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Genetica umana - farmacogenetica

Appunti di Genetica umana per l'esame del professor Piazza sulla farmacogenetica. nello specifico gli argomenti trattati sono i seguenti: l'identità genetica della nostra specie, i polimorfismi che la caratterizzano, come insorgono le mutazioni nel tempo e come variano in una popolazione.

Esame di Genetica umana docente Prof. A. Piazza

Anteprima

ESTRATTO DOCUMENTO

Polymorphic

drug metabolizing enzymes

Amplichip CYP450: The first

commercial clinical test platform

Basel, 25 June 2003

Roche Diagnostics Launches the

AmpliChip CYP450 in the US, the

World’s First Pharmacogenomic

Microarray for Clinical Applications

It is the first chip using Affymetrix

technology that meets federal

standards for clinical use

The route to a new drug…is a

long one

Exploratory Development Full Development

Discovery Phase IV

Phase I Phase II Phase III

0 15

10

5 Years

11-15 Years Marketed

Idea Drug

Patent life 20 years

…and an expensive one!

It costs >$800 million to get a drug to market

$ Millions spent in

3,332 9 months in 2001

2,660

2,487

2,281

1,916 1,955

1,740

1,645

1,499

1,402

1,116

934

SGP ABT AHP BMY LLY MRK PHA AZN AVE JNJ GSK 

Pharmacogenomics defined

The study of genome-derived data,

including human genetic variation, RNA

and protein expression differences, to

predict drug response in individual

patients or groups of patients.

Pharmacogenomics includes Pharmacogenetics

Pharmacogenomics

Human Genetics

• SNPs

• Haplotypes

• Sequencing

Expression Profiling

• Specific transcript levels

• Total RNA profiling Phenotype Prediction

• Drug response

Proteomics • Disease

• Specific biochemical

markers

• Protein profiling

Applying Pharmacogenomics

Discovery Development

DISEASE TARGET SELECTING PHARMACO-

GENETICS RESPONDERS GENETICS

VARIABILITY Improving

Choosing Better Predicting

Early

the Best Understanding Efficacy and

Decision

Targets of Our Targets Safety

Making

.

Goal: use genetics

to broaden drug’s

therapeutic index

Efficacy: % patients cured at a given dose

Toxicity: % patients exhibiting side effects

at a given dose

Therapeutic index: Dose range at which

drug shows highest efficacy and low

toxicity

Drug Efficacy in an

Individual Patient

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

Efficacy

Drug Efficacy in Patient

Population

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6

Patient 7

Patient 8

Patient 9

Patient 10 Drug Toxicity for

Individual Patient

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

Toxicity

Drug Toxicity in Patient

Population

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6

Patient 7

Patient 8

Patient 9

Patient 10

Unsafe drug: small window

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6 Therapeutic window

Use genetic info to enhance

the therapeutic index (TI)

Dose (mg/kg) 0 1 2 3 4 5 6 7 8 9

TI without pharmacogenomics

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6 TI with pharmacogenomics

What are the steps for

translating

pharmacogenomic

information from research

into practice?

Step 1. Identify SNPs in

genes relevant to drug

efficacy or toxicity

Human Genome:

2,900,000,000 billion total base pairs

10,000,000 total single nucleotide polymorphisms (SNP)

300,000 variant haplotypes

10,000 haplotypes in pharmacologically-relevant

genes

Step 2. Retrospectively, find SNPs

associated with response

SNP: single nucleotide

polymorphism

ATGCTTCCCTTTTAAA

Patient 1 Good response

No response ATTGTTCCCTTTTAAA

Patient 2 No response ATTGTTGCCTTTTAAA

Patient 3 Good response

Good response ATGGTTGCCTTTTAAA

Patient 4 No response ATAGTTGCCTTTTAAT

Patient 5 No response ATAGTTGCCTTTTAAT

Patient 6 Good response

Good response ATGATTGCCTTTTAAA

Patient 7 Good response

Good response ATGATTGGCTTTTAAA

Patient 8 Good response

Good response ATGTTTCGCTTTTAAA

Patient 9 Good response ATGTTTTGCTTTTAAA

Good response

Patient 10 No response ATTTTTTGCTTTTAAA

Patient 11 No response ATCTTTTGCTTTTAAA

Patient 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Step 3. Prospectively, determine if

those SNPs affect therapeutic outcome

GG GG GG

GGGGGGGG

GG Treat

GG GGGGGGGG

25% cure 50% cure

Determine statistical significance (the probability

that such a difference is due to random chance)

Clinical significance of DME

polymorphism (1)

Plasma concentrations in the different CYP2C19 genotype

after omeprazole 20 mg dosing Clin Pharmacol Ther

1999;65:552-561.

Omeprazole is mainly metabolized by CYP2C19.

Distinct differences in plasma concentration are

observed between CYP2C19 genotypes.

DME: drug metabolizing enzyme; EM: extensive metabolizers; PM: poor metabolizers

Clinical significance of DME

polymorphism (2)

Median data on 24-hour intragastric pH profiles in the

different CYP2C19 genotype after omeprazole 20 mg dosing

PK difference

between CYP2C19

genotype

PD difference

Clin Pharmacol Ther 1999;65:552-

561.

Genotype is required to rationalize the dosing

PK: pharmacokinetics, PD: pharmacodynamics

Ideal flow considering PK-related

polymorphism No No necessity

Non-clinical Suggested genetically variability in PK to consider

genotype

large small

Clin Pharm PK comparison

Studies between genotypes Genotype data

Δsmall

Δlarge collection

as

Exploratory Dosage regimen demographics

& by genotype,etc.

