak M, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015; 347(6224):1257601. https://doi.org/10.1126/science.1257601.

[38] Boezio B, Audouze K, Ducrot P, et al.Network-based approaches in pharmacology. Mol Inform. 2017; 36(10):48. https://doi.org/10.1002/minf.201700048.

[39] Casas AI, Hassan AA, Larsen SJ, et al. From single drug targets to synergistic network pharmacology in ischemic stroke. Proc Natl Acad Sci U S A. 2019; 116(14):7129-7136. https://doi.org/10.1073/pnas.1820799116.

[40] Nogales C, Mamdouh ZM, List M, et al. Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends Pharmacol Sci. 2022; 43(2):136-150. https://doi.org/10.1016/j.tips.2021.11.004.

[41] DO Valle IF, Roweth HG, Malloy MW, et al. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nat Food. 2021; 2(3):143-155. https://doi.org/10.1038/s43016-021-00243-7.

[42] Xiao W. Decoding Traditional Chinese Medicine. Beijing: China Traditional Chinese Medicine Press, 2022.

[43] Li S.Network pharmacology evaluation methodology guidance. World Chin Med. 2021; 16(4):527-532. https://doi.org/10.3969/j.issn.1673-7202.2021.04.00. doi: 10.3969/j.issn.1673-7202.2021.04.00.

[44] Wang Z, Wang X, Zhang DY, et al. Traditional Chinese medicine network pharmacology: development in new era under guidance of network pharmacology evaluation method guidance. Chin J Chin Mater Med. 2022; 47(1):7-17. https://doi.org/10.19540/j.cnki.cjcmm.20210914.702.

[45] Li S.Network pharmacology evaluation method guidance-draft. World J Tradit Chin Med. 2021; 7(1):148. https://doi.org/10.4103/wjtcm.wjtcm_11_21.

[46] Li S. Network Pharmacology. Beijing: Tsinghua University Press, 2022.

[47] Li S. Network Pharmacology. Springer Press & Tsinghua University Press, ISBN-10: ‎9811607524.

[48] Xu H, Li S, Liu J, et al.Bioactive compounds from Huashi Baidu Decoction possess both antiviral and anti-inflammatory effects against COVID-19. Proc Natl Acad Sci U S A. 2023; 120(18):e2301775120. https://doi.org/10.1073/pnas.2301775120.

[49] Theodoris CV, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature. 2023; 618(7965):616-624. https://doi.org/10.1038/s41586-023-06139-9.

[50] Wu X, Jiang R, Zhang MQ, et al. Network-based global inference of human disease genes. Mol Syst Biol. 2008;4:189. https://doi.org/10.1038/msb.2008.27.

[51] Zhao S, Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One. 2010; 5(7):e11764. https://doi.org/10.1371/journal.pone.0011764.

[52] Lage K, Karlberg EO, Størling ZM, et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol. 2007; 25(3):309-316. https://doi.org/10.1038/nbt1295.

[53] Campillos M, Kuhn M, Gavin AC, et al. Drug target identification using side-effect similarity. Science. 2008; 321(5886):263-266. https://doi.org/10.1126/science.1158140.

[54] Zhao JN, Huang LQ. Traditional Chinese medicine regulatory science: an emerging convergence science in development. Bull Natl Nat Sc Found Chin. 2024; 38(3):396. https://doi.org/10.16262/j.cnki.1000-8217.20240209.003.

[55] Xu HY, Zhang YQ, Liu ZM, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019; 47(D1):D976-D982. https://doi.org/10.1093/nar/gky987.

[56] Huang L, Xie D, Yu Y, et al. TCMID 2.0: a comprehensive resource for TCM. Nucleic Acids Res. 2018; 46(D1):D1117-D1120. https://doi.org/10.1093/nar/gkx1028.

[57] Wu Y, Zhang F, Yang K, et al. SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res. 2019; 47(D1):D1110-D1117. https://doi.org/10.1093/nar/gky1021.

[58] Fang YC, Huang HC, Chen HH, et al. TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining. BMC Complement Altern Med. 2008; 8:58. https://doi.org/10.1186/1472-6882-8-58.

