ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (<i>q</i>²_ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few <i>in silico</i> models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure–activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (<i>q</i>_ext² = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity

Genomic epidemiology and ceftazidime-avibactam high-level resistance mechanisms of <i>Pseudomonas aeruginosa</i> in China from 2010 to 2022

Ceftazidime-avibactam (CZA) resistance is a huge threat in the clinic; however, the underlying mechanism responsible for high-level CZA resistance in Pseudomonas aeruginosa (PA) isolates remains unknown. In this study, a total of 5,763 P. aeruginosa isolates were collected from 2010 to 2022 to investigate the ceftazidime-avibactam (CZA) high-level resistance mechanisms of Pseudomonas aeruginosa (PA) isolates in China. Fifty-six PER-producing isolates were identified, including 50 isolates carrying blaPER-1 in PA, and 6 isolates carrying blaPER-4. Of these, 82.1% (46/56) were classified as DTR-PA isolates, and 76.79% (43/56) were resistant to CZA. Importantly, blaPER-1 and blaPER-4 overexpression led to 16-fold and >1024-fold increases in the MICs of CZA, respectively. WGS revealed that the blaPER-1 gene was located in two different transferable IncP-2-type plasmids and chromosomes, whereas blaPER-4 was found only on chromosomes and was carried by a class 1 integron embedded in a Tn6485-like transposon. Overexpression of efflux pumps may be associated with high-level CZA resistance in blaPER-1-positive strains. Kinetic parameter analysis revealed that PER-4 exhibited a similar kcat/Km with ceftazidime and a high (∼3359-fold) IC50 value with avibactam compared to PER-1. Our study found that overexpression of PER-1 combined with enhanced efflux pump expression and the low affinity of PER-4 for avibactam contributes to high-level resistance to CZA. Additionally, the Tn6485-like transposon plays a significant role in disseminating blaPER. Urgent active surveillance is required to prevent the further spread of high-level CZA resistance in DTR-PA isolates.</p