Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record

Pharmacodynamic Modeling of Warfarin Dosing Algorithm for Cardiovascular Patients in Indonesia: A Tailored Method to Anticoagulation Therapy

Purpose

Warfarin is an anticoagulant drug widely used for treating thromboembolism-related conditions. The main challenge with this drug is the high variability in patients response, which is influenced by both clinical, non-clinical, and genetic factors, such as VKORC1, CYP2C9, and CYP4F2. Therefore, this research aimed to evaluate the impact of clinical and genetic factors on warfarin dose adjustment and to develop a dosing algorithm for patients with cardiovascular disease.

Patients and Methods

A total of 77 research subjects were selected using consecutive sampling based on the inclusion criteria of cardiac outpatients on warfarin for ≥3 months with PT-INR data, complete medical records, and willingness to participate. Exclusion criteria included vitamin K use and inability to follow up. Patients demographic data and clinical characteristics were collected from medical records. Blood samples were obtained for genetic testing of CYP4F2 rs2108622 (sequencing). Statistical analyses included both bivariate and multivariate analyses (logistic regression) with a significance level set at <0.05.

Results

Statistical analysis using the Kruskal-Wallis test showed that the CC, CT, and TT genotypes were significantly associated with warfarin dose (p = 0.02). Furthermore, the Mann-Whitney test results showed that gender did not have a significant relationship with warfarin dose (p = 0.16). The Spearman Rank correlation test showed that age (p = 0.02) and BMI (p = 0.03) had significant relationships with warfarin dose (p < 0.05). However, gender (p = 0.89) had no effect, while age (p = 0.01), BMI (p = 0.01), and genotype (p = 0.01) significantly influenced warfarin dose determination.

Conclusion

In conclusion, the combined contribution of age (8.76%), BMI (7.95%), and CYP4F2 genotype (8.29%) to warfarin dose adjustment was 25%. The linear regression model for predicting warfarin dose was determined to be y = 12.736-0.16*age + 0.55*BMI + 3.55*genotype, where 1 = CC, 2 = CT, and 3 = TT.

Genetic Factors Influencing Warfarin Dose in Han Chinese Population: A Systematic Review and Meta-Analysis of Cohort Studies

OBJECTIVE

To investigate the association of single nucleotide polymorphisms (SNPs) of various genes known to influence mean daily warfarin dose (MDWD) in the Han Chinese population.

METHODS

The study is a systematic review and meta-analysis. Selected studies retrieved by searching Pubmed, Embase (Ovid), Medline, CNKI, Wanfang data, and SinoMed (from their inception to 31 August 2022) for the cohort studies assessing genetic variations that may possibly influence MDWD in Chinese patients were included.

RESULT

A total of 46 studies including a total of 10,102 Han Chinese adult patients were finally included in the meta-analysis. The impact of 20 single nucleotide polymorphisms (SNPs) in 8 genes on MDWD was analyzed. The significant impact of some of these SNPs on MDWD requirements was demonstrated. Patients with CYP4F2 rs2108622 TT, EPHX1 rs2260863 GC, or NQO1 rs1800566 TT genotype required more than 10% higher MDWD. Furthermore, patients with ABCB1 rs2032582 GT or GG, or CALU rs2290228 TT genotype required more than 10% lower MDWD. Subgroup analysis showed that patients with EPHX1 rs2260863 GC genotype required 7% lower MDWD after heart valve replacement (HVR).

CONCLUSION

This is the first systematic review and meta-analysis assessing the association between single nucleotide polymorphisms (SNPs) of various genes known to influence MDWD besides CYP2C9 and VKORC1 in the Han Chinese population. CYP4F2 (rs2108622), GGCX (rs12714145), EPHX1 (rs2292566 and rs2260863), ABCB1 (rs2032582), NQO1 (rs1800566), and CALU (rs2290228) SNPs might be moderate factors affecting MDWD requirements.

REGISTERED INFORMATION

PROSPERO International Prospective Register of Systematic Reviews (CRD42022355130).

Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population

BACKGROUND

Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population.

METHODS

A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles.

RESULTS

We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68).

CONCLUSIONS

Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

Characterization of Reference Materials with an Association for Molecular Pathology Pharmacogenetics Working Group Tier 2 Status: CYP2C9, CYP2C19, VKORC1, CYP2C Cluster Variant, and GGCX: A GeT-RM Collaborative Project

Pharmacogenetic testing is increasingly available from clinical and research laboratories. However, only a limited number of quality control and other reference materials are currently available for many of the variants that are tested. The Association for Molecular Pathology Pharmacogenetic Work Group has published a series of papers recommending alleles for inclusion in clinical testing. Several of the alleles were not considered for tier 1 because of a lack of reference materials. To address this need, the Division of Laboratory Systems, Centers for Disease Control and Prevention-based Genetic Testing Reference Material (GeT-RM) program, in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Institute for Medical Research, has characterized 18 DNA samples derived from Coriell cell lines. DNA samples were distributed to five volunteer testing laboratories for genotyping using three commercially available and laboratory developed tests. Several tier 2 variants, including CYP2C9∗13, CYP2C19∗35, the CYP2C cluster variant (rs12777823), two variants in VKORC1 (rs61742245 and rs72547529) related to warfarin resistance, and two variants in GGCX (rs12714145 and rs11676382) related to clotting factor activation, were identified among these samples. These publicly available materials complement the pharmacogenetic reference materials previously characterized by the GeT-RM program and will support the quality assurance and quality control programs of clinical laboratories that perform pharmacogenetic testing.

