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Hatim, A Natto, Ammar Abdul, Razzak Mahmood, Sriram, T, Rajkumar Krishnan, Vasanthi, Desh Nidhi, Singh. (1404). Artificial intelligence in Combating Antimicrobial Resistance. سامانه مدیریت نشریات علمی, 80(3), 705-713. doi: 10.32592/ARI.2025.80.3.705
A Natto Hatim; Razzak Mahmood Ammar Abdul; T Sriram; Vasanthi Rajkumar Krishnan; Singh Desh Nidhi. "Artificial intelligence in Combating Antimicrobial Resistance". سامانه مدیریت نشریات علمی, 80, 3, 1404, 705-713. doi: 10.32592/ARI.2025.80.3.705
Hatim, A Natto, Ammar Abdul, Razzak Mahmood, Sriram, T, Rajkumar Krishnan, Vasanthi, Desh Nidhi, Singh. (1404). 'Artificial intelligence in Combating Antimicrobial Resistance', سامانه مدیریت نشریات علمی, 80(3), pp. 705-713. doi: 10.32592/ARI.2025.80.3.705
Hatim, A Natto, Ammar Abdul, Razzak Mahmood, Sriram, T, Rajkumar Krishnan, Vasanthi, Desh Nidhi, Singh. Artificial intelligence in Combating Antimicrobial Resistance. سامانه مدیریت نشریات علمی, 1404; 80(3): 705-713. doi: 10.32592/ARI.2025.80.3.705

Artificial intelligence in Combating Antimicrobial Resistance

Archives of Razi Institute
مقاله 7، دوره 80، شماره 3، مرداد و شهریور 2025، صفحه 705-713    اصل مقاله (500.41 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.32592/ARI.2025.80.3.705
نویسندگان
A Natto Hatim1؛ Razzak Mahmood Ammar Abdul2؛ T Sriram3؛ Vasanthi Rajkumar Krishnan4؛ Singh Desh Nidhi* 5
1Associate Professor, Faculty of Public Health, Umm Al-Qura University, Makkah, Saudi Arabia
2Professor, Department of Pharmaceutical Chemistry, College of Pharmacy, University of Baghdad, Iraq
3Assistant Professor, Department of Life Sciences, Kristu Jayanti College, Bangalore, India
4Senior Lecturer, Faculty of Health and Life Sciences, INTI International University, Nilai, Negeri Sembilan, Malaysia.
5Associate Professor Department of Microbiology Autonomous State Medical College, Lalitpur, U. P., India
چکیده
Antibiotic resistance (AR) has become a significant worldwide public health concern in the twenty-first century. Antimicrobial resistance (AMR) occurs when microorganisms, such as bacteria, fungi, parasites, and viruses, acquire genetic changes that make them resistant to antimicrobial drugs, including antibiotics. AMR, often known as the "Silent Pandemic," requires prompt and persistent intervention instead of postponement. Failure in preventative measures will result in AMR being the primary cause of mortality worldwide. In the fight against multidrug-resistant bacteria to halt antibiotic resistance, conventional techniques for developing drugs are expensive and take a long time, however AI systems can rapidly scan through extensive chemical libraries and forecast possible antibacterial substances. Considering the sluggish progress of ongoing antibiotic research, it is essential to accelerate the advancement of novel antibiotics and supplementary treatments. The acceleration is essential for effectively tackling the increasing health risk caused by antibiotic-resistant bacteria, so guaranteeing that we maintain an advantage in combating these developing threats. The use of AI in medical research has significant promise, particularly in addressing multidrug-resistant (MDR) infections to battle AMR. This study focuses on the effective applications of AI in addressing AMR and its potential to benefit humanity. It covers the fundamental concepts of AI, the resources now available for AI, its uses and scope, as well as its benefits and limits.AI algorithms also consistently observe antibiotic usage, occurrences of diseases, and trends of resistance. This review examines the AI to identify AMR markers, diagnosis in AMR, small molecule antibiotic development and also emphasizes emerging research domains, such as AMR detection and novel medication development, that contribute to the management of AMR.
کلیدواژه‌ها
Antimicrobial Resistance؛ AI to identify AMR markers؛ artificial intelligence؛ Deep Learning؛ Machine Language
مراجع
  1. Okeke IN, de Kraker ME, Van Boeckel TP, Kumar CK, Schmitt H, Gales AC, et al. The scope of the antimicrobial resistance challenge. The Lancet. 2024;403(10442):2426-38.