[59] Li X, Tang Q, Meng F, et al. INPUT: an intelligent network pharmacology platform unique for traditional Chinese medicine. Comput Struct Biotechnol J. 2022; 20:1345-1351. https://doi.org/10.1016/j.csbj.2022.03.006.

[60] Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014; 6:13. https://doi.org/10.1186/1758-2946-6-13.

[61] Kong X, Liu C, Zhang Z, et al. BATMAN-TCM 2.0:an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res. 2024; 52(D1):D1110-D1120. https://doi.org/10.1093/nar/gkad926.

[62] Zeng P, Zhou H. Thoughts on the homogenization phenomenon of key component screening for “different diseases and different prescriptions” in network pharmacology. Chin J Exp Tradit Med Form. 2022; 28(18):177-191. https://doi.org/10.13422/j.cnki.syfjx.20221114.

[63] Sun XB. Strategic thinking on high-quality development of traditional Chinese medicine new drugs derived from classic prescriptions. Chin J Pharmacol Toxicol. 2019; 33(9):662.

[64] Xiao W, Zhang XZ, Cao L, et al. Exploration of the whole-process quality control system of traditional Chinese medicine based on efficacy components. J Nanjing Univ Tradit Chin Med. 2022; 38(9):743-747. https://doi.org/10.14148/j.issn.1672-0482.2022.0743.

[65] Hou XJ, Zeng WJ, Ding JB, et al. Optimization of the process of antioxidant functional extracts of Lycium barbarum based on the concept of quality by design and information entropy assignment method. Chin Mod Tradit Chin Med. 2022; 24(9):1745. https://doi.org/10.13313/j.issn.1673-4890.20211014004.

[66] Hu M, Fu J, Hu JH, et al. Analysis of the components and mechanism prediction of Xiao'er Fu Shao He Zhong Granules for the treatment of functional abdominal pain based on HPLC-Q-TOF-MS/MS and network pharmacology. Drug Eval Res. 2023; 46(5):997-1011. https://doi.org/10.7501/j.issn.1674-6376.2023.05.009.

[67] Ke ZP, Zhang XZ, Ding Y, et al. Study on the pharmacodynamic material basis and molecular mechanism of Qigui Tongfeng Tablets based on network pharmacology. Chin J Chin Mater Med. 2015; 40(14):2837-2842. https://doi.org/10.4268/cjcmm20151428.

[68] Huang M, Song YM, Wang XY, et al. Optimization of the ethanol extraction process of Ziziphus jujuba and Schisandra chinensis based on network pharmacology combined with response surface methodology. Chin J Chin Mater Med. 2023; 48(4):966-977. https://doi.org/10.19540/j.cnki.cjcmm.20220712.303.

[69] Hu ZJ, Zheng LL, Xu GY, et al. Study on the extraction process of Zhizi Chi Tang based on network pharmacology and quality by design concept. Chin Herb Med. 2022; 53(7):1973-1982. https://doi.org/10.7501/j.issn.0253-2670.2022.07.006.

[70] Zhang SQ, Lai XX, Wang X, et al. Deciphering the pharmacological mechanisms of Guizhi-Fuling Capsule on primary dysmenorrhea through network pharmacology. Front Pharmacol. 2021;12:613104. https://doi.org/10.3389/fphar.2021.613104.

[71] Xiao W. Establishment and application of modern TCM efficacy-related quality standards system using Guizhi Fuling Capsules as a model. Jiangsu Province, Jiangsu Kanion Pharmaceutical Co., Ltd., Jan. 1, 2010.

[72] Chen S, Yang X, Wei Z, et al. Establishment of an anti-inflammation-based bioassay for the quality control of the 13-component TCM formula (Lianhua Qingwen). Pharm Biol. 2021; 59(1):537-545. https://doi.org/10.1080/13880209.2021.1917627.

