FDA-Approved Drug Labels, Pharmacogenomics and PharmGKB Evaluation

FDA-approved drug label standardization and pharmacogenomics
People often use the wording "FDA drug labels" instead of "FDA-approved drug labels."  To be clear, the FDA does not write drug labels; it is the responsibility of the drug company to provide them.  The FDA reviews and approves labels, and has implemented rules to standardize them.  Since 2005, submitted labels are required to be in Structured Product Labeling (SPL) format.  Starting in 2006, label highlights are required to have bulleted boxed warnings, and include indications and a Table of Contents.  These requirements greatly improve the organization and readability of labels for humans, but stop short of standardization enabling automated parsing and interpretation of labels.  It remains difficult to use techniques like Natural Language Parsing (NLP) to automatically extract information from drug labels.  And even with the current rules, sometimes vital information remains missing from approved drug labels, such as data regarding toxicity and uncertainty regarding the net benefit,  and comparative effectiveness.

The language used in drug labels can be vague and often stops short of requiring action.  For example, the azathioprine label states "It is recommended that consideration be given to either genotype or phenotype patients for TPMT."  Do they recommend testing?  They don't mention testing anywhere on the label, only that consideration should be given to the genotype or phenotype.  With a new patient, how would you have this information without testing?

The FDA maintains a list of labels containing pharmacogenomic information, called the Table of Pharmacogenomic Biomarkers in Drug Labels.  It is important to note that this table is (1) not a complete list of labels that reference genes, nor does it reflect (2) all genes of importance on labels listed or (3) indicate that the genes that are listed in the table are important for prescribing the drug.  Here is an example of each case (as of 10/20/13):

(1)  Many drug labels that discuss G6PD deficiency are not listed in the table (eg. glibenclamide, pegloticase, primaquine - this also mentions CYPB5R3 deficiency on the label!), but the chloroquine label mentioning G6PD deficiency is listed.  Why chloroquine is listed but not other drugs warning against G6PD deficient patients is unclear.

(2) The valproic acid (Depakene) label is listed in the table with NAGS, CPS1, ASS1, OTC, ASL, ABL2.  These are genes related to urea cycle disorder which is a contraindication of the drug, though the genes are not listed by name on the label.   However the label does specifically discuss POLG gene mutations that predispose patients to increased risk of liver failure and death.  POLG is a mitochondrial DNA polymerase associated with hereditary neurometabolic syndromes such as Alpers Huttenlocher Syndrome, and the valproic acid label states "POLG mutation testing should be performed in accordance with current clinical practice for the diagnostic evaluation of such disorders."  POLG is not listed in the Biomarker table with this or any other drug.

(3) The prasugrel (Effient) label is listed in the table with CYP2C19 (no other genes).   The label states "There is no relevant effect of genetic variation in CYP2B6, CYP2C9, CYP2C19, or CYP3A5 on the pharmacokinetics of prasugrel's active metabolite or its inhibition of platelet aggregation." The drug-gene pair is likely in the table because CYP2C19 poor metabolizers may be poor responders to clopidogrel, another anti-platelet drug.  For these cases, prasugrel is a typical alternative because CYP2C19 variation does not affect prasugrel use in any way.   This is an example where a drug-gene pair on the Biomarker table does not indicated a relevant PGx relationship between the two.

PharmGKB
When PharmGKB curators curate drug labels, they need to interpret them, and that can be subjective.   PharmGKB has recently reviewed the drug labels from the FDA's Biomarker Table and improved the label annotations.  Curators have tried to define clearly how they interpret the level of PGx information on labels, resulting in definitions that are really a rather long list of criteria.  These definitions may change over time as new label wording comes up and definitions need to adjust for these new cases. 

Viva La Evidence

We came across this brilliant music video  “Viva La Evidence” by Dr. James McCormack. It’s a parody of Coldplay's Viva La Vida and the lyrics cover many important topics of evidence based medicine, its history, principles and aims. What a fun way to explain the hierarchy of evidence, systematic review and questions to ask in critically appraising a study!

