Progress of artificial intelligence for science (AI4S) applications in drug development and clinical practice in the digital age_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Chunli ^1,2 ,  NIU Gang ^1,2

1. Turing-Darwin Laboratory, Beijing, 100190, P. R. China;
2. Western Institute of Computing Technology, Chongqing, 400000, P. R. China;

Corresponding author：

NIU Gang, Email: g.niu@cqxyy.net

Keywords：

Artificial intelligence for science; digital twins; electronic drugs; virtual clinical trials

DOI：

10.7507/1007-4848.202405020

Video：

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Artificial intelligence (AI) for science (AI4S) technology, the AI technology for scientific research, has shown tremendous potential and influence in the field of healthcare, redefining the research paradigm of medical science under the guidance of computational medicine. We reviewed the main technological trends of AI4S in reshaping healthcare paradigm: knowledge-driven AI, leveraging extensive literature mining and data integration, emerges an important tool for understanding disease mechanisms and facilitating novel drug development; data-driven AI, delving into clinical and human-related omics data, unveils individual variances and disease mechanisms, and further establishes patient-centric digital twins to guide drug development and personalized medicine. Meanwhile, based on authentic patient digital twin models, adaptable strategies are employed to further propel the development of "e-drugs" that mimic the authentic mechanisms. These digital twins of drugs are evaluated for drug efficacy and safety through large-scale cloud-based virtual clinical trials, and followed by rationally designed real-world clinical trials, thus notably reducing drug development costs and enhancing success rates. Despite encountering challenges such as data scale, quality control, model interpretability, the transition from science insights to engineering solutions, and regulatory hurdles, we anticipate the integration of AI4S technology to revolutionize drug development and clinical practices. This transformation brings revolutionary changes to the medical field, offering novel opportunities and challenges for the development of medical science, and more importantly, providing necessary but personalized healthcare solutions for humankind.

Citation： ZHANG Chunli, NIU Gang. Progress of artificial intelligence for science (AI4S) applications in drug development and clinical practice in the digital age. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(10): 1392-1399. doi: 10.7507/1007-4848.202405020 Copy

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25.	Cord M, Cunningham P, Delany SJ. (eds) Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval. Berlin: Springer, 2008: 21-49.
26.	Hastie T, Tibshirani R, Friedman J, et al. Chief Editors. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Berlin: Springer, 2009: 485-585.
27.	Reddy Y, Viswanath P, Reddy BE. Semi-supervised learning: A brief review. Int J Eng Technol, 2018, 7(1-8): 81-85.
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29.	Pan SJ, Yang Q. A survey on transfer learning. IEEE transactions on knowledge and data engineering, 2009, 22(10): 1345-1359.
30.	Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
31.	You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther, 2022, 7(1): 156-179.
32.	Ji X, Tan W, Zhang C, et al. TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2. Drug Dev Res, 2020, 81(8): 1004-1018.
33.	Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
34.	Masison J, Beezley J, Mei Y, et al. A modular computational framework for medical digital twins. Proc Natl Acad Sci USA, 2021, 118(20): e2024287118.
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36.	Finn RS, Martin M, Rugo HS, et al. Palbociclib and letrozole in advanced breast cancer. N Engl J Med, 2016, 375(20): 1925-1936.
37.	Liao L, Deng L, Zhang YL, et al. C9orf142 transcriptionally activates MTBP to drive progression and resistance to CDK4/6 inhibitor in triple-negative breast cancer. Clin Transl Med, 2023, 13(11): e1480.
38.	Yang M, Fan Y, Wu ZY, et al. DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations. EBioMedicine, 2021, 69: 103446.
39.	Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell, 2020, 38(5): 672-684.
40.	Sips FLP, Pappalardo F, Russo G, et al. In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim. BMC Med Inform Decis Mak, 2022, 22(Suppl 6): 294.
41.	Gutiérrez-Casares JR, Quintero J, Jorba G, et al. Methods to develop an in silico clinical trial: Computational head-to-head comparison of lisdexamfetamine and methylphenidate. Front Psychiatry, 2021, 12: 741170.
42.	Kolla L, Gruber FK, Khalid O, et al. The case for AI-driven cancer clinical trials—The efficacy arm in silico. Biochim Biophys Acta Rev Cancer, 2021, 1876(1): 188572.
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44.	Susilo ME, Li CC, Gadkar K, et al. Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a phaseⅠstudy of bispecific antibody, mosunetuzumab, in NHL. Clin Transl Sci, 2023, 16(7): 1134-1148.
45.	Wang H, Arulraj T, Kimko H, et al. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. NPJ Precis Oncol, 2023, 7(1): 55-68.
46.	Milberg O, Gong C, Jafarnejad M, et al. A QSP model for predicting clinical responses to monotherapy, combination and sequential therapy following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade. Sci Rep, 2019, 9(1): 11286.
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48.	Zhang S, Deshpande A, Verma BK, et al. Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial. BioRxiv, 2023: 2023.08. 11.553000.
49.	Sammut SJ, Crispin-Ortuzar M, Chin SF, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature, 2022, 601(7894): 623-629.

