Pharmacogenomics Primer for PRISM

1. Pharmacogenomics Primer for PRISM

2. Presentation Agenda • Introduction to Pharmacogenomics (PGx) • Classic PGx studies: identifying unusual metabolizers by CYP mutation analysis • Terminology • Biomarker, PGx, cohort-based medicine, translational medicine, microdosing, • Forward and reverse TM • New technologies enabling better identification of patient cohorts • Gene expression profiling, rt-PCR, SNP Chips, proteomics, pathways • Biomarker Discovery Case Study • Example of use of above technologies to identify e.g. an at-risk population • IT-Related Issues • Data management • Data federation • Depth vs. breadth in data use • Data exchange: Sponsors, CROs, labs, academic collaborators

3. Objective of this Session To make the case that the PRISM forum should convene a SIG on pharmacogenomics for a future meeting.

4. What is Pharmacogenomics?

5. Pharmacogenomics Description 1 • No two people are the same. Therefore, no two patients respond exactly the same way to a drug. • Anti-psychotics, anti-depressants, oncology drugs, and even aspirin work only for a subset (sometimes a small subset) of the patient population • Some patients are susceptible to drug-related adverse events • Some patients metabolize the drug quickly and never reach therapeutic dose • Some patients metabolize slowly and risk overdosing • One drug may interfere with the response to, or metabolism of, another co-administered drug • For example: • The effective dose for warfarin may vary twenty-fold among the patient population. • The effective propranolol dose may vary forty-fold.

6. Pharmacogenomics Description 2 • Is it possible to discover and use biomarkers (genetic, biochemical and physiological attributes) which will allow us to identify patients who are: • Likely to respond positively to administration of a drug • Likely to respond adversely to administration of a drug due to: • Increased risk of adverse reaction • Unusual metabolic response

7. The Objective of Pharmacogenomics • The objective of pharmacogenomics is to optimize drug therapy by increasing the ability of health care providers to make rational decisions based on: • Differences in drug response attributable to a patient’s genotype or phenotype. • Optimizing drug therapy entails: • Identifying patients likely to be (non)responders • Making better dosing decisions • Identifying under- and over- metabolizers • Identifying patients at risk for serious adverse events

8. The Outcome of Pharmacogenomics • Effective use of pharmacogenomics will change: • Drug discovery and development • Physician decision-making and prescribing behavior • Post-marketing activities and adverse event reporting • These changes will have a profound effect on the way information is managed • Data volumes will increase dramatically • Data will be used throughout the drug lifecycle in unprecedented ways • Traditional organizational boundaries will be challenged • Distinctions between discovery, development and post-marketing activities will become blurred • Information management organizations will need to respond to major changes in the way data are accessed and shared

9. Alcohol Dehydrogenase (ADH) Acetaldehyde Dehydrogenase (ALDH) Alcohol Metabolism

10. Mutation of ADH increases metabolism rate A substitution of the amino acid histidine for arginine at position 47 (ADH1B) significantly increases the rate of ethanol metabolism People with this mutation metabolize ethanol much more quickly than normal and accumulate acetaldehyde

11. Mutation of ALDH2 reduces metabolic rate A substitution of the amino acid lycine for glutamate at position 487 (ALDH2*2) significantly reduces the ability to metabolize acetaldehyde • People with two copies of the ALDH2*2 (homozygotes) have extremely low metabolic rates and it they consume alcohol they suffer from: • Flushing • Nausea • Increased cancer risk • People with one copy of ALDH2*2 (heterozygotes) also have much reduced acetaldehyde metabolism (<10% of normal)

12. These mutations have a clear racial distribution • ALDH2*2 is very common in Asian populations • ADH1B is more common in European populations • Not surprisingly there’s a strong correlation between these genotypes and alcoholism • ADH1B and ALDH2*2 both have a “protective” effect • People with these genotypes often tend to avoid consuming alcohol

13. Classic PGx Studies: Warfarin • Much of the early work in PGx has focused on identifying mutations affecting a patient’s ability to metabolize drugs • The biochemistry and genetics of Absorption, Distribution, Metabolism and Excretion (ADME) has been an active and productive area of research. • The ability to identify patients with abnormal metabolism makes it possible to: • Make better dosing decisions • Identify and avoid potential drug-drug interactions

14. Classic PGx Studies: Warfarin • Historically PGx studies have concentrated on the enzymes critical to metabolism: • Cytochrome P450 (CYP) Enzymes • Phase II Enzymes • Drug Transporters • As an example we’ll look at one study of CYP mutations.

15. CYP Enzymes - Overview • Basic CYP facts: • Humans have 57 CYPs genes grouped into 18 families and 43 subfamilies (as defined by sequence homology) • There are as many as 80 distinct CYPs • Roughly 10 CYPs are responsible for metabolizing the vast majority of small molecule drugs • A drug is metabolized by one CYP depending on that drug’s chemical structure • Mutation of a CYP can alter or eliminate a patient’s ability to metabolize all of the drugs metabolized by that CYP • Gene duplications can increase the rate at which a drug is metabolized • Drugs can decrease or increase CYP activity, altering the patient’s ability to metabolize other drugs • Understanding the genetics of CYPs and a drug’s metabolic pathway makes it possible to: • Identify patients likely to exhibit abnormal metabolism • Identify potential drug-drug interactions

16. CYP Polymorphisms From Williams, et al., 2008, J Clin Pharm 48:849-889 There are hundreds of CYP variants. A CYP mutation can increase that CYP’s activity, decrease it, or eliminate it altogether.

