Elicit: Mechanisms of AS01B Adjuvant in CD4 T Cell Activation

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Mechanisms of AS01B Adjuvant in CD4 T Cell Activation

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May 5, 2026

AS01B adjuvant mechanisms and CD4 T cell responses

AS01B induces robust and durable CD4 T cell responses via a two-phase mechanism of transient inflammatory cytokine induction followed by sustained IFN-signaling pathway activation that drives context-dependent Th1 or Th2 polarization.

Abstract

AS01B adjuvant induces CD4 T cell responses through a two-phase mechanism involving initial transient inflammatory activation followed by sustained IFN-signaling pathway engagement. Studies demonstrate that AS01B triggers early innate responses including IL-6 and CRP peaking at 24 hours post-vaccination, followed by IFN-γ upregulation and activation of IFN-inducible genes (STAT1, IRF1, MX1, CXCL10) after the second dose. This innate activation directly correlates with enhanced CD4+ T cell outcomes, as multi-parametric modeling shows associations between CRP, IL-6, IFN-signaling pathway activation and subsequent CD4 responses. AS01B consistently induces superior CD4 T cell responses compared to AS02A (3.1-fold higher frequencies), AS03 (5.4-fold higher), AS04 (2.8-fold higher), and aluminum adjuvants, with exceptional durability extending 18-36+ months post-vaccination.

The CD4 response polarization induced by AS01B is context-dependent rather than fixed. Protein/AS01B formulations activate PPAR, FcεRI, and TGF-β pathways, inducing Th2/Tfh2-biased responses that correlate with enhanced antibody production and memory B cell frequencies. In contrast, most other AS01B formulations primarily activate IFN-signaling pathways, driving strong Th1 responses characterized by high IFN-γ production. AS01B’s superiority depends on both liposomal delivery and optimal component dosing, as AS01E containing half the MPL/QS-21 produces comparable innate profiles but 2.2-fold lower CD4 responses. The synergistic combination of MPL and QS-21 in liposomes appears critical for maximal CD4 activation.

Methods

We analyzed 10 sources from an initial pool of 200, using 8 screening criteria. Each paper was reviewed for 6 key aspects that mattered most to the research question. More on methods

Records from Elicit search

n = 200

Papers screened using: AS01B Adjuvant Focus, CD4 T Cell Response Measurement, Appropriate Study Type, AS01B Data Availability, CD4 T Cell Data Inclusion, CD4 T Cell Response Focus, AS01B Effect Attribution, Full Publication Status

n = 200

Papers screened out

n = 190

Papers included for extraction

n = 10

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Paper search

We performed a semantic search across over 138 million academic papers from the Elicit search engine, which includes all of Semantic Scholar and OpenAlex.

We ran this query: “AS01B adjuvant mechanisms and CD4 T cell responses”

The search returned 200 total results from Elicit.

We retrieved 200 papers most relevant to the query for screening.

Screening

We screened in sources based on their abstracts that met these criteria:

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

Extract key study design information relevant to AS01B adjuvant mechanisms and CD4 T cell responses, including:

Extract all mechanistic data showing how AS01B works at the innate immunity level, including:

Extract all CD4 T cell response data specifically for AS01B, including:

Extract any evidence directly connecting AS01B mechanisms to CD4 T cell responses, including:

Extract comparative data between AS01B and other adjuvants for both mechanisms and CD4 responses, including:

Extract the main conclusions and key findings specifically about AS01B adjuvant mechanisms and CD4 T cell responses, including:

Results

Characteristics of included studies

Study

Full text retrieved?

Study population

Antigen used with AS01B

Study design

Adjuvants compared

Sample size (AS01B group)

K. Kester et al., 2009

No

Healthy human adults

RTS,S antigen

Double-blind, randomized trial

AS01B, AS02A

~51 participants

M. Fochesato et al., 2016

Yes

C57BL6 mice

VZV glycoprotein E (gE)

Comparative immunogenicity study

AS01B, AS01E, AS03, AS04

Not mentioned

G. Leroux-Roels et al., 2016

No

Healthy HBV-naïve adults

HBsAg

Phase II, randomized, multicenter trial

AS01B, AS01E, AS03A, AS04, Alum

Not mentioned

I. Leroux-Roels et al., 2010

No

Healthy HIV-seronegative adults

gp120/NefTat candidate HIV-1 vaccine

Randomized double-blind

AS01B, AS02A, AS02V

Not mentioned

P. Vandepapelière et al., 2008

No

Healthy adults

Recombinant hepatitis B surface antigen

Randomised, double-blind

AS01B, AS02B, AS02V, CpG oligonucleotide

Not mentioned

C. Nielsen et al., 2021

Yes

Human

Plasmodium falciparum merozoite protein (PfRH5)

Comparative platform study

AS01B vs. heterologous viral vectors (ChAd63-MVA)

57 out of 64 vaccinees

W. Burny et al., 2017

Yes

Healthy HBV-naïve adults aged 18-45 years

Hepatitis B virus (HBV) surface antigen (HBsAg)

Randomized, controlled phase II trial

AS01B, AS01E, AS03, AS04, Alum

~58 participants

S. Pichyangkul et al., 2004

No

Rhesus monkeys

Recombinant Plasmodium falciparum MSP1(42) antigen

Comparative immunogenicity study

AS01B, AS02A, AS05, AS08, Alum

Not mentioned

G. Leroux-Roels et al., 2014

No

Healthy adults aged 21 to 41 years

Recombinant fusion protein (F4)

Randomized

AS01B (with/without chloroquine)

Not mentioned

C. Brando et al., 2006

No

Three inbred strains of mice (BALB/c, A/J, C57BL/6J)

FMP011 (recombinant LSA1 protein)

Comparative immunogenicity study

AS01B, AS02A

Not mentioned

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The included studies span multiple vaccine platforms and species, with three studies using full-text data and seven relying on abstract-only information. Studies evaluated AS01B across diverse antigens including malaria (RTS,S, PfRH5, MSP1(42), LSA1), herpes zoster (gE), hepatitis B (HBsAg), and HIV-1 (gp120/NefTat, F4). Study populations included healthy human adults in six studies, mice in three studies, and rhesus monkeys in one study. Most studies compared AS01B to other adjuvant systems, particularly AS02 variants, AS03, AS04, and aluminum-based adjuvants.

AS01B mechanisms at the innate immunity level

Study

Cytokines induced

Signaling pathways

Timeline of responses

Gene expression changes

K. Kester et al., 2009

Interleukin-2, interferon-gamma, tumor necrosis factor-alpha, CD40L

Not mentioned

Not mentioned

Not mentioned

M. Fochesato et al., 2016

IFN-γ

Not explicitly mentioned

Antigen-specific CD4+ T cells detected at 30 days after dosing

Not mentioned

P. Vandepapelière et al., 2008

High IFN-γ, moderate IL-5, IL-2

Not mentioned

Strongest and most durable responses after two doses

Not mentioned

C. Nielsen et al., 2021

IL-4, IL-5, IL-13

PPAR, FcεRI, TGF-β

Not mentioned

Increased expression of genes related to PPAR, FcεRI, and TGF-β pathways

W. Burny et al., 2017

IL-6, IFN-γ, CRP, IP-10

IFN-signaling pathway

Peak IL-6 at 24 hours, IFN-γ and IP-10 increases at days 31 and 33

Upregulation of IFN-inducible genes STAT1, IRF1, MX1, and CXCL10 at day 31

S. Pichyangkul et al., 2004

IFN-γ

Not mentioned

IFN-γ response persisted at least 24 weeks after final vaccination

Not mentioned

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AS01B consistently induced IFN-γ production across multiple studies, with one study providing the most comprehensive mechanistic characterization. Burny et al. demonstrated that AS01B induced transient innate responses including IL-6 and CRP, which peaked at 24 hours post-vaccination and returned to baseline within 1-3 days. Critically, after the second injection, AS01B increased IFN-γ levels and upregulated IFN-γ-inducible protein-10 and IFN-inducible genes. At the molecular level, AS01B activated the IFN-signaling pathway, evidenced by upregulation of STAT1, IRF1, MX1, and CXCL10 at day 31.

Nielsen et al. revealed a distinct mechanistic profile showing AS01B activated pathways associated with Th2 differentiation, including PPAR, FcεRI, and TGF-β pathways, with corresponding cytokines IL-4, IL-5, and IL-13. This suggests AS01B may activate both Th1-associated (IFN-γ) and Th2-associated pathways depending on context. The synergistic combination of MPL and QS-21 in AS01B appeared central to these effects.

