journal article·2026-03-29Open access

Solid lipid nanoparticles enhance piracetam’s neuroprotective action in streptozotocin-induced cognitive dysfunction

Abhishek Mishra, Bhabani Sankar Satapathy, Pratap Kumar Sahu

Discover Nano· Volume 21· Issue 1

Abstract

Abstract Diabetes-induced cognitive dysfunction remains an unaddressed medical concern across the globe with limited options left with conventional drug therapy. Piracetam, the first identified nootropic agent has been repurposed in the current study via solid-lipid nanoparticle against streptozotocin-induced cognitive dysfunction in rats. Experimental piracetam loaded solid-lipid nanoparticles (PSLNs) were developed by conventional nanoprecipitation method and characterized in terms of SEM, AFM, DLS, zeta potential, drug loading efficiency etc. In vivo efficacy of the selected PSLNs was analysed. Various biochemical parameters were analysed along with histopathology and brain pharmacokinetic studies. Experimental PSLNs were spherical, nanosized, homogenous (PDI: 0.21 ± 0.5) with − 38.2 ± 0.3 mV surface charge. Following administration, selected PSLNs brought significant improvement in behavioural and memory function as compared to plain piracetam and control rat groups. Modulation in key enzyme/ protein expression in terms of SOD, CAT, GSH was observed in vivo in PSLNs treated rats. Histopathology of brain tissue in AD rats indicated revival of normal tissue architecture in PSLNs treated group as compared to piracetam/control groups. Key brain pharmacokinetic parameters like AUC, AUMC, MRT etc. were substantially improved for PSLNs than plain piracetam, signifying higher accumulation and retention of the drug following nanoencapsulation. However, futuristic studies are necessary to establish PSLNs as a potent modality against diabetes-induced cognitive dysfunction in clinical settings.
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Research context

OpenAlex topics: Advanced Drug Delivery Systems · Neurological Disease Mechanisms and Treatments · Medicinal Plants and Neuroprotection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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