Isatuximab plus carfilzomib and dexamethasone <i>versus</i> carfilzomib and dexamethasone in relapsed multiple myeloma patients with renal impairment: IKEMA subgroup analysis

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Abstract

Renal impairment (RI) is common in patients with multiple myeloma (MM) and new therapies that can improve renal function are needed. The phase III IKEMA study (clinicaltrials gov. Identifier: NCT03275285) investigated isatuximab (Isa) with carfilzomib and dexamethasone (Kd) versus Kd in relapsed MM. This subgroup analysis examined results from patients with RI, defined as estimated glomerular filtration rate <60 mL/min/1.73 m². Addition of Isa prolonged progression-free survival (PFS) in patients with RI (hazard ratio: 0.27; 95% confidence interval [CI]: 0.11–0.66; median PFS not reached for Isa-Kd versus 13.4 months for Kd [20.8-month follow-up]). Complete renal responses occurred more frequently with Isa-Kd (52.0%) versus Kd (30.8%) and were durable in 32.0% versus 7.7% of patients, respectively. Treatment exposure was longer with Isa-Kd, with median number of started cycles and median duration of exposure of 20 versus 9 cycles and 81.0 versus 35.7 weeks for Isa-Kd versus Kd, respectively. Among patients with RI, the incidence of patients with grade ≥3 treatment-emergent adverse events was similar between the two arms (79.1% in Isa-Kd vs. 77.8% in Kd). In summary, the addition of Isa to Kd improved clinical outcomes with a manageable safety profile in patients with RI, consistent with the benefit observed in the overall IKEMA study population.

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Most cited references 39

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International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma.

Shaji Kumar, Bruno Paiva, Kenneth Anderson … (2016)

Treatment of multiple myeloma has substantially changed over the past decade with the introduction of several classes of new effective drugs that have greatly improved the rates and depth of response. Response criteria in multiple myeloma were developed to use serum and urine assessment of monoclonal proteins and bone marrow assessment (which is relatively insensitive). Given the high rates of complete response seen in patients with multiple myeloma with new treatment approaches, new response categories need to be defined that can identify responses that are deeper than those conventionally defined as complete response. Recent attempts have focused on the identification of residual tumour cells in the bone marrow using flow cytometry or gene sequencing. Furthermore, sensitive imaging techniques can be used to detect the presence of residual disease outside of the bone marrow. Combining these new methods, the International Myeloma Working Group has defined new response categories of minimal residual disease negativity, with or without imaging-based absence of extramedullary disease, to allow uniform reporting within and outside clinical trials. In this Review, we clarify several aspects of disease response assessment, along with endpoints for clinical trials, and highlight future directions for disease response assessments.

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A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

A S Levey, J Bosch, J. B. Lewis … (1999)

Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.

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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, multicentre, open-label, phase 3 study

Michel Attal, Paul Richardson, S Vincent Rajkumar … (2019)

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Author and article information

Journal

Journal ID (nlm-ta): Haematologica

Journal ID (iso-abbrev): Haematologica

Journal ID (publisher-id): HAEMA

Title: Haematologica

Publisher: Fondazione Ferrata Storti

ISSN (Print): 0390-6078

ISSN (Electronic): 1592-8721

Publication date (Electronic): 14 October 2021

Publication date Collection: 01 June 2022

Volume: 107

Issue: 6

Pages: 1397-1409

Affiliations

[1 ]Centro Integrado de Hematologia e Oncologia, Hospital Mãe de Deus, Porto Alegre, Brazil

[2 ]Department of Medicine, University of California at San Francisco , San Francisco, CA, USA

[3 ]Department of Hematology, University of Nantes , Nantes, France

[4 ]Perth Blood Institute, Murdoch University , Perth, Western Australia, Australia

[5 ]Department of Internal Medicine, Hematology and Oncology, University Hospital Brno , Brno, Czech Republic

[6 ]Department of Hematology, Catholic Hematology Hospital and Leukemia Research Institute , Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

[7 ]Service d'Hématologie et Thérapie Cellulaire, CHU and CIC INSERM 1402, Poitiers Cedex, France

