Pharmacogenetics & Pharmcogenomics Russ B. Altman, MD, PhD

PharmGKB -- http://www.pharmgkb.org/
Pharmacogenetics & Pharmcogenomics Russ B. Altman, MD, PhD Departments of Bioengineering, Genetics, Medicine, Biomedical Data Science & CS (courtesy) PharmGKB --

Pharmacogenetics is Defined
“The role of genetics in drug responses.” F. Vogel, 1959 PharmGKB -- CP

January 15, 2001 PharmGKB --

Genotype <-> Phenotype associations
Relate genetic information (genotype): 1. ATCGCCGGATACCTAGAGAC… 2. ATCGCCGGAGACCTAGAGAC… to observable traits (phenotypes), e.g. Responds well to cholesterol medication Develops hepatotoxicity PharmGKB --

Purine analogs 6-mercaptopurine, 6-thioguanine, azathioprine Used to treat lymphoblastic leukemia, autoimmune disease, inflammatory bowel disease, after transplant Interferes with nucleic acid synthesis Therapeutic index limited by myelosuppression PharmGKB --

Metabolism of 6-MP Weinshilboum (Mayo Clinic) 2001 PharmGKB --

Levels of TPMT can drastically affect levels of thioguanines
More TPMT = less thioguanines Associated with risk of severe marrow toxicity Shows considerable variability in population PharmGKB --

Variation in TPMT Activity
Weinshilboum (Mayo Clinic) 2001 PharmGKB --

6-MP and TPMT Story Summary
Observation of clinical variability (toxicity) Observation of cellular variability (TPMT activity, TGN concentrations) Observation of genetic variability (genome variations in TPMT gene) PharmGKB --

The logic of pharmacogenetics
Identify variation in drug response Associate it with genetic variation Evaluate clinical significance Develop screening tests Individualize drug therapy PharmGKB --

What is the clinical promise?
Focused treatment by pre-identifying genetic backgrounds likely to respond Reduce adverse events by predicting who is at risk A way to save drugs in the pipeline that are very effective only in subpopulations Better understanding of drug interactions PharmGKB --

Defining P-etics vs. P-omics
Pharmacogenetics = study of individual gene-drug interactions, usually the gene that has the dominant effect on a drug response. (SIMPLE relationship) Pharmacogenomics = study of the full set of PK/PD genes, often using high-throughput data (sequencing, expression, proteomics) (COMPLEX interactions) PharmGKB --

Example: Codeine & CYP2D6
Codeine is a commonly used opioid must be metabolized into morphine for activity CYP2D6 is the protein that performs this metabolism 7% of caucasians have a variant version of CYP2D6 with no activity -> codeine does not work PharmGKB --

Candidate Genes Involved in Metabolism of Codeine and Morphine
PharmGKB --

The O-dealkylation of Codeine by CYP2D6 CYP2D6 codeine morphine PharmGKB --

Cytochrome P450 2D6 Absent in 7% of Caucasians Hyperactive in up to 30% of East Africans Catalyzes the primary metabolism of propafenone Codeine -blockers tricyclic antidepressants Inhibited by fluoxteine haloperidol paroxetine quinidine PharmGKB --

CYP2D6 Alleles 125 alleles as of October 2008 10 alleles where haplotype has not been determined 38 alleles have no activity 10 alleles have decreased activity The *2 variant can have 1, 2, 3, 4, 5 or 13 copies resulting in increased activity PharmGKB --

Allelic Frequencies of CYP2D6
PharmGKB --

CYP2D6 and Simvastatin Simvastatin = HMG CoA reductase, used to decrease LDL, increase HDL cholesterol. Dose of simvastatin required to get cholesterol-lowering effect is related to 2D6 mutations and duplications. Clin Pharmacol Ther Dec;70(6): Another report demonstrates that “statins” are metabolized differently. Biopharm Drug Dispos Dec;21(9): PharmGKB --

Copy number polymorphisms = CNPs
Increasing evidence for variation in the number of copies of a gene in humans Won’t necessarily be picked up with normal genotyping technology (e.g. sequencing) Associated with cancers, genetic diseases, and now with drug response variation Methods for quantifying transcript level, to detect CNPs are coming down in costs PharmGKB --

Clinical Implementation of Pharmacogenomics: A Focus on Guidelines
American Heart Association November 4, 2012

