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

Effect of CYP2D6 genetic variation on patient-reported symptom improvement and side effects among children and adolescents treated with amphetamines

OBJECTIVES

Amphetamine-based medications are recommended as a first-line pharmacotherapy for the treatment of attention-deficit/hyperactivity disorder in children and adolescents. However, the efficacy and tolerability of these medications vary across individuals, which could be related to interindividual differences in amphetamine metabolism. This study examined if genotype-predicted phenotypes of the cytochrome P450 isozyme CYP2D6 were associated with self-reported side effects and symptom improvement in youth treated with amphetamines.

METHODS

Two hundred fourteen participants aged 6-24 who had a history of past or current amphetamine treatment were enrolled from Western Canada. Amphetamine dose and duration information was collected from the participants along with questions regarding adherence, concomitant medications, symptom improvement and side effects. DNA was extracted from saliva samples and genotyped for CYP2D6 . Binomial logistic regression models were used to determine the effect of CYP2D6 metabolizer phenotype with and without correction for phenoconversion on self-reported symptom improvement and side effects.

RESULTS

Genotype-predicted CYP2D6 poor metabolizers had significantly higher odds of reporting symptom improvement when compared to intermediate metabolizers (OR = 3.67, 95% CI = 1.15-11.7, P  = 0.029) after correction for phenoconversion and adjusting for sex, age, dose, duration, and adherence. There was no association between CYP2D6 metabolizer phenotype and self-reported side effects.

CONCLUSION

Our findings indicate that phenoconverted and genotype-predicted CYP2D6 poor metabolizer phenotype is significantly associated with higher odds of symptom improvement in children and adolescents treated with amphetamine. If replicated, these results could inform the development of future dosing guidelines for amphetamine treatment in children and adolescents.

QPGx-CARES: Qatar pharmacogenetics clinical applications and research enhancement strategies

Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Pharmacogenomics of Drugs Used in β-Thalassemia and Sickle-Cell Disease: From Basic Research to Clinical Applications

In this short review we have presented and discussed studies on pharmacogenomics (also termed pharmacogenetics) of the drugs employed in the treatment of β-thalassemia or Sickle-cell disease (SCD). This field of investigation is relevant, since it is expected to help clinicians select the appropriate drug and the correct dosage for each patient. We first discussed the search for DNA polymorphisms associated with a high expression of γ-globin genes and identified this using GWAS studies and CRISPR-based gene editing approaches. We then presented validated DNA polymorphisms associated with a high HbF production (including, but not limited to the HBG2 XmnI polymorphism and those related to the BCL11A, MYB, KLF-1, and LYAR genes). The expression of microRNAs involved in the regulation of γ-globin genes was also presented in the context of pharmacomiRNomics. Then, the pharmacogenomics of validated fetal hemoglobin inducers (hydroxyurea, butyrate and butyrate analogues, thalidomide, and sirolimus), of iron chelators, and of analgesics in the pain management of SCD patients were considered. Finally, we discuss current clinical trials, as well as international research networks focusing on clinical issues related to pharmacogenomics in hematological diseases.

ursaPGx: a new R package to annotate pharmacogenetic star alleles using phased whole-genome sequencing data

Long-read sequencing technologies offer new opportunities to generate high-confidence phased whole-genome sequencing data for robust pharmacogenetic annotation. Here, we describe a new user-friendly R package, ursaPGx, designed to accept multi-sample phased whole-genome sequencing data VCF input files and output star allele annotations for pharmacogenes annotated in PharmVar.

Pharmacogenetics in Child and Adolescent Psychiatry: Background and Evidence-Based Clinical Applications

The efficacy and tolerability of psychotropic medications can vary significantly among children and adolescents, and some of this variability relates to pharmacogenetic factors. Pharmacogenetics (PGx) in child and adolescent psychiatry can potentially improve treatment outcomes and minimize adverse drug reactions. This article reviews key pharmacokinetic and pharmacodynamic genes and principles of pharmacogenetic testing and discusses the evidence base for clinical decision-making concerning PGx testing. This article reviews current guidelines from the United States Food and Drug Administration (FDA), the Clinical Pharmacogenetics Implementation Consortium (CPIC), and the Dutch Pharmacogenetics Working Group (DPWG) and explores potential future directions. This review discusses key clinical considerations for clinicians prescribing psychotropic medications in children and adolescents, focusing on antidepressants, antipsychotics, stimulants, norepinephrine reuptake inhibitors, and alpha-2 agonists. Finally, this review synthesizes the practical use of pharmacogenetic testing and clinical decision support systems.

Developments in pharmacogenetics, pharmacogenomics, and personalized medicine

The development of Pharmacogenetics and Pharmacogenomics in Western Europe is highly relevant in the worldwide scenario. Despite the usually low institutional support, many research groups, composed of basic and clinical researchers, have been actively working for decades in this field. Their contributions made an international impact and paved the way for further studies and pharmacogenomics implementation in clinical practice. In this manuscript, that makes part of the Special Issue entitled Spanish Pharmacology, we present an analysis of the state of the art of Pharmacogenetics and Pharmacogenomics research in Europe, we compare it with the developments in Spain, and we summarize the most salient contributions since 1988 to the present, as well as recent developments in the clinical application of pharmacogenomics knowledge. Finally, we present some considerations on how we could improve translation to clinical practice in this specific scenario.

Should secondary pharmacogenomic variants be actively screened and reported when diagnostic genome-wide sequencing is performed in a child?

