Evaluating the pharmacodynamic effect of antimalarial drugs in clinical trials by quantitative PCR

Abstract

The ongoing development of new antimalarial drugs and the increasing use of controlled human malaria infection (CHMI) studies to investigate their activity in early-stage clinical trials require the development of methods to analyze their pharmacodynamic effect. This is especially so for studies where quantitative PCR (qPCR) is becoming the preferred method for assessing parasite clearance as the study endpoint. We report the development and validation of an analytic approach for qPCR-determined parasite clearance data. First, in a clinical trial with the licensed antimalarial combination sulfadoxine-pyrimethamine (S/P), qPCR data were collected from 12 subjects and used to determine qPCR replicate variability and to identify outliers. Then, an iterative analytic approach based on modeling the log-linear decay of parasitemia following drug treatment was developed to determine the parasite reduction ratio (PRR) and parasite clearance half-life, both measures of parasite clearance. This analytic approach was then validated with data from 8 subjects enrolled in a second study with the licensed antimalarial drug mefloquine. By this method, the PRR and parasite clearance half-lives for S/P and Mefloquine were determined to be 38,878 (95% confidence interval [95% CI], 17,396 to 86,889) at 3.15 (95% CI, 2.93 to 3.41) days and 157 (95% CI, 130 to 189) at 6.58 (95% CI, 6.35 to 6.83) days for the respective studies. No serious adverse events occurred in the two trials, and pharmacokinetic values were within expected ranges for sulfadoxine and pyrimethamine. The robust statistical method that we have developed to analyze qPCR-derived pharmacodynamic data from CHMI studies will facilitate the assessment of the activity of a range of experimental antimalarial drugs now entering clinical trials. (This trial was registered with the Australian New Zealand Clinical Trials Registry under registration numbers ACTRN12611001203943 and ACTRN12612000323820.).

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Figures

FIG 1

Effect of lag phase and tail exclusion on the calculation of the clearance rate constant. (Modified from reference with permission of the author.)

FIG 2

Parasitemia curves and pharmacodynamic profiles for the 12 subjects administered sulfadoxine-pyrimethamine. (a) Pre- and posttreatment parasitemia curves; (b and c) pharmacokinetic profiles for pyrimethamine (25-mg dose) and sulfadoxine (500-mg dose), respectively. Average Pre and Average Post, average at pretreatment and posttreatment, respectively.

FIG 3

Example of the algorithm to determine the optimal log-linear decay for subject MQ-16, treated with mefloquine at 10 mg/kg (as a single dose). Linear-regression fit of log10 parasitemia over time for each iteration of the algorithm. (a) The first iteration fits the regression model to all the data. (b) Best model after removal of observations from either the lag or tail phase from panel a. The data show that the best fit corresponds to removal from the lag phase. (c) The best fit is after removal of another observation from lag phase and continues on until panel j, where the final model fits a linear regression to the minimum 4 observations. A total of 10 iterations was performed, and the optimal model corresponding to the minimum P value is iteration 5 (f), which involved removing the first three observations and the last observation shown in panel a. Pr, probability.

FIG 4

Optimal regression models for sulfadoxine-pyrimethamine subjects, with significant regression fits.

FIG 5

Optimal regression models for subjects in the mefloquine study, with significant regression fits.