Guidelines “We encourage reviewing the primary literature and using one’s clinical judgment rather than relying solely on recommendations.” For the 99.9% of clinicians who don’t adequately review primary literature, a peer-reviewed group of experts’ review and recommendations would be better. Archives Int Med Jan 2011

Previous homepage without the yellowbox
Previous homepage without the yellowbox. The rest of the content are the mostly the same as the current one. The main entry to find data from PharmGKB is our homepage. A generalized search function and a specific search option to limit the result to a specific data domain, gene, variants, … for each of these data domains, you can either browse or search. In the case of a drug search You will see that there are 2 main strategies that you could use to find information from the homepage. At the top of the page you could choose to use the general search box, this will search the entire website for information. But if you want to search or browse specifically for genes, you can access the information found in the center specific search box for gene, variants, pathways and drugs. If you type a drug name in the variant search box, we will focus on item available to you specificlly in the variant domain.

Survey: Challenges to implementing pharmacogenetics in the clinic
What do you think is the most challenging aspect of the implementation of pharmacogenetics into the clinic? Translation of genetic information into clinical action Test cost, test reimbursement or other economic issues Availability of high quality genotyping test (CLIA approved) Electronic medical record use, such as the application of CDS Clinician and patient resistance and/or ethical concerns Clin Pharmacol Ther Mar;89(3):464-7.

Survey: top 3 Challenges to implementing pharmacogenetics in the clinic
95% of respondents selected: “process required to translate genetic information into clinical actions” Next 2 responses Genotype test interpretation (e.g. using genotype information to impute phenotype) Providing recommendations for selecting the drug/gene pairs to implement Clin Pharmacol Ther Mar;89(3):464-7.

Clin Pharmacol Ther. 2011 Mar;89(3):464-7.

Key Points about a CPIC guideline
Based on assumption that the test results are in hand and NOT to discuss the merits of doing the test Standardized formats Grading of evidence and of recommendations Peer reviewed Freely available Updated Authorship with COI policy Closely follow IOM practices

CPIC CPIC guidelines are designed to help clinicians understand HOW available genetic test results should be used to optimize drug therapy. Not WHETHER tests should be ordered. Key Assumption: Clinical high-throughput and pre-emptive genotyping will become more widespread. Clinicians will be faced with having patients’ genotypes available even if they did not order test with drug in mind.

CPIC: Clinical Pharmacogenetics Implementation Consortium
As of October 2014: >130 Members Clinicians and scientists 62 institutions 14 countries 12 Observers (NIH and FDA) CPIC Informatics 17 members from 11 organizations 89 in 2013 42 institutions

CPIC guideline genes (n=13) and drugs, September 2014
TPMT MP, TG, azathioprine CYP2D6 Codeine, tramadol, hydrocodone, oxycodone, TCAs CYP2C19 TCAs, clopidogrel, voriconazole VKORC1 warfarin CYP2C9 Warfarin, phenytoin HLA-B Allopurinol, CBZ, abacavir, phenytoin CFTR ivacaftor DPYD 5FU, capecitabine, tegafur G6PD rasburicase UGT1A1 irinotecan SLCO1B1 simvastatin IFNL3 (IL28B) interferon CYP3A5 tacrolimus

Initial Prioritization Considerations for New Gene/Drug Groups
New genes/drugs

Level Definitions for CPIC Genes/Drugs
CPIC Level Clinical Context Level of evidence Strength of Recommendation A Genetic information should be used to change prescribing of affected drug Preponderance of evidence is high or moderate in favor of changing prescribing At least one moderate or strong action (change in prescribing) recommended. B Genetic information could be used to change prescribing of the affected drug because alternative therapies/dosing are extremely likely to be as effective and as safe as non-genetically based dosing Preponderance of evidence is weak with little conflicting data At least one optional action (change in prescribing) is recommended. C There are published studies at varying levels of evidence, some with mechanistic rationale, but no prescribing actions are recommended because (a) dosing based on genetics convincingly makes no difference or (b) alternatives are unclear, possibly less effective, more toxic, or otherwise impractical. Most important for genes that are subject of other CPIC guidelines or genes that are commonly included in clinical or DTC tests. Evidence levels can vary No prescribing actions are recommended. D There are few published studies, clinical actions are unclear, little mechanistic basis, mostly weak evidence, or substantial conflicting data. If the genes are not widely tested for clinically, evaluations are not needed. Level Definitions for CPIC Genes/Drugs

