Artificial Intelligence in Drug Discovery and Development Against Antimicrobial Resistance: A Narrative Review

Ghaderzadeh, Mustafa; Shalchian, Armin; Irajian, Gholamreza; Sadeghsalehi, Hamidreza; Zahedi bialvaei, Abed; Sabet, Babak

year 18, Issue 3 (May - June 2024) Iran J Med Microbiol 2024, 18(3): 135-147 | Back to browse issues page

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Ghaderzadeh M, Shalchian A, Irajian G, Sadeghsalehi H, Zahedi bialvaei A, Sabet B. Artificial Intelligence in Drug Discovery and Development Against Antimicrobial Resistance: A Narrative Review. Iran J Med Microbiol 2024; 18 (3) :135-147
URL: http://ijmm.ir/article-1-2384-en.html

Artificial Intelligence in Drug Discovery and Development Against Antimicrobial Resistance: A Narrative Review

Mustafa Ghaderzadeh

¹, Armin Shalchian²

, Gholamreza Irajian³

, Hamidreza Sadeghsalehi⁴

, Abed Zahedi bialvaei⁵

, Babak Sabet⁶

1- Boukan Faculty of Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran , Mustaf.ghaderzadeh@sbmu.ac.ir
2- Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
3- Microbial Biotechnology Research Center, Iran University of Medical Sciences, Tehran, Iran & Department of Microbiology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
4- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
5- Microbial Biotechnology Research Center, Iran University of Medical Sciences, Tehran, Iran
6- Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran & Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran

Abstract: (288 Views)

Antimicrobial resistance (AMR) presents a formidable global health challenge, jeopardizing the efficacy of current antibiotics and posing a substantial threat to public health. The escalating prevalence of AMR demands innovative solutions. However, the traditional drug discovery process for combating AMR is marked by significant costs, prolonged timelines, frequent inefficacies, and numerous developmental hurdles. This narrative review explores the potential role of artificial intelligence (AI) in addressing AMR through drug discovery and development. It assesses the current state of AMR, critiques the limitations of conventional drug discovery methods, and elucidates the opportunities and advancements afforded by AI. The review delves into various AI applications, encompassing machine learning, deep learning, and language models, for the identification of novel antimicrobial agents, optimization of drug design, and prediction of AMR mechanisms. Additionally, it examines the integration of AI with high-throughput screening, genomics, and proteomics to expedite the discovery and development of new antimicrobial compounds. The review concludes by addressing challenges and ethical considerations linked to AI implementation in AMR research, emphasizing the imperative for collaborative efforts among scientists, policymakers, and healthcare professionals to effectively combat AMR.

Keywords: Artificial Intelligence, Drug Discovery, Drug Development, Antimicrobial Resistance, Machine Learning, Deep Learning

Full-Text [PDF 688 kb] (83 Downloads)

Type of Study: Review Article | Subject: Artificial Intelligence
Received: 2024/04/27 | Accepted: 2024/07/17 | ePublished: 2024/08/18

References

1. Zahedi Bialvaei A, Eslami P, Ganji L, Dolatyar Dehkharghani A, Asgari F, Koupahi H, et al. Prevalence and epidemiological investigation of mgrB-dependent colistin resistance in extensively drug resistant Klebsiella pneumoniae in Iran. Sci Rep. 2023;13(1):10680. [DOI:10.1038/s41598-023-37845-z] [PMID] [PMCID]

2. Majumder MAA, Rahman S, Cohall D, Bharatha A, Singh K, Haque M, et al. Antimicrobial stewardship: Fighting antimicrobial resistance and protecting global public health. Infect Drug Resist. 2020:4713-38. [DOI:10.2147/IDR.S290835] [PMID] [PMCID]

3. World Health Organization (WHO). Antimicrobial resistance. 2023. Retrieved from: [https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance]

4. O'Neill J. Tackling drug-resistant infections globally: final report and recommendations. 2016. London, UK: Wellcome Trust.

