A disease model for Multiple Myeloma developed using Real World Data and validated on Phase 3 clinical trials

Chanu Pascal1, Li Zao2, Samineni Divya2, Susilo Monica2, Jin Y Jin2, Li Chunze2, Bruno René3

1: Clinical Pharmacology, Genentech/Roche, Lyon, France; 2: Clinical Pharmacology, Genentech Inc, South San Francisco, USA; 3:Clinical Pharmacology, Genentech/Roche, Marseille, France

Objectives:

Multiple myeloma (MM) is a neoplasm characterized by the proliferation and accumulation of malignant plasma cells. 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]. Model-derived tumor dynamics also called tumor growth inhibition (TGI) metrics capture the treatment effect as tumor biomarkers and are predictive of overall survival (OS) benefit, based on TGI-OS models, this mathematical framework has been shown to be drug-independent in most cases [2]. M-protein is measured longitudinally during clinical trials, it reflects tumor burden and is one of the criteria used to assess clinical response according to the International Myeloma Working Group Uniform Response Criteria. A drug-independent link between M-protein dynamic metrics and OS was previously established for refractory MM patients [3,4]. Real World Data (RWD) were used to establish the link between M-protein dynamic and OS based on recent clinical data, especially with multiple new therapies made available to MM patients during the last decade.

Methods: 

Data from an electronic health record (EHR)-derived de-identified database, Flatiron Health in 2954 patients with relapsed or refractory MM were used to develop a model linking M-protein dynamic metrics to OS across multiple lines of therapy. Patients received the following treatments either as single agent or in combination: belantamab, bortezomib, daratumumab, dexamethasone, lenalidomide, pomalidomide, selinexor. Individual OS and M-protein data were used. Majority of the data was collected between 2014 and 2021. M-protein dynamic metrics were derived using an empirical bi-exponential model [5,6]. M-protein dynamic metrics such as M-protein ratio to baseline at different time points, time to growth, growth rate, shrinkage rate as well as 23 baseline 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 OS model. A backward deletion stepwise procedure was performed (p<0.01) to retain covariates in the final OS model. External model validations were performed using daratumumab Phase 3 randomized clinical trials POLLUX, CASTOR and APOLLO (NCT02076009, NCT02136134, NCT03180736) obtained in the YODA project (The Yale University Open Data Access, https://yoda.yale.edu/) [7,8,9].

Results: 

OS data followed a log-normal distribution. Among all tested M-protein dynamic metrics and prognostic factors, growth rate (log(KG)) [6] was found to be the best predictor of OS. Longer survival was predicted in patients with slower growth rate, lower ECOG status and lactate dehydrogenase (LDH), higher albumin, hemoglobin and creatinine clearance, earlier line of therapy and in females. Although there was no treatment effect in the model, posterior predictive checks showed that the model was able to simulate the OS distribution for each of the 15 treatments, this confirms that it was treatment-independent.

The model was validated by simulating Phase 3 clinical trial outcomes in POLLUX, CASTOR and APOLLO studies: e.g. for POLLUX and CASTOR the simulated OS hazard ratio [95% PI] was 0.42 [0.25;0.66] and 0.55 [0.37;0.78] respectively while the observed values were 0.50.

Conclusions: 

A model linking M-protein dynamic to OS in MM could be developed based on RWD and qualified in predicting independent Phase 3 trials. The model is consistent with a previous one [3] and can support drug development in MM: e.g., make OS inferences based on early M-protein dynamic data obtained for a new agent. RWD opens new opportunities for Model-Informed Drug Development in the absence of historical Phase 3 clinical trials.

References:
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[7] Dimopoulos MA, Oriol A, Nahi H, San‑Miguel J, Bahlis NJ, Usmani SZ et al. Daratumumab, lenalidomide, and dexamethasone for multiple myeloma. N Engl J Med 2016;375:1319-31.
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[9] Dimopoulos MA, Terpos E, Boccadoro M, Delimpasi S, Beksac M, Katodritou E et al. Daratumumab plus pomalidomide and dexamethasone versus pomalidomide and dexamethasone alone in previously treated multiple myeloma (APOLLO): an open-label, randomised, phase 3 trial. Lancet Oncol 2021;22:801-12.

Reference: PAGE 32 (2024) Abstr 11094 [www.page-meeting.org/?abstract=11094]

Oral: Real-world data (RWD) in pharmacometrics