Infectious diseases are responsible for an overwhelming number of deaths worldwide and their clinical management is often hampered by the emergence of multi-drug-resistant strains. Therefore, prevention through vaccination currently represents the best course of action to combat them. However, immune escape and evasion by pathogens often render vaccine development difficult. Furthermore, most currently available vaccines were empirically designed. In this review, we discuss why rational design of vaccines is not only desirable but also necessary. We introduce recent developments towards specifically tailored antigens, adjuvants, and delivery systems, and discuss the methodological gaps and lack of knowledge still hampering true rational vaccine design. Finally, we address the potential and limitations of different strategies and technologies for advancing vaccine development.
Citation: Rueckert C, Guzmán CA (2012) Vaccines: From Empirical Development to Rational Design. PLoS Pathog 8(11): e1003001. doi:10.1371/journal.ppat.1003001
Editor: Tom C. Hobman, University of Alberta, Canada
Published: November 8, 2012
Copyright: © 2012 Rueckert, Guzman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported in part by grants from the EU (PANFLUVAC, TRANSVAC); BMBF in the context of the programs Gerontosys 2 (Gerontoshield), EuroNanoMed (HCVAX) and ERANetRUS (HCRUS), and the Helmholtz Association (IG-SCID). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Scourges of humanity, such as smallpox, polio, and measles, have been controlled by vaccination. Other epidemics, for instance tuberculosis, have yet to be sufficiently restrained by immunization. Accordingly, policy makers have given a high priority to the development of novel vaccines to induce protective immunity against selected pathogens. Most human vaccines contain attenuated or killed pathogens and were developed empirically, such as the yellow fever vaccine , . Safety concerns were associated with undefined vaccine preparations based on whole pathogens (e.g., inactivated or attenuated bacteria or viruses). Thus, novel subunit vaccines are based on a restricted number of individual components (i.e., antigens) of the specific pathogen, which are able to confer protective immunity. Obviously, the chances of finding effective components of subunit vaccines empirically are low. Immunogenic parts of pathogens that provide antigens for B cell receptors (BCRs) and antigenic peptides that are presentable by MHC molecules to T cell receptors (TCRs) have to be identified. It is critical to compensate for excluded pathogen-associated molecular patterns (PAMPs), which activate the innate immune system to induce an appropriate adaptive immune response. Finally, vaccine delivery systems may be needed. Hence, the rational design of vaccines is mandatory.
Rationally designed vaccines are composed of antigens, delivery systems, and often adjuvants that elicit predictable immune responses against specific epitopes to protect against a particular pathogen. In many cases a vaccine cannot be successfully designed due to insufficient knowledge about the mechanisms of protection. Although the repertoire of immune clearance mechanisms to fight pathogens is known, the specific contributions of different effector mechanisms are well-characterized for only a few pathogens. It is also largely unclear what determines the immunogenicity and selection of particular epitopes among all possible antigenic options offered by a pathogen. Which factors determine dominant or balanced immune responses? What are the mechanisms leading to long-term protection? Investigation of immune responses to known effective and ineffective vaccines and of pathogens' strategies of immune escape and evasion generates the basis to tackle these open questions. The approach relies on data from studies with empirically developed vaccines—for now and in the near future.
There are no universally accepted strategies and tools to rationally design vaccines. Vaccine development is still generally a tedious and costly empiric process. This review focuses on approaches to overcome empirical vaccine development and addresses their potential and limitations. It will become clear that even the latest developments are mostly first steps. Reports may sometimes sound too optimistic with regard to a prompt implementation of the introduced methods. Nevertheless, multiscale interdisciplinary efforts are strongly needed to reach this goal.
Antigen Selection and Optimization
Selecting the optimal antigen represents the cornerstone in vaccine design. With the advent of genomics, the traditional process of selecting candidate antigens one by one has been replaced by reverse vaccinology approaches. Namely, the coding potential of a pathogen's genome is exploited by in silico selection, high throughput screenings, and profiling technologies (e.g., genomics, proteomics) to define promising antigens in relation to in vivo expressed genes and clonal variation –. Importantly, this approach is not suitable for nonproteinaceous antigens. Depending on the desired response, the antigenic protein should contain appropriate BCR epitopes and peptides that can be recognized by the TCR in a complex with MHC molecules. Synthetic peptides produced at comparably low cost can also be incorporated in subunit vaccines. This is relevant especially in epidemic situations when large amounts of vaccine doses need to be produced in a very limited period of time. A peptide-based vaccine meets high safety standards due to the possibility of excluding allergens, toxins, or other functional molecular domains of the pathogen. Restricting the immune response to defined antigenic regions can, furthermore, help avoid effects such as autoimmune responses, dominant responses against epitopes prone to antigenic drift, or responses against epitopes with specificity for a particular strain rather than multiple strains of the pathogen. However, the identification of immunogenic peptide sequences requires a considerable amount of experimental effort. Computational prediction methods can strongly reduce time and costs for vaccine development. Nevertheless, clonal variability and in vivo selection resulting in immune escape could render ineffective a vaccine based on short peptides encompassing a limited number of epitopes. Furthermore, there are technological constraints associated with this approach (e.g., synthesis of long polypeptides).
