Adherence and Oral Therapies in Lymphoma and CLL: A Lymphoma - - PowerPoint PPT Presentation

Adherence and Oral Therapies in Lymphoma and CLL: A Lymphoma Research Foundation Workshop October 19, 2017

Welcome

Meghan Gutierrez Chief Executive Officer Lymphoma Research Foundation

Workshop Co-Chairs

Jonathan Friedberg, MD, MMSc James P. Wilmot Cancer Institute, University of Rochester Michael Williams, MD, ScM University of Virginia Cancer Center

Workshop Steering Committee

Christopher Flowers, MD Winship Cancer Institute of Emory University Neil Kay, MD Mayo Clinic Rochester John P. Leonard, MD Weill Cornell Medicine and New York Presbyterian Sonali Smith, MD The University of Chicago

Workshop Background

• Oral Therapies in Lymphoma

Workshop convened Fall 2015 in Washington, D.C.

• First multi-stakeholder meeting

to explore changing nature of lymphoma/CLL treatment, implications of oral therapies

• Expert presentations and

discussion outlined key issues,

• pportunities, and challenges

facing the community

Workshop Findings

• Three core themes emerged from the 2015 workshop:
• Impact of oral therapies on a heterogeneous disease like lymphoma

is vast and therefore can serve as a case study for other cancers

• Numerous challenges including adherence, monitoring, toxicity

management, patient education, and cost

• Research plays essential role in ensuring HCPs and patients have

the information and treatment choices required to maximize

• utcomes and ensure quality of life

Workshop Findings

• Adherence focus areas:
• Challenges related to adherence highly complex and difficult to

quantify

• Numerous barriers exist to accurately measure adherence
• Existing methods of adherence assessment and emerging

technologies could eventually play role in both measuring and supporting patient adherence

• Little data exists on the clinical impact of adherence

Program Goals

• Review epidemiology/forms

nonadherence, methods of adherence assessment, and barriers to accurately measuring adherence

• Identify primary causes of

nonadherence as well as related adherence interventions and tools

• Discuss the design and

implementation of studies focused

• n adherence in lymphoma/CLL

Workshop Agenda

9:15 AM Patient Adherence and Oral Anticancer Treatment Joseph Greer, PhD, Massachusetts General Hospital 10:00 AM Utilization and Adherence of Oral Anticancer Agents: Perspectives and Opportunities from the National Cancer Institute (NCI) Wendy Nelson, PhD, MPH, Basic Biobehavioral and Psychological Sciences Branch, NCI 10:20 AM Adherence in the TKI Era, or How to Run the CML Marathon Michael J. Mauro, MD, Weill Cornell Medical College 10:45 AM Break 11:00 AM Oral Therapies and Adherence in Lymphoma Roundtable Moderated by Drs. Williams and Friedberg Christopher Flowers, MD, Winship Cancer Institute of Emory University John P. Leonard, MD, Weill Cornell Medical Center Sonali Smith, MD, The University of Chicago 12:15 PM Summary and Next Steps: Areas and priorities for future investigation 12:30 PM Lunch

Patient Adherence & Oral Anticancer Treatment

Joseph Greer, Ph.D.

Program Director, Center for Psychiatric Oncology & Behavioral Sciences Associate Director, Cancer Outcomes Research Program

Overview

Review rates and correlates of adherence to oral chemotherapy Discuss development of a mobile app intervention to improve symptom monitoring and adherence to

• ral chemotherapy

Describe results of an randomized controlled trial of the mobile app intervention

Background

The increased use of oral chemotherapy has revolutionized cancer care through improved:

• Disease outcomes and patient survival
• Convenience of treatment administration
• Quality of life by avoiding problems with IV infusion

However, patients generally receive less supervision and support for adherence and monitoring of side effects as opposed to directly-

• bserved infusion chemotherapy.

• Documentation of
• ral versus IV

chemotherapy plans in 175 patients with metastatic NSCLC

• ASCO Quality

Oncology Practice Initiative

Greer et al. J Oncol Pract, 2014

Background

Background

Rates of adherence to

• ral cancer therapies

range from 16% to 100% A variety of patient, clinician, treatment, and system factors are associated with non-adherence

Ruddy et al. CA: A Cancer Journal for Clinicians, 2009 Greer et al. The Oncologist, 2016

Background

51 studies on rates and/or correlates of adherence (ranging from 46% to 100%)

• Methods of adherence assessment included:
• plasma level (n=1)
• medical chart review (n=3)
• electronic monitoring (n=7)
• pill count (n=5)
• pharmacy/insurance records (n=32)
• self report by patient (n=25), physician (n=7), family (n=3)

Greer et al. The Oncologist, 2016

Pilot study of 90 patients with CML, metastatic NSCLC, RCC, BC

• Adherence per MEMS

– Mean = 89.3% – Less than 90% = 25.6%

• Predictors of better

adherence:

– Improved symptom distress,

mood, QOL, satisfaction with providers and treatment, and perceived burden to others

Jacobs et al. J Oncol Pract, 2017

10 20 30 40 50 60 70 80 90

CML NSCLC RCC BC Percentage of Patients with Greater than 90% Adherent to Oral Chemotherapy

Background

Specific Aims

To develop a patient-centered, smartphone mobile app for individuals with cancer prescribed oral chemotherapy To test the effect of the mobile app on the following:

• Adherence to oral chemotherapy
• Symptoms and treatment side effects
• Quality of life
• Satisfaction with cancer care

Research Design & Methods

Two phase study:

• Mobile app development (year 1)
• Randomized controlled trial (years 2 and 3)

Multidisciplinary team of medical oncologists, psychologists, psychiatrist, and health technology experts Stakeholder engagement throughout the study

Stakeholder Engagement:

FOCUS GROUPS

Patients/ Family Oncology Clinicians Cancer Practice Settings Mobile App Development

FOCUS GROUPS BIANNUAL UPDATES

Complete Analyses Prepare Dissemination Health System, Community, and Society Trial and Data Collection Preliminary Analyses Patients/ Family Oncology Clinicians Cancer Practice Settings Health System, Community, and Society

Research Design & Methods

Patient Eligibility Criteria:

• Adult (age 18 or older)
• Diagnosis of cancer with current prescription for oral

chemotherapy

• Receiving cancer care at MGH or community affiliate
• Possess mobile smartphone (iOS or Android)

Research Design & Methods

Study Outcomes

• Medication adherence (Self-Report & MEMS)
• Symptoms and treatment side effects (MDASI)
• Quality of Life (FACT-G)
• Satisfaction with cancer care (FACIT-TS)

