Marc Cerou (1), Hoai-Thu Thai (1), Dorothee Semiond (2), Franck Dubin (3), Christine Veyrat-Follet (1)
(1) Translation Disease Modeling, Digital and Data Science, Sanofi, France; (2) Translational Medicine and Early Development, Sanofi, United States; (3) Medical Operations, Research and Development, Sanofi, France
Objectives: Isatuximab (Isa) is an IgG1 monoclonal antibody that binds a specific epitope on the CD38 transmembrane glycoprotein. Isa has recently been approved in combination with pomalidomide (P) and dexamethasone (d) for the treatment of adult patients with relapsed/refractory multiple myeloma (RRMM) who have received ≥2 prior therapies, including lenalidomide and a proteasome inhibitor. Isa kills MM cells via multiple modes of action, including direct tumor targeting and immune cell engagement [1-4]. In the randomized Phase 3 study, ICARIA (NCT02990338) [5], Isa in combination with Pd was compared to Pd alone in patients with RRMM. The primary outcome was the prolongation of progression-free survival (PFS).
Models describing the longitudinal dynamics of disease burden are typically based on serum M-protein only [6]. The aim of this analysis was to develop a joint model for multiple composite endpoints (serum and urine M-protein as tumor burden biomarkers and PFS) to evaluate the treatment effect of Isa in combination with Pd and its association to PFS.
Methods: Of 307 patients included in the ICARIA-MM trial, 291 were considered evaluable with ≥2 measurements of serum or urine M-protein. PFS is defined as the time from randomization to the date of progressive disease (PD) or death from any cause, whichever comes first. PD is defined by the International Myeloma Working group response criteria and depends on the level of M-protein (serum and 24 h urine) and radiography (appearance of new lesions, definite increase in the size of existing bone lesion[s], or soft tissue extramedullary disease) [7].
The evolution of tumor burden was characterized by analyzing both the serum and urine M-protein dynamics data. In this model, individual pharmacokinetics (PK) parameters for Isa (from a PK model) and dosing history for Pd (from a Kinetic-Pharmacodynamic [K-PD] model) were used to predict drug exposure. A covariate model was then built using the COSSAC algorithm implemented in Monolix [8] to examine the influence of baseline covariates on M-protein kinetics. Different link functions were tested to estimate the association between longitudinal evaluation and the risk of progression, characterized in a separate step by a parametric time-to-event model.
Parameters were estimated by maximizing the exact joint likelihood with the Stochastic Approximation Expectation-Maximization (SAEM) algorithm implemented in Monolix 2019R1. Model selection was based on the corrected Bayesian information criteria (BICc), and model evaluation was done through residual- and simulation-based graphical diagnostics, parameters uncertainty, relevant interpretation of the parameters, and clinical pertinence.
Results: In the drug effect model that included 148 patients from Isa arm, Isa PK was described through a two-compartment model [9] with parallel linear and nonlinear (Michaelis Menten) elimination and time-varying linear clearance function. A K-PD model using Pd history was used to predict drug exposure in both Isa-Pd and Pd arm. In the mechanistic model for serum/urine M-protein, a latent variable (Precursor [PC] compartment) was used to characterize the tumor burden using a tumor growth inhibition model, with development of drug resistance across time. M-protein was produced from the PC compartment at different rates in serum and in urine with a shared first order elimination. In the time-to-event parametric model for PFS, baseline hazard was described using a log-logistic distribution and the slope of both serum and urine M-protein was the best predictor for progression.
Individual fits show that the model can adequately describe both serum and urine M-protein individual profiles and PFS over time, in different subpopulations. Patients treated with Isa-Pd have a decreased risk of progression. In addition, patients with better prognosis at baseline (higher albumin/alkaline phosphatase, lower β2 microglobulin and lactate dehydrogenase) tend to have a decreased risk of progression. Non-IgG patients tend to have an increased risk of progression after 40 weeks.
Conclusions: The present joint model successfully described multiple composite endpoints in ICARIA trial, i.e., the time-course of both serum and urine M-protein [6] and their impact on PFS in patients with RRMM receiving Isa-Pd or Pd. Several covariates were found to be significantly associated with both the dynamics of disease severity and PFS, thus predicting the benefit of Isa in RRMM patients.
Funding: Sanofi
References:
[1] Deckert J et al. SAR650984, a Novel Humanized CD38-Targeting Antibody, Demonstrates Potent Antitumor Activity in Models of Multiple Myeloma and Other CD38+ Hematologic Malignancies. Clin Cancer Res. 2014; 20(17): 4574-83.
[2] Martin et al. Therapeutic Opportunities with Pharmacological Inhibition of CD38 with Isatuximab. Cells. 2019; 8(12): 1522.
[3] Moreno et al. The Mechanism of Action of the Anti-CD38 Monoclonal Antibody Isatuximab in Multiple Myeloma. Clin Cancer Res. 2019; 25(10): 3176-3187.
[4] Zhu et al. Isatuximab Acts Through Fc-Dependent, Independent, and Direct Pathways to Kill Multiple Myeloma Cells. Front Immunol. 2020; 11: 1771.
[5] Attal M et al. Isatuximab plus pomalidomide and low-dose dexamethasone versus pomalidomide and low-dose dexamethasone in patients with relapsed and refractory multiple myeloma (ICARIA-MM): a randomised, multicenter, open-label, phase 3 study. Lancet. 2019; 394(10214): 2096-2107.
[6] Jonsson F et al. A tumor growth inhibition model based on M-protein levels in subjects with relapsed/refractory multiple myeloma following single-agent carfilzomib use. CPT Pharmacometrics Syst Pharmacol. 2015; 4(12): 711-9.
[7] Kumar S et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17(8):e328-346.
[8] Monolix version 2019R1. Antony, France: Lixoft SAS, 2019. http://lixoft.com/products/monolix/
[9] Fau JB et al. Drug-Disease Interaction and Time-Dependent Population Pharmacokinetics of Isatuximab in Relapsed/Refractory Multiple Myeloma Patients. CPT Pharmacometrics Syst Pharmacol. 2020; 9(11): 649-658.
Reference: PAGE 29 (2021) Abstr 9599 [www.page-meeting.org/?abstract=9599]
Poster: Drug/Disease Modelling - Oncology