Field Of Pharmacogenomics Research Articles

The needs and gaps in pharmacogenomics knowledge and education among healthcare professionals in Malaysia: A multisite Delphi study.

Lack of pharmacogenomics knowledge among healthcare professionals is the most significant cited barrier to implementing pharmacogenomics in clinical settings. Despite the growth in research initiatives and awareness of pharmacogenomics, healthcare professionals continue to report a lack of knowledge and confidence in practicing pharmacogenomics. This study aims to assess the current pharmacogenomics knowledge gaps and learning needs of healthcare professionals in Malaysia. A modified Delphi with a multidisciplinary expert panel was conducted, and a purposive sampling method was used with predefined selection criteria. Fourteen study sites in Malaysia were included. The cut-off value to approach consensus was predefined as a threshold of 60% or higher, and a quantitative descriptive statistical analysis was performed. The study demonstrated that all experts rated the suggested educational content components as essential/important to be included in the educational intervention. Additionally, experts highlighted the significant barriers and gaps to adopting and practicing pharmacogenomics. To conclude, this multisite Delphi study enabled the development of a tailored, effective, evidence-based, competency-based educational intervention in pharmacogenomics for healthcare professionals in Malaysia. To keep up with the rapid evolution of the pharmacogenomics field, healthcare professionals should be equipped with the necessary competencies required to practice pharmacogenomics for better health outcomes. Future research is needed to determine the feasibility of the proposed educational intervention.

B-124 Structure-based Variant Effect Prediction and Design of Functional CYP2C9 Variants by Virtual Evolution

Abstract Background The decryption of massive variants of uncertain significance (VUS) discovered by sequencing poses the main challenge in the post-sequencing era. VUS also resulted in a particular problem in the field of pharmacogenomics, which can be epitomized by drug-metabolizing enzymes, such as CYP2C9. Therefore, an accurate model that incorporates the inner connection between massive protein variants and their function alterations is crucial for clinical annotation of human variome and the virtual evolution of enzyme engineering for biotherapeutics. Methods We developed a model named variant effect recognition network for CYP2C9 (vERnet-P), which learned features from AlphaFold2-predicted protein structures and predicted the enzyme activity of missense single-nucleotide variants in CYP2C9. The two crucial strategies of vERnet-P are the construction of amino acid interaction networks and the application of deep learning. Therefore, vERnet-P leveraged highly accurate protein structures predicted by AlphaFold2, combined with novel techniques for enriching and capturing useful information. Based on the accurate and preemptive prediction of CYP2C9 variants, we performed the saturation mutation prediction on several sites of CYP2C9 to identify new variants with the expected function. Furthermore, the drug metabolic activities of these newly identified variants were investigated with two probe drugs by in vitro metabolic activity assessments. Results An accuracy of up to 93.5% for the classification of activity levels was yielded in the testing dataset. In addition, the tasks for recognizing the high-activity and low-activity variants both achieved 93.5% accuracies, demonstrating that vERnet-P truly learned the features related to enzyme activity, rather than the selection bias to one kind of samples. Strikingly, the AUC values of the ROC and the PR reached 0.971 and 0.966 respectively in the testing cohort for vERnet-P. vERnet-P achieved the strongest agreement with massively parallel assessments relative to the other 8 computational variant effect predictors in the task of predicting CYP2C9 variant enzyme activity. We explored 6 mutant sites in CYP2C9 by using vERnet-P to perform saturation mutation prediction and found 12 novel variants with a high possibility of activity changing. We identified 6 variants with most likely increased activity by using the prediction of wild-type as a cutoff, consequently, 6 variants with most likely decreased activity were selected. According to these 12 discovered variants, 10 were strongly confirmed by the in vitro metabolic activity assays of both probe drugs. Conclusions vERnet-P achieved state-of-the-art performance in predicting the CYP2C9 variant enzyme activity. The preemptive prediction for CYP2C9 variants can efficiently provide the clinical drug dosing guideline. Furthermore, by saturating mutation prediction on several sites, our results indicated the evolutionary direction of CYP2C9 enzyme activity and discovered some brand-new CYP2C9 variants with ultra-strong functional alterations, which have not previously been reported. The results of in vitro metabolic activity assessment were highly consistent with our in silico prediction, indicating the potential of AI models for inferring evolutionary direction and designing novel variants with the expected function.

