(c) Stephen Senn 20071 Pharmacogenetics - difficult or just impossible? Stephen Senn

(c) Stephen Senn 2007 1

Pharmacogenetics - difficult or just impossible?

Stephen Senn


Based on chapter 25 (with some additional material from chapter 24).


“Statistics and the medicine of the future”

Mass-market drugs have successfully treated millions, but they have a corollary: one size has to fit all. Every patient gets the same drug – yet every patient is different and responds differently to drugs, treatments and doses…Each drug each dose, each treatment will be tuned not to the average patient but to the individual. It is the difference between an off-the-peg suit and one made to measure.

Chris Harbron, Significance, June 2006, p67 (My italics)


Genes, Means and Screens

It will soon be possible for patients in clinical trials to undergo genetic tests to identify those individuals who will respond favourably to the drug candidate, based on their genotype, and therefore the underlying mechanism of their disease. This will translate into smaller, more effective clinical trials with corresponding cost savings and ultimately better treatment in general practice. In addition, clinical trials will be capable of screening for genes involved in the absorption, metabolism and clearance of drugs and the genes which are likely to predispose a patient to drug-induced side-effects. In this way, individual patients will be targeted with specific treatment and personalised dosing regimens to maximise efficacy and minimise pharmacokinetic problems and other side-effects.

Sir Richard Sykes, FRS


Claims for Pharmacogenomics

• Clinical trials– Cleaner signal– Non-responders eliminated

• Treatment strategies– “Theranostics”

• Markets– Lower volume– Higher price per patient day


Pharmacogenetics: A cutting-edge science that will start delivering miracle cures the year after next.


Implicit Assumptions

• Most variability seen in clinical trials is genetic– Furthermore it is not revealed in obvious phenotypes

• Example: height and forced expiratory volume (FEV1) in one second• Height predicts FEV1 and height is partly genetically determined but

you don’t need pharmacogenetics to measure height

• We are going to be able to find it– Small number of genes responsible– Low (or no) interactive effects (genes act singly)– We will know where to look

• In fact we simply don’t know if most variation in clinical trials is due to individual response let alone genetic variability


Moerman and Placebos

• Paper of 1984• Investigated 31 placebo-controlled trials of

cimetidine in ulcer• Found considerable variation in response• Considered placebo response rate was an

important factor• Has been cited by others as proof of

variation in treatment effect from trial to trial


0.0 0.4 0.8 1.2

standard error

-2

-1

0

1

2

3

4

log-

odds

rat

io31 Placebo-Controlled Trials of Cimetidine

Significant (Yates)Not-significantUpper control limitLower control limitsignificance boundary


Analysis of Ulcer Data of Moerman Logistic regression model Regression analysis

Response variate: YBinomial totals: nDistribution: BinomialLink function: LogitFitted terms: Constant + Trial + Treat

Accumulated analysis of deviance

mean deviance approxChange d.f. deviance deviance ratio chi pr+ Trial 30 116.627 3.888 3.89 <.001+ Treat 1 170.605 170.605 170.60 <.001 + Treat.Trial 30 34.622 1.154 1.15 0.257Total 61 321.853 5.276


Lessons from Moerman

• There is no evidence of variation in the treatment effect from trial to trial

• We should be wary about concluding that apparent variation signals true variation

• We need to be cautious and think carefully about analysis

• Of course…it is always possible that there was exactly the same genetic mix in each trial

– in which case gene by treatment would not manifest itself as trial by treatment interaction

