Manon Wigbers1, Dennis Reddyhoff1, Rachel Rose1, Andrzej Kierzek1, Piet H. van der Graaf1
1Certara, Applied BioSimulation
Introduction Mathematical Modelling and Simulation is widely used in many areas of drug development. Over the past decade, Quantitative Systems Pharmacology (QSP) has been gaining recognition as a decision-making tool with a growing number of FDA submissions supported by QSP models [1]. Specifically, QSP models are mechanistic models integrating pharmacokinetic-pharmacodynamic (PKPD) and systems biology models and are increasingly being used to support dose and dose regimen predictions for therapeutic modalities for which conventional PKPD approaches are lacking. For example, following the outbreak of Sars-CoV-2 pandemic, we developed the “Vaccine Simulator” [2], a QSP model integrating mechanistic details of the immune response to protein antigen combined with modules of vaccine administration. Here, we present a new case study of using an updated version of our QSP Platform Vaccine Model and we demonstrate the value of QSP modelling to support SARS-CoV-2 vaccine development for both adult and paediatric age groups. Methods Our Vaccine Model integrates a QSP model of liquid nanoparticle (LNP) mRNA administration to an immune response model. The model is calibrated using literature data on B-cell biology and LNP mRNA distribution and translation. To demonstrate the use of our model for vaccine development in real time, we further calibrate the model with two early-phase clinical data sets: SARS-CoV-2 neutralising titers in an adult population [3] and the geometric mean of the S1 antigenic protein concentration profile [4]. For the latter calibration step, we use a filtering approach in which we select individuals (parameter sets) that meet an priori set of parameter and data bounds. This approach allows us to generate a population of individuals that meet an priori set of criteria. We simulate the model for varying doses and dosing intervals for both adult and paediatric age groups. We demonstrate two applications of our Platform Vaccine Model. First, we simulate SARS-CoV-2 neutralising titers in an adult population, predicting the optimal dosing interval between two doses. Next, we simulate SARS-CoV-2 neutralising titers in paediatric age groups to predict dose for paediatric groups. We compare our results to the geometric mean titers of reported clinical trials for two- and three-dose regimen [5,6]. Furthermore, we predict the optimal dose for 2-4 year old children for a two-dose regimen. Results Using our Platform Vaccine Model, we predict SARS-Cov-2 neutralizing titers after LNP mRNA vaccine administration for both adult and paediatric populations. We predict an optimal dosing interval of 8 weeks for all individuals, consistent with clinical results [7]. Furthermore, we calculate the geometric mean ratio (GMR) between paediatric and adult populations for SARS-CoV-2 neutralising titers. We predict that two doses of 3 µg in 2-4 year olds would lead to a GMR of 0.37, and that a third dose is necessary to obtain neutralising titer levels comparable to the adult population, consistent with clinical trial results [6]. Finally, we predict that two doses of 5 µg for 2–4-year-olds would be sufficient to obtain the same neutralising titer levels as predicted for the 5-11 year old age group. Conclusions We demonstrate that prospective use of our QSP model for SARS-CoV-2 vaccine development could have resulted in a more optimal dosing scheme and could have prevented a failed trial, reducing both cost and time for vaccine development. We propose that this case study can be extrapolated to other areas of model-informed vaccine development [8]. Acknowledgement This work was funded by the Gates Foundation (INV-040110). The views expressed in this work do not reflect official views of the Gates Foundation.
1. Bai, Jane PF, et al. “Landscape of regulatory quantitative systems pharmacology submissions to the US Food and Drug Administration: An update report.” CPT: Pharmacometrics & Systems Pharmacology 13.12 (2024): 2102-2110. 2. Giorgi M, Desikan R, van der Graaf PH, Kierzek AM. Application of quantitative systems pharmacology to guide the optimal dosing of COVID-19 vaccines. CPT Pharmacometrics Syst Pharmacol. 2021 Oct;10(10):1130-1133. doi: 10.1002/psp4.12700. 3. Goel, Rishi R., Mark M. Painter, Sokratis A. Apostolidis, Divij Mathew, Wenzhao Meng, Aaron M. Rosenfeld, Kendall A. Lundgreen, et al. 2021. “MRNA Vaccination Induces Durable Immune Memory to SARS-CoV-2 with Continued Evolution to Variants of Concern.” Biorxiv, August. https://doi.org/10.1101/2021.08.23.457229 4. Ogata, Alana F, Chi-An Cheng, Michaël Desjardins, Yasmeen Senussi, Amy C Sherman, Megan Powell, Lewis Novack, et al. 2021. “Circulating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccine Antigen Detected in the Plasma of MRNA-1273 Vaccine Recipients.” Clinical Infectious Diseases, May, ciab465 https://doi.org/10.1093/cid/ciab465 5. Walter EB, Talaat KR, Sabharwal C, et al. Evaluation of the BNT162b2 Covid-19 vaccine in children 5 to 11 years of age. N Engl J Med 2022;386:35-46. 6. Muñoz FM., Sher LD, Sabharwal C et al. Evaluation of BNT162b2 Covid-19 Vaccine in Children Younger than 5 Years of Age. N Engl J Med 388.7 2023: 621-634 7. Payne, Rebecca P., et al. “Sustained T cell immunity, protection and boosting using extended dosing intervals of BNT162b2 mRNA vaccine.” (2021). 8. Desikan R, Germani M, van der Graaf PH, Magee M. A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development. J Pharm Sci. 2024 Jan;113(1):22-32. doi: 10.1016/j.xphs.2023.10.043. Epub 2023 Nov 2. PMID: 37924975.
Reference: PAGE 33 (2025) Abstr 11748 [www.page-meeting.org/?abstract=11748]
Poster: Drug/Disease Modelling - Other Topics