Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Vaccine development using artificial intelligence and machine learning: A review

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.

Targeted Delivery of Nanoparticle-Conveyed Neutrophils to the Glioblastoma Site for Efficient Therapy

Glioblastoma is a common brain tumor that poses considerable challenges in drug delivery. In this study, we investigated the potential of cell-based nanoparticles for targeted drug delivery to the glioblastoma sites. The anticancer drug of temozolomide (TMZ)-loaded T7-cholesterol nanoparticle micelles efficiently delivered nanoparticles to neutrophils and, subsequently, to the tumors. T7 is a cell-penetrating peptide that enhances the delivery of T7/TMZ to the target cells. T7 also serves as a transferrin target peptide, enabling targeted delivery to tumors. T7-conjugated cholesterol can self-assemble into micelles in aqueous solution and attach to the membrane of neutrophils. We confirmed that T7/TMZ nanoparticle micelles were efficiently located inside the neutrophils. Thereafter, T7/TMZ-conveyed neutrophils were administered to a glioblastoma mouse model, enabling neutrophils to penetrate the blood-brain barrier and deliver drugs directly to the tumor site. We evaluated the drug delivery efficiency and therapeutic effects of intravenous injection of T7/TMZ-conveyed neutrophils to a glioblastoma mouse model. These results demonstrate the promising role of neutrophil-based nanoparticle delivery systems in the targeted therapy of glioblastoma.

Recent Biomaterial-Assisted Approaches for Immunotherapeutic Inhibition of Cancer Recurrence

Biomaterials possess distinctive properties, notably their ability to encapsulate active biological products while providing biocompatible support. The immune system plays a vital role in preventing cancer recurrence, and there is considerable demand for an effective strategy to prevent cancer recurrence, necessitating effective strategies to address this concern. This review elucidates crucial cellular signaling pathways in cancer recurrence. Furthermore, it underscores the potential of biomaterial-based tools in averting or inhibiting cancer recurrence by modulating the immune system. Diverse biomaterials, including hydrogels, particles, films, microneedles, etc., exhibit promising capabilities in mitigating cancer recurrence. These materials are compelling candidates for cancer immunotherapy, offering in situ immunostimulatory activity through transdermal, implantable, and injectable devices. They function by reshaping the tumor microenvironment and impeding tumor growth by reducing immunosuppression. Biomaterials facilitate alterations in biodistribution, release kinetics, and colocalization of immunostimulatory agents, enhancing the safety and efficacy of therapy. Additionally, how the method addresses the limitations of other therapeutic approaches is discussed.

The state-of-art polyurethane nanoparticles for drug delivery applications

Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field.