pH-responsive纳米容器中药物释放的数学模型
Mathematical modelling of drug delivery from pH-responsive nanocontainers
摘要：
药物递送系统代表了治疗癌症和克服化疗副作用的有希望的策略。特别是，聚合物纳米容器已经吸引了主要的兴趣，因为它们的结构和形态的优势和各种聚合物，可以使用，允许合成的材料能够响应肿瘤微环境的生化改变。虽然实验方法可以提供很多见解，但在宽参数空间上生成实验数据通常非常耗时和/或昂贵。为了更好地了解不同的设计参数对药物释放曲线和药物动力学的影响，适当设计的数学模型是非常有益的。在这里，我们开发了一种新的数学模型来描述药物在由核和pH响应性聚合物壳组成的中空纳米容器内的运输和释放。双层数学模型充分考虑了药物的溶解、扩散和与聚合物的相互作用。以柔红霉素和[Cu（TPMA）（Phenantroline）]（ClO_4）_2为模型药物，制备了具有良好包封能力的纳米胶囊。体外药物释放行为进行了研究，在不同的条件下，该系统被证明能够响应所选择的pH刺激，释放更大量的药物在酸性比在生理环境中。通过将数学模型的结果与我们的实验数据进行比较，我们能够确定最适合数据的模型参数值，并证明该模型能够描述手头的现象。所提出的方法可用于描述和预测各种药物释放系统的释放曲线。
英文摘要：
Drug delivery systems represent a promising strategy to treat cancer and to overcome the side effects of chemotherapy. In particular, polymeric nanocontainers have attracted major interest because of their structural and morphological advantages and the variety of polymers that can be used, allowing the synthesis of materials capable of responding to the biochemical alterations of the tumour microenvironment. While experimental methodologies can provide much insight, the generation of experimental data across a wide parameter space is usually prohibitively time consuming and/or expensive. To better understand the influence of varying design parameters on the drug release profile and drug kinetics involved, appropriately-designed mathematical models are of great benefit. Here, we developed a novel mathematical model to describe drug transport within, and release from, a hollow nanocontainer consisting of a core and a pH-responsive polymeric shell. The two-layer mathematical model fully accounts for drug dissolution, diffusion and interaction with polymer. We generated experimental drug release profiles using daunorubicin and [Cu(TPMA)(Phenantroline)](ClO_4)_2 as model drugs, for which the nanocontainers exhibited excellent encapsulation ability. The in vitro drug release behaviour was studied under different conditions, where the system proved capable of responding to the selected pH stimuli by releasing a larger amount of drug in an acidic than in the physiological environments. By comparing the results of the mathematical model with our experimental data, we were able to identify the model parameter values that best-fit the data and demonstrate that the model is capable of describing the phenomena at hand. The proposed methodology can be used to describe and predict the release profiles for a variety of drug delivery systems.
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