Application of patient population-derived pharmacokinetic-pharmacodynamic relationships to tigecycline breakpoint determination for staphylococci and streptococci.

Correctly determined susceptibility breakpoints are important to both the individual patient and to society at large. A previously derived patient population pharmacokinetic model and Monte Carlo simulation (9999 patients) were used to create a likelihood distribution of tigecycline exposure, as measured by the area under the concentration-time curve at 24 h (AUC(24)). Each resultant AUC(24) value was paired with a clinically relevant fixed MIC value ranging from 0.12 to 2 mg/L. For each AUC(24)-MIC pair, the probability of microbiologic response was calculated using an exposure-response relationship, which was derived from patients with complicated skin and skin structure infections that involved Staphylococcus aureus or streptococci or both. The median probability of microbiologic success was 94% or greater for MIC values up to and including 0.25 mg/L. The median probability of microbiologic success was 66% or less for MIC values of 0.5 mg/L or greater. These data support a susceptibility breakpoint of 0.25 mg/L for S. aureus and streptococci.

Use of a clinically derived exposure-response relationship to evaluate potential tigecycline-Enterobacteriaceae susceptibility breakpoints.

Potential tigecycline-Enterobacteriaceae susceptibility breakpoints were evaluated using 2 approaches, which differed in the nature of the probabilities assessed by MIC value. Using a previously derived tigecycline population pharmacokinetic model and Monte Carlo simulation, a probability density function of steady-state area under the concentration-time curve for 24 h (AUC(SS(0-24))) values for 9999 patients was generated. AUC(SS(0-24)) values were divided by clinically relevant fixed MIC values to derive AUC(SS(0-24))/MIC ratios, which were used to calculate the clinical response expectation by MIC value based upon a logistic regression model for efficacy (1st approach). For the 2nd approach, the probability of pharmacokinetic-pharmacodynamic (PK-PD) target attainment was calculated as the proportion of patients with AUC(SS(0-24))/MIC ratios greater than the threshold value of 6.96, the PK-PD target associated with optimal clinical response. Probabilities of clinical response and PK-PD target attainment were poorly correlated at MIC values >0.25 mg/L. For instance, the median probability of clinical success was 0.76, whereas the probability of PK-PD target attainment was 0.27 at an MIC value of 1 mg/L, suggesting that the probability of PK-PD target attainment metrics underestimates the clinical performance of tigecycline at higher MIC values.

Evaluation of tigecycline penetration into colon wall tissue and epithelial lining fluid using a population pharmacokinetic model and Monte Carlo simulation.

The objective of these analyses was to assess the penetration of tigecycline into colon wall tissue and epithelial lining fluid (ELF). The analyses included data from subjects without infection (phase 1) and patients with intra-abdominal infections (phase 2/3). Steady-state serum samples were collected from all subjects/patients (n = 577), while colon wall specimens (n = 23) and ELF specimens (n = 30) were obtained from subjects without infection. Tissue and serum data were simultaneously comodeled by using the BigNPAG program, and a four-compartment, open model with zero-order intravenous input and first-order elimination was employed. To examine the full range of tissue penetration and the associated probabilities of occurrence, a 9,999-subject Monte Carlo simulation was performed with two outputs, one for ELF penetration and one for colon wall tissue penetration. Data were well fit using models described above, with all r(2) values above 0.95. For subjects without infection, the median (5th and 95th percentiles) colon wall and ELF penetration ratios were 1.73 (0.160 and 199) and 1.15 (0.561 and 5.23), respectively. Simulation results predict that tissue penetration varies considerably and likely explain unexpected clinical outcomes for those patients infected with strains at margins of the MIC distribution.

Moving goalposts--regulatory oversight of antibacterial drugs.

Is uncertainty concerning the regulation of antimicrobial drug trials stifling investment in infectious disease treatments? Here, experts from a large pharma company and a biotech firm provide their perspectives.