17. The Warfarin Label: PGx Information This is a portion of the label for the anticoagulant warfarin. It shows the range of metabolism rates for some common CYP2C9 variants. Notice the emphasis on race-based categorization

18. The FDA’s list of Valid Genomic Biomarkers FDA guidance is that genetic testing is recommended, not required, prior to warfarin administration. Physicians often use trial-and-error to establish proper dosing for each patient. Often race plays a role in prescribing decisions Does this make sense? Adapted from the Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels at: http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm

19. Using the wrong criterion for decisions presents risks The CYP2D6*10 variant is common in Asians but rare in Caucasians (<2% of the population) A prescription for, e.g. -blockers, might be risky for Dr. Watson if race is the basis for a dosing decision. From Ng, et al., 2008, Clin Pharm & Therap, 84(3):306-309

20. The PGx Tipping Point • We have reached the point where obtaining this biomarker data is technically and economically feasible: • As part of a drug development effort • For individual patients

21. Forward and Reverse TM • Forward and Reverse TM expands the concept of TM to include the idea that research findings will, in addition to moving from “bench to bedside”, will also flow from bedside to bench. Forward TM Discovery Pre-Clinical Development Clinical Development Disease Treatment Reverse TM

22. Essential PGx Technologies • Genetics • Characterization of mutations altering the gene, and possibly, the activity of a protein. • Epigenetics • Biochemical alterations of DNA that alter the regulation of gene expression • Genomics • Characterization of the changes in production of one or many mRNAs in response to a stimulus. • Proteomics • Characterization of changes in the level of one or more proteins in response to a stimulus • Metabonomics • Characterization of metabolic products following a stimulus • Imaging • Characterization of changes in phenotype using diagnostic tools such as X-Ray or CT scan.

23. The Interrelationship of PGx Data What might happen? What is happening? Genetics Epigenetics Genomics Pathways Proteomics Metabonomics What did happen?

24. Role of Technologies through the Drug Lifecycle The various PGx technologies (blue) can be effectively used throughout the drug discovery/development lifecycle (yellow) and its associated activities. Adapted from Marrer and Dieterle, 2007, Chem Biol Drug Des 69:381-394

25. New Technologies Enabling PGx Efforts • Laboratory Technologies: • Genetics • Pyrosequencing, bead arrays, µArrays • Genomics • µArray, qRT-PCR, SAGE, SuperSAGE • Proteomics • LC-MS-MS, ICAT, iTRAQ, MALDI, Immunohistochemistry, in situ hybridization, antibody arrays • Metabolomics • NMR, LS-ms, UPLC-MS • Imaging • X-Ray, CT scan, MRI, fMRI, 2D & 3D ultrasound • IT Technologies: • Pathways Tools • Ontologies • Data exchange standards

26. Is the pharma industry serious about incorporating PGx into the drug discovery/development process?

27. Frequency of DNA Data Collection From Williams, et al., J Clin Pharmacol 2008;48:849-889 A PhRMA survey of 14 major pharma companies found that DNA collection and genotyping was common in early drug development phases

28. Breadth of Current Genotyping Efforts From Williams, et al., J Clin Pharmacol 2008;48:849-889 In the PhRMA survey responses indicated that routine genotyping was common for some of the important ADME genes.

29. Case Study 2: Gene-Drug Interaction

30. Case Study 2 – Adrenergic Receptor Mutations and Drug-Sensitive Mortality in Cardiac Patients • Blockers of the -adrenergic receptor (-blockers) are commonly given to treat cardiac arrhythmias and hypertension • Other anti-hypertensive/anti-arrhythmics are available with different modes of action. • The important question: Does genetic variation on the -adrenergic receptor affect the outcomes for patients taking -blockers relative to other therapies?

31. Genetic Variation in -Adrenergic Receptors There are a number of common adrenergic receptor variants in which one or two amino acids are different from the “standard” protein. The Ser49-Arg-389 variant is predominant in some populations. Patients with this variant are at an increased mortality risk. Do these differences have a bearing on treatment outcomes? From Pacanowski, et al. 2008 Clinical Pharmacology and Therapeutics, advance online publication 09 July 2008. doi:10.1038/clpt.2008.139

32. Differing Therapy-based Outcomes 1 From Pacanowski, et al. 2008 Clinical Pharmacology and Therapeutics, advance online publication 09 July 2008. doi:10.1038/clpt.2008.139 Patients with the Ser49-Arg389 genotype are at higher risk of cardiovascular events and death For Ser49-Arg389 patients the -blocker atenolol reduced mortality, while the L-type Ca-channel blocker verapamil did not.