• We need to understand components of variation


What you learn in your first ANOVA course

• Completely randomised design– One way ANOVA

• Randomised blocks design– Two way ANOVA

• Randomised blocks design with replication– Two way ANOVA with interaction

• No replication, no interaction


1. Senn SJ. Individual Therapy: New Dawn or False Dawn. Drug Information Journal 2001;35(4):1479-1494.



Second cross-over

Responders Non-Responders

Total

First cross-over

Responders 24 0 24

Non-Responders

0 8 8

Total 24 8 32


Second cross-over


Total

First cross-over

Responders 18 6 24

Non-Responders

6 2 8

Total 24 8 32


But Suppose you Only Have one Cross-over

Second cross-over


Total

First cross-over

Responders ? ? 24

Non-Responders

? ? 8

Total 32


Two StrategiesGene led Treatment led

• Identify suitable loci using in vitro studies

• Generate possible treatment hypotheses

• Select suitable patients– ‘Enrichment’ studies

• Prove that the treatment works for these patients

• Identify potential treatments

• Find those that work in general

• Find those where patient by treatment interaction is considerable

• Search for genetic subgroups


Strategy 1 (Treatment led)Whole genome matching

“Drug responses are not persistent affairs; they are temporary characteristics. One therefore may ask whether twin studies are necessary to assess the genetic element in pharmacological responsiveness.To measure the genetic component contributing to their variability, it seems logical to investigate the response variation by repeated drug administration to given individuals, and to compare the variability of the responses within and between individuals.”

Kalow et al, Pharmacogenetics,8, 283-289, 1998.


Physicians like within patient studies but statisticians get cross over them

The Sayings of Confuseus


Possible Strategy

• Run multi-period cross-overs

• Patient by treatment interaction becomes identifiable

• This provides an upper bound for gene by treatment interaction– Because patients differ by more than their

genes


Advantages and DisadvantagesPRO CON

• Cheap• Low tech• Insight into sources

of variation gained• Good at identifying

if there are gene by treatment interactions

• Only suitable for chronic diseases

• Demanding of patient’s time

• Unglamorous• Bad at identifying

which genes are responsible for treatment interactions


In Practice

• We hardly ever run repeated cross-over designs• Hence we are incapable of telling formally which of the

two cases applies• Most researchers simply assume by default that case 1

is the case that applies• They assume that variation in response is a permanent

feature of patients• This is what might be called patient-by-treatment

interaction and provides an upper bound for gene-by-treatment interaction

• Strangely enough, an area in which such repeated cross-overs have been applied is one in which interaction is unlikely to be important: bioequivalence


Shumaker and Metzler

“A single dose (125 mg), two-formulation four-period, bioequivalence trial of phenytoin compared the test product with the reference product. The study used the replicated design:

RT T R TR R T

where R is the reference product and T is the test product. This design can be considered two replications: Replicate 1 Replicate 2 RT and TR TR RT.”

Drug Information Journal, Vol. 32, pp. 1063–1072, 1998


0 5 10 15 20 25

Volunteer

40

60

80

100

AU

CPhenytoin Data: AUC by Subject and Formulation

REFTEST


0.8 0.9 1.0 1.1 1.2

Relative bioavailability first determination

0.8

0.9

1.0

1.1

1.2

Rel

ativ

e bi

oava

ilabi

lity:

sec

ond

dete

rmin

atio

n

1

23

4

5

67

89

1011

1213

14

15

16

17

18

19202122 23

242526


Simple approach ignoring period

Accumulated analysis of variance

Change d.f. s.s. m.s. v.r. F pr.

+ SUB 25 7.748 0.310 82.3 <.001

+ PROD 1 0.00253 0.00253 0.67 0.416

+ SUB.PROD 25 0.0679 0.00272 0.72 0.811

Residual 52 0.196 0.00377

Total 103 8.014 0.0778

Estimated variance components

Random term component s.e.

SUB 0.076800 0.021915

SUB.PROD -0.000524 0.000533


Pharmacogenomics:

A subject with great promise.


Strategy Two (Gene Led) Genetic Subgroups

• In many indications cross-over trials are impossible

• This means that we have to investigate interaction not by whole genome matching (each patient his or her own control) but by genetic subgroups

• Patients provide replication of the subgroup– Which genes should we use?– How should we group genotypes?– Will we have the statistical power to investigate

subgroup interactions?


A Dose-Response View of GeneticsY X EC50 X

X

EC50

0 1 2

0.5

1

DominantRecessiveAdditive

DominantRecessiveAdditive

Allele copies

Phe

noty

pe1


Pairs of Orthogonal Contrasts

Genotype AA Aa aa

Score 0 1 2 Variance multiplier

Linear -1 0 1 2

Quadratic -1 2 -1 6

Dominant -2 1 1 6

Within a 0 -1 1 2

Recessive -1 -1 2 6

Within A -1 1 0 2

See also Balding DJ Nat Rev Genet 2006;7(10):781-91.


