Date: Thursday 25th April 2024
Time: 15:00-17:00 BST
Location: Online via Zoom
Speakers: Dr. Erin Gabriel (University Of Copenhagen) and Dr. Melanie Prague (INRIA Bordeaux Sud-Oest Center).
Who is this event intended for? Statisticians, Mathematicians, Modelers, working in vaccine development.
What is the benefit of attending? Attending this seminar on Correlates of Protection (CoPs) in vaccine development offers several benefits. Firstly, participants will gain insights into statistical models used to identify and validate CoPs, providing them with quantitative tools for assessing vaccine efficacy. Secondly, they will learn about mechanistic models that elucidate the underlying biological mechanisms driving immune responses, enhancing their understanding of vaccine-induced protection.
This event is free of charge to both Members of PSI and Non-Members.
To register for this event, please click here.
This webinar will explore two different approaches to Correlates of Protection in vaccine development. Statistical models providing a quantitative framework for identification and validation of potential CoPs, and mechanistic models aiming to understand the biological mechanisms driving immune responses and protection. By integrating these two approaches, researchers can gain better insights into the immunological factors associated with vaccine efficacy and infection protection. This webinar will include talks from two renowned speakers who will discuss the principles, applications, and challenges of both statistical and mechanistic models, contributing to a better understanding of CoPs and optimizing vaccine clinical trial design.
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Speaker |
Biography |
Abstract |
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Erin Gabriel received her PhD from the University of Washington in 2012. She is an Associate Professor in Biostatistics at the University of Copenhagen. Her research focuses on biostatistical methods development and the proper application of methods to problems in the treatment and prevention of infectious diseases. She is currently working on methodological research in the areas of nonparametric causal bounds, designs, and estimation methods for emulated and randomized clinical trials for the evaluation of prediction-based decision rules, and surrogate evaluation. Her general statistical areas of interest are causal inference and randomized trials. |
Statistical evaluation of correlates of protection: reasonable assumptions are the key. There are a variety of statistical methods for assessing correlates of protection (CoP), the majority of which rely on strong assumptions. I will review the statistical methods for correlates of protection starting with Gilbert and Hudgens (2008), followed by Huang et al's semi-parametric methods, and then my own work which are all under the principal stratification framework. Current methods have moved beyond principal stratification to the controlled vaccine efficacy (VE) framework, which was used to validate CoP for the COVID-19 vaccines. The no unmeasured confounding assumption is needed under the controlled VE evaluation of correlates of protection, as it is an estimand similar to a mediation effect. It could be argued that the controlled VE framework is more robust as the no unmeasured confounding assumption is more common, accessible, and it can be more easily scrutinized by subject-matter experts. There are also more well-developed sensitivity analysis methods for that setting, one of which is nonparametric causal bounds. I will talk quickly about my paper in which we derive bounds for mediation effects, motivated by vaccine efficacy evaluation, and consider how they might be used for controlled vaccine efficacy effects. |
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Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19 |
Definition of mechanistic correlates of protection for vaccine development. Model informed drug development has potentially a great deal to offer for vaccine development and in particular in seeking methods to extrapolate effectiveness. Viral dynamics modeling to define mechanistic correlates of protection for vaccine development. The definition of correlates of protection is critical for the development of next generation SARS-CoV-2 vaccine platforms. The complete chains of causality and interrelationships between vaccination, immune responses, protection and clinical endpoints are likely to be considerably complex. In this work, we propose a model-based approach for identifying mechanistic correlates of protection against disease acquisition based on mathematical modeling of viral dynamics and data mining of immunological markers. We apply the method to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. |
