Literature DB >> 27816373

The TB vaccine H56+IC31 dose-response curve is peaked not saturating: Data generation for new mathematical modelling methods to inform vaccine dose decisions.

Sophie J Rhodes1, Andrea Zelmer2, Gwenan M Knight3, Satria Arief Prabowo2, Lisa Stockdale2, Thomas G Evans4, Thomas Lindenstrøm5, Richard G White6, Helen Fletcher2.   

Abstract

INTRODUCTION: In vaccine development, dose-response curves are commonly assumed to be saturating. Evidence from tuberculosis (TB) vaccine, H56+IC31 shows this may be incorrect. Mathematical modelling techniques may be useful in efficiently identifying the most immunogenic dose, but model calibration requires longitudinal data across multiple doses and time points. AIMS: We aimed to (i) generate longitudinal response data in mice for a wide range of H56+IC31 doses for use in future mathematical modelling and (ii) test whether a 'saturating' or 'peaked' dose-response curve, better fit the empirical data.
METHODS: We measured IFN-γ secretion using an ELISPOT assay in the splenocytes of mice who had received doses of 0, 0.1, 0.5, 1, 5 or 15μg H56+IC31. Mice were vaccinated twice (at day 0 and 15) and responses measured for each dose at 8 time points over a 56-day period following first vaccination. Summary measures Area Under the Curve (AUC), peak and day 56 responses were compared between dose groups. Corrected Akaike Information Criteria was used to test which dose-response curve best fitted empirical data, at different time ranges.
RESULTS: (i) All summary measures for dose groups 0.1 and 0.5μg were higher than the control group (p<0.05). The AUC was higher for 0.1 than 15μg dose. (ii) There was strong evidence that the dose-response curve was peaked for all time ranges, and the best dose is likely to be lower than previous empirical experiments have evaluated.
CONCLUSION: These results suggest that the highest, safe dose may not always optimal in terms of immunogenicity, as the dose-response curve may not saturate. Detailed longitudinal dose range data for TB vaccine H56+IC31 reveals response dynamics in mice that should now be used to identify optimal doses for humans using clinical data, using new data collection and mathematical modelling. Copyright Â
© 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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Year:  2016        PMID: 27816373     DOI: 10.1016/j.vaccine.2016.10.060

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  11 in total

1.  The Quantitative Assessment of the Secreted IgG Repertoire after Recall to Evaluate the Quality of Immunizations.

Authors:  Klaus Eyer; Carlos Castrillon; Guilhem Chenon; Jérôme Bibette; Pierre Bruhns; Andrew D Griffiths; Jean Baudry
Journal:  J Immunol       Date:  2020-07-15       Impact factor: 5.422

Review 2.  Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities.

Authors:  Colby S Shemesh; Joy C Hsu; Iraj Hosseini; Ben-Quan Shen; Anand Rotte; Patrick Twomey; Sandhya Girish; Benjamin Wu
Journal:  Mol Ther       Date:  2020-09-30       Impact factor: 11.454

3.  Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling.

Authors:  Sophie J Rhodes; Gwenan M Knight; Denise E Kirschner; Richard G White; Thomas G Evans
Journal:  J Theor Biol       Date:  2019-01-10       Impact factor: 2.691

4.  Optimising Vaccine Dose in Inoculation against SARS-CoV-2, a Multi-Factor Optimisation Modelling Study to Maximise Vaccine Safety and Efficacy.

Authors:  John Benest; Sophie Rhodes; Matthew Quaife; Thomas G Evans; Richard G White
Journal:  Vaccines (Basel)       Date:  2021-01-22

5.  Long-Term In Vitro Passaging Had a Negligible Effect on Extracellular Vesicles Released by Leishmania amazonensis and Induced Protective Immune Response in BALB/c Mice.

Authors:  Talita Vieira Dupin; Natasha Ferraz de Campos Reis; Elizabeth Cristina Perez; Rodrigo Pedro Soares; Ana Claudia Torrecilhas; Patricia Xander
Journal:  J Immunol Res       Date:  2021-12-24       Impact factor: 4.818

6.  Early events in hepatitis B infection: the role of inoculum dose.

Authors:  Stanca M Ciupe; Naveen K Vaidya; Jonathan E Forde
Journal:  Proc Biol Sci       Date:  2021-02-10       Impact factor: 5.349

7.  High Antigen Dose Is Detrimental to Post-Exposure Vaccine Protection against Tuberculosis.

Authors:  Rolf Billeskov; Thomas Lindenstrøm; Joshua Woodworth; Cristina Vilaplana; Pere-Joan Cardona; Joseph P Cassidy; Rasmus Mortensen; Else Marie Agger; Peter Andersen
Journal:  Front Immunol       Date:  2018-01-15       Impact factor: 7.561

8.  Response Type and Host Species may be Sufficient to Predict Dose-Response Curve Shape for Adenoviral Vector Vaccines.

Authors:  John Benest; Sophie Rhodes; Sara Afrough; Thomas Evans; Richard White
Journal:  Vaccines (Basel)       Date:  2020-03-30

9.  Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design.

Authors:  Andreas Handel; Yan Li; Brian McKay; Kasia A Pawelek; Veronika Zarnitsyna; Rustom Antia
Journal:  PLoS Comput Biol       Date:  2018-10-01       Impact factor: 4.475

10.  Using vaccine Immunostimulation/Immunodynamic modelling methods to inform vaccine dose decision-making.

Authors:  Sophie J Rhodes; Jeremie Guedj; Helen A Fletcher; Thomas Lindenstrøm; Thomas J Scriba; Thomas G Evans; Gwenan M Knight; Richard G White
Journal:  NPJ Vaccines       Date:  2018-09-17       Impact factor: 7.344

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