enrichment strategies for acheiveing greater clinical trial success - icon hosted webinar 2013
TRANSCRIPT
Enrichment Strategies for Achieving Greater Clinical Trial SuccessTuesday, April 23, 2013
ICON Signature Series
ICON Signature Series is ICON’s new thought leadership program that offers expert insight into value-driven strategies for clinical development.
Built on over 20 years of helping biopharma companies accelerate the development of their drugs, ICON Signature Series brings together ICON and industry experts to share knowledge and expertise across all aspects of clinical development and post-approval strategies.
To access the latest materials and schedule of upcoming events, go to http://www.iconplc.com/icon-views/
Upcoming Webinars
• Long-term Cardiovascular Safety - Rising to the Challenge with Early De-risking– 25th April, 2013, 11:00 - 12:00 GMT
• Biomarker Strategies for More Efficient Early-Phase Drug Development in Alzheimers Disease
– 21st May, 2013, 11:00 - 12:00 GMT
• To register visit:– http://www.iconplc.com/news-events/events/webinars/
Introductions
Tim Clark
Vice President Scientific AffairsICON Clinical Research
Klaudius Siegfried MD, PH.D
Vice President CNSICON Clinical Research
Jamal Gasmi MD, Ph.D.
Vice President, OncologyICON Clinical Research
Agenda
• Enrichment Strategies • Making clinical trials more efficient• What is enrichment?• What are the different strategies?• Some words of caution
• Enrichment in Early Phase CNS studies
• Enrichment in Early Phase Oncology studies
• Overall Conclusions
• Q&A
Making Clinical Trials More Efficient
• Cost of clinical research is growing
• May limit our ability to develop useful therapies
• Number of approaches to making trials more efficient:– Making better use of what we already know– Adaptive designs– Better targeted monitoring– Collecting only critical information– Choosing the right patients for the trials (enrichment)
Enrichment
• In December 2012, FDA published a draft guidance: “Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products” [1]
• “Enrichment defined as the prospective use of any patient characteristic to select a study population in which detection of a drug effect (if one is in fact present) is more likely than it would be in an unselected population”
Enrichment
• Not a new concept
• Many studies include some form of enrichment
• Classic enrichment design usually in two phases– First phase: identify responders – Second phase: randomise responders to test or control
• Trials with Tacrine in Alzheimer‘s disease and antiarrhythmic agents used enrichment designs
• Particularly useful when short-term reponse is known to be predictive of long-term efficacy
Unselected Design
All Subjects
Drug
Control
A
Enrichment Design
All subjects All subjects
+ve pts
-ve pts
Drug
Control
B
• Design can result in smaller studies when the treatment effect is greater in the positive group
• A wider number of patients will need to be screened
* Select +ve pts only, labelling in subgroup
Enrichment
• Decrease heterogeneity – Decreased inter-patient and / or intra-patient variability results in
increased study power– Example: exclude patients whose disease or symptoms are
likely to improve spontaneously
• Prognostic enrichment– Choosing patients with a greater likelihood of having an
endpoint event or a substantial worsening in condition – Can lead to an increase in absolute difference
• Predictive enrichment strategies – Choosing patients more likely to respond to the drug – Can lead to a larger absolute and relative difference
Decrease Heterogeneity
• Decrease heterogeneity (noise):– Ensure patients have the disease being studied– Find (prospectively) likely compliers – Choose people who will not drop out– Eliminate placebo-responders in a lead-in period– Exclude people with unstable disease– Exclude people with diseases likely to lead to early death– Exclude people on drugs with the same effect as test drug
• Increase signal to noise ratio
• Increase study power
Prognostic Enrichment
• Use prognostic indicators to identify patients with a:– Greater likelihood of having the event – Large change in a continuous measure
• Clinical and laboratory measures, medical history, and genomic or proteomic measures
• Treatment effect to be more readily discerned
• Prognostic enrichment does not increase the relative risk reduction, but will increase the absolute risk reduction, generally allowing for a smaller sample size
Prognostic Enrichment
