Author(s) :

Yogesh and Sandhya.

Volume/Issue :

Abstract :

The rising prevalence of drug-resistant infections among immunocompromised individuals, including organ transplant recipients, cancer patients undergoing chemotherapy, and individuals with immune deficiencies, presents a growing challenge in modern medicine. These patients are particularly susceptible to infections that do not respond to traditional antibiotics. Nanoantibiotics, which are nanoscale materials with antimicrobial properties or serve as delivery systems for antibiotics, offer promising therapeutic alternatives. However, designing and optimizing these nanoantibiotics require meticulous control over various parameters, such as particle size, shape, surface functionalization, and drug loading. Machine learning has emerged as a transformative tool in accelerating the development of nanoantibiotics by enabling the predictive modeling of complex biological and physicochemical interactions. Machine learning algorithms can analyze large datasets from laboratory and clinical studies to predict antibacterial potency, toxicity, stability, and drug release profiles, thereby streamlining the design process. Additionally, machine learning can assist in optimizing nanoparticle configurations, improving the balance between antimicrobial effectiveness and minimal toxicity to human cells. While challenges such as model interpretability and data quality remain, the integration of machine learning into nanoantibiotic development has the potential to revolutionize personalized treatments for immunocompromised patients, providing safer, more efficient therapeutic options.

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