A PK-Driven Quantitative Systems Pharmacology Model Predicts Cytokine Release Syndrome Severity Across T Cell-Activating Therapies via a Locked Cytokine Amplification Network.

Authors

Hajar Besbassi

Published in

CPT: pharmacometrics & systems pharmacology. Volume 15. Issue 11.

Abstract

Cytokine release syndrome (CRS) is a major dose-limiting toxicity of T cell-engaging immunotherapies. Existing CRS models are drug-class-specific; here we show that a single locked cytokine network captures CRS severity across mechanistically distinct drug classes, changing only drug-specific upstream activation parameters. We developed a PK-driven quantitative systems pharmacology (QSP) model linking drug exposure, T cell activation dynamics, and a macrophage-amplified cytokine network to clinical CRS severity. The 17-parameter downstream amplification network with macrophage-gated STAT3 positive feedback was calibrated on blinatumomab, structurally refined using TGN1412 as a transparently disclosed development case, then locked and tested blind on OKT3. The locked network reproduced the clinically observed CRS severity ordering across all three antibody-based T cell engager classes (bispecific, CD28 superagonist, and anti-CD3 with activation-induced cell death) without re-fitting any shared parameter. The OKT3 blind prediction passed eight qualitative plausibility checks, and its peak TNF- $\alpha$ fell within values measured in patients. Tocilizumab rescue simulation reproduced five clinically validated phenomena. A mechanistic parameter swap reversing the T cell exhaustion rate between OKT3 and TGN1412 reversed CRS severity in the expected direction. Local robustness analysis (926 of 5000 parameter sets accepted, 18.5%) and a 2D stability map over the two threshold-setting parameters (0 of 900 wrong-order combinations) confirmed that the cross-drug severity ordering is a property of a feasible parameter region rather than a single tuned point. The locked model predicted three distinct dose-response shapes without re-fitting. Findings should be interpreted as a mechanistic proof-of-concept; prospective clinical validation remains pending.

PMID:
43023728
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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