5. Muteeb G, Rehman MT, Shahwan M, Aatif M. Origin of antibiotics and antibiotic resistance, and their impacts on drug development: A narrative review. Pharmaceuticals. 2023;16(11):1615. [DOI:10.3390/ph16111615] [PMID] [PMCID]

6. Esmailkhani A, Asadyun M, Nezamzadeh F, Keyvanfar E, Najafabadi N, Zahedi Bialvaei A. Establishing Artificial Intelligence and Reducing Health Care System Costs in Response to COVID-19. Clin Lab. 2022;68(5). [DOI:10.7754/Clin.Lab.2021.210848] [PMID]

7. Shalchian A, Esmailkhani A, Razavi S. A review of the role of artificial intelligence in the diagnosis and treatment of infectious diseases. Razi J Med Sci. 2022;29(7):160-9.

8. Fitria TN, Artificial intelligence (AI) in education: Using AI tools for teaching and learning process. Proceeding Seminar Nasional & Call For Papers. 2021;4(1):134-147.

9. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1):80. [DOI:10.1016/j.drudis.2020.10.010] [PMID] [PMCID]

10. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):14.

11. Malik A, Lischuk Y, Henderson T, Prazenica R. Generating Constant Screw Axis Trajectories With Quintic Time Scaling For End-Effector Using Artificial Neural Network And Machine Learning. 2021 IEEE Conference on Control Technology and Applications (CCTA); 2021: IEEE. [DOI:10.1109/CCTA48906.2021.9658657]

12. Abiodun OI, Jantan A, Omolara AE, Dada KV, Umar AM, Linus OU, et al. Comprehensive review of artificial neural network applications to pattern recognition. IEEE Access. 2019;7:158820-46. [DOI:10.1109/ACCESS.2019.2945545]

13. Shang W, Huang X. A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications. arXiv preprint arXiv:240414809. 2024.

14. Yang J, Jin H, Tang R, Han X, Feng Q, Jiang H, et al. Harnessing the power of llms in practice: A survey on chatgpt and beyond. ACM Trans Knowl Discov Data. 2024;18(6):1-32. [DOI:10.1145/3649506]

15. Swinney DC, Anthony J. How were new medicines discovered?. Nat Rev Drug Discov. 2011;10(7):507-19. [DOI:10.1038/nrd3480] [PMID]

16. Arrowsmith CH, Audia JE, Austin C, Baell J, Bennett J, Blagg J, et al. The promise and peril of chemical probes. Nat Chem Biol. 2015;11(8):536-41. [DOI:10.1038/nchembio.1867] [PMID] [PMCID]

17. Liu R, Li X, Lam KS. Combinatorial chemistry in drug discovery. Curr Opin Chem Biol. 2017;38:117-26. [DOI:10.1016/j.cbpa.2017.03.017] [PMID] [PMCID]

18. Wermuth U, Young D. Classics in Total Synthesis II: More Targets, Strategies, Methods. Synthesis. 2004;2004(05):809-. [DOI:10.1055/s-2004-822995]

19. Harvey A. Strategies for discovering drugs from previously unexplored natural products. Drug Discovery Today. 2000;5(7):294-300. [DOI:10.1016/S1359-6446(00)01511-7] [PMID]

20. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-49. [DOI:10.1111/j.1476-5381.2010.01127.x] [PMID] [PMCID]

21. Salam MA, Al-Amin MY, Salam MT, Pawar JS, Akhter N, Rabaan AA, et al. Antimicrobial Resistance: A Growing Serious Threat for Global Public Health. Healthcare. 2023;11(13):1946. [DOI:10.3390/healthcare11131946]

22. Esmailkhani A, Akhi MT, Sadeghi J, Niknafs B, Zahedi Bialvaei A, Farzadi L, et al. Assessing the prevalence of Staphylococcus aureus in infertile male patients in Tabriz, northwest Iran. Int J Reprod Biomed. 2018;16(7):469. [DOI:10.29252/ijrm.16.7.469] [PMID] [PMCID]