• Reduction of mortality from 10% to 5% in a high-risk population is the same relative effect as a reduction from 1% to 0.5% in a lower risk population
• Smaller sample size needed to show a 5% vs. 0.5% change in absolute risk
• Common to choose patients at high risk for events for the initial outcome study
• If successful, move on to larger studies in lower risk patients
Prognostic Enrichment
• Early CV outcome trials with statins enrolled patients at high risk of an event– e.g., very high cholesterol levels
• Once benefit established, subsequent CV outcome trials enrolled lower-risk patients
• Populations identified as high risk due to concomitant illness – e.g., type 2 diabetes mellitus
• or risk factor – e.g., low HDL cholesterol, elevated high-sensitivity CRP
• Sample sizes increased considerably but prognostic factors made the studies possible
Prognostic Enrichment
• High sensitivity CRP, an inflammatory biomarker, predictive of future vascular events
• JUPITER study (n=17,802) enrolled apparently healthy men and women with [2]:– LDL < 130 mg/dL – CRP ≥ 2 mg/L
• Statin significantly reduced major CV events
Predictive Enrichment
• Identify patients more likely to respond to intervention
• Crucial where responders are a small fraction of the patient population
• Empiric strategies – Open observation followed by randomisation– History of response to treatment class– Results in earlier studies– Adaptation: interim look, then include more responders
Predictive Enrichment
• Pathophysiologic strategies – Known metabolism of drug– Effect on tumour metabolism– Proteomic Markers and Genetic Markers
• Genomic strategies
• Randomised withdrawal studies
• Non-responders or patients intolerant to other therapy
Predictive Enrichment
• Increased efficiency or feasibility:– Increased chance of detecting a treatment effect – Smaller sample size than a study in an unselected population– Particularly useful for early proof of concept studies– Enrichment can facilitate detection of an efficacy signal when
treatment responder population only a small fraction of all patients
• Predictive enrichment may result in both a larger absolute effect and a larger relative effect than the general, unselected population
Predictive Enrichment
Enhanced Benefit–Risk Relationship
Drugs with significant toxicity and a low
overall response in a general population
may not be developed
Identifying a responder population
could result in a positive benefit: risk
assessment and approval
Avoid exposure & potential toxicity in people who cannot
benefit from the drug
Predictive Enrichment
• Trastuzumab– significant survival advantage (approx. 5 months) in patients with
high HER 2/neu expressing tumours– Much smaller effect (approx. 2 months) observed in an unselected
population
• Trastuzumab– approved in marker selected population despite observed
cardiotoxicity of the drug
Stratification Design
All subjects All subjects
+ve pts
-ve pts
Drug
ControlControl
Drug
Control
C
Ref: Kevin Carroll (AstraZeneca)
Select +ve and –ve pts; Further assess the predictive value of the subgroup
Labelling in either the overall population or in subgroup
Some Words of Caution
• Misspecification of a target can have significant consequences
• If agent truly benefits all patients– Slow accrual to trials and increase expense– Produces no improvement in the chance of detecting a benefit– Unnecessary limits the size of the population
• If agent benefits a certain subset, but wrong subgroup of patients identified– Good agent can mistakenly be abandoned
Some Words of Caution
• If proportion of patients identified as having increased likelihood of benefiting from the therapy is large, then:– enrichment may not be cost effective
• Assay development, biomarker screening for trial entry and eventual market size all have cost implications
• Placebo run-in strategy is controversial
• Two main arguments in favour:– screen for compliance– clean comparison
Some Words of Caution
• May permit trialist to screen out poor compliers– Not very good predictors of compliance in the study itself– Time randomised patient in study increased– Larger number of patients screened– Not certain that run-in will increase efficacy
• May result in a clean comparison– Evidence mixed– Placebo lead-in periods (at least those that are single blind)
rarely deliver their theoretical benefit [3].– Meta-analysis of antidepressant trials revealed that a
placebo lead-in did not [4]:• (1) lower the placebo response rate,• (2) increase the drug-placebo difference, or• (3) affect the drug response rate post-randomization…”.
