definition of pgt & pgx · pharmacogenetics and pharmacogenomics • pharmacogenetics – the...

Hong-Hao Zhou, MD

SECOND ANNUAL SYMPOSIUM ON PHARMACOVIGILANCE-Pharmacovigilance Strategy to maximize drug safety

11:15-12:00, March 4, 2010 Hong Kong

Clinical Trials in Pharmacogenetics and

Pharmacogenomics

• PHARMACOGENETICS – the study of the role of inheritance in inter-individual variation in drug response

• PHARMACOGENOMICS – the use of DNA sequence information to measure and predict the reactions of individuals to drugs

• Often, these terms are used interchangeably

Definition of PGt & PGx

Genomic medicine

Ref: Roses, A. Pharmacogenetic and the practice of medicine. Nature, 405, 857-865

Genetic Testing

Disease geneticsDisease prognostics/diagnostics

PharmacogeneticsMedicine response profiles

Rare mendelian diseases:

Causal genes

Common complex diseases,

Susceptibility genes

New disease insights and future medicines Optimal medicine response

Genes for drug

metabolism and/or action

SNP profiles for drug

metabolism

Potential for unsolicited information and ethical, legal and social implication

Utility

What is tested

Benefits

Risks

GWAS PGx

Genomics

Paradox of Modern Drug Development

Clinical trials provide evidence of efficacy and safety at usual doses in populations

Physicians treat individual patients who can vary widely in their response to drug therapy

• Define function of genetic variants of biomarkers related to drug safety and efficacy;

• Define genetic (SNP) profile related to drug safety and efficacy;

• Drug development;

• Personalized medicine.

Use of clinical trial in pharmacogenetics

Two Ways of Clinical Trials in PGt

• Identify suitable loci using in vitro studies

• Generate possible treatment hypotheses

• Select suitable patients– Enrichment studies

• Prove the treatment works for these patients

• Identify potential drugs

• Find those that work in general

• Find the drugs on patients areconsiderable

• Search for genetic subgroups

From Gene From Drug

Functional test of variant

Sampling

+

-

Geneticanalysis

Screen of target volunteers

+/-

Treatment

• Patient numbers for genetic test maybe unbalanced in two arms.

• Sampling maybe limited in some patients.• Results are not confirmative Confirmative trial will be

necessary

Retrospective Design

Placebo

Drug

+

-

Entry

Sampling

Treatment

Randomized, double blind, placebo-controlled trial

+

-Ge

ne

tic

an

aly

sis

To test clinical utility

▬ PGx test is really necessary?▬ Cost-benefit relationship

Results are confirmative

Prospective design 1

Ra

nd

om

ize

d

Genetic analysis

Genotype directed treatmentSampling

Conventional treatment

+

-

+

-

• Increase analytical power of trial• Results are confirmative• May lose a chance of treatment for gene (-) patients

Prospective design 2

Sampling

Placebo

Drug

Treatment

No entry

Ra

nd

om

ize

d

Randomized, double blind, placebo-controlled trial

Enrichment Approach

+

-

Geneticanalysis

Prospective design

Sampling

Placebo

Drug

Placebo

Drug

Treatment

Ra

nd

om

ize

d+

-

Geneticanalysis

Clinical trials identify SNP profile for efficacy or toxicity of drug treatments

Not responds to standard drug treatment

Patient

Individual SNP profile are sorted

SNP profile A SNP profile B

SNP profile D

SNP profile ESNP profile C

Responds to standard drug treatment

Patient with SNP profile A&B given standard drug treatment

IGF2BP2, KCNJ11, and TCF7L2variations influence repaglinide response

and risk of type 2 diabetes

IGF2BP2 binds to insulin-like growth factor 2 (IGF-2), which is an important growth and insulin signaling molecule.

KCNJ11 (potassium inwardly rectifying channel, subfamily J, member 11 gene) encoding Kir6.2 is a candidate gene in relation to T2DM.

TCF7L2 is involved in the growth, differentiation, proliferation, and insulin secretion of pancreatic β-cells. It is involved in the pathogenesis of T2DM.

