Week 8.14: Pharmacogenomics Flashcards
PharmGKB – worth checking out the link
A pharmacogenomics resource: PharmGKB
Provides a database of genomic variants known to relate to drug activity;
Can be searched by drug name, target protein, or SNP identifier
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- <!--[endif]-->Warfarin dosing example
One of the big applications, that has already been rolled out, is Warfarin
What is Warfarin?
It is an anticoagulant (blood thinner) helping prevent blood clots, heart attack, stroke, in at risk individuals.
VKORC1 is essential for activating vitamin K, the activated form of which plays a key role in coagulation. Warfarin is a VKORC1 antagonist, essentially reducing the amount of activated vitamin K available for coagulation.
When prescribing warfarin, getting the correct does is critical;
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· <!--[endif]-->Too little – no benefit (excessive clotting)
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· <!--[endif]-->Too much – increased bleeding (see web for horrible pictures)
Warfarin pharmacokineticsà this is a pathway (no need to go into huge detail) for any particular drug you can find pathways for these drugs.
Different forms of warfarin showing you how all drugs should get broken down
CYP2C9
Is particularly important in metabolism, protein known to metabolise many therapeutic drugs, genetically it’s highly polymorphic over 50 SNPs identified. Many of these SNPs reduce the activity of CYP2C9
Colour coded to indicate what kind of SNPs they are
Warfarin pharmacodynamics
Warfarin dose calculator, the way it is used in a clinical practice. This table can be used to work out does, the left hand side shows the typical clinical diagnosis.
This table can be used to work out does, the left
hand side shows the typical clinical diagnosis.
Age – height – weight – ethnicity (has some genetic basis to them but very vague)
A clinician would put in the numbers and work out a dose bottom
They end up with a dosage; running total
This is how genetic typing can be used, the dosages have been worked out from the effects of different data from clinical data
Available as an on-line tool; where you can calculate dose by input of information
In the results from 23andMe;
Show results of drug response based on genotype of a particular person the variations of this person has an increased sensitivity to warfarin and thus you would expect to have a lower dosage
You don’t even need to know whole genotyping
People in the meantime developed things for a range of tests
** <!--[endif]-->Rare variants in pharmacogenomics**
Every individual has many variants that are not common in the general population (usually defined as <1% incidence).
Traits associated with such variations cannot be discovered in population-wide studies such as GWAS.
Some such variants may not even have been recorded in databases – sometimes called “novel” or “private” SNPs
We cannot rely on existing studies or databases to predict the effect of these variations on drug action.
How can we determine the pharmacogenomics effect of such rare variants?
** Assessing the effect of individual variations**
Let us assume we plan to prescribe someone a particular drug. We can utilise a multiple-step approach for this particular drug;
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1. <!--[endif]-->Map variants to drug-associated genes
2. Predict the functional impact of the variant on the drug
3. ** Asses changes to 3D structure
4. **Molecular modelling
Assessing the effect of individual variations
- *1. Map variants to drug-associated genes
2. Predict the functional impact of the variant on the drug
3. Asses changes to 3D structure
4. Molecular modelling**
1.Map variants to drug-associated genes
We can find the genes associated with a particular drug by looking at pathways in PharamaGKB. DrugBank – database that allows you to search for a particular drug (drugbank.ca) provides similar information. Here is one example for the antihistamine desloratadine;
What we would get is a long page showing the certain protein that is involved in the action of this drug, histamine H1 receptor – an antagonist for that protein and that it how it has pharmaceutical action
Rare variants that lie in or around one of the genes associated with a drug may affect its action. We could avoid prescribing the drug to someone with such a variant or we could… Predict the functional impact of the variant
Assessing the effect of individual variations
1. Map variants to drug-associated genes
2. Predict the functional impact of the variant on the drug
3. Asses changes to 3D structure
4. Molecular modelling
** _ 2. Predict the functional impact of the variant on the drug_**
Most SNPs are not going to affect the drug response at all because;
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· <!--[endif]-->They don’t affect the translated protein sequence
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· <!--[endif]-->They are not in an important protein domain
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· <!--[endif]-->They don’t affect protein structure
By bringing such information about the genomic context of a SNP and other statistics, PolyPhen-2 in one on-line tool that predicts the effect of SNPs on the function of human proteins it classifies SNPs into
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· <!--[endif]-->Benign
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· <!--[endif]-->Possibly damaging
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· <!--[endif]-->Probably damaging
“Damage” to the protein might not necessarily lead to a problematic drug response.
To get a better idea of that we need to…
Assessing the effect of individual variations
1. Map variants to drug-associated genes
2. Predict the functional impact of the variant on the drug
_3. Asses changes to 3D structure _
4. Molecular modelling
** 3. As**ses changes to 3D structure
Many pharmaceutically important proteins have had their structure resolved and are available in the protein databank (PDB).
For example, here is CYP2CP;
An expert biochemist can use interactive visualisations like this to assess the importance of a sequence variant on structure and function.
Assessing the effect of individual variations
1. Map variants to drug-associated genes
2. Predict the functional impact of the variant on the drug
3. Asses changes to 3D structure
4. Molecular modelling
4. ** <!--[endif]-->Molecular modelling**
To obvious next step is use molecular dynamics simulate the interaction between a drug and a protein, and see whether this interaction is affected by a change in protein structure caused by sequence variation.
This is very difficult – ongoing area of research! See example in EPG 7.4.4
Assessing the effect of multiple variations
We already know that drug response is rarely due to a single variation in a single protein. It therefore makes sense to evaluate the collective effect of mutations across a drug-specific pathway.
Mutational load; one gene
For our purposes, the mutational load of a gene is a quantitative representation of how much it differs from the common allele for that gene, i.e the one for which the drug was developed.
To calculate mutation load of a give gene for a given person, we need to:
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· <!--[endif]-->Determine all the variants that the individual has within the gene
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· <!--[endif]-->Gather information on the frequency of the variants in the general population (e.g from the 1000 genomes project).
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· <!--[endif]-->Sum the differences between the individual and the general population
One pathway
We can then sum the mutational load across all genes in the pathways associated with a particular drug.
This gives a score that we can compare with the generation population.
Bioinformatics is the key
We need to genotype individuals, and we rely on data from experimental studies but all the crucial interpretation work for personal genotype is done at a computer
