Machine learning-based quantitative prediction of drug drug interaction using drug label information
10.13699/j.cnki.1001-6821.2024.16.020
- VernacularTitle:基于机器学习的利用药物标签信息定量预测药物-药物相互作用
- Author:
Lu-Hua LIANG
1
;
Yu-Xi XU
;
Bei QI
;
Lu-Yao WANG
;
Chang LI
;
Rong-Wu XIANG
Author Information
1. 沈阳药科大学,生物医药信息教研室,辽宁沈阳 110016;辽宁省医药大数据与人工智能工程技术研究中心,辽宁沈阳 110016
- Keywords:
machine learning;
drug-drug interaction;
bagged tree model
- From:
The Chinese Journal of Clinical Pharmacology
2024;40(16):2396-2400
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct machine learning models that can be used to predict AUC fold change(FC)using a database of existing pharmacokinetic(PK)and drug-drug interaction(DDI)information,which can be used to explore the possibility of predicting existing drug interactions and to provide certain rational recommendations for clinical drug use.Methods PK data of DDIs and AUC fold change data were extracted from FDA-approved drug labels.Peptide and pharmacodynamic(PD)information related to drug interactions were retrieved through DrugBank,and PPDT identification of relevant peptide IDs was performed using Protein Resource(UniProt),and a matrix normalization code was used to generate multidimensional vector data that were easy to analysis.The effect of PPDT on the AUC,and the resulting multiplicity change was used as the dependent variable for machine learning model construction.The model with the smallest root mean square error(RMES)value was used for model construction to train a bagged decision tree(Bagged)prediction model.The models were tested using the trained models for some of the drug tests.The models were evaluated by reviewing the available literature findings on detection of drug interaction pairs and analyzing and comparing the predicted values.Results A total of 16 pairs of model drug pairs were tested for the effects of 16 drugs on tacrolimus,and it was found that the accuracy of the prediction of the presence or absence of drug interactions was 81.25％;the prediction results were classified according to the FDA standard classification of the strong and weak for the strength of drug interactions,and the results showed that the prediction of the strength of drug interactions,with a large deviation from the larger prediction was less.Conclusion The evaluation of the model to predict the presence or absence of drug interactions was general;however,after classifying the strengths and weaknesses of drug interactions,the prediction of drug interactions was better,and the prediction results indicated that the model prediction performance has a certain reference value for potential DDI assessment before clinical trials.