16 genes, 86 drugs with pharmacogenetically-based prescribing
Number of current and planned CPIC genes, drugs and anticipated guidelines. Genes Drugs Anticipated number of unique guidelines Strong or Moderate prescribing action-CPIC level A 13 36 19 (14 published) Optional prescribing actions-CPIC level B 7a 50 10 No prescribing actions-CPIC level C 16b 47 20 aCurrently this is 3 unique genes (four are already subjects of CPIC level A guidelines). bCurrently this is 13 unique genes (three are also subject to CPIC level A or B guidelines for other drugs). Oct 14

Clin Pharmacol Ther. 2013 Feb;93(2):153-8
Clin Pharmacol Ther Apr;93(4):324-5. Clin Pharmacol Ther May;93(5):402-8. Clin Pharmacol Ther Sep;94(3):317-23 Clin Pharmacol Ther Aug 29. Epub Clin Pharmacol Ther Sep;94(3):324-8. Clin Pharmacol Ther Feb;95(2):141-6.

CPIC guidelines are posted on PharmGKB (www.pharmgkb.org

CPIC guidelines linked to “Practice Guideline” filter on PubMed

CPIC is cited in NIH’s Genetic Test Registry (GTR) for clinical pharmacogenetic tests

ASHP is endorsing CPIC guidelines

External interactions with other groups
Endorsement by professional societies ASCPT, ASHP Continue interactions with NIH’s GTR, PubMed, FDA, NHGRI’s Genomic Medicine Working Group, IOM’s Genomic Medicine Roundtable, PGRN, AMIA, and eMERGE Grow interactions with ClinGen/ClinVar

Purpose: To describe the development process of the CPIC guidelines
To compare our process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines

Caudle et al, Current Drug Metab 2014

Uniform Elements of CPIC Guidelines (Main)
Introduction Focused Literature Review Gene: Background Genetic Test Interpretation Table 1. Assignment of likely _____ [gene] phenotypes based on genotypes Available Genetic Test Options Incidental findings Other considerations

Uniform Elements of CPIC Guidelines (Main)
Drug (s): Background linking genetic variability to variability in drug-related phenotypes Dosage Recommendations Table 2. Recommended Dosing of ____ [drug/s] by ____ [gene] phenotype Strength of recommendations grading system Recommendations for Incidental Findings Other considerations Potential Benefits and Risks for the Patient Caveats: Appropriate Use and/or Potential Misuse of Genetic Tests

Uniform Elements of CPIC Guidelines (Supplement)
Literature Review details Genetic Test Interpretation Available Genetic Test Options Supplemental Table . Genotypes that constitute the * alleles for ______ Supplemental Table . Association between allelic variants and _____ [gene function] Supplemental Table . Frequencies of alleles in major race/ethnic groups Supplemental Table . Evidence linking genotype with phenotype Levels of Evidence grading system Resources to facilitate incorporation of pharmacogenetics into an electronic health record with clinical decision support (CDS) (workflow diagrams and example CDS alerts and consults)

Clin Pharmacol Ther :387-91

Linking genotype to phenotype
Clin Pharmacol Ther Mar;89(3):

Dosing recommendations: strength based on evidence

High: Evidence includes consistent results from well-designed, well-conducted studies.
Moderate: Evidence is sufficient to determine effects, but the strength of the evidence is limited by the number, quality, or consistency of the individual studies; generalizability to routine practice; or indirect nature of the evidence. Weak: Evidence is insufficient to assess the effects on health outcomes because of limited number or power of studies, important flaws in their design or conduct, gaps in the chain of evidence, or lack of information

Clin Pharmacol Ther. 2014 Apr;95(4):376-82

Clin Pharmacol Ther. 2012 Apr;91(4):734-8.

Clin Pharmacol Ther. 2012 Jul;92(1):112-7.

CPIC Guidelines Updates
CPIC guidelines are evaluated on an ongoing bases and updated regularly No change: Update reviewed on PharmGKB Update to publications: Minor update: Change to supplemental material only Major update: Changes to both main manuscript and supplement All changes posted to PharmGKB website

Variant annotation lists impact of a genomic variant on drug response phenotype based on individual publications

Strength based on: Implementation Statistics Replications
Clinical annotation is a summary of the clinical impact of a genomic variant on drug response phenotype. Strength based on: Implementation Statistics Replications Population size

Provide therapeutic recommendations based on an individual’s genotype
Dosing Guidelines Provide therapeutic recommendations based on an individual’s genotype

