Pharmacogenomics
Pharmacogenomics
• Pharmacogenomics is the study of how genes
affect a person’s response to drugs. This
relatively new field combines pharmacology
(the science of drugs) and genomics (the study
of genes and their functions) to develop
effective, safe medications and doses that will
be tailored to a person’s genetic makeup.
• Its name (pharmaco- + genomics) reflects its
combining of pharmacology and genomics.
Pharmacogenetic versus
Pharmacogenomic
• Pharmacogenetics used for more than 40 years to denote the science
about how heritability affects the response to drugs.
• Pharmacogenomics is new science about how the systematic identification
of all the human genes, their products, interindividual variation,
intraindividual variation in expression and function over time affects drug
response/metabolism etc.
• The term pharmacogenomics was coined in connection with the human
genome project
• Most use pharmacogenetics to depict the study of single genes and their
effects on interindividual differences in (mainly) drug metabolising
enzymes, and pharmacogenomics to depict the study of not just single
genes but the functions and interactions of all genes in the genome in the
overall variability of drugs response
Pharmacogenetics
• “Pharmacogenetics is the study of how
genetic variations affect the disposition
of drugs, including their metabolism and
transport and their safety and efficacy”
• J. Hoskins et. al NRC 2009
Pharmacogenetics involves both PK
and PD
• Pharmacokinetic
“The process by which a drug is absorbed,
distributed, metabolized, and eliminated by the
body”
• Pharmacodynamic
“the biochemical and physiological effects of drugs
and the mechanisms of their actions”
Goals of Pharmacogen(etics)omics
• Maximize drug efficacy
• Minimize drug toxicity
• Predict patients who will respond to
intervention
• Aid in new drug development
The Hope of Pharmacogenomics
• Individuals genetic makeup with allow
selective use of medications such that
– Efficacy maximized
– Side effect minimized
In the Beginning
• Mendelian genetics “single gene – single
disease”
– single wild type allele and single disease allele
– Patterns of inheritance included autosomal
dominant (need only one disease allele) and
autosomal recessive (need two disease alleles)
• Followed soon thereafter by additive (codominant) model
– Both alleles contribute to phenotype
Dominant/Recessive
Co-dominance
Empiric observations suggesting
Pharmacogenetics important
• Clinical response to many drugs varies widely
amongst individuals
• Same drug-> same dose -> same indication in
different individuals
– Some respond
– Some don’t
– Some don’t respond and have serious toxicity
The beginning of pharmacogenetics
• 1950s
– “Inheritance might explain variation in individuals
response and adverse effects from drugs”
Motulsty
– “Pharmacogenetics defined as “study of role of
Genetics in drug response” Vogel
– Most of studies for next several decades of “high
penetrance monogenic” gene-drug interactions
– Def: Monogenetic disease. Mutation at single
locus sufficient to result in disorder
Penetrance
• Penetrance of a disease-causing mutation is
the proportion of individuals with the
mutation who exhibit clinical symptoms.
– Eg. if a mutation in the gene responsible for a
particular autosomal dominantdisorder has 95%
penetrance, then 95% of those with the mutation
will develop the disease, while 5% will not.
Victor McKusick
• Established Online Mendelian Inheritance in
Man in early 80s
• Categorized majority of Mendelian Disorders
• Became very clear that there are many
different disease alleles for many disorders
(allelic heterogeneity)
• Recently many disorders have associated
modifier genes that modify disease phenotype
– Eg. Age-of-onset and severity
TPMT
• Main metabolizer of chemotherapeutic agents
6MP and azothiopurine (used mainly in blood
based malignancies)
• TPMT deficiency leads to severe toxicity
associated with treatment (potential
mortality)
TPMT enzyme activity distribution
Evans Nature Reviews Cancer 2006
FDA approved pharmacogenetic tests
Gene
Drug
Consequence
TPMT
6MP
Toxicity
CYP2D6
Tamoxifen
Decreased efficacy
UGT1A1
Irinotecan
Toxicity
CYP2D6
Codeine
Ineffective analgesia
These genes all modulate Pharmokinetics
Conclusion
• Pharmacogenetic polymorphisms in the 6-MP
pathway may help identify patients at risk for
associated toxicities and may serve as a guide
for dose individualization.