Warfarin dosing algorithms: A systematic review

AIMS

Numerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms.

METHODS

We systematically searched MEDLINE up to 20 May 2020 and selected studies describing the development, external validation or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment.

RESULTS

Of 10 035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with <2% of algorithm developments and external validations being rated as having a low risk of bias.

CONCLUSION

Most warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under-served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under-represented populations should be prioritized.

Recommendations for Clinical Warfarin Genotyping Allele Selection: A Report of the Association for Molecular Pathology and the College of American Pathologists

The goal of the Association for Molecular Pathology (AMP) Clinical Practice Committee's AMP Pharmacogenomics (PGx) Working Group is to define the key attributes of PGx alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum panel of variant alleles (tier 1) and an extended panel of variant alleles (tier 2) that will aid clinical laboratories when designing assays for PGx testing. The AMP PGx Working Group considered functional impact of the variants, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations for PGx testing when developing these recommendations. The ultimate goal is to promote standardization of PGx gene/allele testing across clinical laboratories. These recommendations are not to be interpreted as prescriptive but to provide a reference guide. Of note, a separate article with recommendations for CYP2C9 allele selection was previously developed by the PGx Working Group that can be applied broadly to CYP2C9-related medications. The warfarin allele recommendations in this report incorporate the previous CYP2C9 allele recommendations and additional genes and alleles that are specific to warfarin testing.

Role of Genetic Variations in the Hepatic Handling of Drugs

The liver plays a pivotal role in drug handling due to its contribution to the processes of detoxification (phases 0 to 3). In addition, the liver is also an essential organ for the mechanism of action of many families of drugs, such as cholesterol-lowering, antidiabetic, antiviral, anticoagulant, and anticancer agents. Accordingly, the presence of genetic variants affecting a high number of genes expressed in hepatocytes has a critical clinical impact. The present review is not an exhaustive list but a general overview of the most relevant variants of genes involved in detoxification phases. The available information highlights the importance of defining the genomic profile responsible for the hepatic handling of drugs in many ways, such as (i) impaired uptake, (ii) enhanced export, (iii) altered metabolism due to decreased activation of prodrugs or enhanced inactivation of active compounds, and (iv) altered molecular targets located in the liver due to genetic changes or activation/downregulation of alternative/compensatory pathways. In conclusion, the advance in this field of modern pharmacology, which allows one to predict the outcome of the treatments and to develop more effective and selective agents able to overcome the lack of effect associated with the existence of some genetic variants, is required to step forward toward a more personalized medicine.

The Genetics of Warfarin Dose-Response Variability in Africans: An Expert Perspective on Past, Present, and Future

Coumarins such as warfarin are prescribed for prevention and treatment of thromboembolic disorders. Warfarin remains the most widely prescribed and an anticoagulant of choice in Africa. Warfarin use is, however, limited by interindividual variability in pharmacokinetics and a narrow therapeutic index. The difference in patients' pharmacodynamic responses to warfarin has been attributed to genetic variation in warfarin metabolism and molecular targets (e.g., CYP2C9 and VKORC1) and host-environment interactions. This expert review offers a synthesis of human genetics studies in Africans with respect to pharmacogenetics-informed warfarin dosing. We identify areas that need future research attention or could benefit from harnessing existing pharmacogenetics knowledge toward rational and optimal therapeutics with warfarin in African patients. A literature search was conducted until January 2019. A total of 343 articles were retrieved from nine African countries: Botswana, Ethiopia, Egypt, Ghana, Kenya, South Africa, Sudan, Tanzania, and Mozambique. We found 19 studies on genetics of warfarin treatment specifically among Africans. Genes examined included CYP2C9, VKORC1, CYP4F2, APOE, CALU, GGCX, and EPHX1. CYP2C9*2 and *3 alleles were highly frequent among Egyptians, while rare in other African populations. CYP2C9*5, *8, *9, and *11, and VKORC1 Asp36Tyr genetic variants explained warfarin variability in Africans better, compared to CYP2C9*2 and *3. In Africa, there is limited pharmacogenetics data on warfarin. Therefore, future research and funding commitments should be prioritized to ensure safe and effective use of warfarin in Africa. Lessons learned in Africa from the science of pharmacogenetics would inform rational therapeutics in hematology, cardiology, and surgical specialties worldwide.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

The Implementation Chasm Hindering Genome-informed Health Care

The promises of precision medicine are often heralded in the medical and lay literature, but routine integration of genomics in clinical practice is still limited. While the "last mile' infrastructure to bring genomics to the bedside has been demonstrated in some healthcare settings, a number of challenges remain - both in the receptivity of today's health system and in its technical and educational readiness to respond to this evolution in care. To improve the impact of genomics on health and disease management, we will need to integrate both new knowledge and new care processes into existing workflows. This change will be onerous and time-consuming, but hopefully valuable to the provision of high quality, economically feasible care worldwide.

Pharmacogenomics

Genomic medicine, which uses DNA variation to individualise and improve human health, is the subject of this Series of papers. The idea that genetic variation can be used to individualise drug therapy-the topic addressed here-is often viewed as within reach for genomic medicine. We have reviewed general mechanisms underlying variability in drug action, the role of genetic variation in mediating beneficial and adverse effects through variable drug concentrations (pharmacokinetics) and drug actions (pharmacodynamics), available data from clinical trials, and ongoing efforts to implement pharmacogenetics in clinical practice.

