Advanced Bioinformatics Lecture 9: Drug resistant & cancerous mutation
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Transcript of Advanced Bioinformatics Lecture 9: Drug resistant & cancerous mutation
Advanced BioinformaticsLecture 9: Drug resistant & cancerous mutation
http://idrb.cqu.edu.cn/Innovative Drug Research Centre in CQU
创新药物研究与生物信息学实验室
1. Differential drug efficacy
2. Pharmacogenetics
3. Pharmacogenetic response
4. Drug resistance mutation
5. Prediction of drug resistance
Table of Content
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Same symptomsSame disease
Same drugSame doseDifferent Effects
Different patients
At a recommended prescribed dosage—
(1) a drug is efficacious in most;
(2) not efficacious in others;
(3) harmful in a few.
Lack of efficacy
Unexpected side-effects
Differential drug efficacy
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Patient population with same disease phenotype
Patients with normal response to drug therapy
Patients with non-response to drug therapy
Patients with drug toxicity
Genotyping
People react differently to drugs“One size does not fit all …”
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Toxic responders
Non-responders
Responders
EthnicityAgePregnancyGenetic factorsDiseaseDrug interactions……
Same symptomsSame disease
Same drugSame doseDifferent Effects
Different patients
Why does drug response vary?
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Possible Reasons: Individual variationBy chance…
Genetic Differences
AA
GGSNP
Primarily 2 types of genetic mutation events create all forms of variations:
Single base mutation which substitutes 1 nucleotide− Single nucleotide polymorphisms (SNPs)
Insertion or deletion of 1 or more nucleotide(s)− Tandem Repeat Polymorphisms
− Insertion/Deletion Polymorphisms
Polymorphism: A genetic variation that is observed at a frequency of >1% in a population
Why does drug response vary?Genetic variation
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SNPs are single base pair positions in genomic DNA at which different sequence alternatives (alleles) exist wherein the least frequent allele has an abundance of 1% or greater.
For example a SNP might change the DNA sequence
from AAGCTTAC
to ATGCTTAC
SNPs are the most commonly occurring genetic differences.
Single nucleotide polymorphism (SNP)
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SNPs are very common in the human population.
Between any two people, there is an average of one SNP every ~1250 bases.
Most of these have no phenotypic effect
− Venter et al. estimate that only <1% of all human SNPs impact protein function (lots of in “non-coding regions”)
Some are alleles of genes.
Single nucleotide polymorphism (SNP)
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Tandem repeats or variable number of tandem repeats (VNTR) are a very common class of polymorphism, consisting of variable length of sequence motifs that are repeated in tandem in a variable copy number.
Based on the size of the tandem repeat units:
− Venter et al. estimate that only <1% of all human SNPs impact protein function (lots of in “non-coding regions”)
Repeat unit: 1-6 (dinucleotide repeat: CACACACACACA)
− Minisatellites
Repeat unit: 14-100
Tandem repeat polymorphisms
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Insertion/Deletion (INDEL) polymorphisms are
quite common and widely distributed throughout
the human genome.
Insertion/deletion polymorphisms
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20-40% of patients benefit from an approved drug
70-80% of drug candidates fail in clinical trials
Many approved drugs removed from the market due to adverse drug effects
The use of DNA sequence information to measure and predict the reaction of individuals to drugs.
Personalized drugs
Faster clinical trials
Less drug side effects
Due to individual variation …
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Pharmacogenetics
“Study of inter-individual variation in DNA sequence related to drug absorption and disposition (Pharmacokinetics) and/or drug action (Pharmacodynamics) including polymorphic variation in genes that encode the functions of transporters, metabolizing enzymes, receptors and other proteins”
“The study of how people respond differently to medicines due to their genetic inheritance is called pharmacogenetics”
“Correlating heritable genetic variation to drug response”
An ultimate goal of pharmacogenetics is to understand how someone's genetic make-up determines, how well a medicine works in his or her body, as well as what side effects are likely to occur.
