Precision oncology

The emergence of artificial intelligence (AI) has proved to be an important development in the field of translational nanomedicine, as it has made possible intelligent and personalized applications for precision oncology. The integration of machine learning (ML), deep learning, nanoinformatics, predictive modeling, and computational methods with nano delivery systems has improved carrier design, formulation optimization, multiscale targeting, and therapy monitoring using AI. This article discusses some of the latest developments in the area of AI-assisted translational nanomedicine. It also highlights the use of AI in tumor identification, patient stratification, theranostics, and the application of AI in brain, liver, breast, and kidney cancers. Moreover, cutting-edge developments in digital twins, federated learning, explainable AI, large language models, and intelligent nanorobotics are described for their capacity to speed up clinical translation. While much has been accomplished, there are issues regarding data standardization, clinical validation, scalable manufacturing, regulatory clearance, and algorithm transparency that need to be addressed.

Artificial intelligence (AI) and translational nanomedicine are revolutionizing the field of precision oncology through the use of smart nanoparticles, personalization of drug delivery, and evidence-based therapeutic decisions. This review highlights some of the recent developments in AI-driven translational nanomedicine, with special emphasis on multistage targeting approaches and clinical utility in liver, breast, renal, and brain tumors. Machine learning, deep learning, radiomics, multimodal omics, and nanoinformatics are among the areas discussed in this review. Tumor heterogeneity, vascular barrier, immune microenvironment, renal clearance, and blood-brain barrier penetration in organs are considered in order to highlight the necessity for the development of personalized nanomedicine strategies. Despite the advancements that have been made by AI-guided nanomedicine in drug delivery, pharmacokinetics and safety, there are several obstacles that hinder the clinical application of such technology. The above review suggests future strategies which include standardization in nanoinformatics, patient-derived organoids, digital twins, federated learning, adaptive clinical trials based on biomarkers, and explainable AI for accelerating clinical translation. It may be concluded that the combination of AI and translational nanomedicine offers an attractive approach towards the development of safer and effective cancer treatment options.

Biodegradable polymers have emerged as essential components in advanced drug delivery systems, enabling controlled, sustained, and site-specific therapeutic release while minimizing systemic toxicity. This comprehensive review covers the design principles, degradation mechanisms, and drug release dynamics of natural and synthetic biodegradable polymers such as PLGA, PCL, chitosan, and alginate. Their adaptability allows fabrication into nanoparticles, microspheres, hydrogels, and scaffolds tailored to various clinical needs, including cancer therapy, vaccine delivery, gene therapy, and tissue engineering. The review discusses hydrolytic and enzymatic degradation processes, surface versus bulk erosion behaviors, and factors influencing polymer degradation and drug release kinetics. Case studies highlight FDA-approved formulations leveraging these polymers for enhanced therapeutic efficacy and patient compliance. Challenges such as variability in degradation rates, formulation stability, manufacturing scale-up, and regulatory hurdles are addressed. Emerging frontiers in smart stimuli-responsive systems, hybrid polymers, AI-driven design, and personalized medicine underscore the future potential of biodegradable polymers as cornerstones of precision and sustainable therapeutics.