Contribution of High Penetrance Monogenic Model to
PG
• Contribution likely not as large as initially
anticipated
• For most pharmacologic traits might be 15-20%
at most
– Could consider this penetrance
• Redundancy likely a major contributing factor
• MANY ENZYMES INVOLVED IN DRUG METABOLISM WITH
MANY ALTERNATE PATHWAYS
• Dichotomous disease versus quantitative trait
• Much more likely polygenic model with geneenvironment interactions
Some of it ain’t genetic
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Age
Co-morbidities
Renal and hepatic function (dysfunction)
Concomitant medications
Diet and smoking
Common Disease Common Variant
Hypothesis
• Most complex
diseases are
strongly
influenced by
combination of
frequent alleles
that each only
exert modest
effect
Approach to polygenic pharmacogenomic traits
Polygenic Model and PG
• Elucidation unlikely possible before advances
in genomics
• Technologic advances
– High throughput sequencing of DNA
– Affordable genotyping of 100ks to 1-2M SNPs
• Genomic knowledge advances:
– Especially Human Genome Project and HapMap
Projects
Cost of Genotyping
• In 2005
– $1600 to genotype 250K SNPs in one individual
• 2009
– $250 to genotype >1Million SNPs
• 2014
-$200-250 to genotype >5 millions SNPs
Hapmap project
• There are an estimated 10 million SNPs with
MAF >1%
• Hapmap project genotyped Chinese,
Japanese, African and European individuals
(families)
HapMap Project
Phase 1
Phase 2
Phase 3
Samples & POP
panels
269 samples
(4 panels)
270 samples
(4 panels)
1,115 samples
(11 panels)
Genotyping
centers
HapMap
International
Consortium
Perlegen
Broad & Sanger
Unique QC+
SNPs
1.1 M
3.8 M
(phase I+II)
1.6 M (Affy 6.0 &
Illumina 1M)
Reference
Nature (2005)
437:p1299
Nature (2007)
449:p851
Draft Rel. 3
(2010)
Tamoxifen metabolism
• Needs to be converted to endoxifen to be
active
– catalysed by the polymorphic enzyme
cytochrome P450 2D6 (CYP2D6)
– 6-10% European population deficient in this
enzyme
• Efficacy of tamoxifen likely low in this population
• Suggests consider alterative treatments
About the CYPs
• Membrane bound enzymatic proteins
– Involved in oxidation, peroxidation and reductive
metabolism
– Responsible for >90% of drug transformation
• Greater than 50 different CYP genes encoding 50
different proteins
• CYP2D6 present mainly in liver and a major player
in drug metabolism from antidepressants to
antihypertensive to chemotherapy
Method
• Plasma concentrations of tamoxifen and its
metabolites were measured. Common alleles
of CYP2D6 and PXR were identified in 202
patients treated with tamoxifen 20 mg daily
for more than 8 weeks. Twelve of the 202
patients and an additional nine patients with
metastatic breast cancer receiving tamoxifen
were assessed for clinical outcome in
correlation with genotypes.
Result
• Patients carrying CYP2D6*10/*10 (n = 49) demonstrated
significantly lower steady-state plasma concentrations of 4hydroxy-N-desmethyltamoxifen and 4-hydroxytamoxifen than
did those with other genotypes (n = 153; 4-hydroxy-Ndesmethyltamoxifen: 7.9 v 18.9 ng/mL, P < .0001; 4hydroxytamoxifen: 1.5 v 2.6 ng/mL, P < .0001), whereas no
difference by PXR genotypes was found. CYP2D6*10/*10 was
significantly more frequent among nonresponders with MBC
(100% v 50%, P = .0186). In Cox proportional hazard
analysis, CYP2D6 genotype and number of disease sites were
significant factors affecting time to progression (TTP). The
median TTP for patients receiving tamoxifen was shorter in
those carrying CYP2D6*10/*10 than for others (5.0 v 21.8
months, P = .0032)
CYP2D6 Genotype and clinical
outcomes
• Several (small trials) have suggested
decreased efficacy of Tamoxifen in poor
(intermediate) metabolizers both in adjuvant
therapy and in treatment of metastatic
disease (see Hoskins NRC 2009 for details)
– All retrospective
– Largest was only statistically significant association
in univariate analysis
– In additions several trials have not confirmed
these results
Reasons for discordant results in
CYP2D6 trials
• Did not genotype many of the rarer poor
metabolizer alleles
• Did not account for concurrent use of other drugs
metabolized by CYP2D6 in many cases
• Different dose of Tamoxifen in several trials
• Did not assay endoxifen levels
• Power (poor metabolizers rare)
• Unknown variants in other genes whose products
involved in tamoxifen metabolism
So what is needed to clarify the issue of
relevance of CYP2D6 genotype and clinical
relevance?