23. Zahedi Bialvaei A, Dolatyar Dehkharghani A, Asgari F, Shamloo F, Eslami P, Rahbar M. Modified CIM test as a useful tool to detect carbapenemase activity among extensively drug-resistant Klebsiella pneumoniae, Escherichia coli and Acinetobacter baumannii. Ann Microbiol. 2021;71(1):23. [DOI:10.1186/s13213-021-01634-8]

24. Lau HJ, Lim CH, Foo SC, Tan HS. The role of artificial intelligence in the battle against antimicrobial-resistant bacteria. Curr Genet. 2021;67(3):421-9. [DOI:10.1007/s00294-021-01156-5] [PMID]

25. Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discovery Today. 2015;20(3):318-31. [DOI:10.1016/j.drudis.2014.10.012] [PMID]

26. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688-702. e13. [DOI:10.1016/j.cell.2020.01.021] [PMID] [PMCID]

27. Fan Y-L, Jin X-H, Huang Z-P, Yu H-F, Zeng Z-G, Gao T, et al. Recent advances of imidazole-containing derivatives as anti-tubercular agents. Eur J Med Chem. 2018;150:347-65. [DOI:10.1016/j.ejmech.2018.03.016] [PMID]

28. Lluka T, Stokes JM. Antibiotic discovery in the artificial intelligence era. Ann N Y Acad Sci. 2023;1519(1):74-93. [DOI:10.1111/nyas.14930] [PMID]

29. Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, et al. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Curr Drug Targets. 2021;22(6):631-55. [DOI:10.2174/1389450122999210104205732] [PMID]

30. Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 2014;66(1):334-95. [DOI:10.1124/pr.112.007336] [PMID] [PMCID]

31. Chew AK. Molecular Dynamics and Machine Learning for Reaction and Nanomaterial Design: The University of Wisconsin-Madison; 2021. 28492007.

32. Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chem Res Toxicol. 2010;23(1):171-83. [DOI:10.1021/tx900326k] [PMID] [PMCID]

33. Lo Y-C, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discovery Today. 2018;23(8):1538-46. [DOI:10.1016/j.drudis.2018.05.010] [PMID] [PMCID]

34. Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360-5. [DOI:10.1126/science.aat2663] [PMID]

35. Y Mahmood H, Jamshidi S, Mark Sutton J, M Rahman K. Current advances in developing inhibitors of bacterial multidrug efflux pumps. Curr Med Chem. 2016;23(10):1062-81. [DOI:10.2174/0929867323666160304150522] [PMID] [PMCID]

36. Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin Microbiol Infect. 2020;26(10):1310-7. [DOI:10.1016/j.cmi.2020.03.014] [PMID]

37. Segler MH, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci. 2018;4(1):120-31. [DOI:10.1021/acscentsci.7b00512] [PMID] [PMCID]

38. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268-76. [DOI:10.1021/acscentsci.7b00572] [PMID] [PMCID]

39. Unterthiner T, Mayr A, Klambauer G, Steijaert M, Wegner JK, Ceulemans H, et al. Deep learning as an opportunity in virtual screening. In Proceedings of the deep learning workshop at NIPS; 2014. Vol. 27, pp. 1-9. Cambridge, MA, United States: MIT Press.

40. Wallach I, Dzamba M, Heifets A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:151002855. 2015.

41. Goh GB, Hodas NO, Vishnu A. Deep learning for computational chemistry. J Comput Chem. 2017;38(16):1291-307. [DOI:10.1002/jcc.24764] [PMID]

42. Liao Z, You R, Huang X, Yao X, Huang T, Zhu S. DeepDock: enhancing ligand-protein interaction prediction by a combination of ligand and structure information. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2019: IEEE. [DOI:10.1109/BIBM47256.2019.8983365]

43. Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017;9(1):1-14. [DOI:10.1186/s13321-017-0235-x] [PMID] [PMCID]

44. Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2018;4(7):eaap7885. [DOI:10.1126/sciadv.aap7885] [PMID] [PMCID]

45. Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, et al. Deep-learning-based drug-target interaction prediction. J Proteome Res. 2017;16(4):1401-9. [DOI:10.1021/acs.jproteome.6b00618] [PMID]

46. Bai P, Miljković F, John B, Lu H. Interpretable bilinear attention network with domain adaptation improves drug-target prediction. Nat Mach Intell. 2023;5(2):126-36. [DOI:10.1038/s42256-022-00605-1]

47. Guan S, Wang G. Drug discovery and development in the era of artificial intelligence: From machine learning to large language models. Artif Intell Chem. 2024;2(1):100070. [DOI:10.1016/j.aichem.2024.100070]

48. Kathiriya S, Nuthakki S, Mulukuntla S, Charllo BV. AI and The Future of Medicine: Pioneering Drug Discovery with Language Models. Int J Sci Res. 2023;12(3):1824-9. [DOI:10.21275/SR24304173757]

49. Han D, Gao P, Li R, Tan P, Xie J, Zhang R, et al. Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing. J Adv Res. 2020;26:111-21. [DOI:10.1016/j.jare.2020.07.010] [PMID] [PMCID]

50. Ruppé E, Ghozlane A, Tap J, Pons N, Alvarez A-S, Maziers N, et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat Microbiol. 2019;4(1):112-23. [DOI:10.1038/s41564-018-0292-6] [PMID]

51. Razavi M, Kristiansson E, Flach C-F, Larsson DJ. The association between insertion sequences and antibiotic resistance genes. Msphere. 2020;5(5):e00418-20. [DOI:10.1128/mSphere.00418-20] [PMID] [PMCID]

52. Torres MD, Cao J, Franco OL, Lu TK, de la Fuente-Nunez C. Synthetic biology and computer-based frameworks for antimicrobial peptide discovery. ACS Nano. 2021;15(2):2143-64. [DOI:10.1021/acsnano.0c09509] [PMID] [PMCID]

53. Anahtar MN, Yang JH, Kanjilal S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J Clin Microbiol. 2021;59(7):10.1128/jcm. 01260-20. [DOI:10.1128/JCM.01260-20] [PMID] [PMCID]

54. Macesic N, Bear Don't Walk IV OJ, Pe'er I, Tatonetti NP, Peleg AY, Uhlemann A-C. Predicting phenotypic polymyxin resistance in Klebsiella pneumoniae through machine learning analysis of genomic data. Msystems. 2020;5(3):10.1128/msystems. 00656-19. [DOI:10.1128/msystems.00656-19] [PMID] [PMCID]

55. Partridge SR, Kwong SM, Firth N, Jensen SO. Mobile genetic elements associated with antimicrobial resistance. Clin Microbiol Rev. 2018;31(4):10.1128/cmr. 00088-17. [DOI:10.1128/CMR.00088-17] [PMID] [PMCID]

56. Yu D, Ryu K, Zhi S, Otto SJ, Neumann NF. Naturalized Escherichia coli in Wastewater and the Co-evolution of Bacterial Resistance to Water Treatment and Antibiotics. Front Microbiol. 2022;13:810312. [DOI:10.3389/fmicb.2022.810312] [PMID] [PMCID]

57. Smith NM, Boissonneault KR, Chen L, Petraitis V, Petraitiene R, Tao X, et al. Mechanistic Insights to Combating NDM-and CTX-M-Coproducing Klebsiella pneumoniae by Targeting Cell Wall Synthesis and Outer Membrane Integrity. Antimicrob Agents Chemother. 2022;66(9):e00527-22. [DOI:10.1128/aac.00527-22] [PMID] [PMCID]