IGF2BP2 variations influence repaglinide response and risk of type 2 diabetes

b: P<0.05, c: P<0.01compared to AA

Fasting plasma glucose Postprandial serum insulin

Huang Q, Acta Pharmacologica Sinica (2010) 31: 709–717

Total cholesterol Low density lipoprotein-cholesterol

Baseline levels inIGF2BP2 rs1470579genotypes

N=350 T2DM


b: P<0.05, c: P<0.01compared to AA

Baseline levels inIGF2BP2 rs4402960genotypes


Fasting plasma glucose Postprandial serum insulin

Total cholesterolPostprandial serum insulin


Changes of FPG (A), PPG (B) (post-Pre repaglinide 3mg/d, 8w) in IGF2BP2 rs1470579genotypes

Change of PINS (C) IGF2BP2 rs4402960genotype after treatment of repaglinide

. b: P<0.05; c: P<0.01

N=42


: Fasting plasma glucose : Postprandial plasma glucose

: Postprandial serum insulin

Yu M, CPT 2010;87:330-5.

Changes of FPG, PPG, HbA1c after Repaglinide (3mg/d, 8w) in KCNJ11 Lys23Glugenotypes

(N= 40 T2DM)

KCNJ11 Lys23Glu polymorphism affect repaglinide therapeutic effect in T2DM

0

1

2

3

4

5

6

*

a

0

1

2

6

8

10

12 *

△FP

G (m

mol

/l)

△H

bA1c

(%)

△PP

G (m

mol

/l)

b

00.5

11.5

22.5

33.5

44.5

5CGGGA/AA

*

* p<0.05

Fasting plasma glucose Postprandial plasma glucose

Glycated hemoglobin

-16-14-12-10

-8-6-4-20

△FI

NS

(mU

/l)

a

*

0

0.5

1

1.5

2

2.5

△LD

L-c

(mm

ol/l)

C -3

-2

-1

0

1

23

4

5

△TG

(mm

ol/l)

b*

CC/CTTT

*

TCF7L2 rs290487(C/T) polymorphism affect repaglinide therapeutic effect in T2DM

Yu M, CPT. 2010;87:330-5.

Changes of FPG, PPG, HbA1c after Repaglinide treatment (3mg/d, 8wks) in TCF7L2 rs290487(C/T)

genotypes (N= 40 T2DM)* p<0.05

Triglyceride

Fasting serum insulin

Lowdensity lipoprotein-cholesterol

Hypertensive Pateints (n=422)

Randomization

One dose fiting all conventionaltherapy (n=218)

Different dose based on Genotype personalized therapy (n=204)

GP1: 2.5mg; GP2: 25mg; GP3: GP3: 50mg bid for 3 months

25mg bid, for 3 monthes

CYP2D6 and B-recepotor genotyping CYP2D6 and B-recepotor genotyping

CYP2D6 genotypingβ1 receptor genotyping

Low 2D 6 activity, High β1 sensitivityMedian 2D6 activity and β1 sensitivityHigh 2D6 activity, Low β1 sensitivity

Stratified to 3 groups

Hypertension patients stratified by CYP2D6 and β1 -adrenergic receptor genotypes

Hypertension patients stratified by CYP2D6 and β1-adrenergic receptor genotypes.

Low 2D 6 activity, High β1 sensitivityCYP2D6*1*10+Arg389ArgCYP2D6*10*10+Arg389Arg and Gly389Arg

Median 2D6 activity and β1 sensitivityCYP2D6*1*1+Arg389Arg/Gly389ArgCYP2D6*1*10+Gly389ArgCYP2D6*10*10+Gly389Gly

High 2D6 activity, Low β1 sensitivityCYP2D6*1*1/CYP2D6*1*10+Gly389Gly

Low dosen=14

Mid. Dosen=90

High dosen=104

Blood pressure response to metoprolol monotherapy in hypertension patients

0

2

4

6

8

10

12

14

16

18

20

⊿SBP ⊿DBP

Blo

od p

ress

ure

decr

ease

(mm

Hg)

AB

*

*⊿DBP P=0.009 compared with group A.

