Population Pharmacokinetic Analyses for Rezafungin (CD101) Efficacy Using Phase 1 Data

• DOI: 10.1128/aac.02603-17
• Publication Date: 2018-03-19T09:57:36Z
• Authors: Elizabeth A. Lakota; Voon Ong; Shawn Flanagan; Christopher M. Ru...
• Keywords: Adult; Male; Antifungal Agents; Candidiasis; Candida glabrata; Clinical Therapeutics; Middle Aged; 3. Good health; Echinocandins; 13. Climate action; Candida albicans; Humans; Administration, Intravenous; Female; Pharmacokinetics

ABSTRACT

Rezafungin (CD101) is a novel echinocandin antifungal agent currently in clinical development for the treatment of candidemia and invasive candidiasis. Rezafungin has potent in vitro activity against Candida albicans and Candida glabrata , including azole- and echinocandin-resistant isolates. The objective of this analysis was to develop a population pharmacokinetic (PK) model to characterize the disposition of rezafungin in plasma following intravenous (i.v.) administration. Data from two phase 1 studies, a single-ascending-dose study and a multiple-ascending-dose study, were available. Candidate population PK models were fit to the pooled data using the Monte Carlo parametric expectation maximization algorithm in S-ADAPT. The data were best described using a linear four-compartment model with zero-order drug input via i.v. infusion and first-order elimination. In order to account for the relationships between the structural PK parameters and subject body weight, all parameters in the model were scaled to subject body weight using standard allometric coefficients (a power of 0.75 for the clearance terms and 1.0 for the volume terms). The final model fit the observed data with very little bias and excellent precision. The prediction-corrected visual predictive check demonstrated that the final model could accurately simulate both the central tendency and the variability of observed rezafungin plasma concentrations. Given this, the final rezafungin population PK model is expected to provide reliable simulated concentration-time profiles and can provide dose selection decision support for future clinical studies.