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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

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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)

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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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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

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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

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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

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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 (idiakite@pharmerit.com)

Scientist – Pharmerit International

Pharmerit International│4350 East-west Highway, Suite 1110│

Bethesda, MD 20878

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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