Effect of CYP4F2, VKORC1, and CYP2C9 in Influencing Coumarin Dose: A Single-Patient Data Meta-Analysis in More Than 15,000 Individuals

The cytochrome P450 (CYP)4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at the individual patients level to capture the possible effect of ethnicity, gene-gene interaction, or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists of 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95% confidence interval (CI) 7-10%), with a higher effect in women, in patients taking acenocoumarol, and in white patients. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived.

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

Background

Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.

Results

In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins.

Conclusions

The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2693-9) contains supplementary material, which is available to authorized users.

Leveraging Big Data to Transform Drug Discovery

The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.

Pharmacogenetics of warfarin dosing in patients of African and European ancestry

Despite the introduction of direct acting oral anticoagulants, warfarin remains the most commonly prescribed oral anticoagulant. However, warfarin therapy is plagued by the large inter- and intrapatient variability. The variability in dosing fueled research to identify clinical and genetic predictors and develop more accurate dosing algorithms. Observational studies have demonstrated the significant impact of single nucleotide polymorphisms in CYP2C9 and VKORC1 on warfarin dose in patients of European ancestry and African-Americans. This evidence supported the design and conduct of clinical trials to assess whether genotype-guided dosing results in improved anticoagulation control and outcomes. The trial results have shown discordance by race, with pharmacogenetic algorithms improving dose and anticoagulation control among European ancestry patients compared with African-American patients. Herein, we review the evidence from observational and interventional studies, highlight the need for inclusion of minority race groups and propose the need to develop race specific dosing algorithms.

Predicting the need for a reduced drug dose, at first prescription

Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients’ prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford’s clinical data warehouse to construct cohorts of patients that either did or did not need a dose change. After feature selection, we trained models to predict the patients who will (or will not) require a dose change after being prescribed one of 34 drugs across 23 drug classes. Overall, we can predict (AUC ≥ 0.70–0.95) a dose reduction for 23 drugs and 22 drug classes. Several of these drugs are associated with clinical guidelines that recommend dose reduction exclusively in the case of adverse reaction. For these cases, a reduction in dosage may be considered as a surrogate for an adverse reaction, which our system could indirectly help predict and prevent. Our study illustrates the role machine learning may take in providing guidance in setting the starting dose for drugs associated with response variability.

Creating and validating a warfarin pharmacogenetic dosing algorithm for Colombian patients

Purpose

Warfarin is an oral anticoagulant associated with adverse reaction to drugs due to wide inter- and intra-individual dosage variability. Warfarin dosage has been related to non-genetic and genetic factors. CYP2C9 and VKORC1 gene polymorphisms affect warfarin metabolism and dosage. Due to the central role of populations’ ethnical and genetic origin on warfarin dosage variability, novel algorithms for Latin American subgroups are necessary to establish safe anticoagulation therapy.

Patients and methods

We genotyped CYP2C9*2 (c.430C > T), CYP2C9*3 (c.1075A > C), CYP4F2 (c.1297G > A), and VKORC1 (−1639 G > A) polymorphisms in 152 Colombian patients who received warfarin. We evaluated the impact on the variability of patients’ warfarin dose requirements. Multiple linear regression analysis, using genetic and non-genetic variables, was used for creating an algorithm for optimal warfarin maintenance dose.

Results

Median weekly prescribed warfarin dosage was significantly lower in patients having the VKORC1-1639 AA genotype and poor CYP2C9*2/*2,*2/*3 metabolizers than their wild-type counterparts. We found a 2.3-fold increase in mean dose for normal sensitivity patients (wild-type VKORC1/CYP2C9 genotypes) compared to the other groups (moderate and high sensitivity); 31.5% of the patients in our study group had warfarin sensitivity-related genotypes. The estimated regression equation accounted for 44.4% of overall variability in regard to warfarin maintenance dose. The algorithm was validated, giving 45.9% correlation (R²=0.459).

Conclusion

Our results describe and validate the first algorithm for predicting warfarin maintenance in a Colombian mestizo population and have contributed toward the understanding of pharmacogenetics in a Latin American population subgroup.

Quality of anticoagulation control and hemorrhage risk among African American and European American warfarin users

OBJECTIVE

We evaluated whether percent time in target range (PTTR), risk of over-anticoagulation [international normalized ratio (INR)>4], and risk of hemorrhage differ by race. As PTTR is a strong predictor of hemorrhage risk, we also determined the influence of PTTR on the risk of hemorrhage by race.

PARTICIPANTS AND METHODS

Among 1326 warfarin users, PTTR was calculated as the percentage of interpolated INR values within the target range of 2.0-3.0. PTTR was also categorized as poor (PTTR<60%), good (60≤PTTR<70%), or excellent (PTTR≥70%) anticoagulation control. Over-anticoagulation was defined as INR more than 4 and major hemorrhages included serious, life-threatening, and fatal bleeding episodes. Logistic regression and survival analyses were carried out to evaluate the association of race with PTTR (≥60 vs. <60) and major hemorrhages, respectively.

