What is NEWMEDS? EFPIA companies: H Lundbeck A/S, Abbvie, AstraZeneca AB, Eli Lilly and

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1 Pre-competitive gains from data sharing across sponsors: the power of big data and colloboration NewMeds Working group: Leads: Jonathan Rabinowitz, Bar Ilan University; Ivo Caers, Janssen Research & Development; Participants: Nomi Werbeloff, Bar Ilan University; Francine Mandel/Nicholas DeMartinis, Pfizer; Shitij Kapur, Institute of Psychiatry, Kings College; Virginia Stauffer/Bruce J. Kinon/Lauren Marangell/Alexander Schacht, Lilly; François Menard; Gerard Marek, AbbVie Supported by Innovative Medicine Initiative Joint Undertaking under grant agreement No of which resources are composed of a European Federation of Pharmaceutical Industries and Associations (EFPIA) in-kind contribution and financial contribution from the European Union s Seventh Framework Programme (FP7/ ). Disclosure: Jonathan Rabinowitz, PhD, has received research grant support and/or travel support and/or speaker fees and/or consultancy fees from Amgen, Janssen, JNJ, Eli Lilly, Pfizer, BiolineRx, F. Hoffmann-La Roche, Avraham Pharmaceuticals, and Newron Pharmaceuticals in on scientific advisory board of MedAvante and is the founder of DupCheck.

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3 What is NEWMEDS? NewMeds is an international consortium of scientists, one of the largest ever research academic-industry collaborations. Goal: Find new methods for development of drugs for schizophrenia and depression. Funding: Innovative Medicines Initiative EFPIA companies: H Lundbeck A/S, Abbvie, AstraZeneca AB, Eli Lilly and Company Ltd, Janssen Pharmaceutica NV, Novartis Pharma AG, Orion Corporation, Pfizer Limited, F. Hoffmann-La Roche AG, Institut de Recherches Servier Universities: King s College London (UK), Karolinska Institutet (Sweden), The University of Cambridge (UK), Central Institute of Mental Health (Germany), CSIC (Spain), The University of Manchester (UK), Bar Ilan University (Israel) SME s: Psynova Neurotech Ltd (UK), decode genetics (Iceland), GABO:mi (Germany)

4 NewMeds (New Methods for Drug Discovery for Depression and Schizophrenia) (1) Private-public colloboration funded by the Innovative Medicines Initiative in colloboration with EFPIA (Abbvie, AstraZeneca, Eli Lilly, Janssen, Lundbeck, Pfizer, Novartis, Roche, Orion) >Likely the largest such precompetitive colloboration, basic to applied. (2) NewMeds repository contains patient level data on over 30,000 patients from 77 randomized controlled trials of antidepressant and antipsychotic medications and data from NIH. >Data used find ways to conducted more efficient drug discover trials. (3) DupCheck web based data sharing tools: a. DupCheck-CT (clinical trial) is used to screen for duplicate enrolment of patients in clinical trials within and across sponsors, trials and indications. b. DupCheck-PV (pharmacovigilance) is used to identify duplicate reporting of adverse events, primarily post-marketing. This includes events reported by different reporters and attributed to different compounds. (4) WORK IN PROGRESS: Registry of placebo arm patient level adverse event data.

5 NEWMEDS consortium and leads

6 Work Package 10: Advanced Data Analysis Techniques Methodological Accomplishments: We have established a consortium that shares clinical trial data coded patient/participant level data- from industry and academia to examine precompetitive questions. Overcome challenges associated with establishing data sharing Pooled and mined data from studies that have sufficiently common experimental designs to have a reasonable chance of valid conclusions. Text borrowed from: Institute of Medicine, Washington, DC, August 2011, Cast as road map.

7 Data sharing NewMeds historical and prospective Improving trial design Historical data sharing: pooling patient level clinical trial data to better understand placebo and active treatment response. Improving clinical trials and quality of life cycle of drug (1) DupCheck-CT (clinical trials)- Real time data sharing to exclude enrolment of duplicate patients within and across sponsors at time of screening. Online tool: DupCheck.org ( (2) DupCheck-PV (pharmacovigallence)- Real time data sharing to exclude duplicate reporting of adverse events; DupCheck-PVPV- (3) Registry of patient level AE data from placebo arms.