1. Chan HCS, Shan H, Dahoun T, et al. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci, 2019, 40(8): 592-604.
2. Lipkova J, Chen RJ, Chen B, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell, 2022, 40(10): 1095-1110.
3. Björnsson B, Borrebaeck C, Elander N, et al. Digital twins to personalize medicine. Genome Med, 2019, 12(1): 4.
4. Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature, 2023, 620(7972): 47-60.
5. Krishnan R, Rajpurkar P, Topol EJ. Self-supervised learning in medicine and healthcare. Nat Biomed Eng, 2022, 6(12): 1346-1352.
6. Townshend RJL, Eismann S, Watkins AM, et al. Geometric deep learning of RNA structure. Science, 2021, 373(6558): 1047-1051.
7. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature, 2015, 521(7553): 452-459.
8. Goenka SD, Gorzynski JE, Shafin K, et al. Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing. Nat Biotechnol, 2022, 40(7): 1035-1041.
9. Qiao Z, Christensen AS, Welborn M, et al. Informing geometric deep learning with electronic interactions to accelerate quantum chemistry. Proc Natl Acad Sci USA, 2022, 119(31): e2205221119.
10. Gao W, Coley CW. The synthesizability of molecules proposed by generative models. J Chem Inf Model, 2020, 60(12): 5714-5723.
11. 王飞跃, 缪青海. 人工智能驱动的科学研究新范式:从AI4S 到智能科学. 中国科学院院刊 , 2023, 38(4): 536-540.Wang FY, Miao QH. Novel paradigm for AI-driven scientific research: From AI4S to intelligent science. Bull Chie Acad Sci (Chinese Version), 2023, 38(4): 536-540.
12. Wu T, He S, Liu J, et al. A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA J Automatica Sinica, 2023, 10(5): 1122-1136.
13. Alaparthi S, Mishra M. Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. J Market Anal, 2021, 9: 118-126.
14. Chen X, Jia S, Xiang Y. A review: Knowledge reasoning over knowledge graph. Expert Syst Appl, 2020, 141: 112948.
15. Stevens R, Goble CA, Bechhofer S. Ontology-based knowledge representation for bioinformatics. Brief Bioinform, 2000, 1(4): 398-414.
16. Getoor L, Friedman N, Koller D, et al. Learning probabilistic relational models. In: Džeroski S, Lavrač N. (eds) Relational Data Mining. Berlin: Springer, 2001: 307-335.
17. Jordan MI. Graphical models. Stat Sci, 2004, 19(1): 140-155.
18. Kirketerp-Møller K, Stewart PS, Bjarnsholt T. The zone model: A conceptual model for understanding the microenvironment of chronic wound infection. Wound Repair Regen, 2020, 28(5): 593-599.
19. Salehinejad H, Sankar S, Barfett J, et al. Recent advances in recurrent neural networks. arXiv preprint arXiv. 2017: 180101078 .
20. Han K, Xiao A, Wu E, et al. Transformer in transformer. In: Ranzato M, Beygelzimer A, Dauphin Y, et al. (eds) Advances in Neural Information Processing Systems. San Diego: Curran Associates, 2021: 15908-15919.
21. Pearl J. Causal inference in statistics: An overview. Stat Surv, 2009, 3: 96-146.
22. Lipovetsky S. Emotion regulation ability and resilience in a sample of adolescents from a suburban area. Technometrics, 2022, 64(3): 423-424.
23. Tchagna Kouanou A, Mih Attia T, Feudjio C, et al. An overview of supervised machine learning methods and data analysis for COVID-19 detection. J Healthc Eng, 2021, 22(2021): 4733167.
24. Atasever S, Azginoglu N, Terzi DS, et al. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clin Imaging, 2023, 94: 18-41.
25. Cord M, Cunningham P, Delany SJ. (eds) Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval. Berlin: Springer, 2008: 21-49.
