School of Pharmacy, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, Jiangsu Province, China.
Changzhou Hospital of Traditional Chinese Medicine, Changzhou, 213003, Jiangsu Province, China.
AAPS PharmSciTech. 2022 Jan 31;23(1):66. doi: 10.1208/s12249-022-02210-2.
Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.
制药制剂的设计受到多种变量的影响，而找到最佳的制剂与通过各种优化任务探索多参数空间密切相关。因此，使这些优化活动更加简单是一项重要的任务。在本研究中，我们提出了一种基于最小二乘支持向量机（LSSVM）的预测模型，其参数通过粒子群优化算法（PSO-LSSVM 模型）进行优化。还使用了其他的计算优化方法并进行了比较，包括 LSSVM 和反向传播（BP）神经网络算法。PSO-LSSVM 在测试数据集上表现出最高的性能，均方误差最低。此外，由于其生产中使用的制剂组成和制造因素广泛，选择了槲皮素固体分散体和芹菜素纳米粒这两种剂型作为模型制剂。使用三种不同的模型来预测两种不同剂型的理想制剂，在实际中，使用田口正交设计数组来优化每种剂型的制剂。显然，使用 PSO-LSSVM 预测的两种制剂的性能与田口正交规划实验的结果一致，表明该模型具有良好的可靠性和高度的实用性。总之，我们的 PSO-LSSVM 预测模型具有准确预测最佳制剂的潜力，减少对实验工作的依赖，加速制剂设计过程，并为药物制剂优化提供低成本解决方案。
