School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, U.K.
Centre for Precision Healthcare, UCL Division of Medicine, University College London, 5 University Street, London WC1E 6JF, U.K.
Mol Pharm. 2022 Feb 7;19(2):602-615. doi: 10.1021/acs.molpharmaceut.1c00699. Epub 2022 Jan 21.
The physical properties of nanoparticles may affect the uptake mechanism, biodistribution, stability, and other physicochemical properties of drug delivery systems. This study aimed to first develop a model exploring the factors controlling the nanogel physical properties using a single drug (propranolol), followed by an evaluation of whether these models can be applied more generally to a range of drugs. Size, polydispersity, ζ potential, and encapsulation efficiency were investigated using a design of experiment (DOE) approach to optimize formulations by systematically identifying the effects of, and interactions between, parameters associated with nanogel formulation and drug loading. Three formulation factors were selected, namely, chitosan concentration, the ratio between the chitosan and cross-linker─sodium triphosphate─and the ratio between the chitosan and drug. The results indicate that the DOE approach can be used not only to model but also to predict the size and polydispersity index (PDI). To explore the application of these prediction models with other drugs and to identify the relationship between the drug structure and nanogel properties, nanogels loaded with 12 structurally distinct drugs and 6 structurally similar drugs were fabricated at the optimal condition for propranolol in the model. The measured size, PDI, and ζ potential of the nanogels could not be modeled using distinct DOE parameters for dissimilar drugs, indicating that each drug requires a separate analysis. Nevertheless, for drugs with structural similarities, various linear and nonlinear trends were observed in the size, PDI, and ζ potential of nanogels against selected molecular descriptors, indicating that there are indeed relationships between the drug molecular structure and the performance outcomes, which may be modeled and predicted using the DOE approach. In conclusion, the study demonstrates that DOE models can be applied to model and predict the influence of formulation and drug loading on key performance parameters. While distinct models are required for structurally unrelated drugs, it was possible to establish correlations for the drug series investigated, which were based on polarity, hydrophobicity, and polarizability, thereby elucidating the importance of the interactions between the drug and the nanogels based on the nanogel properties and thus deepening the understanding of the drug-loading mechanisms in nanogels.
纳米颗粒的物理性质可能会影响药物传递系统的摄取机制、生物分布、稳定性和其他物理化学性质。本研究旨在首先开发一种模型,该模型使用单一药物(普萘洛尔)来探索控制纳米凝胶物理性质的因素,然后评估这些模型是否可以更广泛地应用于一系列药物。使用实验设计(DOE)方法研究了大小、多分散性、ζ 电位和包封效率,通过系统地确定与纳米凝胶配方和药物负载相关的参数的影响和相互作用,优化配方。选择了三个配方因素,即壳聚糖浓度、壳聚糖与交联剂-三磷酸钠的比例以及壳聚糖与药物的比例。结果表明,DOE 方法不仅可用于建模,还可用于预测大小和多分散指数(PDI)。为了探索这些预测模型在其他药物中的应用,并确定药物结构与纳米凝胶性质之间的关系,在模型中为普萘洛尔优化条件下制备了负载 12 种结构不同的药物和 6 种结构相似的药物的纳米凝胶。所测量的纳米凝胶的大小、PDI 和 ζ 电位不能使用不同药物的独特 DOE 参数进行建模,这表明每种药物都需要单独进行分析。然而,对于具有结构相似性的药物,在纳米凝胶的大小、PDI 和 ζ 电位与选定的分子描述符之间观察到各种线性和非线性趋势,这表明药物分子结构与性能结果之间确实存在关系,这些关系可以使用 DOE 方法进行建模和预测。总之,该研究表明 DOE 模型可用于模拟和预测配方和药物负载对关键性能参数的影响。虽然对于结构不相关的药物需要使用独特的模型,但对于所研究的药物系列,可以建立相关性,这些相关性基于极性、疏水性和极化率,从而阐明了药物与纳米凝胶之间相互作用对基于纳米凝胶性质的载药机制的重要性,并加深了对纳米凝胶中载药机制的理解。