26. Hastie T, Tibshirani R, Friedman J, et al. Chief Editors. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Berlin: Springer, 2009: 485-585.
27. Reddy Y, Viswanath P, Reddy BE. Semi-supervised learning: A brief review. Int J Eng Technol, 2018, 7(1-8): 81-85.
28. Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive. IEEE transactions on knowledge and data engineering, 2021, 35(1): 857-876.
29. Pan SJ, Yang Q. A survey on transfer learning. IEEE transactions on knowledge and data engineering, 2009, 22(10): 1345-1359.
30. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
31. You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther, 2022, 7(1): 156-179.
32. Ji X, Tan W, Zhang C, et al. TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2. Drug Dev Res, 2020, 81(8): 1004-1018.
33. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
34. Masison J, Beezley J, Mei Y, et al. A modular computational framework for medical digital twins. Proc Natl Acad Sci USA, 2021, 118(20): e2024287118.
35. Katsoulakis E, Wang Q, Wu H, et al. Digital twins for health: A scoping review. NPJ Digit Med, 2024, 7(1): 77-87.
36. Finn RS, Martin M, Rugo HS, et al. Palbociclib and letrozole in advanced breast cancer. N Engl J Med, 2016, 375(20): 1925-1936.
37. Liao L, Deng L, Zhang YL, et al. C9orf142 transcriptionally activates MTBP to drive progression and resistance to CDK4/6 inhibitor in triple-negative breast cancer. Clin Transl Med, 2023, 13(11): e1480.
38. Yang M, Fan Y, Wu ZY, et al. DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations. EBioMedicine, 2021, 69: 103446.
39. Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell, 2020, 38(5): 672-684.
40. Sips FLP, Pappalardo F, Russo G, et al. In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim. BMC Med Inform Decis Mak, 2022, 22(Suppl 6): 294.
41. Gutiérrez-Casares JR, Quintero J, Jorba G, et al. Methods to develop an in silico clinical trial: Computational head-to-head comparison of lisdexamfetamine and methylphenidate. Front Psychiatry, 2021, 12: 741170.
42. Kolla L, Gruber FK, Khalid O, et al. The case for AI-driven cancer clinical trials—The efficacy arm in silico. Biochim Biophys Acta Rev Cancer, 2021, 1876(1): 188572.
43. Arulraj T, Wang H, Emens LA, et al. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. Sci Adv, 2023, 9(26): eadg0289.
44. Susilo ME, Li CC, Gadkar K, et al. Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a phaseⅠstudy of bispecific antibody, mosunetuzumab, in NHL. Clin Transl Sci, 2023, 16(7): 1134-1148.
45. Wang H, Arulraj T, Kimko H, et al. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. NPJ Precis Oncol, 2023, 7(1): 55-68.
46. Milberg O, Gong C, Jafarnejad M, et al. A QSP model for predicting clinical responses to monotherapy, combination and sequential therapy following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade. Sci Rep, 2019, 9(1): 11286.
47. Anbari S, Wang H, Zhang Y, et al. Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager. Front Pharmacol, 2023, 14: 1163432.
48. Zhang S, Deshpande A, Verma BK, et al. Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial. BioRxiv, 2023: 2023.08. 11.553000.
49. Sammut SJ, Crispin-Ortuzar M, Chin SF, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature, 2022, 601(7894): 623-629.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Progress of artificial intelligence for science (AI4S) applications in drug development and clinical practice in the digital age

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