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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Scientific Meetings
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Training Courses
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Journal Club
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Webinars
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Careers Meetings
Joint PSI/EFSPI Vaccine SIG Webinar: Statistical and Mechanistic Models for Correlates of Protection
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.
Cost
This event is free of charge to both Members of PSI and Non-Members.
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.
Speaker details
Speaker
Biography
Abstract
Dr. Erin Gabriel
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.
Dr. Melanie Prague
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.
Upcoming Events
Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
The event will open with an overview on drug development in women’s health from a clinician perspective. This talk is followed by talks about statistical challenges when planning IVF studies and analysing the menstrual cycles.
This webinar will provide an overview of surrogacy for licensing and reimbursement. In turn, the need of extensions of the SPIRIT and CONSORT statement will be defined and outlined, with case studies to support.
Joint PSI/EFSPI Pre-Clinical SIG Webinar: Virtual Control Groups in Toxicity Studies
Lea Vaas will present how replacement of concurrent control animals by Virtual Control Groups (VCGs) in systemic toxicity studies may help in contributing to the 3R's principle of animal experimentation: Reduce, Refine, Replace.
Joint PSI/EFSPI Data Science SIG Webinar: Developing Digital Measures (Digital Biomarkers) in Drug Development – insights from Mobilise D consortium
We will share a brief overview of what Mobilise D is and why it is an important step stone in the development of digital biomarkers, and how Mobilise D outputs can be relevant for you.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Webinar: Development of Gene Therapies: Strategic, Scientific, Regulatory and Access Considerations
This webinar will cover the history of cell/gene therapy, major regulatory advances, the role of quantitative scientists in drug development of these novel therapeutics, and discuss opportunities for innovation and product advancement.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
PSI Introduction to Industry Training (ITIT) Course - 2024/2025
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
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