Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

Antibiotic resistance and virulence characteristics of four carbapenem-resistant Klebsiella pneumoniae strains coharbouring blaKPC and blaNDM based on whole genome sequences from a tertiary general teaching hospital in central China between 2019 and 2021

OBJECTIVE

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infection is a worldwide health issue that poses a serious threat to public health. This study summarizes the clinical features of four patients with CRKP coproducing NDM and KPC infections and further analyses the molecular typing, resistance and virulence factors of the four CRKP strains.

METHODS

Of the twenty-two CRKP isolates, four strains coharbouring bla_KPC and bla_NDM isolated from four patients were screened by Sanger sequencing between October 2019 and April 2021. Demographics, clinical and pathological data of the four patients were collected through electronic medical records. Antimicrobial susceptibility testing, biofilm formation assays and serum bactericidal assays were performed on the four isolates. The antibiotic resistance and virulence genes were investigated by whole-genome sequencing. Sequence types (STs) were determined by multilocus sequence typing, and serotypes were identified by wzi gene sequencing.

RESULTS

Three patients recovered, and one patient stopped treatment. Four strains were multiple carbapenemase producers: KPC-2, NDM-4, SME-5 and IMI-4 coproducer; KPC-2, NDM-1 and SME-3 coproducer; KPC-2, NDM-1 and IMI-3 coproducer; KPC-2 and NDM-5 coproducer. They also harboured ESBL genes and mutations in the efflux pump regulator genes. They were multidrug resistant but sensitive to tigecycline and colistin. Four isolates had moderate biofilm-forming abilities and carried various virulence genes, including siderophores, type 1 fimbriae and E. coli common pilus. Only the NO. 3 strain was resistant to the serum. The STs and serotypes of the four strains were ST11 and KL64, ST337 and none, ST307 and KL102KL149KL155, and ST29 and K54, respectively.

CONCLUSION

Four CRKP strains coharbouring bla_KPC and bla_NDM also carried other carbapenemase genes. Notably, the NO. 1 isolate carrying four carbapenemase genes has not been reported globally until now. Four strains exhibited a high level of resistance to multiple antibiotics. Additionally, three of the four patients were exposed to invasive medical devices that provided an environment for biofilm formation. Meanwhile, three strains with adhesion genes as moderate biofilm formers might form biofilms resulting in long hospital stays, increasing therapeutic difficulty, and even treatment failure. This study reminds clinicians that CRKP strains with multiple carbapenemase genes emerged in our hospital, and stronger measures should be taken to the control of nosocomial infections.

Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔG_Fs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔG_Fs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c _j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c _j formed from >70 different biological activity parameters ( c₀), >300 different drug targets ( c₁), >230 cell lines ( c₂), and 5 organisms of assay ( c₃) or organisms of the target ( c₄). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

Asymmetric synthesis of δ-amino acid derivatives via diastereoselective vinylogous Mannich reactions between N-tert-butanesulfinyl imines and dioxinone-derived lithium dienolate

A diastereoselective vinylogous Mannich reaction between theN-tert-butanesulfinyl imines and dioxinone-derived lithium dienolate was developed.

How to deal with low-resolution target structures: using SAR, ensemble docking, hydropathic analysis, and 3D-QSAR to definitively map the αβ-tubulin colchicine site

αβ-Tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q(2) of 0.616, r(2) of 0.949, and r(2)pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis, and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin.

Capacity of mesoporous bioactive glass nanoparticles to deliver therapeutic molecules

Inorganic bioactive nanomaterials are attractive for hard tissue regeneration, including nanocomponents for bone replacement composites and nanovehicles for delivering therapeutics. Bioactive glass nanoparticles (BGn) have recently gained potential usefulness as bone and tooth regeneratives. Here we demonstrate the capacity of the BGn with mesopores to load and deliver therapeutic molecules (drugs and particularly genes). Spherical BGn with sizes of 80-90 nm were produced to obtain 3-5 nm sized mesopores through a sono-reacted sol-gel process. A simulated body fluid test of the mesoporous BGn confirmed their excellent apatite forming ability and the cellular toxicity study demonstrated their good cell viability up to 100 μg ml(-1). Small molecules like chemical drug (Na-ampicillin) and gene (small interfering RNA; siRNA) were introduced as model drugs considering the mesopore size of the nanoparticles. Moreover, amine-functionalization allowed switchable surface charge property of the BGn (from -20-30 mV to +20-30 mV). Loading of ampicillin or siRNA saturated within a few hours (~2 h) and reflected the mesopore structure. While the ampicillin released relatively rapidly (~12 h), the siRNA continued to release up to 3 days with almost zero-order kinetics. The siRNA-nanoparticles were easily taken up by the cells, with a transfection efficiency as high as ~80%. The silencing effect of siRNA delivered from the BGn, as examined by using bcl-2 model gene, showed dramatic down-regulation (~15% of control), suggesting the potential use of BGn as a new class of nanovehicles for genes. This, in conjunction with other attractive properties, including size- and mesopore-related high surface area and pore volume, tunable surface chemistry, apatite-forming ability, good cell viability and the possible ion-related stimulatory effects, will potentiate the usefulness of the BGn in hard tissue regeneration.