Phase 1: Mobile App Development

Multistep Process

• Initial interviews with

patients and clinicians

• Software development
• User-testing
• Roll-out
• Ongoing upgrades

Theory Design & Development User Testing/ Quality Assurance Deployment/ Maintenance

Group Feedback Implementation

Patients and Families “Connect patients with the same disease type for social support.” Feature: Education Library Module - Resources and Social Networking App includes a list of reputable, disease-specific resources for patients looking to connect with others. Healthcare Representatives “Provide patients with anchors and definitions of symptoms so they can appropriately determine the severity and urgency of their symptoms.” Feature: Symptom Reporting Module When a patient reports a symptom, app asks several questions about the frequency and duration before providing tailored feedback. Oncology Clinicians “The weekly symptom reports that are sent to clinicians should be concise and easy to understand.” Feature: Symptom Reporting Trends Module Weekly Symptom Reports provide a list of symptoms reported by the patient, as well as a color and numeric value (1-10) denoting severity. Practice Administrators “Provide resources and contact information for patients to use when they miss a dose of their medication.” Feature: Symptom Reporting Module –“Touch to call clinical team” feature Patients are provided with the study team contact information at

• baseline. Embedded in the symptom reporting feature is a

“touch to call” button for their specific clinic.

Phase 1: Mobile App Development

Phase 1: Mobile App Development

App Components:

• Chemo treatment plan
• Weekly symptom and

adherence reporting

• Education library
• Results feedback to
• ncology clinicians
• Energy Tracking
• Healthy Recipes
• Social Networking Sites

Phase 1: Mobile App Development

181 adult patients prescribed

• ral

chemotherapy with access to a Smartphone

Smartphone Mobile App Intervention (n=91) Usual Care Control Group (n=90)

Baseline Data Collection

R A N D O M I Z E D

Post assessment at approximately 12 weeks Post assessment at approximately 12 weeks

Phase 2: RCT Study Design

Approached (n=500) Enrolled (n=212) Baseline Assessment (n=181)

No Smartphone (n=178) Declined (n=110) Dropped out (n=28) Lost to follow-up (n=3)

Usual Care (n=90) Mobile App (n=91) Post Assessment (n=80)

• Withdrew (n=7)
• Deceased (n=3)
• Lost to follow-up (n=1)

MEMS Cap Collected (n=84)

• Did not return (n=6)
• Lost in mail (n=1)

Post Assessment (n=89)

• Deceased (n=1)

MEMS Cap Collected (n=85)

• Did not return (n=4)
• Data lost (n=1)

Phase 2: RCT Results

Phase 2: RCT Results

Sample Demographics Mean (SD) or N (%) Age Mean (SD) 53.5 (12.9) Female 94 (53.6) Race Caucasian Asian African American Hispanic/Latino/a Multiracial Other 159 (87.8) 10 (5.5) 5 (2.8) 4 (2.2) 2 (1.1) 1 (0.6) Ethnicity Hispanic 4 (2.2) Cancer Type Hematologic Lung Brain Breast Other 60 (33.1) 33 (18.2) 26 (14.4) 26 (14.4) 36 (19.9)

From 2/13/15 - 12/31/16, we enrolled and randomized 181 patients in a trial comparing oral chemotherapy mobile app to usual care.

• At baseline, 22% of

patients reported either forgetting or having problems remembering to take oral chemotherapy medication in past week.

Symptom %

Fatigue 88.7 Drowsy 76.8 Disturbed Sleep 68.8 Memory Problems 63.4 Distressed/Upset 61.7 Dry Mouth 51.1 Feeling Sad 48.6 Numbness/Tingling 47.9 Pain 44.6 Appetite Problems 43.0 Shortness of Breath 38.7 Nausea 37.6 Vomiting 13.4

Phase 2: Baseline Symptoms

Symptoms reported on baseline MD Anderson Symptom Inventory (MDASI)

Phase 2: RCT Results

Outcome Measure Usual Care % or M (SE) Mobile App % or M (SE) P- value Self-Report Adherence (MMAS) 76.7% 86.2% .19 Pill Bottle/Cap Adherence (MEMS) 79.2% 81.5% .57 Change in Symptom Severity (MDASI) 0.08 (0.15)

• 0.30 (0.15)

.60 Change in Quality of Life (FACT-G) Physical Wellbeing Social Wellbeing Emotional Wellbeing Functional Wellbeing

• 1.93 (1.13)

0.11 (0.23)

• 2.22 (0.50)

0.53 (0.40)

• 0.40 (0.41)

0.49 (1.19) 0.81 (0.48)

• 0.55 (0.53)

0.04 (0.41) 0.35 (0.43) .14 .29 .03 .39 .22 Change in Satisfaction with Care (FACIT-TS) Explanations Interpersonal Comprehensiveness Trust

• 0.34 (0.19)
• 0.29 (0.13)
• 0.89 (0.49)
• 0.24 (0.12)

0.04 (0.20) 0.07 (0.13) 0.08 (0.52)

• 0.24 (0.13)

.17 .06 .18 .97

Outcomes by Study Group in Overall Sample

Phase 2: RCT Results

MEMS Adherence Moderated by Baseline Self- Reported Adherence

Phase 2: RCT Results

Outcome Measure Usual Care % or M (SE) Mobile App N (%) or M (SE) P- value Pill Bottle/Cap Adherence (MEMS) 63.9% 86.2% .03 Change in Symptom Severity (MDASI) 0.05 (0.29)

• 0.04 (0.34)

.84 Change in Quality of Life (FACT-G) Physical Wellbeing Social Wellbeing Emotional Wellbeing Functional Wellbeing

• 2.70 (2.48)
• 0.85 (0.97)
• 1.69 (0.90)

0.14 (0.74)

• 0.30 (0.81)

1.36 (3.04) 1.46 (1.18)

• 0.53 (1.11)
• 0.001 (0.90)

0.43 (0.99) .31 .14 .42 .90 .57 Change in Satisfaction with Care (FACIT-TS) Explanations Interpersonal Comprehensiveness Trust

• 0.76 (0.45)
• 0.29 (0.14)
• 1.46 (1.01)
• 0.43 (0.25)

0.93 (0.56)

• 0.001 (0.18)

1.15 (1.24)

• 0.14 (0.31)

.03 .22 .11 .48

Outcomes by Study Group in Patients with Poor Self- Reported Adherence at Baseline (N=35)

Phase 2: RCT Results

MEMS Adherence Moderated by Baseline Self- Reported Anxiety Symptoms (HADS-Anxiety>7)

Phase 2: RCT Results

Outcomes by Group in Patients with Anxiety (N=46)

