Gerbert Coen De Waard 1,2, Sander van Tilburg 1,2, Tingjie Guo 3, Harmony consortium 4, Virgil A.S.H. Dalm 5,6, Casper Rokx 7, Marion P. Koopmans 8, P.M.H. Van der Kuy 1, Birgit C.P. Koch 1,2, Rory D. De Vries 8, Tim Preijers 1,2
1 Department of Hospital Pharmacy, Erasmus University Medical Center (Rotterdam, the Netherlands), 2 Rotterdam Clinical Pharmacometrics Group (Rotterdam, the Netherlands), 3 Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University (Leiden, the Netherlands), 4 Harmony consortium, National Institute for Public Health and the Environment (Bilthoven, the Netherlands), 5 Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus University Medical Center (Rotterdam, the Netherlands), 6 Department of Immunology, Erasmus University Medical Center (Rotterdam, the Netherlands), 7 Department of Internal Medicine, Section of Infectious Diseases, and Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Center (Rotterdam, the Netherlands), 8 Department of Viroscience, Erasmus University Medical Center (Rotterdam, the Netherlands)
Objectives
During the pandemic, COVID-19 vaccines were administered to prevent severe disease [1]. Variable immune responses were observed in controls but in certain groups of immunocompromised patients, especially those with primary immunodeficiencies (PID), hematological malignancies, transplant recipients, and people living with HIV (PLWH), primary vaccination induced weaker immune response compared to controls resulting in reduced protection against severe disease [2-5]. Although specific high-risk groups were prioritized for primary and booster vaccinations based on empirical judgment, clear evidence on the optimal within high-risk target populations for vaccination only became available much later. Furthermore, as resources are limited and one-size-fits-all strategies may leave some individuals insufficiently protected and others boosted unnecessarily, a quantitative dose-response framework potentially may enable evidence-based, subgroup-specific vaccination strategies. Therefore, an indirect (pharmaco)kinetic-pharmacodynamic (K-PD) model was developed to (1) shed light on possible covariate relationships influencing SARS-CoV-2-specific antibody levels and (2) assess if individualized vaccination strategies can be pursued.
Methods
Data characteristics
Anti-S1 and anti-nucleocapsid (N) titers, recorded vaccinations and infections, and demographic data was obtained from participants in multiple COVID-19 vaccination studies [2-8] performed in the Netherlands [9]. Participants were controls, recruited by the National Institute of Public Health and Environment or health care workers from participating hospitals, patients with PID, hematological conditions/disorders, chronic kidney disease, solid tumors, and PLWH, and the data collected included demographics and treatments received for the underlying conditions. For the vaccine response read-out, we included the anti-S1 IgG antibodies, in binding antibody units (BAU)/mL, which correlated with neutralizing antibodies early in the pandemic and were considered to be a correlate of protection (CoP) [10,11]. For the development of this model, only the vaccine response measurements were included.
Model development
The structure of the K-PD model assessed the exposure-response dynamics of anti-S1 antibody levels following vaccination. To construct the model, non-linear mixed-effect modeling was performed using NONMEM (v7.5, ICON Development Solutions), Pirana (version 3.0.0, Certara), Pearl-speaks-NONMEM (PsN, version 5.3.0), and R (version 4.4.1, R Foundation for Statistical Computing). The performance of the model was evaluated quantitatively using the drop in the objective function value (dOFV) and relative standard errors (RSEs). Visual diagnostics were applied using goodness-of-fit (GOF) plots and visual predictive checks (VPCs). Robustness of the model predictions was validated by a bootstrap analysis (N=1,000). After evaluating the addition of inter-individual variability (IIV) to the model parameters, a covariate analysis was performed with the forward inclusion (P<0.05) and backward exclusion (P<0.01) method. Covariate effects of treatment, indication, vaccine platform (mRNA or vector-based), age and sex were reviewed. For the categorical covariates, the references were untreated, controls, mRNA vaccines and males. To effectively model the treatments and indications, lumping of certain groups was performed based on the estimated population values of the model, eta vs covariate plots, and physiological plausibility. Finally, Monte Carlo simulations (MCS) were performed assessing the covariate effect and calculating the percentage of simulations with an adequate response by using an anti-S1 level of 300 BAU/mL [4,5,9].
Results
Using the indirect response K-PD model, the vaccination events were linked to the anti-S1 titers. The vaccination events were modelled with an arbitrary dose of 1 as an impulse to initiate the K-PD system. The rate of input (Kin, BAU/mL) was a combined effect of the rate of synthesis (Ks, BAU/mL per vaccine units) and the booster effect (dimensionless). A first-order output rate (Kout, BAU/mL) with a booster effect (dimensionless) best described the waning of the antibodies. As, in general, IgG antibodies are measurable after approximately 1.5 weeks, a delay of 10 days was taken into account to describe antibody production [12]. The booster affected Kin (6.86) as it increased the anti-S1 titers while the booster affected Kout (0.736) by decreasing the waning of antibodies. The latter was expected from previous studies on antibody kinetics after priming and boosting [7,13]. IIV (coefficient of variation) was estimated for Ks (255.7%) and Kout (49.4%).
Treatment, indication, and vaccine platform were significant covariates on Ks, while age and sex were significant on Kout. For the vaccine platform, vector-based vaccination reduced Ks by 51.3% as compared to mRNA vaccination (1). Using a power‑model, an increasing age increased the waning of antibodies. Females were shown to have a 18.0% higher antibody waning than males (1).
MCS demonstrated the influence of the covariate relationships on the anti-S1 titers. Patients treated with rituximab or CAR T-cell therapy, and those diagnosed with X-linked agammaglobulinemia had no to limited antibody responses to vaccination, while anti-S1 titers were comparable between controls and patients in treatment against solid tumors, having an impaired kidney function (<30 estimated glomerular filtration rate (eGFR) in mL/min./1.73m2), hemodialysis, peritoneal dialysis, sickle cell disease/thalassemia, chronic myeloid leukemia treated with tyrosine kinase inhibitors, and autologous stem-cell therapy with high-dose melphalan as treatment against multiple myeloma. Based on the MCS, delaying boosters would reduce the proportion of individuals reaching the threshold of 300 BAU/mL, which may be indicative of reduced protection from severe disease.
Conclusions
To our knowledge, this study presents the first indirect K-PD model characterizing the relationship between vaccination and anti-S1 titers. Using MCS, it was demonstrated that while specific subpopulations necessitate accelerated booster schedules, the interval for other populations (e.g., healthy individuals) can be extended while remaining above the CoP threshold. This model provides a framework to inform vaccination strategies based on clinical indication, vaccine platform, age, and sex. Consequently, the rapid deployment of such modeling approaches in future pandemic settings could significantly optimize population-level vaccine allocation. Future research will integrate infection-acquired immunity data to further enhance the model’s predictive scope.
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Reference: PAGE 34 (2026) Abstr 12215 [www.page-meeting.org/?abstract=12215]
Poster: Drug/Disease Modelling - Other Topics