The overall goal of the Laboratory Center (LC) is to provide the leadership, structure, oversight and execution of high-quality, state-of-the-art laboratory-based research studies on behalf of the HIV Vaccine Trials Network (HVTN) that will accelerate the development of a safe and efficacious HIV vaccine. Within this framework, the LC will also contribute to developing other strategies to prevent HIV (e.g., immune prophylaxis), as well as to advancing vaccines to prevent M. tuberculosis (Mtb) and other infections of public health importance. For more than a decade, our LC has directed HVTN laboratory studies through the application of innovative science and a rigorous lab quality management program. We have overseen the clinical and largely performed the specialized lab studies globally to broadly define the adaptive, and when relevant, the innate and mucosal immune responses induced by candidate HIV vaccines tested from first-in-human to large-scale efficacy trials. We have determined how prime-boost combinations, delivery route and schedule can impact these responses. The development and use of validated and qualified assays have enabled reliable quantitative assessments and direct comparisons of functions and specificities across different vaccine platforms. This unique capability in the field has informed go/no-go decision-making and prioritization of vaccine candidates and specific regimens to advance to large-scale testing. Our HVTN LC has been at the forefront to define experimentally the correlates of risk and protection induced by vaccination, and potential explanations for failed or modest efficacy in the Step Study, HVTN 505, and the RV144 Thai Trial. The LC is now poised to lead lab investigations in five ongoing global phase 2b/3 HVTN vaccine and HVTN/HPTN immune prophylaxis studies. Through these efforts, the LC has elevated the standards and feasibility of conducting groundbreaking studies in human immunology in the global setting and enriched its science through a wide network of collaborations. Over the next seven years, we propose to build upon and enhance our current scientific and operational infrastructure to execute innovative HVTN studies in a good clinical laboratory practice (GCLP) setting that will provide unambiguous measurements of vaccine immunogenicity, vaccine and monoclonal antibody efficacy and insight into correlates of protection (Aims 1, 3, 4) in adults and children (when indicated). To improve monitoring tools, we will continue to develop, optimize and validate assays that can reveal the broad range and distribution of immune functions that candidate agents elicit and in additional compartments beyond blood (Aims 2, 5). We will embrace collaborations among the larger HIV, TB and immunology fields to enhance these efforts (Aim 6) and will continue to provide leadership and expertise to facilitate investigations with other HIV clinical trials and vaccine networks. Headquartered at FHCRC, the Laboratory Center will integrate its activities seamlessly with the HVTN Leadership and Operations Center and the Statistical and Data Management Center, and cooperatively with the DAIDS Vaccine Clinical Branch.
A safe and effective HIV vaccine is needed to halt the pandemic. We will conduct high-quality laboratory studies on behalf of the HTVN that can inform the field about what a candidate HIV vaccine can and cannot do in the populations who would benefit most. Our efforts will provide a rich database that will help advance vaccine development for HIV and other globally important pathogens, such as TB.
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|Wills, Saintedym; Hwang, Kwan-Ki; Liu, Pinghuang et al. (2018) HIV-1-Specific IgA Monoclonal Antibodies from an HIV-1 Vaccinee Mediate Galactosylceramide Blocking and Phagocytosis. J Virol 92:|
|Fong, Youyi; Shen, Xiaoying; Ashley, Vicki C et al. (2018) Modification of the Association Between T-Cell Immune Responses and Human Immunodeficiency Virus Type 1 Infection Risk by Vaccine-Induced Antibody Responses in the HVTN 505 Trial. J Infect Dis 217:1280-1288|
|Fong, Youyi; Huang, Ying; Lemos, Maria P et al. (2018) Rank-based two-sample tests for paired data with missing values. Biostatistics 19:281-294|
|Eren, Kemal; Murrell, Ben (2018) RIFRAF: a frame-resolving consensus algorithm. Bioinformatics 34:3817-3824|
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