To elicit antibody responses, vaccines should include BCR epitopes. Their prediction is particularly challenging, though, and most B cell epitopes are discontinuous; that is, they are comprised of distant parts of the protein's primary structure. In addition, they are of variable length (3–30 amino acids) and conformation-dependent . BCR epitopes do not possess physico-chemical patterns in their amino acid sequences that can be used for in silico prediction . Some epitopes change conformation when interacting with the cognate antibody's paratope, making even 3-D structure-based prediction difficult . The use of learning machines that depend on quantitative data on known antibody epitopes led to the development of prediction tools for linear epitopes such as BCPREDS ,  and IMMUNOPRED , . In contrast, PEPOP ,  is based on 3-D structural data on antigen–antibody complexes, and it predicts discontinuous epitopes, their antigenicity, and immunogenicity, and suggests peptide constructs for synthesis. However, these methods have not yet reached sufficient predictive accuracy to be routinely applied in vaccine design.
The proteins or peptides of a subunit vaccine should also display sequences that allow T cell epitope formation in a complex with MHC molecules. MHC class I and II come in hundreds of alleles that are differentially combined between individuals. Choosing immunogenic peptides presented by MHC faces the challenge of not only predicting sequences appropriate for complexing with a particular MHC allele but also finding peptides that can reliably build epitopes in the diverse genetic background within a human population. Drawbacks of in vitro assay-based TCR epitope identification are (i) time consuming procedures for combinatorial coverage of relevant MHC alleles and candidate pathogenic antigens, (ii) high costs for peptide synthesis and reagents, and (iii) limited sensitivity when using naïve T cell populations. These efforts can be reduced extremely when combined with computational TCR epitope prediction , .
In silico prediction of T cell epitopes cannot be based on physico-chemical properties of presented peptides but depends on the application of learning machines on data sets of known MHC allele–peptide pairs. The development and maintenance of databases is absolutely essential to constantly improve predictions , . Examples for such databases are IEDB ,  or SYFPEITHI, which only lists experimentally validated natural MHC–peptide complexes , . The tools OptiTope ,  and NetMHCcons ,  select for epitope peptides from specific MHC alleles or sets of MHC alleles as they occur naturally in individuals of a certain population. This is achieved by choosing promiscuous peptides that can be presented by several different MHC alleles of a supertype (i.e., universal peptides presented by most known alleles or a mixture of peptides binding to the most prevalent alleles within a population). The final goal is to provide suitable tools to generate immunogenic peptide sequences from any input antigen sequences. However, the broad applicability of these approaches towards rational vaccine design still remains to be proven.
Diversity also occurs at the level of the antigen. Immune escape of pathogen variants through mutation of immunogenic sequences has to be considered when selecting or designing antigens . In silico generation of mosaic polyvalent antigens tackles this problem . Immunization experiments with primates demonstrated the advantage of mosaic constructs over consensus or natural sequences to elicit T cell responses covering a broad selection of viral clades as well as antigenic immune escape variants that may evolve , . The repertoire of possible immunogens can also be widened by exploring glycan antigens . Whenever constructs are designed, one has to ensure their stability and thus bioavailability. For example, HIV-derived peptides display quite variable half-lifes in the cytosol of human cells and this has an impact on their recognition by CD8+ cells . Structural vaccinology is a powerful emerging approach to optimize immunogens based on atomic-level structural information on requirements for conferring protective immunity –. Upon identification of immunogenic domains, it is possible to design constructs that lack decoy or masking portions of the antigen, such as epitope scaffolds that are able to elicit antibody responses against otherwise immune-recessive, cryptic, or transient epitopes . It is also possible to engineer an optimized structure to enable broadly cross-protective responses. As example, chimeric proteins to effectively vaccinate against group B streptococci or Neisseria meningitidis were generated , .
Subunit vaccines are likely to lack the molecular cues needed for efficient activation of the innate immune system, thereby failing to induce vigorous adaptive immunity. PAMPs can act as adjuvants, however many pathogen-derived products might exhibit toxic activity . The only globally approved adjuvant for humans is alum. It facilitates TH2-dependent immune responses but promotes less effective cytotoxic responses and can cause side effects. A number of other adjuvants have been recently approved for use in defined human vaccines, such as MF59 and monophosphoryl lipid A-containing formulations , , and there are other candidates in the pipeline. Adjuvants are not licensed per se, but as part of vaccine formulations. This together with stringent requirements for reagents used on healthy individuals raise the costs of clinical development . Considerable effort was invested in the development of adjuvants for mucosal immunization . Vaccination via mucosal routes is known to elicit both mucosal and systemic immunity , fighting pathogens at the site of entry. However, safety issues were observed following intranasal vaccination with the heat labile toxin of Escherichia coli and its attenuated derivative , . This will need to be considered for current candidate mucosal adjuvants, among them compounds with well-defined molecular targets, such as PAMPs, cytokines, and cyclic di-nucleotides –. For example, the TLR9-agonist CpG enhanced immune responses after vaccination against hepatitis B, anthrax, influenza, and malaria – and proved promising in vaccination of otherwise nonresponsive immune-compromised organisms . However, many molecular mechanisms of adjuvanticity are still elusive. First insights were gained in receptors and signaling pathways involved in the recognition and processing of pathogenic factors and adjuvants in cells of the innate immune system –. Nevertheless, the discovered mechanisms of adjuvanticity do not translate to generally applicable strategies for rationally designed vaccines (see also ). Hence, to date, adjuvantation requires an additional solid theoretical background for systematic implementation in rational vaccine design.