Importance of Pharmacovigilance in Health and Clinical Research

The field of pharmacogenomics has grown to be quite promising for the medical industry. Therefore, it is critical to assess the attitudes and knowledge of healthcare professionals. The study of interindividual differences in DNA sequence linked to pharmacological response is known as pharmacogenomics, or PGx. There have been several attempts to incorporate this area of research into standard therapeutic procedures. Before initiating clinical PGx in a hospital setting, we provide a brief overview of PGx and its function in enhancing treatment outcomes. Prioritization should involve assessing the PGx evidence that is now available, reviewing the medications that are most relevant, and figuring out which variant alleles and drug-gene combinations are the most actionable. Pharmacogenomics, or PGx, is one of the fields of precision medicine with the greatest potential to swiftly transform standard healthcare.Due to the quick advances in PGx knowledge obtained from extensive basic and clinical research as well as the falling costs of laboratory testing, there is presently increased interest in PGx and anticipation of an imminent clinical translation with major therapeutic impact. However, integrating PGx into clinical workflows is not an easy task; multidisciplinary cooperation and thorough procedures are needed. A greater knowledge of unresolved difficulties has resulted from the pioneering models for clinical PGx implementation developed by a number of national and international institutes.

Living systematic review and meta-analysis of plasma-concentrations of antipsychotic drugs in carriers and non-carriers of variant CYP450 genotypes: Living systematic review protocol

Introduction Carriers of variant alleles of genes that encode liver CYP450 and UGT enzymes may experience abnormal plasma levels of antipsychotics and, consequently, worse efficacy or tolerability. Although pharmacogenomics is a rapidly developing field, current guidelines often rely on limited, underpowered evidence. We have previously demonstrated that meta-analysis is a viable strategy for overcoming this problem. Here, we propose a project that will expand our previous work and create a living systematic review and meta-analysis of drug plasma level differences between carriers and non-carriers of variant genotype-predicted phenotypes for every pharmacokinetic drug-gene interaction relevant to commonly used antipsychotic drugs. Protocol First, a baseline systematic review and meta-analysis will be conducted by searching for observational pharmacogenomics-pharmacokinetic studies. Data on dose-adjusted drug plasma levels will be extracted, and participants will be grouped based on their genotype for each drug-gene pair separately. Differences in plasma drug levels between different phenotypes will be compared using a random-effect ratio-of-means meta-analysis. The risk of bias will be assessed using ROBINS-I, and the certainty of evidence will be assessed using GRADE. Following the establishment of baseline results, the literature search will be re-run at least once every six months, and the baseline data will be updated and re-evaluated as new evidence is published. A freely available website will be designated to present up-to-date results and conclusions. Discussion This systematic review will provide evidence-based results that are continuously updated with evidence as it emerges in the rapidly developing field of pharmacogenomics. These results may help psychiatrists in their decision-making, as clinicians are becoming increasingly aware of the patients’ genetic data as testing becomes more widespread and cheaper. In addition, the results may serve as a scientific basis for the development of evidence-based pharmacogenomics algorithms for personalized dosing of antipsychotics to mitigate potentially harmful drug-gene interactions.

Living systematic review and meta-analysis of plasma-concentrations of antipsychotic drugs in carriers and non-carriers of variant CYP450 genotypes: Living systematic review protocol.

