1. 1 CHICAGODALLASMIAMINEW YORK RALEIGHROCKVILLESAN DIEGO SAN FRANCISCO SWITZERLAND Avoiding Medication Errors in Brand Name Selection

2. 2 Patricia Kuker Staub, R.Ph., J.D. Vice-President, Regulatory Affairs BRAND INSTITUTE, INC. Silver Spring, MD - September 19, 2003 Brand Institute

3. 3 Recognition and Memorability: Benefits vs. Reality  Hallmark of Successful Proprietary Name: – Recognition Helps Market Drug, Identify Source, Establish Quality – Memorability Lessens Confusion  Reality: – Over 17, 000 approved generic and brand drug names in the United States – Only 26 letters in the English alphabet

4. 4 Recognition and Memorability: Risk Management Techniques  Pre-Approval – “Coming Soon” Ads – Market Research – Name Safety Testing – DMETS review  Post-Approval – Strong launch (Reminder Ads) – Targeted Advertising – Dear Doctor Letter – Tall Man Letters

5. 5 Best Practices

6. 6 Best Practices: Multifactorial Real-World Approach  Real-World Interpretive Rx Testing  Multiple sound files and handwriting files  Variety in dialect and script imitates reality  Bias is minimized with unaided instructions  Reference Search and Computer Analysis  Analysis of Overlapping Characteristics  Expert Focus Group Assessment of Data

7. 7 Best Practices: Lessons Learned from AERS  DMETS conducted a retrospective analysis of all reported mortality-associated medication errors contained in the AERS database in 2001. (n=5,366)  Proprietary name confusion resulted in 4.8% of fatal medication errors  Nonproprietary name confusion resulted in 4.1% of fatal medication errors.  More written miscommunications (6.7%) than oral miscom- munications (1.7%) resulted in fatal medication errors.

8. 8 Best Practices: Lessons Learned from AERS  Medication error fatalities occurred more frequently in: – Patients over the age of 60 – Patients in hospital settings – Patients receiving injectable drugs – Patients taking only one drug – Patients taking drugs in the therapeutic categories of CNS, Oncology, and CV.  The above factors may be additional considerations in assessing brand name risk

9. 9 Best Practices: Benchmarking  Benchmarking Errors in Rx Interpretation – Error rates are relevant in assessing confusion but may be misleading without additional analysis  Assessment of patient harm is a necessary component of benchmarking  Spelling errors may be harmless and are not necessarily as dangerous as confusion errors

10. 10 Best Practices: Overlapping Characteristics  Overlapping product characteristics can intensify name confusion.

11. 11 Best Practices: Modifiers  Prefix Modifiers  Suffix Modifiers  Modifier Structure

12. 12 Best Practices: Numerical Branding  Numerical Branding inserts numbers into the brand name (beginning, middle, or end) – Single-entity drugs = confusion: – Valium-5 may mean “Take 5 Valium!”  Combination drugs: – Neither number or both numbers may be added to the name; confusion arises when only one ingredient strength is listed:  Aldoril 25/50 – (25 mg methlyldopa/50mg HCTZ)  Percocet 5/325 [vs. Percocet-5] – (5 mg oxycodone/325 mg acetaminophen)

13. 13 Best Practices: Trailing Zeros  Trailing Zeros Cause Confusion – 2.50 mg may look like 250 mg  Leading Zeros Minimize Confusion – 0.25 mg differentiates dose from 25 mg  Most Common Fatal Medication Errors are due to improper dose (40.9%)  Drug Logos should emphasize leading zeros after the drug name, if applicable, and avoid advertising strengths with trailing zeros

14. 14 Best Practices: Tall Man Letters  FDA’s Name Differentiation Project – Generic names use “capital letters” to differentiate parts of a generic name:  AcetaHEXazole vs AcetaZOLamide  Brand Names can also use name differentiation in advertising to counter-detail exiting drug name confusion  i.e. SeroQUEL packaging/advertising to differentiate from SeraFEM

15. 15

16. 16 Best Practices: Electronic Error Control Systems  Bar-Coding to Prevent Confusion – Minimizes order picking confusion – Does not minimize interpretive confusion – Does not assist in order entry confusion  Computerized Order Entry – May minimize illegible prescriber handwriting – May introduce error in picking drug name from a list – Does not correct order-picking confusion  No Electronic System is Error-Free!

17. 17 Best Practices: Orthographic Analysis  Quantitative Measures  Handwriting fatigue and trail-off analysis  Letters that bleed into one another: – “o” and “e,” “m” and “n”  Above and below-line letter tails: – “l” and ‘t,” “j” and “y”

18. 18 Best Practices: Problematic Beginning Letters  Beginning a brand name with the letter “X” or “Z” is discouraged:  Examples of Potential Confusion: – Z can look like C,L,B,2,g,y,j,q – Z can sound like c,s, or x – X can sound like “z”……* * from Evaluating Proprietary Names – A FDA Perspective.” April 17, 2001, Washington, D.C., by Jerry Phillips, R. Ph., Associate Director Office of Post-Marketing Drug Risk Assessment Center for Drug Evaluation and Research Food and Drug Administration

19. 19 Recommendations

20. 20 Recommendations: Process Improvement  Tentatively-Approved Names should be published at time of tentative approval: – Subsequent new drug applicants need to be able to test their proposed names against tentatively-approved names to measure potential confusion – Release of tentatively-approved names would allow sponsors of already-marketed drugs to object to tentatively- approved names on the basis of potential confusion with their own marketed products.

21. 21 Recommendations: Process Improvement FDA Names Testing Procedures:  All orthographic or phonetic models used by the FDA to test proposed brand names, should be fully transparent and available to brand name applicants: – Applicants should be able to examine the model used to test their brand names – Parallel testing of names could improve the accuracy of both models

22. 22 Recommendations: Duplicate Brand Name Exception  A duplicate brand name should be allowed for new indications in cases where the original brand name has become closely associated with mental health conditions that are known to elicit social stigma.  For example:  Wellbutrin vs. Zyban  Prozac vs. Serafem  Risk of confusion from the duplicate brand name is counter-balanced by risk of patient noncompliance due to stigma.

23. 23 Conclusion

24. 24 Conclusion:  Predicting risk is not an exact science. Even after testing, differences of opinion may still exist between regulators and sponsors as to acceptable levels of potential risk  Techniques to evaluate existing testing methodologies continue to evolve.  Nevertheless, we do not see any realistic substitute for a multifactorial approach that incorporates comprehensive names-testing in the real world to assess the risk of confusion between new and existing drug names.