Table 1: Assigning likely CYP2C19 phenotypes based on genotypes1
Likely Phenotype Genotypes Examples of Diplotypes Ultrarapid Metabolizer (UM) Normal or increased activity (~5-30% of patients) An individual carrying two increased activity alleles (*17) or one functional allele (*1) plus one increased activity allele (*17) *1/*17, *17/*17 Extensive Metabolizer (EM) Homozygous wild-type or normal activity (~35-50% of patients) An individual carrying two functional (*1) alleles *1/*1 Intermediate Metabolizer (IM) Heterozygote or intermediate activity (~18-45% of patients) An individual carrying one functional allele (*1) plus one loss-of-function allele (*2-*8) *1/*2, *1/*3 Poor Metabolizer (PM) Homozygous variant, mutant, low, or deficient activity (~2-15% of patients) An individual carrying two loss-of-function alleles (*2-*8) *2/*2, *2/*3, *3/*3 1 Some rare genotype combinations have unclear predicted metabolic phenotypes; see Supplemental Table S3.

Table 2: Clopidogrel therapy based on CYP2C19 phenotype for ACS/PCI patients initiating antiplatelet therapy Phenotype (genotype) Implications for clopidogrel Therapeutic recommendations Classifi-cation of recomen-dations1 Ultrarapid Metabolizer (UM) (*1/*17, *17/*17) and Extensvie Metabolizer (EM) (*1/*1) Normal (EM) or increased (UM) platelet inhibition; normal (EM) or decreased (UM) residual platelet aggregation2 Clopidogrel - label recommended dosage and administration. Strong Intermediate Metabolizer (IM) (*1/*2) Reduced platelet inhibition; increased residual platelet aggregation; increased risk for adverse cardiovascular events Prasugrel or other alternative therapy (if no contraindication) Moderate Poor Metabolizer (PM) (*2/*2) Significantly reduced platelet inhibition; increased residual platelet aggregation; increased risk for adverse cardiovascular events 1 See Supplement, Strength of Therapeutic Recommendations. 2 The CYP2C19*17 allele may be associated with increased bleeding risks (12).

Drugs for “Patient 0” Genome

PharmGKB Annotation Method
Evaluate 2500 SNP annotations for direct drug relevance to patient 0 Evaluate CNVs in known important genes (VIP, PK, PD) Evaluate novel SNPs in known important genes (VIP, PK, PD)

Variant annotation highlight
Patient is heterozygous for a null mutation of CYP2C19 (metabolizing enzyme) CYP2C19 critical for metabolism of: proton pump inhibitors (lansoprazole, omeprazole, pantoprazole, rabeprazole) antiepileptics (diazepam, Norphenytoin, phenobarbitone) Amitryptyline, citalopram, chloramphenicol, clopidogrel, indomethacin, nelfinavir, propranolol, R-warfarin, imipramine...

Summary of Pharmacogenetic Bad News
Drug Summary Level of Evidence PMID Gene rsID Clopidogrel & CYP2C19 substrates CYP2C19 poor metabolizer, many drugs may need adjustment. High CYP2C19 rs Warfarin Requires lower dose VKORC1 rs CYP4F2 rs Metformin Less likely to respond Medium CDKN2A/B rs Troglitazone Cisplatin Increased risk of nephrotoxicity Low SLC22A2 rs316019 Citalopram May increase risk of suicidal ideation during therapy GRIA3 rs Escitalopram; Nortriptyline Depression may not respond as well NR3C1 rs Morphine May require higher dose for pain relief COMT rs4680 Paclitaxel Cancer may respond less well ABCB1 rs Pravastatin May require higher dose SLCO1B1 rs Talinolol ABCC2 rs Sildenafil May not respond as well GNB3 rs5443

Summary of Pharmacogenetic Good News
Drug Summary Level of Evidence PMID Gene rsID HMG CoA Reductase Inhibitors (statins) No increased risk of myopathy High SLCO1B1 rs Statins Desipramine; Fluoxetine Depression may improve more than average Medium BDNF rs Fluvastatin Good response rs Metoprolol and other CYP2D6 substrates Normal CYP2D6 metabolizer. CYP2D6 rs /rs Pravastatin May have good response HMGCR rs Pravastatin, Simvastatin No reduced efficacy rs Caffeine No increased risk of heart problems with caffeine Low CYP1A2 rs762551 Calcium channel blockers No increased risk of Torsades de Pointe KCNH2 rs Carbamazepine SNP is part of protective haplotype for hypersensitivity to carbamazepine HSPA1A rs Neviraprine Reduced risk of hepatoxicity ABCB1 rs Efavirenz; Nevirapine Epoetin Alfa Lower dose of iron and epo required HFE rs Fexofenadine Average blood levels expected Irbesartan Irbesartan may work better than beta-blocker APOB rs Lithium Increased likelihood of response CACNG2 rs Paroxetine May have improved response rs Pramipexole More likely to respond DRD3 rs6280 rs Rosiglitazone LPIN1 rs Succinylcholine No increased risk of apnea BCHE rs rs