Antigen Delivery Systems
Delivery systems become necessary when antigens are not efficiently transported to the inductive sites or presented to the immune system. For example, rapid degradation can result in weak or virtually absent responses to otherwise immunogenic antigens. The coding sequence of an antigen can be integrated into a live virus-vector, which infects antigen-presenting cells (APCs), preferentially dendritic cells (DCs) , . The antigen is then directly presented by MHC molecules and can be recognized by TCRs. The continuous antigen expression leads to its persistent exposure to immune cells. Recombinant viral vectors can be modified with regard to effector cell targeting, expression promoters, and the type of antigenic transgene. Lentiviral vectors with improved safety and efficiency parameters have a comparatively high capacity for encoding transgenes, high transduction efficiency, low anti-vector host immunity, low genotoxicity, and persistent gene expression . They proved promising in vaccination of mice with HIV-derived antigens and in nonhuman primates with SIV-derived antigens , . In spite of the adenoviral vaccine vector's known limited efficacy due to preexisting immunity in large populations , , it still induces protective immune responses with characteristic induction of CD8+ T cells in humans . Recombinant adenoviral vectors derived from uncommon human serotypes, chimpanzee or human/chimpanzee chimeras can circumvent the problem of host immunity –. Human cytomegalovirus (hCMV) vaccine vectors are based on the ability of hCMV strains to superinfect individuals with persistent hCMV infection and immunity. Rhesus macaques developed specific CD4+ and CD8+ responses against SIV antigens delivered by a recombinant CMV vector , . Elucidation of the molecular mechanisms leading to memory inflation during chronic hCMV infections might even lead to hCMV-based strategies to trigger life-long responses. Attenuated recombinant poxviruses are also intrinsically immunogenic, and insights in the promoted innate immune responses have accumulated . The above-described vectors have considerable potential in human vaccination, especially in prime-boost regimens aimed at fine-tuning responses . Different attenuated or commensal bacteria have also been successfully exploited for delivering vaccine antigens and biologicals –.
The delivery to DCs can be achieved by coupling antigens to antibodies specific for surface molecules, such as Clec9A. This method leads to antigen uptake and activation of T and B cells . Similarly, fusion proteins of HIV antigens and antibody fragments targeting the DC surface molecule DEC205 elicited potent cellular immunity in nonhuman primates . The risks related to live vectors in immune-compromised individuals can be eliminated by the application of virus-like particles (VLPs) that are reduced to the structures and antigenic components necessary for delivery and immunogenicity. VLPs are able to elicit efficient humoral immune responses –, contributing to the control of infection , . Plasmid DNA vectors can be delivered to cells and elicit humoral responses , as proven by DNA vaccines against seasonal influenza in phase I trials . Synthetic delivery systems, such as nanoparticles, block-copolymers, DNA nanostructures, and nanogels –, can be loaded or coated with specific antigens and adjuvants. In addition, they can be tailored and functionalized according to specific needs (e.g., transcutaneous or mucosal delivery) , . Trials with nanoparticle vaccines for hepatitis B, leishmaniasis, and malaria demonstrated that they enhance immune responses , , . Although often developed on an empirical base, the given examples are a proof-of-principle essential to rationally design such delivery vehicles in the future.
Immune Response Prediction
Understanding what is needed to confer protection without side effects is a prerequisite to develop a tailored intervention. To date, characterization of human responses to vaccination relies mainly on measuring antibody titers or cellular responses from peripheral blood samples. This does not allow a comprehensive analysis of responses with regard to the effector cells or mechanisms stimulated and the status in all relevant compartments for acquired immunity. Efforts to tackle this problem link the regulation of transcription or protein activity to the prediction of vaccination outcomes . Recent reports suggest the potential of systems vaccinology for the analysis of gene expression profiling experiments to identify patterns or signatures linked to a desired outcome of vaccination –. Human studies showed correlations of gene expression profiles or protein expression patterns with immune system activation upon vaccination against yellow fever and influenza in responders and nonresponders –. Others characterized transcription profiles after treatment of mice or murine DCs with adjuvant molecules , . Correlations between successful immunization or toxic events and cellular expression profiles can be predictive for a particular vaccine. However, no general unambiguous markers were identified that would allow accurate prediction of efficacy or safety for vaccines in trials (introduced, for example, in ).
A quite different approach to predict immune responses upon exposure to potential immunogens is realized by the in silico immune system simulator C-ImmSim –. This model features simulation of different classes of B and T lymphocytes, innate immune cells (e.g., DCs and macrophages), and different immune compartments (e.g., bone marrow, thymus and tertiary lymphoid organs). In silico experiments simulate primary immune responses as well as challenge with a particular antigen in different definable MHC allele backgrounds. The proof-of-principle was performed with antigens of HIV or influenza virus that simulate immunization. The simulations could indeed predict observations in humans, for example that affinity maturation and antigenic dominance evolve, and that MHC diversity can have an impact on immune defense . C-ImmSim can be updated whenever improved versions of the incorporated BCR and TCR epitope prediction methods become available. Though currently not successfully applied, the simulator has potential in vaccine development by testing the immunogenicity of antigens and the potency to induce a robust immune response upon challenge with the antigen. It can be also used as a research tool to elucidate mechanisms of immune responses to fill gaps in knowledge that slow down rational design efforts.
A prevalent problem in vaccine translation is the delayed and costly transition from preclinical to clinical development due to difficulties in predicting human immune responses. Although closer to humans, primate models are associated with ethical, logistic, and financial constraints. An emerging alternative is the use of mice humanized for the immune system. Although they still need to be improved, they can be foreseen as powerful tools to predict human-specific immune responses to vaccines, as well as to investigate vaccine efficacy against pathogens with human tropism –.