This white paper was prepared by the Global Alliance for Genomics and Health Regulatory and Ethics Work Stream's Pediatric Task Team to review and provide perspective with respect to ethical, legal, and social issues regarding the return of secondary pharmacogenomic variants in children who have a serious disease or developmental disorder and are undergoing exome or genome sequencing to identify a genetic cause of their condition. We discuss actively searching for and reporting pharmacogenetic/genomic variants in pediatric patients, different methods of returning secondary pharmacogenomic findings to the patient/parents and/or treating clinicians, maintaining these data in the patient's health record over time, decision supports to assist using pharmacogenetic results in future treatment decisions, and sharing information in public databases to improve the clinical interpretation of pharmacogenetic variants identified in other children. We conclude by presenting a series of points to consider for clinicians and policymakers regarding whether, and under what circumstances, routine screening and return of pharmacogenomic variants unrelated to the indications for testing is appropriate in children who are undergoing genome-wide sequencing to assist in the diagnosis of a suspected genetic disease.

Does Preoperative Pharmacogenomic Testing of Patients Undergoing TKA Improve Postoperative Pain? A Randomized Trial

BACKGROUND

Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear.

QUESTIONS/PURPOSES

(1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns?

METHODS

Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results.

RESULTS

The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99).

CONCLUSION

Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances.

LEVEL OF EVIDENCE

Level I, therapeutic study.

Impact of Pharmacogenomics in Clinical Practice

Polymorphisms of genes encoding drug metabolizing enzymes and transporters can significantly modify pharmacokinetics, and this can be associated with significant differences in drug efficacy, safety, and tolerability. Moreover, genetic variants of some components of the immune system can explain clinically relevant drug-related adverse events. However, the implementation of drug dose individualization based on pharmacogenomics remains scarce. In this narrative review, the impact of genetic variations on the disposition, safety, and tolerability of the most commonly prescribed drugs is reported. Moreover, reasons for poor implementation of pharmacogenomics in everyday clinical settings are discussed. The literature analysis showed that knowledge of how genetic variations can modify the effectiveness, safety, and tolerability of a drug can lead to the adjustment of usually recommended drug dosages, improve effectiveness, and reduce drug-related adverse events. Despite some efforts to introduce pharmacogenomics in clinical practice, presently very few centers routinely use genetic tests as a guide for drug prescription. The education of health care professionals seems critical to keep pace with the rapidly evolving field of pharmacogenomics. Moreover, multimodal algorithms that incorporate both clinical and genetic factors in drug prescribing could significantly help in this regard. Obviously, further studies which definitively establish which genetic variations play a role in conditioning drug effectiveness and safety are needed. Many problems must be solved, but the advantages for human health fully justify all the efforts.

Patient-Level Exposure to Actionable Pharmacogenomic Medications in a Nationally Representative Insurance Claims Database

BACKGROUND

The prevalence of exposure to pharmacogenomic medications is well established but little is known about how long patients are exposed to these medications.

AIM

Our objective was to describe the amount of exposure to actionable pharmacogenomic medications using patient-level measures among a large nationally representative population using an insurance claims database.

METHODS

Our retrospective cohort study included adults (18+ years) from the IQVIA PharMetrics® Plus for Academics claims database with incident fills of 72 Clinical Pharmacogenetics Implementation Consortium level A, A/B, or B medications from January 2012 through September 2018. Patient-level outcomes included the proportion of days covered (PDC), number of fills, and average days supplied per fill over a 12-month period.

RESULTS

Over 1 million fills of pharmacogenetic medications were identified for 605,355 unique patients. The mean PDC for all medications was 0.21 (SD 0.3), suggesting patients were exposed 21% (77 days) of the year. Medications with the highest PDC (0.55-0.89) included ivacaftor, tamoxifen, clopidogrel, HIV medications, transplant medications, and statins; with the exception of statins, these medications were initiated by fewer patients. Pharmacogenomic medications were filled an average of 2.8 times (SD 3.0, range 1-81) during the year following the medication's initiation, and the average days supplied for each fill was 22.3 days (SD 22.4, range 1-180 days).

CONCLUSION

Patient characteristics associated with more medication exposure were male sex, older age, and comorbid chronic conditions. Prescription fill data provide patient-level exposure metrics that can further our understanding of pharmacogenomic medication utilization and help inform opportunities for pharmacogenomic testing.

Pharmacogenomics in clinical trials: an overview

With the trend towards promoting personalised medicine (PM), the application of pharmacogenetics and pharmacogenomics (PGx) is of growing importance. For the purposes of clinical trials, the inclusion of PGx is an additional tool that should be considered for improving our knowledge about the effectiveness and safety of new drugs. A search of available clinical trials containing pharmacogenetic and PGx information was conducted on ClinicalTrials.gov. The results show there has been an increase in the number of trials containing PGx information since the 2000 s, with particular relevance in the areas of Oncology (28.43%) and Mental Health (10.66%). Most of the clinical trials focus on treatment as their primary purpose. In those clinical trials entries where the specific genes considered for study are detailed, the most frequently explored genes are CYP2D6 (especially in Mental Health and Pain), CYP2C9 (in Hematology), CYP2C19 (in Cardiology and Mental Health) and ABCB1 and CYP3A5 (particularly prominent in Transplantation and Cardiology), among others. Researchers and clinicans should be trained in pharmacogenetics and PGx in order to be able to make a proper interpretation of this data, contributing to better prescribing decisions and an improvement in patients' care, which would lead to the performance of PM.

Pharmacogenomics of chloroquine and hydroxychloroquine: current evidence and future implications

As substrates of CYP2C8, CYP3A4/5 and CYP2D6, chloroquine's (CQ) and hydroxychloroquine's (HCQ) efficacy and safety may be affected by variants in the genes encoding these enzymes. This paper aims to assimilate the current evidence on the pharmacogenomics of CQ/HCQ and to identify risk phenotypes affecting the safety or efficacy of these drugs. It has been found that some CYP3A5, CYP2D6 and CYP2C8 genetic variants may affect the safety or effectiveness of CQ/HCQ. The phenotypes predictively representing ultra-rapid and poor metabolizers have been considered high-risk phenotypes. After considering these high-risk phenotypes in different ethnic groups, it is predicted that a considerable proportion of patients taking CQ/HCQ may be at risk of either therapeutic failure or severe toxicities.

Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment

Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

The Value of Pharmacogenomics for White and Indigenous Americans after Kidney Transplantation

BACKGROUND

There is a paucity of evidence to inform the value of pharmacogenomic (PGx) results in patients after kidney transplant and how these results differ between Indigenous Americans and Whites. This study aims to identify the frequency of recommended medication changes based on PGx results and compare the pharmacogenomic (PGx) results and patients' perceptions of the findings between a cohort of Indigenous American and White kidney transplant recipients.

METHODS

Thirty-one Indigenous Americans and fifty White kidney transplant recipients were studied prospectively. Genetic variants were identified using the OneOme RightMed PGx test of 27 genes. PGx pharmacist generated a report of the genetic variation and recommended changes. Pre- and post-qualitative patient surveys were obtained.

RESULTS

White and Indigenous American subjects had a similar mean number of medications at the time of PGx testing (mean 13 (SD 4.5)). In the entire cohort, 53% received beta blockers, 30% received antidepressants, 16% anticoagulation, 47% pain medication, and 25% statin therapy. Drug-gene interactions that warranted a clinical action were present in 21.5% of patients. In 12.7%, monitoring was recommended. Compared to the Whites, the Indigenous American patients had more normal CYP2C19 (p = 0.012) and CYP2D6 (p = 0.012) activities. The Indigenous American patients had more normal CYP4F2 (p = 0.004) and lower VKORC (p = 0.041) activities, phenotypes for warfarin drug dosing, and efficacy compared to the Whites. SLC6A4, which affects antidepressant metabolism, showed statistical differences between the two cohorts (p = 0.017); specifically, SLC6A4 had reduced expression in 45% of the Indigenous American patients compared to 20% of the White patients. There was no significant difference in patient perception before and after PGx.

CONCLUSIONS

Kidney transplant recipients had several drug-gene interactions that were clinically actionable; over one-third of patients were likely to benefit from changes in medications or drug doses based on the PGx results. The Indigenous American patients differed in the expression of drug-metabolizing enzymes and drug transporters from the White patients.

The Role of Gender Pharmacogenetics in the Personalization of Drug Treatment

The use of pharmacogenetic guidelines in personalizing treatments has shown the potential to reduce interindividual variability in drug response by enabling genotype-matched dosing and drug selection. However, other important factors, such as patient gender, may interact strongly with pharmacogenetics in determining the individual profile of toxicity and efficacy but are still rarely considered when planning pharmacological treatment. The literature indicates that males and females respond differently to drugs, with women being at higher risk for toxicity and having different plasma exposure to drugs at standard doses. Recent studies have shown that pharmacogenetic variants may have different predictive value in different sexes, as in the case of treatment with opioids, angiotensin-converting enzyme inhibitors, or proton pump inhibitors. Of particular interest is the case of treatment with fluoropyrimidines for cancer. A significant increase in toxicity has been described in female patients, with a more pronounced effect of specific DPYD and TYMS polymorphisms also noted. This manuscript reviews the major findings in the field of sex-specific pharmacogenomics. SIGNIFICANCE STATEMENT: Interindividual variability in drug response is an emerging issue in pharmacology. The genetic profile of patients, as well as their gender, may play a role in the identification of patients more exposed to the risk of adverse drug reactions or poor efficacy. This article reviews the current state of research on the interaction between gender and pharmacogenetics in addressing interindividual variability.

Pharmacogenomics of Hypertension in Africa: Paving the Way for a Pharmacogenetic-Based Approach for the Treatment of Hypertension in Africans

In Africa, the burden of hypertension has been rising at an alarming rate for the last two decades and is a major cause for cardiovascular disease (CVD) mortality and morbidity. Hypertension is characterised by elevated blood pressure (BP) ≥ 140/90 mmHg. Current hypertension guidelines recommend the use of antihypertensives belonging to the following classes: calcium channel blockers (CCB), angiotensin converting inhibitors (ACEI), angiotensin receptor blockers (ARB), diuretics, β-blockers, and mineralocorticoid receptor antagonists (MRAs), to manage hypertension. Still, a considerable number of hypertensives in Africa have their BP uncontrolled due to poor drug response and remain at the risk of CVD events. Genetic factors are a major contributing factor, accounting for 20% to 80% of individual variability in therapy and poor response. Poor response to antihypertensive drug therapy is characterised by elevated BPs and occurrence of adverse drug reactions (ADRs). As a result, there have been numerous studies which have examined the role of genetic variation and its influence on antihypertensive drug response. These studies are predominantly carried out in non-African populations, including Europeans and Asians, with few or no Africans participating. It is important to note that the greatest genetic diversity is observed in African populations as well as the highest prevalence of hypertension. As a result, this warrants a need to focus on how genetic variation affects response to therapeutic interventions used to manage hypertension in African populations. In this paper, we discuss the implications of genetic diversity in CYP11B2, GRK4, NEDD4L, NPPA, SCNN1B, UMOD, CYP411, WNK, CYP3A4/5, ACE, ADBR1/2, GNB3, NOS3, B2, BEST3, SLC25A31, LRRC15 genes, and chromosome 12q loci on hypertension susceptibility and response to antihypertensive therapy. We show that African populations are poorly explored genetically, and for the few characterised genes, they exhibit qualitative and quantitative differences in the profile of pharmacogene variants when compared to other ethnic groups. We conclude by proposing prioritization of pharmacogenetics research in Africa and possible adoption of pharmacogenetic-guided therapies for hypertension in African patients. Finally, we outline the implications, challenges, and opportunities these studies present for populations of non-European descent.