Novel nonsynonymous damaging SNPs
SNP_loc Ref pt0 Coding PK/PD? Gene related drugs 1: G CG H191D PK AK2 adefovir dipivoxil; tenofovir; 16: AG V793M PD CARD15 infliximab; 12: C CT H578Y ERBB3 trastuzumab; erlotinib; gefitinib; lapatinib; PHA ; chloroquine; cisplatin; gemcitabine; cetuximab; 3: T AA I485F MYLK mercaptopurine; methotrexate; 13: Y21C SLC15A1 atorvastatin; fluvastatin; hmg coa reductase inhibitors; lovastatin; pravastatin; rosuvastatin; simvastatin; 9: S443F SLC28A3 cladribine; fludarabine; uridine; mercaptopurine; thioguanine; antineoplastic agents; gemcitabine; azathioprine; folic acid; 20: P246L AHCY antimetabolites; mercaptopurine; methotrexate; adenosine; antineoplastic agents; azathioprine; folic acid; thioguanine; 16: S431L 6: TT T262K HLA-DRB5 clozapine; 6: I14T MICA 11: R534Q SLC22A8 cimetidine; estrone; antiinflammatory and antirheumatic products, non-steroidals; hmg coa reductase inhibitors;; adefovir dipivoxil; tenofovir; antineoplastic agents; cyanocobalamin; folic acid; leucovorin; pyridoxine; 16: G64R VKORC1 warfarin

A family quartet PLoS Genetics, 2011

Example: Warfarin dosing
Warfarin used for anticoagulation widely in medicine Narrow therapeutic index, final dose impossible to predict -> trial and error Three genes known to affect warfarin dose: CYP2C9, VKORC1 and CYP4F2 Recent results suggest warfarin dose will be predicted precisely using genotypes + a few demographic variables PharmGKB --

Warfarin Dosing (continued)
Long known: Variation in CYP2C9, but only accounted for < 15% of variation Found in rat-poison-resistant-rats: Vitamin K epoxide reductase (VKORC1) SNP in intron explains 35% of variation! VKORC1: G>A allele Clinical trials now underway to predict warfarin dose required based on genotype More later on recent developments and implications of warfarin pharmacogenetics PharmGKB --

Observed vs. Predicted Dose with PGx
PharmGKB --

Example: Herceptin & Breast Cancer
HER2 = human epidermal growth factor HER2 is amplified in 25-35% of breast cancer patients Herceptin = Trastuzumab = antibody against HER2 Herceptin is very effective in those cases, much less effective otherwise Testing HER2 levels becomes critical… Important “First” (survival benefit, ab, test) PharmGKB --

Example: Bidil Combination pill containing two medications for heart failure (hydralazine & isosorbide dinitrate) Clinical trials did not show overall benefit Subgroup of African-descent patients showed benefit BiDil approved for use in African-descent patients PharmGKB --

Example: Irinotecan Powerful anti-neoplastic used in colon/rectal cancers Use is limited by severe life-threatening diarrhea side effect Side effect related to patient genotype of UGT1A1 (helps metabolize irinotecan) Test now marketed for evaluating UGT1A1 genotype prior to initiation of treatment PharmGKB --

Most common major drug ADRs
QT prolongation (heart problems) Liver failure Severe dermatological rash Many other minor ADRs Minor rash Abdominal discomfort Dry mouth Drowsiness or activation Headache PharmGKB --

Other examples Phenylthiourea non-taster phenotype P-glycoprotein transporter variation Aldehyde dehydrogenase CYP2C19 and omeprazole & diazepam Dihydropyridine dehydrogenase and 5-FU UDP glucuronyl transferase 1A1 and bilirubin N-acetylation polymorphism and INH (for TB) NO-sythetase and vascular tone variation… PharmGKB --

Example: Gefitinib (Iressa)
Inhibits the growth factor receptor EGFR Promising early trials - but only improved survival in subset of patients, 10-15% Female, Japanese Non-smokers Example that shows we have to remember about the host genome as well Iressa works better in those with variant EGFR that makes it more susceptible to the drug “Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib”, NEJM : PharmGKB --

