Analytical Advances in the Ex Vivo Challenge Efficacy Assay

Abstract

The ex vivo challenge assay is being increasingly used as an efficacy endpoint during early human clinical trials of HIV prevention treatments. There is no standard methodology for the ex vivo challenge assay, although the use of different data collection methods and analytical parameters may impact results and reduce the comparability of findings between trials. In this analysis, we describe the impact of data imputation methods, kit type, testing schedule and tissue type on variability, statistical power, and ex vivo HIV growth kinetics. Data were p24 antigen (pg/ml) measurements collected from clinical trials of candidate microbicides where rectal (n = 502), cervical (n = 88), and vaginal (n = 110) tissues were challenged with HIV-1_BaL ex vivo. Imputation of missing data using a nonlinear mixed effect model was found to provide an improved fit compared to imputation using half the limit of detection. The rectal virus growth period was found to be earlier and of a relatively shorter duration than the growth period for cervical and vaginal tissue types. On average, only four rectal tissue challenge assays in each treatment and control group would be needed to find a one log difference in p24 to be significant (alpha = 0.05), but a larger sample size was predicted to be needed for either cervical (n = 21) or vaginal (n = 10) tissue comparisons. Overall, the results indicated that improvements could be made in the design and analysis of the ex vivo challenge assay to provide a more standardized and powerful assay to compare efficacy of microbicide products.

Keywords: HIV; HIV prevention; bioinformatics; drug discovery.

FIG. 1.

Tissue virus growth in ex vivo challenge assays for rectal (a), cervical (b), and vaginal (c) data sets. Log₁₀ p24 means and SD are indicated for nonimputed (filled squares, ±1 SD) and imputed (open circles) data. Missing data were imputed using a nonlinear mixed effect model (Method “A”). The solid black line indicates a nonlinear growth curve model fit to each data set.

FIG. 2.

Nonlinear growth curve models for rectal, cervical, and vaginal data sets. The first and last inflection points of the nonlinear curves, per tissue type, are indicated on the x-axis as the beginning and end of the virus growth period.