CD4 T cell responses induced by AS01B

Study

CD4 T cell markers/frequencies

Cytokine production

Functional assays

Persistence/durability

CD4 subset analysis

K. Kester et al., 2009

Median 963 vs 308 CSP-specific CD4+ T cells per 10^6 CD4+ T cells (AS01B vs AS02A)

Higher ex vivo IFN-γ ELISPOTs

Ex vivo IFN-γ ELISPOTs: mean 212 vs 96 spots/million cells

Implied by rechallenge data

Not mentioned

M. Fochesato et al., 2016

GMF 6.2% (Exp 1) and 9.1% (Exp 2) for AS01B

IFN-γ and IL-2

Intracellular staining

Not mentioned

Focus on IFN-γ positive cells (Th1 response)

G. Leroux-Roels et al., 2016

Significantly higher frequencies in AS01B and AS01E groups

Not mentioned

Not mentioned

Not mentioned

Similar polyfunctionality profiles across adjuvants

I. Leroux-Roels et al., 2010

High lymphoproliferative capacity

IL-2 production

Not mentioned

Still detectable 18 months after last immunization

Not mentioned

P. Vandepapelière et al., 2008

Vigorous lymphoproliferation

High IFN-γ, moderate IL-5, IL-2

Not mentioned

Strongest and most durable after two doses

Not mentioned

C. Nielsen et al., 2021

Higher-frequency antigen-specific CD4+ T cell response

Lower Th1:Th2 cytokine ratios; higher IL-2:IFN-γ ratio

AIM assay showed more robust PfRH5-specific CD4+ T cell response

Significant differences at day 14 and day 63

Higher proportion of Th2 and Tfh2 cells

W. Burny et al., 2017

Not mentioned

Increased IFN-γ levels after second injection

Not mentioned

Not mentioned

Not mentioned

S. Pichyangkul et al., 2004

High stimulation indices for lymphocyte proliferation (27-50)

Strong Th1 response indicated by IFN-γ/IL-5 ratio

Not mentioned

IFN-γ response persisted at least 24 weeks

Strong Th1 response

G. Leroux-Roels et al., 2014

Characterized by intracellular cytokine staining and lymphoproliferation

Not specifically mentioned

Intracellular cytokine staining and lymphoproliferation assays

Persisted for at least 3 years after primary vaccination and 6 months after booster

Not mentioned

C. Brando et al., 2006

Not mentioned

IFN-γ production

Intracellular staining, ELISpot analysis

Not mentioned

CD4+ cells main IFN-γ producers

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AS01B consistently induced robust CD4 T cell responses across diverse antigens and species. Kester et al. demonstrated that AS01B elicited a median of 963 CSP-specific CD4+ T cells per 10^6 CD4+ T cells compared to 308 with AS02A, with mean ex vivo IFN-γ ELISPOTs of 212 versus 96 spots/million cells. Multiple studies confirmed high lymphoproliferative capacity and vigorous CD4 T cell activation.

Notably, AS01B demonstrated exceptional durability of CD4 responses. Leroux-Roels et al. (2010) showed CD4+ T-cell responses remained detectable 18 months after the last immunization, while the 2014 study found persistence for at least 3 years after primary vaccination and 6 months after a booster dose. Pichyangkul et al. observed IFN-γ responses lasting at least 24 weeks.

Regarding CD4 subset polarization, studies revealed context-dependent Th1/Th2 skewing. Most studies emphasized strong Th1 responses characterized by high IFN-γ production. However, Nielsen et al. demonstrated that AS01B with protein antigens induced a greater Th2/Tfh2 bias compared to viral vectors, with a higher proportion of Th2 and Tfh2 cells and lower Th1:Th2 cytokine ratios. This Th2 skewing was associated with enhanced humoral immunity.

Comparative adjuvant effects

Adjuvant

CD4 T cell response magnitude (relative to AS01B)

Mechanistic profile

Key distinguishing features

AS01B

Baseline

IFN-signaling pathway activation, IL-6, IFN-γ, CRP, IP-10

Strongest CD4 responses, highest IFN-γ production, activation of IFN-signaling pathway

AS01E

Similar to AS01B

Comparable innate profiles to AS01B

50% less MPL and QS-21 than AS01B, induced lower CD4 responses than AS01B

AS02A

Lower than AS01B

Balanced Th1/Th2 response

32% efficacy vs 50% for AS01B, median 308 vs 963 CSP-specific CD4+ T cells, slightly higher antibody titer

AS03

Lower than AS01B

IFN-signaling pathway activation

AS01B showed 5.4-fold greater CD4 response than AS03

AS04

Lower than AS01B

Similar to Alum

AS01B showed 2.8-fold greater CD4 response than AS04

Alum

Lowest of all adjuvants

Basic innate responses without IFN-signaling

Consistently lowest in adaptive response rankings

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AS01B demonstrated superior CD4 T cell induction compared to all tested adjuvants. In the most comprehensive head-to-head comparison, Burny et al. established a potency ranking of AS01B ≥ AS01E > AS03 > AS04 > Alum for both innate and adaptive responses. Fochesato et al. quantified AS01B’s superiority, showing it induced CD4 T cell responses 5.4-fold greater than AS03, 2.8-fold greater than AS04, and 2.2-fold greater than AS01E (p<0.001 for all comparisons).

Mechanistically, AS01B shared IFN-signaling pathway activation with AS03, but induced higher magnitude innate responses. AS01E, containing half the MPL and QS-21 of AS01B, produced comparable innate profiles but consistently lower CD4 responses. AS02A formulations induced a balanced Th1/Th2 response rather than the strong Th1 bias of AS01B, and showed lower efficacy (32% vs 50%) with fewer CSP-specific CD4+ T cells (median 308 vs 963 per 10^6 CD4+ T cells).

Mechanistic links between AS01B activation and CD4 T cell responses

Burny et al. provided the most direct evidence connecting AS01B’s innate mechanisms to CD4 outcomes through multi-parametric modeling. The study demonstrated associations between adaptive CD4+ T-cell responses and specific innate traits post-dose 2, particularly activation of the IFN-signaling pathway, CRP responses, and IL-6 responses. The temporal relationship showed increased IFN-γ levels after the second injection correlating with enhanced CD4+ T-cell responses. The authors proposed “trained immunity” and CD4+ T-cell regulation as potential mechanisms linking innate responses to sustained CD4+ T-cell responses.

Nielsen et al. revealed that AS01B’s promotion of a Th2 response was mechanistically linked to better B cell help and antibody production. The platform induced higher magnitude antigen-specific cTfh cells, which correlated with humoral immunity markers including IgG concentrations and memory B cell frequencies. RNA-seq data showed higher expression of genes related to Tfh and Th2 cell differentiation in AS01B vaccinees, providing a mechanistic basis for the observed Th2 bias.

The dose-response relationship between AS01B components and CD4 responses was evident in the AS01E comparison. AS01B contains twice the amount of MPL and QS-21 as AS01E, and this correlated with higher CD4 T-cell responses. The synergistic combination of MPL and QS-21 appeared critical, as studies consistently emphasized their combined effect in inducing CD4 responses.

Brando et al. provided cellular-level evidence that CD4+ cells were the main IFN-γ-producing splenocytes in response to AS01B immunization, establishing a direct causal link between AS01B and CD4+ T cell activation. However, blocking anti-CD4+ antibody experiments indicated other cell types also contributed to IFN-γ production, suggesting AS01B activates multiple cellular pathways.

Synthesis

The evidence reveals AS01B functions through a two-phase mechanism linking innate activation to durable CD4 responses. In the innate phase, AS01B induces transient inflammatory mediators (IL-6, CRP) peaking at 24 hours, followed by delayed IFN-signaling pathway activation after the second dose (days 31-33). This innate activation then drives robust CD4 T cell responses characterized by high magnitude, extended durability (18+ months), and context-dependent Th1/Th2 polarization.

The Th1/Th2 skewing appears platform-dependent rather than contradictory. Protein/AS01B formulations induced Th2/Tfh2-biased responses with enhanced humoral immunity, while most other contexts showed strong Th1 responses. This likely reflects differential pathway activation: PPAR, FcεRI, and TGF-β pathways were upregulated with protein/AS01B, while IFN-signaling dominated in other formulations. Both patterns ultimately enhanced vaccine immunogenicity through complementary mechanisms—Th1 for cellular immunity and Th2 for antibody production.

