优化纳米脂质体制剂：使用机器学习评估影响载姜黄素脂质体包封效率的因素。

Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning.

作者信息

Hoseini Benyamin, Jaafari Mahmoud Reza, Golabpour Amin, Momtazi-Borojeni Amir Abbas, Eslami Saeid

机构信息

Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.

Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Pharmaceutical Nanotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.

出版信息

Int J Pharm. 2023 Nov 5;646:123414. doi: 10.1016/j.ijpharm.2023.123414. Epub 2023 Sep 13.

DOI:10.1016/j.ijpharm.2023.123414

PMID:37714314

Abstract

BACKGROUND

Curcumin faces challenges in clinical applications due to its low bioavailability and poor water solubility. Liposomes have emerged as a promising delivery system for curcumin. This study aims to apply ensemble learning, a machine learning technique, to determine the most effective experimental conditions for formulating stable curcumin-loaded liposomes with a high entrapment efficiency (EE).

METHODS

Two liposomal formulations composed of HSPC:DPPG:Chol:DSPE-mPEG2000 and HSPC:Chol:DSPE-mPEG2000 at 55:5:35:5 and 55:40:5 M ratios, respectively, were prepared using the remote loading method, and their particle size and polydispersity index (PDI) were determined using Dynamic Light Scattering. To model the impact of five factors (molar ratios, particle size, sonication time, pH, and PDI) on EE%, the Least-squares boosting (LSBoost) ensemble learning algorithm was employed due to its capability to effectively handle nonlinear and non-stationary problems. The implementation and optimization of LSBoost were performed using MATLAB R2020a. The dataset was randomly split into training and testing sets, with 70% allocated for training. The mean absolute error (MAE) was used as the cost function to evaluate model performance. Additionally, a novel approach was employed to visualize the results using 3D plots, facilitating practical interpretation.

RESULTS

The optimal model exhibited an MAE of 3.61, indicating its robust predictive capability. The study identified several optimal conditions for achieving the highest EE value of 100%. However, to ensure both the highest EE value and a suitable particle size, it is recommended to set the following conditions: a molar ratio of 55:5:35:5, a PDI within the range of 0.09-0.13, a particle size of approximately 130 nm, a sonication time of 30 min, and a pH within the range of 7.2-8. It is worth mentioning that adjusting the molar ratio to 55:40:5 resulted in a maximum EE of 88.38%.

CONCLUSION

These findings underscore the high performance of ensemble learning in accurately predicting and optimizing the EE of the curcumin-loaded liposomes. The application of this technique provides valuable insights and holds promise for the development of efficient drug delivery systems.

摘要

背景

姜黄素由于其低生物利用度和差的水溶性，在临床应用上面临挑战。脂质体已成为一种有前途的姜黄素递送系统。本研究旨在应用集成学习（一种机器学习技术）来确定制备具有高包封率（EE）的稳定姜黄素负载脂质体的最有效实验条件。

方法

使用远程加载方法分别制备了两种脂质体制剂，其组成分别为HSPC:DPPG:Chol:DSPE - mPEG2000和HSPC:Chol:DSPE - mPEG2000，摩尔比分别为55:5:35:5和55:40:5，并用动态光散射法测定其粒径和多分散指数（PDI）。为了模拟五个因素（摩尔比、粒径、超声处理时间、pH值和PDI）对EE%的影响，采用最小二乘提升（LSBoost）集成学习算法，因为它能够有效处理非线性和非平稳问题。LSBoost的实现和优化使用MATLAB R2020a进行。数据集随机分为训练集和测试集，70%用于训练。平均绝对误差（MAE）用作评估模型性能的成本函数。此外，采用了一种新颖的方法用三维图可视化结果，便于实际解释。

结果

最优模型的MAE为3.61，表明其具有强大的预测能力。该研究确定了几个实现最高EE值100%的最优条件。然而，为了确保最高的EE值和合适的粒径，建议设置以下条件：摩尔比为55:5:35:5，PDI在0.09 - 0.13范围内，粒径约为130 nm，超声处理时间为30分钟，pH值在7.2 - 8范围内。值得一提的是，将摩尔比调整为55:40:5时，最大EE为88.38%。