A Novel Approach For Evaluating The Microbiological Efficacy Of Tigecycline In Patients With Complicated Skin And Skin-Structure Infections

Authors: Meagher AK, Ambrose PG, Passarell JA, Cirincione B, Babinchak T, Ellis-Grosse EJ
Conference: ECCMID
Keywords: anti-infectives, antibiotic, AUC:MIC ratio, cSSSI, dermatology, exposure-response, GEE, gram-positive, hosmer-lemeshow, infectious disease, intravenous, IV, logistic regression, ROC, s.aureus, steady state, streptococcus
Division: Clinical Pharmacology and Pharmacometrics (CPP)

Abstract

Objectives: Tigecycline is a glycylcycline in development for the treatment of patients with serious infections, including complicated skin and skin-structure infections (cSSSI). While cSSSI can be caused by a mixture of gram-positive and gram-negative bacteria, Staphylococcus aureus and streptococci are the predominant pathogens. Previous analyses by others combining all pathogens have failed to identify an exposure-response relationship. A prospective method was developed to create more homogenous patient populations for the microbiologic exposure-response analysis of tigecycline in the treatment of cSSSI.

Methods: Patients from 3 cSSSI clinical trials (one phase 2 & two phase 3), with tigecycline pharmacokinetic data and classified as both clinically and microbiologically evaluable, were pooled for analysis. Patients received 100-mg loading dose/50mg q12h (100/50) or 50-mg loading dose/25mg q12h (50/25). At the test of cure visit, microbiologic (eradication or persistence) response was evaluated. Indeterminate responses were excluded. Non-pathogenic baseline isolates were excluded. Five patient cohorts were created based on baseline pathogens: S. aureus only (Cohort 1); S. aureus or streptococci (Cohort 2); 2 gram-positive pathogens (Cohort 3); polymicrobial (Cohort 4); other monomicrobial infections (Cohort 5). Prospective step-wise procedures for combining cohorts to increase sample size were used. Logistic regression was used to evaluate steady-state 24hr area under the concentration-time curve (AUC) to MIC ratio (AUC/MIC) to predict response.

Results: The dataset included 58 patients with 88 observations. Cohort 1 (n=20) and Cohort 2 (n=29) could not be evaluated due to small sample size. Analysis began with pooled Cohorts 2 and 3. Continuous AUC/MIC ratio was marginally significant (p=0.1130); a patient was 5.1% more likely to have successful response for every one-unit increase in AUC/MIC. Adding Cohort 4, including pathogens with MIC values up to 16 g/mL, decreased AUC/MIC, added cures to the lower end of the distribution, and added significant noise to the analysis. Adding Cohort 5 increased sample size and further decreased the ability to detect a relationship.

Conclusion: Analysis of all pathogens combined could not identify an exposure-response relationship. Polymicrobial infections with gram-negative and anaerobic pathogens, associated with high MIC values, added noise to the analysis and decreased the predictive capability of the model. The prospective approach of creating homogenous populations based on two key pathogens in cSSSI, S. aureus and streptococci, was critical for identifying significant exposure-response relationships.

European Congress of Clinical Microbiology and Infectious Disease (ECCMID), Copenhagen, Denmark, April 2005

By AK Meagher, PG Ambrose, Julie A. Passarell, BB Cirincione, T Babinchak, EJ Ellis-Grosse