Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration

Mohammed F.  Al Marjani; Rana K. Mohammed; Ziad O. Ahmed; Yasmin Makki Mohialden

doi:10.37899/journallamultiapp.v5i1.952

Mohammed F. Al Marjani College of Science, Mustansiriyah University, Baghdad, Iraq
Rana K. Mohammed College of science, Baghdad university, Baghdad-Iraq
Ziad O. Ahmed Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad-Iraq
Yasmin Makki Mohialden Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad-Iraq

DOI: https://doi.org/10.37899/journallamultiapp.v5i1.952

Keywords: Antibiotic Resistance (AMR), Machine Learning (ML), Drug Development Deep Learning (DL)

Abstract

The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.

References

Abd-Alrazaq, A., Alajlani, M., Alhuwail, D., Schneider, J., Al-Kuwari, S., Shah, Z., ... & Househ, M. (2020). Artificial intelligence in the fight against COVID-19: scoping review. Journal of medical Internet research, 22(12), e20756.

Agrebi, S., & Larbi, A. (2020). Use of artificial intelligence in infectious diseases. In Artificial intelligence in precision health (pp. 415-438). Academic Press.

Ahmad, I., Malak, H. A., & Abulreesh, H. H. (2021). Environmental antimicrobial resistance and its drivers: a potential threat to public health. Journal of Global Antimicrobial Resistance, 27, 101-111.

Ahmad, I., Siddiqui, S. A., Samreen, Suman, K., & Qais, F. A. (2022). Environmental biofilms as reservoir of antibiotic resistance and hotspot for genetic exchange in bacteria. In Beta-Lactam Resistance in Gram-Negative Bacteria: Threats and Challenges (pp. 237-265). Singapore: Springer Nature Singapore.

Al Marjani, M. F., Mohammed, H. N., Al-Kadmy, I. M., & Aziz, S. N. (2023). Overview of heteroresistance, persistence and optimized strategies to control them. Reviews and Research in Medical Microbiology, 34(2), 110-122.

Al-Garadi, M. A., Khan, M. S., Varathan, K. D., Mujtaba, G., & Al-Kabsi, A. M. (2016). Using online social networks to track a pandemic: A systematic review. Journal of biomedical informatics, 62, 1-11.

Amann, S., Neef, K., & Kohl, S. (2019). Antimicrobial resistance (AMR). European Journal of Hospital Pharmacy, 26(3), 175-177.

Araruna, M. K., Brito, S. A., Morais-Braga, M. F., Santos, K. K., Souza, T. M., Leite, T. R., ... & Coutinho, H. D. (2012). Evaluation of antibiotic & antibiotic modifying activity of pilocarpine & rutin. The Indian Journal of Medical Research, 135(2), 252.

Basu, K., Sinha, R., Ong, A., & Basu, T. (2020). Artificial intelligence: How is it changing medical sciences and its future?. Indian journal of dermatology, 65(5), 365.

Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022). Artificial intelligence in biological sciences. Life, 12(9), 1430.

Boolchandani, M., D’Souza, A. W., & Dantas, G. (2019). Sequencing-based methods and resources to study antimicrobial resistance. Nature Reviews Genetics, 20(6), 356-370.

Capozzi, C., Maurici, M., & Panà, A. (2019). Antimicrobial resistance: it is a global crisis," a slow tsunami". Igiene e sanita pubblica, 75(6), 429-450.

Cavallo, F. M. (2022). All roads lead to Rome: Confronting antibiotic resistant bacteria with unconventional strategies.

Davies, J., & Davies, D. (2010). Origins and Evolution of Antibiotic Resistance. Microbiology and Molecular Biology Reviews : MMBR, 74(3), 417-433. https://doi.org/10.1128/MMBR.00016-10

Division, A. R. (2014, April 1). Antimicrobial resistance: global report on surveillance. https://www.who.int/publications/i/item/9789241564748

Fleming, N. (2018). How artificial intelligence is changing drug discovery. Nature, 557(7706), S55-S55.

Flemming, H. C., Wingender, J., Szewzyk, U., Steinberg, P., Rice, S. A., & Kjelleberg, S. (2016). Biofilms: an emergent form of bacterial life. Nature Reviews Microbiology, 14(9), 563-575.

Ghai, I. (2023). A Barrier to Entry: Examining the Bacterial Outer Membrane and Antibiotic Resistance. Applied Sciences, 13(7), 4238.

Greenfield, C, D. (2019). Artificial Intelligence in Medicine: Applications, implications, and limitations-Science in the News. Science in the News Harvard. https://sitn. hms. harvard. edu/flash/2019/artificial-intelligence-in-medicine-applicationsimplications-and-limitations.

Gudata, D., & Begna, F. (2018). Antimicrobial resistance. Int J Res Granthaalayah, 6(11), 77-93.

Guliy, O. I., Zaitsev, B. D., & Borodina, I. A. (2023). Electroacoustic Biosensor Systems for Evaluating Antibiotic Action on Microbial Cells. Sensors, 23(14), 6292.

Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40.

Harris, M., Fasolino, T., Ivankovic, D., Davis, N. J., & Brownlee, N. (2023). Genetic Factors That Contribute to Antibiotic Resistance through Intrinsic and Acquired Bacterial Genes in Urinary Tract Infections. Microorganisms, 11(6), 1407.

Hassoun, S., Jefferson, F., Shi, X., Stucky, B., Wang, J., & Rosa Jr, E. (2021). Artificial intelligence for biology. Integrative and Comparative Biology, 61(6), 2267-2275.

Hawkins, W. D., & DuRant, S. E. (2020). Applications of machine learning in behavioral ecology: quantifying avian incubation behavior and nest conditions in relation to environmental temperature. Plos One, 15(8), e0236925.

Hayali, Osamah Z.; AL-Marjani, Mohammed. F.; and Maleki, Abbas (2022) "Controlling the heterogeneous vancomycin intermediated Staphylococcus aureus (hVISA) through the use of Rosmarinus officinalis L. leaves extract," Karbala International Journal of Modern Science: Vol. 8 : Iss. 4 , Article 7. Available at: https://doi.org/10.33640/2405-609X.3264

Irfan, M., Almotiri, A., & AlZeyadi, Z. A. (2022). Antimicrobial resistance and its drivers—A review. Antibiotics, 11(10), 1362.

Iskandar, K., Molinier, L., Hallit, S., Sartelli, M., Catena, F., Coccolini, F., ... & Salameh, P. (2020). Drivers of antibiotic resistance transmission in low-and middle-income countries from a “one health” perspective—a review. Antibiotics, 9(7), 372.

Khaledi, A., Schniederjans, M., Pohl, S., Rainer, R., Bodenhofer, U., Xia, B., ... & Häussler, S. (2016). Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrobial agents and chemotherapy, 60(8), 4722-4733.. 2016, 60, 4722–4733.

Kim, J. Y., Baek, K. H., & Lee, S. Y. (2023). Evaluation of Resistance of Phytopathogenic Bacteria to Agricultural Antibiotics. Research in Plant Disease, 29(2), 168-173.

Kuhl, E. (2020). Data-driven modeling of COVID-19—Lessons learned. Extreme Mechanics Letters, 40, 100921.

Laxminarayan, R., Matsoso, P., Pant, S., Brower, C., Røttingen, J. A., Klugman, K., & Davies, S. (2016). Access to effective antimicrobials: a worldwide challenge. The Lancet, 387(10014), 168-175.

Liew, H. M., Hogarth, V. J., & Hay, R. J. (2023). Antibiotics Commonly Used for Skin Infections. In Handbook of Systemic Drug Treatment in Dermatology (pp. 28-41). CRC Press.

Macesic, N., Polubriaginof, F., & Tatonetti, N. P. (2017). Machine learning: novel bioinformatics approaches for combating antimicrobial resistance. Current opinion in infectious diseases, 30(6), 511-517.

Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of family medicine and primary care, 8(7), 2328.

Matinfar, F., & Golpayani, C. A. T. (2022). A fuzzy expert system for early diagnosis of multiple sclerosis. Journal of Biomedical Physics & Engineering, 12(2), 181.

McLachlan, S., Dube, K., Hitman, G. A., Fenton, N. E., & Kyrimi, E. (2020). Bayesian networks in healthcare: Distribution by medical condition. Artificial intelligence in medicine, 107, 101912.

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81.

Multani, H., & Singh, V. A. (2022). Biofilms a Rabble-rouser in the Environment Leading to Antibiotics Resistance. Innovations in Microbiology and Biotechnology Vol. 7, 45-51.

Naveed, M., Chaudhry, Z., Bukhari, S. A., Meer, B., & Ashraf, H. (2020). Antibiotics resistance mechanism. In Antibiotics and Antimicrobial Resistance Genes in the Environment (pp. 292-312). Elsevier.

Nikolic, P., & Mudgil, P. (2023). The Cell Wall, Cell Membrane and Virulence Factors of Staphylococcus aureus and Their Role in Antibiotic Resistance. Microorganisms, 11(2), 259.

Ojkic, N., Serbanescu, D., & Banerjee, S. (2022). Antibiotic resistance via bacterial cell shape-shifting. Mbio, 13(3), e00659-22.

O'Neill, J. (2016). Tackling drug-resistant infections globally: final report and recommendations.

Ozma, M. A., Moaddab, S. R., Hosseini, H., Khodadadi, E., Ghotaslou, R., Asgharzadeh, M., ... & Samadi Kafil, H. (2023). A critical review of novel antibiotic resistance prevention approaches with a focus on postbiotics. Critical Reviews in Food Science and Nutrition, 1-19.

Prasad, S., VP, S., Abbas, H. S., & Kotakonda, M. (2022). Mechanisms of Antimicrobial Resistance: Highlights on Current Advance Methods for Detection of Drug Resistance and Current Pipeline Antitubercular Agents. Current Pharmaceutical Biotechnology, 23(15), 1824-1836.

