Chanu P1, Chen SC2, Wang N3, Li Z3, Uzor Ogbu4, Marchand M5, Li C3, Girish S3, Bruno R1
1 Clinical Pharmacology, Genentech/Roche, France; 2 Retrophin Inc, USA; 3 Clinical Pharmacology, Genentech Inc, USA; 4 Clinical Science, Genentech Inc, USA; 5 Certara Strategic Consulting, France
Objectives:
Multiple myeloma (MM) is a neoplasm characterized by the proliferation and accumulation of malignant plasma cells. End-organ damage resulting from MM includes hypercalcemia, renal insufficiency, anemia, and lytic bone lesions. MM remains incurable despite advances in treatment. Model-based approaches based on tumor dynamics have successfully been used to optimize drug-development in oncology [1]. A drug-independent link between tumor growth inhibition (TGI) metrics and overall survival (OS) was previously established for refractory MM patients where serum M-protein substitutes for tumor size as a marker of tumor burden [2,3]. Indeed M-protein is one of the criteria used to assess clinical response according to the International Myeloma Working Group Uniform Response Criteria. Model-based TGI metrics are biomarkers capturing treatment effect and predict for OS benefit in TGI-OS models, these models have been shown to be drug-independent in most cases [4]. To prevent any confusion, TGI metrics are renamed “M-protein dynamic metrics” in this work. Genentech/Roche has several clinical programs in MM. Flatiron Real World Data (RWD) were used to establish the link between M-protein dynamic metrics and OS based on recent clinical data, especially with multiple new therapies made available to MM patients during the last decade.
Methods:
Flatiron RWD from 1563 patients with relapse or refractory MM were used to develop a model linking M-protein dynamic metrics to OS. Patient data were selected when one the following four treatment combinations was administered: lenalidomide – dexamethasone (N=767), lenalidomide – dexamethasone – daratumumab (N=113), bortezomib – dexamethasone (N=577), and bortezomib – dexamethasone – daratumumab (N=106). Individual OS and M-protein data were used. Majority of the data was collected between 2013 and 2019. Median treatment duration was 4 months with an interquartile interval of 1 to 12 months. M-protein dynamic metrics were derived using an empirical bi-exponential model [5,6]. M-protein dynamic metrics (M-protein ratio to baseline at different time points, time to growth, growth rate, shrinkage rate) and 26 prognostic factors were first tested in a univariate analysis using a Cox proportional hazards regression model. Statistically significant covariates in the univariate analysis (p<0.05) were included in a full multivariate parametric distribution survival model. A backward deletion stepwise procedure was performed (p<0.01) to retain covariates in the final OS model. Patients’ data with complete covariate information were used to estimate model parameters while the remaining patients’ data (with incomplete covariate information) were used for model validation.
Results:
OS data followed a log-normal distribution. The final model was estimated on 438 patients’ data where full covariate information was available. Among all tested M-protein dynamic metrics and prognostic factors, growth rate (G=log(KG)) [6] was found to be the best predictor of OS. Patients with slower growth, better ECOG, higher creatinine clearance and albumin tended to have longer survival.
|
Parameter |
Value |
Std. Error |
p-value |
|
(Intercept) |
-2.664 |
0.8084 |
0.0009811 |
|
Log(KG (day-1)) |
-0.5767 |
0.08927 |
1.046e-10 |
|
ECOG 1,2,3 |
-0.3141 |
0.08445 |
0.0001995 |
|
Creatinine clearance (mL.min-1) |
0.01217 |
0.002523 |
1.429e-06 |
|
Albumin (g/L) |
0.05623 |
0.01446 |
0.0001007 |
|
Log(scale) |
0.1793 |
0.05653 |
0.001516 |
Model qualification was first performed on the same 438 patients using posterior predictive checks based on Kaplan Meier plots: prediction intervals derived from model-based simulations well included observed OS data in each treatment combination. A similar successful model qualification was performed on remaining 1125 patients for whom there was incomplete covariate information. Further model validation will also be performed using daratumumab Phase 3 randomized clinical trials (NCT02076009, NCT02136134) obtained in the YODA project (The Yale University Open Data Access, https://yoda.yale.edu/).
Conclusions:
A model linking M-protein dynamic metrics to OS could be developed and qualified based on RWD in MM and showed consistent results with a previous model [2]. The model can support drug development in MM: e.g. make OS inferences based on M-protein dynamic data obtained for a new agent, or support a Phase 3 design with virtual control. RWD open a new era for Model-Informed Drug Development.
References:
[1] Bruno R, Bottino D, De Alwis DP, Fojo T, Guedj J, Liu C, et al. Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models. Clin Cancer Res. 2019 Dec 23. doi: 10.1158/1078-0432.CCR-19-0287.
[2] Bruno R, Jonsson F, Zaki M, Jacques C, Swern A, Richardson PG, et al.. Simulation of Clinical Outcome for Pomalidomide Plus Low-Dose Dexamethasone in Patients with Refractory Multiple Myeloma Based on Week 8 M-Protein Response. Blood 2011;118:1881.
[3] Jonsson F, Ou Y, Claret L, Siegel D, Jagannath S, Vij R et al. A tumor growth inhibition model based on M-protein levels in subjects with relapsed/refractory multiple myeloma following single-agent carfilzomib use. Clinical Pharmacology Therapeutics: Pharmacometry and System Pharmacology, 2015;4:711-9.
[4] Bruno R, Mercier F, Claret L. Evaluation of tumor size response metrics to predict survival in oncology clinical trials. Clin Pharmacol Ther 2014;95:386-93.
[5] Stein WD, Gulley JL, Schlom J, Madan RA, Dahut W, Figg WD, et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res 2011;17:907-17.
[6] Claret, Jin JY, Ferté C, Winter H, Girish S, Stroh M et al. A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics. Clin Cancer Res. 2018;24:3292-8.
Reference: PAGE () Abstr 9552 [www.page-meeting.org/?abstract=9552]
Poster: Oral: Drug/Disease Modelling