Daily burden and clinical toxicities associated with antiretroviral therapy (ART) emphasize the need for alternative strategies to induce long-term human immunodeficiency virus (HIV) remission upon ART cessation. Broadly neutralizing antibodies (bNAbs) can both neutralize free virions and mediate effector functions against infected cells and therefore represent a leading immunotherapeutic approach. To increase potency and breadth, as well as to limit the development of resistant virus strains, it is likely that bNAbs will need to be administered in combination. It is therefore critical to identify bNAb combinations that can achieve robust polyfunctional antiviral activity against a high number of HIV strains. In this study, we systematically assessed the abilities of single bNAbs and triple bNAb combinations to mediate robust polyfunctional antiviral activity against a large panel of cross-clade simian-human immunodeficiency viruses (SHIVs), which are commonly used as tools for validation of therapeutic strategies targeting the HIV envelope in nonhuman primate models. We demonstrate that most bNAbs are capable of mediating both neutralizing and nonneutralizing effector functions against cross-clade SHIVs, although the susceptibility to V3 glycan-specific bNAbs is highly strain dependent. Moreover, we observe a strong correlation between the neutralization potencies and nonneutralizing effector functions of bNAbs against the transmitted/founder SHIV CH505. Finally, we identify several triple bNAb combinations comprising of CD4 binding site-, V2-glycan-, and gp120-gp41 interface-targeting bNAbs that are capable of mediating synergistic polyfunctional antiviral activities against multiple clade A, B, C, and D SHIVs. Optimal bNAb immunotherapeutics will need to mediate multiple antiviral functions against a broad range of HIV strains. Our systematic assessment of triple bNAb combinations against SHIVs will identify bNAbs with synergistic, polyfunctional antiviral activity that will inform the selection of candidate bNAbs for optimal combination designs. The identified combinations can be validated in future passive immunization studies using the SHIV challenge model.
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http://dx.doi.org/10.1128/JVI.01667-20 | DOI Listing |
NPJ Vaccines
December 2024
Sanofi Vaccines business unit, R&D, Marcy L'Etoile, France.
In the aim of designing and developing a novel saponin-based adjuvant system, we combined the QS21 saponin with low microgram amounts of the fully synthetic TLR4 agonist, E6020, in cholesterol-containing liposomes. The resulting adjuvant system, termed SPA14, appeared as a long-term stable and homogeneous suspension of mostly unilamellar and a few multilamellar vesicles, with an average hydrodynamic diameter of 93 nm, when formulated in citrate buffer at pH 6.0-6.
View Article and Find Full Text PDFLancet HIV
January 2025
Duke University Medical Center, Durham, NC, USA.
Background: Multiple broadly neutralising monoclonal antibodies (mAbs) are in development for HIV-1 prevention. The aim of this trial was to test the PGT121.414.
View Article and Find Full Text PDFHepatology
December 2024
Institut Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université Paris Cité, F-75015 Paris, France.
Front Immunol
November 2024
Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Although HIV infection can be managed with antiretroviral drugs, there is no cure and therapy has to be taken for life. Recent successes in animal models with HIV-specific broadly neutralising antibodies (bNAbs) have led to long-term virological remission and even possible cures in some cases. This has resulted in substantial investment in human studies to explore bNAbs as a curative intervention for HIV infection.
View Article and Find Full Text PDFPLoS Comput Biol
November 2024
Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available training datasets are underrepresented, which likely affects models' generalizability across subtypes.
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