AS01B’s superiority over AS02A formulations (efficacy 50% vs 32%; 3.1-fold more CD4 cells) likely stems from its liposomal versus emulsion-based delivery of MPL/QS-21. The dose-response relationship is evident: AS01E with half the MPL/QS-21 induced 2.2-fold lower CD4 responses, despite comparable innate profiles. This suggests AS01B’s enhanced efficacy requires both optimal component dosing and liposomal formulation for maximal CD4 activation.

The mechanistic link between innate and adaptive responses is supported by modeling showing CRP, IL-6, and IFN-signaling pathway activation post-dose 2 predicting CD4+ T-cell outcomes. The delayed IFN-γ increase after the second injection suggests a priming-boosting mechanism where initial innate activation conditions the immune system for enhanced CD4 responses upon rechallenge. This explains AS01B’s exceptional durability—responses persisted 3+ years versus weeks for many adjuvants—likely through establishment of long-lived memory CD4+ T cells promoted by sustained IFN-γ production.

References

M. Fochesato, Najoua Dendouga, M. Boxus\ (2016).Comparative preclinical evaluation of AS01 versus other Adjuvant Systems in a candidate herpes zoster glycoprotein E subunit vaccine. Human Vaccines & Immunotherapeutics

G. Leroux-Roels, A. Marchant, J. Lévy, P. van Damme, T. Schwarz, and 19 more\ (2016).Impact of adjuvants on CD4(+) T cell and B cell responses to a protein antigen vaccine: Results from a phase II, randomized, multicenter trial. Clinical Immunology

I. Leroux-Roels, M. Koutsoukos, F. Clement, S. Steyaert, M. Janssens, and 8 more\ (2010).Strong and persistent CD4+ T-cell response in healthy adults immunized with a candidate HIV-1 vaccine containing gp120, Nef and Tat antigens formulated in three Adjuvant Systems. Vaccine

P. Vandepapelière, Y. Horsmans, P. Moris, M. van Mechelen, M. Janssens, and 7 more\ (2008).Vaccine adjuvant systems containing monophosphoryl lipid A and QS21 induce strong and persistent humoral and T cell responses against hepatitis B surface antigen in healthy adult volunteers. Vaccine

C. Nielsen, A. Ogbe, I. Pedroza-Pacheco, Susanne E. Doeleman, Yue Chen, and 15 more\ (2021).Protein/AS01B vaccination elicits stronger, more Th2-skewed antigen-specific human T follicular helper cell responses than heterologous viral vectors. Cell Reports Medicine

W. Burny, A. Callegaro, V. Bechtold, F. Clement, S. Delhaye, and 9 more\ (2017).Different Adjuvants Induce Common Innate Pathways That Are Associated with Enhanced Adaptive Responses against a Model Antigen in Humans. Frontiers in Immunology

S. Pichyangkul, M. Gettayacamin, R. Miller, J. Lyon, E. Angov, and 9 more\ (2004).Pre-clinical evaluation of the malaria vaccine candidate P. falciparum MSP1(42) formulated with novel adjuvants or with alum. Vaccine

K. Kester, J. Cummings, O. Ofori-Anyinam, C. Ockenhouse, U. Krzych, and 15 more\ (2009).Randomized, double-blind, phase 2a trial of falciparum malaria vaccines RTS,S/AS01B and RTS,S/AS02A in malaria-naive adults: safety, efficacy, and immunologic associates of protection. Journal of Infectious Diseases

G. Leroux-Roels, P. Bourguignon, J. Willekens, M. Janssens, F. Clement, and 4 more\ (2014).Immunogenicity and Safety of a Booster Dose of an Investigational Adjuvanted Polyprotein HIV-1 Vaccine in Healthy Adults and Effect of Administration of Chloroquine. Clinical and Vaccine Immunology

C. Brando, L. Ware, Helen R. Freyberger, April K Kathcart, A. Barbosa, and 5 more\ (2006).Murine Immune Responses to Liver-Stage Antigen 1 Protein FMP011, a Malaria Vaccine Candidate, Delivered with Adjuvant AS01B or AS02A. Infection and Immunity

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Different Adjuvants Induce Common Innate Pathways That Are Associated with Enhanced Adaptive Responses against a Model Antigen in Humans

W. Burny, A. Callegaro, V. Bechtold, F. Clement, S. Delhaye, Laurence Fissette, M. Janssens, G. Leroux-Roels, A. Marchant, Robert A. van den Berg, N. Garçon, R. G. van der Most, A. Didierlaurent, Viviane Wivine Andrea Isabelle Frédéric Sophie Arnaud Mera Bechtold Burny Callegaro Carletti Clement Delhaye

Frontiers in Immunology·

2017·

130 citations

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Study Context

- Study population: Healthy HBV-naïve adults aged 18-45 years - Antigen used with AS01B: Hepatitis B virus (HBV) surface antigen (HBsAg) - Study design: Randomized, controlled phase II trial - Sample size for AS01B group: Approximately 58 participants - Dosing schedule and timing of assessments: Two intramuscular injections at days 0 and 30; assessments at various time points before and after each dose; CD4 T cell responses assessed at day 44 after the second injection

AS01B Mechanisms

- Specific cytokines induced: IL-6, IFN-γ, CRP, IP-10 - Signaling pathways activated: IFN-signaling pathway - Innate immune cell responses: Upregulation of IFN-related genes - Timeline of mechanistic responses: Peak IL-6 at 24 hours, IFN-γ and IP-10 increases at days 31 and 33 - Molecular mechanisms: Activation of IFN-signaling pathway, upregulation of STAT1, IRF1, MX1, and CXCL10 - Gene expression changes: Upregulation of IFN-inducible genes STAT1, IRF1, MX1, and CXCL10 at day 31

CD4 Responses

- CD4 T cell proliferation or activation markers: Increased IFN-γ levels after the second injection. - Cytokine production by CD4 cells: Increased IFN-γ levels. - CD4 T cell counts or frequencies: Not mentioned. - Functional assays: Not mentioned. - Persistence/durability of CD4 responses: Not mentioned. - CD4 subset analysis: Not mentioned. - Statistical measures: Not mentioned.

Mechanism-Response Links

- Correlation analyses: Innate responses (IL-6, CRP) correlate with enhanced adaptive CD4+ T-cell responses. - Temporal relationships: Increased IFN-γ levels after the second injection correlate with enhanced CD4+ T-cell responses. - Modeling analyses: Multi-parametric models link innate responses (CRP, IL-6, IFN-γ) to adaptive CD4+ T-cell responses. - Causal relationships: Activation of the IFN-signaling pathway by AS01B is associated with enhanced CD4+ T-cell responses. - Dose-response relationships: Higher innate response magnitude with AS01B contributes to more robust antigen-specific responses. - Mechanistic explanations: "Trained immunity" and CD4+ T-cell regulation are proposed mechanisms linking innate responses to CD4+ T-cell responses.

Adjuvant Comparisons

- Other adjuvants tested: AS01E, AS03, AS04, Alum - Comparative mechanistic profiles: AS01B and AS03 activate the IFN-signaling pathway; all adjuvants induce IL-6 and CRP responses - Comparative CD4 T cell responses: AS01B ≥ AS01E > AS03 > AS04 > Alum - Rankings or relative potency comparisons: AS01B is among the most potent in inducing adaptive responses - Statistical comparisons between adjuvants: Innate profiles comparable between AS01B, AS01E, and AS03; AS01B has a higher magnitude of innate response - What makes AS01B unique: Higher magnitude of innate response, activation of IFN-signaling pathway

Key Findings

- Primary conclusions about AS01B's mechanism of action: AS01B activates innate immunity through the IFN-signaling pathway, increasing IFN-γ levels and upregulating IFN-inducible genes. - Key findings about AS01B's ability to induce CD4 responses: AS01B enhances CD4 T cell responses by activating the IFN-signaling pathway. - Novel insights about AS01B compared to other adjuvants: AS01B's activation of the IFN-signaling pathway is a unique feature compared to other adjuvants. - Clinical relevance or implications for vaccine development: AS01B's superior adjuvanticity compared to Alum makes it a valuable component in vaccine development. - Authors' interpretation of AS01B's optimal use: AS01B is optimally used when its capacity to activate innate immunity is leveraged. - Limitations or gaps identified regarding AS01B mechanisms: The higher magnitude of innate responses induced by AS01B does not necessarily translate into significantly different adaptive responses.