• Large randomized trial that compares
standard dosing of tamoxifen to genotype
adjusted dosing
• Until that point clinical utility of testing
(commerically available) unclear
– Should recommend avoiding SSRIs that inhibit
CYP2D6 significantly (see later)
Provocative thoughts
• In post-menopausal breast cancer tamoxifen is
falling out of favor due to the efficacy of
Aromatase Inhibitors (inhibit extragonadal
production of estrogen)
– AI shows increased efficacy c/w tamoxifen
• BUT MUCH MORE EXPENSIVE AND DIFFERENT S/E PROFILE
• Some suggestion that increased efficacy of AI
completely explained by decreased efficacy of
Tamoxifen in CYP2D6
– Punglia (2008) JNCI
More relevant to pre-menopausal
woman
• Can’t use AI alone
• In poor metabolizer could consider
– Increased dose???
– Alternative estrogen receptor modulator not
metabolized by CYP2D6 (eg. raloxifen)
– Consider AI with ovarian ablation (chemical or
otherwise)
Drug Co-administration
• Antidepressant use common in breast cancer patients
– Depression more common in breast cancer patients and
antidepressant often used to treat how flashes associated with
tamoxifen use
• SSRIs (eg. Fluoxetine and paroxetine) inhibit CYP2D6
• Level of inhibition varies between different drugs with
paroxetine having most inhibition and venlafaxine causing
none
• Kelly et al. BMJ 2010
– Population based cohort study of women receiving tamoxifen
adjuvantly for treatment breast cancer
– Mortality from breast cancer increased in group
using paroxtetine concurrent with tamoxifen
Irinotecan – PK example in Colon Cancer
• Excreted after conjugation (glucuronidation) by UGT1A1
• TATA element (consists of TA repeats) in UGT1A1 promoter
shows correlation with transcription levels
– More repeats lower transcription levels
– An example of a non-SNP variant with clinical relevance
• Homozygosity for 7-repeat allele, also known as
UGT1A1*28 associated with severe toxicity (diarrhea and
low WBC counts mainly)
– Results have been somewhat inconsistent but meta-analysis
confirms same especially with higher doses of Irinotecan
– Homozygosity only in 5-15% of individuals
PD example in Colon Cancer Treatment
• EGFR inhibitors used in
treatment of advanced
colon cancer (eg.
Cetuximab)
• Tumors with k-RAS (and
probably BRAF)
mutations will NOT
respond to EGFR
inhibition
Nature Rev. Cancer July 2009
Effect of Clopidogrel as Compared with Placebo on Clinical Outcomes among Patients with
Acute Coronary Syndromes in the CURE trial, Stratified According to Metabolizer Phenotype.
Paré G et al. N Engl J Med 2010;363:1704-1714
Kaplan–Meier Curves for Event-free Survival According to CYP2C19 Loss-of-Function and
Gain-of-Function Allele Carrier Status among European and Latin American Patients with
Acute Coronary Syndromes in the CURE Trial.
Paré G et al. N Engl J Med 2010;363:1704-1714
Effect of Clopidogrel as Compared with Placebo on Clinical Outcomes among Patients with
Atrial Fibrillation in ACTIVE A, Stratified According to Metabolizer Phenotype.
Paré G et al. N Engl J Med 2010;363:1704-1714
Kaplan–Meier Curves for Event-free Survival According to CYP2C19 Loss-of-Function and
Gain-of-Function Allele Carrier Status among European Patients with Atrial Fibrillation in
ACTIVE A.
Paré G et al. N Engl J Med 2010;363:1704-1714
Baseline Characteristics of Genotyped Patients in the CURE and ACTIVE A Trials.
Paré G et al. N Engl J Med 2010;363:1704-1714
Conclusion
• Among patients with acute coronary
syndromes or atrial fibrillation, the effect of
clopidogrel as compared with placebo is
consistent, irrespective of CYP2C19 loss-offunction carrier status.