Carriers of variant alleles of genes that encode liver CYP450 and UGT enzymes may experience abnormal plasma levels of antipsychotics and, consequently, worse efficacy or tolerability. Although pharmacogenomics is a rapidly developing field, current guidelines often rely on limited, underpowered evidence. We have previously demonstrated that meta-analysis is a viable strategy for overcoming this problem. Here, we propose a project that will expand our previous work and create a living systematic review and meta-analysis of drug plasma level differences between carriers and non-carriers of variant genotype-predicted phenotypes for every pharmacokinetic drug-gene interaction relevant to commonly used antipsychotic drugs. First, a baseline systematic review and meta-analysis will be conducted by searching for observational pharmacogenomics-pharmacokinetic studies. Data on dose-adjusted drug plasma levels will be extracted, and participants will be grouped based on their genotype for each drug-gene pair separately. Differences in plasma drug levels between different phenotypes will be compared using a random-effect ratio-of-means meta-analysis. The risk of bias will be assessed using ROBINS-I, and the certainty of evidence will be assessed using GRADE. Following the establishment of baseline results, the literature search will be re-run at least once every six months, and the baseline data will be updated and re-evaluated as new evidence is published. A freely available website will be designated to present up-to-date results and conclusions. This systematic review will provide evidence-based results that are continuously updated with evidence as it emerges in the rapidly developing field of pharmacogenomics. These results may help psychiatrists in their decision-making, as clinicians are becoming increasingly aware of the patients' genetic data as testing becomes more widespread and cheaper. In addition, the results may serve as a scientific basis for the development of evidence-based pharmacogenomics algorithms for personalized dosing of antipsychotics to mitigate potentially harmful drug-gene interactions.

Applications of L-Arginine in Pregnancy and Beyond: An Emerging Pharmacogenomic Approach.

L-arginine is a semi-essential amino acid that plays a critical role in various physiological processes, such as protein synthesis, wound healing, immune function, and cardiovascular regulation. The use of L-arginine in pregnancy has been an emerging topic in the field of pharmacogenomics. L-arginine, an amino acid, plays a crucial role in the production of nitric oxide, which is necessary for proper placental development and fetal growth. Studies have shown that L-arginine supplementation during pregnancy can have positive effects on fetal growth, maternal blood pressure, and the prevention of preeclampsia. This emerging pharmacogenomic approach involves using genetic information to personalize L-arginine dosages for pregnant women based on their specific genetic makeup. By doing so, it may be possible to optimize the benefits of L-arginine supplementation during pregnancy and improve pregnancy outcomes. This paper emphasizes the potential applications of L-arginine in pregnancy and the use of pharmacogenomic approaches to enhance its effectiveness. Nonetheless, the emerging pharmacogenomic approach to the application of L-arginine offers exciting prospects for the development of novel therapies for a wide range of diseases.

Genetics in the diagnosis and treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) remain one of the leading causes of morbidity and mortality worldwide, with genetics being a major risk factor. Genetic cardiovascular disease can occur either because of single variant (Mendelian) or polygenic influences and has been linked to inherited cardiovascular conditions (ICC) such as arrhythmias, cardiomyopathies, dyslipidemias, and aortopathies which are significant factors leading to sudden cardiac death in young adults. Timely screening, diagnosis, and management of ICC can not only provide life-saving treatment to a patient, but also identify at-risk family members. The field of pharmacogenomics (PGx) helped to understand the variable action of medications such as clopidogrel, aspirin, warfarin, and statin according to genotype. Newer technologies such as multi-omics can combine data from multiple sources such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiome. These advancements can contribute to the development of polygenic prediction scores and precision medicine tailored to individual genotypes. Substantial strides have been made in genetic-based therapeutics, gene editing technologies, and drug delivery systems, which have significantly expanded treatment options for patients with acquired or inherited CVDs. Although variable, the country- and society-specific guidelines on genetic testing for ICC and PGx and treatment are being continuously updated to keep up with ongoing research in the field. Along with appropriate knowledge, other factors including cost and availability of genetic testing play a vital role in the usage by both physicians and patients. With the advent of newer genetic testing for CVDs, a key factor is the availability of genetic counselors (GCs) who are specifically trained in cardiovascular genomics. The current review provides a concise summary of the major influences of genetics in the diagnosis and treatment of CVDs.

