PURPOSE: To develop a semimechanistic pharmacokinetic-pharmacodynamic model describing chemotherapy-induced myelosuppression through drug-specific parameters and system-related parameters, which are common to all drugs. PATIENTS AND METHODS: Patient leukocyte and neutrophil data after administration of docetaxel, paclitaxel, and etoposide were used to develop the model, which was also applied to myelosuppression data from 2′-deoxy-2′-methylidenecytidine (DMDC), irinotecan (CPT-11), and vinflunine administrations. The model consisted of a proliferating compartment that was sensitive to drugs, three transit compartments that represented maturation, and a compartment of circulating blood cells. Three system-related parameters were estimated: baseline, mean transit time, and a feedback parameter. Drug concentration-time profiles affected the proliferation of sensitive cells by either an inhibitory linear model or an inhibitory E_max model. To evaluate the model, system-related parameters were fixed to the same values for all drugs, which were based on the results from the estimations, and only drug-specific parameters were estimated. All modeling was performed using NONMEM software. RESULTS: For all investigated drugs, the model successfully described myelosuppression. Consecutive courses and different schedules of administration were also well characterized. Similar system-related parameter estimates were obtained for the different drugs and also for leukocytes compared with neutrophils. In addition, when system-related parameters were fixed, the model well characterized chemotherapy-induced myelosuppression for the different drugs. CONCLUSION: This model predicted myelosuppression after administration of one of several different chemotherapeutic drugs. In addition, with fixed system-related parameters to proposed values, and only drug-related parameters estimated, myelosuppression can be predicted. We propose that this model can be a useful tool in the development of anticancer drugs and therapies.

Colistin is used to treat infections caused by multidrug-resistant gram-negative bacteria (MDR-GNB). It is administered intravenously in the form of colistin methanesulfonate (CMS), which is hydrolyzed in vivo to the active drug. However, pharmacokinetic data are limited. The aim of the present study was to characterize the pharmacokinetics of CMS and colistin in a population of critically ill patients. Patients receiving colistin for the treatment of infections caused by MDR-GNB were enrolled in the study; however, patients receiving a renal replacement therapy were excluded. CMS was administered at a dose of 3 million units (240 mg) every 8 h. Venous blood was collected immediately before and at multiple occasions after the first and the fourth infusions. Plasma CMS and colistin concentrations were determined by a novel liquid chromatography-tandem mass spectrometry method after a rapid precipitation step that avoids the significant degradation of CMS and colistin. Population pharmacokinetic analysis was performed with the NONMEM program. Eighteen patients (6 females; mean age, 63.6 years; mean creatinine clearance, 82.3 ml/min) were included in the study. For CMS, a two-compartment model best described the pharmacokinetics, and the half-lives of the two phases were estimated to be 0.046 h and 2.3 h, respectively. The clearance of CMS was 13.7 liters/h. For colistin, a one-compartment model was sufficient to describe the data, and the estimated half-life was 14.4 h. The predicted maximum concentrations of drug in plasma were 0.60 mg/liter and 2.3 mg/liter for the first dose and at steady state, respectively. Colistin displayed a half-life that was significantly long in relation to the dosing interval. The implications of these findings are that the plasma colistin concentrations are insufficient before steady state and raise the question of whether the administration of a loading dose would benefit critically ill patients.

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure ( in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [ fC _max ]/MIC, the area under the unbound drug concentration-time curve [ f AUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [ fT _>MIC ]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices, with fT _>MIC being the best predictor of the effect for β-lactams and f AUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.

Our objective was to examine the evidence of in vitro synergy of polymyxin-carbapenem combination therapy against Gram-negative bacteria (GNB). A systematic review and meta-analysis were performed. All studies examining in vitro interactions of antibiotic combinations consisting of any carbapenem with colistin or polymyxin B against any GNB were used. A broad search was conducted with no language, date, or publication status restrictions. Synergy rates, defined as a fractional inhibitory concentration index of ≤0.5 or a >2-log reduction in CFU, were pooled separately for time-kill, checkerboard, and Etest methods in a mixed-effect meta-analysis of rates. We examined whether the synergy rate depended on the testing method, type of antibiotic, bacteria, and resistance to carbapenems. Pooled rates with 95% confidence intervals (CI) are shown. Thirty-nine published studies and 15 conference proceeding were included, reporting on 246 different tests on 1,054 bacterial isolates. In time-kill studies, combination therapy showed synergy rates of 77% (95% CI, 64 to 87%) for Acinetobacter baumannii , 44% (95% CI, 30 to 59%) for Klebsiella pneumoniae , and 50% (95% CI, 30 to 69%) for Pseudomonas aeruginosa , with low antagonism rates for all. Doripenem showed high synergy rates for all three bacteria. For A. baumannii , meropenem was more synergistic than imipenem, whereas for P. aeruginosa the opposite was true. Checkerboard and Etest studies generally reported lower synergy rates than time-kill studies. The use of combination therapy led to less resistance development in vitro . The combination of a carbapenem with a polymyxin against GNB, especially A. baumannii , is supported in vitro by high synergy rates, with low antagonism and less resistance development. These findings should be examined in clinical studies.