COVID-19 Scientific Breakthroughs Invigorate Vaccine Market

Vaccines stimulate the immune system to produce a protective humoral and cellular response to a disease, without causing that disease. By harnessing the natural activity of the immune system, vaccines are a powerful and pivotal tool for achieving public health.

The COVID-19 pandemic has catalyzed a remarkable mobilization in vaccine development. The SARS-CoV-2 virus genome was sequenced almost instantly after the first cases were identified, and new vaccines entered clinical trials within a couple of months, followed by regulatory approval and rollout of national vaccination programs within a year. This compares to a more typical development cycle of 5-10 years. Several of these vaccines use platform technologies, in some cases approved for the first time, which will enable even more rapid updates following the discovery of new variants.

Certara’s Vaccine Platform for COVID-19

For the past four years, Certara has been developing a robust, regulatory-ready QSP platform for assessing and managing immunogenicity (IG). As the pandemic gripped the world, the Certara team realized that its IG platform could be reworked and extended for vaccines. Instead of trying to minimize the immune response, the team switched its focus to maximizing the immune response for the COVID-19 vaccine.

The model was calibrated using the structure of actual COVID-19 vaccines, replicating all the published data and demonstrating that they could predict with a virtual trial the outcomes of an actual clinical trial using those vaccines. The model also enabled the prediction of immunological responses that may not be measurable in actual patients, such as the level of memory B cells, which is used to compare and optimize various dosing schedules in the virtual trials. Results from the COVID-19 vaccine platform have already been submitted to a global regulatory agency to support clinical trial designs.

Download a white paper on leveraging Biosimulation for COVID-19 vaccines platform
Predicting Vaccine Dosing for Sub-populations

Most clinical studies of investigational vaccines are initially conducted in healthy adults. After getting regulatory approval for a vaccine for adults, vaccine developers need to determine which vaccine dose will generate the maximum antibody response for different subpopulations such as children, the elderly, and pregnant women. For example, vaccination timing for infants six months and older is scheduled around routine checkups. Until 6 months of age, infants rely on transferred maternal antibodies. Thus, for pregnant women, it’s also important to determine which vaccination timing will provide maximal immunity for the newborn. Modeling and simulation can be used in lieu of clinical studies to assess the timing and efficacy of maternal vaccinations.

Read our blog on using modeling and simulation to inform vaccine development
Assessing Your Vaccine’s Competitive Landscape

In brief, 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.

Learn more about using MBMA to assess your vaccine’s competitive landscape
Piet van der Graaf,药学博士,博士 定量系统药理学部高级副总裁

Piet 曾任职于赛诺菲和辉瑞,在制药行业拥有 20 多年经验,为 QSP 项目带来了丰富的技能和经验,并为 Certara 的战略发展做出了贡献。他还担任《临床药理学与药物治疗学 (CPT)》主编。

Michael Dodds, PhD Executive Director, Integrated Drug Development

Specializing in pharmacometrics application to complex biotechnology products since 2005, Mike’s research focuses on the application of mathematical models of biology, (patho)-physiology, pharmacology and disease that quantify beneficial and undesirable interactions between drugs and patients to predict outcomes. He has held roles at ZymoGenetics and Amgen.

Amy Cheung, PhD Senior Director, Integrated Drug Development

S. Y. Amy Cheung is Senior Director of Integrated Drug Development. Dr. Cheung has over a decade of experience working in the pharmaceutical industry at AstraZeneca (AZ), with her role as Senior Pharmacometrician and Project manager of AZ Paediatric working group. She obtained her Ph.D. from the University of Manchester, on the topic of Structural Identifiability Analysis in Pharmacokinetic and Pharmacodynamic Models. After receiving her Ph.D. she worked as a postdoc on mechanistic modeling at the Centre for Applied Pharmacokinetic Research (CAPKR) at the University of Manchester.
She was work packages co-lead for thoughtflow and cardiac safety training for the IMI DDmoRe project and is also an active member of the EFPIA Model Informed Drug Discovery and Development (MID3) workgroup. She was a chair of IQ Consortium Clinical Pharmacology Leadership Group Pediatric Working Group in 2018 and current co-chair of IQ Consortium TALG, CPLQ PBPK Pediatric group.

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