Outcome Measure Usual Care % or M (SE) Mobile App % or M (SR) P- value Self-Report Adherence (MMAS) 68.0% 95.7% .047 Pill Bottle/Cap Adherence (MEMS) 69.4% 85.5% .044 Change in Symptom Severity (MDASI) 0.02 (0.33)

• 0.30 (0.36)

.52 Change in Quality of Life (FACT-G) Physical Wellbeing Social Wellbeing Emotional Wellbeing Functional Wellbeing

• 4.56 (2.37)
• 0.27 (0.82)
• 4.07 (1.07)

0.63 (0.81)

• 0.85 (0.84)

2.28 (2.60) 1.92 (0.90)

• 1.36 (1.18)

0.62 (0.89) 1.10 (0.92) .06 .09 .10 .99 .13 Change in Satisfaction with Care (FACIT-TS) Explanations Interpersonal Comprehensiveness Trust

• 0.35 (0.34)
• 0.52 (0.23)
• 1.53 (0.85)
• 0.32 (0.28)

0.20 (0.37) 0.24 (0.24) 1.10 (0.93)

• 0.39 (0.30)

.29 .028 .047 .89

• Patients prescribed oral chemotherapy experience

concerning problems with adherence.

• A variety of psychosocial and treatment factors are

associated with adherence.

• Despite the advantages of oral chemotherapy,

symptoms and side effects occur at high rates, especially fatigue, drowsiness and sleep disturbance.

Conclusions

Conclusions

• Through a multi-step process involving numerous stakeholders,

we successfully developed a smartphone mobile app to support patients prescribed oral chemotherapy.

• In the overall sample, the mobile app was generally not

associated with outcomes but lead to improvements in self- reported social wellbeing compared to usual care and marginally significant improvement in satisfaction with interpersonal treatment with clinicians.

• Intervention effects on outcomes were stronger in patients who

reported poor adherence and high anxiety at baseline.

Acknowledgments

Stakeholder Partners Co-Investigators:

• Steven Safren, Ph.D.
• William Pirl, M.D.
• Jennifer Temel, M.D.
• Inga Lennes, M.D.
• Joanne Buzaglo, Ph.D.
• Kamal Jethwani, M.D.

Research Coordinators:

• Jamie Jacobs, Ph.D.
• Nicole Amoyal, Ph.D.
• Lauren Nisotel, B.S.
• Joel Fishbein, B.A.
• James MacDonald, B.A.
• Charn Xin C. Fuh, B.S.
• Molly Ream, B.A.

Funding Source:

PCORI (IHS-1306-03616), PI: Greer

Disclaimer

Funding Source:

PCORI (IHS-1306-03616)

The data and statements reported in this presentation are solely the responsibility

• f the author(s) and do not necessarily

represent the views of the Patient- Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.

LRF October 19, 2017

Oral Anticancer Agents: Utilization, Adherence, and Health Care Delivery

Wendy Nelson, PhD Behavioral Research Program Division of Cancer Control and Population Sciences

LRF October 19, 2017

The views expressed here are those of the presenter and do not represent any official position of the National Cancer Institute or National Institutes of Health.

42

Motivation

§ Targeted oral anticancer (OAC) medications have transformed cancer care

§ OAC medications offer many potential advantages over traditional IV chemotherapy − Superior effectiveness − Convenient home-based treatment − A sense of control over treatment − Avoid IV infusions, trips to hospital § Potential downsides of OAC medications − Complex treatment regimens, often in addition to other medication regimens − OAC medications require strict adherence to be effective − Patients/caregivers are responsible for symptom management − Financial burden − Difficulties acquiring OAC medication

43

Adherence to OAC Medication is Critical for Achieving a Therapeutic Response

§ Many oral agents are cytostatic § Optimal adherence: correct drug, correct dose, correct time, correct conditions § Imatinib adherence in CML <80% → no molecular response § Imatinib adherence in GIST → 2-year progression free survival 80% in continuous group vs. 16% in interrupted group § Full dose ibrutinib adherence in CLL → improved PFS in patients missing < 8 days of treatment compared to patients missing > 8 consecutive days of treatment § Suboptimal adherence → toxicity, more physician visits, higher hospitalization rates, unnecessary treatment changes, breakdown in the patient-provider relationship, increased health care expenditures, biased response rates in clinical trials § OAC medication adherence estimates vary widely: 20% - 100%

44

Treatment Schedules Are Complex

Sun Mon Tues Wed Thurs Fri Sat

Week 1

Lenolidomide Lenolidomide Ixazomib Lenolidomide Dexamethasone Lenolidomide Lenolidomide Lenolidomide Lenolidomide

Week 2

Lenolidomide Lenolidomide Dexamethasone Rest Rest Rest Rest

Week 3

Rest Rest Ixazomib Lenolidomide Dexamethasone Lenolidomide Lenolidomide Lenolidomide Lenolidomide

• Regimens are complex and often in addition to other medications
• Adherence is inversely related to dosing complexity
• “…clinicians must realize that lack of adherence typically reflects the complexity of the

regimen rather than willful or manipulative behavior from the patient.”

(2008 National Comprehensive Cancer Network Task Force Report on Oral Chemotherapy)

45

Characteristics of Medicare Part D Beneficiaries by Type of Oral Cancer Medication Used

Characteristics Imatinib (N=123) Mean [SD] Erlotinib (N=96) Mean [SD] Anastrozole (N=2,397) Mean [SD] Letrozole (N=1,078) Mean [SD]

Age

75.3 [6.39] 76.61 [6.55] 75.33 [7.08] 74.76 [7.07]

Number of comorbidities

1.93 [1.93] 3.13 [2.53] 2.04 [1.93] 1.99 [1.97]

Number of noncancer drugs

9.75 [6.63] 13.24 [6.00] 7.71 [4.87] 8.03 [4.99]

Kaisaeng et al. Journal of Managed Care & Specialty Pharmacy 2014; 20:669-675.

Polypharmacy

46

Polypharmacy

§ Underappreciated barrier to OAC medication adherence § Potential risks of polypharmacy − Drug-drug interactions (e.g., gefitinib/anticoagulants; imatinib/rifampin; lapatinib/fentanyl) − Drug-food interactions (e.g., ibrutinib/grapefruit, Seville oranges; lapatinib/grapefruit) − Prescribing cascade − Unnecessary treatment change − Increased health care costs (physician visits; hospitalizations) § Why do interventions to improve adherence to OAC medication focus only on the OAC medication?

47

Pharmacists

Pharmacists are uniquely positioned to: § Evaluate a patient’s medications for potential drug-drug and drug-food interactions § Provide medication counseling and information about safe handling of medication § Provide medication reconciliation § Contact the prescribing physician § Assist in procuring the OAC medication in a timely fashion § Help patients obtain financial assistance so they can afford their medication

48

Why do we need this funding opportunity announcement?