Ten-year experience with pharmacogenetic testing for DPYD in a national cancer center in Italy: Lessons learned on the path to implementation

Background: Awareness about the importance of implementing DPYD pharmacogenetics in clinical practice to prevent severe side effects related to the use of fluoropyrimidines has been raised over the years. Since 2012 at the National Cancer Institute, CRO-Aviano (Italy), a diagnostic DPYD genotyping service was set up. Purpose: This study aims to describe the evolution of DPYD diagnostic activity at our center over the last 10 years as a case example of a successful introduction of pharmacogenetic testing in clinical practice. Methods: Data related to the diagnostic activity of in-and out-patients referred to our service between January 2012 and December 2022 were retrieved from the hospital database. Results: DPYD diagnostic activity at our center has greatly evolved over the years, shifting gradually from a post-toxicity to a pre-treatment approach. Development of pharmacogenetic guidelines by national and international consortia, genotyping, and IT technology evolution have impacted DPYD testing uptake in the clinics. Our participation in a large prospective implementation study (Ubiquitous Pharmacogenomics) increased health practitioners' and patients' awareness of pharmacogenetic matters and provided additional standardized infrastructures for genotyping and reporting. Nationwide test reimbursement together with recommendations by regulatory agencies in Europe and Italy in 2020 definitely changed the clinical practice guidelines of fluoropyrimidines prescription. A dramatic increase in the number of pre-treatment DPYD genotyping and in the coverage of new fluoropyrimidine prescriptions was noticed by the last year of observation (2022). Conclusion: The long path to a successful DPYD testing implementation in the clinical practice of a National Cancer Center in Italy demonstrated that the development of pharmacogenetic guidelines and genotyping infrastructure standardization as well as capillary training and education activity for all the potential stakeholders are fundamental. However, only national health politics of test reimbursement and clear recommendations by drug regulatory agencies will definitely move the field forward.

How to Run the Pharmacogenomics Clinical Annotation Tool (PharmCAT)

Pharmacogenomics (PGx) investigates the genetic influence on drug response and is an integral part of precision medicine. While PGx testing is becoming more common in clinical practice and may be reimbursed by Medicare/Medicaid and commercial insurance, interpreting PGx testing results for clinical decision support is still a challenge. The Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been designed to tackle the need for transparent, automatic interpretations of patient genetic data. PharmCAT incorporates a patient's genotypes, annotates PGx information (allele, genotype, and phenotype), and generates a report with PGx guideline recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and/or the Dutch Pharmacogenetics Working Group (DPWG). PharmCAT has introduced new features in the last 2 years, including a variant call format (VCF) Preprocessor, the inclusion of DPWG guidelines, and functionalities for PGx research. For example, researchers can use the VCF Preprocessor to prepare biobank-scale data for PharmCAT. In addition, PharmCAT enables the assessment of novel partial and combination alleles that are composed of known PGx variants and can call CYP2D6 genotypes based on single and deletions in the input VCF file. This tutorial provides materials and detailed step-by-step instructions for how to use PharmCAT in a versatile way that can be tailored to users' individual needs.

PharmVar GeneFocus: SLCO1B1

The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.

Pharmacogenetic actionability and medication prescribing in people with cystic fibrosis

Although major advancements have been made in the therapeutics for people with cystic fibrosis (PwCF), many still require the use of multiple medications to manage acute exacerbations of disease and maintain health. Iterative trial and error processes of pharmacotherapeutic management can be optimized by assessing and incorporating pharmacogenetics. For 82 PwCF, we reviewed 2 years of medication use and response history and interrogated metabolizer status for common pharmacogenes, revealing 3336 medication exposure events (MEEs) to 286 unique medications. As expected, the more frequent MEEs were those commonly used to treat cystic fibrosis (CF), such as antibiotics and respiratory medications. Antibiotics also comprised 56.7% of the undesirable drug responses. The impact of gene variants on drug responses was assessed using Pharmacogenomics Knowledgebase (PharmGKB) and Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Thirty-three (11.5%) medications have strong evidence of genetic influence on response as evidenced by gene-based dosing guidelines. 110 (38.5%) unique medications had at least one association with a very important pharmacogene, whereas 143 (50%) were associated with at least one clinical or variant annotation. Over 97% of participants had at least one actionable genotype. Eleven (13.4%) patients with an actionable genotype, taking the impacted medication, had an undesirable drug response described in the CPIC guidelines that could potentially have been mitigated with a priori knowledge of the genotype. PwCF take many medications for disease management, with frequent dose changes to elicit a desired clinical effect. As CF care evolves, implementing pharmacogenetics testing can improve efficiency and safety of prescribing practices using precision selection and dosing at medication initiation.

Pharmacogenetics in critical care: association between CYP3A5 rs776746 A/G genotype and acetaminophen response in sepsis and septic shock

BACKGROUND

Pharmacogenetics could represent a further resource to understand the interindividual heterogeneity of response of the host to sepsis and to provide a personalized approach to the critical care patient.

METHODS

Secondary analysis of data from the prospective observational study NCT02750163, in 50 adult septic and septic shock patients treated with Acetaminophen (ACT) for pyrexia. We investigated the presence of two polymorphisms, located respectively in the genes UGT1A1 and CYP3A5, that encode for proteins related to the hepatic metabolism of ACT. The main dependent variables explored were plasmatic concentration of ACT, body temperature and hepatic parameters.

RESULTS

8% of the patients carried CYP3A5 rs776746 A/G genotypes and showed significantly higher plasma levels of ACT than GG wild type patients, and than patients with UGT1A1 rs8330 C/G genotypes.

CONCLUSIONS

Identifying specific genotypes of response to ACT may be helpful to guide a more personalized titration of therapy in sepsis and septic shock. CYP3A5 might be a good biomarker for ACT metabolism; however further studies are needed to confirm this result.

TRIAL REGISTRATION

NCT02750163.

Impact of pharmacogenomics in achieving personalized/precision medicine in the clinical setting: a symposium report

The Indo-Swiss symposium on pharmacogenomic strategies for the implementation of personalized medicine was conducted as part of the Jawaharlal Institute of Postgraduate Medical Education and Research Integrated Pharmacogenomics Program in Puducherry, India, on 19 November 2022. The symposium was conducted in hybrid mode. The theme of symposium was the impact of pharmacogenomics on the achievement of personalized medicine/precision medicine in the clinical setting. The symposium sought to promote interaction among the participants to initiate future collaborative research projects. The symposium also served as a platform for young researchers to present their research findings as posters to the audience.