Some Words of Caution
• Open trial followed by randomisation– Prior exposure to active treatment may influence outcome and
confound study results– Unethical in some situations to randomise a responder to placebo
• Generalisability of the results– Clearly describe the design of the study and the rationale in the
protocol and the study report– Impact of the selection on the response rate in the overall population– Implications for the claimed indication– Cut-off for marker-positive patients– Data on marker-negative patients will probably be needed– Question is how much? Depends on level of uncertainty
References
1. Draft Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. December 2012
2. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and 1445 women with elevated C-reactive protein. N Engl J Med 2008; 359: 2195-207.
3. Senn S. Statistical issues in drug development. Wiley 2007
4. Trivedi MH, Rush H. Does a placebo run-in or a placebo treatment cell affect the efficacy of antidepressant medications? Neuropsychopharmacology 1994; 11(1):33–43
Enrichment StrategiesApplication in CNS Drug Development
Klaudius Siegfried, MD, Ph.D
Vice President CNSICON Clinical Research
Basic Considerations
• Heterogeneity of Patient Populations in CNS Disorders– The pathophysiology and aetiopathogenesis of
the majority of CNS disorders is only partially known
– Many CNS disorders represent “common final pathways“ of patient subgroups with heterogeneous aetiopathogeneses
– Large variability of response patterns / large variance of outcome variables for patients with the same diagnosis
Basic Considerations
• Consequences of the Heterogeneity of Patient Populations for CNS Drug Development– Large sample sizes needed to show treatment effects
• This is further complicated by the enhanced risk of “placebo effects“ and inter-rater reliability challenges in large multi-center, multi-national trials
– Modest effect sizes– Valuable drugs for specific sub-groups of patients may not be
identified and developed
Basic Considerations
• Caveat and Questions regarding Enrichment Strategies in the development of CNS Disorders:
– How can “prognostic“ and “predictive markers“ be identified in view of the often poorly understood pathophysiology of CNS disorders?
– In case of “predictive enrichment strategies“, we are often left withclinically based “empiric strategies”
– Are the hypothetical pathophysiological and empiric markers sufficiently stable to be used as predictive variables?
– Are they “trait“ or “state markers“ ? and– What proportion of the “response variance“ do they account for ?
• usually only a small proportion
Enrichment StrategiesExample 1
• Semi-Blind Placebo Lead-In Phase to Exclude “Placebo Responders“– Potentially useful to reduce increased “placebo response“ in CNS
trials and avoid indiscriminate study results– But controversial:
• EMEA guidelines raise concerns that this may lead to a “biased“, un-representative patient sample
• Placebo response is probably not a “trait“ but a (fluctuating) “state marker“ – would mean:
• Patients identified as “NO placebo responders“ could still show a placebo response – and,
• “placebo responders“ could attenuate their (placebo) response later
Recommendation:
Questionable value Use blinded randomisation
Enrichment StrategiesExample 2
• Selecting Partial Responders to SSRIs in a Prospective Run-In Phase with MDD Patients (4-6 wks)– “Partial Responders” (clinically defined) will be randomised to
an adjunctive treatment with:• either an (SSRI and placebo) or (SSRI and the new drug)
– The new drug needs to have a different mechanism of action than SSRIs
• e.g. involve other neurotransmitter systems– A frequently used study design
• No objections from regulatory authorities
Comments
Reasonable approach. Some successful studies – but:Further data needed to address the question how valid
