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Modeling cumulative overall prevention efficacy for the VRC01 phase 2b efficacy trials.

ABSTRACT: The Antibody Mediated Prevention trials are assessing whether intravenously-administered VRC01 (10 mg/kg or 30 mg/kg vs placebo) can prevent HIV infection. In a modeling exercise, we used two models to predict the overall prevention efficacy (PE) of each VRC01 dose in preventing HIV infection. For the first per-exposure PE model, parameters were estimated from studies where nonhuman primates (NHPs) were administered high-dose intra-rectal simian-human immunodeficiency virus challenge two days post-VRC01 infusion at various dosages ("NHP model"). To account for the fact that humans may require greater VRC01 concentration to achieve the same level of protection, we next assumed that a 5-fold greater VRC01 serum concentration would be needed to provide the same level of per-exposure PE as seen in the NHP data ("5-fold model"). For the 10 mg/kg regimen, the 5-fold and NHP models predict an overall PE of 37% and 64%, respectively; for the 30 mg/kg regimen, the two models predict an overall PE of 53% and 82%, respectively. Our results support that VRC01 may plausibly confer positive PE in the AMP trials. Given the lack of available knowledge and data to verify the assumptions undergirding our modeling framework, its quantitative predictions of overall PE are preliminary. Its current main applications are to supplement decisions to advance mAb regimens to efficacy trials, and to enable mAb regimen ranking by their potential for PE in humans.

SUBMITTER: Huang Y

PROVIDER: S-EPMC6183277 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Modeling cumulative overall prevention efficacy for the VRC01 phase 2b efficacy trials.

Huang Yunda Y Karuna Shelly S Carpp Lindsay N LN Reeves Daniel D Pegu Amarendra A Seaton Kelly K Mayer Kenneth K Schiffer Joshua J Mascola John J Gilbert Peter B PB

Human vaccines & immunotherapeutics 20180524 9

The Antibody Mediated Prevention trials are assessing whether intravenously-administered VRC01 (10 mg/kg or 30 mg/kg vs placebo) can prevent HIV infection. In a modeling exercise, we used two models to predict the overall prevention efficacy (PE) of each VRC01 dose in preventing HIV infection. For the first per-exposure PE model, parameters were estimated from studies where nonhuman primates (NHPs) were administered high-dose intra-rectal simian-human immunodeficiency virus challenge two days po ...[more]

PMID: 29683765

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Project description:IntroductionLongitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST.Material and methodsJoint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices.ResultsData from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611-0.633 versus 0.586-0.590). The dynamic c-indices from the JM were in the range of 0.627-0.683 indicating good predictive accuracy.ConclusionBoth modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches.

Project description:BackgroundIn the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity.MethodsUsing the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate.ResultsThe level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates.ConclusionOur results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method.Trial registrationThe dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry).

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Modeling cumulative overall prevention efficacy for the VRC01 phase 2b efficacy trials.

Publications

Modeling cumulative overall prevention efficacy for the VRC01 phase 2b efficacy trials.

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