Efficacy of genotype directed metoprolol monotherapy was significantly higher than conventional therapy

Genotype base therapy vs conventional therapy

* ⊿SBP: P=0.014 compared with group A2;

§ ⊿DBP P=0.014 compared with A2;

† ⊿DBP P=0.034 compared with A2.

0

2

4

6

8

10

12

14

16

18

20

⊿SBP ⊿DBP

Blo

od p

ress

ure

decr

ease

(mm

Hg) A1

A2

A3*

§

†

Reduction in Blood pressure after conventional metoprolol monotherapy in hypertensive patients stratified according β1-adrenergic receptor and CYP2D6 genotype.

Different Genotype and same dosage


Reduction in BP after metoprolol monotherapy in hypertension patients stratified according β1-adrenergic receptor and CYP2D6 genotype.

0

2

4

6

8

10

12

14

16

18

⊿SBP ⊿DBP

Blo

od p

ress

ure

decr

ease

(mm

Hg)

A1B1

*

Same Genotype with conventional/genotype base therapy

Reduction in DBP was significantly greater in genotype directed therapy group (12.5mg bid) than the conventional therapy group (25mg bid)

* ⊿DBP P=0.048 vs A1.


Blood pressure response to metoprolol monotherapy in hypertension patients stratified according β1-adrenergic receptor and CYP2D6 genotype.

0

5

10

15

20

⊿SBP ⊿DBP

Blood pressure decrease (mm Hg)

A2

B2

Same Genotype with same dose in conventional/genotype base therapy groups

No difference in reduction of BP between A2 and B2 (both are same genotype) after the same dose by conventional and genotype directed therapy


Reduction in BP after metoprolol therapy in hypertensive patients stratified according β1R and CYP2D6 genotype. compared with A3.

0

5

10

15

20

⊿SBP ⊿DBP

Bloo

d pr

essu

re d

ecre

ase

(mm

Hg)

A3

B3

*

§

Same Genotype with same dose in conventional/genotype base therapy groups

*⊿SBP P=0.015 vs A3;

§⊿DBP P=0.006

Efficacy of metoprolol in same genotype was significantly higher in genotype based dose adjusted therapy than no dose adjusted therapy


Ongoing PGxclinical trial to seek SNPs profile for efficacy and safety of

indapamide, benazepril & losartan

DMET Chip SNP and CNV Correlated analysis

Indapamide n=800400 in each Group

Losartan n= 800 cases400 in each Group

Benazepril n=800400 in each Group

Without ADR

Good Efficacy

Poor Efficacy

Finding of new biomarker for efficacy,dose,toxicy

PyrosequencingRepeating Validation

With ADR

Research Group 400×3=1200

Validation Group 400×3=1200

Grouping Way ⅠGrouping Way Ⅱ (If exists)

Prospective clinical PK study for new biomarker

Essential Hypertensive Patients（2400 cases）

Random Group

863 Program

Design of Ongoing Warfarin Study

5000 patients using warfarin from the 2nd Xangya Hospital

“Derivation” cohort “Validation” cohort

About 1000 patients to test the algorithms

About 4000 patients to create dosing algorithms

Vitamin K

Intake and smoking

INR achieved with a stable warfarin dose

Target INRConcomitant medications

Stable therapeutic dosePrimary indication

Demographic characteristics

Genotype

Retrospective analysis

Endpoint: evaluate the % of validation patients predicted within 20% of the clinically-

correct warfarin dose

Algorithm for initial warfarin dose

Study Design of Tacrolimus

2000 patients using tacrolimus from the 3nd Xiangya Hospital and central south hospital

“Derivation” cohort “Validation” cohort

About 400 patients to test the algorithms

About 1600 patients to create dosing algorithms

Retrospective analysis

Endpoint: evaluate the % of validation patients predicted within 20% of the clinically-

correct tacrolimus dose

Algorithm for initial tacrolimus doseConcomitant

medicationsConcomitant

diseases

Laboratory variables

Target tacrolimus concentration

Stable therapeutic dose

Trough blood concentration

Demographic characteristics

Genotype

Relative use of PGt & PGx

during the process of drug development

TargetID Validate

ScreenDev.

