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

Abstract Cytokine release syndrome (CRS) is a major dose-limiting toxicity of T cell-engaging immunotherapies. Existing CRS models are drug-class-specific and have not addressed whether a single mechanistic cytokine network can capture severity differences across mechanistically distinct drug classes. Here, 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 developed iteratively. The network was calibrated on blinatumomab, structurally refined using TGN1412 as a transparently disclosed development case, then locked and tested blind on OKT3. The same locked network was used to evaluate cross-drug transferability across three antibody-based T cell engager classes: bispecific, CD28 superagonist, and anti-CD3 with activation-induced cell death. The locked network reproduced the clinically observed CRS severity ordering across all three drugs without re-fitting any shared parameter. The OKT3 blind prediction passed eight qualitative plausibility checks and three of three quantitative cytokine peaks within published clinical ranges. Tocilizumab rescue simulation reproduced five clinically validated phenomena. A mechanistic parameter swap test reversing the T cell exhaustion rate between OKT3 and TGN1412 reversed CRS severity in the expected direction, supporting a mechanistic rather than parameter-fitted interpretation. Local robustness analysis (ABC-style accepted ensemble: 692 of 5,000 parameter sets accepted, 13.8%) 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. Profile likelihood analysis of the IL-6 feedback and clearance rates revealed complementary asymmetric profiles consistent with practical identifiability as a ratio. The same locked model predicted three qualitatively distinct dose-response shapes without re-fitting. Findings should be interpreted as a mechanistic proof-of-concept; prospective clinical validation remains pending.