Disk Diffusion Testing for Detection of Methicillin-Resistant Staphylococci: Does Moxalactam Improve upon Cefoxitin?

Disk diffusion testing is widely used to detect methicillin resistance in staphylococci, and cefoxitin is currently considered the best marker for mecA-mediated methicillin resistance. In low-inoculum diffusion testing (colony suspension at 106 CFU/ml), the addition of moxalactam in combination with cefoxitin has been reported to improve on cefoxitin alone for the detection of methicillin-heteroresistant staphylococci. However, moxalactam is absent from EUCAST and CLSI guidelines, which use high-inoculum diffusion testing (colony suspension at 108 CFU/ml), calling into question the potential interest of including moxalactam in their recommendations. The inhibition zone diameters of cefoxitin and moxalactam, alone and in combination, were evaluated for concordance with mecA and mecC positivity in a large collection of clinical Staphylococcus isolates (611 Staphylococcus aureus, Staphylococcus lugdunensis, and Staphylococcus saprophyticus isolates and 307 coagulase-negative staphylococci other than S. lugdunensis and S. saprophyticus isolates, of which 22% and 53% were mecA-positive, respectively) and in 25 mecC-positive S. aureus isolates using high-inoculum diffusion testing. Receiver operating characteristic, sensitivity, and specificity analyses indicated that the detection of mecA- and mecC-positive and negative isolates did not improve with moxalactam, either alone or in combination with cefoxitin, compared to cefoxitin alone. These findings were similar in both the S. aureus/S. lugdunensis/S. saprophyticus group and in the coagulase-negative staphylococci group. Our results do not support the use of moxalactam as an additional marker of methicillin resistance when testing with high-inoculum disk diffusion.

Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach.

Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug-genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.