脂质纳米颗粒药物释放的比较数学分析

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-09-05 DOI:10.1208/s12249-024-02922-7

Pedram Porbaha, Ramin Ansari, Mohammad Reza Kiafar, Rahman Bashiry, Mohammad Mehdi Khazaei, Amirhossein Dadbakhsh, Amir Azadi

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摘要

药物从给药系统中释放的数学模型对于理解和优化配方至关重要。本研究对基于脂质的纳米颗粒的药物释放进行了比较数学分析。研究人员从文献中提取了各种类型脂质纳米颗粒的药物释放曲线，包括脂质体、纳米结构脂质载体（NLCs）、固体脂质纳米颗粒（SLNs）和纳米/微乳（NEMs/MEMs），并用这些曲线评估了八个传统释放数学模型的适用性。对每个数据集都计算了几个指标，包括判定系数 (R2)、调整后的 R2、低于特定阈值（5%、10%、12% 和 20%）的误差数、阿凯克信息准则 (AIC)、回归平方和 (RSS)、回归均方 (RMS)、残差平方和 (rSS) 和残差均方 (rMS)。在评估的模型中，Korsmeyer-Peppas 模型的调整 R2 值最高，NLC 为 0.95，其他脂质体给药系统为 0.93。Weibull 模型排名第二，脂质体系统的调整 R2 值为 0.92，SLN 为 0.94，NEMs/MEMs 为 0.82。因此，这两个模型在预测和表征脂质纳米粒子药物释放方面似乎更有效，可能更适合即将开展的研究工作。

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A Comparative Mathematical Analysis of Drug Release from Lipid-Based Nanoparticles

Mathematical modeling of drug release from drug delivery systems is crucial for understanding and optimizing formulations. This research provides a comparative mathematical analysis of drug release from lipid-based nanoparticles. Drug release profiles from various types of lipid nanoparticles, including liposomes, nanostructured lipid carriers (NLCs), solid lipid nanoparticles (SLNs), and nano/micro-emulsions (NEMs/MEMs), were extracted from the literature and used to assess the suitability of eight conventional mathematical release models. For each dataset, several metrics were calculated, including the coefficient of determination (R²), adjusted R², the number of errors below certain thresholds (5%, 10%, 12%, and 20%), Akaike information criterion (AIC), regression sum square (RSS), regression mean square (RMS), residual sum of square (rSS), and residual mean square (rMS). The Korsmeyer-Peppas model ranked highest among the evaluated models, with the highest adjusted R² values of 0.95 for NLCs and 0.93 for other liposomal drug delivery systems. The Weibull model ranked second, with adjusted R² values of 0.92 for liposomal systems, 0.94 for SLNs, and 0.82 for NEMs/MEMs. Thus, these two models appear to be more effective in forecasting and characterizing the release of lipid nanoparticle drugs, potentially making them more suitable for upcoming research endeavors.