§ Utilization − Little information is available on the adoption of newly approved targeted oral agents − Little information is available on off-label use of oral agents − Are targeted oral agents being prescribed appropriately? § Adherence − Strict adherence is critical to achieving an optimal clinical outcome − Suboptimal adherence may lead to relapse, side-effects, unnecessary treatment changes, increased use of health care resources § Health Care Delivery − Health care systems need to establish new roles, responsibilities, safety standards, patient education strategies for targeted oral therapies − Home-based treatment with targeted oral therapy requires new models of care coordination

49

Purpose

§ Assess and describe the current state of oral anticancer medication utilization, adherence, and delivery § Identify the structural, systemic, and psychosocial barriers to adherence § Develop models and strategies to improve safe and effective delivery of oral anticancer agents so that clinical outcomes are optimized

50

Utilization Challenges

§ Understand how OAC medications are being prescribed, used, and monitored − Disparities in access (e.g., rural and underserved populations, insurance) − Off-label use § Potential research questions: − What are the patient and provider characteristics associated with prescribing and use of OAC agents? − Do genetic testing requirements and health insurance coverage affect the utilization of OAC agents? − What are current trends in utilization of newly approved OAC therapies? − What are the patterns of off-label use of OAC medications?

51

Adherence Challenges

§ Understand how cognitive, affective, and motivational processes influence adherence behavior − Beliefs about the safety and effectiveness of a “pill” − Expectations about side effects, treatment outcome − Depression, anxiety § Understand the adherence needs of patients with cognitive, visual, physical, or health literacy limitations § Potential research questions: − How do patients perceive the risks associated with oral therapy, and how do these risk perceptions influence adherence? − Are there major differences in patterns of adherence depending on the duration or complexity

• f the treatment regimen?

− How do financial considerations (e.g., out-of-pocket costs) influence adherence?

52

Health Care Delivery Challenges

§ Understand how health care delivery systems are adapting to meet safety standards and the educational needs of patients and caregivers − Symptom management − Coordination of care § Potential research questions: − What system, health care team, provider, and patient-level factors are associated with the safe administration of oral agents? − How can pharmacy care models facilitate coordination of care for patients and health care teams? − What features of health care delivery systems lead to improved safety and adherence? − What models of care help to enhance symptom self-management?

53

Considerations

§ Studies may be observational/descriptive or include interventions;

• bservational studies should emphasize modifiable factors for future

interventions § Studies may focus on utilization, adherence, or health care delivery issues related to oral anticancer agents (no need to study everything) § Consult your Program Director if you have questions

54

Research Priorities

§ Cross-disciplinary collaborations (e.g., clinical pharmacists, research nurses, social workers, financial navigators) § Studies focused on newer targeted OAC agents § Primary data collection is encouraged

55

Grant Mechanisms – R01 and R21

NIH Research Project Grant (R01) PA-17-060 NIH Exploratory/Developmental Grant (R21) PA-17-061

§ Supports discrete, specified, circumscribed research projects § Most commonly used grant mechanism § No specific dollar limit − Advance permission required for >$500K direct costs in any year § 3-5 years of funding § Standard receipt dates § Supports exploratory/developmental research projects § May be used for pilot or feasibility studies § Preliminary data not required § Direct costs for the two-year project period may not exceed $275K § 2 years of funding § Standard receipt dates

For more information: grants.nih.gov/grants/funding/funding_program.htm#RSeries

56

Read the funding opportunity announcements carefully

§PA-17-060 (R01) https://grants.nih.gov/grants/guide/pa-files/PA-17-060.html §PA-17-061 (R21) https://grants.nih.gov/grants/guide/pa-files/PA-17-061.html § Upcoming R01 application due dates: February 5, 2018; June 5, 2018 § Upcoming R21 application due dates: February 16, 2018; June 16, 2018 § Start the process early! Allow time for registration in Grants.gov and NIH eRA Commons § Verify successful submission by checking the eRA Commons

57

Program Contacts

Wendy Nelson, PhD Nelsonw@mail.nih.gov 240-276-6971 Adherence Kate Castro, RN, MS, AOCN Kathleen.Castro@nih.gov 240-276-6834 Health Care Delivery Kelly Filipski, PhD Kelly.Filipski@nih.gov 240-276-6841 Utilization

58

U.S. Department of Health & Human Services National Institutes of Health / National Cancer Institute cancercontrol.cancer.gov 1-800-4-CANCER

Michael J. Mauro, MD

Leader, Myeloproliferative Neoplasms Program

Memorial Sloan Kettering Cancer Center, New York, NY

Adherence in the Tyrosine Kinase Inhibitor Era,

• r How to Run the CML Marathon

Background

the success story of ABL kinase inhibitors in CML

The history of CML is long, the kinase inhibitor era short

1845

First description of CML

1865

Fowlers’s solution -1% arsenic trioxide

2001

Imatinib

1879

Staining methods for blood

1903 1953 1983 1965

Radiotherapy Busulfan Hydroxyurea Interferon

1968

BMT

2006

Dasatinib Nilotinib

2012

Bosutinib Ponatinib

1960: Nowell & Hungerford describe the Philadelphia Chromosome 1973: Janet Rowley describes the 9:22 translocation 1998: After seminal preclinical work first clinical trials commence with STI571 (imatinib); 1999, target inhibition validated, resistance identified (T315I)

1845: John Hughes Bennett reported a “Case of Hypertrophy of the Spleen and Liver in which Death Took Place from Suppuration of the Blood” in the Edinburgh Medical Journal; Virchow in Germany wrote up a similar observation

2016

Generic Imatinib

1983: Nora Heisterkamp and John Groffen, with

• thers, describe the BCR-

ABL fusion gene product

Huang et al, Cancer 118:3123-3127, 2012. Bower H et al, J Clin Oncol 34:2851-57, 2016.

20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

• Incidence 4700 per year
• Age-matched mortality ratio vs

normal population = 1.50

• Accounts for increased US

Year Number of Cases

10x greater steady state number

• f CML patients in US

by 2050

CML is an increasingly prevalent and survivable cancer

lifespan prevalence

Saußele S et al. Blood 2015;126:42-49

CML IV Study: Comorbidity effect on CML TKI response, transformation

As measured by the Charleson Comorbidity Index (CCI)

Charlson Comorbidity Index (CCI) calculated including age Charlson Comorbidity Index (CCI) calculated without age

CML IV Study: Comorbidity effect on survival, irrespective of age

Comorbidites trump CML response!

