study title and number outline form 03 2014 … · 1 study title and number outline form title...

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Study title and number Outline Form

Title Patterns and determinants of adherence to medications in

metastatic renal cell carcinoma patients treated with single agent

oral tyrosine kinase or m-tor inhibitors.

A collaboration between EORTC, IKCC, Academic Partners and

Pharmaceutical companies

Study number 1310-GUCG

Final version YES

Study short title Adherence of mRCC to oral TKI and mTor inhibitors

Study coordinator

Study coordinator Bertrand Tombal

[email protected]

Institute Cliniques Universitaires St Luc

Institute number 121

Group affiliation GUCG

Study coordinator Axel Bex

[email protected]

Institute NKI-Antoni van Leeuwenhoekziekenhuis

Institute number 301

Group affiliation GUCG

Participating groups

Other EORTC Group no

Other non-EORTC Group yes

To be confirmed

Non-EORTC Group leading no

EORTC classification

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EORTC classification Other research projects

Tissue stored for future research no

New methodology in clinical

research yes

Project description and

rationale Outline Form

Standard of care EU

Renal cell carcinoma (RCC) used to be considered as an immune-sensitive tumor, as the only active

treatments, although of limited value, were interferon-α (alpha) (IFN-α) and high-dose interleukin-2

(HD-IL2). Since 2005, an understanding of the pathogenesis of RCC at the molecular level has

identified the VEGF pathway and mTOR as targets for therapeutic intervention. The table below

provides an overview of the different classes of drugs approved for treatment of mRCC.

Drug FDA approval Pharmacologic category

Sorafenib 2005 Tyrosine Kinase Inhibitor; Vascular Endothelial

Growth Factor (VEGF) Inhibitor

Sunitinib 2006 Tyrosine Kinase Inhibitor; Vascular Endothelial


Temsirolimus 2007 mTOR Kinase Inhibitor

Everolimus 2009 mTOR Kinase Inhibitor

Bevacizumab in

combination

with interferon

alfa

2009 Monoclonal Antibody; Vascular Endothelial Growth

Factor (VEGF) Inhibitor

Pazopanib 2009 Tyrosine Kinase Inhibitor; Vascular Endothelial


Axitinib 2012 Tyrosine Kinase Inhibitor; Vascular Endothelial


The diversification of treatment options has left treating physicians with a choice of agents including

in either first or second line sorafenib, sunitinib, temsirolimus, everolimus, bevacizumab plus IFN,

pazopanib and axitinib (Refer to Table 1 in appendix).

Standard of care US

same above

Rationale for clinical need

For most trials, the primary endpoint that formed the basis for (accelerated) approval was

progression-free-survival (PFS) (with cross over often allowed at time of progression). However,

there is evidence from prospective studies and retrospective prognosis assessment that several of

these agents provide overall survival (OS) benefit. Consequently the prognosis for advanced RCC

shifts progressively toward that of a chronic disease and patients are nowadays treated for

increasingly longer periods of time with these agents.

This has also induced a major paradigm shift in the management of treatment related toxicities.

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Because these drugs belong to new therapeutic classes, their side effects are new for physicians and

patients, and have raised new challenges for their management. Most of these side effects are not

life threatening but can severely hamper the quality of life (QoL) of patients in the long run. Several

side effects are typical for the class of agents (TKIs and mTOR inhibitors), among which fatigue,

hypertension and diarrhea whereas others are reagent-specific: proteinuria with bevacizumab plus

IFN, hypothyroidism with sunitinib, hand–foot skin reaction (HFSR) with sorafenib, hepatotoxicity

with pazopanib and hyperlipidemia with the mTOR inhibitors. These side effects and their

respective frequency are summarized in Tombal et al publication (Tombal 2013). When administered

to asymptomatic patients, as is most often the case, the only sign observed by the patients will be

the adverse reaction to the medication (Motzer, et al Lancet Oncology 2013) .

