Translation of Pharmacodynamic Biomarkers of Antibiotic Efficacy in Specific Populations to Optimize Doses

Abstract

Antibiotic efficacy determination in clinical trials often relies on non-inferiority designs because they afford smaller study sample sizes. These efficacy studies tend to exclude patients within specific populations or include too few patients to discern potential differences in their clinical outcomes. As a result, dosing guidance in patients with abnormal liver and kidney function, age across the lifespan, and other specific populations relies on drug exposure-matching. The underlying assumption for exposure-matching is that the disease course and the response to the antibiotic are similar in patients with and without the specific condition. While this may not be the case, clinical efficacy studies are underpowered to ensure this is true. The current paper provides an integrative review of the current approach to dose selection in specific populations. We review existing clinical trial endpoints that could be measured on a more continuous rather than a discrete scale to better inform exposure-response relationships. The inclusion of newer systemic biomarkers of efficacy can help overcome the current limitations. We use a modeling and simulation exercise to illustrate how an efficacy biomarker can inform dose selection better. Studies that inform response-matching rather than exposure-matching only are needed to improve dose selection in specific populations.

Keywords: antimicrobials; exposure–response; modeling; pharmacokinetics; simulation; special populations.

Figure 1

Procalcitonin Response Versus Study Day by Patient-Specific Population Across Simulated Trials. Solid lines depict the mean daily average procalcitonin (PCT) level over time from across the 100 simulated trials with the shaded region corresponding to the 90% confidence interval (CI) of the mean response. Figures are organized by expectation of response to be similar or different across obese and non-obese (A,B), and renal impairment and normal renal function (C,D) over time.

Figure 2

Procalcitonin Response Versus Day 14 Vancomycin AUC by Patient-Specific Population Across All Simulated Patients. Solid lines depict the mean daily average procalcitonin (PCT) level versus individual Day 14 vancomycin area under the curve (AUC) binned in 100 mg*h/L increments across all 125,000 simulated patients with the shaded region corresponding to the 90% prediction interval (PI) of individual response. Figures are organized by expectation of response to be similar or different across obese and non-obese (A,B), and renal impairment and normal renal function (C,D) over exposure.