Model Based Approach to Evaluate Isatuximab Monthly Dosing Regimen in Relapsed/Refractory Multiple Myeloma Patients
Bibliographic record
Abstract
Background: Isatuximab (Isa) is a CD38 monoclonal antibody with multiple modes of action for killing tumor cells through direct tumor targeting and immune cell engagement (Moreno, Clin Cancer Res, 2019). The addition of Isa to pomalidomide (P) and dexamethasone (d) was associated with a significant and clinically meaningful benefit in progression-free survival (PFS) in heavily pre-treated patients with relapsed/refractory multiple myeloma (RRMM) (ICARIA-MM, NCT02514668) (Attal, Lancet, 2019). Isa, in combination with Pd, is approved in the United States, the European Union, Canada, Australia, Switzerland, and Japan for the treatment of adult patients with RRMM who have received at least two prior therapies including lenalidomide and a proteasome inhibitor. Aim: The objectives were to characterize the relationship between serum M-protein kinetics and PFS in RRMM using data from the Phase 3 ICARIA-MM study and to simulate expected longitudinal serum M-protein and PFS when switching to a hypothetical monthly dosing regimen after 6 months. Methods: A joint model of serum M-protein dynamics and PFS was developed using data from 256 evaluable ICARIA-MM patients. Patients received Isa intravenously at 10 mg/kg once weekly (QW) for 4 weeks, then every other week (Q2W) for 28-day cycles in combination with standard Pd (Isa-Pd) or Pd alone in the control arm. A tumor growth inhibition model was used to describe the serum M-protein kinetics under treatment effects of Isa-Pd or Pd alone, in which Isa exposure was predicted using individual PK parameters obtained from the population PK analysis (Fau, Population Approach Group in Europe, 2019) and Pd exposure was predicted from K-PD model using dosing history. Trial simulations were then performed using individual PK/PD parameters of ICARIA-MM patients to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. Results: The joint model identified the instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and also identified baseline patient characteristics impacting serum M-protein kinetics (serum albumin and serum β2 microglobulin on the baseline serum M-protein levels and the non-IgG type on the serum M-protein growth rate, the serum M-protein slope), and PFS (presence of plasmacytomas). Non-IgG MM patients have similar behavior on serum M-protein kinetics for the first 60 weeks even with higher exposure and similar progression free survival compared to IgG MM patients supporting the non-dose adjustment based on IgG status. Clinical trial simulation of the ICARIA-MM Isa-Pd regimen demonstrated that switching all patients on treatment at 6 months to a monthly Isa regimen would shorten the median time to progression (TTP) (i.e. increase in serum M-protein greater than 25% and an absolute increase greater than 5 g/L compared to nadir) by 4.1 weeks and would shorten median PFS by 2.3 weeks (from 14.03 to 13.45 months). Based on TTP criteria, patients with no risk of earlier progression (57.7%) due to 6 months switch tend to have lower baseline tumor burden (lower serum M-protein and lower percent of bone marrow plasma cell) and better prognostic factors (higher glomerular filtration rate, higher albumin, lower β2 microglobulin). At 6 months, 85% of these patients had predicted stable "at least" VGPR status. Conclusions: Trial simulations supported the choice of the approved isatuximab 10 mg/kg QW/Q2W regimen and showed that switching to a monthly Isa regimen after 6 months may reduce clinical benefit in overall population. However, a subpopulation of patients with good prognosis and obtaining stable at least VGPR status by 6 months may switch to a monthly regimen after 6 months without compromising disease progression risk; this hypothesis will be tested in a prospective clinical trial. Model-based drug development has been successfully applied to support treatment decisions in RRMM patients. Disclosures Thai: Sanofi: Current Employment. Gaudel-Dedieu:Sanofi: Current Employment. Cerou:Sanofi: Current Employment. Sebastien:Sanofi: Current Employment. van de Velde:Sanofi: Current Employment, Current equity holder in publicly-traded company. Semiond:Sanofi: Current Employment. Veyrat-Follet:Sanofi: Current Employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".