In the past, medicine relied heavily on physicians' experiences and prescribing contained an element of “trial and error” due to the varied individual response within a population. In the current post genomic era, this approach would not be optimal and a systematic, evidence-based approach has emerged as a solution to integrate the best research evidence with clinical expertise, and, patient values and expectations. Preferably, medication prescribing decisions are based not only on population data, but also on the individual patient’s genetic profile and drug metabolizing characteristics. Pharmacogenomics is the study of how the genetic makeup of an individual affects the efficacy and toxicity of drugs and can help move us closer towards the goal of “personalized medicine”.

For fans of evidence based medicine, check it out at :

http://www.youtube.com/watch?v=QUW0Q8tXVUc

Venlafaxine Pathway published in PG&G

Venlafaxine is a serotonin-norepinephrine reuptake inhibitor (SNRI) marketed for the treatment of depression disorders.

We recently have published the Venlafaxine Pathway summary in the Pharmacogenetics and Genomics Journal.

The article describes the metabolism of venlafaxine, with cytochrome P450 2D6 (CYP2D6) being the major enzyme involved in the formation of its metabolites. The pharmacokinetics of venlafaxine is affected by the CYP2D6 metabolizer phenotype.

Read the article for a discussion about the influence of genetic variations on venlafaxine metabolism and treatment outcome.

Find out more...

View the venlafaxine pathway on PharmGKB.

Read the publication:

PharmGKB summary: venlafaxine pathway.

Sangkuhl K, Stingl JC, Turpeinen M, Altman RB, Klein TE.

Pharmacogenet Genomics. 2013 Oct 14

PMID: 24128936

View all pathways on PharmGKB.

NIH funds ClinGen: a resource to establish which genetic variants are clinically relevant

The NIH (NHGRI and NICHD) has awarded more than $25 million over the course of 4 years for the development of the Clinical Genome Resource (ClinGen). The aim of ClinGen is to provide a framework for the evaluation of genetic variants that are clinically relevant, working with the NCBI which will distribute this information through the ClinVar database.

PharmGKB is very excited to be part of ClinGen through the program led at Stanford by co-investigators Carlos Bustamante, Mike Cherry and Teri Klein (for the PGx aspects). PharmGKB will be responsible for curating clinically relevant variants associated with drug response.

Further details:

ClinGen will be developed by a consortium made up of 3 groups, each with different but complementary goals:

1.     Development of standards for data collection and depositing into ClinVar, development of standards to analyze variants and determine whether they cause disease or are medically useful.

Investigators from: Brigham & Women’s Hospital, Boston; Geisinger Health System, Danville, PA; University of Utah, Salt Lake City; University of California, San Francisco.

2.     Develop and apply computing methods to process and analyze variants more effectively, determine which genomic variants have strong evidence for disease risk/ clinically important and prioritize variants for further study utilizing informatics tools and databases, and improve predictions for which variants are associated with disease-risk in non-white populations.      

Investigators from: Stanford University, Palo Alto, CA; Baylor College of Medicine, Houston.

3.     Defining categories of clinical relevance that can be assigned to genetic variants and study ways to integrate this into electronic medical records.

Investigators from: University of North Carolina, Chapel Hill; American College of Medical Genetics and Genomics, Bethesda, MD.

Read more:

•  NIH news
• Genomeweb

The Curation Economy

In an October 7th article in The Huffington Post blog section, Steve Rosenbaum discusses the development of a so-called "Curation Economy". Rosenbaum writes that the creation of content on the web is increasing at an exponential rate, leading to a massive amount of raw and unfiltered data. This includes information ranging from "Likes" on Facebook and YouTube videos, to content such as online news articles, and blog posts like this one. So how do internet users take advantage of this avalanche of information without being overwhelmed? Rosenbaum's answer is through the use of curators.

Though this post discusses general curation of web content, many of his points apply directly to PharmGKB, and the way that the knowledgebase manages its own deluge of information. The era of personalized medicine has brought with it a large increase in studies analyzing pharmacogenetic (PGx) associations. Though this is great for the field, the usefulness of this information is dependent on whether it can be organized and presented in a way that is helpful for all types of interested parties, such as researchers, students or clinicians. PharmGKB addresses this need through the work of curators, who are responsible for annotating, aggregating and integrating PGx study results. Through this process, curators are able to make vast quantities of PGx information useful, accessible and understandable to all types of users. Rosenbaum also makes an important note about how technology is necessary to assist in the finding, filtering and validating of content, and how curation cannot be an exclusively human enterprise. This is certainly true for PharmGKB, since manually keeping pace with the large volume of literature being published is a significant challenge. Current work in natural language processing (NLP) by the knowledgebase aims to assist curators in the identification and extraction of PGx relationships.