CmpdLib.

Screen

Submitfor

ReviewLead

Optim.Preclin.Studies

Clin.Trials

I II III

PharmacogenomicsPharmacogenomics

PharmacogeneticsPharmacogenetics

Genetic assay is available and reliable/validated

Results are prospectively confirmed

Benefit/Risk can be evaluated even in gene (-)

Enrichment effect is reasonable and acceptable

What is needed for good PGt/PGx

PGx clinical trials in drug development

Ref: Roses, A. Pharmacogenetic and the practice of medicine. Nature, 405, 857-865

Abbreviated SNP linkage disequilibrium profile for efficacy and

common AE

Abbreviated SNP linkage disequilibrium profile for

serious rare AE

Comprehensive medicine response

profile predict efficacy and AE

“Non-responsive” profiles define unmet need Research

Phase II clinical trials

Smaller, faster and more efficient phase III

studies

Market approval with medicine response profile;

pharmacogenetic surveillance

Enhancement with comprehensive medicine

response profile; traditional surveillance

Used to select patients for phase III clinical trials

Application of PGt and PGx in Clinical Trials

Data from CMR International Institute for Regulatory Science 2003

0123456789

Understandingmechanism of action

(13)

Identifying newtargets (9)

Investigating targetpolymorphisms (13)

Stratifying patientsfor ADRs (7)

Stratifying patientsfor PK/PD effects

(12)

Stratifying patientsfor response (9)

Phase I Phase II Phase III Phase IVPhase I Phase II Phase III Phase IV

Mechanism

（13）

Identify Target

（9）

Target Polymorphism

（13）

ADR in Stratify Patients（7）

PK/PD in Stratify Patients（12）

Efficacy in Stratify Patients（7）

Current Applications of Pharmacogenomics

• Interpretation of clinical trial results, data quality, study design, and biomarkers.

• Targeting drugs at genetically-defined populations.

• Three areas of greatest activity– Clinical genotyping

– Pre-clinical gene expression

– Clinical gene expression

Traditional Clinical TrialsTraditional Clinical Trials

Roses, Nature Reviews Genetics, 2004.Roses, Nature Reviews Genetics, 2004.

NonNon--respondersresponders RespondersResponders HyperHyper--respondersresponders

Pharmacogenetic PrePharmacogenetic Pre--ScreeningScreeningIn Clinical TrialsIn Clinical Trials


NonNon--respondersresponders RespondersResponders HyperHyper--respondersresponders

-- Smaller, Faster & Less ExpensiveSmaller, Faster & Less Expensive

PGt/PGx in Phase I study

• Phase I studies

– Explain outliers or patient-to-patient variability in PK

– Exclude or include specific patients

– Normalize genotype frequencies

– Bridge to other populations

t 1/2

, hr

1010

2020

3030

4040

5050

Desipramine PK Parameters

CYP2D6 *6/*9CYP2D6 *6/*9

Genotyping can increase trial safety and explain outlying data

CYP2D6 poor metabolizers (2 null alleles) excluded.

One outlier with slow metabolism

Outlier has *6 null allele and *9 allele with reduced enzymatic activity.

Expected occurrence of null/*9 genotype is 0.4%

Adapted from Katz et al., Abbott Labs.Adapted from Katz et al., Abbott Labs.

Extensive metabolizers

Poor metabolizers

35 33

80

140 0

1 2 3Clinical trial center

Nu

mb

er

of

sub

jec

ts

70

140

0

Phase I study on a CYP2D6 substrate

In the trial, any center’s conclusion on tolerance rate, or PK parameters may not be accurately reflect the true of studied population.

