Risk prediction of Reduning Injection batches by near-infrared spectroscopy combined with multiple machine learning algorithms.
10.19540/j.cnki.cjcmm.20240822.302
- Author:
Wen-Yu JIA
1
;
Feng TONG
2
;
Heng-Xu LIU
2
;
Shu-Qin JIN
3
;
Yong-Chao ZHANG
2
;
Chen-Feng ZHANG
2
;
Zhen-Zhong WANG
2
;
Xin ZHANG
2
;
Wei XIAO
2
Author Information
1. State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture,Kanion School of Chinese Materia Medica,Nanjing University of Chinese Medicine Nanjing 210023,China.
2. State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture,Kanion School of Chinese Materia Medica,Nanjing University of Chinese Medicine Nanjing 210023,China Jiangsu Kanion Pharmaceutical Co.,Ltd. Lianyungang 222001,China.
3. Jiangsu Kanion Pharmaceutical Co.,Ltd. Lianyungang 222001,China.
- Publication Type:Journal Article
- Keywords:
Bayesian optimization;
Reduning Injection;
data balance;
machine learning algorithm;
near-infrared spectroscopy;
potential risks of clinical use
- MeSH:
Spectroscopy, Near-Infrared/methods*;
Drugs, Chinese Herbal/administration & dosage*;
Machine Learning;
Algorithms;
Humans;
Quality Control
- From:
China Journal of Chinese Materia Medica
2025;50(2):430-438
- CountryChina
- Language:Chinese
-
Abstract:
In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