In this review we elaborate on recent achievements that facilitate rational vaccine design. There are many visions on the expected impact of reverse vaccinology, epitope prediction, structural vaccinology, systems vaccinology, and personalized medicine on the rational design of effective vaccines , , , . However, the implementation of these concepts towards the development of new and more potent vaccines requires time and considerable financial investment. Rational vaccine design will rely strongly on the availability of clinical data on individuals with different clinical forms of disease or response to vaccination to learn what is needed for protection . The gaps in knowledge on the immune system's specific clearance mechanisms against many pathogens slow down the identification of the immune response that should be evoked by tailored vaccines in different population groups (Table 1). Many aspects of the host pathogen interaction and host immune status during persistent infection are also poorly understood, thereby hindering the development of therapeutic vaccines . Further data from trials with empiric formulations are required to identify patterns or biomarkers that can reliably guide prediction of vaccine efficacy and safety at reasonable success rates (Figure 1). A widely accepted goal in vaccine development is the applicability to huge populations, if not all humankind. Nevertheless, there are reasons for more personalized approaches that consider specific preconditions in recipients, such as genetic background, pre-exposure to pathogens or vaccines, unique physiological background related to local culture/habits, age, and immunodeficiency.
Figure 1. Optimizing the design for more efficient vaccines.
Modern vaccinology focuses on the development of subunit vaccines to maximize efficacy and minimize risks in healthy and immune-compromised individuals. Different enabling technologies and knowledge contribute towards the rational design of formulations that would not only exhibit improved performance but also reduce the time and costs associated with preclinical and clinical development. Promising approaches/enabling factors and roadblocks are highlighted in green and pink, respectively.doi:10.1371/journal.ppat.1003001.g001
Table 1. Needs and challenges for the rational design of vaccines.doi:10.1371/journal.ppat.1003001.t001
Implementation of rational development concepts in vaccinology demands patience, and advances will be incremental. Realization will depend on the application of flanking logistic and regulatory measures and the awareness of the strong impact of vaccine development to solve global health problems. Funding is also required for the basic research needed to provide the basis for rationally developed vaccines. However, we expect to see the advent of new and more efficient vaccines in the coming years as a result of the implementation of this emerging knowledge and enabling technologies.
We are grateful to Dr. Pablo D. Becker and Dr. Blair Prochnow for critical reading of the manuscript and helpful discussions.
- 1. Theiler M, Smith HH (1937) The use of yellow fever virus modified by in vitro cultivation for human immunization. J Exp Med 65: 787–800. doi: 10.1084/jem.65.6.787
- 2. Poland JD, Calisher CH, Monath TP, Downs WG, Murphy K (1981) Persistence of neutralizing antibody 30–35 years after immunization with 17D yellow fever vaccine. Bull World Health Organ 59: 895–900.
- 3. Sette A, Rappuoli R (2010) Reverse vaccinology: developing vaccines in the era of genomics. Immunity 33: 530–541. doi: 10.1016/j.immuni.2010.09.017
- 4. Bagnoli F, Baudner B, Mishra RP, Bartolini E, Fiaschi L, et al. (2011) Designing the next generation of vaccines for global public health. OMICS 15: 545–566. doi: 10.1089/omi.2010.0127
- 5. Poland GA, Kennedy RB, Ovsyannikova IG (2011) Vaccinomics and personalized vaccinology: is science leading us toward a new path of directed vaccine development and discovery? PLoS Pathog 7: e1002344 doi:10.1371/journal.ppat.1002344.
- 6. Kennedy RB, Poland GA (2011) The top five “game changers” in vaccinology: toward rational and directed vaccine development. OMICS 15: 533–537. doi: 10.1089/omi.2011.0012
- 7. Ponomarenko JV, Regenmortel MHVv (2009) B-cell epitope prediction. In: Gu J, Bourne PE, editors. Structural bioinformatics, second ed. John Wiley & Sons, Inc.
- 8. Blythe MJ, Flower DR (2005) Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci 14: 246–248. doi: 10.1110/ps.041059505
- 9. El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear B-cell epitopes using string kernels. J Mol Recognit 21: 243–255. doi: 10.1002/jmr.893
- 10. El-Manzalawy Y, Dobbs D, Honovar V (2008) BCPREDS: B-cell epitope prediction server. Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University of Science and Technology. Available: http://ailab.cs.iastate.edu/bcpreds/. Accessed August 2012.
- 11. Wee LJ, Simarmata D, Kam YW, Ng LF, Tong JC (2010) SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction. BMC Genomics 11 Suppl 4: S21. doi: 10.1186/1471-2164-11-s4-s21
- 12. Wee LJ, Simarmata D, Kam YW, Ng HL, Tong JC (2010) BayesB: server for SVM prediction of linear B-cell epitopes using Bayes Feature Extraction. Available: http://www.immunopred.org/bayesb/index.html. Accessed August 2012.
- 13. Moreau V, Fleury C, Piquer D, Nguyen C, Novali N, et al. (2008) PEPOP: computational design of immunogenic peptides. BMC Bioinformatics 9: 71. doi: 10.1186/1471-2105-9-71
- 14. Moreau V, Fleury C, Piquer D, Nguyen C, Novali N, et al.. (2008) PEPOP. Available: http://pepop.sysdiag.cnrs.fr/PEPOP/. Accessed August 2012.
- 15. Lundegaard C, Lund O, Buus S, Nielsen M (2010) Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology 130: 309–318. doi: 10.1111/j.1365-2567.2010.03300.x
- 16. Li Pira G, Ivaldi F, Moretti P, Manca F (2010) High throughput T epitope mapping and vaccine development. J Biomed Biotechnol 2010: 325720. doi: 10.1155/2010/325720
- 17. Wang P, Sidney J, Kim Y, Sette A, Lund O, et al. (2010) Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 11: 568. doi: 10.1186/1471-2105-11-568
- 18. Tung CW, Ziehm M, Kamper A, Kohlbacher O, Ho SY (2011) POPISK: T-cell reactivity prediction using support vector machines and string kernels. BMC Bioinformatics 12: 446. doi: 10.1186/1471-2105-12-446
- 19. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, et al. (2010) The immune epitope database 2.0. Nucleic Acids Res 38: D854–D862. doi: 10.1093/nar/gkp1004
- 20. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, et al.. (2009) Immune Epitope Database and Analysis Resource. Available: http://www.immuneepitope.org/. Accessed August 2012.
