Title: Essential Bioinformatics and Biocomputing (LSM2104: Section I) Biological Databases and Bioinformatics Software Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, NUS January 2003
1Advanced Bioinformatics Lecture 9 Drug resistant
cancerous mutation
ZHU FENG zhufeng_at_cqu.edu.cn http//idrb.cqu.edu.cn
/ Innovative Drug Research Centre in CQU
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2Table of Content
- Differential drug efficacy
- Pharmacogenetics
- Pharmacogenetic response
- Drug resistance mutation
- Prediction of drug resistance
2
3Differential drug efficacy
Different patients
Same symptoms Same disease
Same drug Same dose
Different Effects
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
3
4People react differently to drugs One size does
not fit all
Patients with drug toxicity
Genotyping
Patients with non-response to drug therapy
Patient population with same disease phenotype
Toxic responders Non-responders Responders
Patients with normal response to drug therapy
4
5Why does drug response vary?
Different patients
Same symptoms Same disease
Same drug Same dose
Different Effects
Genetic Differences
Possible Reasons Individual variation By chance
Ethnicity Age Pregnancy Genetic
factors Disease Drug interactions
SNP
5
6Why does drug response vary? Genetic variation
- 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 gt1 in a population
6
7Single nucleotide polymorphism (SNP)
- 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.
7
8Single nucleotide polymorphism (SNP)
- 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 lt1 of all human
SNPs impact protein function (lots of in
non-coding regions) - Some are alleles of genes.
8
9Tandem repeat polymorphisms
- 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 lt1 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
9
10Insertion/deletion polymorphisms
- Insertion/Deletion (INDEL) polymorphisms are
quite common and widely distributed throughout
the human genome.
10
11Due to individual variation
- 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
Pharmacogenetics
11
12Pharmacogenetics
- 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
12
13Pharmacogenetics vs. pharmacogenomics
- 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.
13
14The study of variations in genes that determine
an individuals response to drug therapy.
Pharmacogenetics
Common variation in DNA sequence (i.e. in gt1 of
population)
Genetic Polymorphism SNPs INDEL VNTRs
Potential Target Genes are those that
encode Drug-metabolizing enzymes Transporters Dru
g targets
14
15Determinants of drug efficacy and toxicity
- Patients response to drug may depend on factors
that can vary according to the alleles that an
individual carries, including
- Pharmacokinetic factors
- Absorption
- Distribution
- Metabolism
- Elimination
- Pharmacodynamic factors
- Target proteins
- Downstream messengers
15
16Examples
- EM phenotype Extensive metabolizer IM
phenotype intermediate metabolizer PM
phenotype poor metabolizer UM phenotype
ultrarapid metabolizers
16
17- 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.
17
18Medicines are not safe or effective in all
patients
18
19Medicines are not safe or effective in all
patients
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.
19
20The needs of prediction of pharmacogenetic
response to drugs
- 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.
20
21The feasibility of prediction of pharmacogenetic
response to drugs
- 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
21
22The approach of prediction of pharmacogenetic
response to drugs
- Reported polymorphisms of ADME-associated
proteins - By a comprehensive search of the abstracts of
Medline database
22
23The approach of prediction of pharmacogenetic
response to drugs
- ADME-associated proteins linked to reported drug
response variations - Also by a comprehensive search of the abstracts
of Medline database
23
24The approach of prediction of pharmacogenetic
response to drugs
- Rule-based prediction of drug responses from the
polymorphisms of ADME-associated proteins
the analysis of clinical samples of the variation
of drug responses
Used as indicators for predicting individual
variations of drug response
the results of genetic analysis of the
participating patients
24
25The approach of prediction of pharmacogenetic
response to drugs
- 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).
25
26Basic idea of using HIV-1 genotype to predict
antiretroviral drug susceptibility
Phenotype resistant drug 1, drug 2, drug 3
HIV-1 genotype 1
Phenotype susceptible drug a, drug b, drug c
Phenotype resistant drug 2, drug 3, drug a
HIV-1 genotype 2
Phenotype susceptible drug b, drug c
Phenotype resistant drug 1, drug 3
HIV-1 genotype 3
Phenotype susceptible drug 2, drug a
Phenotype resistant
Phenotype susceptible
26
27The approach of prediction of pharmacogenetic
response to drugs
- 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.
27
28Limitation of Simple rules based methods
- Low predicting accuracies of simple rules based
methods 50100 (comparable to those of 8197
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
28
29Other methods
- 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
29
30Examples
- Statistical analysis and statistical learning
methods used for pharmacogenetic prediction of
drug responses
30
31What is the drug resistance?
- 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
31
32Example of drug resistance mutations
- HIV-1
- Protease mutations (could be quickly developed)
- Integrase mutations
Henderson L. and Arthur L. 2005. NIH AIDS
Research and Reference Reagent Program
32
33The needs for drug resistance mutations prediction
- The molecular analysis of drug resistance
mechanisms - Design new agents to against resistant strains
- Guide the clinical regimen to fight with disease
33
34Methods for mechanistic study and prediction of
resistance mutations
- 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)
34
35Methods for mechanistic study and prediction of
resistance mutations
Phenotypic
Drugs
Protein Mutations
Genotypic
35
36Methods for mechanistic study and prediction of
resistance mutations
susceptible potential low-level
resistance low-level resistance Intermediate
resistance high-level resistance
36
37Methods for mechanistic study and prediction of
resistance mutations
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38Projects QA!
Biological pathway simulation
2. Computer-aided anti-cancer drug design
3. Disease-causing mutation on drug target
Any questions? Thank you!
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