Evidence map›Paper›PMID 41872417›Full record

ArticleAAPS PharmSciTech2026

Design, Optimization, and In Vitro Evaluation of Lactoferrin-Coated Brexpiprazole-Loaded Nanostructured Lipid Carriers for Brain Targeting.

Jaimini Parmar, Pranav Shah, Dipika Chavda, Mahavir Bhupal Chougule

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Article in AAPS PharmSciTech, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jaimini ParmarDepartment of Pharmaceutics & Pharmaceutical Technology, Maliba Pharmacy College, Uka Tarsadia University, Maliba Campus, Gopal Vidyanagar, Bardoli-Mahuva Road, Tarsadia, Surat, Gujarat, 394350, India.
Pranav ShahDepartment of Pharmaceutics & Pharmaceutical Technology, Maliba Pharmacy College, Uka Tarsadia University, Maliba Campus, Gopal Vidyanagar, Bardoli-Mahuva Road, Tarsadia, Surat, Gujarat, 394350, India. pranav.shah@utu.ac.in.ORCID http://orcid.org/0000-0003-1057-2566
Dipika ChavdaDepartment of Pharmaceutics, Anand Pharmacy College, Opp. Town Hall, Rahtlav, Mathiya Chora, Anand, Gujarat, 388001, India.
Mahavir Bhupal ChouguleIngenious Biopharma-Engineered Drugs and Biologics Delivery (iBD-2) Laboratory, Department of Pharmaceutical Sciences, Mercer University, Atlanta, Georgia, 30341, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to develop lactoferrin-coated Brexpiprazole-loaded nanostructured lipid carriers (Lf-BXP-NLCs) using a Design of Experiments (DoE) approach combined with an artificial neural network (ANN) to improve brain targetability and prolong residence time.

methodDrug solubility and interactions of Brexpiprazole with solid lipid, liquid lipid, surfactant, and co-surfactant were evaluated using in silico molecular docking. NLCs were prepared by hot high-speed homogenization. Plackett-Burman design was employed to screen critical formulation and process variables, while the Box-Behnken design enabled optimization. A feed-forward ANN model was developed to predict particle size (PS) and drug release (DR), and to assist in selecting formulations meeting critical quality attributes (CQAs). NLCs were characterized for physicochemical properties, encapsulation efficiency, morphology, in vitro release, ex vivo studies, and stability.

resultsBrexpiprazole showed higher solubility in GMS, oleic acid, Tween®80, and Span®80, supported by favorable docking interactions (-1.6 to -2.0 kcal/mol). Liquid lipid content, homogenization speed, and sonication time significantly influenced PS and DR, with liquid lipid amount identified as the most critical factor. Optimized Lf-BXP-NLCs exhibited a PS of 132.8 ± 2.4 nm, PDI 0.249 ± 0.013, and zeta potential -26.2 ± 1.3 mV. High entrapment efficiency (87.8 ± 0.25%) and drug loading (11.71 ± 0.03%) were achieved, along with extended drug release (55.00 ± 2.26%-66.97 ± 2.25% at 12-24 h, 96.14 ± 2.96% at 28 h) and higher permeation flux (75.35 µg/cm

conclusionThe integrated DoE-Assessment of a predicted model-based successful optimization of lactoferrin-functionalized extended release Brexpiprazole-loaded NLCs with 132.8 ± 2.4 nm, and zeta potential -26.2 ± 1.3 mV, which demonstrated strong compatibility, sustained release, and promising potential for targeted nose-to-brain delivery of Brexpiprazole.

Indexed as

BrainDrug CarriersLactoferrinLipidsNanostructuresQuinolonesThiophenesAnimalsChemistry, PharmaceuticalDrug Delivery SystemsDrug LiberationMolecular Docking SimulationNeural Networks, ComputerParticle SizeSolubilitySurface-Active AgentsbrexpiprazoleDrug CarriersLactoferrinLipidsQuinolonesSurface-Active AgentsThiophenesArtificial neural networkBox-Behnken designBrexpiprazoleNanostructured lipid carriersPlackett–Burman design

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.