4 2 0 2 4

4

2

2

4

One t-test versus one 2 DF F-test

Linear contrast

Qua

drat

ic c

ontr

ast

1.96 1.96

Second approach either the linear or quadratic approach is tested


4 2 0 2 4

4

2

2

4

Two t-tests versus one 2 DF F-test

Linear contrast

Qua

drat

ic c

ontr

ast

2.236

2.236

2.236 2.236


Impact on trial design

• Suppose that you know that a dominant (with a as dominant allele) model applies

• Then optimal clinical trial design implies that you should have half the patients on AA and the other half on Aa or aa

• But if HW equilibrium applies this will only happen naturally if the probability of allele A is √2

• Of course, since disease is a selection process HW equilibrium may not apply anyway but this does not get around the problem

• The distribution of genotypes may be very unfavourable for efficient investigation


0 0.2 0.4 0.6 0.8 10

0.5

1

AAAaaaTotal

Genotype frequency for Hardy-Weinberg equilibrium

Probability of allele a

Pro

babi

lity

of g

enot

ype


0 0.2 0.4 0.6 0.8 1

1

1

AaAAaa

Contrast multipliers for three genotypes

Probability of allele a

Gen

otyp

e m

ultip

lier

1

1

0.5


0.2 0.4 0.6 0.8

LinearDominantRecessiveUniversal

Variances for gene-by-treatment contrasts

Allele relative frequency

Var

ianc

e of

con

tras

t

4

11

2

1

2

N2


‘Enrichment’ studies?

• Could we fix enrollment so that we have optimal genotype frequencies?

• Problems– Recruitment time increases– Only optimal for one given locus– Requires knowledge of allele copy response

• Dominant, recessive, linear etc

– Requires knowledge of relevant locus– Interferes with other purposes of trial


Pharmacoeconomics and genotyping

• Finding a subset of patients who benefit has the potential to make the market smaller

• This might imply that it is not in the economic interests of sponsors to do so

• In fact models can be produced that suggest subsetting is valuable

• An adaptation of a model of Kwerel(1980), which was originally applied to another situation, will be considered


Economic Model

probability side effect, loss to patient

benefit, price, cost of sale

1,0 ,

11 proportion benefitting

1 marginal revenue per patient

L p

L

b p c

f b b L p

L pdb

L pp c

Crucial assumption: the sponsor can change the price


Pharmacogentic model

1

2

1 2

1 2

probability low risk

1 probability low risk

probability side effect given low risk

probability side effect given low risk

= 1

Suppose 0.86, 0.05, 0.3

Position is shown on next slide


0 0.2 0.4 0.6 0.8

0.05

0.1

Perceived average riskLow risk marketHigh risk marketGenotyped market

Price

Mar

gina

l rev

enue


0.45 0.5 0.550.1

0.102

0.104

0.106

0.108

Perceived average riskLow risk marketHigh risk marketGenotyped market

Price

Mar

gina

l rev

enue

0.1055

0.1025

0.52 0.55


An Issue with Covariates• Covariate adjustment in clinical trials is generally beneficial and to

be recommended– However a point to note is that the covariates in question should be

measured prior to allocation of treatment– Otherwise problems arise with causal inference– Some of the treatment effect may be removed

• However, when looking at gene-by-treatment interaction there is a potential problem

• Covariates can be pre treatment allocation and hence unaffected by treatment but can be affected by genetics

• Hence fitting the covariate could remove some of the gene effect• Will inference about gene-by-treatment interaction still be sound?• This issue requires careful thought


An Overlooked Source of Genetic Variability

• Humans may be classified into two important genetic subtypes

• One of these suffers from a massive chromosomal deficiency

• This is expressed in – important phenotypic differences

– a huge disadvantage in life expectancy

• Many treatment strategies take no account of this• The names of these subtypes are...


Males and females

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(c) Stephen Senn 20071 Pharmacogenetics - difficult or just impossible? Stephen Senn