This article reminds us that though the exponential creation of data is exciting prospect, is it essential to be able to manage this information, and extract meaningful and relevant data. This is true for normal web users, pharmacogenetic researchers, and people in various fields worldwide.

You can read Steve Rosenbaum's article here or here, and visit PharmGKB here.

CPIC publishes guidelines for IFNL3 (IL28B) and peginterferon alpha based regimens

Guidelines regarding the use of IFNL3 (formerly known as IL28B) genotypes in peginterferon alpha based therapies have been accepted for publication in the Clinical Pharmacology and Therapeutics by the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Hepatitis C virus (HCV) infection affects more than 150 million people worldwide and is one of the leading causes of cirrhosis and hepatocellular carcinoma. Treatment for chronic HCV infection includes combination pegylated-interferon alpha 2a or 2b (PEG-IFN α) and ribavirin (RBV) therapy as well as protease inhibitors. The response rate varies greatly among patients and had been especially low for patients with HCV genotype 1 and 4.  Currently IFNL3 (IL28B) variations (rs12979860 and rs8099917) are the strongest baseline predictor of response to PEG-interferon-α and ribavirin therapy in HCV genotype 1 patients.  Patients with the favorable response genotype (rs12979860 CC) have increased likelihood of response (higher SVR rate) to PEG-IFN α and RBV therapy as compared to patients with unfavorable response genotypes (rs12979860 CT or TT). With protease inhibitor regimens, the IFNL3 genotype predicts response and also predicts eligibility for the shorter durations of therapy. The IFNL3 genotypes along with other clinical and genetic factors may guide patients and clinicians in their treatment decisions. 


For details, see the CPIC guideline, accepted article preview and supplement for IFNL3 (IL28B) and peginterferon alpha based regimens.

For other CPIC guidelines see the list of CPIC publications and guidelines in progress.

 

CYP2D6 genotype and tamoxifen response: meta-analysis of heterogeneous population

Tamoxifen treatment results in substantial inter-individual variability of outcome, possibly due to genetic variability in CYP2D6.  The clinical usefulness of CYP2D6 genotyping for prediction of tamoxifen outcome has not been established because of conflicting reports.   This controversy is well illustrated by secondary analyses from 3 large adjuvant tamoxifen trials (ATAC, BIG 1-98, and ABCSG 8), which came to different conclusions regarding the association between CYP2D6 genotype variants.  The International Tamoxifen Pharmacogenomics Consortium (ITPC) was established to aggregate the worldwide experience regarding CYP2D6 and the clinical outcomes of tamoxifen as adjuvant therapy for breast cancer. It comprises twelve research projects from nine countries and three continents that contributed clinical and genetic data for a total of 4,973 breast cancer patients treated with tamoxifen. Investigators from ITPC performed a meta-analysis on data from 4,973 tamoxifen treated patients and the result was just released in Journal Clinical Pharmacology and Therapeutics. The meta-analysis showed that “CYP2D6 poor metabolizer status was associated with poorer Invasive Disease-Free Survival (IDFS: HR=1.25; 95% CI 1.06, 1.47; P=0.009)” in patients meeting strict criterion. “However, CYP2D6 was not statistically significant when tamoxifen duration, menopausal status, and annual follow-up were not specified (Criterion 2, n=2443; 49%; P=0.25) nor when no exclusions were applied (Criterion 3, n=4935; 99%; P=0.38)”. This study demonstrates the complexity of performing a retrospective biomarker study. The lack of effect in the entire heterogeneous population highlights the need for prospective studies to fully establish the value of CYP2D6 genotyping in tamoxifen therapy.

The data used in the meta-analysis are available for download on the PharmGKB.

 --Blog post written by Li Gong