Fraud & Misconduct at Investigator Sites

1. Fraud & Misconduct at Investigator Sites Paul Below Clinical Research Consultant P. Below Consulting, Inc. SoCRA 15th Annual Conference Chicago, IL September 23, 2006

2. Disclosure & Disclaimer • I have a consulting relationship with the following companies: • MGI Pharma • Medical Research Management • Minneapolis Heart Institute Foundation • The views expressed here are my own and I am solely responsible for the content of this presentation

3. Presentation Topics • Definition of fraud • Prevalence • Famous cases • Consequences • Reasons why fraud occurs • Warning signs/identifiers • Detection strategies • Fraud prevention

4. FDA Definition of Fraud • Falsification of data in proposing, designing, performing, recording, supervising or reviewing research, or in reporting research results • Falsification includes both acts of omission (consciously not revealing all data) and commission (consciously altering or fabricating data)

5. Fraud Definition (cont.) • Fraud does not include honest error or honest differences in opinion • Deliberate or repeated noncompliance with the protocol and GCP can be considered fraud, but is considered secondary to falsification of data

6. Who Commits Fraud? • Investigators • Study coordinators • Data management personnel • Lab personnel • IRB staff • CRAs and sponsor personnel • FDA

7. Study Coordinator Nurse Hospital Sponsor Investigator Office Staff Sub-investigator CRA Who Gets Blamed? 4% 4% 9% 39% 9% 9% 9% 17% Source: FDA Presentation, DIA 2000

8. Prevalence of Fraud • Difficult to determine but still considered rare • Reported to significantly impact 1-5% of pharmaceutical clinical trials – F. Wells, Reuters Health, January 2002 • Only ~3% of FDA inspections uncover serious GCP violations resulting in Warning Letters

9. Famous Cases - Investigators • Robert Fiddes, MDPrivate practice, Whittier, CA – 1997 • Richard Borison, MD and Bruce Diamond, PhD Medical College of Georgia – 1998 • Michael McGee, MDUniversity of Oklahoma, Tulsa – 2000 • Maria Kirkman (aka Ann Campbell), MDPrivate practice, Alabama – 2003

10. Famous Cases - Coordinators • Anne ButkovitzPediatric private practice, Newton, MA – 2005 • Paul KornakStratton VA Medical Center, Albany – 2005

11. Robert Fiddes, MD – “Of Mice and Men”, 60 Minutes, April 1, 2001

12. Richard Borison, MD – “Drug Money,” 48 hours, July 31, 2000

13. Richard Borison, MD – “Drug Money,” 48 hours, July 31, 2000

14. Bruce Diamond, PhD – “Drug Money,” 48 hours, July 31, 2000

15. Bruce Diamond, PhD – “The Lessons of Greed,” PharmaVOICE, July 2004

16. Paul Kornak – “Abuses Endangered Veterans in Cancer Drug Experiments,” New York Times, February 6, 2005

17. Consequences of Fraud • Sponsor– data validity compromised, submission jeopardized, additional costs • Investigator– fines, legal expenses, disqualification/debarment, license revocation, incarceration, ruined career • Institution– lawsuits • Subject– safety at risk, loss of trust in clinical trial process

18. Consequences (cont.) • Fraudulent investigators are often used by multiple sponsors on multiple trials • A small number of investigators can have a broad impact on many NDA submissions • One fraudulent investigator, Dr. Fiddes, was involved in 91 submissions with 47 different sponsors

19. Dr. Fiddes and staff on the FDA Debarment List

20. Federal Register Notice for Study Coordinator Debarment

21. Why Does Fraud Occur? • Lack of resources (staff, time, subjects) • Lack of GCP training • Lack of regulatory oversight • Laziness • Loss of interest • Pressure to perform or to publish • Money, greed

22. General Warning Signs • High staff turnover • Staff are disgruntled, fearful, anxious, depressed, defensive • High pressure work environment • Obsession with study payments • Absent investigators • Lack of GCP training • Unusually fast recruitment

23. Data Identifiers of Fraud • Implausible trends/patterns: • 100% drug compliance • Identical lab/ECG results • No SAEs reported • Subjects adhering perfectly to a visit schedule • Perfect efficacy responses for all subjects

24. Layout the primary efficacy data for all subjects at a site to look for trends

25. Data Identifiers (cont.) • Site data not consistent with other centers (statistical outlier) • Source records lack an audit trail - no signatures and dates of persons completing documentation • All source records & CRFs completed with the same pen • Perfect diary cards, immaculate CRFs

26. Source: British Medical Journal, 2002; 324; 1193-1194

27. Data Identifiers (cont.) • Subject handwriting and signatures are inconsistent across documents (consents, diaries) • Questionable subject visit dates (Sundays, holidays, staff vacations) • Impossible events (eg, subject randomized before IP even available at the site)

28. Data Identifiers (cont.) • Subject visits cannot be verified in the medical chart or appointment schedule • Data contains “digit preference” – some digits used more frequently than others (0, 5, and even digits) • “Halo” around the date or test value indicating the original was obliterated with correction fluid

29. Detection Strategies • Expect fraud – start from the assumption that records are bogus and work backwards • Question missing, altered, and/or inconsistent data – offer to retrieve records yourself, keep pulling on loose ends and see what unravels • Don’t be intimidated – challenge to explain suspicious data

30. Detection Strategies (cont.) • Be suspicious of blame shifting –remind the investigator that he/she is responsible for study conduct • Cultivate whistleblowers – pay attention to staff complaints, listen to grievances, establish rapport, and be approachable

31. Whistleblowers • Many fraud cases uncovered by staff whistleblowers • Ethical commitment to report fraud (SoCRA): • I recognize my right and responsibility as a clinical research professional to question suspected falsified data, and if necessary, proceed with appropriate reporting procedures as mandated by the appropriate regulatory agencies. • Many institutions have an Office of Compliance with reporting hotlines • US government encourages whistleblowers through False Claims Act awards

32. False Claims Act • Unlawful to submit a false or fraudulent claim for payment to the United States government • Private citizens who know of people or companies defrauding the government may sue on the government's behalf (qui tam relator) • Plaintiff shares in the proceeds of the suit (15-30% of amount recovered by government) • Contains protections for whistleblowers who are harassed, threatened, discharged or otherwise discriminated against in their employment

33. Recent False Claims Act settlement with the Mayo Clinic (Rochester, MN)

36. Cherlynn Mathias - University of Oklahoma Melanoma Trial Whistle Blower

37. Fraud Prevention • During pre-study evaluation, sponsors should carefully scrutinize sites for interest in the study, stability of the staff, investigator/staff interactions, workload, and level of training • Everyone involved in the clinical trial process should complete regular GCP training • CRAs should be expert on the protocol particularly with parameters that determine eligibility (inclusion/exclusion criteria) and primary efficacy endpoints

38. Fraud Prevention (cont.) • Sponsors should emphasize their policy on fraud at the initiation visit • Institutions should set-up systems to encourage fraud reporting and protect whistleblowers

39. This presentation and related references are posted on my corporate website at:www.pbelow-consulting.com/fraud.html

41. Thanks • Kerrin Young, Study Manager, Takeda, and Jeri Weigand, Quality Assurance Auditor, 3M Pharmaceuticals, for their collaboration in the development of this presentation

42. Contact Information • Office: (952) 882-4083 • E-mail:paul@pbelow-consulting.com