Pharmacogenomics: Importance in Exploration of Target Molecule, Drug Resistance and Mechanism in Cancer Chemotherapy

Background: The field of pharmacogenomics investigates the ways in which genes impact the body's reaction to drugs. Through the integration of genetics and medical knowledge, it is possible to develop individualised and efficient therapies that are based on genetic profiles. Specifically, this evaluation focuses on three aspects: Finding molecular targets and gaining a grasp of their structures is the first step. 2. Using pharmacogenomics to lessen the harmful effects of drugs 1. 3. Predicting pharmacological mechanisms based on genetic variances. Methods: A systematic review examining pharmacogenomics' role in cancer chemotherapy, focusing on target molecules, drug resistance, and mechanism, was conducted using PRISMA-compliant databases. The review included English-published journals from 2000-2023, aiming to reduce bias and ensure all relevant research is found, chosen, and critically assessed. Results: The results show that over the past 20 years, cytotoxic medications and personalized treatments have significantly improved cancer treatment strategies. Targeted drugs, small molecule drugs, and pharmacogenomic biomarkers offer targeted treatment for carcinoma tissues. Conclusion: Understanding genetic profiles and drug resistance is crucial for effective cancer treatment. Combination therapies have advanced treatment for advanced or metastatic cancers.

Application of computer on Pharmacogenomics

Background Pharmacogenomics is an emerging field that explores the relationship between a person’s genetic makeup and their response to drugs.
 Objectives: Computer applications have been instrumental in advancing pharmacogenomics research and facilitating personalized medicine. In this abstract, we review some of the key computer applications used in pharmacogenomics research, including bioinformatics tools for analyzing genetic data, machine learning algorithms for predicting drug response, and clinical decision support systems for guiding drug selection and dosing. We also highlight the challenges and opportunities in the field of pharmacogenomics, including the need for large-scale, multi-omics datasets, and the importance of developing robust and interpretable machine learning models. Overall, computer applications have played a critical role in enabling pharmacogenomics research and have the potential to revolutionize the practice of medicine by facilitating the development of more personalized and effective drug therapies.
 Bangladesh Journal of Medical Science Vol. 23 No. 02 April’24 Page : 300-308

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.

MMDRP: drug response prediction and biomarker discovery using multi-modal deep learning.

A major challenge in cancer care is that patients with similar demographics, tumor types, and medical histories can respond quite differently to the same drug regimens. This difference is largely explained by genetic and other molecular variabilities among the patients and their cancers. Efforts in the pharmacogenomics field are underway to understand better the relationship between the genome of the patient's healthy and tumor cells and their response to therapy. To advance this goal, research groups and consortia have undertaken large-scale systematic screening of panels of drugs across multiple cancer cell lines that have been molecularly profiled by genomics, proteomics, and similar techniques. These large data drug screening sets have been applied to the problem of drug response prediction (DRP), the challenge of predicting the response of a previously untested drug/cell-line combination. Although deep learning algorithms outperform traditional methods, there are still many challenges in DRP that ultimately result in these models' low generalizability and hampers their clinical application. In this article, we describe a novel algorithm that addresses the major shortcomings of current DRP methods by combining multiple cell line characterization data, addressing drug response data skewness, and improving chemical compound representation. MMDRP is implemented as an open-source, Python-based, command-line program and is available at https://github.com/LincolnSteinLab/MMDRP.

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.

Understanding the Human RECQ5 Helicase-Connecting the Dots from DNA to Clinics.

