Dataset Information

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

ABSTRACT:

Background

The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data.

Methods

A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy - a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2.

Findings

Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant.

Interpretation

The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines.

Funding

This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

SUBMITTER: Kandala B

PROVIDER: S-EPMC9514977 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

Kandala Bhargava B Plock Nele N Chawla Akshita A Largajolli Anna A Robey Seth S Watson Kenny K Thatavarti Raj R Dubey Sheri A SA Cheung S Y Amy SYA de Greef Rik R Stone Julie J Sachs Jeffrey R JR

EBioMedicine 20220928

<h4>Background</h4>The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data.<h4>Methods</h4>A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level dat ...[more]

PMID: 36182824

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Dataset Information

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

Background

Methods

Findings

Interpretation

Funding

Publications

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

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