[73] Ye J, Li RM, Zeng HW, et al. Discovery and research advances in quality markers of Chinese materia medica based on holistic characteristics. Chin Tradit Herb Drugs. 2019; 50(19):4529-4537. https://doi.org/10.7501/j.issn.0253-2670.2019.19.002.

[74] Fan XH, Cheng YY, Zhang BL. Network pharmacology: a new strategy for the modern research of Chinese medicinal formulas. Chin J Chin Mater Med. 2015; 40(1):1-6. https://doi.org/10.4268/cjcmm20150101.

[75] Tao XQ, Ke ZP, Zhang XZ, et al. Study on the mechanism of Jinzhen Oral Liquid in the intervention of COVID-19 based on network pharmacology and molecular docking technology. Chin Herb Med. 2020; 51(9):2326-2333. https://doi.org/10.7501/j.issn.0253-2670.2020.09.006.

[76] Zhang XZ, Xiao W, Xu XJ, et al. Study on the molecular mechanism of Guizhi Fuling Capsules in the treatment of dysmenorrhea, uterine fibroids, and pelvic inflammatory disease based on network pharmacology. Chin Herb Med. 2016; 47(1):81-94. https://doi.org/10.7501/j.issn.0253-2670.2016.01.013.

[77] Li N, Zhang XZ, Wang YR, et al. Study on the mechanism of Tongsai Mai Pian in the treatment of atherosclerosis based on network pharmacology. Chin J Chin Mater Med. 2016; 41(8):66-72. https://doi.org/10.4268/cjcmm20160922.

[78] Zhu Y, Ouyang Z, Du H, et al. New opportunities and challenges of natural products research: when target identification meets single-cell multiomics. Acta Pharm Sin B. 2022; 12(11):4011-4039. https://doi.org/10.1016/j.apsb.2022.08.022.

[79] Huang Y, Li S. Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network. BMC Bioinformatics. 2010; 11(Suppl 1):S32. https://doi.org/10.1186/1471-2105-11-S1-S32.

[80] Tann WT, Hu BJ, Huang B, et al. Effect of Anchusa italica Retz on cerebral ischemia-reperfusion injury in rats based on disease and active compound target network. Her Med. 2024; 43(4):535-544. https://doi.org/10.3870/j.issn.1004-0781.2024.04.009.

[81] Deng G, Zhou L, Wang B, et al. Targeting cathepsin B by cycloastragenol enhances antitumor immunity of CD8 T cells via inhibiting MHC-I degradation. J Immunother Cancer. 2022; 10(10):e004874. https://doi.org/10.1136/jitc-2022-004874.

[82] Zhang X, Gu J, Cao L, et al. Network pharmacology study on the mechanism of traditional Chinese medicine for upper respiratory tract infection. Mol Biosyst. 2014; 10(10):2517-2525. https://doi.org/10.1039/C4MB00164H.

[83] Zhou W, Lai XX, Wang X, et al. Network pharmacology to explore the anti-inflammatory mechanism of Xuebijing in the treatment of sepsis. Phytomedicine. 2021;85:153543. https://doi.org/10.1016/j.phymed.2021.153543.

[84] Wang Y, Li X, Zhang JH, et al. Study on the mechanism of Xuanfei Baidu Decoction in the treatment of COVID-19 based on network pharmacology. Chin J Chin Mater Med. 2020; 45(10):2249-2256. https://doi.org/10.19540/j.cnki.cjcmm.20200325.401.

[85] Tu C, Niu M, Li C, et al. Network pharmacology oriented study reveals inflammatory state-dependent dietary supplement hepatotoxicity responses in normal and diseased rats. Food Funct. 2019; 10(6):3477-3490. https://doi.org/10.1039/c8fo01974f.

[86] Bai ZF, Wang JB, Xiao XH. Cognition innovation of toxicity of Chinese medicine and safe and precise medication. Chin J Chin Mater Med. 2022; 47(10):2557-2564. https://doi.org/10.19540/j.cnki.cjcmm.20220211.601.

[87] Zhang L, Wang T, Xu ZY, el al. Potential hepatotoxic compounds and mechanisms of Epimedii Folium based on network toxicology and cell experimental validation. Chin J Chin Mater Med. 2021; 46(10):2413-2423. https://doi.org/10.19540/j.cnki.cjcmm.20210202.401.