PAnno: A pharmacogenomics annotation tool for clinical genomic testing

Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.

Refinement of a pharmacogenomics app for dosing guidelines for oncology: findings from the usability evaluation

Background

This study extended a precision medicine clinical decision support mobile application (app) for use with oncology medications. Two gene variants (CYP2D6 and DPYD) associated with pharmacogenomic dosing algorithms in oncology was added to a prototype app. Usability of the app was evaluated. The use of smartphones and mobile apps for prescribing medications has exponentially increased since the introduction of physician order entry. Decision support apps have improved provider performance and studies have shown broader adoption is crucial for the success of these tools. Therefore, successful use of mobile apps will depend on perceptions of users. Rogers' Diffusion of Innovation theory will be the guiding framework for this study.

Methods

The main research variable is usability as measured by effectiveness, efficiency, and satisfaction. A mixed method design was used. The setting was inpatient and outpatient oncology practices within North Carolina. The sample included registered nurses and nurse practitioners within the oncology field. A functioning mobile app was extended based on the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines to address the most common gene variants seen in oncology patients. Usability testing is divided into two main categories, inspection and testing methods. Prior to the field study, a heuristic evaluation was conducted. This evaluation inspected the user interface, comparing the elements and aspects of it to a set of principles, heuristics, as a guideline to evaluate the usability of the mobile app.

Results

The testing evaluation was conducted with a sample of 51 health care providers to evaluate usability, measured by the System Usability Scale and open-ended questions. Descriptive statistics was used to summarize usefulness and end-user perceived ease of use. In addition, a thematic analysis of the open-ended questions was conducted.

Conclusions

The development of this mobile app is relevant to nurses who have prescriptive privileges, as well as an educational tool for nurses to understand the rationale behind prescribing certain medications and alternate dosages by providing specific recommendations. Translation of precision medicine into practice will benefit patients by improving care, reducing adverse reactions, and lowering costs.

Cost Effectiveness of Pharmacogenetic Testing for Drugs with Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines: A Systematic Review

The objective of this study was to evaluate the evidence on cost-effectiveness of pharmacogenetic (PGx)-guided treatment for drugs with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. A systematic review was conducted using multiple biomedical literature databases from inception to June 2021. Full articles comparing PGx-guided with nonguided treatment were included for data extraction. Quality of Health Economic Studies (QHES) was used to assess robustness of each study (0-100). Data are reported using descriptive statistics. Of 108 studies evaluating 39 drugs, 77 (71%) showed PGx testing was cost-effective (CE) (N = 48) or cost-saving (CS) (N = 29); 21 (20%) were not CE; 10 (9%) were uncertain. Clopidogrel had the most articles (N = 23), of which 22 demonstrated CE or CS, followed by warfarin (N = 16), of which 7 demonstrated CE or CS. Of 26 studies evaluating human leukocyte antigen (HLA) testing for abacavir (N = 8), allopurinol (N = 10), or carbamazepine/phenytoin (N = 8), 15 demonstrated CE or CS. Nine of 11 antidepressant articles demonstrated CE or CS. The median QHES score reflected high-quality studies (91; range 48-100). Most studies evaluating cost-effectiveness favored PGx testing. Limited data exist on cost-effectiveness of preemptive and multigene testing across disease states.

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

Introduction

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.

Method

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.

Results

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.

Discussion

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.

Current pharmacogenomic recommendations in chronic respiratory diseases: Is there a biomarker ready for clinical implementation?

INTRODUCTION

The study of genetic variants in response to different drugs has predominated in fields of medicine such as oncology and infectious diseases. In chronic respiratory diseases, the available pharmacogenomic information is scarce but not less relevant.

AREAS COVERED

We searched the pharmacogenomic recommendations for respiratory diseases in the Table of Pharmacogenomic Biomarkers in Drug Labeling (U.S. Food and Drug Administration), the Clinical Pharmacogenomics Implementation Consortium (CPIC), and PharmGKB. The main pharmacogenomics recommendation in this field is to assess CFTR variants for using ivacaftor and its combination. The drugs' labels for arformoterol, indacaterol, and umeclidinium indicate a lack of influence of genetic variants in the pharmacokinetics of these drugs. Further studies should evaluate the contribution of CYP2D6 and CYP2C19 variants for formoterol. In addition, there are reports of potential pharmacogenetic variants in the treatment with acetylcysteine (TOLLIP rs3750920) and captopril (ACE rs1799752). The genetic variations for warfarin also are presented in PharmGKB and CPIC for patients with pulmonary hypertension.

EXPERT OPINION

The pharmacogenomics recommendations for lung diseases are limited. The clinical implementation of pharmacogenomics in treating respiratory diseases will contribute to the quality of life of patients with chronic respiratory diseases.

How to Implement a Pharmacogenetics Service at your Institution

The vast majority of patients possess one or more pharmacogenetic variants that can influence optimal medication use. When pharmacogenetic data are used to guide drug choice and dosing, evidence points to improved disease outcomes, fewer adverse effects, and lower healthcare spending. Although its science is well established, clinical use of pharmacogenetic data to guide drug therapy is still in its infancy. Pharmacogenetics essentially involves the intersection of an individual's genetic data with their medications, which makes pharmacists uniquely qualified to provide clinical support and education in this field. In fact, most pharmacogenetics implementations, to date, have been led by pharmacists as leaders or members of a multidisciplinary team or as individual practitioners. A successful large-scale pharmacogenetics implementation requires coordination and synergy among administrators, clinicians, informatics teams, laboratories, and patients. Because clinical implementation of pharmacogenetics is in its early stages, there is an urgent need for guidance and dissemination of shared experiences to provide a framework for clinicians. Many early adopters of pharmacogenetics have explored various strategies among diverse practice settings. This article relies on the experiences of early adopters to provide guidance for critical steps along the pathway to implementation, including strategies to engage stakeholders; evaluate pharmacogenetic evidence; coordinate laboratory testing, results interpretation and their integration into the electronic health record; identify reimbursement avenues; educate providers and patients; and maintain a successful program. Learning from early adopters' published experiences and strategies can allow clinicians leading a new pharmacogenetics implementation to avoid pitfalls and adapt and apply lessons learned by others to their own practice.

