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

Personalised Medicine: Personalised Medicine: Beyond the buzzwordBeyond the buzzword

Dr. Oscar Della PasquaDr. Oscar Della Pasqua

CClinical PharmacologyGlaxoSmithKline, United Kingdom

OutlineOutline

- 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

‘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.

All patients with same diagnosis

1

2

Responders and patients not predisposed to toxicity

Non-responders or toxic

responders

Treat with alternative drug

Treat with most suitable dose

Treatment Decisions Treatment Decisions

Biomarkers of Drug ResponseBiomarkers of Drug Response

Clinical Relevance - Predictive ValueClinical Relevance - Predictive ValueUtility of the information/biomarkerUtility of the information/biomarker

Examples– ErbB-2 over-expression and response to Herceptin

– ALOX5 promoter in asthma

– CrCL

– Bone marrow density

Variation VariationY YY YN N N N

Res

pon

se YY

N N

Best Good Poor Not a biomarker

Co

nc

(nm

ol/

L)

Hours

Number of functionalCYP2D6 genes

0

0

1

2

3

13

30

60

0 24 48 72

CYP2D6 - Polymorphisms CYP2D6 - Polymorphisms • 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

Clinical relevance of CYP2D6Clinical relevance of CYP2D6Nortriptyline dosing recommendation in Europe Nortriptyline dosing recommendation in Europe

Clinical Relevance of CYP2D6 Clinical Relevance of CYP2D6 Strattera - No Dosing AdjustmentStrattera - No Dosing Adjustment

Initial approval 2002, USA

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

(Am J Psychiatry 2002; 159:122–129)

C

G A T G G A A T

A G C C T A C A T A C T A A

A G

T A

A C C A T T A A G G T

T G T

A C T A

A C A C

G A A C

G C G A

C T G T

G A C C T T C A A T G G A T T A A G C C

A GA

AT

A A T A A C C T A T

T T G

T C A A A G T A C

G A G C A A T A G T

A

T A C G A T G

Consider Genetics

Epidemiology / Genetics / Clinical Pharmacology

Disease / Pharmacokinetics / Pharmacodynamics

Can one predict the impact of variability Can one predict the impact of variability or noise in drug effect with a single or noise in drug effect with a single

marker?marker?

What do you see when you have spent 8 months designing a sports car?

D

D H E R B A L S

C O M E D I C A T I O N S U

E G

T M

A B O D Y W E I G H T

P R T

R Y T A

O F R B

T A A O

E C N L

I T S I

G E N E T I C P O L Y M O R P H I S M S

B R O I

I S R E N Z Y M E S

N T G

D I S E A S E S E

I R E C E P T O R S

N S

T A R G E T S

Consider Intrinsic and Extrinsic Factors



D

D H E R B A L S


E G

T M


P R T

R Y T A

O F R B

T A A O

E C N L

I T S I


B R O I

I S R E N Z Y M E S

N T G

D I S E A S E S E

I R E C E P T O R S

N S

T A R G E T S

Model-based Approaches for Prediction of ResponseModel-based Approaches for Prediction of Response



BeSt study designBeSt 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

BeST study: summaryBeST 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%

DAS >2.4

DAS 2.4

Treatment outcome

Host

Disease Environment

Genes

Drug

Disease activity scoreACR criteria

Health assessment questionnaireRadiographic score

agegenderhormonal statusco-morbidityethnicityprevious DMARD use

Life style e.g. smoking and dietsocial class

HLA-DRB1 alleles (shared epitope)PTPN22Cytochrome P450 enzymesCandidate genes

Disease durationdisease activityAnti-CCPrheumatoid factor

Factors influencing outcome

Measures to evaluate outcome

RFC

Folate pathway Folate pathway

46

62

80

46

66

88

50

77

100

Number of MTHFR 677C-1298A haplotype copies

MTHFR haplotype as factor for MTX response

good clinicalresponse

good clinicalimprovement

moderate clincalimrpovement

MTHFR testing maydetermine which RA patients will benefit from MTX

genetics contribute to MTX treatment outcome in RA

AMPD

ITPA

‘‘Adenosine release’ Adenosine release’

AMPD1 T-allele, ATIC CC genotype, ITPA CC genotype are 2-3 fold more likely to achieve good clinical response

Favor

able

geno

types

ITPA C

C

ATIC C

C

AMPD T

-alle

le

ITPA C

C + A

TIC C

C

AMPD T

-alle

le +

ITPA C

C

ATIC C

C + A

MPD T

-alle

le

all th

ree

favo

rable

over

all p

opula

tion

5058 60

6168

75

93

3726

4237

4143

4347

Good clinical response with MTX

at 6 months (%)

From associations with genes

to a predictive clinical tool

“MTX sensitive RA”

- Simple model

- validation in 2nd cohort

Current MTX pharmacogenetic researchCurrent MTX pharmacogenetic research

24 baseline variables believed

to influence RA disease state

and MTX drug response were

selected based on literature

Development of a predictive model of Development of a predictive model of clinical responseclinical response

RFC

AMPD

ITPA

17 SNPs in 13 genes involved in the MTX mechanism of action, purine and pyrimidine synthesis

Factors determining efficacy for Factors determining efficacy for individual MTX monotherapy individual MTX monotherapy