RECQ5, a member of the conserved RECQ helicase family, is the sole human RECQ homolog that has not been linked to a hereditary developmental syndrome. Nonetheless, dysregulation of RECQ5 has emerged as a significant clinical concern, being linked to cancer predisposition, cardiovascular disease, and inflammation. In cells, RECQ5 assumes a crucial role in the regulation of DNA repair pathways, particularly in the repair of DNA double-strand breaks and inter-strand DNA crosslinks. Moreover, RECQ5 exhibits a capacity to modulate gene expression by interacting with transcription machineries and their co-regulatory proteins, thus safeguarding against transcription-induced DNA damage. This review aims to provide an overview of the multifaceted functions of RECQ5 and its implications in maintaining genomic stability. We will discuss the potential effects of clinical variants of RECQ5 on its cellular functions and their underlying mechanisms in the pathogenesis of cancer and cardiovascular disease. We will review the impact of RECQ5 variants in the field of pharmacogenomics, specifically their influence on drug responses, which may pave the way for novel therapeutic interventions targeting RECQ5 in human diseases.

An in-silico analysis predicting the impact of coding single nucleotide polymorphisms (SNPs) in the human multidrug pump ABCG2

ATP-Binding Cassette transporter G2 (ABCG2) is a multidrug resistance pump involved in pathophysiology and pharmacoresistance in various neurological, and neurogenerative disorders. Single nucleotide polymorphisms (SNPs) of ABCG2 affecting expression or function may be involved in the pharmacokinetics of its substrate drugs, influencing the bioavailability leading to disease progression. Thus, pathophysiology and pharmacogenetics linked to ABCG2 can be comprehended by understanding the functions of its SNPs. Identification of these SNPs predictive of pharmacoresistance and disease progression is still a major challenge. Here, we have prioritized ABCG2 SNPs for the identification and validation of deleterious/damaging coding SNPs (cSNPs) using an in-silico approach. Out of 965 cSNPs, only 6 were considered to be highly deleterious by five in-silico computational tools. These cSNPs were submitted to MutPred and PMut for further validation followed by subjected to I-Mutant 2.0 for protein stability analysis. We found 5 cSNPs with a DDG score less than −0.5, suggesting a damaging effect. Based on the conservation of amino acid residues, post-translational modification sites, and structural superimposition, rs3116448 (S248A) and rs41282401 (D296H) were predicted as high-risk cSNPs. Molecular dynamics (MD) simulations revealed that the substitution of the residues S248A in the ABCG2 model altered the structural conformation of the protein and may influence the functional activity as per previously reported in vitro studies. Thus, findings from our study, for the first time, identified clinically important ABCG2 variations utilizing a substantial bioinformatics pipeline that not only provides important potential targets in the field of pharmacogenomics but also curtails study costs.

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.

Pharmacogenomics and personalized medicine: A Descriptive study of current trends and future perspective

At the nexus of genetics and healthcare, the powerful fields of pharmacogenomics and personalized medicine have evolved, revolutionizing both the prescription of pharmaceuticals and the provision of patient care. Pharmacogenomics receives attention because of its potential to enhance drug therapy and reduce negative side effects. Pharmacogenomic testing helps to identify genetic variations among people who are get affected drug response and metabolism. The goal of personalized medicine is to increase therapeutic efficacy while lowering the risk of adverse events by customizing a patient's course of therapy based on the genetic profile. Improvements in patient outcomes has become possible by pharmacogenomics' significant achievements in fields like oncology, psychiatry, and cardiovascular medicine. Prospects for pharmacogenomics and personalized medicine are very positive. Pharmacogenomic testing capabilities will increase because of the quick development of genomic technologies like high-throughput genotyping and next-generation sequencing. Additionally, pharmacogenomic data linkage with electronic medical records and decision support systems will improve clinical implementation. Personalized therapy suggestions can be created using artificial intelligence and machine learning algorithms after extensive analysis of patient data that considers clinical, genetic, and lifestyle aspects.