8 NewMeds Antipsychotic (AP) Database Data from: Astra Zeneca, Janssen, Lilly, Lundbeck, Pfizer 64 Industry sponsored studies 34 placebo controlled 30 active comparator 25,900 patients: 16,105 study drug; 7,119 active comparator; 2,676 placebo 1 NIMH sponsored study CATIE 1,493 patients; 1 European Union sponsored study EUFEST 498 patients Antidepressant (AD) Database Data from: Astra Zeneca, Lundbeck, Pfizer and Lilly 39 placebo controlled Industry sponsored studies 12,217 patients: 8,260 active drug, 3,957 placebo NIMH STAR*D, n=2,876

9 Findings to be presented Duration of both randomized controlled drug discovery studies of antipsychotic (AP) and antidepressant (AD) studies can be reduced by 20% How sample sizes can be reduced by: segmentation using clinical and demographic variables to select enriched populations (AP) increasing proportion of patients on placebo (AD) using interim analysis which can reliably be used to reestimate sample sizes excluding centers with high placebo response screening within and across studies for duplicate or professional patients

10 Can trials be shorter? Percent of the baseline to 6 week LOCF (last observation carried forward) difference between placebo and active treatment on efficacy measure (PANSS/HAMD/MADRS) discernable at previous weeks (pooled data) Antipsychotic Antidepressant Week 5 95% 87% Week 4 90% 79% Week 3 80% 60% Week 2 67% 45% Week 1 44% 13% e.g., if WK 6 LOCF PANSS total difference=-5, WK 5 difference=-4, 80% (4/5) discernable at week 5

11 Would statistical significance be lost if trial ended at week 4? Antipsychotic 19 trials no change 2 trials placebo active difference became non-significant 3 trials results that were not significant at week 6 were significant earlier Depression % change at week 6 - active is significantly superior to placebo in 15 trials (24 negative or failed studies at wk 6 not included). At week 4 all these trials still show significant differences, as do 2 additional trials that were not significant at week 6.

12 Segmentation using clinical and demographic variables to select enriched populations (AP) Sex by PANSS improvement (decline) Total Negative General Psychopathology Positive

13 Placebo active-treatment difference on PANSS improvement (decline) by age quartiles and years ill quartiles (mean 95% confidence interval) age > 30; illness < 4 age <= 30; illness < 4 age > 30; illness >= 4 age <= 30; illness >= 4

14 Placebo active-treatment difference on PANSS improvement (decline) by symptom prominence (mean 95% confidence interval) Prominent negative Prominent positive Prominent both Prominent: A score 4 moderate on at least 3, or 5 moderately severe on at least 2 subscale items in the respective subscale

15 Estimating sample size implications of findings on subgroups with heightened treatment response Bootstrap resampling used. For each scenario of interest 1000 simulated data trials created by resampling from our database. Bootstrapping for Dummies: Random samples are drawn from data. This is the best guess of the population from which the sample was taken.

16 Sample sizes needed per arm (90% power, p of.05) (based on bootstrap/resampling of data) Current studies 64 Current enriched 71 6 Weeks 60 50% Female 50% female enriched 57 50% Early episode % Early episode enriched 46 50% Female 50% F & 50% & 50% Early EE enriched episode Current=70% male; 20% early episode; 40% enriched Enriched=prominent positive and negative symptoms

17 Reliability of interim analysis Effect size differences placebo vs. active treatment in first and second cohort of patients enrolled ordered by difference Antipsychotic Antidepressant Worse results after interim Better results after interim 1.5 Worse results after interim Better results after interim trials differences were positive (.03 to.58) 4 trials differences were negative but small (less than -.08) 7 trials differences were negative (-.12 to -.44) In 13 of 20 interim results were a good estimate. 22 trials differences were positive (.01 to 1.38) 8 trials differences were negative but small (less than -.06) 9 trials differences were negative (-.11 to -.89) In 30 of 39 interim results were a good estimate. 17

18 Scatter plot percent placebo by placebo active difference (ES) Antidepressant: r=0.33, p=.04 Antipsychotic: r=-0.02, p=.91 18