Saußele S et al. Blood 2015;126:42-49

At present, five oral, small molecular kinase inhibitors approved in the US for Ph+ Leukemia: a ‘spoil of riches’; more on the way?

1st Gen. TKI 2nd Gen. TKIs 3rd Gen. TKI

2001 Novartis (1st line) 2007/2010 BMS (1st, 2nd line) 2012/2015 IL-YANG: (1st, 2nd line) 2012 Pfizer (2nd/3rd line) 2012 Ariad (2nd?/3rd line) 2007/2010 Novartis (1st, 2nd line)

Radotinib (IY5511) Imatinib (STI571) Dasatinib (BMS354825) Nilotinib (AMN107) Bosutinib (SKI606) Ponatinib (AP24534)

(1st/2nd/3rd line 2018?)

4th Gen. TKI (allosteric): ABL001

South Korea

• nly

Know Your Tools: Comparing TKI Toxicity in CML

Issue Imatinib Nilotinib Dasatinib Bosutinib Ponatinib

Dosing QD/BID, with food BID, without food (2h) QD, w/ or w/o food QD, with food QD, w/ or w/o food Long term safety Most extensive Extensive; Emerging toxicity Extensive; Emerging toxicity Extensive, No emerging toxicity More limited but increasing; Emerging toxicity Heme toxicity intermediate least Most severe; ASA-like effect; lymphocytosis ~dasatinb in 2nd, 3rd line; ~nilotinib in 1st line éthrombocytopenia ASA-like effect Non- Heme toxicity Edema, GI effects, êPhos élipase, ébili, échol, églu Black box: QT prolongation; screening req’d Pleural / pericardial effusions Diarrhea; transaminitis élipase, pancreatitis; rash; hypertension; Black box: vascular

• cclusion, heart failure,

and hepatotoxicity Emerging toxicities early question re: CHF; ?late renal effects

Vascular events (ICVE, IHD, PAD)

PAH (pulmonary arterial hypertension) ? Mild renal effects

Vascular events (ICVE, IHD, PAD, VTE)

NCCN Guidelines Version 1.2018 Chronic Myeloid Leukemia

NCCN Guidelines Index Table of Contents Discussion BCR-ABL1 (IS) 3 months 6 months 12 months >12 months >10%f YELLOW RED >1%–10% GREEN YELLOW RED 0.1%–1% GREEN YELLOW <0.1% GREEN RED

• Evaluate patient compliance and drug interactions
• Mutational analysis

Switch to alternate TKI (CML-5) and Evaluate for HCT (CML-6) YELLOW

• Evaluate patient compliance and drug interactions
• Mutational analysis

Switch to alternate TKI (CML-5)

• r Continue same TKI (CML-F)g
• r Dose escalation of imatinib (to a max of 800 mg)

and Evaluate for HCT (CML-6) GREEN

• Monitor response (CML-F) and side efgects

Continue same TKI (CML-F)h

Patients with Achievement of response milestones must be interpreted within the clinical context. Patients with more than 50% reduction compared to baseline or minimally above the 10% cutofg can continue the same dose of dasatinib or nilotinib for another 3 months.

RESPONSE MILESTONESc,e CLINICAL CONSIDERATIONS SECOND-LINE AND SUBSEQUENT TREATMENT OPTIONS

Green/Yellow/Red Lights

1Inferred from MR2.0; 2MR4.0 rather than MR4.5

IRIS (IM400) IM400 ENEST/DA SISION TIDEL I (IM600) TIDEL II (IM600) SPIRIT FRANCE (IM600) ENESTnd (NIL) DASISION (DAS) >10%@3mo

• 33%/36%

24% 12%

• 9%

16% CCyR @12mo 69% 65%/73% 88% 87%1 65% 80% 85% MMR@12mo 40% 27%/28% 47% 64% 49% 55% 46% MMR@24mo 55% 44%/46% 73% 73% 53% 71% 64% MR4.5@12mo

• 4%/---

18%2 19% 22%2 11% 5% MR4.5@24mo

• 9%/8%
• 34%

26%2 25% 17% OS@3y 92% 94%/93%

• 96%
• 95%

94%

International Standard (IS) qPCR

10% 1% 0.1% 0.01% 0.0032%

Early Molecular Response: <10% or 1-log (10x) drop from starting level Complete Cytogenetic Response: <1% or 2-log (100x) drop Major Molecular Response: <0.1% or 3-log (1000x) drop 4-log drop (<0.01%) 4.5 log drop, ‘MR4.5’, Complete Molecular Remission: <0.0032%; below the level of detection for standard labs

Early Molecular Response Complete Cytogenetic Response Major Molecular Response MR4

MR5-6?

MR4.5 ‘CMR’ Early Molecular Response Complete Cytogenetic Response Major Molecular Response MR4 MR4.5 ‘CMR’

eligible for ‘treatment free remission’ trials

Shrinking the iceberg: Response Expectations

Criteria for consideration of treatment free remission (TKI cessation): the rules as noted by the NCCN

Age ≥18 years. Chronic phase CML. No prior history of accelerated or blast phase CML. On approved TKI therapy (imatinib, dasatinib, nilotinib, bosutinib, or ponatinib) for at least three years. Prior evidence of quantifiable BCR-ABL1 transcript. Stable molecular response (MR4; ≤0.01% IS) for ≥2 years, as documented on at least four tests, performed at least three months apart. No history of resistance to any TKI. Access to a reliable QPCR test with a sensitivity of detection of ≥4.5 logs that reports results on the IS and provides results within 2 weeks. Monthly molecular monitoring for the first six months following discontinuation, bimonthly during months 7–24, and quarterly thereafter (indefinitely) for patients who remain in MMR (MR3; ≤0.1% IS). Consultation with a CML Specialty Center to review the appropriateness for TKI discontinuation and potential risks and benefits of treatment discontinuation, including TKI withdrawal syndrome. Prompt resumption of TKI, with a monthly molecular monitoring for the first six months following resumption of TKI and every 3 months thereafter is recommended indefinitely for patients with a loss of

• MMR. For those who fail to achieve MMR after six months of TKI resumption, BCR-ABL1 kinase domain

mutation testing should be performed, and monthly molecular monitoring should be continued for another six months. Reporting of the following to a member of the NCCN CML panel is strongly encouraged:

• Any significant adverse event believed to be related to treatment discontinuation.
• Progression to accelerated or blast phase CML at any time.

fantastic therapy + good monitoring= fantastic outcomes… ‘treatment free remission’

100

80 60 40 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Months from discontinuation of TKI

EURO-SKI: Survival without loss of MMR n=200; MR4 or greater, >2y (inclusion) Relapses, n=86 Relapses within 6 months , n=77

Saussele S, et al. EHA. 2014: [abstract LB-6214]

Relapsemol-free survival at 6 months : 61% (54-68)

Relapse free survival

Do Adverse Events Occur With TKI Withdrawal?

