Vaccine Simulator
Vaccine Simulator: A QSP Platform Model to Guide and Inform Vaccine Dosing
Optimize Vaccine Dosing through Quantitative Systems Pharmacology
A QSP Model to guide and inform vaccine dosing
Certara’s Vaccine Simulator uses quantitative systems pharmacology (QSP) modeling to determine the optimal dosing strategies for patients. The Vaccine Simulator is based on Certara’s quantitative systems pharmacology (QSP) technology. It is calibrated to data from a large set of clinical studies reporting changes to multiple biomarkers following vaccination. It is used to develop sophisticated models that answer ‘what-if’ scenarios to determine the best dosing strategies for various patient cohorts, such as the elderly and children. Many global pharmaceutical companies have also used the Vaccine Simulator, and its findings have been presented to regulatory agencies, including a U.S. Food and Drug Administration (FDA) workshop in June 2021.
In 2017, Certara established a biosimulation consortium with seven major pharmaceutical companies to build a mechanistic model of the human immune system. Certara’s immunogenicity model was born from this collaboration and has been validated using data from more than 20 clinical cases. With the COVID-19 pandemic’s onset, the Certara team quickly began to rework its immunogenicity model for use as a COVID-19 vaccine model. Instead of trying to minimize the immune response as was the goal for the original immunogenicity model for biotherapeutics, the team switched its focus to maximizing the immune response for the COVID-19 vaccine.
The new Totality of Evidence
- 32 Clinical Datasets
- 3 Vaccines
- 8 Dosing regimens
- 10 Biomarkers
1 Model
Case Study
In July 2021, Certara announced that its Vaccine Simulator accurately predicted that eight weeks was the optimal timing between the first and second dose of COVID-19 vaccines. Read about how we applied quantitative systems pharmacology to guide the optimal doing of COVID-19 vaccines.
Assessing Your Vaccine’s Competitive Landscape
MBMA incorporates parametric models based on literature data and in-house data. It quantifies the effect of treatment, time, and patient population characteristics on the outcomes.
It can include trial-level covariate relationships on the dose-response models to account for between trial differences in patient populations. It also allows for simultaneous modeling of multiple endpoints and can therefore link biomarkers (e.g. immunogenicity) to clinical endpoints (e.g. incident rate or hospitalization). Like network meta-analysis, it can provide indirect comparisons and simulations of head-to-head trials, but it uses longitudinal dose- response models for individual vaccine or vaccine classes. It can also be used for simulations of trials and trial success predictions.
Meet the Expert
Rachel Rose, Senior Director, ABS
Rachel has 15 years’ experience in developing QSP and PBPK/PD models for a broad range of drug modalities from small molecules to biotherapeutics including therapeutic proteins, antibody drug conjugates, gene therapies and vaccines. Her current work is focused on developing mechanistic models to predict immunogenicity of biotherapeutics and vaccines.
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