Why is pharmacogenomics not widely
utilized in the clinic
• It required a shift in clinician attitude and beliefs “not
one dose fits all”
• Paucity of studies demonstrating improved clinical
benefit from use of pharmacogenomic data
– Still much to be learned
• Even some of the black block warnings
currently on drug labels may be overcalls of
importance
• Genome wide interrogation will likely be important
to get the entire picture
Conclusion
• Genetic variation contributes to inter-individual differences
in drug response phenotype at every pharmacologic step
• Through individualized treatments, pharmacogenetics and
pharmacogenomics are expected to lead to:
• Better, safer drugs the first time
• More accurate methods of determining appropriate drug
dosages
• Pharmacogenomics offers unprecedented opportunities to
understand the genetic architecture of drug response
• HOWEVER IN MANY CASES NOT YET READY FOR PRIME
TIME!!!
Biomarkers
Biological Markers
A biological marker is a measurable indicator that can
tell us something about a person’s health or disease
state, for example,
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disease (pathological) processes in the body, for example, disease stage,
biological processes in the body (heart rate, blood pressure, temperature),
a person’s response to a treatment or medicine,
or a psychological condition.
Biomarkers are used in many scientific fields, in
different ways at different stages of medicines
development. The accuracy of biomarkers can vary,
therefore, not all biomarkers are suitable for medicines
development.
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Examples of Biomarkers
• Glucose levels that are used as a biomarker in
managing diabetes.
• Brain images such as Magnetic Resonance
Imaging (MRI) that can provide information
about the progression of Multiple Sclerosis.
• biological substances such as enzymes, which
may be found in the blood or in tissue samples
and are often used in cancer research,
• genetic (DNA) changes,
• medical images, or X-rays.
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Aims of Biomarker Use
1. Improving the processes of medicines
development
• Clinical trials seek to measure patients’
responses to a treatment.
• If it is not possible to measure the response
directly, biomarkers may provide an
alternative way of measuring an outcome they serve as surrogate endpoints.
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There are advantages of using validated
biomarkers as surrogate endpoints,
• they might be able to be measured earlier,
more easily, or frequently, with high precision;
• they may be less affected by other treatments,
reduce the sample size required, and allow
researchers to make faster decisions;
• they offer important ethical advantages as
surrogate endpoints in diseases with poor
prognoses.
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Example of the use of a biomarker
as a surrogate endpoint
• An example of the use of a biomarker as a surrogate
endpoint comes from the development of antiretroviral
medicines for HIV and AIDS.
• Previously, studies would have been based on hard
clinical endpoints such as the progression of the HIV
infection to AIDS and/or patient survival.
• Now, cell changes such as levels of ‘CD4 lymphocytes’
and changes in the levels of HI-virus RNA in plasma can
be used as surrogate endpoints.
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Aims of Biomarker Use
2. Tailoring treatment to individuals
Biomarker research is helping to
• improve how well we can predict a person’s risk of disease,
• how a disease might progress once it is diagnosed,
• how an individual will respond to a medicine.
This will enable safer and more effective treatment decisions.
• Examples:
• Blood sugar levels in a patient’s blood can be used to monitor if an
individual is responding to diabetes treatment.
• Magnetic Resonance Imaging (MRI) scans of a patient’s brain can be used
to monitor the progress of the disease in Multiple Sclerosis.
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• Many new biomarkers are being discovered
and used during the development of new
medicines.
• Many of these use:
• genomics (analyses of changes occurring at the gene level),
• proteomics (analyses of changes on the protein level),
• and/or metabolomics (analyses of differences in chemical molecules that
play an important role in body and cell function).
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Biomarkers in Medicines Development
• Cancer (oncology) research was one of the first areas
where the use of biomarkers was adopted.
• Biomarkers are used to make exploratory trials and Phase
II trials (‘Proof of Concept’ trials) of medicines more
efficient.
• Only a limited number of biomarkers can be used for
clinical endpoints in Phase III trials (confirmatory trials).
• Biomarkers may be used in late-stage trials in combination
with clinical outcomes (clinical endpoints).
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• For some medicines, only a minority of
patients might respond.
• It is helpful to identify these patients for clinical
trials using biomarkers.
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Companion Diagnostics
Companion diagnostics are tests that are
validated and approved for marketing alongside
a new medicine.
These tests may help to:
• select patients likely to respond to a medicine,
• exclude those patients likely to have an adverse reaction, and
• determine the best dose for a patient.
