Birses Debir 1, Andrzej M. Kierzek 1, Robert Mines 2, Douglas W. Chung 2, David Flowers 2, Rachel Rose 1, Harsbir Singh Sandu 3
1 Certara (Sheffield,, UK), 2 Certara (Radnor,, USA), 3 Certara (Secunderabad ,, India)
Introduction:
Blinatumomab is a bispecific T-cell engager (TCE) antibody that simultaneously binds CD3 on T-cells and CD19 on B-cells, thereby promoting immune-mediated cytotoxicity in B-cell acute lymphoblastic leukemia (B-ALL). Quantitative systems pharmacology (QSP) approaches provide a powerful framework to mechanistically link drug exposure to downstream biological responses and clinical outcomes. We have previously developed a mechanistic TCE platform model to inform Phase I and Phase II dose range selection, including cytokine release syndrome (CRS) analysis, for a novel CD3xCD19 bispecific antibody by integrating non-clinical and clinical data for non-Hodgkin lymphoma and B-ALL indications. In this work, we apply the TCE platform framework to Blinatumomab, performing virtual trial simulations within the platform framework, with a focus on B cell depletion and cytokine dynamics associated with CRS risk.
Objectives:
A modular TCE platform model was implemented in QSP Designer software [1]. Model components include a physiologically based pharmacokinetic (PBPK) model, a Bispecific T cell Engager (BiTE) module incorporating avidity and trimer formation [2], a cytotoxicity model describing B-cell killing, and a cytokine distribution module adapted from the Immuno-Oncology simulator platform [3]. The modular architecture of QSP Designer enabled seamless integration of the cytokine distribution module between platforms. Additional model features include T-cell margination and B-cell migration processes to represent immune cell trafficking between plasma and tissue compartments. Together these components represent dynamic interactions between circulating B-cells, T-cells, and cytokines. Literature data were used to refine parameters governing Blinatumomab pharmacokinetics [4], efficacy [5], and downstream cytokine induction pathways including IL2, IL6, IL10, IFN-γ, and TNF-α [6]. A virtual patient population was generated using literature-reported variability in disease characteristics and key model parameters to capture inter-individual heterogeneity and enable probabilistic predictions of response and safety endpoints.
Results:
The integrated TCE platform model mechanistically describes the Blinatumomab mode of action, linking systemic exposure to target binding, cytotoxic activity, and cytokine dynamics. Model qualification was performed using clinical studies MT103-202, MT103-203, and MT103-211 [7]. Simulated pharmacokinetic profiles accurately captured Blinatumomab steady-state concentrations, with predictions within two-fold of reported clinical observations [4-5, 9]. In addition, the model reproduced key pharmacodynamic endpoints reported, including time to B-cell depletion (3.35 days) and time to T-cell nadir (0.3 days) [5]. A virtual population of 3030 patients was generated and filtered against observed pharmacokinetic, cytokine, and clinical response criteria [5, 8, 9]. The resulting virtual population closely matched reported efficacy outcomes, with simulated complete remission/complete remission with partial hematologic recovery (CR/CRh) rates of 43%, consistent with published clinical data [10]. Virtual trial simulations also reproduced observed cytokine dynamics and enabled prediction of CRS incidence probability across different dosing schedules. The framework further enabled exploratory dose optimization by evaluating regimens from study MT103-104, originally conducted in non-Hodgkin lymphoma patients. Among the schedules tested, a dose of 30 µg/m² was identified as the most favorable for simulated B-ALL patients, demonstrating robust cytotoxic responses while maintaining acceptable cytokine levels. Overall, the platform supports learn-and-confirm refinement using Phase II data and enables quantitative exploration of dose–response versus CRS possibility relationships.
Conclusions:
We present a mechanistic case study demonstrating application of a TCE platform model with the Cancer Immunity Cycle in blood cancers for Blinatumomab efficacy and CRS incidence. The platform approach provides a reusable foundation for rapid adaptation to emerging TCE modalities and targets, facilitating model-informed drug development across oncology programs. The legacy models with modular architectures highlight the value of reusable, interoperable components for accelerating QSP model development. The TCE platform enables evaluation of alternative dosing strategies while supporting early risk–benefit optimization to inform translational decision-making across drug development.
References:
References:
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Reference: PAGE 34 (2026) Abstr 11863 [www.page-meeting.org/?abstract=11863]
Poster: Drug/Disease Modelling - Oncology