3 research outputs found
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><sup>2</sup><sub>ext</sub> = 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><sub>ext</sub><sup>2</sup> = 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