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

Abstract

Vaccine dose-response curves can follow both saturating and peaking shapes. Dose-response curves for adenoviral vector vaccines have not been systematically described. In this paper, we explore the dose-response shape of published adenoviral animal and human studies. Where data were informative, dose-response was approximately five times more likely to be peaking than saturating. There was evidence that host species and response type may be sufficient for prediction of dose-response curve shape. Dose-response curve shape prediction could decrease clinical trial costs, accelerating the development of life-saving vaccines.

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

“…This is likely the result of using too few doses or not dosing at a sufficiently large dose to observe peaking or saturating dose-response behaviour. We have previously shown that curve shape could not be determined for 75% of adenoviral dose-response data [ 8 ]. However, in this case, it is possible that dosing at a large enough magnitude to determine curve shape could cause an unacceptable number of grade 3+ adverse events.…”

“…We also calibrated a representative non-saturating (“peaking”) curve, using the methods discussed in [ 8 ]. However, as these methods did not support the non-saturating curve providing a better description of the data, we assume that the dose-response follows a sigmoidal function ( Supplementary S1 ).…”

“…Model-based drug development is commonly used to accelerate drug decision making, and the field of Immunostimulation/Immunodynamic (IS/ID) modelling has been developed to adapt these methods for vaccine development [ 5 ]. IS/ID modelling has shown promise in discovering optimal dose for TB and influenza inoculations [ 6 , 7 ] and for exploring dose-response trends for adenoviral-vectored vaccines [ 8 ], however, the previous works have focused entirely on optimising dose with respect to immunological response. In these studies, the modelling of dose-response and hence finding the dose that maximises response can be considered single-objective optimisation problems.…”

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity

“…Whereas for many drugs, we can assume that an increased dose increases efficacy, for vaccines, this may not be the case. It is possible that there exists some dose for which the probability of efficacy is maximised and that increasing this dose decreases the probability of efficacious response [ 3 , 24 , 25 , 26 ]. Below, we define approaches for modelling vaccine efficacy.…”

Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling

“…To address this, we have launched the novel field of vaccine ‘Immunostimulation/Immunodynamic’ (IS/ID) modelling, an adaptation of methods used in drug development to systematically and quantitatively identify ‘best’ dose. We have shown its potential in a novel TB vaccine [14] , [15] and Adenoviral-based vaccines [16] , [17] , [18] and IS/ID modelling has been recognised by vaccine developers and modellers as the future methodology to optimise vaccine development [19] , [20] , [21] , [22] , [23] .…”