[88] Wu X, Wu DL, Wei LB, et al. Mechanism of Astragali Radix in reducing the nephrotoxicity of Tripterygium wilfordii based on network toxicology combined with network pharmacology and experimental verification. Pharmacol Clin Chin Mater Med. 2024; 40(9):83-88. https://doi.org/10.13412/j.cnki.zyyl.20240423.009.

[89] Jiang HY, Gao HY, Li J, et al. Integrated spatially resolved metabolomics and network toxicology to investigate the hepatotoxicity mechanisms of component D of Polygonum multiflorum Thunb. J Ethnopharmacol. 2022;298: 115630. https://doi.org/10.1016/j.jep.2022.115630.

[90] Li JL, Liang H, Cai SZ, et al. Mechanism of detoxification of Chebulae Fructus against Aconiti kusnezoffii Radix toxicity based on network pharmacology. Acta Pharm Sin. 2018; 53(10):1670-1679. https://doi.org/10.16438/j.0513-4870.2018-0506.

[91] Su SB, Lu A, Li S, et al. Evidence-based ZHENG: a traditional Chinese medicine syndrome. Evid Based Complement Alternat Med. 2012;2012:246538. https://doi.org/10.1155/2012/246538.

[92] Wei FX, Liu HL, Fan YH, et al. Overview of mathematical models applying in property theory and compatibility rules of traditional Chinese medicines. Drug Eval Res. 2021; 44(1):205-212. https://doi.org/10.7501/j.issn.1674-6376.2021.01.029.

[93] Niu M, Zhang CE, Wu SN, et al. Evaluation of the “multi-efficacy of one drug” of traditional Chinese medicines based on the biological target network: towards precision medicine. Modern Tradit Chin Med Mater Med World Sci Technol. 2017; 19(1):44-49. https://doi.org/10.11842/wst.2017.01.006.

[94] Fu Y, Fan GJ, Huang HY, et al. Thinking of research methods on inheritance of famous TCM doctors’ experience based on big data. Chin J Tradit Chin Med Pharm. 2017; 32(4):1644-1646.

[95] Zhou W, Yang K, Zeng J, et al. FordNet: recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule. Pharmacol Res. 2021;173:105752. https://doi.org/10.1016/j.phrs.2021.105752.

[96] Li Y, Wang X, Yang Z, et al. A new approach for optimizing empirical prescriptions of famous physicians based on network target: taking Qingluo Decoction as an example. Chin J Chin Mater Med. 2022; 47(19):5264-5273. https://doi.org/10.19540/j.cnki.cjcmm.20220613.704.

[97] Zuo J, Wang X, Liu Y, et al. Integrating network pharmacology and metabolomics study on anti-rheumatic mechanisms and antagonistic effects against methotrexate-induced toxicity of Qing-Luo-Yin. Front Pharmacol. 2018;9:1472. https://doi.org/10.3389/fphar.2018.01472.

[98] Wang JH, Lei X, Cheng XR, et al. LW-AFC, a new formula derived from Liuwei Dihuang Decoction, ameliorates behavioral and pathological deterioration via modulating the neuroendocrine-immune system in PrP-hAβPPswe/PS1^ΔE9 transgenic mice. Alzheimers Res Ther. 2016; 8(1): 57. https://doi.org/10.1186/s13195-016-0226-6.

[99] Qiao LS, Li J, Xie L, et al. The exploration of a new approach for developing innovative Chinese herbal formulae by combining targeted transcriptome, expert experience and artificial intelligence: taking the development of antichronic heart failure innovative formula as an example. J Tradit Chin Med. 2023; 64(3):217-224. https://doi.org/10.13288/j.11-2166/r.2023.03.001.

[100] Liao S, Han L, Zheng X, et al. Tanshinol borneol ester, a novel synthetic small molecule angiogenesis stimulator inspired by botanical formulations for angina pectoris. Br J Pharmacol. 2019; 176(17):3143-3160. https://doi.org/10.1111/bph.14714.