Meta-analysis of pharmacogenetic clinical decision support systems for the treatment of major depressive disorder

The study aimed to conduct a meta-analysis of studies comparing pharmacogenetically guided dosing of antidepressants with empiric standard of care. Publications referring to genotype-guided antidepressant therapy were identified via PubMed, Google Scholar, Scopus, Web of Science, Embase, and Cochrane databases from the inception of the databases to 2021. In addition, bibliographies of all articles were manually searched for additional references not identified in primary searches. Studies comparing clinical outcomes between two groups of patients who received antidepressant treatment were included in meta-analysis. Analysis of the data revealed statistically significant differences between the experimental group receiving pharmacogenetically guided dosing and the empirically treated controls. Specifically, genotype-guided treatment significantly improved response and remission of patients after both eight and twelve weeks of therapy, whereas no effect on the development of adverse drug reactions was observed. This meta-analysis indicates that the use of preemptive genotyping to guide dosing of antidepressants might increase treatment efficacy.

Advancing Pharmacogenomics from Single-Gene to Preemptive Testing

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

Population genetic polymorphisms of pharmacogenes in Zimbabwe, a potential guide for the safe and efficacious use of medicines in people of African ancestry

OBJECTIVE

Pharmacogenomics (PGx) is a clinically significant factor in the safe and efficacious use of medicines. While PGx knowledge is abundant for other populations, there are scarce PGx data on African populations and is little knowledge on drug-gene interactions for medicines used to treat diseases common in Africa. The aim of this study was to use a custom-designed open array to genotype clinically actionable variants in a Zimbabwean population. This study also identified some of the commonly used drugs in Zimbabwe and the associated genes involved in their metabolism.

METHODS

A custom-designed open array that covers 120 genetic variants was used to genotype 522 black Zimbabwean healthy volunteers using TaqMan-based single nucleotide polymorphism genotyping. Data were also accessed from Essential Drugs' List in Zimbabwe (EDLIZ), and the medicines were grouped into the associated biomarker groups based on their metabolism. We also estimated the national drug procurement levels for medicines that could benefit from PGx-guided use based on the data obtained from the national authorities in Zimbabwe.

RESULTS

The results demonstrate the applicability of an open-array chip in simultaneously determining multiple genetic variants in an individual, thus significantly reducing cost and time to generate PGx data. There were significantly high frequencies of African-specific variants, such as the CYP2D6*17 and *29 variants and the CYP2B6*18 variant. The data obtained showed that the Zimbabwean population exhibits PGx variations in genes important for the safe and efficacious use of drugs approved by the EDLIZ and are procured at significantly large amounts annually. The study has established a cohort of genotyped healthy volunteers that can be accessed and used in the conduct of clinical pharmacogenetic studies for drugs entering a market of people of predominantly African ancestry.

CONCLUSION

Our study demonstrated the potential benefit of integrating PGx in Zimbabwe for the safe and efficacious use of drugs that are commonly used.

Variability Between Clinical Pharmacogenetics Implementation Consortium (CPIC®) Guidelines and a Commercial Pharmacogenetics Laboratory in Genotype to Phenotype Interpretations For Patients Utilizing Psychotropics

Clinical practice environments without in-house pharmacogenetic testing often rely on commercial laboratories, especially in the setting of pharmacogenetic testing intended to guide psychotropic use. There are occasionally differences in phenotype assignment and medication recommendations between commercial laboratories and the Clinical Pharmacogenetics Implementation Consortium (CPIC). This may be problematic as many institutions that implement pharmacogenetics consider CPIC to be an important source of guidelines for recommended prescribing actions based on genetics, as well as a tool towards standardizing pharmacogenetics implementation. Here, we completed a retrospective chart review of our academic health system’s (Michigan Medicine) electronic health record with the goal of comparing phenotypic assignment of CYP2D6 and CYP2C19 genotypes between the commercial pharmacogenetic lab used most at our institution, and CPIC. Ultimately, we identified 205 patients with available pharmacogenetic results from this lab. The prevalence of conflicting phenotype assignment was 28.8% for CYP2D6 and 32.2% for CYP2C19 genotypes when comparing the commercial lab to CPIC guidelines. In several cases, the phenotypic assignment differences for antidepressants led to significant differences in medication recommendations when comparing the commercial lab report and CPIC guidelines. These results may also have implications for medications outside of psychiatry with recommendations for dose adjustments based on CYP2D6 or CYP2C19 metabolizing phenotype.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Pharmacogenomics: a tool to improve medication safety and efficacy in patients with cystic fibrosis

Cystic fibrosis is a genetic, multiorgan system disease that involves the use of many medications to control symptoms associated with the underlying condition. Many of these medications have Clinical Pharmacogenetics Implementation Consortium evidence-based guidelines for pharmacogenomics that are available to guide dosing. The aim of this article is to review relevant literature and evaluate the utility of preemptive pharmacogenomics testing for persons with cystic fibrosis and propose a pharmacogenomics panel that could be considered standard of care for persons with cystic fibrosis.

Prospective Randomized Study Using Pharmacogenetics to Customize Postoperative Pain Medication Following Hip and Knee Arthroplasty

BACKGROUND

The purpose of this study is to determine whether pharmacogenetic testing could be used to effectively customize postoperative pain medicine following total joint replacement.

