Advanced BioinformaticsLecture 9: Drug resistant & cancerous mutation

ZHU FENGzhufeng@cqu.edu.cn

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!