33. Differing Therapy-based Outcomes 2 Differing Therapy-based Outcomes 2 But it’s not so simple! Patients with a common variant of another -adrenergic receptor exhibited the opposite response profile. They did better with verapamil than atenolol. From Pacanowski, et al. 2008 Clinical Pharmacology and Therapeutics, advance online publication 09 July 2008. doi:10.1038/clpt.2008.139

34. PGx can get complex We are only scratching the surface!

35. Implications of PGx Studies • PGx studies cut across traditional divisions in a drug lifecycle : • Discovery • Development • Patient selection • Adaptive trial design • Clinical practice • Patient testing • Diagnostics • Patient data management • Post-marketing activities • Adverse Event Reporting

36. Information Management Challenges • The major information management challenges are: • Effectively managing the expected flood of data • Integrating data from different sources • Providing users with the appropriate tools to analyze, visualize and mine data • Enabling the required changes as organizations struggle to adapt to new a research and development paradigm

37. Managing the Flood of Data • What kind of data volumes might we begin to expect as PGx data are generated as part of clinical trials? • The next example is a back-of the envelop calculation of data generated in the course of: • Four 5,000 patient Phase III studies • Each using a different imaging modality

38. Data Generated by PGx Technologies

39. Integrating Data from Different Sources • Not only will data volumes increase, but so will the need for efficient integration. • Each data modality has strengths and weaknesses • A useful biomarker will probably not be a single unambiguous datum, but a “signature” comprised of different data from different sources This SNP and This expression pattern and These serum proteins Example Biomarker Signature

40. Providing Appropriate Tools • Users, depending on job and expertise, will require very different tools to access data • Some users will require the ability to dive deeply into a particular set of data. These users will: • Develop and maintain deep domain knowledge • Develop expertise with the appropriate software tools • Other users will require breadth of access; integrating data from a variety of sources. These users will not: • Have a deep understanding of all (or any) of the different data domains • Be willing to develop expertise with all the different software tools needed to effectively manage the data they wish to use

41. Bioinformatician; Biostatistician Depth vs. Breadth in Data Access 1 For instance: Genomic (gene expression) analyses have multiple steps: Clinical Researcher Data Acquisition Initial data processing: background subtraction, normalization, QA/QC, noise analysis Discovery of population patterns: Merge with patient data, Stratify patients, Identify covariates Identify expression pattern: Define patterns of up- and down-regulated genes In this scenario, the bioinformatician, biostatistician and clinical researcher will almost certainly not use the same tools to explore and analyze the data.

42. Depth vs. Breadth in Data Access 2 Genomics Data Stream Genetics Data Stream Imaging Data Stream When integrating data across types it is very unlikely that a researcher will be willing or able to examine any one data type deeply. It is essential to identify the “sweet spots” in the data stream where data may have broad utility.

43. Depth vs. Breadth in Data Access 3 • Identification of the “sweet spots” in the data stream can have significant impact on decisions about: • Storage requirements • Data retention policies • Security policies • Validation/qualification requirements • Data exchange with CROs and external collaborators • Software purchases and the use of open-source software/freeware

44. Other Data Management Issues • The need to manage increase data volumes, and to integrate data across different modalities touches on many issues including: • Data exchange standards (MAGE/MIAME, DICOM, etc.) • Retention of data by CROs and external collaborators • Infrastructure and capacity planning • Introduction of new information technologies (e.g. Semantic Web) • Re-engineering existing systems to meet anticipated future use

45. Conclusion • Pharmacogenomics has the capacity to revolutionize drug development and patient care • The information management challenges PGx represents are significant • Responding proactively to these challenges is essential

46. Backup Slides

47. A PGx Lexicon • Pharmacogenomics (PGx) attempts to establish a causal relationship between the genotypes in a population and variations in a drug’s efficacy, metabolism or toxicity. • A biomarker characteristic that is objectively measured as an indicator of normal biological processes, pathogenic processes of a pharmacological response to a therapeutic intervention. (from the Biomarkers Definition Working Group, 2001, Clin Pharm Ther, 69:89-95) • Personalized medicine (or Cohort-based medicine) is the concept that knowledge of a patient’s genotype and phenotype should make it possible to design a therapeutic intervention specifically tailored to that patient. Advocates of personalized medicine contend that, with sufficient knowledge of the patient’s biological status and disease status it should be possible to design therapeutic interventions that are more likely to be efficacious and less likely to have undesirable side effects. • Translational medicine ( TM ) is often called “bench to bedside” medicine . It refers to the desire of the medical community to quickly take advantage of research discoveries in a clinical setting. TM has come to mean the quick application of discoveries about the molecular basis of disease and the discovery of biomarkers to patient care and the development of diagnostics. TM is often used synonymously with Personalized Medicine and Molecular Medicine. PGx, TM, Personalized Medicine and Molecular Medicine are often used interchangeably

48. A PGx Lexicon • An allele is one of a pair of forms of a gene. Each human has, typically, two copies of each gene. These copies are frequently not identical. Each copy is an allele. • Homozygote: an individual who has two (functionally) identically copies of a gene. • Heterozygote: an individual who has two dissimilar variants of a gene.