Patients All Grade (n) Patients Grade 3 (n) AEs All Grade (n) AEs Grade 3 (n) Musculoskeletal pain, joint pain, arthralgia 23 3 39 6 Other (sweating, skin disorders, folliculitis, depressive episodes, fatigue, urticaria, weight loss) 8 18 3

Musculoskeletal pain in CML patients after discontinuation of imatinib: a tyrosine kinase inhibitor withdrawal syndrome?

• J. Richter et al. J Clin Oncol. 2014 Sep 1;32(25):2821-3.

Tyrosine kinase inhibitor withdrawal syndrome: a matter of c-kit ? Response to Richter et al.

• Ph. Rousselot et al.

N=200; 222 AEs in 98 patients were reported 57 AEs in 31 patients were related to treatment stop, no grade 4

Mahon FX et al, Blood 2014 124:151

Summary I

• Highly treatable, functionally curable disease
• New paradigm of chronic therapy x years or indefinitely; normal life

span expected

• Multiple choices of oral agents:
• Overlapping and unique side effects
• ‘dealer’s choice’ algorithm; careful monitoring, toxicity management, etc.

navigation required

• Momentum towards ‘treatment free remission’; realistic expectation for smaller

minority @ present

Adherence in CML

What do we know

Adherence to therapy is a critical factor for achieving deep molecular response linked to treatment free remission

• MMR
• adherence to

imatinib therapy, RR=11.17 (p=0.001)

• CMR
• adherence to

imatinib therapy, RR=19.35 (p=0.004)

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

“Drugs don’t work in patients who don’t take them”

C Everett Koop, M.D. former U.S. Surgeon General

“The most likely cause of sudden molecular relapse is that the patient stopped therapy”

T Hughes, Adelade AUS

Cartoon drawn by CML patient (OHSU, Portland, OR)

2,921 2,913 2,908 2,883 2,742 2,617 2,448 2,269 2,126 1,997 1,866 1,671 1,368 685 500 1,000 1,500 2,000 2,500 3,000 3,500

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13+

Months Patients

Tsang, J-P, Rudychev I, Pescatore SL. J Clin Onc. 2006; 24:330s. Abs 6119.

Pharmacy Record Analysis of 4043 Patients Prescribed Imatinib

• Patient adherence with imatinib therapy estimated at 75%.

– Only 41% of patients were more than 90% adherent.

Dramatic decline in persistency

– Month 4: near 100% – Month 5: 94% – Month 14: 23%

≥100% 95–99% 90–95% 80–90% <80% 90 80 70 60 50 40 30 20 10 Proportion of patients (%) Percentage of intended dose 13.8% 12.6% 8% 25.3% 40.2%

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

Long term adherence to imatinib

We’ve got issues: Doctor and Patient

• Challenging for busy oncologists to

be up to date on fine points of managing a relatively rare cancer such as CML (very diffuse CML care in US)

• TKI side effects not as dramatic as

those form traditional chemo and thus may be discounted

• Appointment/MD time short;

extenders (PAs, etc.) involved and nursing time limited/absent to counsel patients re: oral meds

• “Of course they are taking their

meds, it is cancer…”

• New paradigm in CML: chronic

maintenance therapy, ?indefinitely

• r at least x several years, unique
• Increasing attention needed to
• ptimize early tolerability and

monitor late effects for ‘endurance’ during chronic therapy

• Ongoing side effects may tempt

patients to secretly quit meds; not being taken seriously re: side effects may frustrate patients

• Financial burden, inadequate

insurance coverage, government costs, generics versus copies, etc.

• Medical system and society more
• riented to time-limited treatment,

not living life fully with cancer as a chronic disease: paradox of having cancer and still on treatment with no outward change in appearance

Why is toxicity management so important?

Adherence to therapy very likely to suffer in the case of poorly managed or under- managed side effects: ‘self-management’… Adherence to therapy, given the chronicity of therapy still necessary for patients with CML (advice ranges from 3+, 5+ to 8-10 yrs+) is one of the strongest predictors of

• utcome…

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

6-year probability of MMR according to the measured adherence rate

p<0.001

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

6-year probability of CMR according to the measured adherence rate

p=0.002

Imatinib plasma levels, viewed in the setting of an adherence study, were not an independent predictor of molecular response

Total population Adherent patients Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

Adherence is critical after dose escalation

72 66 60 54 48 42 36 30 24 18 12 6 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Months from start of imatinib therapy Probability of MMR Adherence >90%, n= 18 Adherence ≤90%, n= 14

p =0.0002

72 66 60 54 48 42 36 30 24 18 12 6 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 72 66 60 54 48 42 36 30 24 18 12 6 72 66 60 54 48 42 36 30 24 18 12 6 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Months from start of imatinib therapy Probability of MMR Adherence >90%, n= 18 Adherence ≤90%, n= 14

p =0.0002

72 66 60 54 48 42 36 30 24 18 12 6 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Months from start of imatinib therapy Probability of 4 log reduction

p =0.01

Adherence >90%, n= 18 Adherence ≤90%, n= 14

72 66 60 54 48 42 36 30 24 18 12 6 72 66 60 54 48 42 36 30 24 18 12 6 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Months from start of imatinib therapy Probability of 4 log reduction

p =0.01

Adherence >90%, n= 18 Adherence ≤90%, n= 14

• In 32 patients who failed to achieve MMR at 18 months the dose of

imatinib was increased to 600 mg od

• The adherence rate in these 32 patients was 86%, which is

significantly lower that that observed in the 55 subjects who remained on 400 mg (98.8%, p=0.02)

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

Cumulate incidence of loss of CCyR 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6

p<0.0001

Adherence rate ≤85%, n=18 Adherence rate >85%, n=69 Cumulate incidence of loss of CCyR 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 Cumulate incidence of loss of CCyR 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6

p<0.0001

Adherence rate ≤85%, n=18 Adherence rate >85%, n=69 Probability of imatinib failure 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 p<0.0001 Adherence rate ≤85%, n=18 Adherence rate >85%, n=69 Probability of imatinib failure 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 Probability of imatinib failure 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 p<0.0001 Adherence rate ≤85%, n=18 Adherence rate >85%, n=69

Poor adherent patients have a higher probability of losing the CCyR and a lower EFS

