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1
AN UPDATED TWO-YEAR SURVIVAL MIXTURE MODELING OF AXICABTAGENE CILOLEUCEL
(AXI-CEL) IN RELAPSED OR REFRACTORY LARGE B-CELL LYMPHOMA (R/R-LBCL)
Diakite I1, Lin VW2, Klijn SL3, Navale L2, Purdum AG2, Fenwick E4,Botteman M1, van Hout B5
1Pharmerit International, Bethesda, MD, USA, 2Kite, A Gilead Company, Santa Monica, CA, USA, 3Pharmerit International, Rotterdam, Netherlands, 4Pharmerit International, Oxford, United Kingdom,
5University of Sheffield, Sheffield, United Kingdom
Disclosures
Lin VW, Navale L, and Purdum AG are employees of andown equity in Kite, a Gilead Company
Diakite I, Klijn SL, Fenwick E, Botteman M, and van Hout Bare consultants retained by Kite, a Gilead Company
Disclosures
Lin VW, Navale L, and Purdum AG are employees of andown equity in Kite, a Gilead Company
Diakite I, Klijn SL, Fenwick E, Botteman M, and van Hout Bare consultants retained by Kite, a Gilead Company
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Diakite et al ISPOR 2019
Patients With Relapsed/Refractory LBCL Have Consistently Poor Outcomes• SCHOLAR-1
- Retrospective, international, patient-level, multi-institution study
- Largest reported analysis of outcomes in patients with relapsed/refractory large B cell lymphoma (LBCL)
- Demonstrated that these patients have a very poor prognosis1
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1. Crump M, et al. Blood. 2017
Diakite et al ISPOR 2019
Axicabtagene Ciloleucel (Axi-Cel) is a Promising Alternative for Patients With Relapsed/Refractory LBCL• In the two-year update of the ZUMA-1 trial of axi-cel, median OS was not reached
after a median follow-up of 27.1 months1
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1. Locke et al. Lancet Oncology 2019
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Diakite et al ISPOR 2019 5
Previously, Bansal et al Analyzed The ZUMA-1 One-Year Data Using Mixture Cure Modeling (MCM)
Bansal et al. Medical Decision Making. 2019
Diakite et al ISPOR 2019 6
Previously, Bansal et al Analyzed The ZUMA-1 One-Year Data Using Mixture Cure Modeling (MCM)
Bansal et al. Medical Decision Making. 2019
4
Diakite et al ISPOR 2019
Objective
• Analyze the 2-year ZUMA-1 survival data and compare additional extrapolation approaches, aiming to further explore the robustness of mixture cure modeling
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Diakite et al ISPOR 2019
Methods
• Model 1: Mixture cure modeling (MCM)
• Model 2: General mixture modelling (GMM)
• Model 3: Integrated Markov cure modeling (IMCM)
8
5
Diakite et al ISPOR 2019 9
Model 1: Mixture Cure Model and Clinical Rationale
Mixture
Progression free
Alive
Progressed
Disease-related death
Non-cured Patients
Diakite et al ISPOR 2019 10
Model 1: Mixture Cure Model and Clinical Rationale
Mixture
Non-disease-related death
Progression free
Alive
Progressed
Disease-related death
Non-cured Patients
Progression free
Alive
Cured Patients (only experience general mortality)
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Diakite et al ISPOR 2019 11
Results: Mixture Cure Model = Good Fit
KM of OS fitted to MCM (Weibull)
Months
Surv
ival
pro
bab
ility “Cure fraction” = 51.3%
Diakite et al ISPOR 2019
Model 2: General Mixture Model and Clinical Rationale
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Mixture
Non-disease-related death
Progression free
Alive
Progressed
Disease-related death
Non-cured Patients
Progression free
Alive
Cured Patients (only experience general mortality)
Progressed
Disease-related death
GMM relaxes the assumption of general population mortality of the cure group
7
Diakite et al ISPOR 2019 13
Results: General Mixture Model = Good Fit
KM of OS fitted to GMM (log-logistic & gamma)Su
rviv
al p
rob
abili
ty
Months
“Good prognosis fraction” = 53%
Diakite et al ISPOR 2019
• All states modeled explicitly • State membership determined via transition
probability matrices• Clinical events explicitly related• Structural link between OS and PFS• A unique “cure fraction” can be determined
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Methods: Using the Structural Link Between OS and PFS to Inform OS Extrapolation
Progression free
Post-progression
Death
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Diakite et al ISPOR 2019
Progression free
Post-progression
cured
Post-progression
non-cure
Progression and/or Death
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Model 3: Integrated Markov Cure Model
Mixture
Disease-related death
Diakite et al ISPOR 2019
Pre-progression Cure (only experience general mortality)
Progression free
Progression free
Post-progression
cured
Post-progression
non-cure
Progression and/or Death
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Model 3: Integrated Markov Cure Model
Mixture
Disease-related death
Non-disease-related death
Post-progression
cured
Post-progression
non-cure
Disease-related death
9
Diakite et al ISPOR 2019 17
Results: Integrated Markov Cure Model Showed Consistent Results With Other Mixture Models
KM of OS & PFS fitted to IMCM
“Cure fraction” = 50.5%A “cure fraction” here is defined as the sum of the proportion of patients who neither progress nor die due to disease and those who achieved post-progression cure
Months
Surv
ival
pro
bab
ility
Diakite et al ISPOR 2019
NA
51% 53% 51%
0%
20%
40%
60%
TM MCM GMM IMCM
Cure fraction
10.5813.47 13.46 13.35
0
5
10
15
TM MCM GMM IMCM
Life Years
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Models Results and Comparison
TM, traditional model; MCM, mixture cure model; GMM, general mixture model; IMCM, integrated Markov cure model
Months
Surv
ival
pro
bab
ility
10
Diakite et al ISPOR 2019
Conclusions
• Analyzing the updated ZUMA-1 two-year data, both GMM and IMCM showed good model fit and consistent results to MCM.
• All three mixture models (MCM, GMM, and IMCM) are relevant and supported by the clinical rationale
• These results support the long-term survival associated with axi-cel in R/R-LBCL, driven by ≥50% long-term OS rates.
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Diakite et al ISPOR 2019
Contacts
Ibrahim Diakite, PhD ([email protected])
Scientist – Pharmerit International
Pharmerit International│4350 East-west Highway, Suite 1110│
Bethesda, MD 20878
20
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Diakite et al ISPOR 2019
• All models goodness of fit were based on Akaike’s Information Criterion (AIC)
• The AIC of the IMCM cannot be directly compared to that of the other models as it includes the additional log-likelihood estimate for the PFS
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Model Selection Criteria
452.2 439.3 442.1
916.6
0
200
400
600
800
1000
TM MCM GMM IMCM
AIC