Another challenging factor is that amongst the approved drugs, 5 are oral agents (sorafenib,

sunitinib, everolimus, pazopanib and axitinib). Oral intake is generally preferred over intravenous

treatment for reasons of convenience. Therefore, the daily use of oral drugs can be a challenging

commitment for many patients. Oral therapy requires patient self-medication and depends on the

ability and willingness of the patients to adhere to a prescribed regimen which may involve, one or

several daily doses, drug holidays, or dosing based on the timing of meals.

Adherence rates average 50% for long-term drug treatment of chronic conditions, according to a

2003 report by the WHO (Sabate 2003). Adherence also received the attention of the European

Commission who supported under the FP7 framework, the Ascertaining Barriers for Compliance

(ABC) project (www.abcproject.eu) between January 2009 and June 2012. This project, amongst

other outputs, produced a new consolidated taxonomy and related terminology for adherence.

(Refer to Figure 1 in appendix). The process by which patients take their medications as prescribed

is composed of initiation, implementation and discontinuation. Initiation occurs when the patient

takes the first dose of a prescribed medication. Discontinuation occurs when the patient stops taking

the prescribed medication, for whatever reason(s). Implementation is the extent to which a patient’s

actual dosing corresponds to the prescribed dosing regimen, from initiation until the last dose.

Persistence is the length of time between initiation and the last dose, which immediately precedes

discontinuation. (Vrijens et al, 2011)

Until now, limited research has been conducted on adherence to medication in cancer patients but

the matter seems to gain growing interest, as indicated by two recent reviews concerning adherence

to oral anticancer medications (Geynisman and Wickersham 2013, Krikorian and Shamin 2013). The

most solid evidence about adherence to anticancer agents stems from research conducted in breast

cancer: notably in patients taking hormone therapy (e.g; Partridge 2003, Partridge 2008, Sedjo 2011)

or capecitabine (Partridge 2010). Other studies were conducted in patients with CML (ADAGIO study

Noens 2009, Marin et al 2010) or in patients with GIST taking imatinib (ADAGIO Study, Mazzeo 2011)

or in patients with non- small cell lung cancer taking erlotinib (Gebbia 2013). Little documentation of

adherence rates to oral medications in mRCC is available until now, with only two studies. A Belgian

national study (IPSOC trial, Wolter et al 2012) suggested little non-adherence to treatment.

However, this study included only 49 patients. Adherence was assessed by patient self-reports

(MMAS questionnaires) and evidence suggested that both patients and doctors overestimated

compliance rates. An additional concern with this study is the free supply of the drugs (Mazzeo 2001,

Krikorian and Shamin 2013). Another study with commercial healthcare insurance, including 1080

patients treated with sunitinib, sorafenib and everolimus showed that 81% of patients had

adherence rates >80%, but this was based on claims records rather than by actual patient recorded

intake (Hess et al 2011).

Taken together, the above evidence suggests that adherence ranges from 50% to 97.5% depending

on the type of therapy and the measurement and definition of adherence.

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This is contrary to the general belief that adherence in cancer patients would be higher than in other

chronic illnesses, due to the life-threatening nature of the disease.

Published reports also suggest that cancer patients with poor adherence to treatment may not

receive the full benefit of the treatment and may consequently experience poor clinical outcome.

A cohort study involving 1633 women treated with tamoxifen for a median of 2.4 years illustrates

this point (Mc Cowan 2008): median adherence was 93%, however 19% of women had adherence

rates < 80%. In multivariate analysis, low adherence was independently associated with an increased

risk of death (HR=1.10, P=0.046).

Likewise, studies in patients with chronic myeloid leukemia (CML) treated with TKIs showed that

patients who were less adherent were more likely to have suboptimal response than those with

good adherence (Marin 2010). A recent review suggests that in the treatment of patients with

metastatic/unresectable GIST, non-adherence to imatinib is associated with a number of problems,

including tumor progression, poor clinical outcomes, and healthcare costs (Blay and Rutkowski,

2013).