“Right medicine for the right patient”
Pharmacogenetics
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Pharmacogenetics: Study of variability in drug
response determined by single genes.
Pharmacogenomics: Study of variability in drug
response determined by multiple genes within
the genome.
Pharmacogenetics vs. pharmacogenomics
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Pharmacogenetics
The study of variations in genes that determine an individual’s response to drug therapy.
Common variation in DNA sequence (i.e. in >1% of population)
Genetic Polymorphism: SNPs; INDEL; VNTRs
Potential Target Genes are those that encode:Drug-metabolizing enzymesTransportersDrug targets
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Patient’s response to drug may depend on factors that can vary according to
the alleles that an individual carries, including:
Determinants of drug efficacy and toxicity
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Pharmacologic effect
Clinical response
Toxicity Efficacy
DISTRIBUTION
ABSORPTION
ELIMINATION
Pharmacokinetics
Pharmacodynamics
dose administered
drug in tissuesof distribution
concentration insystemic circulation
concentration atsite of action
metabolism and/or excretion
Pharmacokinetic factors − Absorption
− Distribution
− Metabolism
− Elimination
Pharmacodynamic factors− Target proteins
− Downstream messengers
EM phenotype: Extensive metabolizer; IM phenotype: intermediate metabolizer;
PM phenotype: poor metabolizer; UM phenotype: ultrarapid metabolizers
Examples
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Individual variations in drug response are frequently associated with three groups of protein:
ADME-associated proteins: proteins responsible for the absorption, distribution, metabolism and excretion (ADME) of drugs
Therapeutic targets: proteins that can be modified by an external stimulus (drug molecules).
ADR related proteins: drug adverse reaction related proteins
The factors in variations of drug responses:
Sequence polymorphism
Transcriptional processing of proteins: altered methylations of genes, differential splicing of mRNAS
Post-transcriptional processing of proteins: differences in protein folding, glycosylation, turnover and trafficking.
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Medicines are not safe or effective in all patients
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Medicines are not safe or effective in all patients
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Drug Group Efficacy Incomplete/Absent
SSRI 10-25%
Beta blockers 15-25%
Statins 30-70%
Beta2 agonists 40-70%
…… ……
when considered in further detail, we can see that efficacy of some of our major drug classes vary from 10-70% incomplete efficacy.
Pharmacogenetic prediction and mechanistic elucidation
of individual variations of drug responses is important
for facilitating the design of personalized drugs and
optimum dosages.
For most drugs, not all of the ADME-associated proteins
responsible for metabolism and disposition of
pharmaceutical agents are known.
The needs of prediction of pharmacogenetic response to drugs
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A number of studies have explored the possibility of using polymorphisms as indicators of specific drug responses.
Computational methods have been developed for analyzing complex genetic, expression and environmental data to analyze the association between drug response and the profiles of polymorphism, expression and environmental factors and to derive pharmacogenetic predictors of drug response
A number of Freely accessible internet resources
The feasibility of prediction of pharmacogenetic response to drugs
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Reported polymorphisms of ADME-associated proteins:By a comprehensive search of the abstracts of Medline database
The approach of prediction of pharmacogenetic response to drugs
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ADME-associated proteins linked to reported drug response variationsAlso by a comprehensive search of the abstracts of Medline database
The approach of prediction of pharmacogenetic response to drugs
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Rule-based prediction of drug responses from the polymorphisms of ADME-associated proteins
The approach of prediction of pharmacogenetic response to drugs
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the analysis of clinical samples of the variation of drug responses
+the results of genetic analysis of the participating patients
Used as indicators for predicting individual variations of drug response
Similar to the “Simple rules-based” method for using
HIV-1 genotype to predict antiretroviral drug
susceptibility (HIV drug resistant genotype
interpretation systems)*
* Comparative Evaluation of Three Computerized Algorithms for Prediction
of Antiretroviral Susceptibility from HIV Type 1 Genotype. J
Antimicrob Chemother 53, 356-360 (2004).