- 21. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50: 213–219. doi: 10.1007/s002510050595
- 22. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) Available: http://www.syfpeithi.de/. SYFPEITHI: a database of MHC ligands and peptide motifs. Accessed August 2012.
- 23. Toussaint NC, Kohlbacher O (2009) OptiTope–a web server for the selection of an optimal set of peptides for epitope-based vaccines. Nucleic Acids Res 37: W617–W622. doi: 10.1093/nar/gkp293
- 24. Toussaint NC, Kohlbacher O (2009) Available: http://www.epitoolkit.org/optitope. Accessed August 2012.
- 25. Karosiene E, Lundegaard C, Lund O, Nielsen M (2012) NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64: 177–186. doi: 10.1007/s00251-011-0579-8
- 26. Karosiene E, Lundegaard C, Lund O, Nielsen HM (2012) NetMHCcons 1.0 server. Available: http://www.cbs.dtu.dk/services/NetMHCcons/. Accessed August 2012.
- 27. Kaur K, Sullivan M, Wilson PC (2011) Targeting B cell responses in universal influenza vaccine design. Trends Immunol 32: 524–531. doi: 10.1016/j.it.2011.08.007
- 28. Fischer W, Perkins S, Theiler J, Bhattacharya T, Yusim K, et al. (2007) Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants. Nat Med 13: 100–106. doi: 10.1038/nm1461
- 29. Barouch DH, O'Brien KL, Simmons NL, King SL, Abbink P, et al. (2010) Mosaic HIV-1 vaccines expand the breadth and depth of cellular immune responses in rhesus monkeys. Nat Med 16: 319–323. doi: 10.1038/nm.2089
- 30. Santra S, Liao HX, Zhang R, Muldoon M, Watson S, et al. (2010) Mosaic vaccines elicit CD8+ T lymphocyte responses that confer enhanced immune coverage of diverse HIV strains in monkeys. Nat Med 16: 324–328. doi: 10.1038/nm.2108
- 31. Clo E, Kracun SK, Nudelman AS, Jensen KJ, Liljeqvist JA, et al. (2012) Characterization of the viral O-glycopeptidome: a novel tool of relevance for vaccine design and serodiagnosis. J Virol 86: 6268–6278. doi: 10.1128/jvi.00392-12
- 32. Lazaro E, Kadie C, Stamegna P, Zhang SC, Gourdain P, et al. (2011) Variable HIV peptide stability in human cytosol is critical to epitope presentation and immune escape. J Clin Invest 121: 2480–2492. doi: 10.1172/jci44932
- 33. Dimitrov JD, Kazatchkine MD, Kaveri SV, Lacroix-Desmazes S (2011) “Rational vaccine design” for HIV should take into account the adaptive potential of polyreactive antibodies. PLoS Pathog 7: e1002095 doi:10.1371/journal.ppat.1002095.
- 34. Wang Y, Keck ZY, Foung SK (2011) Neutralizing antibody response to hepatitis C virus. Viruses 3: 2127–2145. doi: 10.3390/v3112127
- 35. Haynes BF, Kelsoe G, Harrison SC, Kepler TB (2012) B-cell-lineage immunogen design in vaccine development with HIV-1 as a case study. Nat Biotechnol 30: 423–433. doi: 10.1038/nbt.2197
- 36. Ofek G, Guenaga FJ, Schief WR, Skinner J, Baker D, et al. (2010) Elicitation of structure-specific antibodies by epitope scaffolds. Proc Natl Acad Sci U S A 107: 17880–17887. doi: 10.1073/pnas.1004728107
- 37. Nuccitelli A, Cozzi R, Gourlay LJ, Donnarumma D, Necchi F, et al. (2011) Structure-based approach to rationally design a chimeric protein for an effective vaccine against Group B Streptococcus infections. Proc Natl Acad Sci U S A 108: 10278–10283. doi: 10.1073/pnas.1106590108
- 38. Scarselli M, Arico B, Brunelli B, Savino S, Di Marcello F, et al. (2011) Rational design of a meningococcal antigen inducing broad protective immunity. Sci Transl Med 3: 91ra62. doi: 10.1126/scitranslmed.3002234
- 39. Adkins I, Holubova J, Kosova M, Sadilkova L (2012) Bacteria and their toxins tamed for immunotherapy. Curr Pharm Biotechnol 13: 1446–1473. doi: 10.2174/138920112800784835
- 40. Montomoli E, Piccirella S, Khadang B, Mennitto E, Camerini R, et al. (2011) Current adjuvants and new perspectives in vaccine formulation. Expert Rev Vaccines 10: 1053–1061. doi: 10.1586/erv.11.48
- 41. Harandi AM, Davies G, Olesen OF (2009) Vaccine adjuvants: scientific challenges and strategic initiatives. Expert Rev Vaccines 8: 293–298. doi: 10.1586/147605126.96.36.1993
- 42. Harandi AM, Medaglini D, Shattock RJ (2010) Vaccine adjuvants: a priority for vaccine research. Vaccine 28: 2363–2366. doi: 10.1016/j.vaccine.2009.12.084
- 43. Harandi AM, Medaglini D (2010) Mucosal adjuvants. Curr HIV Res 8: 330–335. doi: 10.2174/157016210791208695
