Article ; Online: Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer.
CPT: pharmacometrics & systems pharmacology
2021 Volume 10, Issue 3, Page(s) 220–229
Abstract: A semimechanistic 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 ... ...
| Abstract | A semimechanistic 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 patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. 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 (i.v.) 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. 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. |
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| MeSH term(s) | Administration, Intravenous ; Animals ; Antibodies, Monoclonal, Humanized/administration & dosage ; Antibodies, Monoclonal, Humanized/pharmacokinetics ; Antibodies, Monoclonal, Humanized/therapeutic use ; Computer Simulation ; Dose-Response Relationship, Drug ; Drug Development ; Evaluation Studies as Topic ; Humans ; Immune Checkpoint Inhibitors/administration & dosage ; Immune Checkpoint Inhibitors/pharmacokinetics ; Killer Cells, Natural/drug effects ; Killer Cells, Natural/immunology ; Killer Cells, Natural/metabolism ; Male ; Metabolic Clearance Rate ; Mice ; Models, Animal ; NK Cell Lectin-Like Receptor Subfamily C/antagonists & inhibitors ; NK Cell Lectin-Like Receptor Subfamily C/chemistry ; NK Cell Lectin-Like Receptor Subfamily C/immunology ; Neoplasms/drug therapy ; Sensitivity and Specificity ; Tissue Distribution |
| Chemical Substances | Antibodies, Monoclonal, Humanized ; Immune Checkpoint Inhibitors ; KLRC1 protein, human ; NK Cell Lectin-Like Receptor Subfamily C ; monalizumab (3ZXZ2V0588) |
| Language | English |
| Publishing date | 2021-02-13 |
| Publishing country | United States |
| Document type | Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2697010-7 |
| ISSN | 2163-8306 ; 2163-8306 |
| ISSN (online) | 2163-8306 |
| ISSN | 2163-8306 |
| DOI | 10.1002/psp4.12592 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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