Öztürk A Alper, Gündüz A Bilge, Ozisik Ozan
Department of Pharmaceutical Technology, Faculty of Pharmacy, Anadolu University, Eskisehir, Turkey.
Department of Computer Science, Electrical & Electronics Faculty, Yildiz Technical University, Istanbul, Turkey.
Comb Chem High Throughput Screen. 2018;21(9):693-699. doi: 10.2174/1386207322666181218160704.
Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric.
SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated.
PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods.
Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.
固体脂质纳米粒（SLNs）是一种药物递送系统，具有药物控释、长期稳定性等优点。粒径（PS）是固体脂质纳米粒的重要标准之一。这些因素会影响药物释放速率、生物分布等。在本研究中，对采用高速均质技术制备固体脂质纳米粒的配方进行了评估。这项工作的主要重点是研究混合时间和配方成分对粒径的影响是否可以建模。为此，应用了不同的机器学习算法，并使用平均绝对误差指标进行评估。
通过高速均质法制备固体脂质纳米粒。对新制备的样品进行粒径、粒度分布和zeta电位测量。为了根据混合时间和配方成分对颗粒配方进行建模，并评估粒径对这些参数的可预测性，对所制备的数据集应用了不同的机器学习算法，并对算法的性能进行了评估。
所获得的固体脂质纳米粒的粒径在263 - 498nm范围内。结果表明，基于决策树的方法能最好地估计固体脂质纳米粒的粒径，其中随机森林的平均绝对误差值最小，为0.028。因此，机器学习算法的估计表明，粒径可以通过基于决策规则的机器学习方法和函数拟合机器学习方法进行估计。
我们的研究结果表明，机器学习方法对于确定进一步研究的配方参数非常有用。