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

ADAGIO: About One Third of CML Patients Report Poor Adherence to Imatinib

• 169 CML patients treated with imatinib prospectively followed for 90

days

Noens L, et al. Blood. 2009;13:5401-5411. Patients Who Reported in Past 4 Wks (%) 64% 80 100 Adherent 60 40 20 Overall adherence and nonadherence by behavior, self-reported Dose Not Taken Consecutive Doses Not Taken Dose Taken > 2-Hr Delay Dose Reduced 67% 16% 13% 3% 2% 22% 25% 1% 2% Baseline Follow-up

Microelectronic Monitoring System (MEMS 6 Trackcap) CML Study

• Records the time of opening

the container

• Most reliable method of

measuring adherence

• Marin study: patients

told that adherence was measured by pill counts, not told about bottle cap electronic chip

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

The Ideal: 100% adherence as recorded by MEMS

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

The Reality: ‘Catching up’ to empty the bottle, as recorded by MEMS

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

The Reality: Poor adherence due to drug holiday, as recorded by MEMS

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

The Reality: Poor adherence and Human Nature, as recorded by MEMS

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

The dosing of medication may be very erratic as well

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

6-year probability of response Adherence rate n MMR (%) 4-log (%) CMR (%) 100% <99% 36 51 p=0.01 91.1 58.6 p=0.02 79.9 38.6 p=0.02 46.7 22.7 >95% <95% 57 30 p<0.001 94.5 29.3 p<0.001 77.2 15.0 p=0.002 45.2 8.2 >90% <90% 64 23 p<0.001 93.7 13.9 p<0.001 76.0 4.3 p=0.002 43.8 >85% <85% 69 18 p<0.001 85.8 11.8 p=0.001 69.2 5.6 p=0.007 40.8 >80% <80% 75 12 p=0.001 81.2 p=0.005 63.8 p=0.04 37.1

MMR=major molecular response; CMR=complete molecular response. Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

Achievement of a molecular response is related to the adherence to imatinib therapy

Probability of imatinib failure 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 p<0.0001 CCyR, no MMR, Adherence Rate ≤85%, n=11 MMR, n=53 CCyR, no MMR, Adherence Rate >85%, n=23

p=0.003 p<0.0001

Cumulate incidence of loss of CCyR 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Months from enrolment 0.0 24 18 12 6 p<0.0001 CCyR, no MMR, Adherence Rate ≤85%, n=11 MMR, n=53 CCyR, no MMR, Adherence Rate >85%, n=23

p=0.0009 p<0.0001

Conclusion to the Marin Study: Adherence and the achievement of MMR are the

• nly independent predictors for outcome

Marin D, et al. J Clin Oncol 2010; 28(14): 2381–2388.

Complexities, Actions, Algorithms

CV toxicity, switching TKIs, management deficits

SIMPLICITY Study Overview: Landmark Observational Study in CML

Eligibility:

• Newly-diagnosed CP-CML

patients

• Receiving 1st line imatinib,

dasatinib or nilotinib

• ≥18 years at time of diagnosis
• Not participating in a CML

clinical trial Retrospective (imatinib) N=252

• Bone marrow aspirates
• Blood tests
• Molecular response
• Cytogenetics

Prospective (nilotinib) N=408

Primary Research Objective

• To better understand the use of dasatinib and
• ther TKIs in first-line CML in a real world setting

Secondary Research Objectives

• Evaluate patient benefit of CML treatment
• Determine healthcare resource utilization

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Enrollment (Retrospective cohort) Enrollment (Prospective cohort) End of study follow-up

Data Collection

Strategic Objective: Determine effectiveness of available CP-CML treatments, understand impact of CML and treatment on QoL and evaluate HRU and associated cost burden in real-world setting

• Hospitalizations
• QoL outcomes
• Treatment adherence
• Discontinuation
• Clinical characteristics
• TKI treatment
• Comorbidities
• Spleen assessment

Prospective (dasatinib) N=418 Prospective (imatinib) N=416

• Demographics
• Vital signs
• Physical exam
• CV assessments

Improve outcomes for CP-CML patients Inform clinical practice 3

1. Goldberg SL, Cortes J, Gambacorti-Passerini C, et al. Cytogenetic and molecular testing in patients with chronic myeloid leukemia (CML) in a prospective

• bservational study (SIMPLICITY). J Clin Oncol. 2014;32:5s (suppl; abstr 7050).

2. Goldberg SL, Cortes J, Gambacorti-Passerini C, et al. Predictors of performing response monitoring in patients with chronic-phase chronic myeloid leukemia (CP-CML) in a prospective observational study (SIMPLICITY). J Clin Oncol. 2014;32:(suppl 30; abstr 116).

58% 51% 38% Patients not tested for CyR at 12 months1

SIMPLICITY: Lack of Adherence to Monitoring Guidelines in Clinical Practice

17% 13% Patients not tested for MR by 12 months1 19%

Age <65 years at initiation of first-line TKI, patients who had switched from first-line TKI and those seen in academic centres were more likely to be monitored by 12 months (p<0.05)2 About 1 in every 5 patients are not tested for MR at 12 months and almost half are not tested for CyR

Overall (N=862) US (n=573) Europe (n=289) Overall (N=862) US (n=573) Europe (n=289)

Hehlmann R, Cortes J, Gambacorti-Passerini C et al. Tyrosine kinase inhibitor (TKI) switching experience from SIMPLICITY, a prospective observational study

• f chronic phase chronic myeloid leukemia (CP-CML) patients in clinical practice. 19th European Hematology Association (EHA) Annual Meeting; June 12–14,

2014, Milan, Italy: abstract P883.

862 pts with at least 12 months of follow-up

Pts who discontinued index Tx:

24%

(N=207)

21% 17% 30%

Proportion of patients discontinuing treatment (%)*

Nilotinib (n=232) Imatinib (n=400) Dasatinib (n=230)

SIMPLICITY: Patients Discontinuing First-Line TKI

Quarter of SIMPLICITY patients discontinue TKI treatment in first 12 months

*P<0.001

Hehlmann R, Cortes J, Gambacorti-Passerini C et al. Tyrosine kinase inhibitor (TKI) switching experience from SIMPLICITY, a prospective observational study

• f chronic phase chronic myeloid leukemia (CP-CML) patients in clinical practice. 19th European Hematology Association (EHA) Annual Meeting; June 12–14,

2013, Milan, Italy: abstract P883

862 pts with at least 12 months of follow-up

SIMPLICITY: Reasons for Treatment Discontinuation

Intolerance most common reason for TKI discontinuation in first 12 months

Hehlmann R, Cortes J, Gambacorti-Passerini C et al. Tyrosine kinase inhibitor (TKI) switching experience from SIMPLICITY, a prospective observational study

• f chronic phase chronic myeloid leukemia (CP-CML) patients in clinical practice. 19th European Hematology Association (EHA) Annual Meeting; June 12–14,