Much effort has been expended to understand the barriers to medication adherence in all chronic

diseases (Kardas et al 2013, Walsh 2001, Horne et al 2009) and in anticancer treatments (Ruddy et al

2009, Geynisman and Wickersham 2013). All consistently show that medication non adherence is

affected by multiple determinants among which some are patient related (health beliefs, quality of

life/health status, understanding of disease/therapy, psychological state, social support, socio-

economic status, forgetfulness/lifestyle factors..), some are disease related (characteristics/severity

of the disease), and some are treatment related factors (complexity of the regimen, interaction with

food intake, side effects, schedule, cost of the drug, cost of side effects, cost of follow-up of care)

and some are related to the system (interaction with doctor and nursing staff, ease of obtaining

drugs, satisfaction with care …) (Refer to Figure 2 in appendix). For oral targeted anticancer agents,

specific barriers are still to be elucidated but may include cost, side effects and timing with food

(Geynisman and Wickersham 2013).

Patients may be non-adherent for non-intentional reasons, such as forgetfulness or prescription

error, or for intentional reasons such as a lack of belief in the medication or believing that the cost of

the medication and related side effects outweigh its benefits. Intentional non-adherence is

considered a behavioral issue, and non-intentional non-adherence is "process oriented." (Horne

2001).

Research in a variety of chronic illnesses suggests that intentional non-adherence to medication is

the result of three factors: (1) a lack of information about the advantages and disadvantages of the

treatment; (2) perceptions of necessity or personal need for treatment and (3) concerns about

potential adverse effects (Horne and Weinman 1999, Horne 1997, Horne 2003, Atkins and

Fallowfield 2006).

Using a validated questionnaire (Beliefs about Medicine Questionnaire) to quantify patients’

necessity beliefs and concerns (Horne et al 1999) this ‘‘necessity-concerns’’ framework has been

found to predict adherence to medication across a range of chronic illnesses, including asthma, renal

disease, heart disease, cancer, and cardiac failure. (Horne et al 1999, Horne and Weinmann 2002,

Horne et al 2004, Horne et al 2009).

A further challenge comes from the fact that all existing methods to quantify adherence are

imperfect and that there is no “gold standard” for measuring adherence (Geynisman and

Wickersham 2013, Osterberg and Blaschke 2005) (Refer to Table 2 in appendix). Bias may come in

the form of the so called “Howthorne effect” whereby patient’ awareness that adherence is being

measured may impact the degree of adherence (Partridge et al, 2002). Self-report, in which patients

are asked to recall how accurately they followed their prescribed regimen, has been repeatedly

5

shown to suffer response bias, with patients usually over-reporting rates of adherence because of a

desire to please providers. A novel method for the measurement of adherence is the microelectronic

monitoring system (MEMS). The MEMS system consists of an “intelligent” cap on a pill bottle that

electronically records every time the cap of the pill bottle is removed. MEMS data thus provide a

computerized record of each date and time the bottle is opened. Although MEMS monitoring is

quantitative and less subject to patient manipulation it may not be exempt of the Hawthorne effect

and it is quite expensive. (Ruddy et al, 2013, Osterberg and Blashke 2005)

With a presumed transition of mRCC to a chronic disease, it becomes extremely important to

identify populations at risk and factors leading to non-adherence so that targeted interventions can

be developed. This, for the patient himself, but also given the challenging economics of healthcare,

to avoid that patients and third-party payers are paying for medications that may not be used

correctly (Wu et al 2010).

Robust knowledge of the barriers for adherence to any treatment is required to allow for the design

of interventions that may help improve adherence.

Rationale

Given the often narrow therapeutic margin for some oral targeted anticancer medications, their

enormous costs and significant side effects, it is critical to understand barriers to adherence for

individuals taking these drugs and ways to maximize adherence, possibly through the development

of oncology specific or even individualized adherence-enhancing tools (Geynisman and Wickersam

2013).

