# Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines

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## Abstract

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## 1. Introduction

## 2. Model

#### 2.1. Parameter Fitting

#### 2.2. Sensitivity Analysis

## 3. Results

#### 3.1. Sensitivity Analysis

#### 3.2. Mechanism of Vaccine-Induced Immunity with Booster Delay and Sparing

#### 3.2.1. Antibody and Cytotoxic T-Cell Responses

#### 3.2.2. Cytokines, B and Th${}_{0}$ Cell Responses

#### 3.2.3. Protective Capacity

## 4. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

APC | Antigen-Presenting Cell |

MHC | Major Histocompatibility Complex |

$T{h}_{0}$ | T Helper Cell Type 0 |

$T{h}_{1}$ | T Helper Cell Type 1 |

$T{h}_{2}$ | T Helper Cell Type 2 |

NK | Natural Killer Cell |

CTL | Cytotoxic T Lymphocyte |

IL | Interleukin |

$IFN$ | Interferon |

$TGF$ | Transforming Growth Factor |

$NAb$ | Neutralizing Antibody |

$IgG$ | Immunoglobulin G |

## References

- Ramasamy, M.N.; Minassian, A.M.; Ewer, K.J.; Flaxman, A.L.; Folegatti, P.M.; Owens, D.R.; Voysey, M.; Aley, P.K.; Angus, B.; Babbage, G.; et al. Safety and immunogenicity of ChAdOx1 nCoV-19 vaccine administered in a prime-boost regimen in young and old adults (COV002): A single-blind, randomised, controlled, phase 2/3 trial. Lancet
**2020**, 396, 1979–1993. [Google Scholar] [CrossRef] - Quinn, K.M.; Zak, D.E.; Costa, A.; Yamamoto, A.; Kastenmuller, K.; Hill, B.J.; Lynn, G.M.; Darrah, P.A.; Lindsay, R.W.; Wang, L.; et al. Antigen expression determines adenoviral vaccine potency independent of IFN and STING signaling. J. Clin. Investig.
**2015**, 125, 1129–1146. [Google Scholar] [CrossRef] [Green Version] - Wu, F.; Wang, A.; Liu, M.; Wang, Q.; Chen, J.; Xia, S.; Ling, Y.; Zhang, Y.; Xun, J.; Lu, L.; et al. Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications. medRxiv
**2020**. [Google Scholar] [CrossRef] - Zhang, L. Multi-epitope vaccines: A promising strategy against tumors and viral infections. Cell. Mol. Immunol.
**2018**, 15, 182–184. [Google Scholar] [CrossRef] [Green Version] - Kar, T.; Narsaria, U.; Basak, S.; Deb, D.; Castiglione, F.; Mueller, D.M.; Srivastava, A.P. A candidate multi-epitope vaccine against SARS-CoV-2. Sci. Rep.
**2020**, 10, 1–24. [Google Scholar] [CrossRef] [PubMed] - Estrada, E. COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. Phys. Rep.
**2020**. [Google Scholar] [CrossRef] [PubMed] - Janeway, C.A., Jr.; Travers, P.; Walport, M.; Shlomchik, M.J. The complement system and innate immunity. In Immunobiology: The Immune System in Health and Disease, 5th ed.; Garland Science: New York, NY, USA, 2001. [Google Scholar]
- Clem, A.S. Fundamentals of vaccine immunology. J. Glob. Infect. Dis.
**2011**, 3, 73. [Google Scholar] [CrossRef] [PubMed] - Lees, J.R. Interferon gamma in autoimmunity: A complicated player on a complex stage. Cytokine
**2015**, 74, 18–26. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Costela-Ruiz, V.J.; Illescas-Montes, R.; Puerta-Puerta, J.M.; Ruiz, C.; Melguizo-Rodríguez, L. SARS-CoV-2 infection: The role of cytokines in COVID-19 disease. Cytokine Growth Factor Rev.
**2020**. [Google Scholar] [CrossRef] [PubMed] - Morris, R.; Kershaw, N.J.; Babon, J.J. The molecular details of cytokine signaling via the JAK/STAT pathway. Protein Sci.
