Quantitative modeling predicts competitive advantages of a next generation anti‐NKG2A therapy over monalizumab for the treatment of cancer

Collaboration with KSQ Therapeutics - Published in CPT: Pharmacometrics Systems Pharmacology


A semi‐mechanistic pharmacokinetic (PK)/ receptor occupancy (RO) model was constructed to differentiate a next generation anti‐NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three‐compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in rheumatoid arthritis patients, the model successfully predicted the published PK and RO observed in gynecological tumors and in squamous cell carcinoma of the head and neck (SCCHN) patients. Simulations predicted that the KSQ mAb requires a 10‐fold lower dose than monalizumab to achieve a similar RO over a 3‐week period following Q3W intravenous (IV) infusion dosing. A global sensitivity analysis of the model indicated that the drug‐target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition (TMDD). The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg Q2W. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.


Phillip Spinosa, Monika Musial‐Siwek, Marc Presler, Alison Betts, Emily Rosentrater, Janice Villali, Lucia Wille, Yang Zhao, Tom McCaughtry, Kalyanasundaram Subramanian, Hanlan Liu (2021)

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