RESULTS

Compared with African Americans, European Americans had higher PTTR (57.6 vs. 49.1%; P<0.0001) and were more likely to attain 60≤PTTR<70% (22.9 vs. 13.1%; P<0.001) or PTTR of at least 70% (26.9 vs. 18.2%; P=0.001). Older (>65 years) patients without venous thromboembolism indication and chronic kidney disease were more likely to attain PTTR of at least 60%. After accounting for clinical and genetic factors, and PTTR, African Americans had a higher risk of hemorrhage [hazard ratio (HR)=1.58; 95% confidence interval (CI): 1.04-2.41; P=0.034]. Patients with 60≤PTTR<70% (HR=0.62; 95% CI: 0.38-1.02; P=0.058) and PTTR of at least 70% (HR=0.27; 95% CI: 0.15-0.49; P<0.001) had a lower risk of hemorrhage compared with those with PTTR less than 60%.

CONCLUSION

Despite the provision of warfarin management through anticoagulation clinics, African Americans achieve a lower overall PTTR and have a significantly higher risk of hemorrhage. Personalized medicine interventions tailored to African American warfarin users need to be developed.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.

VKORC1-1639A allele influences warfarin maintenance dosage among Blacks receiving warfarin anticoagulation: a retrospective cohort study

AIM

The study objectives were to investigate the association between selected CYP2C9 and VKORC1 single nucleotide polymorphisms with serious bleeding or thrombotic risk, and to estimate mean daily maintenance dose of warfarin and international normalized ratio measurements among Blacks receiving warfarin anticoagulation.

METHODS

We conducted a retrospective cohort study among 230 Black adults receiving warfarin for a minimum of three consecutive months with a confirmed date of first dosage.

RESULTS

A lower mean daily maintenance dosage of warfarin was required to maintain an international normalized ratio measurement within the therapeutic range among Blacks with the VKORC1-1639G>A variant alleles ([G/A vs G/G, p = 0.02], [A/A vs G/A, p = 0.008] and [A/A vs G/G, p = 0.001]).

CONCLUSION

Data indicated that VKORC1-1639A variant allele influenced warfarin daily maintenance dosage among our small, likely admixed Black patient population.

Pharmacogenetics in Cardiovascular Medicine

PURPOSE OF REVIEW

Pharmacogenetics is an important component of precision medicine. Even within the genomic era, several challenges lie ahead in the road towards clinical implementation of pharmacogenetics in the clinic. This review will summarize the current state of knowledge regarding pharmacogenetics of cardiovascular drugs, focusing on those with the most evidence supporting clinical implementation- clopidogrel, warfarin and simvastatin.

RECENT FINDINGS

There is limited translation of pharmacogenetics into clinical practice primarily due to the absence of outcomes data from prospective, randomized, genotype-directed clinical trials. There are several ongoing randomized controlled trials that will provide some answers as to the clinical utility of genotype-directed strategies. Several academic medical centers have pushed towards clinical implementation where the clinical validity data are strong. Their experiences will inform operational requirements of a clinical pharmacogenetics testing including the timing of testing, incorporation of test results into the electronic health record, reimbursement and ethical issues.

SUMMARY

Pharmacogenetics of clopidogrel, warfarin and simvastatin are three examples where pharmacogenetics testing may provide added clinical value. Continued accumulation of evidence surrounding clinical utility of pharmacogenetics markers is imperative as this will inform reimbursement policy and drive adoption of pharamcogenetics into routine care.

Warfarin Pharmacogenomics in Diverse Populations

Genotype-guided warfarin dosing algorithms are a rational approach to optimize warfarin dosing and potentially reduce adverse drug events. Diverse populations, such as African Americans and Latinos, have greater variability in warfarin dose requirements and are at greater risk for experiencing warfarin-related adverse events compared with individuals of European ancestry. Although these data suggest that patients of diverse populations may benefit from improved warfarin dose estimation, the vast majority of literature on genotype-guided warfarin dosing, including data from prospective randomized trials, is in populations of European ancestry. Despite differing frequencies of variants by race/ethnicity, most evidence in diverse populations evaluates variants that are most common in populations of European ancestry. Algorithms that do not include variants important across race/ethnic groups are unlikely to benefit diverse populations. In some race/ethnic groups, development of race-specific or admixture-based algorithms may facilitate improved genotype-guided warfarin dosing algorithms above and beyond that seen in individuals of European ancestry. These observations should be considered in the interpretation of literature evaluating the clinical utility of genotype-guided warfarin dosing. Careful consideration of race/ethnicity and additional evidence focused on improving warfarin dosing algorithms across race/ethnic groups will be necessary for successful clinical implementation of warfarin pharmacogenomics. The evidence for warfarin pharmacogenomics has a broad significance for pharmacogenomic testing, emphasizing the consideration of race/ethnicity in discovery of gene-drug pairs and development of clinical recommendations for pharmacogenetic testing.

Genetic determinants of variability in warfarin response after the dose-titration phase

OBJECTIVES

Genetic factors contribute considerably toward variability in warfarin dose requirements and are important in the dose-titration phase; their effects on the stability of anticoagulation later in therapy are not known.

METHODS

Using deidentified electronic medical records linked to a DNA-biobank, we studied 140 African-Americans and 943 European-Americans after the warfarin dose-titration phase. We genotyped 12 single nucleotide polymorphisms in genes (CYP2C9, VKORC1, CYP4F2, GGCX, EPHX1, CALU) associated with altered warfarin dose requirements and tested their associations with international normalized ratio variability (INRVAR) and percent time in therapeutic range in European-Americans and African-Americans.