**2018**, 27, 1984–2009. [Google Scholar] [CrossRef] [Green Version] - Kishimoto, T. Interleukin-6: Discovery of a pleiotropic cytokine. Arthritis Res. Ther.
**2006**, 8, 1–6. [Google Scholar] [CrossRef] [Green Version] - Velazquez-Salinas, L.; Verdugo-Rodriguez, A.; Rodriguez, L.L.; Borca, M.V. The role of interleukin 6 during viral infections. Front. Microbiol.
**2019**, 10, 1057. [Google Scholar] [CrossRef] [Green Version] - Akira, S.; Kishimoto, T. IL-6 and NF-IL6 in acute-phase response and viral infection. Immunol. Rev.
**1992**, 127, 25–50. [Google Scholar] [CrossRef] - Tanaka, T.; Narazaki, M.; Kishimoto, T. IL-6 in inflammation, immunity, and disease. Cold Spring Harb. Perspect. Biol.
**2014**, 6, a016295. [Google Scholar] [CrossRef] - Miller, J.P.; Mitchell, G. Cell to cell interaction in the immune response: I. Hemolysin-forming cells in neonatally thymectomized mice reconstituted with thymus or thoracic duct lymphocytes. J. Exp. Med.
**1968**, 128, 801–820. [Google Scholar] [CrossRef] - Kopf, M.; Baumann, H.; Freer, G.; Freudenberg, M.; Lamers, M.; Kishimoto, T.; Zinkernagel, R.; Bluethmann, H.; Köhler, G. Impaired immune and acute-phase responses in interleukin-6-deficient mice. Nature
**1994**, 368, 339–342. [Google Scholar] [CrossRef] [PubMed] - Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y.; et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet
**2020**, 395, 507–513. [Google Scholar] [CrossRef] [Green Version] - Wenjun, W.; Xiaoqing, L.; Sipei, W.; Puyi, L.; Liyan, H.; Yimin, L.; Linling, C.; Sibei, C.; Lingbo, N.; Yongping, L.; et al. The definition and risks of Cytokine Release Syndrome-Like in 11 COVID-19-Infected Pneumonia critically ill patients: Disease Characteristics and Retrospective Analysis. MedRxiv
**2020**. [Google Scholar] [CrossRef] [Green Version] - Mehta, P.; McAuley, D.F.; Brown, M.; Sanchez, E.; Tattersall, R.S.; Manson, J.J. COVID-19: Consider cytokine storm syndromes and immunosuppression. Lancet
**2020**, 395, 1033–1034. [Google Scholar] [CrossRef] - Chen, L.; Liu, H.; Liu, W.; Liu, J.; Liu, K.; Shang, J.; Deng, Y.; Wei, S. Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi = Zhonghua Jiehe He HUXI Zazhi= Chin. J. Tuberc. Respir. Dis.
**2020**, 43, E005. [Google Scholar] - Chu, H.; Chan, J.F.W.; Wang, Y.; Yuen, T.T.T.; Chai, Y.; Hou, Y.; Shuai, H.; Yang, D.; Hu, B.; Huang, X.; et al. Comparative replication and immune activation profiles of SARS-CoV-2 and SARS-CoV in human lungs: An ex vivo study with implications for the pathogenesis of COVID-19. Clin. Infect. Dis.
**2020**, 71, 1400–1409. [Google Scholar] [CrossRef] [Green Version] - Diao, B.; Wang, C.; Tan, Y.; Chen, X.; Liu, Y.; Ning, L.; Chen, L.; Li, M.; Liu, Y.; Wang, G.; et al. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front. Immunol.
**2020**, 11, 827. [Google Scholar] [CrossRef] - Dong, L.; Tian, J.; He, S.; Zhu, C.; Wang, J.; Liu, C.; Yang, J. Possible vertical transmission of SARS-CoV-2 from an infected mother to her newborn. Jama
**2020**, 323, 1846–1848. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Luo, P.; Liu, Y.; Qiu, L.; Liu, X.; Liu, D.; Li, J. Tocilizumab treatment in COVID-19: A single center experience. J. Med. Virol.