2014, Milan, Italy: abstract P883

862 pts with at least 12 months of follow-up

SIMPLICITY: Switching Patterns in Pts Discontinuing in First Year

Imatinib (n=119) Dasatinib (n=40) Nilotinib (n=48)

FIRST-LINE DAS 45% NIL 32% SECOND-LINE All patients DAS 33% IM 29% IM 40% NIL 17% DAS 36% NIL 42% SECOND-LINE Pts in Europe DAS 25% IM 12% IM 33% NIL 0% DAS 58% NIL 26% SECOND-LINE Pts in US DAS 35% IM 32% IM 41% NIL 21%

Second-line TKI choices vary by region; in the US, DAS was most common second-line TKI, while in Eu, the second-line TKI choice was more equally distributed

ENRICH STUDY: Changes in Toxicity and QoL in Patients Switched from Imatinib to Nilotinib

• 45 / 52 patients had a total of 182 low-grade (grade 1/2), imatinib-related,

nonhematologic AEs at baseline.

• Of the 182 AEs, 130 were grade 1 and 52 were grade 2.

71.4%

ASH 2012

ENRICH: Change in Severity of Most Frequently Reported Imatinib-Related Nonhematologic AEs*

Cortes J, et al. Blood. 2012;120(21):[abstract 3782].

ENRICH: Change from Baseline in QoL by Cycle

Cortes J, et al. Blood. 2012;120(21):[abstract 3782].

TKI General AE profile

Colors represent the frequency of side effect: green 0–1%; light green 2–5%; yellow 6–15%; orange 16–29%; red ≥ 30% Patel, et al. Exp Rev Hematol. 2017;10:659–74

Kantarjian, et al. Blood. 2012;119:1981-1987.

The most significant ‘late effects’: BCR-ABL1 TKI Associated Cardiovascular Adverse Effects

105

Cerebrovascular Disease Coronary Heart Disease Myocardial Infarction Pulmonary Arterial Hypertension Venous Thrombosis Peripheral Arterial Disease Cardiomyopathy Congestive Heart Failure

è Morbidity and mortality; ? Effect on OS observations in front-line studies è ? Delay/deferral of advantageous therapy both in front-line and salvage

Cardiomyocyte Injury? Endothelial Dysfunction? Atherosclerosis? Endothelial Dysfunction? Atherosclerosis? Endothelial Dysfunction? Atherosclerosis? Endothelial Dysfunction? Platelet dysfunction? Prothrombotic state?

• Fatigue
• Musculoskeletal Sx / Cramping
• Exercise-Induced Symptoms

Other:

Front-line nilotinib in CML: CVEs over time (5y data- ENESTnd)

Larson RA, et al. J Clin Oncol. 2014;32:5s (suppl; abstr 7073).

Summary of VOEs: ponatinib phase I and phase II (PACE) trials

Treatment-Emergent Adverse Events Treatment-Emergent Serious Adverse Events All, n/N (%) Treatment- Related, n /N (%) All, n/N (%) Treatment- Related, n/N (%) Phase I All patients (N=81) 37/81 (46) 7/81 (9) 16/81 (20) 5/81 (6) Phase I CML and Ph+ ALL (N=65) 31/65 (48) 7/65 (11) 14/65 (22) 5/65 (8) Phase II (PACE) All patients (N=449) 109/449 (24) 45/449 (10) 67/449 (15) 27/449 (6)

27% Dec 2013 USPI

Phase I Data as of 26 Sep 2013; PACE Data as of 03 Sept 2013

Meta-analysis of VOEs: Imatinib versus subsequent generation TKIs

CI, confidence interval; CML, chronic myeloid leukemia; OR, odds ratio; Ph, Philadelphia chromosome; TKI, tyrosine kinase inhibitor; VOE, vascular occlusive event. Douxfils et al. JAMA Oncol. 2016 Feb 4. doi: 10.1001/jamaoncol.2015.5932.

Meta-analysis: Arterial and Venous Occlusive Events

Haguet et al. Expert Opin Drug Saf. 2016

Favors imatinib Favors imatinib

‘ABCDE’ Step Approach to CV Intervention

A: Awareness of cardiovascular disease signs and symptoms A: Aspirin (in select patients) A: Ankle-brachial index measurement at baseline and follow- up to document peripheral arterial disease B: Blood pressure control C: Cigarette/tobacco cessation C: Cholesterol (regular monitoring and treatment if indicated) D: Diabetes mellitus (regular monitoring, dose of radiation/chemotherapy, and treatment if indicated) D: Diet and weight management E: Exercise (echocardiogram)

= Recommended = As clinically indicated Imatinib Bosutinib Dasatinib Nilotinib Ponatinib Baseline Assessment Cardiovascular assessment Blood pressure check Fasting glucose Fasting lipid panel Echocardiogram * Electrocardiogram Ankle-brachial index 1-month follow up Cardiovascular assessment Blood pressure check 3- to 6-month follow-up Cardiovascular assessment Blood pressure check Fasting glucose Fasting lipid panel Echocardiogram * Electrocardiogram Ankle-brachial index *Patients treated with dasatinib should be considered for echocardiogram if cardiopulmonary symptoms are present.

‘Complete Molecular Remission’ ‘Treatment Free Remission’ MI CVA PAD Dyslipidemia DM/Glu Intol Barber M, Mauro M and Moslehi J, in press

Gu Guidelines in active dev develo lopm pment for CML pa patie ients and and CV ris isk

Summary II

• Adherence has significant impact on response
• Landmark responses key to avoid progression (MMR)
• Landmark response key to consider TFR (MR4, CMR)
• Multiple liabilities:
• chronicity of therapy with oral therapy, uncertain end date
• Minimal disease symptoms, rapid/deep response (many “NED”)
• Low grade adverse events common
• More significant late effects under investigation, surveillance and risk assessment tools

needed

• Under-monitoring and lack of expertise are risks on the provider side
• Monitoring basic landmarks of response lacking
• Switch of therapy common

Take-aways

• CML has opened the door to ‘cure’ with oral, low risk therapy

(prior to imatinib, hormonal therapy for breast cancer and trastuzumab were only ‘targeted’ therapies)

• All the challenges of chronic oral therapy exist and are

magnified by impact on response and outcome

• ‘Good problems: many drug choices; rapid response; lack or

reinforcement of disease presence; common to be managing disease detected at minute levels or ‘NED’

• Uncertainty about length of therapy and treatment free

remission leaves the ‘end game’ nebulous…

Many TKIs Response Remission Cure? = Long, Happy, Healthy Life!

Thank you for your attention! +212-639-3107

maurom@mskcc.org