As of today, most of the work relating to this question was conducted in CML. Therefore the EORTC,

with IKCC and academic partners in collaboration with the pharmaceutical companies (Bayer, GSK,

Pfizer, Novartis) producing those drugs decided to launch a large scale prospective cohort study in

mRCC.

Data supporting choice of dose

Treatment will be prescribed as per standard of care in each country.

Data supporting choice of reference value for design

Experience regarding adherence rates with MEMS was obtained from Aardex company, who

provided us with the value for the intra-patient correlation coefficient (rho=0.2) used the in sample

size calculation. This value may be adjusted at the time of the interim analysis

Median PFS in first line is approximately 8 months and approximately 5 months in second line. These

values were used to estimate the study duration.

Few data are available that document the distribution of the answers to the BMQ and PSM

questionnaires (respectively, belief about medicine & perceived sensitivity to medicine) in cancer

patients, and little is known regarding adherence to therapy and acceptability of the questionnaires

to patients with mRCC. Further insight into the applicability and compliance to the questionnaires

will be obtained at the interim analysis.

Outline Form

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Patient population

Inclusion criteria

� Histologically confirmed diagnosis of renal cell carcinoma (all subtypes)

� Proven metastatic disease

� Schedule to initiate oral single agent TKI or mTOR inhibitors within 2 weeks as first line or

second line treatment as per national guidelines

� Performance status ECOG grade <=2

� Age >= 18 years

� Ability to understand the questionnaires and to comply with the study requirements

� Written informed consent

Main objective

The final aim of this trial is to gain the understanding of factors impacting on patient’s adherence

treated with oral single agent therapy (TKI or mTOR). We will assess how adherence impacts

outcome of mRCC patients in order to pave the way for rationale development of adherence

enhancing -and or personalized support that would induce achievement of adherence and

persistence.

The proposed study is a phased non interventional prospective cohort study that includes an interim

analysis to allow adaption of the study design parameters and early termination

The main objective of the study is the following:

- To characterize patterns of (non-) adherence to oral targeted agents mRCC in a number of

European countries.

Secondary objectives are

- To assess how most to efficaciously measure adherence (questionnaire MARS-5 vs pill

counts through MEMS)

- To study how adherence to therapy ultimately affects progression-free survival

- To study the predictors of non-adherence by assessing how a) patient and disease

characteristics, b) chronic side effects of oral treatment, c) patient perceptions and beliefs

about medicine, d) quality of life and satisfaction about care jointly impact treatment

adherence in order to characterise predictors of non-adherence

- To study how the various factors measured in the study interact between themselves and

how they evolve over time on treatment

- To study a number of subgroups in relation to their pattern of adherence and the predictors

of their behaviour (eg. by line of treatment, by country, by socio economic factors, gender,

age, symptoms at entry etc.. , the final list will be developed in the statistical analysis plan)

Trial design Observational (non-interventional) prospective cohort study

Randomized No

Timing of entry in the study Patients who are diagnosed with mRCC and in whom single agent

oral therapy (TKI or mTOR) is initiated as first or second line therapy

for mRCC will be recruited to the trial within 4 weeks of initiation of

treatment (see eligibility criteria).

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Stratification factors The study is not randomized therefore “stratification” here pertains

to baseline variables that will be recorded and that may serve as

adjustment variables or subgrouping factors.

All analyses will be stratified by drug and by treatment line.

The analyses will include adjustments for a number of baseline

characteristics that could be relevant to the endpoints of adherence

and/or PFS: gender, age, presence of symptoms at baseline (pain),

socio-economic factors (employment, social environment),

education level , performance status at baseline, type of treating

institution country, whether patients are part of a patient advocacy

group or not… The detailed list will be finalized in the full protocol.

Treatment group description and

assessments

All drugs will be administered at the standard dose as single agent.