and efficient the selection of “partial responders“ is, using this procedure
Enrichment StrategiesExample 3
• Randomised Withdrawal Designs in (stabilised) Responders to Test “Prevention-of-Relapse Treatment” in MDD, Bipolar Disorders, Schizophrenia– Stabilised Responders are randomised to a 6- or 12-month
preventive treatment on either active drug or placebo– Outcome measure: time of relapse
• survival analysis using Kaplan-Meier estimator– Procedure recommended by Regulatory Authorities for
testing long-term preventing effects
Enrichment StrategiesExample 4
• Selecting Patients with a more pronounced progressive course in trials with disease-modifying agents in progressive, degenerative illnesses– e.g. Alzheimer's, Parkinson's & Multiple Sclerosis diseases
– Variations in progression:• More rapid and constant decline vs. Slow constant progression
vs. Course with lengthy “plateaus“ in-between– Issue:
• The course of patients in the placebo group often determines the success or failure of an efficacy trial
– Selection of Patients on:• Genetic grounds (patients with the E4 allele variant of the APO E gene)• Clinical grounds (Assessment: type of progression during
last 6 months)
Results: Varying success; stratification recommended
Enrichment StrategiesExample 5
• Selecting Subjects at Risk for Alzheimer’s Diseases based on Biomarkers
• Background– Studies with potentially disease-modifying agents in AD patients so
far not successful• One possible explanation:
Treatment comes too late. By the time, a clinical diagnosis is made, the disease has already been ongoing for 15-20 yrs
• Suggestion– Use potential biomarkers (predictors of AD) to identify subjects at
risk for AD – Examples:
• Imaging markers (MRI hippocampal volume; rate of hippocampal and entorhinal cortex atrophy;…)
• CSF markers: CSF A, A42 and tau
ExecutiveCommittee
OperationalCommittee
Contracts & Finance
• Enrichment useful in CNS indications because of the heterogeneity of patient populations
• Some strategies fairly established– e.g. randomised withdrawal design with responders to test preventive effects
• In other areas, unclear situation– e.g. exclusion of placebo responders
• In many other areas, there are suggestions of (so far) “hypothetical markers” which need further research to establish– their sensitivity and specificity – their predictive value
• i.e. the amount of variance accounted for by the predictor
Enrichment Strategies in CNS - Conclusions
These predictors could be “experimentally” used in early phases of development
(e.g. POC studies) – or for patient stratification
Enrichment StudiesApplication in Oncology Drug Development
Jamal Gasmi MD, Ph.D
Vice President, OncologyICON Clinical Research
Shift of paradigm: Toward Molecular Subsets of Cancer & Personalised Oncology (1)
• Advances knowledge of the disease process• To tailor therapy based on biological target• To improve therapeutic effect• Only to treat those that benefit• To improve cost effectiveness
Shift of paradigm: Toward Molecular Subsets of Cancer & Personalised Oncology (2)
Personalised OncologyBiomarker
( predictive or prognostic marker)
Clinical Trial Validation of predictive marker
VUnselected (untargeted = all comers)
Enrichment (targeted= restrictive)
Unselected Design
All Patients
IP
Control
Enrichment Design (of Targeted Design)
Register Test Marker
Marker (+) Randomise
Treatment A
Treatment B
Examples of Recent Successful Enrichment Strategy in Early Development
• Crizotinib in ALK + NSCLC− ALK translocation occurs in 5-7% of patients with NSCLC− Mainly in adenocarcinoma & younger patients− ALK+ inhibitor: Crizotinib
• Vemurafenib in BRAF V600E mutation metastatic melanoma − Approximately 50% of melanoma harbor BRAF V600E mutation− BRAF V600E mutation inhibitor: Vemurafenib
Crizotinib in ALK positive NSCLC : First In-Human Study
Part 1: Dose Escalation
250 mg BIDMTD/RP2D