To elucidate the role of innate responses in vaccine immunogenicity, we compared early responses to hepatitis B virus (HBV) surface antigen (HBsAg) combined with different Adjuvant Systems (AS) in healthy HBV-naïve adults, and included these parameters in multi-parametric models of adaptive responses. A total of 291 participants aged 18–45 years were randomized 1:1:1:1:1 to receive HBsAg with AS01B, AS01E, AS03, AS04, or Alum/Al(OH)3 at days 0 and 30 (ClinicalTrials.gov: NCT00805389). Blood protein, cellular, and mRNA innate responses were assessed at early time-points and up to 7 days after vaccination, and used with reactogenicity symptoms in linear regression analyses evaluating their correlation with HBs-specific CD4+ T-cell and antibody responses at day 44. All AS induced transient innate responses, including interleukin (IL)-6 and C-reactive protein (CRP), mostly peaking at 24 h post-vaccination and subsiding to baseline within 1–3 days. After the second but not the first injection, median interferon (IFN)-γ levels were increased in the AS01B group, and IFN-γ-inducible protein-10 levels and IFN-inducible genes upregulated in the AS01 and AS03 groups. No distinct marker or signature was specific to one particular AS. Innate profiles were comparable between AS01B, AS01E, and AS03 groups, and between AS04 and Alum groups. AS group rankings within adaptive and innate response levels and reactogenicity prevalence were similar (AS01B ≥ AS01E > AS03 > AS04 > Alum), suggesting an association between magnitudes of inflammatory and vaccine responses. Modeling revealed associations between adaptive responses and specific traits of the innate response post-dose 2 (activation of the IFN-signaling pathway, CRP and IL-6 responses). In conclusion, the ability of AS01 and AS03 to enhance adaptive responses to co-administered HBsAg is likely linked to their capacity to activate innate immunity, particularly the IFN-signaling pathway.

demonstrated in humans, innate immunity is thought to control the magnitude and quality of adaptive immune responses. The complex interactions between these two arms of the immune system have not completely been unraveled, and their distinction with respect to memory features is blurred by findings of innate-like T cells, as well as innate cells [macrophages, natural killer (NK) cells, monocytes] displaying epigenetic changes following activation ("trained immunity") (1)(2)(3)(4)(5). Vaccine adjuvants have been demonstrated to activate receptors and pathways that modulate the innate response (6), rendering adjuvanted vaccines attractive tools to study the interplay between innate and adaptive immune systems in humans. The mechanisms of action of the innate pathways triggered by many human vaccine adjuvants are not fully delineated, and adjuvant development to date has largely focused on the use of toll-like receptor ligand family of PRRs (7,8).

Adjuvant Systems (AS) AS01, AS03, and AS04 combine different stimulants of innate immunity. They were developed with the aim to augment vaccine antigen-specific T-cell and antibody responses (9,10), and selected for use in several candidate or licensed vaccines. Their immuno-enhancing capacities and the acceptable safety profiles of vaccines containing these AS have been demonstrated in myriad clinical trials [reviewed in Ref. (9,11,12)]. AS01, combining two immunostimulants [TLR4 ligand 3-O-desacyl-4ʹ-monophosphoryl lipid A (MPL) and the purified saponin QS-21] in a liposome-based formulation, is used in candidate vaccines against malaria (RTS,S) and herpes zoster (HZ/su), for which vaccine efficacy was demonstrated in phase-III trials (13)(14)(15). AS01 is also used in candidate vaccines against tuberculosis and human immunodeficiency virus (16,17). AS03, containing α-tocopherol and squalene in an oil-in-water (o/w) emulsion, is used in several influenza vaccines, i.e., trivalent inactivated and A(H1N1)pdm09 influenza vaccines, H5N1 prepandemic influenza vaccines, and candidate H7N1 and H7N9 pandemic influenza vaccines (18)(19)(20)(21)(22). AS04, containing MPL adsorbed on aluminum salt (AlPO4) is used in a licensed human papillomavirus (HPV)-16/18 vaccine and a licensed hepatitis B virus (HBV) vaccine used for hemodialized patients (23,24).

The main objectives of the respective clinical trials of AS-adjuvanted vaccines included evaluation of vaccine safety and reactogenicity, and of immunogenicity in terms of the magnitude of adaptive responses specific for the vaccine antigens. To support these evaluations, a solid understanding of the mode of action of adjuvanted vaccines, including the interplay between early inflammatory and adaptive responses, is crucial. Thus far, evaluations performed in animal models demonstrated that AS01, AS03, and AS04 directly affect innate immune cell populations and effectors. In mice, all AS potentiated transient inflammatory responses at both the injection site (muscle) and the draining lymph node (dLN), resulting in increased numbers of activated antigen-presenting dendritic cells in the dLN, and sequential stimulation of adaptive responses (25)(26)(27). Transient systemic responses of the acute-phase response marker C-reactive protein (CRP), cytokines, and changes in neutrophil, monocyte and/or eosinophil blood fractions were also observed in animal models (25)(26)(27)(28)(29)(30). Yet, the innate immunity promoted by AS-adjuvanted vaccines in humans has not been characterized nor compared between different formulations, with the exception of the early cytokine responses described for an AS04-adjuvanted HPV-16/18 vaccine (31).

Previously, we reported head-to-head comparisons of the safety and adaptive responses for adjuvanted vaccines containing the prototypic HBV surface antigen (HBsAg) in young, HBV-naïve adults (32)(33)(34), the most recent of which compared formulations adjuvanted with AS01B, AS01E (half-dose AS01B with respect to MPL and QS-21 quantities), AS03, AS04 or aluminum salt (Al(OH)3; Alum) (34). From this study, a pattern emerged in which HBs-specific CD4 + T-cell and antibody responses could be generally ranked, in order of decreasing magnitudes, from AS01, to AS03, to AS04, to Alum. Since a similar ranking seemed to apply to the prevalence of solicited adverse events (AEs), which may reflect inflammatory signals, we hypothesized that the adaptive and early inflammatory (innate) responses to these vaccines could be correlated. To evaluate this hypothesis, we characterized innate vaccine responses at the protein, gene expression, and cellular level in peripheral blood, in order to use these data in linear regression models of the adaptive responses. The modeling of gene expression data was performed on a limited but robust dataset generated by qPCR. This was done in order to provide sufficient power to the model and to enhance the likelihood of obtaining meaningful results in this hypothesis-driven study. Innate responses were summarized using principal component (PC) analysis, which allowed dissecting out the complex mix of early innate variables and identifying innate signatures governing these associations.

In this first-time comparison of blood innate responses to AS01, AS03, and AS04 in humans, we observed rapid (starting at 3-6 h) yet transient changes in blood innate parameters, some of which were shown to correlate with the magnitude of the adaptive responses.

Study Design

The randomized, controlled phase II trial (ClinicalTrials.gov: NCT00805389) was performed at 14 study centers (34). The protocol was approved by all institutional Ethics Committees and conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines. Written informed consent was obtained from each participant before trial participation. Participants were healthy HBV-naïve men or women 18-45 years of age who received two intramuscular injections of vaccine containing HBsAg (20 µg dose) adjuvanted with AS01B, AS01E, AS03A, AS04 (FENDrix), or Alum (Engerix-B) at days 0 and 30. Participants were followed up to day 360. Safety and reactogenicity up to day 60 in the total vaccinated cohort, and adaptive responses in the according-to-protocol (ATP) cohort for adaptive immunogenicity up to day 60 were described previously (34).

One of the study's secondary endpoints, innate immunogenicity, was evaluated for the ATP cohort for innate immunogenicity, which included all participants not meeting elimination criteria during the study and for whom innate immunogenicity data were available (

innate response Evaluations

Blood samples for innate response evaluations were collected before vaccination (days 0 and 30), 3-6 h, 1 day and, for qPCR analysis only, 14 days after dose 1 (3-6 h, day 1 and day 14), and 3-6 h, 1, 3, and 7 days after dose 2 (3-6 h on day 30, day 31, day 33, and day 37, respectively).

Cytokines

Cytokine concentrations in serum were measured using cytometric bead array (CBA) commercial kits, i.e., BD CBA Human Enhanced Sensitivity Master Buffer kits [for interleukin (IL)-1β, IL-6, IL-5, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and BD CBA Human Flex Set kits [for IFN-γ-inducible protein (IP)-10 and monocyte chemoattractant protein (MCP)-1], according to the manufacturer's instructions. Since these kits were not validated, qualification was performed internally to establish their cutoff values, which were subsequently set at 0.822 pg/mL for IL-1β, IL-6, TNF-α, IL-5, and IL-10, 40 pg/mL for IP-10 and MCP-1, and 7.407 pg/mL for IFN-γ. Of note, the latter cutoff for IFN-γ was higher than the limit of quantitation of the IFN-γ ELISA used in a recent study, i.e., 1.0 pg/mL (35). Concentrations below these assay cutoffs were given an arbitrary value of one-half of the cutoff value.

