Desai Jagruti, Dhameliya Prince, Patel Swayamprakash
Department of Pharmaceutical Technology, Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus, Changa, Gujarat, India.
Drug Dev Ind Pharm. 2024 Dec;50(12):995-1007. doi: 10.1080/03639045.2024.2434640. Epub 2024 Dec 8.
The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critical quality attributes (CQAs) such as hardness, friability, and disintegration time of fast disintegrating tablets (FDTs).
A dataset of 864 batches of FDTs was generated by varying binder types and amounts, disintegrants, diluents, punch sizes, and compression forces. Preprocessing steps included normalizing numerical features based on industry standards, one-hot encoding for categorical variables, and addressing outliers to ensure data consistency. Four machine learning models were trained and evaluated on R values and mean squared error (MSE). Feature importance was analyzed using permutation importance, and statistical validation ( < 0.05) and confidence intervals were computed for model performance. The 'differential_evolution' function was used to optimize the formulation.
Among the models, ANN demonstrated the highest predictive accuracy, achieving R values up to 0.9550 with the lowest MSE across training and test datasets, outperforming kNN, SVM, and linear regression. The ANN's ability to model complex, non-linear interactions between formulation variables was statistically significant, as validated through six checkpoint batches of acetylsalicylic acid FDTs. The feature importance analysis revealed compression force, binder type, and punch size as the most influential factors affecting hardness, while disintegrant type influenced friability. The 'differential_evolution' function effectively optimized the CQAs, resulting in FDTs with ideal characteristics.
The ANN model, integrated with differential evolution, provided a robust tool for optimizing FDT formulations by accurately predicting CQAs and reducing the need for extensive experimental trials. Compared to traditional optimization methods, ANN excels in capturing intricate multi-variable relationships, making it a valuable approach for scaling beyond acetylsalicylic acid to other formulations. This method enhances the consistency and efficiency of tablet formulation, supporting broader pharmaceutical applications.
本研究的目的是利用机器学习算法创建预测模型，包括人工神经网络（ANN）、k近邻算法（kNN）、支持向量机（SVM）和线性回归，以预测快速崩解片（FDT）的关键质量属性（CQA），如硬度、脆碎度和崩解时间。
通过改变粘合剂类型和用量、崩解剂、稀释剂、冲头尺寸和压缩力，生成了一个包含864批次FDT的数据集。预处理步骤包括根据行业标准对数值特征进行归一化、对分类变量进行独热编码以及处理异常值以确保数据一致性。在R值和均方误差（MSE）上对四个机器学习模型进行训练和评估。使用排列重要性分析特征重要性，并计算模型性能的统计验证（<0.05）和置信区间。使用“差分进化”函数优化配方。
在这些模型中，ANN表现出最高的预测准确性，在训练和测试数据集中，R值高达0.9550，MSE最低，优于kNN、SVM和线性回归。通过六批乙酰水杨酸FDT的检查点验证，ANN对配方变量之间复杂非线性相互作用进行建模的能力具有统计学意义。特征重要性分析表明，压缩力、粘合剂类型和冲头尺寸是影响硬度的最主要因素，而崩解剂类型影响脆碎度。“差分进化”函数有效地优化了CQA，得到了具有理想特性的FDT。
与传统优化方法相比，结合差分进化的ANN模型通过准确预测CQA并减少大量实验试验的需求，为优化FDT配方提供了一个强大的工具。ANN在捕捉复杂的多变量关系方面表现出色，使其成为从乙酰水杨酸扩展到其他配方的有价值方法。该方法提高了片剂配方的一致性和效率，支持更广泛的制药应用。
