Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens

Huang, Yunda; Zhang, Yuanyuan; Seaton, Kelly E.; Rosa, Stephen De; Heptinstall, Jack; Carpp, Lindsay N.; Randhawa, April; McKinnon, Lyle R.; McLaren, Paul J.; Viegas, Edna; Gray, Glenda; Churchyard, Gavin; Buchbinder, Susan; Edupuganti, Srilatha; Bekker, Linda‐Gail; Keefer, Michael C.; Hosseinipour, Mina C.; Goepfert, Paul; Cohen, Kristen W.; Williamson, Brian D.; McElrath, M. Juliana; Tomaras, Georgia D.; Thakar, Juilee; Kobie, James J.

doi:10.1016/j.ebiom.2022.104271

Cited by 5 publications

(3 citation statements)

References 91 publications

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“…A total of 400 participants were enrolled at 20 United States and African clinical research sites between 23 June 2016 and 25 July 2018. The median age at enrollment was 25 years (interquartile range [22][23][24][25][26][27][28], 214 (53.5%) were assigned female sex at birth, and participants had diverse racial backgrounds (Table 1). Vaccinations were completed in 369 (92.3%) participants, and 343 (85.8%) reached study completion (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…HIV-1-specific IgG and IgG3 binding antibody responses were measured by Binding Antibody Multiplex assay, as described previously, 9,19–24 at a 1:50 dilution. Tested antigens and assay reagents included vaccine-matched subtype C 96ZM651.C gp140, V1V2 antigens 1086.C V1V2 and CaseA2_gp70_V1V2.B, and heterologous antigen to assess breadth (Clade A.…”

Section: Methodsmentioning

confidence: 99%

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Safety and Immunogenicity of a DNA Vaccine With Subtype C gp120 Protein Adjuvanted With MF59 or AS01B: A Phase 1/2a HIV-1 Vaccine Trial

Garrett,

Dintwe,

Monaco

et al. 2024

JAIDS Journal of Acquired Immune Deficiency Syndromes

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Despite progress in HIV prevention and treatment, an estimated 1.3 million people were newly infected with HIV in 2022,1 highlighting the urgent need for an effective vaccine. To date, the RV144 trial remains the only HIV vaccine trial that has demonstrated partial efficacy against acquisition.2 The Pox-Protein Public-Private Partnership (P5) was established with the aims of improving on RV144 by developing a vaccine capable of protecting against a broader diversity of HIV strains and achieving a better understanding of immune responses associated with preventing HIV infection.3 Vaccine concepts in the P5 program have focused on clade C immunogens, targeting predominant strains of East and Southern Africa, where approximately half of the 39 million people living with HIV reside.1 The RV144 regimen, originally designed to protect against subtype B/E strains, was adapted to incorporate clade C antigens and adjuvanted with MF59®.4 This regimen demonstrated adequate immunogenicity in the HVTN100 phase 1/2a trial,5 and was further evaluated in the HVTN702 efficacy trial in South Africa, but ultimately discontinued due to non-efficacy.6 In parallel, the P5 designed the correlates program: a series of phase 1/2a trials to evaluate vaccine candidates based on favorable immune profiles of putative correlates of protection. These trials employed novel prime-boost and co-administration regimens, varied protein doses, and used new adjuvants and vaccine delivery systems, with an emphasis on shared immunological endpoints to allow for cross-study comparisons. Preclinical studies have shown promising immune responses using DNA/protein combination vaccines.7,8 A comparison of responses between HVTN100 (ALVAC) and HVTN111 (DNA) trials indicated that DNA priming with a protein boost led to increased antibody and cellular responses compared to priming with the canarypox vector.9 In the HVTN105 trial, both a DNA prime-protein boost and a co-administration regimen induced potent and durable V1/V2 binding antibody responses (a known correlate of lower HIV-1 infection risk in RV144), with co-administration inducing early antibody responses.10 Furthermore, in the HVTN096 trial, including gp120 Env protein at the priming stage, co-administered with either NYVAC or DNA, elicited earlier and even greater antibody responses.11 The adjuvant system 01 (AS01) has been successfully tested in vaccine trials for other infectious diseases including malaria,12 shingles,13,14 and tuberculosis.15 Some HIV vaccine studies have also used AS01 and have shown that it contributes to the induction of robust and persistent cellular and humoral responses.16,17 MF59® has likewise been used in several licensed vaccines and pre-clinical studies,18 inducing strong and durable T-cell memory and humoral responses. MF59® was also used in HVTN studies with ALVAC 5 and was therefore chosen for comparison with AS01B in this trial. Thus, the aim of the HVTN108 trial was to evaluate the safety and immunogenicity of the DNA vaccine with different HIV clade C protein doses, adjuvanted with MF59® or AS01B, and dosed in prime-boost or co-administration regimens.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Safety and Immunogenicity of a DNA Vaccine With Subtype C gp120 Protein Adjuvanted With MF59 or AS01B: A Phase 1/2a HIV-1 Vaccine Trial

Garrett,

Dintwe,

Monaco

et al. 2024

JAIDS Journal of Acquired Immune Deficiency Syndromes

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“…Furthermore, in recent years, some scholars have delved deeper into analyzing the treatment and immune changes of HIV-infected individuals with concurrent infections (tuberculosis [ 58 ], COVID-19 [ 59 ]) using multiple machine learning models. The application of vaccine-induced immune factors [ 60 ] has also found relevance in this domain. In order to enhance our understanding of survival status in individuals living with HIV, it is crucial to continue the discourse on this topic, despite the previous meta-analyses conducted.…”

Section: Discussionmentioning

confidence: 99%

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

Li,

Feng,

et al. 2024

BMC Infect Dis

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Background Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain. Methods We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths. Results After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75–0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78–0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models. Conclusions The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.

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Safety and immunogenicity of CD40.HIVRI.Env, a dendritic cell-based HIV vaccine, in healthy HIV-uninfected adults: a first-in-human randomized, placebo-controlled, dose-escalation study (ANRS VRI06)

Levy,

Moog,

Wiedemann

et al. 2024

eClinicalMedicine

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Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens

Cited by 5 publications

References 91 publications

Safety and Immunogenicity of a DNA Vaccine With Subtype C gp120 Protein Adjuvanted With MF59 or AS01B: A Phase 1/2a HIV-1 Vaccine Trial

Safety and Immunogenicity of a DNA Vaccine With Subtype C gp120 Protein Adjuvanted With MF59 or AS01B: A Phase 1/2a HIV-1 Vaccine Trial

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

Safety and immunogenicity of CD40.HIVRI.Env, a dendritic cell-based HIV vaccine, in healthy HIV-uninfected adults: a first-in-human randomized, placebo-controlled, dose-escalation study (ANRS VRI06)

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