RESULTS

One allele copy of rs2108622 in CYP4F2 was associated with a 15% [95% confidence interval (CI): 1-26, P=0.03] decrease in the median INRVAR in European-Americans. In African-Americans, GGCX variants rs11676382 and rs699664 were associated with 4.16-fold (95% CI: 1.45-11.97, P=0.009) and 1.50-fold (95% CI: 1.07-2.08, P=0.02) changes in the median INRVAR per variant allele copy, respectively; rs11676382 was also significantly associated with a 23.19% (95% CI: 5.89-40.48, P=0.01) decrease in time in therapeutic range. The total variation in INRVAR explained by both clinical factors and rs2108622 was 5.2% for European-Americans. In African-Americans, the inclusion of GGCX variants rs11676382 and rs699664, and the CYP2C9*8 variant rs7900194 explained ∼29% of the variation in INRVAR.

CONCLUSION

The stability of anticoagulation after the warfarin dose-titration phase is differentially affected by variants in CYP4F2 in European-Americans and GGCX loci in African-Americans.

STRATEGIES FOR EQUITABLE PHARMACOGENOMIC-GUIDED WARFARIN DOSING AMONG EUROPEAN AND AFRICAN AMERICAN INDIVIDUALS IN A CLINICAL POPULATION

The blood thinner warfarin has a narrow therapeutic range and high inter- and intra-patient variability in therapeutic doses. Several studies have shown that pharmacogenomic variants help predict stable warfarin dosing. However, retrospective and randomized controlled trials that employ dosing algorithms incorporating pharmacogenomic variants under perform in African Americans. This study sought to determine if: 1) including additional variants associated with warfarin dose in African Americans, 2) predicting within single ancestry groups rather than a combined population, or 3) using percentage African ancestry rather than observed race, would improve warfarin dosing algorithms in African Americans. Using BioVU, the Vanderbilt University Medical Center biobank linked to electronic medical records, we compared 25 modeling strategies to existing algorithms using a cohort of 2,181 warfarin users (1,928 whites, 253 blacks). We found that approaches incorporating additional variants increased model accuracy, but not in clinically significant ways. Race stratification increased model fidelity for African Americans, but the improvement was small and not likely to be clinically significant. Use of percent African ancestry improved model fit in the context of race misclassification.

Frequency of Common VKORC1 Polymorphisms and Their Impact on Warfarin Dose Requirement in Pakistani Population

Polymorphisms in vitamin K epoxide reductase complex subunit 1 (VKORC1) gene lead to interindividual variability in warfarin dose requirement. The characterization of genotype frequency distribution is required in different populations for construction of customized dosing algorithms to enhance the efficacy and reduce the toxicity of warfarin therapy. This study was carried out in Pakistani population to evaluate the contribution of common VKORC1 polymorphisms to warfarin therapy. A total of 550 stable patients taking warfarin were enrolled after medical history, physical examination, and laboratory investigations. Single blood sample was collected after informed consent. Genomic DNA was extracted and genotype analysis for VKORC1 1173C>T and VKORC1-1639G>A polymorphisms was done by polymerase chain reaction-restriction fragment length polymorphism assay. A number of samples were also analyzed by direct DNA sequencing for validation of results. Data were analyzed using SPSS version 20. Genotype frequency distributions of VKORC1 1173C>T and VKORC1-1639G>A were found to be different from other populations. Both of these polymorphisms did not demonstrate significant effect on warfarin dose requirement. Although Cytochrome P450 2C9 (CYP2C9) and VKORC1 polymorphisms together attributed only 3.8% variability in warfarin dose but it was statistically significant ( p value = .004). It is concluded that there is a need to study genotype frequency distribution and their effect on warfarin dose variability among different populations due to diversity in outcome. At the same time, no effect on warfarin dose variation explained by VKORC1 polymorphisms and small variability explained by studied genotypes stresses the need for exploration of more genetic and nongenetic factors in Pakistani population.

Pharmacogenetic-guided Warfarin Dosing Algorithm in African-Americans

We aim to develop warfarin dosing algorithm for African-Americans. We explored demographic, clinical, and genetic data from a previously collected cohort of 163 African-American patients with a stable warfarin dose. We explored 2 approaches to develop the algorithm: multiple linear regression and artificial neural network (ANN). The clinical significance of the 2 dosing algorithms was evaluated by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual dose. Linear regression model and ANN model predicted the ideal dose in 52% and 48% of the patients, respectively. The mean absolute error using linear regression model was estimated to be 10.8 mg compared with 10.9 mg using ANN. Linear regression and ANN models identified several predictors of warfarin dose including age, weight, CYP2C9 genotype *1/*1, VKORC1 genotype, rs12777823 genotype, rs2108622 genotype, congestive heart failure, and amiodarone use. In conclusion, we developed a warfarin dosing algorithm for African-Americans. The proposed dosing algorithm has the potential to recommend warfarin doses that are close to the appropriate doses. The use of more sophisticated ANN approach did not result in improved predictive performance of the dosing algorithm except for patients of a dose of ≥49 mg/wk.

Phenome-Wide Association Studies as a Tool to Advance Precision Medicine

Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health records (EHRs) made possible genome-phenome studies (i.e., comparative analyses of genetic variants and phenotypes) using only data collected as a by-product of typical health care. In addition to disease and trait genetics, EHRs proved a valuable resource for analyzing pharmacogenetic traits and developing reverse genetics approaches such as phenome-wide association studies (PheWASs). PheWASs are designed to survey which of many phenotypes may be associated with a given genetic variant. PheWAS methods have been validated through replication of hundreds of known genotype-phenotype associations, and their use has differentiated between true pleiotropy and clinical comorbidity, added context to genetic discoveries, and helped define disease subtypes, and may also help repurpose medications. PheWAS methods have also proven to be useful with research-collected data. Future efforts that integrate broad, robust collection of phenotype data (e.g., EHR data) with purpose-collected research data in combination with a greater understanding of EHR data will create a rich resource for increasingly more efficient and detailed genome-phenome analysis to usher in new discoveries in precision medicine.

