Tailoring Medication to Patient Characteristics

1. Tailoring Medication to Patient Characteristics Farrokh Alemi, Ph.D.George Mason University

2. Patent • This presentation is based on a patent application on personalized medication held by George Mason University • Scientists and government organizations have free access to this patent

3. Privilege

4. What is informatics?

5. What is informatics? Anything you want it to be

6. It could be … Level 1 1. Identify the need for IT applications in medicine and healthcare 2. Demonstrate competence in the use of appropriate technologies, communication and organisational skills 3. Apply organisational techniques to interpret information and use knowledge 4. Deploy skills required in the administration of patients and their records 5. Describe the characteristics of health and social care information systems. 6. List the strengths and weaknesses of e-communication in healthcare. 7. Explain statistical reports. Level 2 8. Discuss and apply advanced theoretical and practical applications of informatics and computer science. 9. Demonstrate use and design of software. 10. Present data and information processing skills, analyse and assess different coding systems in healthcare. 11. Define and evaluate informatics standards. 12. Display an awareness of the fields of Medicine, health and biosciences and NHS organisation. 13. Show appropriate and professional customer service skills. 14. Describe applications of biomedical informatics specialities. Level 3 15. Critically discuss ethical issues and patients privacy. 16. Manage, implement and assess Information and Communication Technology. 17. Identify and synthesize solutions for technical/security faults 18. Present information regarding image and signal processing 19. Plan, implement, monitor, evaluate and complete projects. 20. Exhibit managerial skills and knowledge, demonstrate financial awareness, apply problem-solving skills and describe different project management frameworks.

7. It could be … • Computational biogenetics • Text processing & social networks • Bioinformatics • Electronic Health Record • Robotics • Expert systems & machine learning • Decision support systems • Online management of patients • Ergonomics • …

8. Technology is Seductive • Promises efficiency

9. Technology is Seductive • Promises efficiency • Takes you to new directions A classical bait and switch

10. You cannot take a car for a walk • Go short distances • Drive to new destinations • Roads, evolution

11. Case of Personalized Medicine What is it and how it changes us?

12. Medication Benchmarks: Selection of Antidepressants • Many options are possible: • tricyclic antidepressants (TCAs), • monoamine oxidase inhibitors, • selective serotonin reuptake inhibitors (SSRIs), • nonselective serotonin–norepinephrine reuptake inhibitors (SNRIs), • the selective norepinephrine reuptake inhibitors (selective NRIs) • other miscellaneous agents, such as mirtazapine.

13. Case of George • Military service • Industrial manager, polite, defines himself a “medieval knight.” • First depression episode at 26, treated with clomipramine, dose unknown. • At 30 married with a daughter • At 45 return of depressive symptoms, treated with fluvoxamine 200–300 mg and mirtazapine 15 mg • Depression continues, loss of interest in work, difficulty with bi-polar daughter • Loss of daughter, divorce and loss of work • At 48, suicide

14. First Treatment not Enough • Who responds to SSRI citalopram? • Highly educated • Currently employed • Caucasian women • Few complicating psychiatric or medical disorders. At least 70% of patients did not respond. Beyond Efficacy: The STAR*D Trial. By Thomas R. Insel Am J Psychiatry. available in PMC 2006 September 30.

15. First Treatment not Enough • Who responds to SSRI citalopram? Highly educated Currently employed Caucasian women Few complicating psychiatric or medical disorders. • At least 70% of patients did not respond. Beyond Efficacy: The STAR*D Trial. By Thomas R. Insel Am J Psychiatry. available in PMC 2006 September 30.

16. First Treatment not Enough • Who responds to SSRI citalopram? Highly educated Currently employed Caucasian women Few complicating psychiatric or medical disorders. • At least 70% of patients did not respond. Not George Beyond Efficacy: The STAR*D Trial. By Thomas R. Insel Am J Psychiatry. available in PMC 2006 September 30.

17. Medication Benchmarks: Selection of Antidepressants • 70% not responsive • Six weeks before efficacy can be examined • Sometimes 2-3 years searching for right medication

18. Variability in Outcomes George is not alone, poor management of depression is affecting many.

19. What We Want? Medication That Works for Me

20. EHR Patient data stored Patient data retrieved Care reminders Patient info displayed Care Decisions Data of others Data warehousing Clinical Education Discovery Rethinking Role of Data

21. Rethinking Role of Data EHR Patient data stored Patient data retrieved Care reminders Patient info displayed Care Decisions Data of others Similar patients Data warehousing Clinical Education Discovery

22. Rethinking Role of Data EHR Patient data stored Patient data retrieved Analytics: care forecasts Patient info displayed Care Decisions Data of others Similar patients Data warehousing Clinical Education Discovery

23. Age Gender Race Ethnicity Concurrent drugs Methadone Buprenorphine Diet Grapefruit Genetics CYP2D6 CYP2C19 CYP3A4 CYP1A2 Concurrent illness Cancer Diabetes Medication Benchmarks: Selection of Antidepressants

24. Medication Benchmarks: Selection of Antidepressants • Steps in Algorithm • Select characteristics that make patient different from norm • Calculate similarity to patients in the database Based on GMU patent. Confidential communication

25. Medication Benchmarks: Selection of Antidepressants • Steps in Algorithm • Select characteristics that make patient different from norm • Calculate similarity to patients in the database Number of features matched Number of features matched Features in one but not the other Based on GMU patent. Confidential communication

26. Medication Benchmarks: Selection of Antidepressants • Steps in Algorithm • Reported average outcomes for different anti-depressants weighted by similarity of patients: Based on GMU patent. Confidential communication

27. Medication Benchmarks: Selection of Antidepressants • Easy to implement • 8 lines of SQL code • Can work within any EHR • Concurrent analysis • No need to export data. No need for consent • No need to use data warehouses • No need to require same data on all patients

28. Medication Benchmarks: Selection of Antidepressants • Easy to implement • 8 lines of SQL code • Can work within any EHR • Concurrent analysis • No need to export data. No need for consent • Current or warehoused data • No need to require same data on all patients

29. Medication Benchmarks: Selection of Antidepressants • Easy to implement • 8 lines of SQL code • Can work within any EHR • Concurrent analysis • No need to export data. No need for consent • Current or warehoused data • No need to require same data on all patients

30. Data Source • Sequenced Treatment Alternatives to Relieve Depression (STAR*D) • 4,041 outpatients with non-psychotic depression • 23 psychiatric and 18 primary care sites • 12-week course of the SSRI citalopram • Adjunct or replacement treatment in three subsequent phases

31. Technological Fix • Technology is available • Data is available • Analytical procedure is simple Will there be fewer George’s among us?

32. Transformation at Hand: Suppose the Dog Catches with the Car • Process changes • Confusion in responding • Patient first? • Liability for follow up • Organizational changes • Formulary • Must have software • Consent to exclude • Other • FDA approval • Publishing data

33. Informatics Can Be Anything You Want Be Careful What You Wish For What is 1+1?