[8 ]Department of Hematology, Hôpital Saint-Antoine, Sorbonne University , INSERM UMRS 938, Paris, France

[9 ]Hospital Universitario Virgen del Rocio, Sevilla, Spain

[10 ]Department of Hematology, Ondokuz Mayıs University Faculty of Medicine , Samsun, Turkey

[11 ]Hôpital Maisonneuve-Rosemont, Université de Montréal , Montréal, Quebec, Canada

[12 ]Sanofi Research and Development, Vitry/Alfortville, France

[13 ]Aixial, Boulogne-Billancourt, France

[14 ]Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens School of Medicine , Athens, Greece

Author notes

Marcelo Capra marcelocapra@ 123456hotmail.com

Disclosures

MC is part of the speaker’s bureau of Amgen, Janssen, and Sanofi. TM has received research funding from Amgen, Janssen, and Sanofi; consults for GSK. PM has received honoraria from Amgen, Celgene, Janssen, Novartis, and Takeda; has a consulting or advisory role at Amgen, Celgene, Janssen, Novartis, and Takeda. RB has received research funding from AbbVie, Acerta Pharma, Alexion, Amgen, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, CSL Behring, Daiichi Sankyo, Janssen-Cilag, MorphoSys AG, Pfizer, Rigel Pharmaceuticals, Roche, Sanofi, and Takeda; has received honoraria from Bayer; has a consulting or advisory role at Janssen-Cilag, Roche; is part of the speaker’s bureau of Bayer. LP, C-KM, MRS, and MT have no conflicts of interest to disclose. XL has received honoraria from AbbVie, Amgen, Bristol Myers Squibb, Carsgen Therapeutics Ltd, Celgene, Gilead Sciences, Janssen-Cilag, Karyopharm Therapeutics, Merck, Mundipharma, Novartis, Oncopeptides, Pierre Fabre, Roche, Sanofi, and Takeda; has received non-financial support from Takeda. MM has received research funding from Adaptive, Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Jazz, Novartis, Sanofi, Stemline Therapeutics, and Takeda; has received honoraria from Adaptive, Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Jazz, Novartis, Sanofi, Stemline Therapeutics, and Takeda; has received non-financial support from Takeda; has a consulting or advisory role at Adaptive, Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Jazz, Novartis, Sanofi, Stemline Therapeutics, and Takeda. RLB has a consulting or advisory role at Celgene/Bristol Myers Squibb, Janssen, Amgen, Sanofi, and Takeda; has received research funding from Celgene/Bristol Myers Squibb. M-LR and SS are employed by Sanofi; may hold stock and/or stock options in the company. LM has a consulting or advisory role at Aixial (contracted by Sanofi). MD has a consulting or advisory role at Amgen, Bristol Myers Squibb, Celgene, Janssen, and Takeda.

Contributions

IKEMA Study Steering Committee members (TM, PM, MD) and employees of Sanofi (M-LR, SS) contributed to the conception/design of this study. All authors contributed to the provision of study material, data collection and analysis, as well as development/final approval of the manuscript.

Data sharing statement

Qualified researchers can request access to patient-level data and related study documents including the clinical study report, study protocol with any amendments, blank case report forms, statistical analysis plan, and dataset specifications. Patient-level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi’s data-sharing criteria, eligible studies, and process for requesting access are at: https://www.clinicalstudydatarequest.com.

Article

DOI: 10.3324/haematol.2021.279229

PMC ID: 9152981

PubMed ID: 34647444

SO-VID: f6f58a84-d604-4828-8999-eafe60de2f61

License:

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

History

Date received : 28 May 2021

Date accepted : 05 October 2021

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Figures: 3, Tables: 5, Equations: 0, References: 42, Pages: 13

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Isatuximab plus carfilzomib and dexamethasone versus carfilzomib and dexamethasone in relapsed multiple myeloma patients with renal impairment: IKEMA subgroup analysis

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Most cited references 39

International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma.

A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

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, multicentre, open-label, phase 3 study

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