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Purpose <p>This 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.</p> Method <p>Drug 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.</p> Results <p>Brexpiprazole showed higher solubility in GMS, oleic acid, Tween®80, and Span®80, supported by favorable docking interactions (–1.6 to –2.0&#xa0;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&#xa0;nm, PDI 0.249 ± 0.013, and zeta potential –26.2 ± 1.3&#xa0;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&#xa0;h, 96.14 ± 2.96% at 28&#xa0;h) and higher permeation flux (75.35&#xa0;µg/cm<sup>2</sup>/h).</p> Conclusion <p>The integrated DoE-Assessment of a predicted model-based successful optimization of lactoferrin-functionalized extended release Brexpiprazole-loaded NLCs with 132.8 ± 2.4&#xa0;nm, and zeta potential -26.2 ± 1.3&#xa0;mV, which demonstrated strong compatibility, sustained release, and promising potential for targeted nose-to-brain delivery of Brexpiprazole.</p>

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

  • 期刊论文

  • 发表日期：

    2026年3月23日

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

摘要

Purpose

This 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.

Method

Drug 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.

Results

Brexpiprazole 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/cm2/h).

Conclusion

The 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.