- 44. Mutsch M, Zhou W, Rhodes P, Bopp M, Chen RT, et al. (2004) Use of the inactivated intranasal influenza vaccine and the risk of Bell's palsy in Switzerland. N Engl J Med 350: 896–903. doi: 10.1056/nejmoa030595
- 45. Hemmi H, Takeuchi O, Kawai T, Kaisho T, Sato S, et al. (2000) A Toll-like receptor recognizes bacterial DNA. Nature 408: 740–745. doi: 10.1038/35047123
- 46. Libanova R, Becker PD, Guzman CA (2012) Cyclic di-nucleotides: new era for small molecules as adjuvants. Microb Biotechnol 5: 168–176. doi: 10.1111/j.1751-7915.2011.00306.x
- 47. Thompson AL, Staats HF (2011) Cytokines: the future of intranasal vaccine adjuvants. Clin Dev Immunol 2011: 289597. doi: 10.1155/2011/289597
- 48. Asanuma H, Zamri NB, Sekine S, Fukuyama Y, Tokuhara D, et al. (2012) A novel combined adjuvant for nasal delivery elicits mucosal immunity to influenza in aging. Vaccine 30: 803–812. doi: 10.1016/j.vaccine.2011.10.093
- 49. Chiang CY, Huang MH, Hsieh CH, Chen MY, Liu HH, et al. (2012) Dengue-1 envelope protein domain III along with PELC and CpG oligodeoxynucleotides synergistically enhances immune responses. PLoS Negl Trop Dis 6: e1645 doi:10.1371/journal.pntd.0001645.
- 50. Krieg AM (2012) CpG still rocks! Update on an accidental drug. Nucleic Acid Ther 22: 77–89.
- 51. Shafique M, Wilschut J, de Haan A (2012) Induction of mucosal and systemic immunity against respiratory syncytial virus by inactivated virus supplemented with TLR9 and NOD2 ligands. Vaccine 30: 597–606. doi: 10.1016/j.vaccine.2011.11.054
- 52. Bode C, Zhao G, Steinhagen F, Kinjo T, Klinman DM (2011) CpG DNA as a vaccine adjuvant. Expert Rev Vaccines 10: 499–511. doi: 10.1586/erv.10.174
- 53. Akira S (2011) Innate immunity and adjuvants. Philos Trans R Soc Lond B Biol Sci 366: 2748–2755.
- 54. Kwissa M, Kasturi SP, Pulendran B (2007) The science of adjuvants. Expert Rev Vaccines 6: 673–684. doi: 10.1586/147605188.8.131.523
- 55. Pulendran B, Ahmed R (2011) Immunological mechanisms of vaccination. Nat Immunol 12: 509–517. doi: 10.1038/ni.2039
- 56. Brave A, Ljungberg K, Wahren B, Liu MA (2007) Vaccine delivery methods using viral vectors. Mol Pharm 4: 18–32. doi: 10.1021/mp060098+
- 57. Liu MA (2010) Immunologic basis of vaccine vectors. Immunity 33: 504–515. doi: 10.1016/j.immuni.2010.10.004
- 58. Hu B, Tai A, Wang P (2011) Immunization delivered by lentiviral vectors for cancer and infectious diseases. Immunol Rev 239: 45–61. doi: 10.1111/j.1600-065x.2010.00967.x
- 59. Buffa V, Negri DR, Leone P, Bona R, Borghi M, et al. (2006) A single administration of lentiviral vectors expressing either full-length human immunodeficiency virus 1 (HIV-1)(HXB2) Rev/Env or codon-optimized HIV-1(JR-FL) gp120 generates durable immune responses in mice. J Gen Virol 87: 1625–1634. doi: 10.1099/vir.0.81706-0
- 60. Buffa V, Negri DR, Leone P, Borghi M, Bona R, et al. (2006) Evaluation of a self-inactivating lentiviral vector expressing simian immunodeficiency virus gag for induction of specific immune responses in vitro and in vivo. Viral Immunol 19: 690–701. doi: 10.1089/vim.2006.19.690
- 61. Frahm N, DeCamp AC, Friedrich DP, Carter DK, Defawe OD, et al. (2012) Human adenovirus-specific T cells modulate HIV-specific T cell responses to an Ad5-vectored HIV-1 vaccine. J Clin Invest 122: 359–367. doi: 10.1172/jci60202
- 62. Buchbinder SP, Mehrotra DV, Duerr A, Fitzgerald DW, Mogg R, et al. (2008) Efficacy assessment of a cell-mediated immunity HIV-1 vaccine (the Step Study): a double-blind, randomised, placebo-controlled, test-of-concept trial. Lancet 372: 1881–1893. doi: 10.1016/s0140-6736(08)61591-3
- 63. Bassett JD, Swift SL, Bramson JL (2011) Optimizing vaccine-induced CD8(+) T-cell immunity: focus on recombinant adenovirus vectors. Expert Rev Vaccines 10: 1307–1319. doi: 10.1586/erv.11.88
- 64. Dudareva M, Andrews L, Gilbert SC, Bejon P, Marsh K, et al. (2009) Prevalence of serum neutralizing antibodies against chimpanzee adenovirus 63 and human adenovirus 5 in Kenyan children, in the context of vaccine vector efficacy. Vaccine 27: 3501–3504. doi: 10.1016/j.vaccine.2009.03.080
- 65. Tatsis N, Blejer A, Lasaro MO, Hensley SE, Cun A, et al. (2007) A CD46-binding chimpanzee adenovirus vector as a vaccine carrier. Mol Ther 15: 608–617. doi: 10.1038/sj.mt.6300078
- 66. von Bubnoff A (2012) Adenovirus vectors: promise and possible pitfalls. IAVI Rep 16: 4–7.