58. Magiorakos A-P, Suetens C, Monnet DL, Gagliotti C, Heuer OE, Group E-NC, et al. The rise of carbapenem resistance in Europe: just the tip of the iceberg?. Antimicrob Resist Infect Control. 2013;2:1-3. [DOI:10.1186/2047-2994-2-6] [PMID] [PMCID]

59. Delgado FM, Gómez-Vela F. Computational methods for Gene Regulatory Networks reconstruction and analysis: A review. Artif Intell Med. 2019;95:133-45. [DOI:10.1016/j.artmed.2018.10.006] [PMID]

60. Mann M, Kumar C, Zeng W-F, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021;12(8):759-70. [DOI:10.1016/j.cels.2021.06.006] [PMID]

61. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463-77. [DOI:10.1038/s41573-019-0024-5] [PMID] [PMCID]

62. Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine learning in drug discovery: a review. Artif Intell Rev. 2022;55(3):1947-99. [DOI:10.1007/s10462-021-10058-4] [PMID] [PMCID]

63. Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V. Massively multitask networks for drug discovery. arXiv preprint arXiv:150202072. 2015.

64. Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm. 2016;13(7):2524-30. [DOI:10.1021/acs.molpharmaceut.6b00248] [PMID] [PMCID]

65. Sleire L, Førde HE, Netland IA, Leiss L, Skeie BS, Enger PØ. Drug repurposing in cancer. Pharmacol Res. 2017;124:74-91. [DOI:10.1016/j.phrs.2017.07.013] [PMID]

66. M Honorio K, L Moda T, D Andricopulo A. Pharmacokinetic properties and in silico ADME modeling in drug discovery. Med Chem. 2013;9(2):163-76. [DOI:10.2174/1573406411309020002] [PMID]

67. Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038-40. [DOI:10.1038/s41587-019-0224-x] [PMID]

68. Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, Alvarez-Ordóñez A. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes. 2018;9(5):268. [DOI:10.3390/genes9050268] [PMID] [PMCID]

69. Zankari E, Hasman H, Kaas RS, Seyfarth AM, Agersø Y, Lund O, et al. Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. J Antimicrob Chemother. 2013;68(4):771-7. [DOI:10.1093/jac/dks496] [PMID]

70. Lakin SM, Dean C, Noyes NR, Dettenwanger A, Ross AS, Doster E, et al. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 2017;45(D1):D574-D80. [DOI:10.1093/nar/gkw1009] [PMID] [PMCID]

71. Bengtsson-Palme J, Larsson DJ, Kristiansson E. Using metagenomics to investigate human and environmental resistomes. J Antimicrob Chemother. 2017;72(10):2690-703. [DOI:10.1093/jac/dkx199] [PMID]

72. Forslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, et al. Country-specific antibiotic use practices impact the human gut resistome. Genome Res. 2013;23(7):1163-9. [DOI:10.1101/gr.155465.113] [PMID] [PMCID]

73. Hu Y, Yang X, Qin J, Lu N, Cheng G, Wu N, et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat Commun. 2013;4(1):2151. [DOI:10.1038/ncomms3151] [PMID]

74. Westermann AJ, Förstner KU, Amman F, Barquist L, Chao Y, Schulte LN, et al. Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions. Nature. 2016;529(7587):496-501. [DOI:10.1038/nature16547] [PMID]

75. Chen L, Zhao X, Li R, Yang H. Integrated metabolomics and transcriptomics reveal the adaptive responses of Salmonella enterica serovar Typhimurium to thyme and cinnamon oils. Food Res J. 2022;157:111241. [DOI:10.1016/j.foodres.2022.111241] [PMID]

76. Malmström J, Beck M, Schmidt A, Lange V, Deutsch EW, Aebersold R. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature. 2009;460(7256):762-5. [DOI:10.1038/nature08184] [PMID] [PMCID]

77. Pérez-Llarena FJ, Bou G. Proteomics as a tool for studying bacterial virulence and antimicrobial resistance. Front Microbiol. 2016;7:410. [DOI:10.3389/fmicb.2016.00410] [PMID] [PMCID]

