Lecture 10: Prediction system for pharmacogenetic response to drugs

1. CZ5225 Methods in Computational Biology Lecture 10: Prediction system for pharmacogenetic response to drugs

2. Introduction 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.

3. Medicines are not Safe or Effective in all Patients Source: CDER Report to the Nation: 2004--Drug Safety and Quality

4. Medicines 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.

5. 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.

6. 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

7. 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

8. 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

9. 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

10. 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 Chemother53, 356-360 (2004).

11. 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-1genotype 2 Phenotype susceptible: drug b, drug c… Phenotype resistant : drug 1, drug 3… HIV-1genotype 3 Phenotype susceptible: drug 2, drug a… Phenotype resistant : … … Phenotype susceptible:…

12. 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.

13. 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 systems

14. 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]

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

16. About the project: • Distinguish subgroups of patients who respond differently to drug treatment. • The study of genome-derived data, including human genetic variation, RNA and protein expression differences, to predict drug response in individual patients or groups of patients by using simple-rule based methods. • “Develop a simple rule-based computational drug response prediction system”

17. A guide to the project • Step 1: Choose the research object • - one protein with quite a lot of corresponding drugs • - several function relevant proteins • Step 2: Drug Response Data collection • - by extensive searching web-sources • - developing a database of the information collected • Step3: Design a simple rule-based computational drug response prediction system • - using simply “penalty/score” to describe the simple rules • - comparing the variant sequence with wild type sequence • - predicting the possible drug responses

18. Physician Dx; clinical info Pharmacogenetics: to deliver ‘right medicine, right dose, to right patient’ prediction system Genetic profiles

19. Summary • Genetic profiles of new patients can be used to prescribe drugs more effectively and avoid adverse reactions. • Can also speed clinical trials by testing on those who are likely to respond well. • The primary goal is to reduce the time and cost of drug development. Drug – Gene Interactions  Optimal Dosing

20. Any questions? • Thank you