1. Combating participant misbehavior in analgesic clinical trials Robert H. Dworkin, PhD Professor of Anesthesiology, Neurology, and Psychiatry Professor of Neurology in the Center for Human Experimental Therapeutics University of Rochester School of Medicine and Dentistry

3. Mundt JC, et al. J Clin Psychopharmacol 2007;27:121-125

4. “Both investigators and patients were blinded to the following information: entry criteria for patients’ pain intensity, baseline pain intensity, definition of responder groups, visit at which randomization occurred, treatment during the withdrawal phase, efficacy failure criteria, and computation rules and time windows in the IVRS system used to calculate the baseline intensity and pain response.” Hewitt DJ, et al. Pain 2011;152:514-521

6. Potential recommendations, I Blind patients and site personnel to absolutely everything they don’t need to know (also blind www.clinicaltrials.gov to this same information)www.clinicaltrials.gov

9. This IOM report describes the existence in psychiatry trials of “professional patients” — individuals who participate in multiple trials as a source of income and medication — noting the example of a 300 patient schizophrenia trial in which 30 subjects were found to have been randomized to the same study by multiple study sites.

10. Improving the quality of clinical trials and drug discovery. www.DupCheck.org Prof. Jonathan Rabinowitz, PhD jrabin@DupCheck.org J. Rabinowitz ©2013 All rights reserved. Patent pending

11. What duplicate patients think when they answer no to “have you been on an investigational drug in the last 180 days?” They believe that they know better and that it’s a silly requirement. Investigational drug, not me I am not a criminal. I am on a study for a cream, this is a study for a pill, its not the same. They don’t mean me. They miscalculate, previous study ended 100 days ago. They don’t understand the question and are embarrassed to ask. They sense the enrollment pressure at the site. They want to get paid for an additional study. They, or their accompanying family member, are desperate and want to increase the chances of getting active treatment. Presented: J. Rabinowitz, Annual ASCP Meeting, June 16, 2014, Hollywood, Florida, Presentation: Evidence and Risks of Duplicate Subjects in Clinical Trials and How You Can Minimize the Risk

12. DupCheck can be used in two ways At time of screening Using already collected data

13. 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. PIDDOBSe x CountryRaceMarital statusEducation 16 & 4Yes 14 & 7Yes 5 & 20Yes NoYes 13 & 22Yes 8 & 15Yes 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 Presented: J. Rabinowitz, Pipeline Session, Schizophrenia International Research Society, Florence, April 2014.

16. A significant number of subjects attempting to screen in clinical trials were found to be problematic:  4% of the subjects attempted dual enrollment across all phases of clinical research and all disease states.  A much higher incidence was seen in early phase trials where stipends were higher and in certain disease states such as healthy volunteers, CNS, pain and other subjective conditions.  Attempted dual enrollment was successfully thwarted with the database registry.  Furthermore, overall costs were reduced by stopping the attempted dual enrollment prior to screening and preventing costly screenings:  The system detected 7% attempted enrollment during their lockout period across all phases of clinical research with similar statistics across early and late phase trials.  5% attempted to screen while actively screening at another clinical trial center. Mitchell Efros, MD, FACS* & Kerri Weingard, ANP** Verified Clinical Trials, Garden City, NY Financial Disclosures: Accumed Research Associates & Verified Clinical Trials * President & Medical Director, ** COO Background Enrollment in subjective disease states and in particular CNS clinical trials can present unique and considerable challenges to research sites, pharmaceutical companies and CROs:  The research subject’s failure to admit to simultaneous participation in more than one clinical trial and jumping from one trial to another without allowing sufficient time to lapse between treatments compromises: 1) the health of the subjects, 2) data quality and 3) efficacy and placebo rates of the investigational compound.  The issue of dual enrollment in clinical trials is widespread, especially in the CNS clinical trials. When a research subject combines multiple investigational products or alternatively does not actually take the study product and provides false data, this often leads to altered efficacy rates, placebo rates, and potential for increased adverse events to screening for the clinical trial. Objective To develop a system to prevent dual enrollment in clinical trials to ultimately improve safety and data quality in clinical trials Methods  Using a proprietary identity-based (Idmetric), HIPAA compliant de-identified research subject clinical trials registry database system, Verified Clinical Trials has successfully performed 184,590 total verifications and multiple security alerts over the past 2 years in North America.  Both early phase and late phase trials were incorporated in to the Verified Clinical Trials system. * Following informed consent, each subject is verified and several different types of security alerts are completed just prior to screening for the clinical trial. Results Conclusions  Dual enrollment is a serious problem that can be costly to the research site, pharmaceutical company and most importantly harmful to patients. CNS clinical trials are inherently prone to increased attempted dual enrollment with significant safety and data quality ramifications  By tracking the dual screening activity, Verified Clinical Trials was able to alert CNS research centers; thus allowing them to be pro-active with finding qualified alternates to meet their enrollment and dosing numbers  99.8% of research subjects accepted the verification system at screening and the added security and safety measures of a clinical trials research subject database system had very little, if no impact on successful screening and enrollment in CNS clinical trials. The Impact of Implementing a National Research Subject Database to Prevent Dual Enrollment in Early and Late Phase Central Nervous System Trials Report for CNS Events* Number of Subjects (N=17,859) Previously enrolled in the same protocol11 Subject is currently randomized in a protocol138 Subject is still within the lockout period359 Compound Exposure278 Compound half-life did not expire2 In-Screening at a different site196 In-Screening at the same site702 Biologic compound0 Attempted protocol violations9%

17. “The Verified Clinical Trials system prevents many protocol violations Duplicate enrollment Subjects screening at multiple sites Subjects in their washout period and not eligible Exposure to the same IP which may be exclusionary Verified Clinical Trials is well entrenched (covers the majority of phase 1 units) in early phase research where Verified Clinical Trials detects and prevents many of these protocol violations. We see 8% - 16% protocol violations prevented in phase 1 units across North America. Verified Clinical Trials has excellent late phase coverage as well. In later phase trials (phase 2-4) the degree of attempted dual enrollment varies with the therapeutic study. In subjective type trials (pain, CNS, psychiatric, substance abuse), there is an 8%-10% prevention rate. In metabolic studies such as diabetes and COPD/asthma, the prevention is 4-5%. In oncology and dermatology studies, the number drops to 1-2%. While there is not 100% adoption of such a system to date, there is significant protection. The main message being that use of a system is better than not using one at all.”

18. Potential recommendations, II Implement efforts to identify and eliminate fraudulent and duplicate patients verification by primary care clinician obtain medical records consider using networks that identify duplicate patients within and across trials

20. A hypothetical example of a baseline week daily pain rating exclusion algorithm: 1.Completion of < 6/7 baseline ratings 2.Mean of baseline ratings 9 3.> 2/7 baseline ratings ≥ 9 and > 2/7 baseline ratings ≤ 2 (or some other approach to exclude patients with excessively variable ratings) 4.Poor agreement between 2 different pain measures (e.g., average and worst, NRS and VAS) 5.1 or more days with worst pain < average pain 6.Poor medication adherence during a placebo run-in phase

21. Potential recommendations, III Blinded baseline pain rating exclusion criteria/algorithm may improve assay sensitivity by excluding patients who show excessive improvement when randomized to placebo group (but not to active medication) may possibly exclude fraudulent patients, whose pain reporting can be expected to differ from that of patients who truly have the target painful condition