机构：[1]School of Pharmacy, Southwest Medical University, Luzhou 646000, China.[2]School of Basic Medical Science, Southwest Medical University, Luzhou 646000, China.[3]Key Laboratory of Medical Electrophysiology, Southwest Medical University, Luzhou 646000, China.[4]Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou 646000, China.[5]State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China.四川大学华西医院[6]Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.[7]Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.
基金：
Southwest Medical University Applied Basic Research
Program Project [2021ZKMS006, 2021ZKQN013], the Open Fund of Key Laboratory of Medical
Electrophysiology [KeyME2020-003], the National Natural Science Foundation of China [82074129],
the Science and Technology Planning Project of Sichuan Province, China [2022JDJQ0061].
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类|2 区医学
小类|2 区药学
最新[2023]版：
大类|3 区医学
小类|2 区药学
第一作者：
第一作者机构：[1]School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
共同第一作者：
通讯作者：
通讯机构：[2]School of Basic Medical Science, Southwest Medical University, Luzhou 646000, China.[3]Key Laboratory of Medical Electrophysiology, Southwest Medical University, Luzhou 646000, China.[4]Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou 646000, China.[6]Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.[7]Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
推荐引用方式(GB/T 7714)：
Wang Binyou,Tan Xiaoqiu,Guo Jianmin,et al.Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning.[J].Pharmaceutics.2022,14(5):doi:10.3390/pharmaceutics14050943.
APA：
Wang Binyou,Tan Xiaoqiu,Guo Jianmin,Xiao Ting,Jiao Yan...&Wang Yiwei.(2022).Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning..Pharmaceutics,14,(5)
MLA：
Wang Binyou,et al."Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning.".Pharmaceutics 14..5(2022)
