Population PK-PD modeling and exposure-response analysis in multiple myeloma patients treated with intravenous forimtamig, a T-cell engaging 2:1 bispecific antibody targeting GPRC5D and CD3
Emilie Schindler (1), Cristina Santini (1), Suresh Vatakuti (1), Sara Belli (1)
(1) Roche Pharma Research and Early Development, Roche Innovation Center Basel, Switzerland
Introduction: Forimtamig is a bispecific T-cell engaging bispecific antibody that redirects T-cells to target and eliminate cells expressing GPRC5D, including malignant plasma cells. Forimtamig is currently under development in relapsed refractory multiple myeloma (RRMM) patients. Forimtamig is administered with double step-up dosing during Cycle 1 to mitigate the risk for severe cytokine release syndrome (CRS). B-cell maturation antigen (sBCMA) is expressed by malignant plasma cells and the dynamics of its soluble form (sBCMA) is a promising biomarker to identify early tumor response.
Objectives: To characterize forimtamig intravenous (IV) pharmacokinetics (PK) with a population PK (popPK) model, to describe sBCMA response using a tumor growth inhibition (TGI) model, and to investigate the relationship between CRS and exposure in patients with RRMM.
Methods: The popPK model was built using 1041 PK samples from 50 RRMM patients involved in the dose escalation part of a phase 1 study and treated with 0.006-10 mg of IV forimtamig, with step-up dosing on Day 1 and Day 8, followed by target dose Q2W or Q3W from Day 15 onward. The model explored linear and non-linear elimination. 309 sBCMA samples from 49 patients were used for the development of the popPK-TGI model. Models were developed using NONMEM v7.4.3. In addition, the relationships between model-derived exposure and the occurrence and severity of CRS after each of the doses in Cycle 1 were explored graphically and using logistic regression.
Results: Forimtamig PK was adequately described by a two-compartment model with linear clearance estimated to 29 mL/h. An additional time-varying clearance estimated to 123 mL/h at the start of treatment and decreasing exponentially with a half-life of 84 hours was necessary to obtain a good description of the data during step-up dosing. The estimated central and peripheral distribution volumes were 3.91 and 2.39 L, respectively. All parameters were estimated with good precision (RSE < 25%) and prediction-corrected visual predictive checks demonstrated the good predictive ability of the model. No significant trend between sex, body weight, or age with any of the PK parameters was identified. The TGI model described adequately the overall rapid decrease in sBCMA after treatment initiation. Forimtamig anti-tumor effect was driven by predicted forimtamig concentrations in bone marrow (assuming a serum to bone marrow ratio of 1) via an Emax model, with an estimated EC50 of 2.38 ng/mL. Finally, none of the evaluated relationships between exposure (maximum or average concentration) and CRS was statistically significant (p<0.01) after any IV dosing step during Cycle 1.
Conclusions: The developed popPK model properly characterized forimtamig clinical PK after IV administration of a wide range of doses. It will be further extended to support the development of subcutaneous administration. The popPK-TGI model describing sBCMA dynamics is suitable for in silico explorations of the tumor cell killing and probability of clinical response for a range of IV doses/schedules of interest.