Baseline Variable Score

premenopausal1Gender Female

postmenopausal 1Male 0

Disease activity DAS at baseline 3.8 0DAS at baseline >3.8, but 5.1 3DAS at baseline >5.1 3.5

Immunological factors Rheumatoid factor negative and non-smoker 0Rheumatoid factor negative and smoker 1Rheumatoid factor positive and non-smoker 1Rheumatoid factor positive and smoker 2

Genetic factors MTHFD1 1958 AA genotype 1AMPD1 34 CC genotype 1ITPA 94 A- allele carrier 2ATIC 347 G-allele carrier 1Other genotypes 0

Baseline Variable Score

premenopausal1Gender Female

postmenopausal 1Male 0

Disease activity DAS at baseline 3.8 0DAS at baseline >3.8, but 5.1 3DAS at baseline >5.1 3.5

Immunological factors Rheumatoid factor negative and non-smoker 0Rheumatoid factor negative and smoker 1Rheumatoid factor positive and non-smoker 1Rheumatoid factor positive and smoker 2

Genetic factors MTHFD1 1958 AA genotype 1AMPD1 34 CC genotype 1ITPA 94 A- allele carrier 2ATIC 347 G-allele carrier 1Other genotypes 0

≤≤

Suggestions for clinical applicationSuggestions for clinical applicationof the modelof the model

Categories Clinical consequence

Scores ≥ 6 Low probability to respond to MTX monotherapy. Consider a combination strategy.

Scores < 6, but > 3.5

Scores ≤ 3.5 High probability to respond to MTX monotherapy Dose escalation to 25 mg/weekif necessary.

Intermediate probability to respond to MTX monotherapy. Evaluate after 3 months therapy.

Categories Clinical consequence





Categories Clinical consequenceCategories Clinical consequence









Receiver Operating Curves (ROC)Receiver Operating Curves (ROC)

1,00,80,50,30,0

0,8

0,5

0,3

1,0

sensitivity

1- specificity

non-genetic model

pharmacogenetic modelPG Model:

True positive response 95% (36 out of 38)

True negative response 87% (62 out of 72)

Percentage of patients categorized: 60%

Non-genetic model

Percentage of patients categorised: 32%

Conclusions - BeSTConclusions - 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)

D

D H E R B A L S


E G

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P R T

R Y T A

O F R B

T A A O

E C N L

I T S I


B R O I

I S R E N Z Y M E S

N T G

D I S E A S E S E

I R E C E P T O R S

N S

T A R G E T S

Model-based Approaches for Dose OptimisationModel-based Approaches for Dose Optimisation



New Technologies – Old tools? New Technologies – Old tools? From Biomarker data to Treatment Decision

JAMA, 296 (12), 2006

Exposure Adverse Events

Effi

cacy

The concentration-response surface:What is the surface for a given population /patient group?

Where are you during development?

Multidimensional Diseases - Multiple Endpoints -

1. Migraine (4)

2. Alzheimers (2)

3. Acute Pain (3)

4. Lower Back Pain (3)

5. Sleep Disorders (3 or 6)

6. RA (4)

7. OA for symptom modif. (2)

8. Asthma, COPD (2)

9. ED (3)

10. Skin Aging (2)

11. Menopausal Symptoms (3)

12. Fracture Healing (2)

13. Acne (4)

14. Male Pattern Baldness (2)

15. Glaucoma (9)

16. Ophthalmology – dry eye (2)

17. Hepatitis B (up to 3)

18. Vaginal Atrophy (3)

19. Organ Transplantation (2)

20. Primary Biliary Cirrhosis (4)

21. BPH (2)

22. Multiple Sclerosis (2)

23. Epilepsy (3)

24. Vaccines (up to 23)

25. Operable Breast Cancer(with + auxiliary lymph nodes) (2)

26. Fibromyalgia (2-3)

Model-based risk assessment Model-based risk assessment

Model-based risk assessment Model-based risk assessment

Model-based Approaches: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

% C

ss <

0.5

UI/m

l%

Css

>1.

2 U

I/ml

% C

ss o

ut

of

ran

ge

(1, CrCL <30 ml min−1; 2, CrCL 30–50 ml min−1; 3, CrCL >50 ml min−1).

8.3 IU/ kh/h5.8 IU/kg/h5.0 IU/kg/h4.2 IU/kg/h

intensive care unit general medical unit

Model-based Dose RecommendationsModel-based Dose Recommendations

Barras et al. (2007) Clin Pharmacol Ther advance online publication doi:10.1038/sj.clpt.6100399

Sotalol in SVTSotalol in SVTP

roba

bilit

y of

Res

pons

e

Sotalol conc (ug/mL)

PK/PD relationship Effect of Age on Clearance

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).

Age (years)

Sot

alol

ora

l Cle

aran

ce

(m

l/min

/kg)

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.

Dose Dose RecommendationRecommendation

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. Arrowsindicate start and target doses.

Age-specific Dosing regimen for sotalol in children with SVT

SummarySummary

- 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

Personalised Treatment: Delicate Balance Between Personalised Treatment: Delicate Balance Between Benefit and RiskBenefit and Risk

The greatest obstacle to discovery is not

ignorance, but the illusion of knowledge

by Daniel Boorstinby Daniel Boorstin

Documents

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