Brain targeting of drugs via intranasal route in conjunction with nanoparticle-based systems: an updated review
Source abstract
Abstract
The prevalence of neurological illnesses, such as epilepsy, Alzheimer’s disease, Parkinson’s disease, glioblastoma, and multiple sclerosis, is rising globally; there is still a shortage of effective drug delivery to central nervous system (CNS). The blood-brain barrier (BBB) is directly related to restricted drug entrance to the brain and prevents the major dose of potentially effective therapeutic moieties to reaching the brain from the systemic circulation. Drugs that have minimal oral bioavailability and are active at low dosages can be delivered effectively using intranasal route of drug delivery in conjunction with nanoparticulate structures. This route facilitates the movement of therapeutic molecules through and by circumventing the BBB. The nasal route is convenient for self-medication and avoids the hepatic first-pass elimination associated with oral delivery. The critical evaluation of reported studies showed dose reduction, effectiveness, safety, stability, improved blood circulation time, controlled release characteristics, and targeting in brain for drug-loaded nanocarriers administered intranasally. The size range of nanoparticles from published works was found to be in between 70 and 200 nm. In this review, recent developments in nanocarrier-based intranasal drug delivery systems (nanostructured lipid carriers, solid lipid nanoparticles, liposomes, niosomes, polymeric nanoparticles, nanoemulsions, nanomicelles, carbon nanotubes, and metallic nanoparticles) are described, along with basic aspects of nasal drug delivery. Patent reports related to intranasal delivery of drugs through nanocarriers in brain targeting are also summarized.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Drug Delivery Systems · Lipid Membrane Structure and Behavior · Nanoparticle-Based Drug Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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