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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-1BaL 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.

SUBMITTER: Richardson-Harman N 

PROVIDER: S-EPMC5372762 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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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, statis  ...[more]

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