The approach of prediction of pharmacogenetic response to drugs
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Basic idea of using HIV-1 genotype to predict antiretroviral drug susceptibility
HIV-1 genotype 1
HIV-1 genotype 2
Phenotype resistant : drug 1, drug 2, drug 3…
Phenotype susceptible: drug a, drug b, drug c…
HIV-1 genotype 3
…
Phenotype resistant : drug 2, drug 3, drug a…
Phenotype susceptible: drug b, drug c…
Phenotype resistant : drug 1, drug 3…
Phenotype susceptible: drug 2, drug a…
Phenotype resistant : …
Phenotype susceptible:…
Drug 1: Genotype1: phenotype (penalty / score); Genotype2: phenotype (penalty / score); …Drug 2: Genotype1: phenotype (penalty / score); Genotype2: phenotype (penalty / score); …
Examples of the ADME-associated proteins having a known pharmacogenetic polymorphism and a sufficiently accurate rule for predicting responses to a specific drug or drug group reported in the literature.
The approach of prediction of pharmacogenetic response to drugs
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Low predicting accuracies of simple rules based methods: 50%~100% (comparable to those of 81%~97% for predicting HIV drug resistance mutations from the HIV resistant genotype interpretation systems)
Variation of response to some drugs: associated with complex interaction of polymorphisms in multiple proteins
Simple rules:
Limited predicting capacity for prediction of drug responses
The basis for developing more sophisticated interpretation systems like those of the HIV resistant genotype interpretation system
Limitation of Simple rules based methods
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Computational methods for analysis and prediction of pharmacogenetics of drug responses from the polymorphisms of ADME-associated proteins
Examples recently explored for pharmacogenetic prediction of drug responses:
Discriminant analysis (DA) [Chiang et al., 2003]
Unconditional logistic regression [Yu et al., 2000]
Random regression model [Zanardi et al., 2001]
Logistic regression, 2004 [Zheng et al., 2004b]
Artificial neural networks (ANN) [Chiang et al., 2003; Serretti et al., 2004]
Maximum likelihood context model from haplotype structure provided by hapmap [Lin et al., 2005]
Other methods
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Statistical analysis and statistical learning methods used for pharmacogenetic prediction of drug responses
Examples
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Organisms are said to be drug-resistant when drugs meant to
neutralize them have reduced effect or even no effect.
Main cause of drug fail during the treatment of infectious disease ,
cancers (chemotherapy)
Main cause of the drug resistance:
Mutation in drug-interacting disease proteins (genetic resistance)
Development of alternative disease related pathway
What is the drug resistance?
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Example of drug resistance mutations
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HIV-1
Protease mutations
(could be quickly
developed)
Integrase mutations
……Henderson L. and Arthur L. 2005. NIH AIDS Research and Reference Reagent Program
The molecular analysis of drug resistance mechanisms
Design new agents to against resistant strains
Guide the clinical regimen to fight with disease
The needs for drug resistance mutations prediction
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Structure-based approaches molecular modeling approach evolutionary simulation model neural network model
Sequence-based approaches Statistical learning methods Neural networks (NN) (classification, association, regression) Support vector machines (SVM) )(classification, regression) Decision tree (DT) Simple rules (HIVdb, HIValg, ARS, and VGI etc)
Methods for mechanistic study and prediction of resistance mutations
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Methods for mechanistic study and prediction of resistance mutations
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– Simple rules
Protein Mutations
Drugs
Genotypic
Phenotypic
PenaltyPenalty
PenaltyPenalty
PenaltyPenalty
PenaltyPenalty
PenaltyPenalty
Penalty
Methods for mechanistic study and prediction of resistance mutations
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– Simple rules
Penalty
Penalty
Penalty
PenaltyPenalty
PenaltyPenalty
Penalty
susceptiblepotential low-level resistancelow-level resistanceIntermediate resistancehigh-level resistance
Methods for mechanistic study and prediction of resistance mutations
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– Simple rules
Projects Q&A!
1. Biological pathway simulation
2. Computer-aided anti-cancer drug design
3. Disease-causing mutation on drug target
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Any questions? Thank you!