1. Advanced Bioinformatics Lecture 9: Drug resistant & cancerous mutation ZHU FENG zhufeng@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

2. 1.Differential drug efficacy 2.Pharmacogenetics 3.Pharmacogenetic response 4.Drug resistance mutation 5.Prediction of drug resistance Table of Content 2

3. Same symptoms Same disease Same drug Same dose Different 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 3

4. 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 …” 4 Toxic responders Non-responders Responders

5. Ethnicity Age Pregnancy Genetic factors Disease Drug interactions …… Same symptoms Same disease Same drug Same dose Different Effects Different patients Why does drug response vary? 5 Possible Reasons: Individual variation By chance… Genetic Differences A G SNP

6. 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 6

7. 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) 7

8.  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) 8

9.  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 9

10.  Insertion/Deletion (INDEL) polymorphisms are quite common and widely distributed throughout the human genome. Insertion/deletion polymorphisms 10

11.  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 … 11 Pharmacogenetics

12.  “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 12

13.  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 13

14. 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 enzymes Transporters Drug targets 14

15.  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 15  Pharmacokinetic factors − Absorption − Distribution − Metabolism − Elimination  Pharmacodynamic factors − Target proteins − Downstream messengers

16. EM phenotype: Extensive metabolizer; IM phenotype: intermediate metabolizer; PM phenotype: poor metabolizer; UM phenotype: ultrarapid metabolizers Examples 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

18. Medicines are not safe or effective in all patients 18

19. Medicines are not safe or effective in all patients 19 Drug GroupEfficacy Incomplete/Absent SSRI10-25% Beta blockers15-25% Statins30-70% Beta2 agonists40-70% …… when considered in further detail, we can see that efficacy of some of our major drug classes vary from 10-70% incomplete efficacy.

20.  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 20

21.  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 21

22.  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 22

23.  ADME-associated proteins linked to reported drug response variations Also by a comprehensive search of the abstracts of Medline database The approach of prediction of pharmacogenetic response to drugs 23

24.  Rule-based prediction of drug responses from the polymorphisms of ADME-associated proteins The approach of prediction of pharmacogenetic response to drugs 24 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

25.  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 25

26. 26 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); …

27.  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 27

28.  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 28

29.  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 29

30.  Statistical analysis and statistical learning methods used for pharmacogenetic prediction of drug responses Examples 30

31.  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? 31

32. Example of drug resistance mutations 32  HIV-1  Protease mutations (could be quickly developed)  Integrase mutations  …… Henderson L. and Arthur L. 2005. NIH AIDS Research and Reference Reagent Program

33.  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 33

34.  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 34

35. Methods for mechanistic study and prediction of resistance mutations 35 –Simple rules Protein Mutations Drugs Genotypic Phenotypic Penalty

36. Methods for mechanistic study and prediction of resistance mutations 36 –Simple rules Penalty susceptible potential low-level resistance low-level resistance Intermediate resistance high-level resistance

37. Methods for mechanistic study and prediction of resistance mutations 37 –Simple rules

38. Projects Q&A! 1.Biological pathway simulation 2. Computer-aided anti-cancer drug design 3. Disease-causing mutation on drug target 38 Any questions? Thank you!