Part 2:Molecularly enriched cohorts
(ALK positive )
37 patients with solidtumors were treated in
the dose escalation
Cohort of 82 patients with prospectively identified ALK-
positive NSCLC enrolled after preliminary observation of impressive activity in a few patients treated in Part 1
ORR: 57%
Kwak et al. N Engl J Med 2010; 363:1693-1703
ALK-Positive & Crizotinib Timeline
• ALK translocation occurs in 5 -7% of patients with NSCLC
• Mainly in adenocarcinoma & younger patients
• Development of ALK inhibitor: crizotinib
• Impressive clinical data in early phases
• Development of diagnostic companion
• FDA approval based on 2 single arms phase II data
• Short time between discovery of biological target and approved therapeutic : 4 years
ALK-Positive & Crizotinib Timeline
EML4-ALK chromosomal rearrangements reported
in NSCLC[1]
Crizotinib antitumor activity in advanced cancers with
EML4-ALK rearrangement[4]
FDA approves crizotinib for treatment of ALK+
NSCLC[6]
EMA approval basedon a phase III trial
2007 2009 2011 2012
2008 2010
Preclinical studies document antitumor activity of ALK inhibitors
in lung cancer cell lines and xenografts[2,3]
Crizotinib produces a response in 47/82 ALK+ patients and a 6-month
PFS of 72%[5]
Crizotinib improvesPFS over chemo in
ALK+ patients
1. Soda M, et al. Nature. 2007;448:561-566. 2. McDermott U, et al. Cancer Res. 2008;68:3389-3395. 3. Koivunen JP, et al. Clin Cancer Res. 2008;14:4275-4283. 4. Kwak EL, et al. ASCO 2009. Abstract 3509.
5. Kwak EL, et al. N Engl J Med. 2010;363:1693-1703. 6. US Food and Drug Administration.7.Shaw A,et al.ESMO meeting 2012
Vemurafenib in BRAF V600E Mutation Melanoma: First In-Human Study
Part 1: Dose Escalation
RP2D960 mg twice
daily
Part 2:Molecularly enriched cohorts
(V600E BRAF mutation)
55 patients (majority withmetastatic melanoma)
were treated in the dose escalation
Cohort of 32 patients with prospectively identified BRAF
mutation were enrolled
ORR: 81%
Flaherty et al. N Engl J Med 2010; 363:809-819
BRAF V600E Mutation & Vemurafenib Timelines
• Approximately 50% of melanoma harbor BRAF V600E mutation
• Vemurafenib target BRAF V600E mutation
• Single arm phase II in previously pretreated pts: Impressive response rate > 50%
• Development of diagnostic companion• Phase III trial in first line : Vemurafenib vs Dacarbazine
– 6mOS: 84% vs 64%– ORR: 48% vs 5.5%
• Relatively short time from FIM to FDA approval : 2 years
• New treatment paradigm for metastatic melanoma
Ribas; ASCO 2011, Chapman; NEJM 2011
Enrichment Challenges (1)
• Heterogeneous disease– Every patient and every tumor is unique– Every cancer becomes a rare cancer
• Requires early development of hypotheses – Preclinical & clinical
• Identification of appropriate biomarker– Qualification & utility – Prognosis and/or predictive factors
Enrichment Challenges (2)
• Sample/tissue collection– For trial eligibility
• Development & validation of assay
• Development issues– Validation in clinical trials
Enrichment Strategies in Oncology - Conclusions
• Provide adequate information from preclinical data
• Better patient selection based upon– Biomarker expression/mutation
• Better drug selection based on robust inhibition
Overall Conclusions
• Approaches to reduce heterogenicity used in most studies
• Enrichment can shorten development time
• Enrichment strategies must be clearly described upfront
• Key issue is defining the criteria for selection
• What is the likely benefit in the non-selected population?
• Analytical validity of proteomic or genetic test is critical
• The sensitivity and specificity and positive and negative predictive values of any marker used to select patients must be well characterised
Tim Clark
Vice President Scientific AffairsICON Clinical Research
Klaudius Siegfried, MD, Ph.D.
Vice President CNSICON Clinical Research
Jamal Gasmi, MD, Ph.D.
Vice President, OncologyICON Clinical Research
