Personalised Medicine: Beyond the buzzword Dr. Oscar Della Pasqua C linical Pharmacology

1. Personalised Medicine: Beyond the buzzword Dr. Oscar Della Pasqua Clinical Pharmacology GlaxoSmithKline, United Kingdom

2. Outline • - Does ‘personalised’ effectively mean the same for clinicians, patients and industry? • - What are the implications for drug development ? Effectiveness – integrated measure(s) of efficacy and safety • shift in paradigm from ‘one dose fits all’ • shift in paradigm from ‘one endpoint fits all’ • shift in paradigm from ‘large, non-enriched trials’ • Model-based approach to integrate data: • right dose, right patient, right methods • Conclusions

3. ‘Clinical Reality….We’ve got a new wonder drug! - But I wonder what it will do for you. We’ve got a new wonder drug! - But I wonder which dose to prescribe.

4. All patients with same diagnosis 1 Non-responders or toxic responders Treat with alternative drug 2 Responders and patients not predisposed to toxicity Treat with most suitable dose Treatment Decisions Biomarkers of Drug Response

5. Clinical Relevance - Predictive ValueUtility of the information/biomarker Best Good Poor Not a biomarker Y Y • Examples • ErbB-2 over-expression and response to Herceptin • ALOX5 promoter in asthma • CrCL • Bone marrow density Response N N Y Y Y N N Y N N Variation Variation

6. CYP2D6 - Polymorphisms 60 0 1 30 2 3 13 • Number of functional CYP2D6 alleles (0 - 13) determines concentrations of nortriptyline. 2 allele patients had greater clearance than 1 or 0 allele patients. • Lack of efficacy in CYP2D6 x 13 patients Nortriptyline 25 mg dose Number of functional CYP2D6 genes Conc (nmol/L) 0 0 24 48 72 Hours

7. Clinical relevance of CYP2D6Nortriptyline dosing recommendation in Europe

8. Clinical Relevance of CYP2D6 Strattera - No Dosing Adjustment Initial approval 2002, USA

9. Are the answers to personalised medicine really here or does one need to look beyond?

10. (Am J Psychiatry 2002; 159:122–129)

11. Consider Genetics Disease / Pharmacokinetics / Pharmacodynamics Epidemiology / Genetics / Clinical Pharmacology

12. Can one predict the impact of variability or noise in drug effect with a single marker? What do you see when you have spent 8 months designing a sports car?

13. Consider Intrinsic and Extrinsic Factors Disease / Pharmacokinetics / Pharmacodynamics Epidemiology / Genetics / Clinical Pharmacology

14. Model-based Approaches for Prediction of Response Disease / Pharmacokinetics / Pharmacodynamics Epidemiology / Genetics / Clinical Pharmacology

15. BeSt study design • Retrospective, multi-centre, open • 509 patients with active RA enrolled in this study are participants in a trial to test the effectiveness of different treatment strategies (BeSt- study) • all patients have active disease according to ACR criteria, disease duration < 2 years • 247 patients are treated with monotherapy MTX • Wessels et al. Arthritis Rheum.56:1765-75, 2007

16. DAS >2.4 DAS  2.4 BeST study: summary 205 RA patients Active RA at baseline DAS 4.5 MTX 15 mg/week or 25 mg/week, folic acid 1 mg/day RESPONSE 47% at 6 months ADVERSE DRUG EVENTS 30%

17. Factors influencing outcome Measures to evaluate outcome

18. RFC Folate pathway MTHFR testing may determine which RA patients will benefit from MTX genetics contribute to MTX treatment outcome in RA

19. ITPA AMPD ‘Adenosine release’ Good clinical response with MTX at 6 months (%) AMPD1 T-allele, ATIC CC genotype, ITPA CC genotype are 2-3 fold more likely to achieve good clinical response 93 75 68 58 60 50 61 37 26 42 37 41 43 ITPA CC 43 ATIC CC 47 Favorable genotypes AMPD T-allele ITPA CC + ATIC CC all three favorable AMPD T-allele + ITPA CC overall population ATIC CC + AMPD T-allele

20. Current MTX pharmacogenetic research From associations with genes to a predictive clinical tool “MTX sensitive RA” - Simple model - validation in 2nd cohort

21. Development of a predictive model of clinical response 24 baseline variables believed to influence RA disease state and MTX drug response were selected based on literature

22. RFC ITPA AMPD 17 SNPs in 13 genes involved in the MTX mechanism of action, purine and pyrimidine synthesis

23. Baseline Variable Baseline Variable Score Score premenopausal premenopausal Gender Gender Female Female 1 1 postmenopausal postmenopausal 1 1 Male Male 0 0 Disease activity Disease activity DAS at baseline DAS at baseline 3.8 3.8 0 0 DAS at baseline >3.8, but DAS at baseline >3.8, but 5.1 5.1 3 3 DAS at baseline >5.1 DAS at baseline >5.1 3.5 3.5 Immunological factors Immunological factors Rheumatoid factor negative and non Rheumatoid factor negative and non - - smoker smoker 0 0 Rheumatoid factor negative and smoker Rheumatoid factor negative and smoker 1 1 Rheumatoid factor positive and non Rheumatoid factor positive and non - - smoker smoker 1 1 Rheumatoid factor positive a Rheumatoid factor positive a nd nd smoker smoker 2 2 Genetic factors Genetic factors MTHFD1 MTHFD1 1958 1958 AA genotype AA genotype 1 1 AMPD1 AMPD1 34 CC 34 CC genotype genotype 1 1 ITPA ITPA 94 A 94 A - - allele carrier allele carrier 2 2 ATIC ATIC 347 G 347 G - - allele carrier allele carrier 1 1 Other Other ge ge notypes notypes 0 0 Factors determining efficacy for individual MTX monotherapy ≤ ≤