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Companion Diagnostics
• Many companies developing targeted
therapies for cancer have also begun to
consider the potential benefits of developing a
diagnostic to pair with that treatment.
• The trend is to develop medicines and
companion diagnostics together, rather than
have both developments happen separately.
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Biomarkers potential to increase
the efficiency of medicines
development
Speeding up clinical trials
• Biomarkers can be used to detect an effect (or lack of effect) earlier and
more frequently than if only a clinical outcome (endpoint) is used.
Example: a panel of biomarkers has been used in the early phases of a clinical
trial for a psoriasis treatment.
• The biomarkers included ‘epidermal thickness’ (thickness of the outer
layer of skin) and the activity levels of several genes.
• These were both measured in tissue samples.
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Biomarkers potential to increase
the efficiency of medicines
development
Streamlining clinical trials
Biomarkers are used to identify those patients who are most suitable for a
treatment, specifically, genomic biomarkers can be used to:
• identify patients with a particular disease sub-type or severity,
• exclude patients at increased risk of serious side effects (adverse
reactions), for example, melanoma patients are at risk of their condition
getting worse if their tumours do not have a certain mutation in the ‘BRAF’
gene and they are treated with kinase inhibitors,
• identify patients with a high chance of benefiting from a particular
medicine.
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Biomarkers potential to increase
the efficiency of medicines
development
Improve our understanding
• Biomarkers can improve understanding of how new medicines work and
may lead to novel approaches to medicines development in both nonclinical and clinical phases
Improving the ethics of trial recruitment
• Biomarkers can help exclude people who won’t benefit from starting a nonhelpful treatment, thus providing an ethical benefit.
Improving trial monitoring and stopping
unhelpful trials early
• Biomarkers may help decide whether to stop a trial early if there is no
benefit to be gained by the patients in the trial or an obvious benefit where
withholding treatment would be unethical.
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Biomarkers potential to increase
the efficiency of medicines
development
Speeding up authorisation
• A medicine that is having a positive effect may be authorised sooner
based on information provided using biomarkers and therefore may be
prescribed earlier to patients who will benefit.
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Challenges of using biomarkers in
medicines development
As the use of biomarkers in pharmaceutical
research grows, companies face new
• technical challenges,
• regulatory challenges,
• ethical challenges.
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Technical Challenges
• Biomarkers used in clinical trials must be validated
through scientific evidence to ensure that the biomarker
test is sufficiently
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accurate,
reliable,
sensitive, and
specific.
• The biomarker needs show a reasonable relationship to
the disease being studied.
• If a biomarker is used to predict how severe a disease
may become, is there enough evidence of the ‘predictive
ability’ with this biomarker?
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Technical Challenges
• IT systems for data management and data
analysis must be reliable and fast to cope with
the amount of data generated.
• All biomarker measurements must be
correctly linked with individual patients.
• Where the use of a companion diagnostic is
required for a new medicine to be prescribed,
a new platform or kit for testing patients in
the clinic may need to be developed.
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Regulatory Challenges
• The regulation of the use of novel methods such
as biomarkers in medicines development is
evolving.
• ‘Biomarker’ and ‘surrogate endpoint’ are not
interchangeable terms.
• For a biomarker to be used as a surrogate
endpoint, studies will be done to assess the direct
relationship of the biomarker with:
• the development of the disease,
• a treatment intervention with an important clinical
endpoint.
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Regulatory Challenges
• Developers of novel biomarkers are being encouraged to
engage with regulators at an early stage.
• They can submit their plans to use biomarkers to the EMA.
• Validating biomarkers to meet regulatory standards can be
complex and expensive.
• This is especially challenging if a biomarker is intended to
be used as a surrogate endpoint.
• In this case, a dedicated clinical trial is required, designed
to test the link between the biomarker and the clinical
endpoint.
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Regulatory Challenges
• In the EU, medicines and diagnostics are
regulated differently.
• Licensing a medicine and its companion
diagnostic together adds an extra layer of
complexity to the approval process.
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Ethical Challenges
• Many of the ethical issues that arise in biomarker
research are linked to the storage and use of tissue
samples and the associated handling of personal
medical data.
• Wider concerns have been raised about the impact
of targeted medicine (which is largely based on
biomarker research).
• As targeted treatments only bring benefits to the
sub-population of patients that respond to them, the
challenge is to ensure that medicines are developed
for those who fall outside of this sub-population. 82
• End.