An expanded pharmacogenomics warfarin dosing table with utility in generalised dosing guidance

Pharmacogenomics (PGx) guided warfarin dosing, using a comprehensive dosing algorithm, is expected to improve dose optimisation and lower the risk of adverse drug reactions. As a complementary tool, a simple genotype-dosing table, such as in the US Food and Drug Administration (FDA) Coumadin drug label, may be utilised for general risk assessment of likely over- or under-anticoagulation on a standard dose of warfarin. This tool may be used as part of the clinical decision support for the interpretation of genetic data, serving as a first step in the anticoagulation therapy decision making process. Here we used a publicly available warfarin dosing calculator (www.warfarindosing.org) to create an expanded gene-based warfarin dosing table, the CPMC-WD table that includes nine genetic variants in CYP2C9, VKORC1, and CYP4F2. Using two datasets, a European American cohort (EUA, n=73) and the Quebec Warfarin Cohort (QWC, n=769), we show that the CPMC-WD table more accurately predicts therapeutic dose than the FDA table (51 % vs 33 %, respectively, in the EUA, McNemar's two-sided p=0.02; 52 % vs 37 % in the QWC, p<1×10(-6)). It also outperforms both the standard of care 5 mg/day dosing (51 % vs 34 % in the EUA, p=0.04; 52 % vs 31 % in the QWC, p<1×10(-6)) as well as a clinical-only algorithm (51 % vs 38 % in the EUA, trend p=0.11; 52 % vs 45 % in the QWC, p=0.003). This table offers a valuable update to the PGx dosing guideline in the drug label.

Warfarin Pharmacogenetics Reevaluated: Subgroup Analysis Reveals a Likely Underestimation of the Maximum Pharmacogenetic Benefit by Clinical Trials

OBJECTIVES

Various patient subgroups were examined to determine which ones obtain the largest pharmacogenetic improvements in warfarin dose accuracy. Subgrouping schemes of recent clinical trials were analyzed for comparison.

METHODS

The accuracy of a pharmacogenetic dose algorithm was determined retrospectively in comparison to that of a clinical algorithm in subgroups of the International Warfarin Pharmacogenetics Consortium (IWPC) patient database (n = 2,274) and of newly studied clinic patients (n = 146).

RESULTS

White patients with low-dose genotypes (*1*3/AA, *2*2/AA, *2*3/GA, *2*3/AA, *3*3/GG, *3*3/GA, and *3*3/AA) achieved the largest pharmacogenetic improvements in warfarin dose accuracy. Mean absolute dosing error (MAE) in this subgroup of IWPC and newly studied patients was reduced 75.7% and 89.7%, respectively. White IWPC patients with >2 variants or ≥2 mg/day absolute difference between pharmacogenetic and clinical dose predictions obtained MAE reductions of 71.1% and 65.3%, respectively. By comparison, unstratified populations and subgroups of a major clinical trial, when replicated in IWPC patients, obtained smaller MAE reductions of 31.8% to 48.2%. Blacks and Asians obtained substantially smaller dose accuracy improvements overall than whites.

CONCLUSIONS

Patient subgroups were identified that obtained the largest pharmacogenetic improvements in warfarin dose accuracy. These subgroups have not been analyzed in clinical trials to date, likely resulting in underestimation of the pharmacogenetic benefit.

Influence of the CYP4F2 polymorphism on the risk of hemorrhagic complications in coumarin-treated patients

Objectives:

To evaluate the impact of the CYP4F2 polymorphism on bleeding complications and over-anticoagulation due to coumarin.

Methods:

A comprehensive literature search was performed to look for eligible studies published prior to February 2015 in EMBASE and PubMed. References were strictly identified by inclusion and exclusion criteria, and authors of primary studies were consulted for additional information and data. Revman 5.3 software was used to analyze the impact of the CYP4F2 polymorphism on hemorrhagic complications and over-anticoagulation events (international normalized ratio >4).

Results:

Eight studies involving 3,101 samples met the specified inclusion criteria. Compared with wild-type homozygotes (CYP4F2*1*1), carriers of the CYP4F2*3 variant had no significant effects on total bleeding events (odds ratio [OR]: 0.86; 95% confidence interval [CI]: 0.71-1.05; p=0.15), major hemorrhage complications in coumarin users (OR: 0.80; 95% CI: 0.64-1.01; p=0.06). Patients carried CYP4F2*3 also had nonsignificant associations with the risk of over-anticoagulation (relative risk [RR]: 079; 95% CI: 0.59-1.06; p=0.12). We found a lower risk in patients with homozygotes for CYP4F2*3, but there was no statistical significance (RR: 0.66; 95% CI: 0.43-1.01; p=0.05).

Conclusion:

This meta-analysis indicated the impact of the CYP4F2 polymorphism on bleeding complications and over-anticoagulation in coumarin-treated patients failed to reach the level of statistical significance. However, large-scale and well designed studies are necessary to determine conclusively the association between the CYP4F2 polymorphism and hemorrhage risk.

Integrating electronic health record genotype and phenotype datasets to transform patient care

The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 mandates the development and implementation of electronic health record (EHR) systems across the country. While a primary goal is to improve the care of individual patients, EHRs are also key enabling resources for a vision of individualized (or personalized or precision) medicine: the aggregation of multiple EHRs within or across healthcare systems should allow discovery of patient subsets that have unusual and definable clinical trajectories that deviate importantly from the expected response in a "typical" patient. The spectrum of such personalized care can then extend from prevention to choice of medication to intensity or nature of follow-up.

Novel regulatory variant detected on the VKORC1 haplotype that is associated with warfarin dose

AIM

Warfarin dose requirement is associated with VKORC1 rs9923231, and we studied whether it is a functional variant.

MATERIALS & METHODS

We selected variants in linkage disequilibrium with rs9923231 that bind transcription factors in an allele-specific way. Representative haplotypes were cloned or constructed, nuclear protein binding and transcriptional activity were evaluated.

RESULTS

rs56314408C>T and rs2032915C>T were detected in a liver enhancer in linkage disequilibrium with rs9923231. The rs56314408-rs2032915 C-C haplotype preferentially bound nuclear proteins and had higher transcriptional activity than T-T and the African-specific T-C. A motif for TFAP2A/C was disrupted by rs56314408T. No difference in transcriptional activity was detected for rs9923231G>A.

CONCLUSION

Our results supported an activating role for rs56314408C, while rs9923231G>A had no evidence of being functional.

Role of genomics in eliminating health disparities

The Texas Center for Health Disparities, a National Institute on Minority Health and Health Disparities Center of Excellence, presents an annual conference to discuss prevention, awareness education, and ongoing research about health disparities both in Texas and among the national population. The 2014 Annual Texas Conference on Health Disparities brought together experts in research, patient care, and community outreach on the “Role of Genomics in Eliminating Health Disparities.” Rapid advances in genomics and pharmacogenomics are leading the field of medicine to use genetics and genetic risk to build personalized or individualized medicine strategies. We are at a critical juncture of ensuring such rapid advances benefit diverse populations. Relatively few forums have been organized around the theme of the role of genomics in eliminating health disparities. The conference consisted of three sessions addressing “Gene-Environment Interactions and Health Disparities,” “Personalized Medicine and Elimination of Health Disparities,” and “Ethics and Public Policy in the Genomic Era.” This article summarizes the basic science, clinical correlates, and public health data presented by the speakers.

Poor warfarin dose prediction with pharmacogenetic algorithms that exclude genotypes important for African Americans

OBJECTIVES

Recent clinical trial data cast doubt on the utility of genotype-guided warfarin dosing, specifically showing worse dosing with a pharmacogenetic versus clinical dosing algorithm in African Americans. However, many genotypes important in African Americans were not accounted for. We aimed to determine whether omission of the CYP2C9*5, CYP2C9*6, CYP2C9*8, CYP2C9*11 alleles and rs12777823 G > A genotype affects performance of dosing algorithms in African Americans.

METHODS

In a cohort of 274 warfarin-treated African Americans, we examined the association between the CYP2C9*5, CYP2C9*6, CYP2C9*8, CYP2C9*11 alleles and rs12777823 G > A genotype and warfarin dose prediction error with pharmacogenetic algorithms used in clinical trials.

RESULTS

The http://www.warfarindosing.org algorithm overestimated doses by a median (interquartile range) of 1.2 (0.02-2.6) mg/day in rs12777823 heterozygotes (P<0.001 for predicted vs. observed dose), 2.0 (0.6-2.8) mg/day in rs12777823 variant homozygotes (P = 0.004), and 2.2 (0.5-2.9) mg/day in carriers of a CYP2C9 variant (P < 0.001). The International Warfarin Pharmacogenetics Consortium (IWPC) algorithm underdosed warfarin by 0.8 (-2.3 to 0.4) mg/day for patients with the rs12777823 GG genotype (P < 0.001) and overdosed warfarin by 0.7 (-0.4 to 1.9) mg/day in carriers of a variant CYP2C9 allele (P = 0.04). Modifying the http://www.warfarindosing.org algorithm to adjust for variants important in African Americans led to better dose prediction than either the original http://www.warfarindosing.org (P < 0.01) or IWPC (P < 0.01) algorithm.

CONCLUSION

These data suggest that, when providing genotype-guided warfarin dosing, failure to account for variants important in African Americans leads to significant dosing error in this population.

Desiderata for computable representations of electronic health records-driven phenotype algorithms

BACKGROUND

Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).

METHODS

A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.

RESULTS

We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.

CONCLUSION

A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Genotype and risk of major bleeding during warfarin treatment

AIM

To determine whether genetic variants associated with warfarin dose variability were associated with increased risk of major bleeding during warfarin therapy.

MATERIALS & METHODS

Using Vanderbilt's DNA biobank we compared the prevalence of CYP2C9, VKORC1 and CYP4F2 variants in 250 cases with major bleeding and 259 controls during warfarin therapy.

RESULTS

CYP2C9*3 was the only allele that differed significantly among cases (14.2%) and controls (7.8%; p = 0.022). In the 214 (85.6%) cases with a major bleed 30 or more days after warfarin initiation, CYP2C9*3 was the only variant associated with bleeding (adjusted odds ratio: 2.05; 95% CI: 1.04, 4.04).

CONCLUSION

The CYP2C9*3 allele may double the risk of major bleeding among patients taking warfarin for 30 or more days.

