ArticleHuman vaccines & immunotherapeutics2020
Combining immunoprofiling with machine learning to assess the effects of adjuvant formulation on human vaccine-induced immunity.
Article in Human vaccines & immunotherapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
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- Could artificial intelligence gradually replace classical adjuvants?Frontiers in immunology · 2026Review
- Advances in vaccine adjuvant development and future perspectives.Drug delivery · 2025Review
- Machine learning insights into vaccine adjuvants and immune outcomes.Frontiers in immunology · 2025Article
- Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- Comprehensive immunoprofiling and systematic adjuvant comparisons for identifying suitable vaccine: Adjuvant pairings.Human vaccines & immunotherapeutics · 2023Article
- Evolution of Vaccines Formulation to Tackle the Challenge of Anti-Microbial Resistant Pathogens.International journal of molecular sciences · 2023Review
- Review: Current trends, challenges, and success stories in adjuvant research.Frontiers in immunology · 2023Review
- Potentials of saponins-based adjuvants for nasal vaccines.Frontiers in immunology · 2023Review
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- Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting.Frontiers in medicine · 2022Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design.Briefings in functional genomics · 2021Review
- Emergency preparedness for public health threats, surveillance, modelling & forecasting.The Indian journal of medical research · 2021Review
- SIMON: Open-Source Knowledge Discovery Platform.Patterns (New York, N.Y.) · 2021Article
- Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective.Pharmaceutics · 2020Review
- The COVID-19 Vaccine Race: Challenges and Opportunities in Vaccine Formulation.AAPS PharmSciTech · 2020Review
- Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adjuvants produce complex, but often subtle, effects on vaccine-induced immune responses that, nonetheless, play a critical role in vaccine efficacy. In-depth profiling of vaccine-induced cytokine, cellular, and antibody responses ("immunoprofiling") combined with machine-learning holds the promise of identifying adjuvant-specific immune response characteristics that can guide rational adjuvant selection. Here, we profiled human immune responses induced by vaccines adjuvanted with two similar, clinically relevant adjuvants, AS01B and AS02A, and identified key distinguishing characteristics, or immune signatures, they imprint on vaccine-induced immunity. Samples for this side-by-side comparison were from malaria-naïve individuals who had received a recombinant malaria subunit vaccine (AMA-1) that targets the pre-erythrocytic stage of the parasite. Both adjuvant formulations contain the same immunostimulatory components, QS21 and MPL, thus this study reveals the subtle impact that adjuvant formulation has on immunogenicity. Adjuvant-mediated immune signatures were established through a two-step approach: First, we generated a broad immunoprofile (serological, functional and cellular characterization of vaccine-induced responses). Second, we integrated the immunoprofiling data and identify what combination of immune features was most clearly able to distinguish vaccine-induced responses by adjuvant using machine learning. The computational analysis revealed statistically significant differences in cellular and antibody responses between cohorts and identified a combination of immune features that was able to distinguish subjects by adjuvant with 71% accuracy. Moreover, the in-depth characterization demonstrated an unexpected induction of CD8
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