A Multi-Vector, Multi-Envelope HIV-1 Vaccine

The St. Jude Children's Research Hospital (St. Jude) HIV-1 vaccine program is based on the observation that multiple antigenically distinct HIV-1 envelope protein structures are capable of mediating HIV-1 infection. A cocktail vaccine comprising representatives of these diverse structures (immunotypes) is therefore considered necessary to elicit lymphocyte populations that prevent HIV-1 infection. This strategy is reminiscent of that used to design a currently licensed and successful 23-valent pneumococcus vaccine. Three recombinant vector systems are used for the delivery of envelope cocktails (DNA, vaccinia virus, and purified protein), and each of these has been tested individually in phase I safety trials. A fourth FDA-approved clinical trial, in which diverse envelopes and vectors are combined in a prime-boost vaccination regimen, has recently begun. This trial will continue to test the hypothesis that a multi-vector, multi-envelope vaccine can elicit diverse B- and T-cell populations that can prevent HIV-1 infections in humans.

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First phase I clinical trial of an HIV-1 subtype D gp140 envelope protein vaccine: immune activity induced in all study participants

AIDS ◽

10.1097/qad.0b013e3282f174ed ◽

2008 ◽

Vol 22 (1) ◽

pp. 149-151 ◽

Cited By ~ 7

Author(s):

Julia L Hurwitz ◽

Timothy D Lockey ◽

Bart Jones ◽

Pamela Freiden ◽

Robert Sealy ◽

...

Keyword(s):

Clinical Trial ◽

Phase I ◽

Envelope Protein ◽

Phase I Clinical Trial ◽

Protein Vaccine ◽

Immune Activity ◽

Gp140 Envelope ◽

Study Participants ◽

Hiv 1

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A Clinical Trial to Evaluate the Safety and Immunogenicity of Recombinant HIV-1 Envelope Protein BG505 SOSIP.GT1.1 gp140 Vaccine, Adjuvanted in Healthy, HIV-uninfected Adults

Case Medical Research ◽

10.31525/ct1-nct04224701 ◽

2020 ◽

Author(s):

Keyword(s):

Clinical Trial ◽

Envelope Protein ◽

Hiv 1

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Phase I clinical trial safety of DNA- and modified virus Ankara-vectored human immunodeficiency virus type 1 (HIV-1) vaccines administered alone and in a prime-boost regime to healthy HIV-1-uninfected volunteers

Vaccine ◽

10.1016/j.vaccine.2005.08.041 ◽

2006 ◽

Vol 24 (4) ◽

pp. 417-425 ◽

Cited By ~ 98

Author(s):

Inese Cebere ◽

Lucy Dorrell ◽

Helen McShane ◽

Alison Simmons ◽

Sheena McCormack ◽

...

Keyword(s):

Clinical Trial ◽

Human Immunodeficiency Virus ◽

Phase I ◽

Human Immunodeficiency Virus Type ◽

Virus Type ◽

Phase I Clinical Trial ◽

Immunodeficiency Virus ◽

Hiv 1

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Phase i trials in melanoma: A framework to translate preclinical findings to the clinic

10.1101/015925 ◽

2015 ◽

Cited By ~ 1

Author(s):

Eunjung Kim ◽

Vito W. Rebecca ◽

Keiran S.M. Smalley ◽

Alexander R.A. Anderson

Keyword(s):

Mathematical Model ◽

Clinical Trial ◽

Combination Therapy ◽

Phase I ◽

Phase 1 ◽

Cell Populations ◽

Model Parameters ◽

Phase I Trials ◽

Virtual Patients ◽

Akt Inhibitor

We present a, mathematical model driven, framework to implement virtual or imaginary clinical trials (phase i trials) that can be used to bridge the gap between preclinical studies and the clinic. The trial implementation process includes the development of an experimentally validated mathematical model, generation of a cohort of heterogeneous virtual patients, an assessment of stratification factors, and optimization of treatment strategy. We show the detailed process through application to melanoma treatment, using a combination therapy of chemotherapy and an AKT inhibitor, which was recently tested in a phase 1 clinical trial. We developed a mathematical model, composed of ordinary differential equations, based on experimental data showing that such therapies differentially induce autophagy in melanoma cells. Model parameters were estimated using an optimization algorithm that minimizes differences between predicted cell populations and experimentally measured cell numbers. The calibrated model was validated by comparing predicted cell populations with experimentally measured melanoma cell populations in twelve different treatment scheduling conditions. By using this validated model as the foundation for a genetic algorithm, we generated a cohort of virtual patients that mimics the heterogeneous combination therapy responses observed in a companion clinical trial. Sensitivity analysis of this cohort defined parameters that discriminated virtual patients having more favorable versus less favorable outcomes. Finally, the model predicts optimal therapeutic approaches across all virtual patients.

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