Dose adjustment will be performed by the investigators according

to local standard of care. Only oral drugs will be considered in this

study:

• Sorafenib (Nexavar®, Bayer): 400 mg BID (800 mg / d total

dose). (currently tablets of 200 mg)

• Sunitinib (Sutent®, Pfizer): 50 mg/d , schedule 4/2,

(currently capsules exist in 12.5,25,37.5,50 mg)

• Everolimus (Afinitor®, Novartis): 10 mg daily dosing

(currently tablets of 2.5 mg, 5 mg or 10 mg)

• Pazopanib (Votrient®, GSK): 800 mg daily dosing (currently

tablets of 200 mg (or 400 mg)

• Axitinib (Inlyta®,Pfizer): 5 mg BID (10 mg/d total dose)

(currently tablets of 1 mg (and 5 mg available)

Evaluations refer to Tables 3 &4

Primary endpoint

Adherence to therapy measured by MEMS

Secondary endpoints 1. Adherence to treatment will be measured through

a. quantitative measurement using electronic monitoring

devices (microelectronic event measurement system

MEMS, Urquhart and Vrijens) and/or

b. qualitative measurement using patient self-report

about medication intake through completion of the

Medication Adherence Report Scale (MARS-5)

(Horne, R et al 2009)

2. Beliefs about Medicine and perceived sensitivity to medicine

(PSM) will be assessed through the BMQ General and BMQ

specific questionnaires (Horne et al 1999) supplemented with

the PSM questionnaire (Horne, Faasse et al. 2013)

3. Toxicity of the treatments will be assessed using CTCAE version

4

4. Patient-reported outcomes including

8

a. Quality of life EQ-5D – short Quality of Life

questionnaire (http://www.euroqol.org/about-eq-

5d/publications.html)

b. HADS (Hospital anxiety and depression questionnaire)

(Zigmond and Snaith, Acta psychiatrica Scandinavica 1983)

c. Specific questionnaires addressing symptomatology of

kidney cancer and side effects of the treatments? (from

COMPARZ, FKSI-19 functional assessment cancer

therapy for kidney symptom index (Rao et al 2009),

Supplementary quality of life questionnaire (SQLQ) that

addresses mouth, throat, hand or foot soreness . To

include rash.

d. Satisfaction about care CTSQ (cancer therapy

satisfaction questionnaire (a validated 16-items

questionnaire developed by Pfizer US and used in the

Comparz trial (Motzer et al 2013) for which translations

are currently available in most languages

5. Progression-free survival and date of death

Statistical design for

primary endpoint Outline Form

Trial design Prospective non-interventional parallel cohorts study

Null hypothesis There is perfect adherence at all times for all studied drugs

(implementation rate>=90%)

Alternative hypothesis A significant proportion of patients are less than perfectly adherent

to therapy either from the start of the treatment or become non

adherent with time (implementation rate <80%)

Total number of patients, all arms 500 pts (i.e. approximately 100 patients/ drug across lines of

treatment). Patients are entered over a period of 24 months (and

followed up for another 6 months after the last patient enters the

study. A steady recruitment rate of 333 pts/year (27.75/m) is

assumed to be reached after 1 year. Recruitment rate increases

during the first year from 7/m during m1-3, 11/m during m 4-6,

16/m during m 7-9, 21/m during m 10-12 and 27.75 from m 13

onwards.

Alpha 0.05 (2-sided)

Beta 0.15

Other design details

The sample size is calculated for the aspect of adherence that

pertains to implementation. To that effect, we will study the profiles

9

of adherence per patient, and code for each day the adherence to

the patient as a binary indicator (drug taken or not taken as

scheduled). We can then model the probability of being adherent

for one day (p, the implementation rate), as well as the probability

of a patient being adherent for a study period (which we take here

as 365 days).

The sample size is calculated to provide given degree of precision in

the estimation of these two probabilities, within each study strata.