PGt/PGx in Phase II/III study

• Phase II/III studies

– Identify genetically-defined groups with more pronounced or rapidly progressing disease

– Exclude/include at-risk individuals

– Stratify studies based on genotypes

• Clinical response

• Risk of adverse events

– Where appropriate, develop drugs for specific groups

– Identify genetic markers associated with clinical outcomes

Extensive metabolizers

Poor metabolizers

95

6380

20

5 7

1 2 3Clinical trial site

Nu

mb

er

of

sub

jec

ts

140-

120-

100-

80-

60-

40-

20-

0-

Phase II study on a CYP2C19 substrate

1 2 3Clinical trial site

Plas

ma

conc

entr

atio

n

50

100

0

5% 10% 20%

Phase II study on a CYP2C19 substrate

ADR rate

5000 events

Genotype, Risk and Events

0.1

1

10

100

1000

10000

100000

1E-10 1E-09 1E-08 1E-07 0.0000010.00001 0.0001 0.001 0.01 0.1 1

Genotype frequency

Indi

vidu

al ri

sk

I I I I I I I I I I I

—

—

—

—

—

—

5000 Events

High frequency of CYP2D6 variants in Caucasians

Highly polymorphic: deletions, critical SNPs, duplications

In Europe

Ingelman-Sundberg TRENDS Pharm Sciences 25, 193-200 (2004)

Relevant for 15 % of the drugs used

CYP2D6DuplicatedMutiplied

CYP2D6 HomozygousDeletionFrameshiftStop codons

CYP2D6 HeterozygousDeletionFrameshiftStop codons

CYP2D6 Two normal alleles

High frequency of CYP2C19 variants in Chinese

Highly polymorphic: critical SNPs/*2, *3

CYP2C19 Homozygous*1 /*1

CYP2C19Homozygous*2, *3, *5

CYP2C19Heterozygous*1/*2, *3j, *5

In China

Log10% 4hydroxymephenytoin excreted

-2.0 -1.0 0 1.0 2.0

180-260 million subjects have no CYP2D6 enzymes (PM)

Too slow drug metabolism

Too high drug levels at ordinary dosage

High risk for ADRs

No response from certain prodrugs

PM

IM

EM

Num

ber o

f sub

ject

s

20

10

0

Zhou HH et al, CPT 1989

Issues Raised by Ethics Committees

• Patient confidentiality/data privacy

• Specify genes

• Scope of sample use for future research

• Length of storage period

• Disclosure of individual results to patients

• Limited sample withdrawal period

• Samples cannot be used for commercial purpose

• Sample ownership

• Investigator role in access/use of samples/data

• Fairness in the use of genetic information– Who should have access to personal genetic

information, and how will it be used?

• Privacy and confidentiality of geneticinformation.– Who owns and controls genetic information?

• Psychological impact and stigmatization due to an individual's genetic differences. – How does personal genetic information affect an

individual and society's perceptions of that individual?

Ethical, Legal and Social Implications

• Reproductive issues– Do healthcare personnel properly counsel

parents about the risks and limitations of genetic technology? How reliable and useful is fetal genetic testing?

• Clinical issues– How will genetic tests be evaluated and

regulated for accuracy, reliability, and utility?Currently, there is little regulation at the federallevel.


• Uncertainties associated with• gene tests for susceptibilities• complex conditions (e.g., heart disease) linked

to multiple genes and gene-environment interactions.

– Should testing be performed when no treatmentis available?

– Should parents have the right to have their minor children tested for adult-onset diseases?

– Are genetic tests reliable and interpretable by the medical community?


• Conceptual and philosophical implications– Do people's genes make them behave in a

particular way? Can people always control their behavior? What is considered acceptablediversity

• Safety and environmental issues concerning genetically altered foods and microbes

• Commercialization of products including property rights (patents, copyrights, and trade secrets)– Who owns genes and other pieces of DNA?


Therapeutic specificity

Direct information receiver

Indirect information receiver

Now

Future

Diagnosis

Diagnosis+ Gene profile


Pharmacy

National health care systems

Insurance companies

Family

Friends

Health care personnel

Other surroundings

Ele

ctr

on

icp

ati

en

tjo

urn

al

Direct information receivers refers to systems that automatically receive information about the patients’ drug usage by the use of for example the electronic patients journal.

Indirect information receivers refers to individuals that intentionally or unintentionally receive information about the patients’ drug usage.

