Synergistic Combinations of FDA-Approved Drugs with Ceftobiprole against Methicillin-Resistant Staphylococcus aureus

Efficacy of Ceftobiprole and Daptomycin at Bone Concentrations Against Methicillin-Resistant Staphylococcus aureus Biofilm: Results of a Dynamic In Vitro PK/PD Model

Background: The presence of biofilms and low antimicrobial concentrations in bone tissue make prosthetic joint infections (PJI) difficult to treat. Ceftobiprole (CTO) has a potential role in MRSA PJI. This study evaluated the efficacy of ceftobiprole and daptomycin (DAP) alone and in combination against MRSA biofilms at expected bone tissue concentrations. We assessed whether CTO-DAP outperformed DAP combined with a non-anti-MRSA beta-lactam (cefazolin [CZO]). Methods: A dynamic in vitro PK/PD biofilm model (CDC biofilm reactor) was used to simulate concentrations expected in cortical bone at a standard dosing of DAP (10 mg/kg/24 h), CTO (500 mg/8 h), and CZO (2 g/8 h), and assess performance against a 48-h MRSA biofilm from two clinical isolates that cause PJI (MRSA-1811 and MRSA-1733). Time-kill curves using the log change method (Δlog10 CFU/cm2) assessed antimicrobial efficacy over 56 h. Resistance emergence was monitored. Results: Although both monotherapies were active, neither reached bactericidal levels nor was one superior to the other (Δlog10 CFU/cm2 CTO vs. DAP: -1.44 ± 0.25 vs. -1.50 ± 0.01 [p = 0.686] and -1.55 ± 0.74 vs. -0.56 ± 0.36 [p = 0.108] for MRSA-1811 and MRSA-1733, respectively). Only in the MRSA-1811 isolate did the CTO-DAP combination improve the activity of each monotherapy, without achieving a synergistic effect (Δlog10 CFU/cm2: CTO-DAP -2.087 ± 0.048 vs. CTO -1.436 ± 0.249 [p = 0.013] and vs. DAP -1.503 ± 0.011 [p = 0.006]). No combination therapy (CTO-DAP vs. DAP-CZO) outperformed the other in either strain. No resistant bacterial subpopulations appeared with any antibiotic regimen. Conclusions: At clinically relevant concentrations, ceftobiprole and daptomycin showed similar activity against MRSA biofilms. The CTO-DAP combination showed comparable efficacy to DAP-CZO.

Checkerboard synergistic data analysis using a Hill function-based approach

This study presents a checkerboard data analysis approach using a Hill function (y = 1/(1+(x/K)n) to fit each column and row of checkerboard assay data. Column fits give a K (MIC) value in units of row concentration for each column antibiotic concentration (MIC_row vs [Col]), and row fits give an MIC_col value for each row antibiotic (MIC_row vs [Row]). Since the corresponding row and column concentrations are themselves MICs, this provides two sets of MIC vs MIC data pairs which can be plotted together as an isobologram. These MIC_A vs MIC_B values can be subjected to a second round of Hill function fitting, separately in x-y and y-x directions. Finally, a fit based on overlapping Hill functions is developed that allows x-y and y-x dimension fits to be performed simultaneously. Formula for fractional inhibitory concentrations (FICIs) as a function of fit parameters, and other features of these curves, are derived. This analysis also provides "n" (steepness) values from column and row fits, which are themselves dependent on the other antibiotic concentration and can be exceptionally, as in the case of ceftobiprole alone (n>10). This synergistic checkerboard analysis approach is implemented in MATLAB, which performs the fits and provides statistics variable values and alternative models significance.

Machine Learning-Assisted High-Throughput Screening for Anti-MRSA Compounds

BACKGROUND

Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.

AIMS

A machine learning (ML) model was developed for the in silico screening of low molecular weight molecules.

METHODS

We used the results of a high-throughput Caenorhabditis elegans methicillin-resistant Staphylococcus aureus (MRSA) liquid infection assay to develop ML models for compound prioritization and quality control.

RESULTS

The compound prioritization model achieved an AUC of 0.795 with a sensitivity of 81% and a specificity of 70%. When applied to a validation set of 22,768 compounds, the model identified 81% of the active compounds identified by high-throughput screening (HTS) among only 30.6% of the total 22,768 compounds, resulting in a 2.67-fold increase in hit rate. When we retrained the model on all the compounds of the HTS dataset, it further identified 45 discordant molecules classified as non-hits by the HTS, with 42/45 (93%) having known antimicrobial activity.

CONCLUSION

Our ML approach can be used to increase HTS efficiency by reducing the number of compounds that need to be physically screened and identifying potential missed hits, making HTS more accessible and reducing barriers to entry.

Analyses of the Antibiofilm Activity of o-Phenanthroline Monohydrate against Enterococcus faecalis and Staphylococcus aureus and the Mechanisms Underlying These Effects

Enterococcus faecalis and Staphylococcus aureus exhibit robust biofilm formation capabilities, the formation of which is closely linked to pathogenicity and drug resistance, thereby resulting in host infection and treatment failure. o-Phenanthroline monohydrate (o-Phen) and its derivatives demonstrate a wide range of antibacterial and antifungal activities. In this study, we aimed to explore the antibiofilm activity of o-Phen to E. faecalis and S. aureus and provide insights into the molecular mechanisms for combating biofilm resistance. We demonstrated that o-Phen possesses significant antibacterial and antibiofilm properties against E. faecalis and S. aureus, inducing alterations in bacterial morphology, compromising cell membrane integrity, and exhibiting synergistic effects with β-lactam antibiotics at sub-MIC concentrations. The adhesion ability and automatic condensation capacity of, and synthesis of, extracellular polymers by E. faecalis cells were reduced by o-Phen, resulting in the inhibition of biofilm formation. Importantly, transcriptome analysis revealed 354 upregulated and 456 downregulated genes in o-Phen-treated E. faecalis. Differentially expressed genes were enriched in 11 metabolism-related pathways, including amino acid metabolism, pyrimidine metabolism, and glycolysis/gluconeogenesis. Moreover, the oppA, CeuA, and ZnuB genes involved in the ABC transport system, and the PBP1A penicillin-binding protein-coding genes sarA and mrcA were significantly downregulated. The multidrug efflux pump system and membrane permeability genes mdtG and hlyD, and bacterial adhesion-related genes, including adcA and fss2 were also downregulated, while mraZ and ASP23 were upregulated. Thus, o-Phen is anticipated to be an effective alternative drug for the treatment of E. faecalis and S. aureus biofilm-associated infections.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.