Hematology and CRP

Blood samples for hematology assessment were analyzed within 24 h after collection using a standard hematology analyzer. Serum CRP concentrations were measured and counts of white blood cells (WBC: lymphocytes, monocytes, eosinophils, basophils, neutrophils) recorded. Tests were conducted by ISO 15189-accredited labs. As this was a multicentric study, a set of normal ranges was provided by each study center. In order to compute summary statistics, results were first normalized.

To facilitate interpretation, normalization was done using reference ranges from one center (ImmuneHealth), as follows: normalized data = Ls + (x-Lx) × [(Us-Ls)/(Ux-Lx)], where x = raw data; Ux/Lx = upper/lower normal limit of the local normal range applicable to x; Us/Ls = upper/lower normal limit of the corresponding reference range. For differential cell counts, this formula was only applied to subjects from the centers expressing their results in absolute counts (i.e., in the specific unit as was used in the reference center), for whom the results are shown here.

ATP Cohort Descriptions

Descriptive statistical analyses were conducted using SAS v9.

Characterization of the three model parameters was performed for the ATP cohort for innate immunogenicity (for innate responses and reactogenicity evaluations) and for the same ATP cohort excluding the participants who were previously (34) excluded from the ATP cohort for adaptive immunogenicity (for adaptive responses; Table 1 ). Given the different laboratory quantitation standards, responses of the innate variables were expressed in FCs over their pre-vaccination baselines (days 0 and 30). The median FCs were visualized in heat maps generated in R (https://www.r-project.org/), in which data were zero-mirrored for symmetrical presentation of over-and under-expression, as follows:

where A and B are post-and pre-vaccination responses). Significant differences in post-vaccination IL-6 and IP-10 levels between an AS group and the Alum group were assessed using a testing cascade with α-recycling (36) (starting with a Kruskal-Wallis rank sum group test and cascading into Wilcoxon rank sum tests), and overall statistical significance level α of 0.05.

Datasets Multi-Parametric Analyses

Adaptive responses after the second injection (HBs-specific CD40L + CD4 + T-cell frequencies or antibody concentrations at day 44) were modeled separately, as a function of the innate responses after the first (pI) or the second (pII) injection, and of reactogenicity pII (Figure 1A ). The output parameters were selected as displaying the highest difference between the AS groups (34), and, for anti-HBs antibodies, serving as a proxy for protective immunity. Modeling was performed on participants of the ATP cohorts for whom data were available for each time-point and variable of immunogenicity and reactogenicity evaluations. Thus, each dataset in the model had the same size for the collective innate or adaptive responses and reactogenicity data (i.e., N = 256 or N = 84 for models including clinical laboratory/serum data or gene expression data, respectively; Table 1 ). Solicited local AEs (pain, redness, swelling) and solicited systemic AEs [fatigue, fever (axillary temperature ≥ 37.5°C), headache, malaise, myalgia] as described previously (34) were represented in the model by the sum of either all individual local scores, or all individual systemic scores, by treatment group. These scores were derived from the maximum AE grading [based on the intensity grading described in Ref. (34)] reported by subject over all local or all systemic AEs. Of the 14-day safety follow-up period pII (from day 30 through day 43), only the first week (from day 30 through day 36) was considered in the modeling, since in the vast majority of subjects the solicited local and general AEs had resolved by day 6 post vaccination (34).

Principal component analysis was performed on the clinical laboratory/serum and qPCR datasets, with data for all variables and time-points expressed in FCs over pre-vaccination as described. Data were organized in a matrix with n rows (one per subject) and K*T columns of [VAR1_time1, VAR1_time2, …, VAR_K_timeT], where K = number of variables and T = number of post-vaccination time-points. The first three PCs (PC1, PC2, and PC3) of each dataset were included in the model.

Multi-Parametric Analyses

In the linear regression models, the adaptive response pII of each subject (ApII,i) was modeled as a function of: the regression coefficients β for the local and systemic reactogenicity scores pII by subject (LRpII,i and SRpII,i), the individual innate variables pII (INpII,i) as summarized by their first three PCs, and the treatment effect on the innate responses (per AS group relative to the Alum group), corrected for the error term (εi). The Alum group was the primary comparator as displaying the lowest overall responses among groups and the β of its intercept (β0) was considered as the baseline. The following equation was used:

.

In a third model, the impact of the adaptive response pI was evaluated by adjusting the first model for individual responses of HBs-specific CD40L + CD4 + T-cell pI (CD4pI,i) and HBs-specific antibodies pI (ABpI,i) responses pI, as follows:

The resulting ApII,i values were then included in a model to study the relationships between the parameters. The strength of a given association was described by an estimate of its effect size, variability (standard error; SE), and statistical significance, as summarized by the p-value. PCs with p < 0.05 were considered significantly associated with the adaptive responses.

rESUlTS

To elucidate the role of early responses in vaccine immunogenicity, we modeled the adaptive responses after two injections as a function of both the innate response (measured after the first or second injection) and the reactogenicity scores reported after the second injection (Figure 1A ). We used multi-parametric analyses to examine the strengths of the linear associations between these parameters, in terms of their estimated effect sizes, variability, and statistical significance, as determined for the AS groups relative to the Alum group (considered as the baseline). For the monitoring of the innate response, two separate analyses were performed on the per-protocol cohorts: one including clinical laboratory parameters and serum proteins (N = 291), and the second including gene expression data (N = 112; Table 1 ).

The adaptive responses were represented by either HBsspecific CD40L + CD4 + T-cell or antibody responses measured 2 weeks after the second immunization (day 44), which were modeled separately since only limited associations between these responses were observed previously (34). The data reflected similar trends between adjuvant groups as reported previously for a larger cohort (34) (Figures 1B, C ). The local and systemic reactogenicity scores reported after dose 2 also exhibited a comparable adjuvant ranking, but with greater similarity between the AS03 and AS04 groups (Figure 1D ).

aS induce a Transient increase in levels of innate blood Parameters

To highlight the innate parameters most impacted by the adjuvanted vaccines, the responses were first expressed as median FCs from the two pre-vaccination time-points (days 0 and 30), and represented as heat maps [considering biological significance at a FC of ≥|1.5|

Absolute values of a selection of these parameters are presented in Figures 2 and 3 .

Evaluation of the clinical laboratory data revealed CRP responses at 1 and 3 days post vaccination (Figure S1A in Supplementary Material). Indeed, median CRP levels were increased in the AS01 and AS03 groups at day 1, and in each AS group at days 31 and 33 (10-, 5-, 3-and 2-fold for AS01B, AS01E, AS03 and AS04, respectively at day 33). Changes in blood cell counts were detected mainly at day 1 post each vaccination (Figure 2 ). All parameters returned to baseline within 1 week post vaccination (Figure S1A in Supplementary Material; Figure 2 ). Among the myeloid lineage, the increase in neutrophil counts after the second dose was the most prominent signature. While this increase was, in fold-changes, only seen at day 31 in the AS01B and AS01E groups (1.8-and 1.5-fold, respectively), evaluation of the interquartile ranges of the absolute neutrophils and monocytes counts revealed a trend for increased responses in all AS groups at both day 1 and day 31. The monocyte counts tended to remain slightly elevated through day 33 in the AS01 and AS03 groups and returned to baseline at day 37. Likely associated with this increase in myeloid cells at day 31 was the concurrent transient decrease in the relative lymphocyte fractions, which was, in fold-changes, observed in the AS01B group (-1.6-fold), and, in absolute counts, also in the AS01E and AS03 groups. No clear CRP or hematology responses were observed in the Alum group.

All formulations triggered transient cytokine responses (Figure S1B in Supplementary Material). After the first injection, at 3-6 h, only IL-6 was detected in the AS01B and AS01E groups (3-and 1.6-fold, respectively). At day 1, most of the pro-inflammatory markers measured (listed in Table S1 in Supplementary Material) were increased in at least one group, of which the IL-6 and TNF-α levels were increased in all groups (1.6-to 4-fold, and 1.7-to 2-fold, respectively; Figure S1B in Supplementary Material). The IL-6 levels in the AS01B group surpassed those in the Alum group at both time points (p = 0.001). Of note, median IL-5, IL-1β, and IL-10 responses were ambiguous, since the individual concentrations often approached the assay cutoffs. After the second injection, IL-6 levels were increased at 3-6 h on day 30 in the AS01 groups (1.9-fold) and at day 31 in each AS group (1.7-to 4-fold), and had returned to baseline at day 33. At 3-6 h on day 30 and at day 31, IL-6 levels in both AS01 groups exceeded those in the Alum group (p = 0.001).