19 Effect of removing sites with unrealistically high rates of placebo response Efficacy at wk 4 (shortest trial) using LOCF analysis was done on each study Antidepressant: Site identification was available in 19 studies (1 of which was an active run-in study). 7 studies no centers with inflated placebo change scores (>50% a priori). In all the rest (k=12; including the active run-in study) there was improvement after removing centers with inflated change scores. Mean improvement in effect size= 0.15 At 90% power, p<.05 (2 tail) an effect size of.30 needs 235 per arm (2 arms) Same with ES=.45, 105 per arm. Antipsychotic: Site identification was available in 9 studies. All had at least one site with inflated placebo change scores (>40%). Removing the centers with inflated change scores, increased placeboactive difference. Mean improvement in effect size= 0.07 At 90% power, p<.05 (2 tail) an effect size of.40 needs 133 per arm (2 arms) Same with ES=.45, 97 per arm. 19

20 Study results are sensitive to even a few duplicate patients. Data from placebo controlled antidepressant study were examined (n=301, approximately 1:1 active treatment/ placebo). Analyses focused on answering question, what if some placebo patients were also on active treatment in another study? Change from baseline was altered for 8 placebo patients, reflecting greater reduction in symptoms, aligning with values of active treatment patients. 20

21 Original data Data from study of an anti-depressant drug trial (LOCF change from baseline on MADRS) Active treatment Modified Data Unchanged Placebo Values of 8 of 150 placebo patients changed to simulate duplicate placebo patients also on active treatment Left: LS mean (se) change: Active treatment= (.75) n=154 vs. Placebo= (.77) (n=147), p=0.019, ES=.27 Right: LS mean (se) change: Active treatment= (.75) n=154 vs. Placebo= (.76) (n=147), p=0.15, ES=.17 21

22 Death of patient concurrently enrolled in 2 phase I studies Benjamin Brody, MD Andrew C. Leon, PhD James H. Kocsis, MD the study monitor informed us that a subject with the identical birth date and initials had just completed the same trial at another local institution 22

23 How many variables are needed to find duplicates: Population based data Data from birth cohort of 91,578 unique consecutive births over 10 years (Jerusalem Perinatal Study). Date of birth Sex Birth weight Hospital (5 hospitals) Neighborhood (10 categories) Example % Example % Example % Uniquely identified 23

24 Possible duplicate patients within CATIE * (n=1460) Matching on all of the following: Age (date of birth not captured) Height BMI category (5 categories) Sex Race (Caucasian yes/no) Hispanic (yes/no) Marital status (5 categories) Level of education (8 categories) DupCheck yielded 49 matches representing 87 patients (based on simulations less than 7% of these 49 could be false positives). (29 had one match, seven had two matches and two had three). *NIMH-funded Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study 24

25 Possible duplicate patients within STAR*D (n=4042) Matching on all of the following: Age within one year (date of birth not available) Age of onset of first depression Sex Ethnicity (White, Black, Asian, American Indian, Hawaiian) Hispanic (yes/no) Marital status (6 categories) Year of schooling (within one year) Residence (8 categories: Detached house, Rowhouse or Townhouse, Mobile home, Apartment or condo, Rooming house/hotel, Retirement complex, Nursing home, Homeless) DupCheck yielded 103 matches representing 213 patients. (96 had one match, seven had three matches, less than 5% of these could be false positives). *NIMH-funded Sequenced Treatment Alternatives to Relieve Depression

26 Sharing ongoing data to improve trials in real time, screening for duplicate patients online using Working together to identify duplicate patients Using central database of key demographic variables of current study participants. With sufficient data to give high certainty of duplicate patient, but insufficient to locate patient (using encrypted data). Access secure web site at time of screening, input data and receive immediate feedback about likely duplicates. 26

27 DupCheck Search and Results screens (screening across sponsors and protocols) RESULTS for patients #32 1 match on all of the following variables: Variable First initial Second initial Sex Year of birth Month of birth Day of birth Height Source of matches demographic info (DOB, initials) Was matches height measured? City where match seen State where match seen Match Yes Yes Yes Yes Yes Yes Near match Driver s license Yes Queens New York Default variables

