Cancer cure with immunotherapy is an innovative step towards cancer treatment with better survivability, but it is mostly dependent on the response of the patient’s immune system to the immunother

The next-generation nanoparticles overcome the drawbacks of early nanoplatforms by integrating multiple functions, such as drug delivery, controlled drug release, and combination therapy, into a single system. This study examines the biomedical applications of quantum dots, carbon nanotubes, superparamagnetic iron oxide nanoparticles, and layered double hydroxides for the delivery of breast cancer drugs. They are termed as “nextgeneration” nanoparticles, as they are advanced nanocarriers that offer a comprehensive and alternative approach towards breast cancer treatment, providing enhanced specificity and efficacy compared to their predecessors. The development of these nanoplatforms has significantly enhanced drug bioavailability and reduced toxicity. A comprehensive analysis of a nanotechnology-based drug delivery system was conducted. The keywords used for this review were “Breast Cancer”, “Targeted Drug Delivery”, “Quantum Dots”, “Carbon Nanotubes”, “Layer Double Hydroxides”, and “Superparamagnetic Iron Oxide Nanoparticles”. The inclusion criteria consisted of studies focusing on breast cancer, targeted drug delivery, and therapeutic applications of these nanocarriers. In contrast, exclusion criteria included studies focusing on the synthesis of nanocarriers and the diagnostic applications of these nanostructures. The study underscores their mechanisms, limitations, and future development directions. Additionally, the study tracks the evolution of the nanocarriers since their early discovery. Next-generation nanocarriers (QDs, CNTs, SPIONs, and LDHs) have strong therapeutic potential owing to their precisely engineered properties, such as size, shape, morphology, and surface modifications. Their trigger-initiated drug release mechanisms enable targeted delivery with a better rate of tumor penetration, while their ability to co-deliver multiple therapeutic agents addresses drug resistance issues and provides synergistic effects. Comparative analyses have revealed that these advanced nanoplatforms significantly outperform early-generation carriers in terms of bioavailability, reduced toxicity, and treatment efficacy across various breast cancer types. Next-generation nanoplatforms offer unprecedented opportunities for targeted and efficient cancer treatment. Continued research and innovation are necessary to address existing challenges and to optimize their therapeutic potential for clinical applications.

Aim: The PI3K (phosphoinositide 3-kinase)-alpha isoform is found upregulated in 30–40% of breast cancer. Currently, there are limited selective and specific drugs that target PI3K-alpha, and no natural therapeutic option is available. This study aims to develop natural hybrid antagonists of PI3K-alpha for breast cancer therapeutics. Methods: 25 pan-PI3K and PI3K-alpha targeting drugs were obtained from various sources, including the COCONUT (Collection of Open Natural Products) database. On the parent dataset, high throughput virtual screening (HTVS), standard precision (SP) docking, and extra precision (XP) docking were performed to produce Murcko scaffolds and heterogenous fragments. Murcko scaffolds are hybridized with fragments of natural compounds (Category 1) and drugs (Category 2), respectively. Hybrids are docked with HTVS, SP, and XP, followed by induced fit docking and ADME (absorption, distribution, metabolism, and excretion) prediction. MM/GBSA (molecular mechanics/generalized Born and surface area) was performed on the docked poses. Results: Highest docking scores of –13.354 kcal/mol and –12.670 kcal/mol were achieved by hybrids in Category 1 and Category 2, respectively. MM/GBSA free energy ranged from –51.14 kcal/mol to –72.66 kcal/mol. In terms of binding docking, pharmacological properties, and Lipinski’s rule of five, the natural hybrids outperformed the parent drugs. Conclusions: PI3K-alpha kinase proteins can be targeted with natural-drug hybrid antagonists for breast cancer treatment. Hybrid molecules, such as NH-01 and NH-06, show better binding with promising ADME properties. Thus, in vivo and in vitro testing is necessary to prove the value of such hybrids.

Artificial Intelligence (AI) has become an unprecedented force in healthcare with significant impact on personalized medicine and Biosensor Engineering. This chapter discusses the evolution of AI in healthcare from the early Expert Systems to the advanced Machine Learning (ML) algorithm in practice. Integration of AI in personalized medicine has revolutionized Genomic Analysis, Predictive Analytics, and Drug Discovery and Manufacture with customized treatment. The advancement of AI and AI empowered Biosensor Engineering Technologies has brought future of real time health management and diagnosis by the development of wearable and implantable devices with advanced technologies like ML. The research reveals that AI improves diagnostic accuracy, shortens the time to treatment, and streamlines the drug development process. Additionally, progress in AI-driven biosensor engineering has led to innovations in real-time health monitoring and diagnostics, particularly through the creation of wearable and implantable devices that leverage machine learning technologies.

In cancer research, tumour complexity and treatment resistance have become more prevalent. Especially microRNA (miRNA) modulation has recently come into light as a promising strategy in cancer immunotherapy, particularly in converting "cold" tumours with limited immune cell infiltration into "hot" tumours responsive to immunotherapy. Tumour microenvironments are influenced by miRNAs, while specific miRNAs are targeted in cold tumours to enhance the immune response, potentially improving therapeutic efficacy. A miRNA profiling approach may enable earlier cancer detection and personalized treatments, emphasizing its importance for oncology in the future. This review sheds light on miRNA's role in cold and hot tumour microenvironments, that how they can modulate depending on the tumour niche. Furthermore, therapeutic resistance might develop over time and aid to decrease the efficacy of miRNA regulation. To address the hurdles of finding relevant miRNAs to target, researchers may use high-throughput sequencing methods to characterise miRNA expression across tumour subtypes. They may also use bioinformatics tools and databases to analyse massive amounts of miRNArelated data and find prospective candidate miRNAs.