**2020**, 92, 814–818. [Google Scholar] [CrossRef] [PubMed] - Ma, J.; Xia, P.; Zhou, Y.; Liu, Z.; Zhou, X.; Wang, J.; Li, T.; Yan, X.; Chen, L.; Zhang, S.; et al. Potential effect of blood purification therapy in reducing cytokine storm as a late complication of critically ill COVID-19. Clin. Immunol.
**2020**, 214, 108408. [Google Scholar] [CrossRef] [PubMed] - Pedersen, S.F.; Ho, Y.C. SARS-CoV-2: A storm is raging. J. Clin. Investig.
**2020**, 130, 2202–2205. [Google Scholar] [CrossRef] [PubMed] - Ruan, Q.; Yang, K.; Wang, W.; Jiang, L.; Song, J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med.
**2020**, 46, 846–848. [Google Scholar] [CrossRef] [Green Version] - Sun, D.; Li, H.; Lu, X.X.; Xiao, H.; Ren, J.; Zhang, F.R.; Liu, Z.S. Clinical features of severe pediatric patients with coronavirus disease 2019 in Wuhan: A single center’s observational study. World J. Pediatr.
**2020**, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Wang, Z.; Yang, B.; Li, Q.; Wen, L.; Zhang, R. Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China. Clin. Infect. Dis.
**2020**, 71, 769–777. [Google Scholar] [CrossRef] [Green Version] - Wu, C.; Chen, X.; Cai, Y.; Zhou, X.; Xu, S.; Huang, H.; Zhang, L.; Zhou, X.; Du, C.; Zhang, Y.; et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern. Med.
**2020**, 180, 934–943. [Google Scholar] [CrossRef] [Green Version] - Yang, Y.; Shen, C.; Li, J.; Yuan, J.; Yang, M.; Wang, F.; Li, G.; Li, Y.; Xing, L.; Peng, L.; et al. Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcome. MedRxiv
**2020**. [Google Scholar] [CrossRef] [Green Version] - Eagar, T.N.; Miller, S.D. 16 - Helper T-Cell Subsets and Control of the Inflammatory Response. In Clinical Immunology, 5th ed.; Rich, R.R., Fleisher, T.A., Shearer, W.T., Schroeder, H.W., Frew, A.J., Weyand, C.M., Eds.; Elsevier: London, UK, 2019; pp. 235–245.e1. [Google Scholar]
- Nezlin, R. The Immunoglobulins: Structure and Function; Academic Press: Cambridge, MA, USA, 1998. [Google Scholar]
- Schroeder, H.W., Jr.; Cavacini, L. Structure and function of immunoglobulins. J. Allergy Clin. Immunol.
**2010**, 125, S41–S52. [Google Scholar] [CrossRef] [PubMed] [Green Version] - McKaya, M.; Beckmana, R.; Conoverb, W. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics
**1979**, 21, 239–245. [Google Scholar] - Blower, S.M.; Hartel, D.; Dowlatabadi, H.; Anderson, R.M.; May, R.M. Drugs, sex and HIV: A mathematical model for New York City. In Philosophical Transactions of the Royal Society of London; Series B: Biological Sciences; Royal Society: London, UK, 1991; Volume 331, pp. 171–187. [Google Scholar]
- Wu, J.; Dhingra, R.; Gambhir, M.; Remais, J.V. Sensitivity analysis of infectious disease models: Methods, advances and their application. J. R. Soc. Interface
**2013**, 10, 20121018. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Gomero, B. Latin Hypercube Sampling and Partial Rank Correlation Coefficient Analysis Applied to an Optimal Control Problem. Ph.D. Thesis, University of Tennessee, Knoxville, TN, USA, 2012. [Google Scholar]
- Sanchez, S.; Palacio, N.; Dangi, T.; Ciucci, T.; Penaloza-MacMaster, P. Limiting the priming dose of a SARS CoV-2 vaccine improves virus-specific immunity. bioRxiv
**2021**. [Google Scholar] [CrossRef] - Geoffroy, F.; Traulsen, A.; Uecker, H. Vaccination strategies when vaccines are scarce: On conflicts between reducing the burden and avoiding the evolution of escape mutants. medRxiv
**2021**. [Google Scholar] [CrossRef]