Diagnosis-Therapy-Information flow associated with prescription of a drug

Patient

Patient

Inaccurate choice

Select the exact

PGt & PGxClinical Trial

Center

Phamacogenomic Database

for Chinese People

Technical Systemfor

High-Throughput Assays

Pharmacogenomics Evaluation System

For New Drug

Development

Pharmacogenomics Evaluation System

For Major Disease

Treatment

Technical System for High-ThroughputGene PolymorphismFunctional Testing

Software System forPharmacogenomics

Study

Integrated Technical System for supporting PGt/PGx Clinical Trial in CSU

• Too much enrichment may limit target patients/Indication• Enriched population may not represent a real

population in a practical medicine• Enriched approach may limit the indication for

approval and increase off label use• How much enrichment effect is useful and reasonable?

Prospective design

Out of indication

Target patient

Real target practical medicine

General target in clinical trial

Limited target

Small LargeEnrichment effect

Traditional Clinical TrialsTraditional Clinical TrialsPhase I: Safety and Dosage

Phase II A: Small Safety and Efficacy Study

Phase II B: Large Safety, Efficacy and Dose Ranging Study

Phase III: Comparative Safety and Efficacy; Randomized and Controlled

Non-Responders Responders Hyper-responders

Tested Drug Placebo

Pharmacogenetic PrePharmacogenetic Pre--ScreeningScreeningIn Clinical TrialsIn Clinical Trials


Pharmacogenetic Screen

Phase I Phase IIA Phase IIB Phase III Phase III

Non-Responders Responders Hyper-responders

Translation of PGx to Clinical Trials in Drug Development

• DNA samples are being collected in almost all clinical trials by major pharmaceutical companies

• DMET (drug metabolism) chips are used routinely to determine polymorphisms in CYP and related enzymes

• Regulatory agency reviewers always look for PGx factors influencing PK, PD and, through subset analysis, clinical outcomes

Proposal• SFDA should establish policies on categories of PG studies• “Submission requirement” should be included in policy

• Submission not required for some types• Submission required for other types• Some with no regulatory impact

• “Regulatory impact” should be included in policy• Results from some study categories should not have any

regulatory impact• Other study results should be utilized as part of

safety/efficacy evaluation• Possible threshold determination: Does genomic

information represent valid biomarker with known predictive characteristics?

• Develop threshold and policies using public and transparent process with advisory committee oversight

The objectives of this Proposal

Free exchange of data?

Ability of SFDA scientists to begin developing framework for new findings?

Advance the use of the new science?

Examples: With Regulatory Impact (cont.)

• Safety rationale based on animal genomic data--i.e., explaining why a toxic finding is unique to that species

• In general- results intended to influence the course of the clinical development process will be considered part of the S&E evaluation

• Trial enrollment by genotype: enrichment of respondersavoiding bad outcomes

• Selection of dose based on metabolizer genotype

PG results without regulatory impact

• Evaluation of new transporter gene diversity vs response in clinical subjects

• Genomic SNP data collection in clinical trial subjects

• Gene expression microarray screen in trial subjects

• Gene expression microarray screen in animal toxicology study

Unresolved Issues in Application of Pharmacogenomics

What are reasonable expectations of the role of genetics in drug responses?

If a relationship is identified between a genotype and a response, will that lead to specific labeling requirements, even if the drug is safe and effective for the general population?

Will collection of DNA in a clinical trial be a green light for the FDA to request pharmacogenetic studies?

• Will the division of the patient population into multiple genetic subgroups lead to a request for larger studies to enable statistical power for each group?

• What will be the regulatory requirements for tests indicated on the drug label (IVD vs homebrew) and for tests used in genotyping for registrational studies?

• Under what conditions will it be possible to label a drug based on testing of only a pharmacogenetically defined patient group?

Unresolved Issues in Application of Pharmacogenomics

PK Basis for Differences in Drug Response

• Extrinsic factors– environment (smoking, diet, alcohol)– drug interactions (Rx, OTC and herbal)

• Intrinsic factors– demographic (gender, age, race)– disease (renal, hepatic)– pharmacogenetics (PGt)

• polymorphisms in genes encoding metabolic enzymes

definition of pgt & pgx · pharmacogenetics and pharmacogenomics • pharmacogenetics – the...

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