Clinical Implementation Of Pharmacogenomics Research Articles

Optimizing pharmacogenomic decision-making by data science

Healthcare systems have made rapid progress towards combining data science with precision medicine, particularly in pharmacogenomics. With the lack of predictability in medication effectiveness from patient to patient, acquiring the specifics of their genotype would be highly advantageous for patient treatment. Genotype-guided dosing adjustment improves clinical decision-making and helps optimize doses to deliver medications with greater efficacy and within safe margins. Current databases demand extensive effort to locate relevant genetic dosing information. To address this problem, Patient Optimization Pharmacogenomics (POPGx) was constructed. The objective of this paper is to describe the development of POPGx, a tool to simplify the approach for healthcare providers to determine pharmacogenomic dosing recommendations for patients taking multiple medications. Additionally, this tool educates patients on how their allele variations may impact gene function in case they need further healthcare consultations. POPGx was created on Konstanz Information Miner (KNIME). KNIME is a modular environment that allows users to conduct code-free data analysis. The POPGx workflow can access Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines and subsequently be able to present relevant dosing and counseling information. A KNIME representational state transfer (REST) application program interface (API) node was established to retrieve information from CPIC and drugs that are exclusively metabolized through CYP450, and these drugs were processed simultaneously to demonstrate competency of the workflow. The POPGx program provides a time-efficient method for users to retrieve relevant, patient-specific medication selection and dosing recommendations. Users input metabolizer gene, genetic allele data, and medication list to retrieve clear dosing information. The program is automated to display current guideline recommendations from CPIC. The integration of this program into healthcare systems has the potential to revolutionize patient care by giving healthcare practitioners an easy way to prescribe medications with greater efficacy and safety by utilizing the latest advancements in the field of pharmacogenomics.

Knowledge, perceptions, and attitudes toward pharmacogenomics among pharmacists and pharmacy students: A systematic review.

Pharmacists have been recognized as one of the most qualified healthcare professionals in the clinical implementation of pharmacogenomics, yet its widespread implementation in clinical pharmacy practice has remained limited. The review aims to systematically investigate knowledge, perceptions, and attitudes toward pharmacogenomics among pharmacists and pharmacy students to inform the future delivery of pharmacogenomics education programs. PubMed, MEDLINE, Embase, Scopus, and the International Pharmaceutical Abstracts were searched up to May 17, 2022. Studies were selected if they included data on pharmacists' or pharmacy students' knowledge, perception, or attitude about pharmacogenomics and were published in a peer-reviewed, English-language journal with full-text availability. Any published study not deemed original research was excluded. All included studies were critically appraised using the Center for Evidence-Based Management's critical appraisal tools. The data were descriptively analyzed and presented based on pharmacists' and pharmacy students' knowledge/awareness, perception/attitudes toward pharmacogenomic (PGx), confidence in using or interpreting PGx testing results, and their desire to get further PGx education or their most preferred method of further education. A combined total of 12,430 pharmacists and pharmacy students from 26 countries are represented in the 52 included studies. Despite overwhelmingly positive attitudes and perceptions toward pharmacogenomics among pharmacists and pharmacy students, an overall lack of adequate knowledge and confidence was found. The review also found a strong desire for further pharmacogenomics education among pharmacists and pharmacy students. Pharmacists and pharmacy students have positive perceptions and attitudes toward pharmacogenomics, which is hindered by a lack of knowledge and confidence. However, inadequate control for confounders, limited representativeness of the studied population or region, and small sample sizes diminish the generalizability of the review results. Knowledge and confidence could be improved through enhanced delivery of pharmacogenomic courses within the pharmacy curriculum and continuing education programs.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward.

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

The Utility of CYP2D6 and CYP2C19 Variants to Guide Pharmacological Treatment in Complex Unipolar Major Depression: A Pilot Longitudinal Study

Major depression is a frequent condition which variably responds to treatment. In view of its high prevalence, the presence of treatment resistance in major depression significantly impacts on quality of life. Tailoring pharmacological treatment based on genetic polymorphisms is a current trend to personalizing pharmacological treatment in patients with major depressive disorders. Current guidelines for the use of genetic tests in major depression issued by the Clinical Pharmacogenomics Implementation Consortium (CPIC) are based on CYP2D6 and CYP2C19 polymorphisms which constitute the strongest evidence for pharmacogenomic guided treatment. There is evidence of increased clinical response to pharmacological treatment in major depression although largely in non-treatment resistant patients from Western countries. In this study, well characterised participants (N = 15) with complex, largely treatment resistant unipolar major depression were investigated, and clinical improvement was measured at baseline and at week-8 after the pharmacogenomics-guided treatment with the Montgomery Åsberg Depression Rating Scale (MÅDRS). Results suggested a statistically significant improvement (p = 0.01) of 16% at endpoint in the whole group and a larger effect in case of changes in medication regime (28%, p = 0.004). This small but appreciable effect can be understood in the context of the level of treatment resistance in the group. To our knowledge, this is the first study from the Middle East demonstrating the feasibility of this approach in the treatment of complex major depressive disorders.

Veterans Health Administration: Implementation of pharmacogenomic clinical decision support with statin medications and the SLCO1B1 gene as an exemplar.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To describe the implementation of clinical decision support tools for alerting prescribers of actionable drug-gene interactions in the Veterans Health Administration (VHA). Drug-gene interactions have been the focus of clinicians for years. Interactions between SCLO1B1 genotype and statin medications are of particular interest as these can inform risk for statin-associated muscle symptoms (SAMS). VHA identified approximately 500,000 new users of statin medications prescribed in VHA in fiscal year 2021, some of whom could benefit from pharmacogenomic testing for the SCLO1B1 gene. In 2019, VHA implemented the Pharmacogenomic Testing for Veterans (PHASER) program to offer panel-based, preemptive pharmacogenomic testing and interpretation. The PHASER panel includes SLCO1B1, and VHA utilized Clinical Pharmacogenomics Implementation Consortium statin guidelines to build its clinical decision support tools. The program's overarching goal is to reduce the risk of adverse drug reactions such as SAMS and improve medication efficacy by alerting practitioners of actionable drug-gene interactions. We describe the development and implementation of decision support for the SLCO1B1 gene as an example of the approach being used for the nearly 40 drug-gene interactions screened for by the panel. The VHA PHASER program identifies and addresses drug-gene interactions as an application of precision medicine to reduce veterans' risks for adverse events. The PHASER program's implementation of statin pharmacogenomics utilizes a patient's SCLO1B1 phenotype to alert providers of the risk for SAMS with the statin being prescribed and how to lower that risk through a lower dose or alternative statin selection. The PHASER program may help reduce the number of veterans who experience SAMS and may improve their adherence to statin medications.

Comparison of targeted vs. expanded pharmacogenomic testing: What are we missing?

BackgroundPharmacogenomics (PGx) is used as a medication management strategy by a small but growing number of institutions. PGx allows prescribers to individually treat patients concordant with their genes. Recent litigation for preventable PGx-mediated adverse events highlights the need to accelerate PGx implementation for patient safety. Genetic variations cause drug metabolism, transport, and target changes, affecting medication response and tolerability. PGx testing often consists of targeted testing aimed at specific gene-drug pairs or disease states. Conversely, expanded panel testing can evaluate all known actionable gene-drug interactions, enhancing proactive clarity regarding patient response. ObjectivesEvaluate the divergence of targeted PGx testing with a single gene-drug pair test (cardiac), a two-gene panel, and a focused psychiatric panel compared to expanded PGx testing. MethodsAn expanded PGx panel (≥25 genes) was compared to a single gene-drug pair test of CYP2C19/clopidogrel, a dual gene test of CYP2C19/CYP2D6, a 7-gene psychiatric list, and a 14-gene psychiatric panel to inform specific depression and pain management drugs. The expanded panel provided a baseline to evaluate total PGx variations compared to those possibly missed by targeted testing. ResultsTargeted testing did not identify up to 95% of total PGx gene-drug interactions discovered. The expanded panel reported all gene-drug interactions for any medication with Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance or U.S. Food and Drug Administration (FDA) labeling for that gene. Single gene CYP2C19/clopidogrel testing missed or did not report on ∼95% of total interactions, CYP2C19/CYP2D6 testing missed or did not report ∼89%, and the 14-gene panel missed or did not report on ∼73%. The 7-gene list missed ∼20% of discovered potential PGx interactions but was not designed to identify gene-drug interactions. ConclusionsTargeted PGx testing for limited genes or by specialty may miss or not report significant portions of PGx gene-drug interactions. This can lead to potential patient harm from the missed interactions and subsequent failed therapies and/or adverse reactions.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.

A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain’s performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries

The clinical implementation of pharmacogenomics (PGx) could be one of the first milestones towards realizing personalized medicine in routine care. However, its widespread adoption requires the availability of suitable clinical decision support (CDS) systems, which is often impeded by the fragmentation or absence of adequate health IT infrastructures. We report results of CDS implementation in the large-scale European research project Ubiquitous Pharmacogenomics (U-PGx), in which PGx CDS was rolled out and evaluated across more than 15 clinical sites in the Netherlands, Spain, Slovenia, Italy, Greece, United Kingdom and Austria, covering a wide variety of healthcare settings. We evaluated the CDS implementation process through qualitative and quantitative process indicators. Quantitative indicators included statistics on generated PGx reports, median time from sampled upload until report delivery and statistics on report retrievals via the mobile-based CDS tool. Adoption of different CDS tools, uptake and usability were further investigated through a user survey among healthcare providers. Results of a risk assessment conducted prior to the implementation process were retrospectively analyzed and compared to actual encountered difficulties and their impact. As of March 2021, personalized PGx reports were produced from 6884 genotyped samples with a median delivery time of twenty minutes. Out of 131 invited healthcare providers, 65 completed the questionnaire (response rate: 49.6%). Overall satisfaction rates with the different CDS tools varied between 63.6% and 85.2% per tool. Delays in implementation were caused by challenges including institutional factors and complexities in the development of required tools and reference data resources, such as genotype-phenotype mappings. We demonstrated the feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting. Remaining challenges for future wide-scale roll-out include the harmonization of existing PGx information in guidelines and drug labels, the need for strategies to lower the barrier of PGx CDS adoption for healthcare institutions and providers, and easier compliance with regulatory and legal frameworks.

A population study of clinically actionable genetic variation affecting drug response from the Middle East

Clinical implementation of pharmacogenomics will help in personalizing drug prescriptions and alleviate the personal and financial burden due to inefficacy and adverse reactions to drugs. However, such implementation is lagging in many parts of the world, including the Middle East, mainly due to the lack of data on the distribution of actionable pharmacogenomic variation in these ethnicities. We analyzed 6,045 whole genomes from the Qatari population for the distribution of allele frequencies of 2,629 variants in 1,026 genes known to affect 559 drugs or classes of drugs. We also performed a focused analysis of genotypes or diplotypes of 15 genes affecting 46 drugs, which have guidelines for clinical implementation and predicted their phenotypic impact. The allele frequencies of 1,320 variants in 703 genes affecting 299 drugs or class of drugs were significantly different between the Qatari population and other world populations. On average, Qataris carry 3.6 actionable genotypes/diplotypes, affecting 13 drugs with guidelines for clinical implementation, and 99.5% of the individuals had at least one clinically actionable genotype/diplotype. Increased risk of simvastatin-induced myopathy could be predicted in ~32% of Qataris from the diplotypes of SLCO1B1, which is higher compared to many other populations, while fewer Qataris may need tacrolimus dosage adjustments for achieving immunosuppression based on the CYP3A5 diplotypes compared to other world populations. Distinct distribution of actionable pharmacogenomic variation was also observed among the Qatari subpopulations. Our comprehensive study of the distribution of actionable genetic variation affecting drugs in a Middle Eastern population has potential implications for preemptive pharmacogenomic implementation in the region and beyond.

Gene-drug pairings for antidepressants and antipsychotics: level of evidence and clinical application.

Substantial inter-individual discrepancies exist in both therapeutic effectiveness and adverse effects of antidepressant and antipsychotic medications, which can, in part, be explained by genetic variation. Here, we searched the Pharmacogenomics Knowledge Base for gene-antidepressant and gene-antipsychotic pairs with the highest level of evidence. We then extracted and compared the associated prescribing recommendations for these pairs developed by the Clinical Pharmacogenomics Implementation Consortium, the Dutch Pharmacogenetics Working Group or approved product labels in the US, Canada, Europe, and Asia. Finally, we highlight key economical, educational, regulatory, and ethical issues that, if not appropriately considered, can hinder the implementation of these recommendations in clinical practice. Our review indicates that evidence-based guidelines are available to assist with the implementation of pharmacogenetic-guided antidepressant and antipsychotic prescribing, although the maximum impact of these guidelines on patient care will not be realized until key barriers are minimized or eliminated.

Clinical implementation of drug metabolizing gene-based therapeutic interventions worldwide.

Over the last few years, the field of pharmacogenomics has gained considerable momentum. The advances of new genomics and bioinformatics technologies propelled pharmacogenomics towards its implementation in the clinical setting. Since 2007, and especially the last-5years, many studies have focused on the clinical implementation of pharmacogenomics while identifying obstacles and proposed strategies and approaches for overcoming them in the real world of primary care as well as outpatients and inpatients clinics. Here, we outline the recent pharmacogenomics clinical implementation projects and provide details of the study designs, including the most predominant and innovative, as well as clinical studies worldwide that focus on outpatients and inpatient clinics, and primary care. According to these studies, pharmacogenomics holds promise for improving patients' health in terms of efficacy and toxicity, as well as in their overall quality of life, while simultaneously can contribute to the minimization of healthcare expenditure.

An interprofessional education experience to promote the role of the pharmacist in precision medicine

BackgroundAn interprofessional (IP) experience was created that demonstrated the roles and responsibilities of pharmacists and physicians in clinical implementation of pharmacogenomics (PGx). The IP experience focused on PGx-themed patient cases and application of genotyping results to drug therapy management. Interprofessional education activityIn 2016 and 2017, third-year pharmacy students and first-year medical students were placed on interprofessional teams with two to three students each. The teams resolved PGx patient cases, medical students wrote prescriptions for altered drug therapy based on the PGx profiles of the patients, and pharmacy students assessed and provided feedback to medical students about the prescriptions. Student could also volunteer to be genotyped for CYP2C19*2, and the results were compared. DiscussionThe IP experience significantly enhanced PGx knowledge and increased the confidence of using PGx in patient cases for the majority of participants. The experience did not increase the recognition of each discipline's role in precision medicine in a statistically significant manner. Accurate prescription writing was challenging for the first-year medical students (44.3% prescriptions written correctly). The genotyping results did not deviate from a Hardy Weinberg equilibrium for this population. ImplicationsIP experiences focused on PGx present an ideal opportunity to educate and initiate collaborations between pharmacists and physicians and to promote utilization of PGx in precision medicine. The roles and responsibilities for each discipline can be easily recreated in an IP experience to provide robust training to the students.

Catalyzing clinical implementation of pharmacogenomics and personalized medicine interventions in Africa

Pharmacogenomics is considered to be the low-hanging fruit in the tree of genomic medicine with numerous examples of its successful implementation in the clinic. In this perspective, we provide details about the potential clinical application of pharmacogenomics in African populations by using relevant drug cases and high-throughput genomics approaches; involving numerous countries and stakeholders; and most importantly exploiting the existing knowledge of respective large-scale initiatives. We emphasize on the necessity of constructing appropriate frameworks for government policies in African countries. We also provide input about different initiatives in the field of genomics medicine implementation in Africa, not only for their potential for synergy and collaboration among them, but also as models for replication in other regions worldwide, aiming for healthcare improvement.

Pharmacist Consult Reports to Support Pharmacogenomics Report Interpretation.

BackgroundThe clinical implementation of pharmacogenomics (PGx) has often involved teams that include pharmacists. PGx laboratories often provide baseline information within the laboratory report that is based on Food and Drug Administration and Clinical Pharmacogenomics Implementation Consortium guidance, but information is often provided independent of concurrent disease states or medication use, among other clinical factors. Major challenges to widescale implementation of PGx include lack of physician experience or confidence in interpreting the data. The purpose of this paper is to describe how pharmacists can help further personalize PGx information and identify clinical recommendations for a given patient.MethodsThis work was performed as a secondary objective of a study evaluating genetic biomarkers of opioid addiction risk. This portion of the study utilized a descriptive analysis of pharmacist consult reports that consist of individualized, patient-level clinical recommendations that take into account current medications, current health conditions, and PGx data. A panel of 60 common PGx targets were tested among patients being treated for chronic pain or opioid use disorder (OUD). A pharmacist consult report was generated and compared with standard laboratory reporting of general PGx information.ResultsOf the 252 patients, PGx reports for 198 (78.6%) contained red and/or yellow clinical decision support flags for medications with actionable or informative PGx guidance for currently prescribed medications. Pharmacists recommended modifications to current prescriptions for 31 (53%) of the patients with actionable flags and 17 (12%) of the patients with informative flags. Drug classes most commonly included medications for cardiology, depression and anxiety, pain (opioids) and gastrointestinal management. Taken together, 24.2% of the actionable and informative flags had immediate clinical value based on the pharmacist’s review. An additional 217 (86%) received one or more clinical recommendations not related to PGx.ConclusionWhile PGx provides another opportunity for pharmacotherapy personalization, PGx data must be considered within the context of other patient-specific factors. Pharmacists were able to streamline the PGx report flags and identify other pharmacotherapy interventions following application of patient-specific data, thereby developing a brief report of recommendations for the patient’s prescriber(s). Engaging clinical pharmacists in the PGx clinical decision process may help to facilitate more widespread PGx implementation.

A review of the existing literature on buprenorphine pharmacogenomics.

Buprenorphine is an effective treatment for opioid dependence; however, it demonstrates individual variability in efficacy. Pharmacogenomics may explain this drug response variability and could allow for tailored therapy on an individual basis. The Food and Drug Administration and the Clinical Pharmacogenomics Implementation Consortium have guidelines on pharmacogenomic testing for some opioids (e.g., codeine); however, no guidelines exist for the partial opioid agonist buprenorphine. Pharmacogenomic testing targets for buprenorphine include pharmacodynamic genes like the mu-opioid receptor (MOP receptor) and catechol-O-methyltransferase (COMT), as well as the pharmacokinetic genes like the CYP enzymes. In this review we identified genotypes in patients with opioid addiction receiving buprenorphine that may result in altered therapeutic dosing and increased rate of relapse. The OPRM1 A118G single nucleotide polymorphism (SNP rs1799971) gene variant encoding the N40D MOP receptor has been associated with variable efficacy and response to treatment in both adult and neonatal patients receiving buprenorphine for treatment of opioid withdrawal. An SNP associated with rs678849 of OPRD1, coding for the delta opioid receptor, was associated with opioid relapse as indicated by opioid positive urine drug screens; there was also sex specific SNP identified at rs581111 and rs529520 in the European American population. COMT variability, particularly in rs4680, has been associated with length of stay and need for opioid treatment in patients with neonatal abstinence syndrome. Variations of the pharmacokinetic gene for CYP3A4 showed that the ultrarapid metabolizer phenotype required higher doses of buprenorphine. Genotyping of patients may allow us to appropriately tailor buprenorphine therapy to individual patients and lead to improved patient outcomes; however, further research on the pharmacogenomics of buprenorphine is needed.

Prevalence and types of inconsistencies in clinical pharmacogenetic recommendations among major U.S. sources

Clinical implementation of pharmacogenomics (PGx) is slow. Previous studies have identified some inconsistencies among clinical PGx recommendations, but the prevalence and types of inconsistencies have not been comprehensively analyzed among major PGx guidance sources in the U.S. PGx recommendations from the Clinical Pharmacogenetics Implementation Consortium, U.S. Food and Drug Administration drug labels, and major U.S. professional medical organizations were analyzed through May 24, 2019. Inconsistencies were analyzed within the following elements: recommendation category; whether routine screening was recommended; and the specific biomarkers, variants, and patient groups involved. We identified 606 total clinical PGx recommendations, which contained 267 unique drugs. Composite inconsistencies occurred in 48.1% of clinical PGx recommendations overall, and in 93.3% of recommendations from three sources. Inconsistencies occurred in the recommendation category (29.8%), the patient group (35.4%), and routine screening (15.2%). In conclusion, almost one-half of clinical PGx recommendations from prominent U.S. guidance sources contain inconsistencies, which can potentially slow clinical implementation.

Costing Methods as a Means to Measure the Costs of Pharmacogenomics Testing.

Clinical implementation of pharmacogenomics and personalized medicine interventions relies on addressing important financial aspects of the delivery of genetic testing to the patients, be it from public or private providers. Details on how to determine the cost items of the genetic testing are often limited. The goal of this study is to present a costing methodology in order to estimate and measure the costs as far as the technical process of pharmacogenomics testing is concerned. Moreover, an overall cost mindset strategy based on the selective genotyping workflow to guide specialized laboratories of interest effectively is provided. We particularly accounted for the resources consumed within the laboratory premises such as cost of reagents for DNA isolation, cost of consumables, cost of personnel, while costs associated with patient recruitment, blood sample collection and maintenance, administration costs in the hospital, and costs of blood sample shipment were not taken into consideration. Our article presents the first-time detailed information on a costing framework for pharmacogenomic testing that could be employed to laboratories involved in routine clinical implementation of pharmacogenomics.

Precision medicine for rheumatologists: lessons from the pharmacogenomics of azathioprine.

Precision medicine aims to personalize treatment for both effectiveness and safety. As a critical component of this emerging initiative, pharmacogenomics seeks to guide drug treatment based on genetics. In this review article, we give an overview of pharmacogenomics in the setting of an immunosuppressant frequently prescribed by rheumatologists, azathioprine. Azathioprine has a narrow therapeutic index and a high risk of adverse events. By applying candidate gene analysis and unbiased approaches, researchers have identified multiple variants associated with an increased risk for adverse events associated with azathioprine, particularly bone marrow suppression. Variants in two genes, TPMT and NUDT15, are widely recognized, leading drug regulatory agencies and professional organizations to adopt recommendations for testing before initiation of azathioprine therapy. As more gene-drug interactions are discovered, our field will continue to face the challenge of balancing benefits and costs associated with genetic testing. However, novel approaches in genomics and the integration of clinical and genetic factors into risk scores offer unprecedented opportunities for the application of pharmacogenomics in routine practice. Key Points • Pharmacogenomics can help us understand how individuals' genetics may impact their response to medications. • Azathioprine is a success story for the clinical implementation of pharmacogenomics, particularly the effects of TPMT and NUDT15 variants on myelosuppression. • As our knowledge advances, testing and dosing recommendations will continue to evolve, with our field striving to balance costs and benefits to patients. • As we aim toward the goals of precision medicine, future research may integrate increasingly individualized traits-including clinical and genetic characteristics-to predict the safety and efficacy of particular medications for individual patients.

Evaluation of Current Regulation and Guidelines of Pharmacogenomic Drug Labels: Opportunities for Improvements.

Pharmacogenomic drug labels in the Summary of Product Characteristics (SmPC) provide an instrument for clinical implementation of pharmacogenomics. We compared pharmacogenomic guidance by Clinical Pharmacogenetics Implementation Consortium (CPIC), Dutch Pharmacogenetics Working Group (DPWG), the US Food and Drug Administration (FDA), and by the European agencies the European Medicines Agency (EMA), College ter Beoordeling van Geneesmiddelen Medicines Evaluation Board (CBG‐MEB), and Federal Institute for Drugs and Medical Devices (FIDMD), collectively assigned as EMA/FIDMD+MEB shortened as EMA/FM. Of 54 drugs with an actionable gene–drug interaction in the CPIC and DPWG guidelines, only 50% had actionable pharmacogenomic information in the SmPCs and the agencies were in agreement in only 18% of the cases. We further compared 450 additional drugs, lacking CPIC or DPWG guidance, and found 126 actionable gene–drug labels by the FDA and/or the EMA/FM. Based on these 126 drugs in addition to the 54 above, the consensus of actionable pharmacogenomic labeling between the FDA and the EMA/FM was only 54%. In conclusion, guidelines provided by CPIC/DPWG are only partly implemented into the SmPCs and the implementation of pharmacogenomic drug labels into the clinics would strongly gain from a higher extent of consensus between agencies.