- 67. Hansen SG, Powers CJ, Richards R, Ventura AB, Ford JC, et al. (2010) Evasion of CD8+ T cells is critical for superinfection by cytomegalovirus. Science 328: 102–106. doi: 10.1126/science.1185350
- 68. Hansen SG, Ford JC, Lewis MS, Ventura AB, Hughes CM, et al. (2011) Profound early control of highly pathogenic SIV by an effector memory T-cell vaccine. Nature 473: 523–527. doi: 10.1038/nature10003
- 69. Lousberg EL, Diener KR, Brown MP, Hayball JD (2011) Innate immune recognition of poxviral vaccine vectors. Expert Rev Vaccines 10: 1435–1449. doi: 10.1586/erv.11.121
- 70. Rollier CS, Reyes-Sandoval A, Cottingham MG, Ewer K, Hill AV (2011) Viral vectors as vaccine platforms: deployment in sight. Curr Opin Immunol 23: 377–382. doi: 10.1016/j.coi.2011.03.006
- 71. Link C, Ebensen T, Standner L, Dejosez M, Reinhard E, et al. (2006) An SopB-mediated immune escape mechanism of Salmonella enterica can be subverted to optimize the performance of live attenuated vaccine carrier strains. Microbes Infect 8: 2262–2269. doi: 10.1016/j.micinf.2006.04.013
- 72. Juarez-Rodriguez MD, Arteaga-Cortes LT, Kader R, Curtiss R 3rd, Clark-Curtiss JE (2012) Live attenuated Salmonella vaccines against Mycobacterium tuberculosis with antigen delivery via the type III secretion system. Infect Immun 80: 798–814. doi: 10.1128/iai.05525-11
- 73. Juarez-Rodriguez MD, Yang J, Kader R, Alamuri P, Curtiss R 3rd, et al. (2012) Live attenuated Salmonella vaccines displaying regulated delayed lysis and delayed antigen synthesis to confer protection against Mycobacterium tuberculosis. Infect Immun 80: 815–831. doi: 10.1128/iai.05526-11
- 74. Galen JE, Simon R, Ernst RK (2011) Salmonella expressing detoxified lipopolysaccharide is immunogenic and protective both as an attenuated vaccine and for delivery of foreign antigens. Expert Rev Vaccines 10: 1679–1682. doi: 10.1586/erv.11.155
- 75. Johnson PV, Blair BM, Zeller S, Kotton CN, Hohmann EL (2011) Attenuated Listeria monocytogenes vaccine vectors expressing influenza A nucleoprotein: preclinical evaluation and oral inoculation of volunteers. Microbiol Immunol 55: 304–317. doi: 10.1111/j.1348-0421.2011.00322.x
- 76. Caminschi I, Shortman K (2012) Boosting antibody responses by targeting antigens to dendritic cells. Trends Immunol 33: 71–77. doi: 10.1016/j.it.2011.10.007
- 77. Flynn BJ, Kastenmuller K, Wille-Reece U, Tomaras GD, Alam M, et al. (2011) Immunization with HIV Gag targeted to dendritic cells followed by recombinant New York vaccinia virus induces robust T-cell immunity in nonhuman primates. Proc Natl Acad Sci U S A 108: 7131–7136. doi: 10.1073/pnas.1103869108
- 78. Giles BM, Crevar CJ, Carter DM, Bissel SJ, Schultz-Cherry S, et al. (2012) A computationally optimized hemagglutinin virus-like particle vaccine elicits broadly reactive antibodies that protect nonhuman primates from H5N1 infection. J Infect Dis 205: 1562–1570. doi: 10.1093/infdis/jis232
- 79. Ramirez K, Wahid R, Richardson C, Bargatze RF, El-Kamary SS, et al. (2012) Intranasal vaccination with an adjuvanted Norwalk virus-like particle vaccine elicits antigen-specific B memory responses in human adult volunteers. Clin Immunol 144: 98–108. doi: 10.1016/j.clim.2012.05.006
- 80. Ruiss R, Jochum S, Wanner G, Reisbach G, Hammerschmidt W, et al. (2011) A virus-like particle-based Epstein-Barr virus vaccine. J Virol 85: 13105–13113. doi: 10.1128/jvi.05598-11
- 81. Pastori C, Tudor D, Diomede L, Drillet AS, Jegerlehner A, et al. (2012) Virus like particle based strategy to elicit HIV-protective antibodies to the alpha-helic regions of gp41. Virology 431: 1–11. doi: 10.1016/j.virol.2012.05.005
- 82. Ferraro B, Morrow MP, Hutnick NA, Shin TH, Lucke CE, et al. (2011) Clinical applications of DNA vaccines: current progress. Clin Infect Dis 53: 296–302. doi: 10.1093/cid/cir334
- 83. Smith LR, Wloch MK, Ye M, Reyes LR, Boutsaboualoy S, et al. (2010) Phase 1 clinical trials of the safety and immunogenicity of adjuvanted plasmid DNA vaccines encoding influenza A virus H5 hemagglutinin. Vaccine 28: 2565–2572. doi: 10.1016/j.vaccine.2010.01.029
- 84. Nochi T, Yuki Y, Takahashi H, Sawada S, Mejima M, et al. (2010) Nanogel antigenic protein-delivery system for adjuvant-free intranasal vaccines. Nat Mater 9: 572–578. doi: 10.1038/nmat2784
- 85. Hansen S, Lehr CM (2012) Nanoparticles for transcutaneous vaccination. Microb Biotechnol 5: 156–167. doi: 10.1111/j.1751-7915.2011.00284.x