Changes in IP-10 and IFN-γ levels were only observed after the second injection. Of these responses, IP-10 levels were only increased in the AS01 groups at days 31 and 33 (1.6-to 3-fold) and were at both of these time-points significantly different from the decreased levels in the Alum group (i.e., -1.6 and -1.47, respectively; p ≤ 0.002). These responses had subsided to baseline at day 37. IFN-γ was only increased in the AS01B group at day 31 (1.5-fold). MCP-1 levels were decreased in all groups, and predominantly at day 33.

Evaluation of the absolute cytokine concentrations revealed that the variability was relatively high across subjects, groups, and parameters (Figure 3 ). Interestingly, discrete IFN-γ responses were observed in some individuals of each group. In particular, in 14% of the participants who received AS01, IFN-γ levels were already detectable at day 1 (Figure S2A in Supplementary Material).

Early Changes in CrP, il-6, iFn-γ, and iP-10 levels are associated with the Magnitude of the adaptive response

We next tested our hypothesis that adaptive and early innate responses after vaccination are associated. In order to perform association analyses, the innate parameters were summarized by PC analysis and the first three PCs were used in the multiparametric model. After the first vaccine dose, and following adjustment for treatment effect, no association between the PCs representing the innate responses, and the CD4 + T-cell response at day 44, could be found (Figure 4A ). As expected, significant associations were seen between the CD4 + T-cell responses and the AS01 and AS03 treatments, as well as between the antibody responses and all four AS treatments (p < 0.001; upper panel). Weaker associations were seen between the CD4 + T-cell responses and systemic reactogenicity (p = 0.03), and between the antibody responses and the PC3 of the innate response (p = 0.005). Visualization of this PC3 in a PC1, PC3 plot allowed grouping of the individual subjects with overall similar expression profiles (middle panel). Consistent with the cytokine expression profiles of the AS after the first dose (see Figure S1B in Supplementary Material and Figure 3 ), the patterns of all AS groups were largely overlapping, with only the AS01B and Alum groups exhibiting a clear separation on the PC3. To identify the relative contributions of the individual variables to the statistically significant association observed for the PC3, as well as to represent the association between different parameters, we visualized the loadings of the analyte-time-point combinations in a second PC1, PC3 plot (Figure 4A , lower panel). IL-6 at 3-6 h and day 1 and CRP at day 1 exhibited the strongest separation on the PC3 axis and were consequently most strongly associated with antibody response. In line with the data in Figures S1A, B in Supplementary Material, these parameters were mostly activated by AS01B.

When the innate responses after the second injection rather than those after the first injection were included in the model (Figure 4B ), stronger associations of CD4 + T-cell and antibody responses with the PC2 (p < 0.001) and, to a lesser extent, the PC3 (p = 0.03; CD4 + T cells only) were seen (upper panel). No associations with reactogenicity were observed. A PC1, PC2 plot showed that the subjects clustered largely separately from each other by group, with less overlap than was observed after the first dose (middle panel). Loading analyses by analyte and time-point along the relevant PCs revealed that for the PC2, the association engaged more variables than after the first dose (lower panel). The largest separations on the PC2 were observed for IL-6 and IFN-γ at day 31, and CRP and IP-10 at days 31 and 33. A PC1, PC3 plot revealed that the weak association of the CD4 + T-cell response with the PC3 was mostly determined by a cluster Input parameters included local and systemic reactogenicity scores calculated from the solicited adverse events (AEs) pII, and the innate responses pI and pII. Intercept, β for Alum group (β0). Adjuvant systems (AS) groups were compared with the Alum group (considered as baseline). HBs-specific CD4 + T-cell and antibody responses were measured at day 44. Principal components (PCs) that were significantly associated (p < 0.05) with the adaptive response are indicated by bold font. Middle panels: PC analysis of the innate response dataset was performed by subject and treatment group and visualized in bivariate plots. The variance explained by the first three PCs was 73% after the first injection and 58% after the second injection. Each dot represents the expression profile of an individual subject. Arbitrary aggregation of the subjects into treatment groups is visualized by the colored ellipses, according to the color coding presented in the left-hand corners of the plots. The PC1 accounted for 52 and 35% of the variance after the first and second dose, respectively, and is plotted against the PCs showing the strongest association with the adaptive response in the table in the upper panels, i.e., the PC3 after the first dose and the PC2 after the second dose. Lower panels: as for the middle panels, but with PCs representing the variables at the post-vaccination time-point indicated by the color coding in the upper corners of the PC plots. An overview of PC plots for each PC1, PC2, PC3 combination by variable/time-point is presented in Figure S3 in Supplementary Material. LYM, lymphocytes; MON, monocytes; NEU, neutrophils; WBC, white blood cells.

formed by TNF-α, IL-5, IL-1β, and IL-10 at day 31 (Figure S3 in Supplementary Material). Thus, key innate markers associated with the magnitude of adaptive responses were, after the first dose, CRP (at day 1) and IL-6 (at 3-6 h or day 1), and after the second dose, CRP and IP-10 (at days 31 and 33) and IFN-γ and IL-6 (at day 31). Of note, other important markers, such as neutrophil or monocyte counts (which were both increased post vaccination), were not associated with the adaptive response.

To evaluate the impact of the adaptive response to the first vaccine dose on the adaptive response to the second vaccine dose, day 14 CD4 + T-cell responses and day 30 antibody responses were added to the model (Table S2 in Supplementary Material). The levels of the adaptive response after the first dose were associated with the levels of both antibody and CD4 + T-cell responses after the second dose (p ≤ 0.04; |β| range: 0.1-0.4). However, associations with the innate parameters after two doses were no longer observed, suggesting that the influence of the adaptive response after one vaccine dose on the day 44 adaptive response was stronger than that of the innate response.

Gene Expression analysis Supports the association of Early iFn Pathways and adaptive response

To further explore our hypothesis to the gene expression level, we quantified the expression of 14 genes encoding major cytokines or transcription factors implicated in inflammation, cell proliferation, and the IFN pathway (listed in Table S1 in Supplementary Material), by whole blood qPCR for a subset of subjects (N = 112). This selection was based on published literature describing either innate responses to different AS-containing vaccines and other vaccines in humans (31,35,37,38) and animal models (25)(26)(27), or early IFN-related responses to AS01-adjuvanted candidate vaccines in humans (32,35,39) and animals (40).

No changes in gene expression patterns were seen in the Alum and AS04 groups after either dose (Figure S1C in Supplementary Material). Median responses in the other groups were only seen at day 1 (AS01 groups), day 31 (all three groups), and day 33 (AS01B group only), with comparable signatures between the AS01B and AS01E groups. After the first dose, at day 1, only STAT1 was slightly upregulated in the AS01B and AS01E groups (2-and 3-fold, respectively). Of note, increased STAT1 mRNA levels after the first vaccine dose were also seen in a minority of individuals in the AS03 group (Figure 5 ). After the second dose, at day 31, upregulation of the IFN-inducible genes STAT1, IRF1, MX1, and CXCL10 was observed in the AS01 groups (4-or 5-, 2.6-or 3-, 2-or 3-, and 3-or 6-fold, respectively; Figure S1C in Supplementary Material), consistent with the concurrent detection of IFN-γ (AS01B group) and IP-10 (both AS01 groups) proteins in serum (see Figure S1B in Supplementary Material; Figure 3 ). Yet, no change in the levels of IFNG mRNA could be detected at any time point (Figure S2B in Supplementary Material). At day 33, only the STAT1 and MX1 upregulation in the AS01B group persisted (Figure S1C in Supplementary Material). Interestingly, a slight downregulation of NFATc2, a gene that is upregulated in T lymphocytes, was detected at day 31. This coincided with the observed decrease in lymphocyte counts at the same time point (see Figure S1A in Supplementary Material; Figure 2 ) and potentially reflected the recruitment of antigen-specific T cells (and bystander cells) to the dLN. In the AS03 group, only the median MX1 and STAT1 mRNA levels were increased, at day 31 (2-and 3-fold, respectively). In sum, the AS01-or AS03-adjuvanted vaccines shared the same INF-related signature at day 31 (although more and stronger signals were detected with AS01), while the AS04-or Alum-adjuvanted vaccines had no apparent effect on the whole-blood gene expression.