METHODS

Buccal swabs were collected preoperatively from 107 patients. Pharmacogenetic testing was performed for genetic variants on a panel of 16 genes, including CYP2D6, CYP2C9, OPRM1, and CYP1A2, which affect the pharmacodynamics and pharmacokinetics of non-steroidal anti-inflammatory drugs and many opioids. Patients were randomized to a control group or custom group and blinded to their group. The control group was prescribed oxycodone, tramadol, and celecoxib for postoperative pain management. If any of those were not normally metabolized, they were not prescribed to the patients in the custom group, who were given an alternative drug (hydromorphone for narcotics, meloxicam for non-steroidal anti-inflammatory drugs). Patients recorded their pain level (0-10 numeric scale) and all medications taken daily for the first 10 days following surgery. Medication was converted to milligram morphine equivalents (MMEs).

RESULTS

Genetic variations to medications in our standard postoperative pain management protocol occurred in 24 of the 107 patients (22.4%). The 10-day MME consumed by patients in the control group with genetic variants was 162.6 mg. Patients with variants who had custom postoperative medication consumed only 86.7 MME in the same timeframe (P = .126). The control group demonstrated a higher 10-day average pain level of 4.2 vs the custom group pain level of only 3.1 (P < .05).

CONCLUSION

With custom postoperative pain prescriptions based on pharmacogenetic testing, patients were able to achieve lower pain levels while reducing the consumption of pain medication.

The promise of digital health in healthcare equity and medication adherence in the disadvantaged dementia population

Tweetable abstract Pharmacogenomics cascade testing in a digital health solution can improve medication adherence in dementia care for disadvantaged populations.

The genetics of drug-induced QT prolongation: evaluating the evidence for pharmacodynamic variants

Drug-induced long QT syndrome (diLQTS) is an adverse effect of many commonly prescribed drugs, and it can increase the risk for lethal ventricular arrhythmias. Genetic variants in pharmacodynamic genes have been associated with diLQTS, but the strength of the evidence for each of those variants has not yet been evaluated. Therefore, the purpose of this review was to evaluate the strength of the evidence for pharmacodynamic genetic variants associated with diLQTS using a novel, semiquantitative scoring system modified from the approach used for congenital LQTS. KCNE1-D85N and KCNE2-T8A had definitive and strong evidence for diLQTS, respectively. The high level of evidence for these variants supports current consideration as risk factors for patients that will be prescribed a QT-prolonging drug.

Common UGT1A6 Variant Alleles Determine Acetaminophen Pharmacokinetics in Man

Acetaminophen (paracetamol) is a widely used drug that causes adverse drug events that are often dose-dependent and related to plasma drug concentrations. Acetaminophen metabolism strongly depends on UGT1A enzymes. We aimed to investigate putative factors influencing acetaminophen pharmacokinetics. We analyzed acetaminophen pharmacokinetics after intravenous administration in 186 individuals, and we determined the effect of sex; body mass index (BMI); previous and concomitant therapy with UGT1A substrates, inhibitors, and inducers; as well as common variations in the genes coding for UGT1A1, UGT1A6, and UGT1A9. We identified sex and UGT1A6 genetic variants as major factors influencing acetaminophen pharmacokinetics, with women showing lower clearance (p < 0.001) and higher area under the plasma drug concentration-time curve (AUC) values than men (p < 0.001). UGT1A6 genetic variants were related to decreased acetaminophen biodisposition. Individuals who were homozygous or double-heterozygous for variant UGT1A6 alleles showed a 22.5% increase in t1/2 values and a 22.8 increase in drug exposure (p < 0.001, and 0.006, respectively) after correction by sex. The effect is related to the UGT1A6*2 and UGT1A6*4 variant alleles, whereas no effect of UGT1A6*3 and UGT1A9*3 alleles, BMI, or drug−drug interaction was identified in this study. We conclude that sex and UGT1A6 variants determine acetaminophen pharmacokinetics, thus providing evidence to eventually developing pharmacogenomics procedures and recommendations for acetaminophen use.

Integration of Omics and Phenotypic Data for Precision Medicine

Over the past two decades, biomedical research is moving toward a big-data-driven approach. The underlying causes of this transition include the ability to gather genetic or molecular profiles of humans faster, the increasing adoption of electronic health record (EHR) system, and the growing interest in linking omics and phenotypic data for analysis. The integration of individual's biology data (e.g., genomics, proteomics, metabolomics), and health-care data has created unprecedented opportunities for precision medicine, that is, a medical model that uses a patient's unique information, mainly genetic, to prevent, diagnose, or treat disease. This chapter reviewed the research opportunities and applications of integrating omics and phenotypic data for precision medicine, such as understanding the relationship between genotype and phenotype, disease subtyping, and diagnosis or prediction of adverse outcomes. We reviewed the recent advanced methods, particularly the machine learning and deep learning-based approaches used for harnessing and harmonizing the multiomics and phenotypic data to address these applications. We finally discussed the challenges and future directions.

Establishing the Medical Actionability of Genomic Variants

Actionability is an important concept in medicine that does not have a well-accepted standard definition, nor is there a general consensus on how to establish it. Medical actionability is often conflated with clinical utility, a related but distinct concept. This lack of clarity contributes to practice variation and inconsistent coverage decisions in genomic medicine, leading to the potential for systematic bias in the use of evidence-based interventions. We clarify how medical actionability and clinical utility are distinct and then discuss the spectrum of actionability, including benefits for the person, the family, and society. We also describe applications across the life course, including prediction, diagnosis, and treatment. Current challenges in assessing the medical actionability of identified genomic variants include gaps in the evidence, limited contexts with practice guidelines, and subjective aspects of medical actionability. A standardized and authoritative assessment of medical actionability is critical to implementing genomic medicine in a fashion that improves population health outcomes and reduces health disparities. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Automated Pharmacogenomic Reports for Clinical Genome Sequencing

Clinical laboratories offering genome sequencing have the opportunity to return pharmacogenomic findings to patients, providing the added benefit of preemptive testing that could help inform medication selection or dosing throughout the lifespan. Implementation of pharmacogenomic reporting must address several challenges, including inherent limitations in short-read genome sequencing methods, gene and variant selection, standardization of genotype and phenotype nomenclature, and choice of guidelines and drugs to report. An automated pipeline, lmPGX, was developed as an end-to-end solution that produces two versions of a pharmacogenomic report, presenting either Clinical Pharmacogenetics Implementation Consortium or US Food and Drug Administration guidelines for 12 genes. The pipeline was validated for performance using reference samples and pharmacogenetic data from the Genetic Testing Reference Materials Coordination Program. To determine performance and limitations, lmPGX was compared with three additional publicly available pharmacogenomic pipelines. The lmPGX pipeline offers clinical laboratories an opportunity for seamless integration of pharmacogenomic results with genome reporting.