[101] World Health Organization.Clinical Management of COVID-19: Living Guideline.https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2023.2. License: CC BY-NC-SA 3.0 IGO.

[102] Li S, Wang R, Zhang Y, et al. Symptom combinations associated with outcome and therapeutic effects in a cohort of cases with SARS. Am J Chin Med. 2006; 34(6):937-947. https://doi.org/10.1142/S0192415X06004417.

[103] Corona Virus Disease-19 Prevention and Control·Consensus Diagnosis and Treatment of Corona Virus Disease-19 (9^th trial edition). Chin Med. 2022; 17(4):481-487. https://doi.org/10.3760/j.issn.1673-4777.2022.04.001.

[104] Li S, Wang BY, Cao L, et al. Research and development practice of traditional Chinese medicine based on network target theory and technology. Chin J Chin Mater Med. 2023; 48(22):5965-5976. https://doi.org/10.19540/j.cnki.cjcmm.20230923.701.

[105] Xu HY, Wang SS, Yang HJ, et al. Study on action mechanism of adjuvant therapeutic effect compound Ejiao slurry in treating cancers based on network pharmacology. Chin J Chin Mater Med. 2014; 39(16):3148-3151. https://doi.org/10.4268/cjcmm20141628.

[106] Guo Y, Nie Q, Maclean AL, et al. Multiscale modeling of inflammation-induced tumorigenesis reveals competing oncogenic and onco-protective roles for inflammation. Cancer Res. 2017; 77(22):6429-6441. https://doi.org/10.1158/0008-5472.CAN-17-1662.

[107] Zhang P, Li S. Human cross-tissue cell atlases: unprecedented resources towards systematic understanding of physiology and diseases. Signal Transduct Target Ther. 2022; 7(1):352. https://doi.org/10.1038/s41392-022-01201-w.

[108] Hou SY, Zhang P, Yang K, et al. Decoding multilevel relationships with the human tissue-cell-molecule network. Brief Bioinform. 2022; 23(5):bbac170. https://doi.org/10.1093/bib/bbac170.

[109] Zhang P, Yang M, Zhang Y, et al. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep. 2019; 27(6):1934-1947. https://doi.org/10.1016/j.celrep.2019.04.052.

[110] Cui J, Cui H, Yang M, et al. Tongue coating microbiome as a potential biomarker for gastritis including precancerous cascade. Protein Cell. 2019; 10(7):496-509. https://doi.org/10.1007/s13238-018-0596-6.

[111] Guo JC, Zhang P, Zhou L, et al. Prognostic and predictive value of a five-molecule panel in resected pancreatic ductal adenocarcinoma: a multicentre study. EBioMedicine. 2020;55:102767. https://doi.org/10.1016/j.ebiom.2020.102767.

[112] Zhang YX, Cheng XR, Zhou WX. Drug repositioning: an important application of network pharmacology. Chin J Pharmacol Toxicol. 2012; 26(6):779-786. https://doi.org/10.3867/j.issn.1000-3002.2012.06.001.

[113] Zhang P, Wang B, Li S. Network-based cancer precision prevention with artificial intelligence and multi-omics. Sci Bull (Beijing). 2023; 68(12):1219-1222. https://doi.org/10.1016/j.scib.2023.05.023.

[114] National Natural Science Foundation of China. Condensing the Case of Scientific Problems. Beijing: Science Press, 2023.

[115] Wang B, Zhang D, Zhang T, et al. Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on network target analysis. Chin Med. 2023; 18(1):88. https://doi.org/10.1186/s13020-023-00781-1.

[116] U. S. Food & Drug Administration.Good Machine Learning Practice for Medical Device Development: Guiding Principles.https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles.

[117] European Medicines Agency.Multidisciplinary: Artificial Intelligence (AI).

[118] Wang Z, Liu J. Network pharmacology and drug discovery for new combinations in traditional Chinese medicine. Chin J Pharmacol Toxicol. 2018; 32(11):862-864.