Evolution of ceftazidime-avibactam resistance driven by mutations in double-copy to during treatment of ST11 carbapenem-resistant .

Unlabelled: carbapenemase (KPC) variants can contribute to resistance to ceftazidime-avibactam (CZA) in (KP). However, two-copy KPC variant-mediated resistance to CZA has rarely been reported to date. Here, we aimed to clarify the evolutionary trajectory of CZA resistance driven by mutations in double-copy to carried by the tandem core structure (IS-IS-IS) during treatment of ST11 carbapenem-resistant (CRKP).

Discovery of novel BfmR inhibitors restoring carbapenem susceptibility against carbapenem-resistant by structure-based virtual screening and biological evaluation.

The emergence and spread of pose a severe threat to public health, highlighting the urgent need for the next generation of therapeutics due to its increasing resistance to existing antibiotics. BfmR, a response regulator modulating virulence and antimicrobial resistance, shows a promising potential as a novel antimicrobial target. Developing BfmR inhibitors may propel a new therapeutic direction for intractable infection of resistant strains.

Role of tcaA, a potential target as a ceftobiprole resistance breaker in MRSA β-lactam resistance.

Objectives: Using a random forest algorithm, we previously found that teicoplanin-associated gene A (tcaA) might play a role in resistance of methicillin-resistant Staphylococcus aureus (MRSA) to β-lactams, which we have investigated further here.

Methods: Representative MRSA strains of prevalent clones were selected to identify the role of tcaA in the MRSA response to β-lactams. tcaA genes were deleted by homologous recombination in the selected MRSA strains, and antibiotic susceptibility tests were applied to evaluate the effect of tcaA on the minimum inhibitory concentrations (MICs) of glycopeptides and β-lactams.

Genomic epidemiology and ceftazidime-avibactam high-level resistance mechanisms of 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 (PA) isolates remains unknown. In this study, a total of 5,763 isolates were collected from 2010 to 2022 to investigate the ceftazidime-avibactam (CZA) high-level resistance mechanisms of (PA) isolates in China. Fifty-six PER-producing isolates were identified, including 50 isolates carrying in PA, and 6 isolates carrying .

Remimazolam attenuates myocardial ischemia-reperfusion injury by inhibiting the NF-ĸB pathway of macrophage inflammation.

Background: Inflammation is a major contributing factor in myocardial ischemia/reperfusion (I/R) injury, and targeting macrophage inflammation is an effective strategy for myocardial I/R therapy. Though remimazolam is approved for sedation, induction, and the maintenance of general anesthesia in cardiac surgery, its effect on cardiac function during the perioperative period has not been reported. Therefore, this research aimed to explore the impact of remimazolam on inflammation during myocardial ischemia/reperfusion (I/R) injury.

A Novel CMY Variant Confers Transferable High-Level Resistance to Ceftazidime-Avibactam in Multidrug-Resistant Escherichia coli.

Here, our objective was to explore the molecular mechanism underlying ceftazidime-avibactam resistance in a novel CMY-178 variant produced by the clinical Escherichia coli strain AR13438. The antibiotic susceptibility of the clinical isolate, its transconjugants, and its transformants harboring transferable were determined by the agar dilution method. S1-PFGE, cloning experiments, and whole-genome sequencing (WGS) were performed to investigate the molecular characteristics of ceftazidime-avibactam resistance genes.

Structural Basis of PER-1-Mediated Cefiderocol Resistance and Synergistic Inhibition of PER-1 by Cefiderocol in Combination with Avibactam or Durlobactam in Acinetobacter baumannii.

Cefiderocol is a novel siderophore cephalosporin that displays activity against Gram-negative bacteria. To establish cefiderocol susceptibility levels of Acinetobacter baumannii strains from China, we performed susceptibility testing and genomic analyses on 131 clinical isolates. Cefiderocol shows high activity against the strains.

A novel KPC-113 variant conferring carbapenem and ceftazidime-avibactam resistance in a multidrug-resistant Pseudomonas aeruginosa isolate.

Objectives: This study aimed to characterize a novel KPC-113 variant from a clinical Pseudomonas aeruginosa isolate R20-14.

Methods: Genomic DNA of R20-14 was subjected to Illumina and Oxford Nanopore sequencing. The horizontal transmission of plasmid was evaluated with conjugation experiments.

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug-drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug-drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints.

ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.

Adverse effects induced by drug-drug interactions may result in early termination of drug development or even withdrawal of drugs from the market, and many drug-drug interactions are caused by the inhibition of cytochrome P450 (CYP450) enzymes. Therefore, the accurate prediction of the inhibition capability of a given compound against a specific CYP450 isoform is highly desirable. In this study, three ensemble learning methods, including random forest, gradient boosting decision tree, and eXtreme gradient boosting (XGBoost), and two deep learning methods, including deep neural networks and convolutional neural networks, were used to develop classification models to discriminate inhibitors and noninhibitors for five major CYP450 isoforms (1A2, 2C9, 2C19, 2D6, and 3A4).

Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I NIK Inhibitors.

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I NIK inhibitors.

Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power.

Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibitors, have attracted extensive attention owing to their potential high selectivity.

farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods.

Summary: Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively 'undruggable' features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms.

Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor.

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants ().

Importance of protein flexibility on molecular recognition: modeling binding mechanisms of aminopyrazine inhibitors to Nek2.

NIMA-related kinase 2 (Nek2) plays a significant role in cell cycle regulation, and overexpression of Nek2 has been observed in several types of carcinoma, suggesting it is a potential target for cancer therapy. In this study, we attempted to gain more insight into the binding mechanisms of a series of aminopyrazine inhibitors of Nek2 through multiple molecular modeling techniques, including molecular docking, molecular dynamics (MD) simulations and free energy calculations. The simulation results showed that the induced fit docking and ensemble docking based on multiple protein structures yield better predictions than conventional rigid receptor docking, highlighting the importance of incorporating receptor flexibility into the accurate predictions of the binding poses and binding affinities of Nek2 inhibitors.

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 in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures.

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.

ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling.

Background: Determination of acute toxicity, expressed as median lethal dose (LD50), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity.

Results: In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD50 values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting.