Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process

PharmVar GeneFocus: CYP2C19

The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C19 gene. CYP2C19 genetic variation impacts the metabolism of many drugs and has been associated with both efficacy and safety issues for several commonly prescribed medications. This GeneFocus provides a comprehensive overview and summary of CYP2C19 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records

Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.

Pharmacist's attitudes and knowledge of pharmacogenomics and the factors that may predict future engagement

BACKGROUND

While pharmacists are well positioned to implement pharmacogenomic testing in healthcare systems, uptake has been limited.

OBJECTIVE

The primary objective of this survey was to determine how post-graduate education and training influences pharmacist's knowledge and attitudes of pharmacogenomic testing.

METHODS

Survey questions were developed by the study team, and responses were collected electronically using REDCap™. The electronic survey was sent to all pharmacists (n=161) within a large, multi-state healthcare system by email.

RESULTS

A total of 75 (47%) respondents completed all aspects of the survey. The majority of respondents were female (60%), worked in acute care settings (57%), were full-time employees (80%), and worked in an urban area (85%), with many graduating in or after 2010 (43%). For post-graduate education, 36% of respondents completed a Post-Graduate Year One Residency (PGY-1), and 27% had a board certification. Those that completed a PGY-1 residency were significantly more likely to have received formal training or education on pharmacogenomics than those who had not. They also assessed their own knowledge of pharmacogenomic resources and guidelines higher than those without PGY-1 training. More recent graduates were also significantly more likely to have received formal training or education on pharmacogenomics. Additionally, pharmacists who completed a PGY-1 residency were more likely to respond favorably to pharmacogenomics being offered through pharmacy services. Pharmacists with board certification were more comfortable interpreting results of a pharmacogenomic test than those without board certification.

CONCLUSIONS

Pharmacists who have completed a PGY-1 residency or received board certification appear more comfortable with interpretation and implementation of pharmacogenomic testing.

Pharmacogenetic testing in psychiatric inpatients with polypharmacy is associated with decreased medication side effects but not via medication changes

In psychiatric patients, medication adverse effects are regularly attributed to psychosomatic causes. However, many psychotropic medications are metabolized by cytochrome P450 (CYP450) enzymes. In the setting of polypharmacy, the activity of these enzymes may produce unfavorable drug-drug interactions (DDI) and drug-genotype interactions (DGI) that contribute to morbidity and mortality. This study sought to estimate the risk of adverse DDI and DGI in psychiatric inpatients with polypharmacy. We assessed whether medication changes made after pharmacogenetics (PGx) testing correlated with changes in side effects and overall improvement. Adult psychiatry inpatients with polypharmacy, defined as 5 or more scheduled prescription medications, completed the 24-item Antidepressant Side Effect Checklist (ASEC) questionnaire on enrollment and underwent PGx testing. Analysis of PGx results focused on whether the CYP2D6 and CYP2C19 phenotypes were "extreme," defined as poor, poor to intermediate, or ultrarapid. Approximately 30 days after PGx results were sent to outpatient providers, patients were contacted to obtain their current medication list and ASEC and Clinical Global Impression Improvement (CGI-I) scores. A total of 80 patients were enrolled, and 52 (65%) completed follow-up. ASEC scores improved from 11.5 (±8.1) to 7.2 (±6.0) (p = 0.0009). Mean CGI-I score was 2.7 (±1.4), between "minimal" to "much improved." However, linear regression revealed that these improvements were not correlated with whether medications were changed. We concluded that the impact of drug-genotype interactions in this small sample of inpatients with polypharmacy was low, and that patient improvement was related not to PGx-guided medication changes but to other treatments during hospitalization.

PARC report: a perspective on the state of clinical pharmacogenomics testing

In this Perspective, the authors discuss the state of pharmacogenomics testing addressing a number of advances, challenges and barriers, including legal ramifications, changes to the regulatory landscape, coverage of testing and the implications of direct-to-consumer genetic testing on the provision of care to patients. Patient attitudes toward pharmacogenomics testing and associated costs will play an increasingly important role in test acquisition and subsequent utilization in a clinical setting. Additional key steps needed include: further research trials demonstrating clinical utility and cost–effectiveness of pharmacogenetic testing, evidence review to better integrate genomic information into clinical practice guidelines in target therapeutic areas to help providers identify patients that may benefit from pharmacogenetic testing and engagement with payers to create a path to reimbursement for pharmacogenetic tests that currently have sufficient evidence of clinical utility. Increased adoption of testing by payers and improved reimbursement practices will be needed to overcome barriers, especially as the healthcare landscape continues to shift toward a system of value-based care.

Assessment of healthcare professionals' knowledge, attitudes, and perceived challenges of clinical pharmacogenetic testing in Egypt

Aim: We evaluated healthcare practitioners' perspectives regarding clinical pharmacogenetics in Cairo, Egypt. Materials & methods: We administered a paper-based survey to pharmacists and physicians practicing at Children's Cancer Hospital Egypt. The survey assessed practitioners' knowledge, attitudes, and perspectives about pharmacogenetic testing. Results: The study included 184 respondents (67.9% pharmacists; 32.1% physicians. Overall, the pharmacogenetic knowledge was low (mean = 41.7%) but attitudes toward pharmacogenetic testing and its potential clinical application were generally positive. Pharmacists responded more favorably than physicians to statements attributing the responsibility of applying pharmacogenetics in the clinical setting to their profession. However, several challenges were identified; the most common being: lack of pharmacogenetic knowledge and skill, lack of pharmacogenetic testing devices, and limited funding. Conclusion: Future efforts to promote pharmacogenetic implementation should focus on foundational education, practical training, and exploration of potential funding sources.

Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

Motivation

The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.

Results

To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.

Availability and implementation

LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic and phenotypic frequency distribution of CYP2C9, CYP2C19 and CYP2D6 in over 3200 Han Chinese

PURPOSE

This retrospective study analyzed the polymorphisms and phenotypic frequencies of CYP2C9, CYP2C19 and CYP2D6 in a Han Chinese population.

METHODS

Tests for polymorphisms of CYP2C9, CYP2C19 and CYP2D6 were performed in over 3000 (3099-3931) samples using an Illumina HiSeq X Ten sequencer. Following the guidance of the PharmGKB and PharmVar databases, the polymorphisms of CYP2C9, CYP2C19 and CYP2D6 were transformed into phenotypes, which included ultrarapid metabolizers (UMs), rapid metabolizers (RMs), normal metabolizers (NMs), intermediate metabolizers (IMs) and poor metabolizers (PMs).

RESULTS

A total of 3122 samples were tested for polymorphisms in CYP2C9 and the overall polymorphism frequency was found to be 8.8%; the phenotypic frequency for CYP2C9 was 91.2% NMs, 8.23% IMs and 0.16%, PMs. The overall polymorphism frequency of CYP2C19 was tested in 3099 samples and found to be 60.1%; the phenotypic frequency for CYP2C19 was 39.9% NMs, with 1.06% RMs, 45.62% IMs and 13.42% PMs. The overall polymorphism frequency of CYP2D6 was tested in 3931 samples and found to be 88.04%; the phenotypic frequency of CYP2D6 was 95.43% NMs, 3.35% IMs and 0.52% PMs. Using 2690 samples, the polymorphisms and phenotypic distributions of CYP2C9, CYP2C19 and CYP2D6 were examined simultaneously. We found that 96.36% of the samples contained mutations while 66.51% corresponded with phenotypic changes.

CONCLUSIONS

Polymorphisms and phenotypic changes of CYP2C9, CYP2C19 and CYP2D6 are relatively frequent in the Han Chinese population. Thus, preemptive pharmacogenetic testing of CYP2C9, CYP2C19 and CYP2D6 should be recommended prior to dosing substrate drugs.

Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care

Providing maximal therapeutic efficacy without toxicity is a universal goal of rational drug therapy. However, substantial between-patient variability in drug response often impedes such successful treatments and brings the necessity of tailoring drug dose to individual needs for more precise therapy. In many cases plenty of patient characteristics, such as body size, genetic makeup and environmental factors, need to be taken into consideration to find the optimal dose in clinical practice. A pharmacokinetics and pharmacodynamics (PK/PD) model-informed approach offers integration of various patient information to provide an expectation of drug response and derive practical dose estimates to support clinicians' dosing decisions. Such an approach was pioneered in the late 1970s, but its broad clinical acceptance and implementation have been hampered by the lack of widespread computer technology, including user-friendly software tools. This has significantly changed in recent years. With the advent of electronic health records (EHRs) and the ubiquity of user-friendly software tools, we now experience a convergence of clinical information, pharmacogenetics, systems pharmacology and pharmacometrics, and technology. Advanced pharmacometrics research is now more appliable and implementable to improve health care. This article presents examples of successful development and implementation of pharmacogenetics-guided and PK/PD model-informed decision support to facilitate precision dosing, including the development of an EHR-embedded decision support tool. Through the integration of clinical decision support tools in EHRs, clinical pharmacometrics support can be brought directly to the clinical team and the bedside.

Pharmacogenomics education in medical and pharmacy schools: conclusions of a global survey

Aim: The need for pharmacogenomic education is becoming more and more urgent. Our aim was to evaluate the progress in pharmacogenomics education since then, and to put forward further recommendations. Methods: A survey was sent to 248 schools of medicine, pharmacy, nursing and health professions around the world. Results: The majority of the study programs (87%) include pharmacogenomics education, which is generally taught as part of the pharmacology curriculum. On average, educators and teachers have selected appropriate and highly relevant pharmacogenomics biomarkers to include in their teaching programs. Conclusions: Based on the results, we can conclude that the state of pharmacogenomics education at the surveyed universities has improved substantially since 2005.

Projected impact of pharmacogenomic testing on medications beyond antiplatelet therapy in percutaneous coronary intervention patients

Aim: CYP2C19 genotyping is used to guide antiplatelet therapy after percutaneous coronary intervention (PCI). This study evaluated the potential impact of CYP2C19 and multigene pharmacogenomics (PGx) testing on medications beyond antiplatelet therapy in a real-world cohort of PCI patients that underwent CYP2C19 testing. Methodology & results: Multiple medications with actionable PGx recommendations, including proton pump inhibitors, antidepressants and opioids, were commonly prescribed. Approximately 50% received a CYP2C19 metabolized medication beyond clopidogrel and 7% met criteria for a CYP2C19 genotype-guided intervention. A simulation analysis projected that 17.5 PGx-guided medication interventions per 100 PCI patients could have been made if multigene PGx results were available. Conclusion: This suggests that CYP2C19 and multigene PGx results could be used to optimize medication prescribing beyond antiplatelet therapy in PCI patients.

Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR

INTRODUCTION

Pre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective.

METHODS AND ANALYSIS

The EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment.

ETHICS AND DISSEMINATION

The protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences.

TRIAL REGISTRATION NUMBER

German Clinical Trials Register, DRKS00013909.

Challenges and Future Prospects of Precision Medicine in Psychiatry

Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

The clinical utility of combinatorial pharmacogenomic testing for patients with depression: a meta-analysis

Aim: To perform a meta-analysis of prospective, two-arm studies examining the clinical utility of using the combinatorial pharmacogenomic test, GeneSight Psychotropic, to inform treatment decisions for patients with major depressive disorder (MDD). Patients & methods: The pooled mean effect of symptom improvement and pooled relative risk ratio (RR) of response and remission were calculated using a random effect model. Results: Overall, 1556 patients were included from four studies, with outcomes evaluated at week 8 or week 10. Patient outcomes were significantly improved for patients with MDD whose care was guided by the combinatorial pharmacogenomic test results compared with unguided care (symptom improvement Δ = 10.08%, 95% CI: 1.67-18.50; p = 0.019; response RR = 1.40, 95% CI: 1.17-1.67; p < 0.001; remission RR = 1.49, 95% CI: 1.17-1.89; p = 0.001). Conclusion: GeneSight Psychotropic guided care improves outcomes among patients with MDD.

Pharmacogenomics in pregnancy

Pregnant women frequently take prescription and over the counter medications. The efficacy of medications is affected by the many physiological changes during pregnancy, and these events may be further impacted by genetic factors. Research on pharmacogenomic and pharmacokinetic influences on drug disposition during pregnancy has lagged behind other fields. Clinical investigators have demonstrated altered activity of several drug metabolizing enzymes during pregnancy. Emerging evidence also supports the influence of pharmacogenomic variability in drug response for many important classes of drugs commonly used in pregnancy. Prescribing medications during pregnancy requires an understanding of the substantial dynamic physiologic and metabolic changes that occur during gestation. Pharmacogenomics also contributes to the inter-individual variability in response to many medications, and more research is needed to understand how best to manage drug therapy in pregnant women.

An update on the pharmacogenomics of NSAID metabolism and the risk of gastrointestinal bleeding

Introduction: Several reports suggest a possible association between polymorphisms in the cytochrome P450 2C9 (CYP2C9) gene and the risk for non-steroidal anti-inflammatory drug (NSAID)-related adverse gastrointestinal events, including gastrointestinal bleeding. Because findings were controversial, a systematic review and a meta-analysis of eligible studies on this putative association was conducted.Areas covered: The authors have revised the relationship between CYP2C9 polymorphisms and the risk of developing NSAID-related gastrointestinal bleeding, as well as other adverse gastrointestinal events, and performed meta-analyzes. The bias effect and potential sources of heterogeneity between studies was analyzed.Expert opinion: Individuals classified as poor metabolizers after CYP2C9 genotyping (activity scores equal to 0 or 0.5) have an increased risk of developing NSAID-related gastrointestinal adverse events with an odds ratio (OR) = 1.86, (p = 0.004) and the OR for subjects with gastrointestinal bleeding is = 1.90, (p = 0.003). Gene-dose effect for variant CYP2C9 alleles (p = 0.005 for all gastrointestinal adverse events, and p = 0.0001 for bleeding patients) was observed. Also, there is an allele-specific effect in the association: CYP2C9*2 is a poor risk predictor, whereas CYP2C9*3 is a highly significant predictor of gastrointestinal adverse events (p = 0.006) and gastrointestinal bleeding (p = 0.0007).

Analysis of comprehensive pharmacogenomic profiling to impact in-hospital prescribing

INTRODUCTION

In-hospital adverse medication events result in increased morbidity and mortality. Many implicated drugs carry pharmacogenomic information. We hypothesized that comprehensive pre-emptive pharmacogenomic profiling could have high relevance for in-hospital prescribing.

PATIENTS AND METHODS

We retrospectively analyzed the in-hospital medications of a genotyped outpatient cohort admitted at our institution from 2012 to 2015. The endpoints were medication changes (new medications initiated, dose adjustments, or medications discontinued) involving drugs with pharmacogenomic annotations from three sources: Clinical Pharmacogenetics Implementation Consortium guidance, Food and Drug Administration label information, and drugs with clinical decision supports in our institutional pharmacogenomic Genomic Prescribing System.

RESULTS

Of 867 genotyped outpatients, 20 were hospitalized (mean: 78.2 years, 65% male). This hospitalized cohort was significantly older (78.2 vs. 61.3 years, P<0.0001) and took more medications (8.9 vs. 5.0 medications, P<0.0001). Out of 159 medication changes made, most (67.9%) were new medications (average: 2.5/hospitalization) with one-third of these having clinically annotated pharmacogenomic information. Half of all hospitalizations involved at least one pharmacogenomic medication. Over half (55%) of the hospitalized cohort was newly prescribed at least one of eight key pharmacogenomic medications, including high-risk drugs such as clopidogrel, codeine, and warfarin.

CONCLUSION

Our study suggested that older patients and those with polypharmacy were at increased risk for hospitalizations, where many new prescriptions included frequently used pharmacogenomic drugs. Targeting this group for pre-emptive genotyping would facilitate the delivery of highly relevant information to inform inpatient prescribing.

Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

Personalized Medicine and the Power of Electronic Health Records

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

Pharmacogenetics in Practice: Estimating the Clinical Actionability of Pharmacogenetic Testing in Perioperative and Ambulatory Settings

Most literature describing pharmacogenetic implementations are within academic medical centers and use single‐gene tests. Our objective was to describe the results and lessons learned from a multisite pharmacogenetic pilot that utilized panel‐based testing in academic and nonacademic settings. This was a retrospective analysis of 667 patients from a pilot in 4 perioperative and 5 outpatient cardiology clinics. Recommendations related to 12 genes and 65 drugs were classified as actionable or not actionable. They were ascertained from Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines and US Food and Drug Administration (FDA) labeling. Patients displayed a high prevalence of actionable results (88%, 99%) and use of medications (28%, 46%) with FDA or CPIC recommendations, respectively. Sixteen percent of patients had an actionable result for a current medication per CPIC compared with 5% per FDA labeling. A systematic approach by a health system may be beneficial given the quantity and diversity of patients affected.

Focus group testing of a mobile app for pharmacogenetic-guided dosing

BACKGROUND

A common barrier to implementation of precision medicine is the lack of use of published clinical practice guidelines. Consequently, a user-friendly mechanism to easily adopt these guidelines is imperative.

PURPOSE

The purpose of this study was to evaluate the perceptions of a prototype of a clinical decision support tool through a mobile application for pharmacogenetics.

METHODOLOGICAL ORIENTATION

A case study on a patient requiring pharmacogenetic testing was provided to the participants. The participants were given up to 30 minutes to identify the correct dosing in the clinical decision support tool based on clinical evidence-based guidelines. Immediately after the utilization of the mobile app, focus group interviews were conducted to identify the perceptions of the tool, obstacles associated with the tool, and suggestions for improvement of the tool.

SAMPLE

Focus group interviews with 23 nurse practitioners and nurse practitioner students were conducted. Field notes and audio recordings were taken.

CONCLUSIONS

Specific feedback for improvement in the font and size of text, color contrast, use of drug calculator, automatic input, and desire for further development of education portal were found within the data. The findings revealed useful feedback to adjust the prototype to improve the ease of use among nurse practitioners.

IMPLICATIONS FOR PRACTICE

The revision of this mobile app will improve user friendliness to increase applicability within health care. The mobile app can be used for future research to identify improvements in patient outcomes after implementing this tool.

[Perspectives of the use of pharmacogenetic tests in neurology and psychiatry]

The review is devoted to the analysis of the current state of pharmacogenetic research and their use in psychiatric practice. The main genes responsible for the pharmacodynamics and pharmacokinetics of drugs used in psychiatry are listed. Foreign pharmacogenetic clinical recommendations and progress on their implementation in medical practice in various countries of Europe and the USA are analyzed. The need to create Russian clinical guidelines on pharmacogenomics to improve the effectiveness of patient care and to implement a personalized approach to therapy is discussed.

Clinical application of genomic high-throughput data: Infrastructural, ethical, legal and psychosocial aspects

Genomic high-throughput technologies (GHTT) such as next-generation sequencing represent a fast and cost-effective tool toward a more comprehensive understanding of the molecular background of complex diseases. However, technological advances contrast with insufficient application in clinical practice. Thus, patients, physicians, and other professionals are faced with tough challenges that forestall the efficient and effective implementation. With the increasing application of genetic testing, it is of paramount importance that physicians and other professionals in healthcare recognize the restrictions and potential of GHTT, in order to understand and interpret the complex data in the context of health and disease. At the same time, the growing volume and complexity of data is forever increasing the need for sustainable infrastructure and state-of-the-art tools for efficient data management, including their analysis and integration. The large pool of sensitive information remains difficult to interpret and fundamental questions spanning from billing to legal, social, and ethical issues have still not been resolved. Here we summarize and discuss these obstacles in an interdisciplinary context and suggest ways to overcome them. Continuous discussion with clinicians, data managers, biostatisticians, systems medicine experts, ethicists, legal scholars, and patients illuminates the strengths, weakness, and current practices in the pipeline from biomaterial to sequencing and data management. This discussion also highlights the new, cross-disciplinary working collaborations to realize the wide-ranging challenges in clinical genomics including the exceptional demands placed on the staff preparing and presenting the data, as well as the question as to how to report the data and results to patients.

Pharmacogenomic potential in advanced cancer patients

PURPOSE

The prevalence of pharmacogenetically actionable medications in advanced cancer patients whose therapy may be optimized with genotype data was determined.

METHODS

Patients enrolled in our institutional molecular tumor board observational cohort were included in this study. Collected data included demographics, type(s) of cancer, and outpatient medications. Medications were classified as "pharmacogenetically actionable" if there are Clinical Pharmacogenetics Implementation Consortium (CPIC) therapeutic recommendations for that medication based on the presence of germline variations. The prevalence of pharmacogenetically actionable medications in the study population was determined, and the frequency of opportunities for pharmacogenetic prescribing and adverse event (AE) mitigation were estimated.

RESULTS

In a cohort of 193 patients with advanced cancer, 65% of patients were taking a pharmacogenetically actionable medication. Approximately 10% of the outpatient medications taken by the study population had a pharmacogenetic association. The most common pharmacogenetically actionable medications being used were ondansetron (47%), capecitabine (10%), and sertraline (7%). Using published genetic variation frequencies and AE risk, we conservatively estimated that 7.1% of cancer patients would be eligible for genetic-based medication adjustment, and 101 AEs would be prevented in 10,000 patients genotyped.

CONCLUSION

Medications with pharmacogenetic associations are used commonly in the advanced cancer patient population. This widespread exposure supports the implementation of prospective genotyping in the treatment of these high-risk patients.

Influence of Genetic Variants on Steady-State Etonogestrel Concentrations Among Contraceptive Implant Users

OBJECTIVE

To identify genetic variants that influence steady-state etonogestrel concentrations among contraceptive implant users.

METHODS

We enrolled healthy, reproductive-age women in our pharmacogenomic study using etonogestrel implants for 12-36 months without concomitant use of hepatic enzyme inducers or inhibitors. We collected participant characteristics, measured serum etonogestrel concentrations, and genotyped each participant for 120 single nucleotide variants in 14 genes encoding proteins involved in steroid hormone (ie, estrogens, progestins) metabolism, regulation, or function. We performed generalized linear modeling to identify genetic variants associated with steady-state etonogestrel concentrations.

RESULTS

We enrolled 350 women, who had a median serum etonogestrel concentration of 137.4 pg/mL (range 55.8-695.1). Our final generalized linear model contained three genetic variants associated with serum etonogestrel concentrations: NR1I2(PXR) rs2461817 (β=13.36, P=.005), PGR rs537681 (β=-29.77, P=.007), and CYP3A7*1C (β=-35.06, P=.025). Variant allele frequencies were 69.4%, 84.9%, and 5.1%, respectively. Our linear model also contained two nongenetic factors associated with etonogestrel concentrations: body mass index (BMI) (β=-3.08, P=7.0×10) and duration of implant use (β=-1.60, P=5.8×10); R for the model =0.17.

CONCLUSION

Only BMI and duration of implant use remained significantly associated with steady-state etonogestrel concentrations. Of the three novel genetic associations found, one variant associated with increased etonogestrel metabolism (CYP3A7*1C) causes adult expression of fetal CYP3A7 proteins and can consequently alter steroid hormone metabolism. Women with this variant may potentially have increased metabolism of all steroid hormones, as 27.8% (5/18) of CYP3A7*1C carriers had serum etonogestrel concentrations that fell below the threshold for consistent ovulatory suppression (less than 90 pg/mL). More pharmacogenomic investigations are needed to advance our understanding of how genetic variation can influence the effectiveness and safety of hormonal contraception, and lay the groundwork for personalized medicine approaches in women's health.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT03092037.

Pharmacy students' attitudes and perceptions toward pharmacogenomics education

PURPOSE

To evaluate final-year pharmacy students' perceptions toward pharmacogenomics education, their attitudes on its clinical relevance, and their readiness to use such knowledge in practice.

METHODS

A 19-question survey was developed and modified from prior studies and was pretested on a small group of pharmacogenomics faculty and pharmacy students. The final survey was administered to 978 final-year pharmacy students in 8 school/colleges of pharmacy in New York and New Jersey between January and May 2017. The survey targeted 3 main themes: perceptions toward pharmacogenomics education, attitudes toward the clinical relevance of this education, and the students' readiness to use knowledge of pharmacogenomics in practice.

RESULTS

With a 35% response rate, the majority (81%) of the 339 student participants believed that pharmacogenomics was a useful clinical tool for pharmacists, yet only 40% felt that it had been a relevant part of their training. Almost half (46%) received only 1-3 lectures on pharmacogenomics and the majority were not ready to use it in practice. Survey results pointed toward practice-based trainings such as pharmacogenomics rotations as the most helpful in preparing students for practice.

CONCLUSIONS

Final-year student pharmacists reported varying exposure to pharmacogenomics content in their pharmacy training and had positive attitudes toward the clinical relevance of the discipline, yet they expressed low confidence in their readiness to use this information in practice.

PGxCorpus, a manually annotated corpus for pharmacogenomics

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. In this article, we present the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge.

A pediatric perspective on genomics and prevention in the twenty-first century

We present evidence from diverse disciplines and populations to identify the current and emerging role of genomics in prevention from both medical and public health perspectives as well as key challenges and potential untoward consequences of increasing the role of genomics in these endeavors. We begin by comparing screening in healthy populations (newborn screening), with testing in symptomatic populations, which may incidentally identify secondary findings and at-risk relatives. Emerging evidence suggests that variants in genes subject to the reporting of secondary findings are more common than expected in patients who otherwise would not meet the criteria for testing and population testing for variants in these genes may more precisely identify discrete populations to target for various prevention strategies starting in childhood. Conversely, despite its theoretical promise, recent studies attempting to demonstrate benefits of next-generation sequencing for newborn screening have instead demonstrated numerous barriers and pitfalls to this approach. We also examine the special cases of pharmacogenomics and polygenic risk scores as examples of ways genomics can contribute to prevention amongst a broader population than that affected by rare Mendelian disease. We conclude with unresolved questions which will benefit from future investigations of the role of genomics in disease prevention.

Coverage of pharmacogenetic tests by private health insurance companies

OBJECTIVE

To assess the coverage of clinically relevant pharmacogenetic tests by the top 41 private insurance companies in the United States.

DESIGN

Websites of insurance companies were searched for medical policies addressing 34 common and clinically relevant pharmacogenetic tests referenced by the Clinical Pharmacogenetics Implementation Consortium, PharmGKB, and Food and Drug Administration product labeling. Those policies were subsequently reviewed for the coverage of the tests by gene-drug pair and by company. Policies were subsequently reviewed to determine coverage of pharmacogenetic tests by gene-drug indication group (GDIG) and an insurance company.

SETTINGS AND PARTICIPANTS

Not applicable.

OUTCOME MEASURES

Within unique policy sets, the following were analyzed: (1) the number of times each GDIG was mentioned; (2) the percentage of times each GDIG was mentioned; (3) when mentioned, the number of times each GDIG was covered; (4) when mentioned, the percentage of times each GDIG was covered; and (5) regardless of being mentioned, the percentage of times each GDIG was covered.

RESULTS

A total of 223 medical policies mentioning pharmacogenetic tests were retrieved, representing 34 unique policy sets from 41 companies. Thirty-three companies had their policies accessible on their website. Approximately 50% of GDIGs were unanimously mentioned in all policies but were covered only < 20% of the time. When mentioned in a policy, 7 GDIGs were uniformly covered, and 11 GDIGs were uniformly not covered. Overall, insurance companies covered approximately 40% of GDIGs mentioned in their policies.

CONCLUSION

The medical policies addressing recommended pharmacogenetic tests were not readily accessible on websites of the top private health insurance companies. The coverage and payments of the tests varied by the company and gene-drug pairs and remain suboptimal.

Unveiling the guidance heterogeneity for genome-informed drug treatment interventions among regulatory bodies and research consortia

Pharmacogenomics and personalized medicine interventions hold promise to optimize drug treatment modalities and hence, improve the quality of life of the patients by minimizing the occurrence of adverse drug reactions and/or maximizing drug treatment efficacy. To this end, proper guidance for accurately prescribing the correct drug at the right dose is empowered by major regulatory bodies, namely the U.S. Food and Drug Administration (FDA) and the European Medicine Agency (EMA), and well-recognized research consortia, like the Clinical Pharmacogenetics Implementation Consortium (CPIC), that propose therapeutic recommendations after the thorough evaluation of the existing scientific evidence base. In this context, the consistency of these recommendations is crucial for smoothly integrating pharmacogenomics into the clinic. Here, we collected all of the important and clinically actionable pharmacogenomics information provided by the aforementioned renowned sources and documented it in order to assess potential similarities and, most importantly, differences. Our data show that the level of concordance regarding the guidance provided for the same drug-gene association pairs varies significantly, despite the fact that it all derives from a single evidence base. In particular, apart from the expected similarities in a number of association pairs, especially the ones related to cancer genomics, there are still major discrepancies that create confusion as to which guidance should be followed in order to properly inform drug prescribing. This regulatory deficiency calls for the fruitful engagement of the regulatory agencies involved with the contribution of other experts engaged in the field of pharmacogenomics in an effort to harmonize the existing arsenal of guidance for genome-informed drug prescription. The achievement of harmonization would in turn expedite bringing personalized medicine closer to clinical fruition.

PharmVar GeneFocus: CYP2D6

The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus. CYP2D6 genetic variation impacts the metabolism of numerous drugs and, thus, can impact drug efficacy and safety. This GeneFocus provides a comprehensive overview and summary of CYP2D6 genetic variation and describes how the information provided by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

The Clinical Pharmacogenetics Implementation Consortium: 10 Years Later

In 2009, the Clinical Pharmacogenetics Implementation Consortium (CPIC, www.cpicpgx.org), a shared project between Pharmacogenomics Knowledge Base (PharmGKB, http://www.pharmgkb.org) and the National Institutes of Health (NIH), was created to provide freely available, evidence-based, peer-reviewed, and updated pharmacogenetic clinical practice guidelines. To date, CPIC has published 23 guidelines (of which 11 have been updated), covering 19 genes and 46 drugs across several therapeutic areas. CPIC also now provides additional resources to facilitate the implementation of pharmacogenetics into routine clinical practice and the electronic health record. Furthermore, since its inception, CPIC's interactions with other resources, databases, websites, and genomic communities have grown. The purpose of this paper is to highlight the progress of CPIC over the past 10 years.

Projected Cost-Effectiveness for 2 Gene-Drug Pairs Using a Multigene Panel for Patients Undergoing Percutaneous Coronary Intervention

BACKGROUND

For patients undergoing percutaneous coronary intervention, gene-drug associations exist relevant to first-line treatment options-antiplatelet agent, clopidogrel, and pain medication, tramadol. Knowledge of genotype information may allow for avoidance of adverse drug events during critical clinical windows.

OBJECTIVE

This evaluation estimated cost-effectiveness associated with a multi-gene panel pre-emptively testing two genes providing CYP2C19 genotype-guided strategy for antiplatelet therapy, with CYP2D6 genotype-guided pain management, compared to single gene test for CYP2C19 with random assignment for pain treatment, and to no testing (empiric clopidogrel with random assignment for pain treatment).

METHODS

Decision analysis modeling was used to project costs from a payer perspective and patient quality-adjusted life years (QALYs) from the three strategies. The model captured composite risks of major adverse cardiovascular events and pain therapy-related adverse drug events and associated utility estimates. We conducted sensitivity analyses to assess influential input parameters.

RESULTS

Over 15 months, multi-gene testing was least costly and yielded more QALYs compared to both single gene and no testing; total incremental costs were $1646 lower with incremental gains of 0.04 QALYs for multi-gene compared with single gene and $11 368 lower with 0.17 QALY gains compared to no test. Base case analyses revealed multi gene was dominant compared to both single gene and no test, as it demonstrated cost savings with increased QALYs.

CONCLUSIONS

For these patients, a multi-gene-guided strategy yields a favorable incremental cost-effectiveness ratio compared to the other two treatment strategies. Pre-emptively ascertaining additional gene-drug pair information can inform clinical and economic decision-making at the point of care.

Pharmacogenomics: Prescribing Precisely

Pharmacogenomics (PGx) is a powerful tool that can predict increased risks of adverse effects and sub-therapeutic response to medications. This article establishes the core principles necessary for a primary care provider to meaningfully and prudently use PGx testing. Key topics include in which patients PGx testing should be considered, how PGx tests are ordered, how the results are translated into clinical recommendations, and what further advancements are likely in the near future. This will provide clinicians with a foundational knowledge of PGx that can allow incorporation of this tool into their practice or support further personal investigation.

Essential Characteristics of Pharmacogenomics Study Publications

Pharmacogenomics (PGx) can be seen as a model for biomedical studies: it includes all disease areas of interest and spans in vitro studies to clinical trials, while focusing on the relationships between genes and drugs and the resulting phenotypes. This review will examine different characteristics of PGx study publications and provide examples of excellence in framing PGx questions and reporting their resulting data in a way that maximizes the knowledge that can be built on them.

Translating pharmacogenomics into clinical decisions: do not let the perfect be the enemy of the good

The field of pharmacogenomics (PGx) is gradually shifting from the reactive testing of single genes toward the proactive testing of multiple genes to improve treatment outcomes, reduce adverse events, and decrease the burden of unnecessary costs for healthcare systems. Despite the progress in the field of pharmacogenomics, its implementation into routine care has been slow due to several barriers. However, in recent years, the number of studies on the implementation of PGx has increased, all providing a wealth of knowledge on different solutions for overcoming the obstacles that have been emphasized over the past years. This review focuses on some of the challenges faced by these initiatives, the solutions and different approaches for testing that they suggest, and the evidence that they provide regarding the benefits of preemptive PGx testing.

Pharmacogenomic Testing: Clinical Evidence and Implementation Challenges

Pharmacogenomics can enhance patient care by enabling treatments tailored to genetic make-up and lowering risk of serious adverse events. As of June 2019, there are 132 pharmacogenomic dosing guidelines for 99 drugs and pharmacogenomic information is included in 309 medication labels. Recently, the technology for identifying individual-specific genetic variants (genotyping) has become more accessible. Next generation sequencing (NGS) is a cost-effective option for genotyping patients at many pharmacogenomic loci simultaneously, and guidelines for implementation of these data are available from organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG). NGS and related technologies are increasing knowledge in the research sphere, yet rates of genomic literacy remain low, resulting in a widening gap in knowledge translation to the patient. Multidisciplinary teams—including physicians, nurses, genetic counsellors, and pharmacists—will need to combine their expertise to deliver optimal pharmacogenomically-informed care.

Implementation of wide-scale pharmacogenetic testing in primary care

The convergence of translational genomics and biomedical informatics has changed healthcare delivery. Institutional consortia have begun implementing lab testing and decision support for drug–gene interactions. Aggregate datasets are now revealing the impact of clinical decision support for drug–gene interactions. Given the pleiotropic nature of pharmacogenes, interdisciplinary teams and robust clinical decision support tools must exist within an informatics framework built to be flexible and capable of cross-talk between clinical specialties. Navigation of the challenges presented with the implementation of five steps to build a genetics program infrastructure requires the expertise of multiple healthcare professionals. Ultimately, this manuscript describes our efforts to place pharmacogenomics in the hands of the primary care provider integrating this information into a patient’s healthcare over their lifetime.

Use of Pharmacogenetic Drugs by the Dutch Population

Introduction

The Dutch Pharmacogenetics Working Group (DPWG) indicated a list of actionable genotypes that affect patients’ response to more 50 drugs; these drugs which show variable effects based on patients’ genetic traits were named as pharmacogenetics (PGX) drugs. Preemptive genetic testing before using these drugs may protect certain patients from serious adverse reactions and could help in avoiding treatment failures. The objectives of this study include identifying the rate of PGX drug usage among Dutch population, estimating the level of users who carry the actionable genotypes and determining the main genes involved in drug’s effect variability.

Methods

Usage of PGX drugs over 2011–2017 by the insured population (an average of 11.4 million) in outpatient clinics in Netherlands was obtained from the publically available GIP databank. The data of 45 drugs were analyzed and their interactions with selected pharmacogenes were estimated. Frequency of actionable genotypes of 249 Dutch parents was obtained from the public database: Genome of Netherlands (GoNL), to identify the pattern of genetic characteristics of Dutch population.

Results

Over a 7 year period, 51.3 million exposures of patients to PGX drugs were reported with an average of 5.3 exposures per each drug user. One quarterof the exposures (12.4 million) are predicted to be experienced by individuals with actionable genotypes (risky exposures). Up to 60% of the risky exposures (around 7.5 million) were related to drugs metabolized by CYP2D6. SLCO1B1, and CYP2C19 were also identified among the top genes affecting response of drugs users (involved in about 22 and 12.4% of the risky exposures, respectively). Cardiovascular medications were the top prescribed PGX drug class (43%), followed by gastroenterology (29%) and psychiatry/neurology medications (15%). Women use more PGX drugs than men (55.8 vs. 44.2%, respectively) with the majority (84%) of users in both sexes are above 45 years.

Conclusion

PGX drugs are commonly used in Netherlands. Preemptive panel testing for CYP2D6, SLCO1B1, and CYP2C19 only could be useful to predict 95% of vulnerable patients’ exposures to PGX drugs. Future studies to assess the economic impact of preemptive panel testing on patients of older age are suggested.

Pharmacogenetic and pharmacogenomic discovery strategies

Genetic/genomic profiling at a single-patient level is expected to provide critical information for determining inter-individual drug toxicity and potential efficacy in cancer therapy. A better definition of cancer subtypes at a molecular level, may correspondingly complement such pharmacogenetic and pharmacogenomic approaches, for more effective personalized treatments. Current pharmacogenetic/pharmacogenomic strategies are largely based on the identification of known polymorphisms, thus limiting the discovery of novel or rarer genetic variants. Recent improvements in cost and throughput of next generation sequencing (NGS) are now making whole-genome profiling a plausible alternative for clinical procedures. Beyond classical pharmacogenetic/pharmacogenomic traits for drug metabolism, NGS screening programs of cancer genomes may lead to the identification of novel cancer-driving mutations. These may not only constitute novel therapeutic targets, but also effector determinants for metabolic pathways linked to drug metabolism. An additional advantage is that cancer NGS profiling is now leading to discovering targetable mutations, e.g., in glioblastomas and pancreatic cancers, which were originally discovered in other tumor types, thus allowing for effective repurposing of active drugs already on the market.

Big data in pharmacogenomics: current applications, perspectives and pitfalls

The efficiency of new generation sequencing methods and the reduction of their cost has led pharmacogenomics to gradually supplant pharmacogenetics, leading to new applications in personalized medicine along with new perspectives in drug design or identification of drug response factors. The amount of data generated in genomics fits the definition of big data, and need a specific bioinformatics processing following standard steps: data collection, processing, analysis and interpretation. Pitfalls of pharmacogenomics studies are directly related to these steps. This review aims to describe these steps from a pharmacogenomic point of view, focusing on bioinformatics aspects.

g-Nomic: a new pharmacogenetics interpretation software

We present g-Nomic, a pharmacogenetics interpretation software that analyzes globally a prescribed medication taking into account the personal background genetics, drug–drug interactions, lifestyle, nutritional supplements, inhibitors, inducers, and other risks to analyze primary or secondary metabolism pathways. G-Nomic provides a set of recommendations describing the suitability of a given combination of drugs for each patient according to their genes and polymedication. G-Nomic is updated monthly including data from the new drugs to be included, their known interactions, and the relevant pharmacokinetic biomarkers. For the interactions, the list is curated manually, only keeping those with clinical relevance. For each drug, their FDA and EMA drug labels are accessed, to check for relevant enzymes and transport proteins that influence its pharmacokinetics, and for their ability to induce or inhibit other enzymes, particularly the CYP-450 system. When this information is not available, a PubMed search is made to look for these characteristics. In addition, a distinction is made between drugs and prodrugs. A query on the g-Nomic software begins with entering the medication by either their common or commercial name. Non-pharmacological substances can be also added or selected under “lifestyle habits”. The lifestyle list is dynamic, showing only the substances known to interact with the drugs that are currently selected, and includes herb compounds, such as St. John’s wort, as well as proper lifestyle substances such as grapefruit or cigarette smoking. The software provides a list of the genes classified as primary biomarkers as candidates for genetic testing, and a list of the interactions that have been detected. If genetic information is available then, or is made available at a later point, these results can also be entered and the software returns pharmacogenetics recommendations regarding specific genotypes. g-Nomic takes all the above-mentioned parameters in an easy and user-friendly tool making prescription safer.

[Pharmacogenetics in psychiatry: state of the art]

In this article, the current literature on pharmacogenetics of antidepressants, antipsychotics and lithium are summarized by the section of Neurobiology and Genetics of the German Society of Psychiatry, Psychotherapy and Neurology (DGPPN). The publications of international expert groups and regulatory authorities are reviewed and discussed. In Germany, a statement on pharmacogenetics was also made by the gene diagnostics committee of the Ministry of Health. The DGPPN supports two recommendations: 1) to perform CYP2D6 genetic testing prior to prescription of tricyclic antidepressants and 2) to determine the HLA-B*1502 genotype in patients of Asian origin before using carbamazepine. The main obstacle for a broad application of pharmacogenetic tests in psychiatry remains the lack of large prospective studies, for both single gene-drug pair and cobinatorial pharmacogenetic tests, to evaluate the benefits of genetic testing. Psychiatrists, geneticists and funding agencies are encouraged to increase their efforts for the future benefit of psychiatric patients.

Pharmacogenetic Factors Affecting Asthma Treatment Response. Potential Implications for Drug Therapy

Asthma is a frequent disease, mainly characterized by airway inflammation, in which drug therapy is crucial in its management. The potential of pharmacogenomics testing in asthma therapy has been, to date, little explored. In this review, we discuss pharmacogenetic factors affecting asthma treatment, both related to drugs used as controller medications for regular maintenance, such as inhaled corticosteroids, anti-leukotriene agents, long-acting beta-agonists, and the new biologic agents used to treat severe persistent asthma. In addition, we discuss current pharmacogenomics knowledge for rescue medications provided to all patients for as-needed relief, such as short-acting beta-agonists. Evidence for genetic variations as a factor related to drugs response has been provided for the following genes and groups of drugs: Inhaled corticosteroids: FCER2; anti-leukotriene agents: ABCC1, and LTC4S; beta-agonists: ADRB2. However, the following genes require further studies confirming or rejecting association with the response to asthma therapy: ADCY9, ALOX5, ARG1, ARG2, CRHR1, CRHR2, CYP3A4, CYP3A5, CYSLTR1, CYSLTR2, GLCCI1, IL4RA, LTA4H, ORMDL3, SLCO2B1, SPATS2L, STIP1, T, TBX21, THRA, THRB, and VEGFA. Although only a minority of these genes are, at present, listed as associated with drugs used in asthma therapy, in the Clinical Pharmacogenomics Implementation Consortium gene-drug pair list, this review reveals that sufficient evidence to start testing the potential of clinical pharmacogenomics in asthma therapy already exists. This evidence supports the inclusion in pilot pharmacogenetics tests of at least four genes. Hopefully these tests, if proven useful, will increase the efficiency and the safety of asthma therapy.

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.