AIMC Topic: Liposomes
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Self-Driving and Detachable Lab-Microrobots Tailor Drug Delivery for Closed-Loop Stimulation of the Antitumor Immune Cycle.
ACS nano
Jun 16, 2025
Hypoxia arises in most solid tumors with insufficient blood flow, which hinders the delivery and efficacy of therapeutic agents to tumors. In this work, utilizing anaerobic bacteria capable of seeking out hypoxic areas for flourishing, we constructed...
Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics.
Mikrochimica acta
Jun 16, 2025
Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target releas...
A potential new strategy for BC treatment: NPs containing solanine and evaluation of its anticancer and antimetastatic properties.
BMC cancer
May 12, 2025
Solanine has been shown to inhibit cancer by regulating the expression of apoptosis (Bax, Bcl-2) and metastasis (CDH-1, MMP2) genes in various cancer cell types. We synthesized optimized niosome NPs (NPs) with high solubility and capacity for solanin...
Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.
Drug delivery
Mar 3, 2025
Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticle...
'Applications of machine learning in liposomal formulation and development'.
Pharmaceutical development and technology
Jan 11, 2025
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 algo...
Deep Learning for the Accurate Prediction of Triggered Drug Delivery.
IEEE transactions on nanobioscience
Jan 2, 2025
The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nano...
Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.
Nature communications
Dec 30, 2024
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial i...
Artificial intelligence-driven hydrogel microneedle patches integrating 5-fluorouracil inclusion complex-loaded flexible pegylated liposomes for enhanced non-melanoma skin cancer treatment.
International journal of pharmaceutics
Dec 13, 2024
The current study focused on the development of crosslinked hydrogel microneedle patches (cHMNs) incorporating 5-FU-hydroxypropyl beta-cyclodextrin inclusion complex-loaded flexible PEGylated liposomes (5-FU-HPβCD-loaded FP-LPs) to enhance treatment ...
Leveraging machine learning to streamline the development of liposomal drug delivery systems.
Journal of controlled release : official journal of the Controlled Release Society
Nov 8, 2024
Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufa...
Review of machine learning for lipid nanoparticle formulation and process development.
Journal of pharmaceutical sciences
Sep 27, 2024
Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes ...
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