Hypertension Beta-adrenergic receptor polymorphism at amino acid 389 (Arg -> Gly) Less susceptible to beta-blockers (atenolol, metorprolol, propranolol, etc…) 9 mm Hg vs. 2 mm Hg response to atenolol May suggest need for testing before giving certain types of anti-hypertensives PharmGKB --

ADRB1 haplotypes and metoprolol
Freq of SR/SR: Whites - 45% Blacks - 10% Freq of SR/SR or SR/GR: Whites – 57% Blacks – 22% Decrease in BP p = between groups PharmGKB --

Apolipoprotein 4 Allele and Statin Benefits APOE also implicated in Parkinson’s PharmGKB --

Arrhythmia QT prolongation and arrhythmia risk is the single commonest cause of drug withdrawal or relabeling in the last decade Wall Street Journal Oct. 28, 1999 PharmGKB --

Gene-Drug FDA examples
HLA-B*1501 and 1502 alleles hypersensitivity responses which can lead to a rash and skin reactions that may become serious and life-threatening abacavir (Ziagen), carbamezepine (Carbatrol, Tegretol) CYP2D6*3, *4, *5, *6, *10, *17 and *29 alleles confer "poor metabolism” tamoxifen (Novaldex); CYP forms appear to predict poorer prognosis for long term survival fluoxetine (Prozac) and atomoxetine (Strattera) VKORC1 A/A and A/B haplotypes and CYP2C9*2 and *3 alleles interactions at the target site and the removal from the body warfarin (Coumadin) Patients with combinations of these alleles can experience elevated INR measures and increased risk of dangerous bleeding events PharmGKB --

Gene-Drugs FDA Examples
UGT1A1*28 allele reduced clearance of the active form SN-38 of the anticancer drug irinotecan (Camptosar) which can lead to neutropenia and a life-threatening diarrhea TPMT*2, *3A, and *3C alleles in homozygous patients leads to decreased clearance 6-mercaptopurine (Purinethol) or the pro-drug azothiaprine (Imuran) used to treat acute lymphoblastic leukemia and autoimmune diseases leading to rapid bone marrow suppression that is severe and can become fatal SLC01B1 (rs ) and OATP1B (rs ) found in liver can determine the blood levels of a wide range of drugs simvastatin (Zocor) and pravastatin (Pravachol) are associated with development of myopathies ADRB2 Arg16 allele of the beta-2 adrenergic receptor linked in homozygous patients to a sub-class of asthma and lack of long-term responsiveness to treatment with albuterol (Ventolin) PharmGKB --

PGx info in drug labels approved by FDA Frueh et al, Pharmacotherapy : Reviewed labels of FDA approved drugs to identify those that contained PGx biomarker information to collect prevalence information on the use of those drugs for which PGx information was included in drug labelling 1200 drug labels reviewed for 121 drug labels contain PGx info 69 labels refer to human genomic biomarkers 62% (43) pertained to cytochrome p450s with CYP2D6 most common 52 labels referred to microbial genomic biomarkers 24.3% of prescriptions in 2006 (8.8 million out of 36.1 million) processed by a large pharmacy benefits manager received one or more drugs with human genomic biomarker information in the drug label. PharmGKB --

PGx info in drug labels approved by FDA Frueh et al, Pharmacotherapy : Pharmacogenomic biomarkers identified in drug labels with human genomic information (1945–2005), and percentage of drug labels associated with each. Number of drugs that were approved With pharmacogenomic information in their drug labels during each 10-year period from 1945–2005 PharmGKB --

Conclusions from Drug Data “Nearly one fourth of all outpatients received one or more drugs that have pharmacogenomic information in the label for that drug. The incorporation and appropriate use of pharmacogenomic information in drug labels should be tested for its ability to improve drug use and safety in the United States.” Frueh et al, Pharmacotherapy : PharmGKB --

Table of Valid Genomic Biomarkers
in the Context of Approved Drug Labels PharmGKB --

Prevalence of Use of Required or Recommended Pharmacogenomic Test in Drug Labels PharmGKB --

PharmGKB --

P450 Substrates PharmGKB --

P450 Inhibitors PharmGKB --

P450 Inducers and Genetics
PharmGKB --

Pharmacogenetics & Pharmcogenomics Russ B. Altman, MD, PhD

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