- 86. Liu X, Xu Y, Yu T, Clifford C, Liu Y, et al. (2012) A DNA nanostructure platform for directed assembly of synthetic vaccines. Nano Lett 12: 4254–4259. doi: 10.1021/nl301877k
- 87. Tafaghodi M, Saluja V, Kersten GF, Kraan H, Slutter B, et al. (2012) Hepatitis B surface antigen nanoparticles coated with chitosan and trimethyl chitosan: impact of formulation on physicochemical and immunological characteristics. Vaccine 30: 5341–5348. doi: 10.1016/j.vaccine.2012.06.035
- 88. Li N, Peng LH, Chen X, Nakagawa S, Gao JQ (2011) Transcutaneous vaccines: novel advances in technology and delivery for overcoming the barriers. Vaccine 29: 6179–6190. doi: 10.1016/j.vaccine.2011.06.086
- 89. Moon JJ, Suh H, Li AV, Ockenhouse CF, Yadava A, et al. (2012) Enhancing humoral responses to a malaria antigen with nanoparticle vaccines that expand Tfh cells and promote germinal center induction. Proc Natl Acad Sci U S A 109: 1080–1085. doi: 10.1073/pnas.1112648109
- 90. Doroud D, Rafati S (2012) Leishmaniasis: focus on the design of nanoparticulate vaccine delivery systems. Expert Rev Vaccines 11: 69–86. doi: 10.1586/erv.11.166
- 91. Poland GA, Ovsyannikova IG, Kennedy RB, Haralambieva IH, Jacobson RM (2011) Vaccinomics and a new paradigm for the development of preventive vaccines against viral infections. OMICS 15: 625–636. doi: 10.1089/omi.2011.0032
- 92. Haining WN, Wherry EJ (2010) Integrating genomic signatures for immunologic discovery. Immunity 32: 152–161. doi: 10.1016/j.immuni.2010.02.001
- 93. De Gregorio E, Rappuoli R (2012) Vaccines for the future: learning from human immunology. Microb Biotechnol 5: 149–155. doi: 10.1111/j.1751-7915.2011.00276.x
- 94. Six A, Bellier B, Thomas-Vaslin V, Klatzmann D (2012) Systems biology in vaccine design. Microb Biotechnol 5: 295–304. doi: 10.1111/j.1751-7915.2011.00321.x
- 95. Gaucher D, Therrien R, Kettaf N, Angermann BR, Boucher G, et al. (2008) Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J Exp Med 205: 3119–3131. doi: 10.1084/jem.20082292
- 96. Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, et al. (2009) Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 10: 116–125. doi: 10.1038/ni.1688
- 97. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, et al. (2011) Systems biology of vaccination for seasonal influenza in humans. Nat Immunol 12: 786–795. doi: 10.1038/ni.2067
- 98. Amit I, Garber M, Chevrier N, Leite AP, Donner Y, et al. (2009) Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326: 257–263. doi: 10.1126/science.1179050
- 99. Lindqvist M, Nookaew I, Brinkenberg I, Samuelson E, Thorn K, et al. (2011) Unraveling molecular signatures of immunostimulatory adjuvants in the female genital tract through systems biology. PLoS One 6: e20448 doi:10.1371/journal.pone.0020448.
- 100. Rapin N, Lund O, Bernaschi M, Castiglione F (2010) Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 5: e9862 doi:10.1371/journal.pone.0009862.
- 101. Rapin N, Lund O, Castiglione F (2011) Immune system simulation online. Bioinformatics 27: 2013–2014. doi: 10.1093/bioinformatics/btr335
- 102. Rapin N, Lund O, Castiglione F (2011) C-Immsim 10.1 server. Available: http://www.cbs.dtu.dk/services/C-ImmSim-10.1/. Accessed August 2012.
- 103. Legrand N, Ploss A, Balling R, Becker PD, Borsotti C, et al. (2009) Humanized mice for modeling human infectious disease: challenges, progress, and outlook. Cell Host Microbe 6: 5–9. doi: 10.1016/j.chom.2009.06.006
- 104. Legrand N, Weijer K, Spits H (2006) Experimental models to study development and function of the human immune system in vivo. J Immunol 176: 2053–2058. doi: 10.4049/jimmunol.176.4.2053
- 105. Becker PD, Legrand N, van Geelen CM, Noerder M, Huntington ND, et al. (2010) Generation of human antigen-specific monoclonal IgM antibodies using vaccinated “human immune system” mice. PLoS One 5: e13137 doi:10.1371/journal.pone.0013137.
- 106. Oberg AL, Kennedy RB, Li P, Ovsyannikova IG, Poland GA (2011) Systems biology approaches to new vaccine development. Curr Opin Immunol 23: 436–443. doi: 10.1016/j.coi.2011.04.005
- 107. Andersen-Nissen E, Heit A, McElrath MJ (2012) Profiling immunity to HIV vaccines with systems biology. Curr Opin HIV AIDS 7: 32–37. doi: 10.1097/coh.0b013e32834ddcd9
- 108. Hilleman MR (2004) Strategies and mechanisms for host and pathogen survival in acute and persistent viral infections. Proc Natl Acad Sci U S A 101 Suppl 2: 14560–14566. doi: 10.1073/pnas.0404758101