Pricop, L. (2022). Antimicrobial Resistance: The Moral Compass of Health. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 13(4), 29-39.

Puvača, N., Tankosić, J. V., Ignjatijević, S., Carić, M., & Prodanović, R. (2022). Antimicrobial resistance in the environment: review of the selected resistance drivers and public health concerns. J. Agron. Technol. Eng. Manag, 5, 793-802

Rajkumar, N., & Mohiddin, S. K. (2022). Biofilm aggravates antibiotic resistance: Molecular mechanisms behind. International Journal of Health Sciences, 6(S4), 10285–10297. https://doi.org/10.53730/ijhs.v6nS4.11055

Reghukumar, A. (2023). Drivers of Antimicrobial Resistance. In Handbook on Antimicrobial Resistance: Current Status, Trends in Detection and Mitigation Measures (pp. 1-16). Singapore: Springer Nature Singapore.

Roberts, K., Alam, T., Bedrick, S., Demner-Fushman, D., Lo, K., Soboroff, I., ... & Hersh, W. R. (2020). TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19. Journal of the American Medical Informatics Association, 27(9), 1431-1436.

Saarenmaa, H., Stone, N. D., Folse, L. J., Packard, J. M., Grant, W. E., Makela, M. E., & Coulson, R. N. (1988). An artificial intelligence modelling approach to simulating animal/habitat interactions. Ecological Modelling, 44(1-2), 125-141.

Sagar, S., Kaistha, S., Das, A. J., Kumar, R., Sagar, S., Kaistha, S., ... & Kumar, R. (2019). Chemical-Mediated Alteration of Antibiotics. Antibiotic Resistant Bacteria: A Challenge to Modern Medicine, 105-126.

Sharma, R., Thakur, A., Saini, A., Giri, S. K., Kumar, A., Priya, K., & Singh, G. (2023). Antibiotics stress response of bacteria as mechanism of development of drug resistance. In Microbial Stress Response: Mechanisms and Data Science (pp. 23-42). American Chemical Society.

Shree, P., Singh, C. K., Sodhi, K. K., Surya, J. N., & Singh, D. K. (2023). Biofilms: Understanding the structure and contribution towards bacterial resistance in antibiotics. Medicine in Microecology, 100084.

Shusterman, E., Mottahedeh, A., & McCarthy, M. (2021). The Synergistic Effects of Rhamnolipids and Antibiotics Against Bacteria. Journal of Student Research, 10(2).

Sikchi, S. S., Sikchi, S., & Ali, M. S. (2013). Fuzzy expert systems (FES) for medical diagnosis. International Journal of Computer Applications, 63(11).

Smith, J. S., Roitberg, A. E., & Isayev, O. (2018). Transforming computational drug discovery with machine learning and AI. ACS medicinal chemistry letters, 9(11), 1065-1069.

Suganya, T., Packiavathy, I. A. S. V., Aseervatham, G., Carmona, A., Rashmi, V., Mariappan, S., ... & Ananth, D. A. (2022). Tackling multiple-drug-resistant bacteria with conventional and complex phytochemicals. Frontiers in Cellular and Infection Microbiology, 12, 883839.

Sukumaran, J., Economo, E. P., & Lacey Knowles, L. (2016). Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis. Systematic Biology, 65(3), 525-545.

Tang, K. W. K., Millar, B. C., & Moore, J. E. (2023). Antimicrobial resistance (AMR). British Journal of Biomedical Science, 80, 11387.

Trubenová, B., Roizman, D., Rolff, J., & Regoes, R. R. (2022). Modeling Polygenic Antibiotic Resistance Evolution in Biofilms. Frontiers in Microbiology, 13, 916035.

Turel, M. K., Meshram, B., & Rajshekhar, V. (2021). Survey of Prophylactic use of Antibiotics among Indian Neurosurgeons. Neurology India, 69(6), 1737.

Wang, X., Yu, D., & Chen, L. (2023). Antimicrobial resistance and mechanisms of epigenetic regulation. Frontiers in Cellular and Infection Microbiology, 13, 1199646.

Wang, Y., Lu, J., Engelstädter, J., Zhang, S., Ding, P., Mao, L., ... & Guo, J. (2020). Non-antibiotic pharmaceuticals enhance the transmission of exogenous antibiotic resistance genes through bacterial transformation. The ISME Journal, 14(8), 2179-2196.

Wiemken, T. L., & Kelley, R. R. (2020). Machine learning in epidemiology and health outcomes research. Annu Rev Public Health, 41(1), 21-36.

Zakaria, N. F. S., fakharul zaman raja Yahya, M., & Jamil, N. M. (2023). Multiple Bacterial Strategies to Survive Antibiotic Pressure: A Review.

Zeden, M. S., & C, A. (2023). Agar Plate-Based Method for the Selection of Antibiotic-Resistant Bacterial Strains. Cold Spring Harbor Protocols.