Cardiovascular pharmacogenomics: current status and future directions

Drugs are widely used and highly effective in the treatment of heart disease. Nevertheless, in some instances, even drugs effective in a population display lack of efficacy or adverse drug reactions in individual patients, often in an apparently unpredictable fashion. This review summarizes the genomic factors now known to influence variability in responses to widely used cardiovascular drugs such as clopidogrel, warfarin, heparin and statins. Genomic approaches being used to discover new pathways in common cardiovascular diseases and thus potential new targets for drug development are described. Finally, the way in which this new information is likely to be used in an electronic medical record environment is discussed.

Extracting Primary Open-Angle Glaucoma from Electronic Medical Records for Genetic Association Studies

Electronic medical records (EMRs) are being widely implemented for use in genetic and genomic studies. As a phenotypic rich resource, EMRs provide researchers with the opportunity to identify disease cohorts and perform genotype-phenotype association studies. The Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study, has genotyped more than 15,000 individuals of diverse genetic ancestry in BioVU, the Vanderbilt University Medical Center’s biorepository linked to a de-identified version of the EMR (EAGLE BioVU). Here we develop and deploy an algorithm utilizing data mining techniques to identify primary open-angle glaucoma (POAG) in African Americans from EAGLE BioVU for genetic association studies. The algorithm described here was designed using a combination of diagnostic codes, current procedural terminology billing codes, and free text searches to identify POAG status in situations where gold-standard digital photography cannot be accessed. The case algorithm identified 267 potential POAG subjects but underperformed after manual review with a positive predictive value of 51.6% and an accuracy of 76.3%. The control algorithm identified controls with a negative predictive value of 98.3%. Although the case algorithm requires more downstream manual review for use in large-scale studies, it provides a basis by which to extract a specific clinical subtype of glaucoma from EMRs in the absence of digital photographs.

Race influences warfarin dose changes associated with genetic factors

Warfarin dosing algorithms adjust for race, assigning a fixed effect size to each predictor, thereby attenuating the differential effect by race. Attenuation likely occurs in both race groups but may be more pronounced in the less-represented race group. Therefore, we evaluated whether the effect of clinical (age, body surface area [BSA], chronic kidney disease [CKD], and amiodarone use) and genetic factors (CYP2C9*2, *3, *5, *6, *11, rs12777823, VKORC1, and CYP4F2) on warfarin dose differs by race using regression analyses among 1357 patients enrolled in a prospective cohort study and compared predictive ability of race-combined vs race-stratified models. Differential effect of predictors by race was assessed using predictor-race interactions in race-combined analyses. Warfarin dose was influenced by age, BSA, CKD, amiodarone use, and CYP2C9*3 and VKORC1 variants in both races, by CYP2C9*2 and CYP4F2 variants in European Americans, and by rs12777823 in African Americans. CYP2C9*2 was associated with a lower dose only among European Americans (20.6% vs 3.0%, P < .001) and rs12777823 only among African Americans (12.3% vs 2.3%, P = .006). Although VKORC1 was associated with dose decrease in both races, the proportional decrease was higher among European Americans (28.9% vs 19.9%, P = .003) compared with African Americans. Race-stratified analysis improved dose prediction in both race groups compared with race-combined analysis. We demonstrate that the effect of predictors on warfarin dose differs by race, which may explain divergent findings reported by recent warfarin pharmacogenetic trials. We recommend that warfarin dosing algorithms should be stratified by race rather than adjusted for race.

Extracting research-quality phenotypes from electronic health records to support precision medicine

The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.

Development of phenotype algorithms using electronic medical records and incorporating natural language processing

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

Biobanks and electronic medical records: enabling cost-effective research

The use of electronic medical record data linked to biological specimens in health care settings is expected to enable cost-effective and rapid genomic analyses. Here, we present a model that highlights potential advantages for genomic discovery and describe the operational infrastructure that facilitated multiple simultaneous discovery efforts.

Size matters: how population size influences genotype-phenotype association studies in anonymized data

OBJECTIVE

Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.

METHODS

We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.

RESULTS

Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.

CONCLUSIONS

This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans

The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes (≤ 35 and ≥ 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 × 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 × 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.

A pharmacogenetics-based warfarin maintenance dosing algorithm from Northern Chinese patients

Inconsistent associations with warfarin dose were observed in genetic variants except VKORC1 haplotype and CYP2C9*3 in Chinese people, and few studies on warfarin dose algorithm was performed in a large Chinese Han population lived in Northern China. Of 787 consenting patients with heart-valve replacements who were receiving long-term warfarin maintenance therapy, 20 related Single nucleotide polymorphisms were genotyped. Only VKORC1 and CYP2C9 SNPs were observed to be significantly associated with warfarin dose. In the derivation cohort (n = 551), warfarin dose variability was influenced, in decreasing order, by VKORC1 rs7294 (27.3%), CYP2C9*3(7.0%), body surface area(4.2%), age(2.7%), target INR(1.4%), CYP4F2 rs2108622 (0.7%), amiodarone use(0.6%), diabetes mellitus(0.6%), and digoxin use(0.5%), which account for 45.1% of the warfarin dose variability. In the validation cohort (n = 236), the actual maintenance dose was significantly correlated with predicted dose (r = 0.609, P<0.001). Our algorithm could improve the personalized management of warfarin use in Northern Chinese patients.

Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality.

Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index.

Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR.

Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.