Exploring the Pharmacokinetic/Pharmacodynamic Relationship of Relebactam (MK-7655) in Combination with Imipenem in a Hollow-Fiber Infection Model.

Resistance to antibiotics among bacterial pathogens is rapidly spreading, and therapeutic options against multidrug-resistant bacteria are limited. There is an urgent need for new drugs, especially those that can circumvent the broad array of resistance pathways that bacteria have evolved. In this study, we assessed the pharmacokinetic/pharmacodynamic relationship of the novel ß-lactamase inhibitor relebactam (REL; MK-7655) in a hollow-fiber infection model. REL is intended for use with the carbapenem ß-lactam antibiotic imipenem for the treatment of Gram-negative bacterial infections. In this study, we used an in vitro hollow-fiber infection model to confirm the efficacy of human exposures associated with the phase 2 doses (imipenem at 500 mg plus REL at 125 or 250 mg administered intravenously every 6 h as a 30-min infusion) against imipenem-resistant strains of Pseudomonas aeruginosa and Klebsiella pneumoniae Dose fractionation experiments confirmed that the pharmacokinetic parameter that best correlated with REL activity is the area under the concentration-time curve, consistent with findings in a murine pharmacokinetic/pharmacodynamic model. Determination of the pharmacokinetic/pharmacodynamic relationship between ß-lactam antibiotics and ß-lactamase inhibitors is complex, as there is an interdependence between their respective exposure-response relationships. Here, we show that this interdependence could be captured by treating the MIC of imipenem as dynamic: it changes with time, and this change is directly related to REL levels. For the strains tested, the percentage of the dosing interval time that the concentration remains above the dynamic MIC for imipenem was maintained at the carbapenem target of 30 to 40%, required for maximum efficacy, for imipenem at 500 mg plus REL at 250 mg.

Modeling Heterogeneous Bacterial Populations Exposed to Antibiotics: The Logistic-Dynamics Case.

In typical in vitro tests for clinical use or development of antibiotics, samples from a bacterial population are exposed to an antibiotic at various concentrations. The resulting data can then be used to build a mathematical model suitable for dosing regimen design or for further development. For bacterial populations that include resistant subpopulations-an issue that has reached alarming proportions-building such a model is challenging. In prior work, we developed a related modeling framework for such heterogeneous bacterial populations following linear dynamics when exposed to an antibiotic. We extend this framework to the case of logistic dynamics, common among strongly resistant bacterial strains. Explicit formulas are developed that can be easily used in parameter estimation and subsequent dosing regimen design under realistic pharmacokinetic conditions. A case study using experimental data from the effect of an antibiotic on a gram-negative bacterial population exemplifies the usefulness of the proposed approach.

Discovery of efficacious Pseudomonas aeruginosa-targeted siderophore-conjugated monocarbams by application of a semi-mechanistic pharmacokinetic/pharmacodynamic model.

To identify new agents for the treatment of multi-drug-resistant Pseudomonas aeruginosa, we focused on siderophore-conjugated monocarbams. This class of monocyclic ß-lactams are stable to metallo-ß-lactamases and have excellent P. aeruginosa activities due to their ability to exploit the iron uptake machinery of Gram-negative bacteria. Our medicinal chemistry plan focused on identifying a molecule with optimal potency and physical properties and activity for in vivo efficacy. Modifications to the monocarbam linker, siderophore, and oxime portion of the molecules were examined. Through these efforts, a series of pyrrolidinone-based monocarbams with good P. aeruginosa cellular activity (P. aeruginosa MIC90 = 2 µg/mL), free fraction levels (>20% free), and hydrolytic stability (t1/2 ≥ 100 h) were identified. To differentiate the lead compounds and enable prioritization for in vivo studies, we applied a semi-mechanistic pharmacokinetic/pharmacodynamic model to enable prediction of in vivo efficacy from in vitro data.