24. Suggestions for clinical applicationof the model

25. Receiver Operating Curves (ROC) 1,0 0,8 sensitivity 0,5 PG Model: True positive response 95% (36 out of 38) True negative response 87% (62 out of 72) Percentage of patients categorized: 60% pharmacogenetic model 0,3 non-genetic model Non-genetic model Percentage of patients categorised: 32% 0,0 0,3 0,5 0,8 1,0 1- specificity

26. Conclusions - BeST The chance to achieve clinical response with MTX treatment is predictable in recent onset RA. It is feasible to assist initial treatment decisions to tailor therapy in RA patients according to their baseline criteria (symptoms, signs and genotype)

27. Model-based Approaches for Dose Optimisation Disease / Pharmacokinetics / Pharmacodynamics Epidemiology / Genetics / Clinical Pharmacology

28. New Technologies – Old tools? From Biomarker data to Treatment Decision JAMA, 296 (12), 2006

29. Efficacy Adverse Events Exposure The concentration-response surface:What is the surface for a given population /patient group?Where are you during development?

30. Multidimensional Diseases - Multiple Endpoints - • Organ Transplantation (2) • Primary Biliary Cirrhosis (4) • BPH (2) • Multiple Sclerosis (2) • Epilepsy (3) • Vaccines (up to 23) • Operable Breast Cancer(with + auxiliary lymph nodes) (2) • Fibromyalgia (2-3) • Menopausal Symptoms (3) • Fracture Healing (2) • Acne (4) • Male Pattern Baldness (2) • Glaucoma (9) • Ophthalmology – dry eye (2) • Hepatitis B (up to 3) • Vaginal Atrophy (3) • Migraine (4) • Alzheimers (2) • Acute Pain (3) • Lower Back Pain (3) • Sleep Disorders (3 or 6) • RA (4) • OA for symptom modif. (2) • Asthma, COPD (2) • ED (3) • Skin Aging (2)

31. Model-based risk assessment

32. Model-based risk assessment

33. Model-based Approaches:Dosage strategy for enoxaparin Observed vs. population predicted anti-Xa concentrations for the two-compartment model with CrCL and weight covariates in the model. Individual data points are shown as dots and the unity as a solid line Three-dimensional surface showing the relationship between CrCL, weight and predicted Css. The surface shows how the Css changes with both weight and CrCL simultaneously Feng et al (2007), Br J Clin Pharmacol 62:165–176

34. intensive care unit general medical unit % Css < 0.5 UI/ml 8.3 IU/ kh/h 5.8 IU/kg/h 5.0 IU/kg/h 4.2 IU/kg/h % Css >1.2 UI/ml % Css out of range (1, CrCL <30 ml min−1; 2, CrCL 30–50 ml min−1; 3, CrCL >50 ml min−1).

35. Model-based Dose Recommendations Barras et al. (2007) Clin Pharmacol Ther advance online publication doi:10.1038/sj.clpt.6100399

36. Sotalol in SVT PK/PD relationship Effect of Age on Clearance Sotalol oral Clearance (ml/min/kg) Probability of Response Sotalol conc (ug/mL) Age (years) Measured (closed diamonds) and model predicted oral sotalol clearance based on body weight (open diamonds). Median (solid line) and the 10th and 90th percentile (dashed line) of 1,000 simulated data sets. Probability of arrhythmia suppression in the 15 children with supraventricular tachycardia vs sotalol trough concentration under steady-state conditions and an 8-h dosing interval. Filled circles 6 neonates (28 days).

37. Dose Recommendation Age-specific Dosing regimen for sotalol in children with SVT Black box plots and hatched bars indicate recommended dosing range. (A) Simulated sotalol trough concentrations (125 patients per group and dose level) for paediatric patients with supraventricular tachycardia. Lines indicate 50% and more than 95% efficacy. (B) Patient fraction with 50% and more than 95% probability of arrhythmia suppression. Arrows indicate start and target doses.

38. Summary • - Does ‘personalised’ effectively mean the same for clinicians, patients and industry? • - What are the implications for drug development ? Effectiveness – integrated measure(s) of efficacy and safety • shift in paradigm from ‘one dose fits all’ • shift in paradigm from ‘one endpoint fits all’ • shift in paradigm from ‘large, non-enriched trials’ • Model-based approach to integrate data: • right dose, right patient, right methods • Conclusions

39. Personalised Treatment: Delicate Balance Between Benefit and Risk

40. The greatest obstacle to discovery is not ignorance, but the illusion of knowledge by Daniel Boorstin