Multi-parametric analyses of the adaptive responses for a subset of subjects (N = 84) revealed only a weak association with the PC1 of the innate responses post dose 2 (p = 0.01 or p = 0.03; Figures 6A, B ; upper panels). As expected, the PC1, PC2 plots after each dose showed that the AS01 and AS03 treatments were separated from the AS04 and Alum treatments (middle panels). As shown in the PC1, PC2 (lower panel) and PC1, PC3 (Figure S4 in Supplementary Material) plots by variable, the association with the PC1 of the innate responses was predominantly governed by STAT1, IRF1, MX1, and CXCL10 at day 31, and MX1 at day 33. Thus, upregulation of IFN-inducible genes post dose 2, as induced by AS01 and AS03, was associated with higher adaptive responses.

When the model was adjusted for adaptive responses post dose 1, the latter responses but not the innate responses were significantly associated with the adaptive responses post dose 2 (Table S2 in Supplementary Material), confirming the observations made for the clinical laboratory/serum dataset.

DiSCUSSiOn

Previously, we described the reactogenicity and adaptive responses induced by HBsAg adjuvanted with AS01B, AS01E, AS03, AS04, or Alum, in close to 600 young, HBV-naïve adults (34). As part of this study, we also characterized the innate responses in blood collected from 291 of these participants and used these data in multi-parametric models of adaptive responses as described here. We found that the vaccines provoked transient responses which started at 3-6 or 24 h after vaccination. These responses comprised inflammatory markers (for all AS), neutrophils (for AS01), and mRNAs encoding cytokines implicated in the innate response (for AS01 and AS03). Yet, no unique protein, cellular, or gene expression signature was identified for one particular vaccine. Furthermore, adaptive responses post dose 2 were found to be associated with, in order of decreasing strength, adaptive responses post dose 1, then innate responses post dose 2, and then innate responses post dose 1, while reactogenicity was not identified as a significant predictor of adaptive responses. The association between innate and adaptive responses after two doses was largely driven by increased levels of CRP and IL-6 promoted by all AS, and of parameters of the IFN-signaling pathway promoted by AS01 or AS03. Importantly, the multi-parametric model proved to be able to specifically identify those innate parameters that were associated with the adaptive response. This was exemplified by the observation that for some innate parameters (e.g., neutrophil counts), the levels were markedly changed after the first and/ or second vaccination, while the modeling showed associations with neither the CD4 + T-cell response nor the antibody response. The adjuvant ranking for innate responses was overall similar to that observed previously for adaptive responses (34). The superior adjuvanticity of AS over Alum is overall consistent with mice data showing comparable differences between these adjuvants in innate cellular and cytokine responses in dLNs (25)(26)(27), and similar observations were made for other adjuvants (6). For AS04, the difference with AS01 may be explained by its lack of QS-21, although this comparison is hampered by the difference in the formulations of these AS. The presence of MPL in AS04 may explain its difference with Alum, but this comparison is confounded by the different aluminum salts these adjuvants contain. Among the AS01-adjuvanted vaccines, the magnitude of the innate response was overall higher with AS01B, and although this did not translate into significantly different adaptive responses, it was consistent with the lower inter-subject variability of the adaptive responses observed in the AS01B group (34). This suggests that a more potent innate response can contribute to generate a more robust antigen-specific response, which is particularly relevant for individuals with a tendency to respond less efficiently to vaccines. For example, in evaluations of an varicella zoster virus glycoprotein E vaccine in older adults, who are anticipated to be less responsive to vaccines in general, the antibody and CD4 + T-cell responses were significantly higher for the AS01B-adjuvanted vaccine than for the AS01E-adjuvanted vaccine (41). This is consistent with a recent publication of Nakaya et al. showing that the impaired IFN-related genes signature following influenza vaccination in the elderly population in that study was associated with the decreased antibody response (42). This was, however, not seen in the responses induced by a tuberculosis vaccine (M72/AS01) in healthy adults (43), suggesting that this effect might be dependent on the immune status of individuals and the specific combination of antigen and adjuvant. Overall, our data suggest that for the current antigen and naive adult population, the potency of an AS in inducing systemic innate responses was positively correlated with the magnitude of adaptive responses.

Production of both IL-6, a cytokine promoting T helper (TH) cell stimulation, and CRP were features shared by the AS. This aligns with non-clinical data showing IL-6 responses in the mouse dLN, muscle and serum and/or in human primary cell cultures (for MPL, QS-21, or AS03), and CRP responses in rabbits (for AS01 and AS03) (25)(26)(27)(28)44). The time-courses for the sequential peaks of IL-6 (3-6 h) and CRP (24 h) were reminiscent of those found for a non-adjuvanted bacterial vaccine in humans (45). This sequence is also consistent with CRP production in the liver being, at least in part, under transcriptional control of the IL-6 pathway (46,47). A hallmark of the response to the vaccines containing AS01 and AS03 (but not AS04 or Alum) was the upregulation of the IFN pathway, manifested by changes in the expression of IFN-related genes and increases in serum IFN-γ and IP-10 levels. IP-10 may be downstream of IFN signals, as it is produced by several cell types in response to IFN-γ or IFN-α (48,49). Of interest, IFN-related protein and gene expression was also observed in the predominantly H1N1-primed recipients of AS03-adjuvanted H1N1 influenza vaccine (38). However, in the naïve participants of the present study, the IFN-γ response induced by the HBsAg/ AS03 vaccine was less prominent and mainly seen after the second dose. Interestingly, in mouse models, AS01 has been shown to directly drive early IFN-γ production by both NK cells and CD8 + T cells, resulting from a synergistic effect of MPL and QS-21 on macrophages in the dLN, which eventually led to the synergistic enhancement of polyfunctional CD4 + T-cell responses (11,40,50). Furthermore, IFN-γ derived from innate immune cells enhances protective anti-parasitic Th1 responses (51), and recent data suggested a link between the detection of early IFN signatures in blood from RTS,S/AS01 vaccinees and their subsequent protection from malaria (52). These mechanistic clues to the mode of action of AS01 may explain why both AS01B and AS01E ranked highest among the AS with respect to IFN-related gene expression and IL-6 and CRP levels, and why they were eventually shown to be most strongly associated with adaptive responses.

One of the aims of this study was to identify potential interrelationships between innate and adaptive responses. IFN-γ specifically is known to be critical for both adaptive and innate immunity. In addition to antigen-specific T cells, IFN-γ is secreted by innate lymphoid cells, such as innate-type T cells or NK cells, among others [reviewed in Ref. (53)], that can be involved in the onset of the immune response as well as in protection mechanisms. In our study, IFN-γ signaling pathways were found to be increased after the second dose vs after the first dose. Similar trends of increased serum IFN-γ after repeated administration of AS-adjuvanted vaccines were observed in clinical trials evaluating either HBsAg/ AS01 (32), or other antigens (M72, RTS,S) combined with either AS02 (an emulsion containing MPL and QS21) or AS01 (35,54). Moreover, peripheral IFNγ-producing NK cells were observed after M72/AS01 and RTS,S/AS01 vaccination in humans (55,56). Taken together, the current observations with respect to IFN-associated responses may be explained by two, not mutually exclusive hypotheses, involving either "trained immunity" or a CD4 + T-cell regulated mechanism (Figure 7 ). Trained immunity, involving epigenetic imprinting of the IFN-γ loci in NK cells following activation, could drive higher IFN-γ production by memory NK cells upon re-challenge (2,3,5,57). Alternatively or in addition, stimulation of IFN-γ secretion by NK cells or monocytes could be mediated by IL-2 produced by vaccine-induced effector memory T cells (55,(58)(59)(60). Since in our study, only AS01 and AS03 provoked IFN-associated responses, their efficiency in inducing adaptive responses may, thus, possibly be linked to their capacity to trigger IFN-signaling.