**Figure 1.**Vaccine-induced immune activation pathway for an adenovirus vaccine. Faint background: The subsequent downstream of signaling pathways activated through adaptive immunity when SARS-CoV-2 enters the human cell. Highlighted compartments describe vector-based vaccine-induced immune system stimulation that is modeled explicitly in this study. The dashed arrows show implicit communications between cells and cytokines, and the only solid arrow indicates the production of antibodies by B-cells.

**Figure 2.**Antibody and IFN$\gamma $ fit to the clinical trial data [1]. Blue and red solid lines: predicted results for participants who received one (blue) or two doses (red), with a boost dose at day 28 (shown by black vertical dashed lines). Left: The purple horizontal dashed line shows the maximum stimulated antibody level post-boost.

**Figure 3.**Sensitivity analysis of Model (1) using 10,000 iterations of a Latin hypercube sampling (LHS) method with a partial rank correlation coefficient (PRCC). PRCC values with magnitude close to unity indicate that the parameter has a strong impact on the model output [38].

**Figure 4.**Antibody (IgG) and CTL outcomes with standard (SD) and low dose (LD), with and without delay. Model predictions of antibody, first row, and cytotoxic T-cells, second row, with second dose vaccination on days: 28 (week:4), 42 (week:6), 56 (week:8), 70 (week:10), 84 (week:12), 98 (week:14), 112 (week:16), 126 (week:18), and 140 (week:20, shown by colored vertical dashed lines.). The second dose value (sd) in panel (

**a**) is the same as the initial dose (id) value: (sd = Id = 1000 vaccine particles), in panel (

**b**) it is decreased by half (sd = Id/2 = 500 vaccine particles), and in panel (

**c**) is decreased by a quarter (sd = Id/4 = 250 particles).

**Figure 5.**Model predictions of IFN$\gamma $, IL-6, plasma B-cells and T helper type 0 cells Th${}_{0}$ for the received boost dose (1000 vaccine particles) on days 28 (blue curve), 42 (red curve), 56 (green curve) and 70 (purple curve). The vertical dashed lines show the second dose injection days.

Variable | Definition |
---|---|

V | Vaccine cell |

T | T helper type 0 cell (Th${}_{0}$) |

F | Interferon gamma ($IFN\gamma $) |

I | Interleukin 6 ($IL-6$) |

B | Plasma B-cell |

A | Antibody |

C | Cytotoxic T-cell |

Parameter | Definition | Value | Unit | Comment |
---|---|---|---|---|

${\alpha}_{16}$ | Vaccine neutralizing rate by antibody molecules | 1 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-6}$ | day${}^{-1}$(a.u.)${}^{-1}$ | Handle et al., 2018 |

${\gamma}_{v}$ | Vaccine clearance rate | 0.2 | day${}^{-1}$ | Cao et al., 2016 |

${\mu}_{21}$ | Th${}_{0}$ cells activation rate by vaccine particles | 0.035 | day${}^{-1}$ | Chosen |

${\gamma}_{t}$ | Th${}_{0}$ cells natural death rate | 0.055 | day${}^{-1}$ | Cao et al., 2016 |

${\mu}_{32}$ | IFN$\gamma $ stimulation rate by Th${}_{0}$ | 2.55 | day${}^{-1}$ | Fitted |

${\gamma}_{f}$ | IFN$\gamma $ natural degradation rate | 0.13 | day${}^{-1}$ | Fitted |

${\alpha}_{37}$ | IFN$\gamma $ absorption rate by CTL for mitotic signals | 0.006 | day${}^{-1}$(a.u.)${}^{-1}$ | Fitted |

${\mu}_{42}$ | IL6 release rate by Th${}_{0}$ | 1.3 | day${}^{-1}$ | Fitted |

${\gamma}_{i}$ | IL6 natural degradation rate | 0.0008 | day${}^{-1}$ | Chosen |

${\alpha}_{45}$ | IL6 absorption rate by B-cells for mitotic signals | 0.0001 | day${}^{-1}$(a.u.)${}^{-1}$ | Fitted |

${\mu}_{52}$ | B-cell activation rate by Th${}_{0}$ | 0.02 | day${}^{-1}$ | Fitted |

${\alpha}_{54}$ | B-cell stimulation rate by IL | 0.05 | day${}^{-1}$(a.u.)${}^{-1}$ | Fitted |

${S}_{i}$ | B-cell duplication threshold due to IL | 1000 | a.u. | Chosen |

${\gamma}_{b}$ | B-cell natural death rate | 0.06 | day${}^{-1}$ | Fitted |

$\epsilon {\mu}_{65}$ | Released Ab rate by B-cells | 7 | day${}^{-1}$ | Fitted |