28 NewMeds DupCheck improves results in completed study (1020-CLIN- 201) Exact matches found and removed within study post-hoc. ITT analysis would have rendered significant results were this to have been done at time of screening. PID DOB Sex Country Race Marital status Education 16 & 4 Yes Yes Yes Yes Yes Yes 14 & 7 Yes Yes Yes Yes Yes Yes 5 & 20 Yes Yes Yes Yes No Yes 13 & 22 Yes Yes Yes Yes Yes Yes 8 & 15 Yes Yes Yes Yes Yes Yes Results improved Change from baseline to endpoint (ANCOVA) on efficacy measure n=216 (1:1 allocation) Before removing duplicates p=.054 After removing 10 duplicates p=.03

29 checks for duplicates across and within studies and sponsors & whether a subject is unique to development program 29

30 DupCheck-PV Helps identify duplicate adverse event reporting within and across medications. Regulators and sponsors strive to improve accuracy of adverse event reporting.

31 Same event, same patient reported twice Report A Report B Physician reporting Dr. Jones (neurologist) Dr. Smith (GP) Attributed drug Anti-convulsant x Anti-convulsant x Adverse event Dizziness Dizziness Date of reporting Dec. 18, 2013 Dec. 20, 2013 DOB of patient June 1, 1960 June 1, 1960 Sex of patient Female Female Initials of patients C.S. C.S. City, State, Country of physicians office Chicago, Ill, USA Chicago, Ill, USA

32 Same event, same patient attributed to 2 drugs Report A Report B Physician reporting Dr. Jones (orthopedist) Dr. Smith (ENT) Attributed drug Pain killer x Antibiotic y Adverse event Bad rash on arms Bad rash on arms Date of reporting Dec. 18, 2013 Dec. 20, 2013 DOB of patient June 1, 1960 June 1, 1960 Sex of patient Female Female Initials of patients C.S. C.S. City, State, Country of physicians office Chicago, Ill, USA Chicago, Ill, USA

33 Before or as part of regulatory reporting Prior to reporting to regulatory authorities Pharmaceutical company or regulatory authority uploads data file reporting either single or multiple events (e.g., Electronic Transmission of Individual Case Safety Reports (ICSRs) in ICH format to DupCheck-PV). DupCheck inserts a flag variable and comment into data set indicating suspected duplicate adverse event and evidence of duplication (e.g., same DOB, sex, initials and city). Decision support system

34 DupCheck-PV is easy to use: Enroll at Similar to enrolling at Gmail. Click on button upload AE file. Comma separated file with ICH E2B(R3)DATA ELEMENTS Either single or multiple cases Click on button to customize matching criteria. Click on search for duplicates. This will produce: downloadable enhanced AE file with duplicate flag and comment field indicating reason for duplication. Report of duplicate events on screen. Each ICSR uploaded is added to database. Designed to interface with other databases as access become available (e.g. MedWatch, EudraVigilance etc.)

35 Sharing placebo safety data to improve signal detection of rare AE s in clinical trials Why: 400 patients on active treatment (4 dosage arms) 100 placebo patients Serious adverse events: treatment 3, placebo 0 Decision point: abandon?/collect more data? Alternative: Estimate adjusted placebo prevalence. Placebo AE registry: Establish registry of patient level AE reports, clinical and demographic data from placebo arms. Collecting data from complete studies. Ongoing feed from completed new studies (enabled by CDISC). 35

36 Conclusions Data sharing can enhance quality in drug life cycle. Drug discovery Duration of studies can be reduced. Sample sizes can be reduced (segmentation, increasing proportion of placebo patients, interim analysis, excluding center with high placebo response). Signal detection & safety improved Dupchecking for duplicate or professional patients. Safety detection of rare events: Placebo safety repository Post marketing dupchecking within and across sponsors for duplicate adverse events. More information: Determinants of Antipsychotic Response in Schizophrenia: Implications for Practice and Future Clinical Trials (2014). Rabinowitz, J., Werbeloff, N., Caers, I., Mandel, F.S., Stauffer, V., Ménard, F. Kinon, B.J., Kapur, S. J Clin Psychiatry 2014;75:4