Pharmacogenetics difficult or just impossible

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Pharmacogenetics - difficult or just impossible?

• Stephen Senn

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Based on chapter 25 (with some additional
material from chapter 24).
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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 dosesEach
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)
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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
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Claims for Pharmacogenomics

• Clinical trials
• Cleaner signal
• Non-responders eliminated
• Treatment strategies
• Theranostics
• Markets
• Lower volume
• Higher price per patient day

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Pharmacogenetics A cutting-edge science that
will start delivering miracle cures the year
after next.
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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 dont 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 dont know if most variation in
  clinical trials is due to individual response let
  alone genetic variability

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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

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Analysis of Ulcer Data of Moerman Logistic
regression model Regression analysis
Response variate Y Binomial totals
n Distribution Binomial Link function
Logit Fitted terms Constant Trial
Treat Accumulated analysis of deviance
mean deviance
approx Change d.f. deviance deviance ratio
chi pr Trial 30 116.627 3.888
3.89 lt.001 Treat 1 170.605 170.605
170.60 lt.001 Treat.Trial 30 34.622
1.154 1.15 0.257 Total 61
321.853 5.276
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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 courseit 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

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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

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1. Senn SJ. Individual Therapy New Dawn or False
Dawn. Drug Information Journal 200135(4)1479-149
4.
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But Suppose you Only Have one Cross-over
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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

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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.
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Physicians like within patient studies but
statisticians get cross over them The
Sayings of Confuseus
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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

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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 patients time
• Unglamorous
• Bad at identifying which genes are responsible
  for treatment interactions

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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

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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. 10631072,
1998
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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 lt.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
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Pharmacogenomics A subject with great promise.
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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?

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A Dose-Response View of Genetics
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Pairs of Orthogonal Contrasts
See also Balding DJ Nat Rev Genet
20067(10)781-91.
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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 v2
• 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

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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

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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

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Economic Model
Crucial assumption the sponsor can change the
price
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Pharmacogentic model
Position is shown on next slide
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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

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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...

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Males and females