Evaluating Polymyxin B-Based Combinations against Carbapenem-Resistant Escherichia coli in Time-Kill Studies and in a Hollow-Fiber Infection Model.

Polymyxin B-based combinations have emerged as a mainstay treatment against carbapenem-resistant Escherichia coli (CREC). We investigated the activity of polymyxin B-based two-antibiotic combinations against CREC using time-kill studies (TKS) and validated the findings in a hollow-fiber infection model (HFIM). TKS were conducted using 5 clinical CREC strains at 5 log10 CFU/ml against 10 polymyxin B-based two-antibiotic combinations at maximum clinically achievable concentrations. HFIMs simulating dosing regimens with polymyxin B (30,000U/kg/day) and tigecycline (100 mg every 12 h) alone and in combination were conducted against two CREC strains at 5 log10 CFU/ml over 120 h. Emergence of resistance was quantified using antibiotic-containing media. Phenotypic characterization (growth rate and stability of resistant phenotypes) of the resistant isolates was performed. All five CREC strains harbored carbapenemases. Polymyxin B and tigecycline MICs ranged from 0.5 mg/liter to 2 mg/liter and from 0.25 mg/liter to 8 mg/liter, respectively. All antibiotics alone did not have bactericidal activity at 24 h in the TKS, except for polymyxin B against two strains. In combination TKS, only polymyxin B plus tigecycline demonstrated both bactericidal activity and synergy in two out of five strains. In the HFIM, polymyxin B alone was bactericidal against both CREC strains before regrowth was observed at 8 h. Phenotypically stable polymyxin B-resistant mutants were observed for both strains, with a reduced growth rate observed in one strain. Tigecycline alone resulted in a slow reduction in bacterial counts. Polymyxin B plus tigecycline resulted in rapid and sustained bactericidal killing up to 120 h. Polymyxin B plus tigecycline is a promising combination against CREC. The clinical relevance of our results warrants further investigations.

[Recent advances in metabolism-based drug-drug interactions].

The pharmacological or toxicological efficacy of drugs can be influenced significantly when their metabolic pathway is induced or inhibited by co-administrated drugs.Metabolism-based drug-drug interactions (DDIs) have a high incidence and are important in clinical therapeutics. Studies on metabolism-based DDIs are now moved to the early stages of drug development, so that adequate assessment of its safety and effectiveness can be facilitated. These studies comprise in vitro and in vivo investigations and an appropriate design of studies is important. In many cases, negative findings from early in vitro and early clinical studies can eliminate the need for later clinical investigations. This article summarizes the background and mechanism of metabolism-based DDIs and focuses on the strategies of these studies.

Discrimination analysis of mass spectrometry proteomics for ovarian cancer detection.

AIM: A discrimination analysis has been explored for the probabilistic classification of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS). METHODS: The method employs data normalization, clustering, and a linear discriminant analysis on surface-enhanced laser desorption ionization (SELDI) time-of-flight MS data. The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra. Cross-validation and training/testing data-split experiments are conducted to verify the optimal discriminant and demonstrate the accuracy and robustness of the method. RESULTS: The cluster discrimination method achieves excellent performance. The sensitivity, specificity, and positive predictive values are above 97% on ovarian cancer. The protein fraction peaks, which significantly contribute to the classification, can be available from the analysis process. CONCLUSION: The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.