  2. Mahindroo J, Narayan C, Modgil V, Kaur H, Shahi V, Sharma B, et al. Antimicrobial resistance in food-borne pathogens at the human-animal interface: Results from a large surveillance study in India. One Health. 2024;18:100677.
  3. Nashwan AJ, Barakat M, Niaz F, Tariq S, Ahmed SK, Tariq Sr S. Antimicrobial resistance: stewardship and one health in the Eastern Mediterranean region. Cureus. 2024;16(4).
  4. Kumawat M, Nabi B, Daswani M, Viquar I, Pal N, Sharma P, et al. Role of bacterial efflux pump proteins in antibiotic resistance across microbial species. Microbial Pathogenesis. 2023;181:106182.
  5. Islam N, Reid D. Inhaled antibiotics: A promising drug delivery strategies for efficient treatment of lower respiratory tract infections (LRTIs) associated with antibiotic resistant biofilm-dwelling and intracellular bacterial pathogens. Respiratory Medicine. 2024:107661.
  6. Ali WMB, Alhumaidi MS. Artificial Intelligence for Cancer Diagnosis & Radiology. International Journal of Trends in OncoScience. 2023:13-8.
  7. Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, et al. The impact of artificial intelligence on microbial diagnosis. Microorganisms. 2024;12(6):1051.
  8. Behling AH, Wilson BC, Ho D, Virta M, O’Sullivan JM, Vatanen T. Addressing antibiotic resistance: computational answers to a biological problem? Current opinion in microbiology. 2023;74:102305.
  9. Yu P, Dong P, Zou Y, Wang H. Effect of pH on the mitigation of extracellular/intracellular antibiotic resistance genes and antibiotic resistance pathogenic bacteria during anaerobic fermentation of swine manure. Bioresource Technology. 2023;373:128706.
  10. Geethalakshmi S, Yadav S. Advancements in artificial intelligence for the diagnosis of multidrug resistance and extensively drug-resistant tuberculosis: a comprehensive review. Cureus. 2024;16(5).
  11. Ahmed SK, Hussein S, Qurbani K, Ibrahim RH, Fareeq A, Mahmood KA, et al. Antimicrobial resistance: Impacts, challenges, and future prospects. Journal of Medicine, Surgery, and Public Health. 2024;2:100081.
  12. Otaibi NN, Almutairi M, Al-Namshan YZ, AL Dhohayan MA, Alotibi RD. Factors influencing misuse of antibiotic therapy in Al-Qassim region, Saudi Arabia. Int J Life Sci Pharma Res. 2019;9:1-6.
  13. Hatim A, Abdul A, Mahmood R, Sriram T, Rajkumar Krishnan V, Desh Nidhi S. Artificial intelligence in combating antimicrobial resistance. Archives of Razi Institute. 2024.
  14. Mulchandani R, Zhao C, Tiseo K, Pires J, Van Boeckel TP. Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021. Emerging infectious diseases. 2024;30(1):96.
  15. Zhang T, Nickerson R, Zhang W, Peng X, Shang Y, Zhou Y, et al. The impacts of animal agriculture on One Health—Bacterial zoonosis, antimicrobial resistance, and beyond. One Health. 2024:100748.
  16. Parker EM, Ballash GA, Mollenkopf DF, Wittum TE. A complex cyclical One Health pathway drives the emergence and dissemination of antimicrobial resistance. American Journal of Veterinary Research. 2024;85(4).
  17. Adelman MW, Connor AA, Hsu E, Saharia A, Mobley CM, Victor III DW, et al. Bloodstream infections after solid organ transplantation: clinical epidemiology and antimicrobial resistance (2016–21). JAC-Antimicrobial Resistance. 2024;6(1):dlad158.
  18. Shang K, Kim J-H, Park J-Y, Choi Y-R, Kim S-W, Cha S-Y, et al. Comparative studies of antimicrobial resistance in Escherichia coli, Salmonella, and Campylobacter isolates from broiler chickens with and without use of Enrofloxacin. Foods. 2023;12(11):2239.
  19. Bianchessi L, De Bernardi G, Vigorelli M, Dall’Ara P, Turin L. Bacteriophage therapy in companion and farm animals. Antibiotics. 2024;13(4):294.
  20. Moulic AG, Deshmukh P, Gaurkar SS. A comprehensive review on biofilms in otorhinolaryngology: understanding the pathogenesis, diagnosis, and treatment strategies. Cureus. 2024;16(4).
  21. Khosravi P, Mohammadi S, Zahiri F, Khodarahmi M, Zahiri J. AI‐enhanced detection of clinically relevant structural and functional anomalies in MRI: Traversing the landscape of conventional to explainable approaches. Journal of Magnetic Resonance Imaging. 2024;60(6):2272-89.
  22. Jarocki VM, Cummins ML, Donato CM, Howden BP, Djordjevic SP. A One Health approach for the genomic surveillance of AMR. Microbiology Australia. 2024.
  23. Wang B, Yu JF, Lin SY, Li YJ, Huang WY, Yan SY, et al. Intraoperative AI‐assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery. Head & Neck. 2024;46(8):1975-87.
  24. Bhatia M, Shamanna V, Nagaraj G, Gupta P, Omar BJ, Rohilla R, et al. Assessment of in vitro colistin susceptibility of carbapenem-resistant clinical Gram-negative bacterial isolates using four commercially available systems & Whole-genome sequencing: A diagnostic accuracy study. Diagnostic Microbiology and Infectious Disease. 2024;108(3):116155.
  25. Bose R, Roy AM. Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows. Engineering Applications of Artificial Intelligence. 2024;128:107483.
  26. Corbin CK, Sung L, Chattopadhyay A, Noshad M, Chang A, Deresinksi S, et al. Personalized antibiograms for machine learning driven antibiotic selection. Communications medicine. 2022;2(1):38.
  27. Feretzakis G, Loupelis E, Sakagianni A, Kalles D, Martsoukou M, Lada M, et al. Using machine learning techniques to aid empirical antibiotic therapy decisions in the intensive care unit of a general hospital in Greece. Antibiotics. 2020;9(2):50.
  28. van Loon G, Van Steenkiste G, Vera L, Decloedt A. Catheter-based electrical interventions to study, diagnose and treat arrhythmias in horses: from refractory period to electro-anatomical mapping. The Veterinary Journal. 2020;263:105519.
  29. Rabaan AA, Alhumaid S, Mutair AA, Garout M, Abulhamayel Y, Halwani MA, et al. Application of artificial intelligence in combating high antimicrobial resistance rates. Antibiotics. 2022;11(6):784.
  30. Fortes de Arruda FC. The Revised Baux Score as a Predictor of Burn Mortality: A Systematic Review and Meta-analysis. Journal of Burn Care & Research. 2024;45(1):256-.
  31. Somineni H, Mukherjee S, Amar D, Pei J, Guo K, Light D, et al. Machine learning across multiple imaging and biomarker modalities in the UK Biobank improves genetic discovery for liver fat accumulation. medRxiv. 2024:2024.01. 06.24300923.
  32. Elsherbini AM, Elkholy AH, Fadel YM, Goussarov G, Elshal AM, El-Hadidi M, et al. Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques. BMC bioinformatics. 2024;25(1):131.
  33. Kuang X, Wang F, Hernandez KM, Zhang Z, Grossman RL. Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN. Scientific reports. 2022;12(1):2427.
  34. Gambato S, Bellotto O, Mardirossian M, Di Stasi A, Gennaro R, Pacor S, et al. Designing new hybrid antibiotics: proline-rich antimicrobial peptides conjugated to the aminoglycoside tobramycin. Bioconjugate Chemistry. 2023;34(7):1212-20.
  35. Bush K. Commentary on The antibiotic explosion by H. Boyd Woodruff and Richard W. Burg. Hemodynamics and Immune Defense: Elsevier; 2024. p. 301-6.
  36. Alanjary M, Kronmiller B, Adamek M, Blin K, Weber T, Huson D, et al. The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery. Nucleic acids research. 2017;45(W1):W42-W8.
  37. Román Hurtado F. Genome Mining of the Strain Streptomyces sp. CA-170360: Identification and Heterologous Expression of Secondary Metabolite Biosynthetic Gene Clusters. 2022.
  38. Goff A, Cantillon D, Muraro Wildner L, Waddell SJ. Multi-omics technologies applied to tuberculosis drug discovery. Applied Sciences. 2020;10(13):4629.
  39. Singh S, Numan A, Cinti S. Point-of-care for evaluating antimicrobial resistance through the adoption of functional materials. Analytical Chemistry. 2021;94(1):26-40.
  40. Salam MA, Al-Amin MY, Salam MT, Pawar JS, Akhter N, Rabaan AA, et al., editors. Antimicrobial resistance: a growing serious threat for global public health. Healthcare; 2023: Multidisciplinary Digital Publishing Institute.
  41. Teillant A, Gandra S, Barter D, Morgan DJ, Laxminarayan R. Potential burden of antibiotic resistance on surgery and cancer chemotherapy antibiotic prophylaxis in the USA: a literature review and modelling study. The Lancet infectious diseases. 2015;15(12):1429-37.
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