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

AI-driven innovations in smart multifunctional nanocarriers for drug and gene delivery: A mini-review

The convergence of artificial intelligence (AI) and nanomedicine has revolutionized the design of smart multifunctional nanocarriers (SMNs) for drug and gene delivery, offering unprecedented precision, efficiency, and personalization in therapeutic applications. AI-driven approaches enhance the development of these nanocarriers by accelerating their design, optimizing drug loading and release kinetics, improving biocompatibility, and predicting interactions with biological barriers. This review explores the transformative role of AI in the fabrication and functionalization of SMNs, emphasizing its impact on overcoming challenges in targeted drug delivery, controlled release, and theranostics. We discuss the integration of AI with advanced nanomaterials-such as polymeric, lipidic, and inorganic nanoparticles-highlighting their potential in oncology and hematology. Furthermore, we examine recent clinical and preclinical case studies demonstrating AI-assisted nanocarrier development for personalized medicine. The synergy between AI and nanotechnology paves the way for next-generation precision therapeutics, addressing critical limitations in traditional drug delivery systems. However, data standardization, regulatory compliance, and translational scalability challenges remain. This review underscores the need for interdisciplinary collaboration to unlock AI's potential in nanomedicine fully, ultimately advancing the clinical application of SMNs for more effective and safer patient care.

Biological mechanisms contradict AI consciousness: The spaces between the notes

The presumption that experiential consciousness requires a nervous system and brain has been central to the debate on the possibility of developing a conscious form of artificial intelligence (AI). The likelihood of future AI consciousness or devising tools to assess its presence has focused on how AI might mimic brain-centered activities. Currently, dual general assumptions prevail: AI consciousness is primarily an issue of functional information density and integration, and no substantive technical barriers exist to prevent its achievement. When the cognitive process that underpins consciousness is stipulated as a cellular attribute, these premises are directly contradicted. The innate characteristics of biological information and how that information is managed by individual cells have no parallels within machine-based AI systems. Any assertion of computer-based AI consciousness represents a fundamental misapprehension of these crucial differences.

'Applications of machine learning in liposomal formulation and development'

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algorithms are accelerating formulation processes, predicting key parameters, and enabling personalized therapies. ML-driven approaches are restructuring formulation development by optimizing liposome size, stability, and encapsulation efficiency while refining drug release profiles. Additionally, the integration of ML enhances therapeutic outcomes by enabling precision-targeted delivery and minimizing side effects. This review presents current breakthroughs, challenges, and future opportunities in applying ML to liposomal systems, aiming to improve therapeutic efficacy and patient outcomes in various disease treatments.

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.

Nanocarrier-mediated cancer therapy with cisplatin: A meta-analysis with a promising new paradigm

Aims

Cisplatin is a frontline chemotherapeutic utilized to attenuate multiple cancers in the clinic. Given its side-effects, a new cisplatin formulation which could prevent cytotoxicity, metabolic deficiencies and metastasis is much needed. This study investigates whether nanocarriers can provide a better mode of drug delivery in preclinical cancer models seeking a potent anticancer therapeutic agent.

Materials and methods

The PubMed database was searched, and 242 research articles were screened from which 94 articles qualified for selection from those published by December 31, 2023 and the data was synthesized using the Review Manager software.

Key findings

Cisplatin encapsulated as a nanomedicine confirmed the versatility of nanocarriers in significantly diminishing cancer cell viability, half maximal inhibitory concentration, tumour volume, biodistribution of platinum in tumours and kidney; at p < 0.00001 and a 95% confidence interval.

Significance

An estimated 19.3 million global cancer incidence is reported with 50% mortality worldwide for which nanocarrier-mediated cisplatin therapy is most promising. Our findings offer new vistas for future cancer treatment when combined with chemo-immunotherapy that utilizes the recently advanced nanozymes.

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.