${\gamma}_{a}$ | Ab natural degradation rate | 0.06 | day${}^{-1}$ | Fitted |

${\alpha}_{61}$ | Ab - V cells binding rate | 1 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-7}$ | day${}^{-1}$(a.u.)${}^{-1}$ | Chosen |

${\mu}_{71}$ | CTL activation rate by vaccine | 0.002 | day${}^{-1}$ | Fitted |

${\alpha}_{73}$ | CTL stimulation rate by IFN$\gamma $ | 0.09 | day${}^{-1}$(a.u.)${}^{-1}$ | Fitted |

${S}_{f}$ | CTL duplication threshold due to IFN$\gamma $ | 600 | a.u. | Chosen |

${\gamma}_{c}$ | CTL natural death rate | 0.01 | day${}^{-1}$ | Wang et al., 2016 |

Variable | Parameter | Absolute PRCC Value |
---|---|---|

A (Antibody) | ${\gamma}_{b}$ | $0.8<PRCC<0.9$ |

${\mu}_{21}$ | ≈$0.8$ | |

${\gamma}_{v}$ | ≈$0.8$ | |

$\epsilon {\mu}_{65}$ | ≈$0.7$ | |

${\alpha}_{54}$ | $0.6<PRCC<0.7$ | |

${\gamma}_{t}$ | ≈$0.6$ | |

${\mu}_{52}$ | ≈$0.6$ | |

${\gamma}_{a}$ | $0.5\le PRCC<0.6$ | |

${\alpha}_{73}$ | $0.8<PRCC<0.9$ | |

${\gamma}_{v}$ | $0.8<PRCC<0.9$ | |

${\gamma}_{t}$ | ≈$0.7$ | |

C (CTL) | ${\mu}_{21}$ | $0.6<PRCC<0.7$ |

${\mu}_{32}$ | $0.6<PRCC<0.7$ | |

${S}_{f}$ | ≈$0.7$ | |

${\mu}_{71}$ | ≈$0.6$ | |

F (IFN$\gamma $) | ${\mu}_{21}$ | ≈$0.9$ |

${\mu}_{32}$ | ≈$0.9$ | |

${\gamma}_{v}$ | $0.7<PRCC<0.8$ | |

${\gamma}_{t}$ | ≈$0.6$ | |

${\mu}_{71}$ | ≈$0.5$ | |

${\alpha}_{37}$ | ≈$0.5$ | |

${\mu}_{21}$ | ≈1 | |

T (Th${}_{0}$) | ${\gamma}_{v}$ | ≈1 |

${\gamma}_{t}$ | ≈$0.7$ | |

Plasma B | ${\mu}_{21}$ | ≈$0.8$ |

${\gamma}_{b}$ | ≈$0.8$ | |

${\gamma}_{v}$ | $0.7<PRCC<0.8$ | |

${\mu}_{52}$ | ≈$0.7$ | |

${\alpha}_{54}$ | $0.6<PRCC<0.7$ | |

${\gamma}_{t}$ | ≈$0.6$ | |

I (IL6) | ${\mu}_{42}$ | ≈$0.9$ |

${\mu}_{21}$ | ≈$0.9$ | |

${\gamma}_{v}$ | $0.8<PRCC<0.9$ | |

${\gamma}_{t}$ | $0.8<PRCC<0.9$ |

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**MDPI and ACS Style**

Farhang-Sardroodi, S.; Korosec, C.S.; Gholami, S.; Craig, M.; Moyles, I.R.; Ghaemi, M.S.; Ooi, H.K.; Heffernan, J.M.
Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines. *Vaccines* **2021**, *9*, 861.
https://doi.org/10.3390/vaccines9080861

**AMA Style**

Farhang-Sardroodi S, Korosec CS, Gholami S, Craig M, Moyles IR, Ghaemi MS, Ooi HK, Heffernan JM.
Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines. *Vaccines*. 2021; 9(8):861.
https://doi.org/10.3390/vaccines9080861

**Chicago/Turabian Style**

Farhang-Sardroodi, Suzan, Chapin S. Korosec, Samaneh Gholami, Morgan Craig, Iain R. Moyles, Mohammad Sajjad Ghaemi, Hsu Kiang Ooi, and Jane M. Heffernan.
2021. "Analysis of Host Immunological Response of Adenovirus-Based COVID-19 Vaccines" *Vaccines* 9, no. 8: 861.
https://doi.org/10.3390/vaccines9080861