An intra-patient correlation of 0.20 is used, based on former

experience with MEMS. Then from the variance of cluster sampling

formula (Cluster Randomization Trials, Donner and Klar,

2000, pg8), where each patient is considered as a cluster, and day to

day binary patients’ adherence is considered as the random

variable. The correlation of day to day binary patients’ adherence

within each patient is represented by rho = 0.20, we can derive the

variance for the probability of a patient being adherent over a

period one year, if N patients are used for the estimation as

var=sqrt[ p*(1-p) *(1-(N-1)*rho) /(N*365)]

Using this formula, the precision when estimating the

implementation rate over one year is <4.5% (ie. Half width 0.5w of

the 95% CI) and is +/-4.4% if the implementation rate is 50% and

+/- 2% if the implementation rate is 95% (0.5w=0.019).

With 100 of patients the study also has 85% power to reject the null

hypothesis of an implementation rate >=90% if the true

implementation rate is <=80% with 1-sided significance level 0.025.

Interim analysis Yes

Describe interim analysis At 150 patients or month 12 whichever occurs first, an interim

analysis will be conducted with the aim of fine-tuning the study

parameters and decide on continuation or stop of each of the study

strata (drugs and/or lines of treatment) on the basis of observed

recruitment rate and of observed adherence pattern. Recruitment

will not be interrupted during the interim analysis.

Based on the recruitment assumptions indicated above (333

pts/year at peak rate), the 150 pts are expected to have been

recruited in the study at m12 from first patient in.

The interim analysis will document the following elements:

- Overall recruitment rate and recruitment by line of

treatment and by study drug.

o Consideration for stopping the trial will be

recommended if the <120 patients have been

recruited over the first 12 months, or if the average

recruitment rate in the last 3 months is below 15

pts/m. In this case, the projected trial completion

10

would be delayed by >6 months compared to the

original plan. However, further consideration of the

recruitment rate by line of treatment and/or by

drug will be given, in order to offer the possibility to

continue the trial with a limited number of strata,

should lack of recruitment be related to specific

drug(s) and/or line(s) of treatment.

o Consideration for stopping recruitment in a given

drug will be made if less than 50 patients were

recruited to the drug by month 12.

o Consideration for stopping a line of treatment will

be made if that line of therapy represents <10% of

the total sample and the next line of treatment also

fulfills the condition (this is to ensure consistency

and avoid continuation with lines 1 and 3 and

dropping line 2 for example)

- The completion rate for all questionnaires administered to

the patients will be documented for baseline and for follow-

up. Distribution by country will also be assessed. A

questionnaire will be considered for removal from the study

should the compliance rate at baseline fall below 80% or

compliance rate during follow-up fall below 60%. The

average compliance to the drug (as measured by MEMS) will

be documented for each drug and for each line of

treatment. Consideration of futility of the study for a drug

or combination of drug /line of treatment will be made

should the adherence be >90% in that subgroup.

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13

Appendices

Table 1: Summary of benefit of new-targeted agents used in RCC (Grunvald and al, EJC, 2013)

Figure 1: New Taxonomy and Terminology

14

Figure 2 from Ruddy et al 2009

Table 2: method for measuring adherence (from Osterberg and Blaschke, NEJM 2005)

15

Tables 3 & 4 : Clinical evaluation and questionnaires

Assessments by Physicians

Baseline Q12 wks

Written consent x

Standard work-up x x

Disease evaluation x x

Assessments AEs x x

Sample for genomics x

Assessments by patients

Questionnaires measures Baseline At week

12

Q12 wks

(from

registration)

At week

24

Q24 wks

(from

registration)

At PD

MEMS adherence CONTINUOUS

MARS adherence X

BMQ general -

12 and PSM-5

Belief

about

medicines

&

perceived

sensitivity

to

medicines

x x x x X

CTSQ Satisfaction

with care

X x x x

HADS

(7 questions)

Anxiety

depression

X X x X X

FKSI-19 Kidney

symptoms

index

x X x x x

SQLQ Side effects x x x x

EQ-5D HR-QoL X X x X x

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