Machine Learning Approaches to Engineer Nanoantibiotics for Treating Infections in Immunocompromised Patients

Authors- PAVAN T.K

Abstract-– The increasing prevalence of drug-resistant infections among immunocompromised individuals presents a critical challenge in modern medicine, as these patients are particularly vulnerable to infections that often fail to respond to traditional antibiotics. Nanoantibiotics, which include nanoscale materials with inherent antimicrobial properties or those serving as delivery systems for antibiotics, offer a promising therapeutic solution. However, the design of nanoantibiotics requires careful optimization of multiple parameters to ensure their efficacy and safety. Machine learning (ML) has emerged as a transformative tool in the development of nanoantibiotics, enabling the prediction of complex biological and physicochemical interactions. By utilizing large datasets from experimental and clinical studies, ML models can predict antimicrobial potency, toxicity, drug release profiles, and stability, thus reducing the need for extensive trial-and-error experimentation. Moreover, ML facilitates the identification of non-linear relationships between nanoparticle features and therapeutic outcomes, providing deeper insights into nanoparticle design. The integration of machine learning with experimental synthesis platforms can expedite the development of optimized nanoantibiotics, while also enabling the personalization of therapies tailored to individual patient needs. This paper explores the applications, challenges, and future potential of machine learning in the rational design of nanoantibiotics for immunocompromised patients.

DOI: /10.61463/ijset.vol.13.issue2.427