78. Yamashita S, Hattori N, Fujii S, Yamaguchi T, Takahashi M, Hozumi Y, et al. Multi-omics analyses identify HSD17B4 methylation-silencing as a predictive and response marker of HER2-positive breast cancer to HER2-directed therapy. Sci Rep. 2020;10(1):15530. [DOI:10.1038/s41598-020-72661-9] [PMID] [PMCID]

79. Chakraborty S, Hosen MI, Ahmed M, Shekhar HU. Onco-multi-OMICS approach: a new frontier in cancer research. Biomed Res Int. 2018; 2018(1):9836256. [DOI:10.1155/2018/9836256] [PMID] [PMCID]

80. Wang H, Jia C, Li H, Yin R, Chen J, Li Y, et al. Paving the way for precise diagnostics of antimicrobial resistant bacteria. Front Mol Biosci. 2022;9:976705. [DOI:10.3389/fmolb.2022.976705] [PMID] [PMCID]

81. Tamma PD, Fan Y, Bergman Y, Pertea G, Kazmi AQ, Lewis S, et al. Applying rapid whole-genome sequencing to predict phenotypic antimicrobial susceptibility testing results among carbapenem-resistant Klebsiella pneumoniae clinical isolates. Antimicrob Agents Chemother. 2019;63(1):10.1128/aac. 01923-18. [DOI:10.1128/AAC.01923-18] [PMID] [PMCID]

82. Tacconelli E, Goepel S, Gladstone BP, Eisenbeis S, Hoelzl F, Buhl M, et al. Development and validation of BLOOMY prediction scores for 14-day and 6-month mortality in hospitalised adults with bloodstream infections: a multicentre, prospective, cohort study. Lancet Infect Dis. 2022;22(5):731-41. [DOI:10.1016/S1473-3099(21)00587-9] [PMID]

83. Nguyen M, Olson R, Shukla M, VanOeffelen M, Davis JJ. Predicting antimicrobial resistance using conserved genes. PLoS Comput Biol. 2020;16(10):e1008319. [DOI:10.1371/journal.pcbi.1008319] [PMID] [PMCID]

84. Lodise TP, Lomaestro BM, Drusano GL. Application of antimicrobial pharmacodynamic concepts into clinical practice: focus on β‐lactam antibiotics: insights from the Society of Infectious Diseases Pharmacists. Pharmacotherapy: J Hum Pharmacol Drug Ther. 2006;26(9):1320-32. [DOI:10.1592/phco.26.9.1320] [PMID]

85. Avedissian SN, Rohani R, Bradley J, Le J, Rhodes NJ. Optimizing aminoglycoside dosing regimens for critically ill pediatric patients with augmented renal clearance: a convergence of parametric and nonparametric population approaches. Antimicrob Agents Chemother. 2021;65(4):10.1128/aac.02629-20. [DOI:10.1128/AAC.02629-20] [PMID] [PMCID]

86. Losier M, Ramsey TD, Wilby KJ, Black EK. A systematic review of antimicrobial stewardship interventions in the emergency department. Ann Pharmacother. 2017;51(9):774-90. [DOI:10.1177/1060028017709820] [PMID]

87. Rawson T, Moore L, Hernandez B, Charani E, Castro-Sanchez E, Herrero P, et al. A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?. Clin Microbiol Infect. 2017;23(8):524-32. [DOI:10.1016/j.cmi.2017.02.028] [PMID]

88. Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the age of artificial intelligence: a comprehensive review. Interdiscip Sci Comput Life Sci. 2021;13:153-75. [DOI:10.1007/s12539-021-00431-w] [PMID] [PMCID]

89. Haeusler GM, Thursky KA, Mechinaud F, Babl FE, De Abreu Lourenco R, Slavin MA, et al. Predicting infectious complications in children with cancer: an external validation study. Br J Cancer. 2017;117(2):171-8. [DOI:10.1038/bjc.2017.154] [PMID] [PMCID]