While we did not evaluate a putative association between inflammatory markers and reactogenicity, such association may plausibly exist, given the parallel trends in innate responses and the prevalence of reactogenicity events [i.e., both were higher with the formulations with AS vs Alum, and (for systemic reactogenicity) both were increased after the second dose of AS01B-adjuvanted vaccine (34)]. Yet, reports of immunological correlates of reactogenicity in humans are scarce and often conflicting. For instance, in recipients of non-adjuvanted influenza vaccine, local or systemic reactogenicity events corresponded with post-vaccination levels of MIF and/or TNF-α, but not of IL-6, IL-8, or IL-1β (61), and CRP increases seen in other clinical vaccine trials were also not clearly associated with reactogenicity (62)(63)(64). Furthermore, reports of severe AEs in recipients of AS03-adjuvanted A(H1N1) pdm09 influenza vaccine did not correlate with blood TNF, IL-6, IFN-γ, or CRP levels, nor, albeit ambiguous, with IP-10 levels (38). In non-clinical studies, however, the data suggested correlations between systemic IL-6 responses and increased body temperature (65), and between upregulation of IFN-inducible and innate phase genes and several reactogenicity parameters (66). Still, animal models do not faithfully mimic human responses to inflammation, as evidenced by the limited correlations between the human endotoxemia model and mouse endotoxin model (a common proxy for inflammation in humans) (67). Nonetheless, if innate immunity and reactogenicity are linked, reduction of reactogenicity while preserving immunogenicity poses an interesting opportunity for vaccine development. For AS01, this was addressed by the dose reduction from AS01B to AS01E for a candidate tuberculosis vaccine, which generally decreased reactogenicity without affecting adaptive response magnitudes (43). Importantly, the acceptable safety profiles described for the current AS01-, AS03-, or AS04-adjuvanted HBsAg vaccines [as described in Ref. (32,34,68)] were supported by our observation that the inflammatory responses typically returned to baseline within 1 or 3 days. Still, given the variability of the human population and limited number of markers assessed, the inflammatory marker signatures obtained in the current young, naive adult population cannot be extended to other populations, nor serve to predict risks of rare safety signals.

Previous preclinical assessment has shown that the effect of AS is local, as the antigen needs to be co-localized with the adjuvant at the site of injection, presumably engaging the same dLN (25)(26)(27). As we only probed blood in the current study, the systemic data may not fully reflect the direct events occurring in the muscle at the site of injection and the dLN. For example, in AS04-treated mice, the cytokine concentrations measured locally were ~10fold higher than those in peripheral blood (25). Nonetheless, the changes in STAT1, CXCL10, and IRF1 mRNA and concurrent IFN-γ and IP-10 responses suggested that the IFN-γ canonical pathway was activated in circulating cells. This is supported by the fact that monocytes and lymphocytes can produce IP-10 in response to IFN-γ responses. Since STAT1 is transcriptionally activated by IFN-γ as well as by IFN-α/β (which were not measured) a role of type-1 IFNs cannot be excluded. Cytokine consumption or dilution effects from using whole blood instead of sorted cells may also have been confounding factors. Overall, because no changes in INFG mRNA were detected, the systemic IFN-γ responses may indicate local production in the injected muscle or dLN, as supported by the IP-10 protein and CXCL10 mRNA responses.

The PC modeling revealed overlapping innate immune profiles for a portion of the participants in the AS01, AS03, and AS04 groups. Furthermore, AS01 and AS03 were shown to display similar innate marker profiles, although the responses they induced were of different magnitudes. Such resemblance was not obvious from previous evaluations of these adjuvants in preclinical models, in which the set of markers partly overlapped the selection assessed in the current study (26)(27)(28). Comparative analyses of several adjuvants in mice revealed that, while the adjuvants shared a core inflammatory signature, they also displayed distinctly different specific signatures (6,69). Yet, the relevance of such adjuvant-specific signatures obtained in inbred mouse models for human vaccine studies remains unclear, necessitating further comparative evaluations in a clinical context. Furthermore, it is likely that the current observations are at least partially a consequence of our marker selection. Indeed, the input data of the model assessing gene expression were generated by qPCR, a method chosen for its robustness, and included a limited selection of genes, to gain sufficient power in the modeling. As a consequence and potential limitation, the number of genes was lower as compared to that in assays evaluating genome-wide expression. Hence, as a next step, we are currently in the process of generating microarray data for this study, focusing on both the group-averages and the individual participants' gene expressions. These data may serve to (1) confirm the current observations, (2) uncover specific differences in the modes of action of AS01, AS03, and AS04 underlying the differential impacts these AS were shown to have on the adaptive response. Similarly, more work is needed to better define any potential differences between these adjuvants in the quality of the induced antigen-specific responses, including the breadth, avidity, polyfunctionality, memory phenotype, or persistence, which were previously shown to be altered by other adjuvants (32,(70)(71)(72)(73). It will also be of interest to define such differences as a function of the physicochemical properties of these adjuvants. The kinetics of the innate response induced by the different AS described here has been studied mainly in mice (25)(26)(27), rabbits (28), and sheep (74). These animal data showed that all AS trigger a transient innate response regardless of their composition. In AS04, MPL is adsorbed on Alum, but the kinetics of the innate immune response induced by MPL is not significantly impacted by the depot effect of Alum (26). The difference in the compositions of AS01 (liposome-based) and AS03 (oil-in-water emulsion) is reflected in discrepancies of their respective response. In rabbits, the CRP levels induced by AS03 declined at a slower rate as compared to those induced by AS01 (28), which may be related to the greater retention of the oil-in-water emulsion at the injection site (29). By contrast, AS01 rapidly drained to the local lymph node (26), and 3 days after injection no signs of inflammation were detected at the injection sites of AS01-treated rabbits (75). While this may be inconsistent with the similar kinetics of the AS-adjuvanted vaccines observed here, it could also be a function of the selected time points, or of biological differences between animals and humans.

Because the study was designed to allow a head-to-head comparison of the innate responses to the adjuvanted vaccines, neither placebo controls nor adjuvant-or antigen-only groups were included in the study design. Therefore, the data cannot serve to ascertain the impacts of the needle insertion or the HBsAg by itself. Engerix-B was used as an adequate benchmark because it is the current standard of prevention against hepatitis B viral infection. In addition, the use of Alum as a benchmark adjuvant is relevant because Alum is used in many vaccines and has a well-established safety profile. Engerix-B had little impact on the innate markers studied here, in agreement with murine data (27), suggesting that the antigen itself would have limited impact on the innate response directly. Collectively, this suggests that the innate response induced by the AS-containing HBsAg vaccines were directly attributable to these adjuvants.

COnClUSiOn

A feature shared by AS01, AS03, and AS04 is that the innate immune responses promoted by these adjuvants in humans led to increased adaptive responses to the co-administered antigen, confirming the mechanism of action of these adjuvants investigated in animal models. Despite the distinctly different compositions of these adjuvants, the innate immune responses activated by AS01 and AS03 converged toward a common pathway, the IFN pathway, which was associated with enhanced adaptive responses.

TraDEMarK STaTEMEnTS

FENDrix and Engerix-B are trademarks of the GSK group of companies.

ETHiCS STaTEMEnT

The protocol was approved by all institutional Ethics Committees and conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines.

aUTHOr COnTribUTiOnS WB, AD, SD, NG, GL-R, AM, and RM participated in the conception, planning, and/or design of the study. SD, VB, RB, and MJ participated in the data generation. WB, VB, FC, AD, NG, MJ, GL-R, AM, RB, and RM performed or supervised the analysis of data and interpreted the results. AC and LF provided with statistical expertise for the modeling and/or analysis and interpretation of the results. GL-R was the principal investigator of CEVAC and FC coordinated CEVAC's participation. WB led the development of the outline. All authors participated in the development of this manuscript. All authors had full access to the data, gave final approval before submission, and agreed to be accountable for all aspects of the work. The corresponding author was responsible for submission of the publication. ). The authors would like to thank the study participants and staff members of the different study sites for their contribution to the study. The authors also thank Claus Andersen for producing the heat maps and Alix Collard for statistical support. Finally, the authors would also like to thank Ellen Oe (XPE Pharma & Science, on behalf of GSK) for medical writing support and Marie-Line Seret and Sonia Dopico (XPE Pharma & Science, on behalf of GSK) for editorial assistance and publication coordination.

FUnDinG

GlaxoSmithKline Biologicals SA was the funding source and was involved in all stages of the study conduct and analysis. GlaxoSmithKline Biologicals SA also took responsibility for all costs associated with the development and publishing of the present manuscript.

SUPPlEMEnTarY MaTErial

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fimmu.2017.00943/ full#supplementary-material.

Conflict of Interest Statement: AC, AD, LF, MJ, RB, RM, VB, and WB are employees of the GSK group of companies. AD, RB, RM, VB, and WB hold shares in the GSK group of companies as part of their employee remuneration. AD and RM have a patent on AS01 (fractional dose) pending. AM's institution received payment from the GSK group of companies for the performance of the clinical trial as well as consulting fee. GL-R's and FC's institutions received payment from the GSK group of companies for the performance of the clinical trial as well for execution of immune assays. NG and SD were employees of the GSK group of companies. NG owns stocks from GSK group of companies or stock options and has issued patents for AS01, AS03, and AS04.

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