CYP1A2 mRNA Expression Rather than Genetic Variants Indicate Hepatic CYP1A2 Activity

CYP1A2, one of the most abundant hepatic cytochrome P450 enzymes, is involved in metabolism of several drugs and carcinogenic compounds. Data on the significance of CYP1A2 genetic polymorphisms in enzyme activity are highly inconsistent; therefore, the impact of CYP1A2 genetic variants (−3860G>A, −2467delT, −739T>G, −163C>A, 2159G>A) on mRNA expression and phenacetin O-dealkylation selective for CYP1A2 was investigated in human liver tissues and in psychiatric patients belonging to Caucasian populations. CYP1A2*1F, considered to be associated with high CYP1A2 inducibility, is generally identified by the presence of −163C>A polymorphism; however, we demonstrated that −163C>A existed in several haplotypes (CYP1A2*1F, CYP1A2*1L, CYP1A2*1M, CYP1A2*1V, CYP1A2*1W), and consequently, CYP1A2*1F was a much rarer allelic variant (0.4%) than reported in Caucasian populations. Of note, −163C>A polymorphism was found to result in an increase of neither mRNA nor the activity of CYP1A2. Moreover, hepatic CYP1A2 activity was associated with hepatic or leukocyte mRNA expression rather than genetic polymorphisms of CYP1A2. Consideration of non-genetic phenoconverting factors (co-medication with CYP1A2-specific inhibitors/inducers, tobacco smoking and non-specific factors, including amoxicillin+clavulanic acid therapy or chronic alcohol consumption) did not much improve genotype−phenotype estimation. In conclusion, CYP1A2-genotyping is inappropriate for the prediction of CYP1A2 function; however, CYP1A2 mRNA expression in leukocytes can inform about patients’ CYP1A2-metabolizing capacity.

Clinical Practice Guidelines Using GRADE and AGREE II for the Impact of Genetic Variants on Plasma Lipid/Lipoprotein/Apolipoprotein Responsiveness to Omega-3 Fatty Acids

Background

A recent systematic review, which used the GRADE methodology, concluded that there is strong evidence for two gene-diet associations related to omega-3 and plasma triglyceride (TG) responses. Systematic reviews can be used to inform the development of clinical practice guidelines (CPGs).

Objective

To provide guidance for clinical practice related to genetic testing for evaluating responsiveness to dietary/supplemental omega-3s and their impact on plasma lipids/lipoproteins/apolipoproteins.

Design

Using the results of the abovementioned systematic review, the first CPGs in nutrigenetics were developed using the established GRADE methodology and AGREE II approach.

Results

Three clinical practice recommendations were developed. Most gene-diet associations identified in the literature lack adequate scientific and clinical validity to warrant consideration for implementing in a practice setting. However, two gene-diet associations with strong evidence (GRADE quality: moderate and high) can be considered for implementation into clinical practice in certain cases: male APOE-E4 carriers (rs429358, rs7412) and TG changes in response to the omega-3 fatty acids eicosapentaenoic acid (EPA) and/or docosahexaenoic acid (DHA) as well as a 31-SNP nutrigenetic risk score and TG changes in response to EPA+DHA among adults with overweight/obesity. Ethical and regulatory implications must be considered when providing APOE nutrigenetic tests given the well-established link between APOE genetic variation and Alzheimer's Disease.

Conclusion

Most of the evidence in this area is not ready for implementation into clinical practice primarily due to low scientific validity (low quality of evidence). However, the first CPGs in nutrigenetics have been developed for two nutrigenetic associations with strong scientific validity, related to dietary/supplemental omega-3 and TG responses.

DNA Genotyping Based on Isothermal Amplification and Colorimetric Detection by Consumer Electronics Devices

The point-of-care testing of DNA biomarkers requires compact biosensing systems and consumer electronic technologies provide fascinating opportunities. Their portability, mass-produced components, and high-performance readout capabilities are the main advantages for the development of novel bioanalytical methods.This chapter describes the detection of single nucleotide polymorphisms (SNP) through methods based on user-friendly optical devices (e.g., USB digital microscope, flatbed scanner, smartphone, and DVD drive). Loop mediated isothermal amplification (LAMP) enables the required discrimination of each specific variant prior to the optical reading. In the first method, products are directly hybridized to the allele-specific probes attached to plastic chips in an array format. The second method, allele-specific primers are used, enabling the direct end-point detection based a colorimetric dyer and a microfluidic chamber chip. In both approaches, devices are employed for chip scanning.A representative application to the genotyping of a clinically relevant SNP from human samples is provided, showing the excellent features achieved. Consumer electronic devices are able to register sensitive precise measurements in terms of signal-to-noise ratios, image resolution, and scan-to-scan reproducibility. The integrated DNA-based method lead a low detection limit (100 genomic DNA copies), reproducible (variation <15%), high specificity (genotypes validated by reference method), and cheap assays (<10 €/test). The underlying challenge is the reliable implementation into minimal-specialized clinical laboratories, incorporating additional advantages, such as user-friendly interface, low cost, and connectivity for telemedicine needs.

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.