Advanced Bioinformatics Lecture 9: Drug resistant & cancerous mutation

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. Table of Content Differential drug efficacy Pharmacogenetics Pharmacogenetic response Drug resistance mutation Prediction of drug resistance 2

3. Differential drug efficacy Different patients Same drug Same dose Same symptoms Same disease 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

4. People 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

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

6. Why 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 >1% in a population 6

7. Single 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

8. Single 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 <1% of all human SNPs impact protein function (lots of in “non-coding regions”) • Some are alleles of genes. 8

9. Tandem 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 <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 9

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

11. Due 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

12. 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” 12

13. Pharmacogenetics 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

14. The study of variations in genes that determine an individual’s response to drug therapy. Pharmacogenetics 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. dose administered Pharmacokinetics ABSORPTION DISTRIBUTION concentration in drug in tissues systemic circulation of distribution ELIMINATION metabolism and/or excretion concentration at site of action Pharmacologic effect Clinical response Pharmacodynamics Toxicity Efficacy Determinants of drug efficacy and toxicity • Patient’s 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

16. Examples 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

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

19. Medicines are not safe or effective in all patients when considered in further detail, we can see that efficacy of some of our major drug classes vary from 10-70% incomplete efficacy. 19

20. The 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

21. The 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

22. The 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

23. The 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

24. The 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

25. The 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

26. Drug 1: Genotype1: phenotype (penalty / score); Genotype2: phenotype (penalty / score); … Drug 2: Genotype1: phenotype (penalty / score); Genotype2: phenotype (penalty / score); … Basic 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

27. The 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

28. Limitation of Simple rules based methods • 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 28

29. Other 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

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

31. What 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

32. Example 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

33. The 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

34. Methods 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

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

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

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

38. Any questions? Thank you! 38