Confirmatory

Studies Population PK/PD:

genotype

To confirm Genotyping

as covariate

utility of is useful?

genotyping Yes No

No necessity

Product Dosage regimen by genotype to consider

Launch •

Pharmacogenomics-oriented TDM genotype

Polymorphic

drug metabolizing enzymes

Drug metabolizing enzymes

for β-blocker

Drug metabolizing

β-blocker enzymes

metoprolol CYP2D6

bisoprolol CYP2D6/3A4/1A2

carvedilol

Effect of CYP2D6*10 allele

on PK of S-metoprolol

500

(nM) CYP2D6* 10/ * 10

400

plasma 300

in

Concentration 200

100 2D6* 1/ * 1

0 0 2 4 6 8 10 12 14

Time (hr)

Clin Pharmacol Ther 1999 ; 65 : 402-407

Chronic Heart

Failure (CHF)

βblocker

responder non-responder

CAUSES:

Plasma Concentration Polymorphisms

of β blocker Drug Metabolizing Enzyme

Function of Target Polymorphisms

Molecules of β blocker Adrenergic Receptor (AR)

and Target Molecules

β AR Ser49Gly and Risk in CHF

1 △

Ser49 homozygotes without β-blockers (n=63)

Gly49 variant without β-blockers (n=28)

Ser49 homozygotes with β-blockers (n=59)

60 ★

Gly49 variant with β-blockers (n=33)

β-blocker is more effective

%)

( in Patients with Gly allele p = 0.12

end-point 40 ☆

▲ p = 0.016

of 20

Risk ★

0 0 2 5

3 4

1 Follow-up (years)

Eur Heart J 2000;21:1853-8.

β Adrenergic Receptor

2 polymorphism

Ratio of Responders

Gln/Gln 26%

Gln/Glu Glu/Glu 62%

Gln27Glu is a potential determinant for

the response to carvedilol in heart failure

Kaye DM et al. (2003) Pharmacogenetics 13: 379-382

Scientific Basis for Using

Pharmacogenomics to

Rationalize Dosing

• Top 27 drugs more frequently cited

in reports

– 59% (16/27) metabolized by at least one

enzyme having poor metabolizer (PM)

genotype

– 38% (11/27) metabolized by CYP 2D6

• mainly drugs acting on central nervous

and cardiovascular systems

Phillips et al. (2001) JAMA, 286 (18): 2270-2279

Summary of CYP2D6 activity

Japanese Caucasoids

activity genotype phenotype genotype

phenotype PM

PM

Low Mainly CYP2D6 5 3, 4, 5

* * * * etc

~1% 5-10%

??? ( 2 with -1584CG SNP)

*

10/PM

* gene (about 3 %)

IM 10/ 10

* * (about 15 %) hetEM: wt / PM gene

EM hetEM: wt / PM gene EM

wt / 10

*

wt / wt(wi wt / wt(wi

l d t ype ) l d t ype)

UM Ultra Rapid (ethnic difference )

UM Ultra Rapid ( )

low frequency

High Multiple active genes

Genetica della malattia

cardiovascolare

Dati in parte della

British Heart Foundation

Heart disease statistics

• Leading cause of premature death in UK

– Deaths under 75, 39% of men, 30% of women

• 270,000 heart attacks per year

– 43% fatal within 28 days, 32% within 24 hours

350 UK

0

0 300

,0 Germany

0 250 Sweden USA

0

/1 200

s

th Italy

150

a

e France

100

D Japan

50

0

Mortality from CVD and CHD in selected countries

Rate per 100,000 population (Men aged 35 –74 years)

CVD deaths CHD deaths

1500

1000

500

0 Russia Poland Finland New England/ USA Italy Spain Japan

Zealand Wales (Adapted from 1998 World Health Statistics)

Genetics of CVD

• Positive Family History

– 7-fold increase in mortality in first degree relatives of

CAD patients compared with control subjects

– Families share environment as well as genes

• CAD is not a monogenic trait

– rare exceptions involving mutation of genes e.g. LDL

receptor, apolipoprotein B

Sibling recurrence rate (λ ) for CVD

s

CHD (MI<55yr) =4

Hypertension = 2.5 TEXTBOOK

IDDM = 15

Cystic fibrosis = 500  = 2 to 12 (premature CHD)

RANGE IN LITERATURE s

 = 3 (fatal CHD <65yr)

DZT

 TWINS

Male = 7 ; Female = 15 (fatal CHD <65yr)

MZT (Marenberg NEJM 1994) Female > Male

HERITABILITY Early > Late disease

What is a heart attack?

Blockage of the coronary arteries, preventing blood

flow and hence oxygen delivery to heart muscle

Atherosclerosis in vivo

Angiogram


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DETTAGLI
Corso di laurea: Corso di Laurea in Medicina e Chirurgia
SSD:
Università: Torino - Unito
A.A.: 2013-2014

I contenuti di questa pagina costituiscono rielaborazioni personali del Publisher cecilialll di informazioni apprese con la frequenza delle lezioni di Genetica umana e studio autonomo di eventuali libri di riferimento in preparazione dell'esame finale o della tesi. Non devono intendersi come materiale ufficiale dell'università Torino - Unito o del prof Piazza Alberto.

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