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

Current methods to optimize vaccine dose are purely empirically based, whereas in the drug development field, dosing determinations use far more advanced quantitative methodology to accelerate decision-making. Applying these established methods in the field of vaccine development may reduce the currently large clinical trial sample sizes, long time frames, high costs, and ultimately have a better potential to save lives. We propose the field of immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate mathematical frameworks used for drug dosing towards optimizing vaccine dose decision-making. Analogous to Pharmacokinetic/Pharmacodynamic (PK/PD) modelling, the mathematical description of drug distribution (PK) and effect (PD) in host, IS/ID modelling approaches apply mathematical models to describe the underlying mechanisms by which the immune response is stimulated by vaccination (IS) and the resulting measured immune response dynamics (ID). To move IS/ID modelling forward, existing datasets and further data on vaccine allometry and dose-dependent dynamics need to be generated and collate, requiring a collaborative environment with input from academia, industry, regulators, governmental and non-governmental agencies to share modelling expertise, and connect modellers to vaccine data.

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

Unlike drug dose optimisation, mathematical modelling has not been applied to vaccine dose finding. We applied a novel Immunostimulation/Immunodynamic mathematical modelling framework to translate multi-dose TB vaccine immune responses from mice, to predict most immunogenic dose in humans. Data were previously collected on IFN-γ secreting CD4+ T cells over time for novel TB vaccines H56 and H1 adjuvanted with IC31 in mice (1 dose groups (0.1-1.5 and 15 µg H56 + IC31), 45 mice) and humans (1 dose (50 µg H56/H1 + IC31), 18 humans). A two-compartment mathematical model, describing the dynamics of the post-vaccination IFN-γ T cell response, was fitted to mouse and human data, separately, using nonlinear mixed effects methods. We used these fitted models and a vaccine dose allometric scaling assumption, to predict the most immunogenic human dose. Based on the changes in model parameters by mouse H56 + IC31 dose and by varying the H56 dose allometric scaling factor between mouse and humans, we established that, at a late time point (224 days) doses of 0.8-8 µg H56 + IC31 in humans may be the most immunogenic. A 0.8-8 µg of H-series TB vaccines in humans, may be as, or more, immunogenic, as larger doses. The Immunostimulation/Immunodynamic mathematical modelling framework is a novel, and potentially revolutionary tool, to predict most immunogenic vaccine doses, and accelerate vaccine development.

Using Data from Macaques To Predict Gamma Interferon Responses after Mycobacterium bovis BCG Vaccination in Humans: a Proof-of-Concept Study of Immunostimulation/Immunodynamic Modeling Methods.

Macaques play a central role in the development of human tuberculosis (TB) vaccines. Immune and challenge responses differ across macaque and human subpopulations. We used novel immunostimulation/immunodynamic modeling methods in a proof-of-concept study to determine which macaque subpopulations best predicted immune responses in different human subpopulations. Data on gamma interferon (IFN-γ)-secreting CD4+ T cells over time after recent Mycobacterium bovis BCG vaccination were available for 55 humans and 81 macaques. Human population covariates were baseline BCG vaccination status, time since BCG vaccination, gender, and the monocyte/lymphocyte cell count ratio. The macaque population covariate was the colony of origin. A two-compartment mathematical model describing the dynamics of the IFN-γ T cell response after BCG vaccination was calibrated to these data using nonlinear mixed-effects methods. The model was calibrated to macaque and human data separately. The association between subpopulations and the BCG immune response in each species was assessed. The macaque subpopulations that best predicted immune responses in different human subpopulations were identified using Bayesian information criteria. We found that the macaque colony and the human baseline BCG status were significantly (P < 0.05) associated with the BCG-induced immune response. For humans who were BCG naïve at baseline, Indonesian cynomolgus macaques and Indian rhesus macaques best predicted the immune response. For humans who had already been BCG vaccinated at baseline, Mauritian cynomolgus macaques best predicted the immune response. This work suggests that the immune responses of different human populations may be best modeled by different macaque colonies, and it demonstrates the potential utility of immunostimulation/immunodynamic modeling to accelerate TB vaccine development.

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

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.

Individual-level factors associated with variation in mycobacterial-specific immune response: Gender and previous BCG vaccination status.

INTRODUCTION: A more effective tuberculosis (TB) vaccine is needed to eliminate TB disease. Many new vaccine candidates enhance the immunogenicity of the existing vaccine, Bacillus Calmette-Guérin (BCG). Understanding BCG induced immune variation is key to developing a new vaccine. AIMS: We aimed to establish if individual-level covariates were associated with cell-mediated immune response (interferon gamma (IFN-γ)) at vaccine trial enrolment (baseline) in a long-term retrospective analysis (LTR) and after BCG vaccination in a short-term prospective analysis (STP). METHODS: Four covariates were analysed: gender, country, BCG vaccination history and monocyte/lymphocyte cell count ratio. Univariable and multivariable linear regression were conducted on IFN-γ response at baseline for LTR, and area under the curve (AUC), 24 week and peak IFN-γ response for STP. RESULTS: Previous BCG vaccination was strongly associated with higher IFN-γ response at baseline (LTR analysis) (p-values < 0.05). Being male showed a weak association with higher baseline response (p-value = 0.1). BCG revaccination was strongly associated with a larger response increase than primary-vaccination (AUC & peak p-values < 0.01), but did not differ at 24 weeks (STP analysis). All other covariates were non-significant (p-values > 0.1). CONCLUSION: This analysis suggests that previous BCG vaccination and gender are associated with durable IFN-γ responses. Vaccine trials may need to stratify by BCG vaccination history and gender.