Association of the Gene FTO Single Nucleotide Polymorphism, rs9939609 with Type 2 Diabetes Mellitus in Pakistani Cohort

To date, inconclusive data is available about the insight of the FTO gene variant with type 2 diabetes mellitus. T2DM is a chronic disease and a rising problem worldwide. Its complications lead to an increase in the burden of mortality specifically in lower and medium-income countries. Genome-wide association studies have spotted many genetic loci that are related to T2DM and validate the complicated polygenic traits. Many variants of different genes including FTO are associated with T2DM hence, this study was designed to inspect and unfold obscure data in South Asians. The main objective of present study is to identify the relation of FTO intronic variant rs9939609 with T2DM in Karachi-based Sindhi population of Pakistan. Total recruited individuals were grouped as diabetic cases and controls. Out of the total recruited subjects, genotyping was done on 152 samples using T-ARMS PCR however, demographic and clinical data were recorded of all individuals. The results showed that the frequency of variant genotypes in the diabetic case group was 11 % for AA, 45 % for AT and 44 % for TT though, the frequency of the lethal allele (T) was 34 %. These outcomes concluded, rare T allele frequency is higher among diabetic cases as compared to controls and provides the contribution from the Pakistani population to support the previous controversial findings. This study concluded FTO gene-single nucleotide polymorphism, rs9939609 is associated with T2DM but still, it is a growing need to do further studies on T2DM susceptible genes with different polymorphisms to recognize targets in the field of pharmacogenomics for clinical implementation.

Characterization of pharmacogenomic variants in a Brazilian admixed cohort of elderly individuals based on whole-genome sequencing data.

Introduction: Research in the field of pharmacogenomics (PGx) aims to identify genetic variants that modulate response to drugs, through alterations in their pharmacokinetics (PK) or pharmacodynamics (PD). The distribution of PGx variants differs considerably among populations, and whole-genome sequencing (WGS) plays a major role as a comprehensive approach to detect both common and rare variants. This study evaluated the frequency of PGx markers in the context of the Brazilian population, using data from a population-based admixed cohort from Sao Paulo, Brazil, which includes variants from WGS of 1,171 unrelated, elderly individuals. Methods: The Stargazer tool was used to call star alleles and structural variants (SVs) from 38 pharmacogenes. Clinically relevant variants were investigated, and the predicted drug response phenotype was analyzed in combination with the medication record to assess individuals potentially at high-risk of gene-drug interaction. Results: In total, 352 unique star alleles or haplotypes were observed, of which 255 and 199 had a frequency < 0.05 and < 0.01, respectively. For star alleles with frequency > 5% (n = 97), decreased, loss-of-function and unknown function accounted for 13.4%, 8.2% and 27.8% of alleles or haplotypes, respectively. Structural variants (SVs) were identified in 35 genes for at least one individual, and occurred with frequencies >5% for CYP2D6, CYP2A6, GSTM1, and UGT2B17. Overall 98.0% of the individuals carried at least one high risk genotype-predicted phenotype in pharmacogenes with PharmGKB level of evidence 1A for drug interaction. The Electronic Health Record (EHR) Priority Result Notation and the cohort medication registry were combined to assess high-risk gene-drug interactions. In general, 42.0% of the cohort used at least one PharmGKB evidence level 1A drug, and 18.9% of individuals who used PharmGKB evidence level 1A drugs had a genotype-predicted phenotype of high-risk gene-drug interaction. Conclusion: This study described the applicability of next-generation sequencing (NGS) techniques for translating PGx variants into clinically relevant phenotypes on a large scale in the Brazilian population and explores the feasibility of systematic adoption of PGx testing in Brazil.

Discovering alignment relations with Graph Convolutional Networks: A biomedical case study

Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task encompasses the identification, within an aggregated knowledge graph, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster. We conducted experiments with this approach on the real world application of aligning knowledge in the field of pharmacogenomics, which motivated our study. We particularly investigated the interplay between domain knowledge and GCN models with the two following focuses. First, we applied inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Second, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the “strength” of these different relations (e.g., smaller distances for equivalences), letting us considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.

Analysis of a panel-based pharmacogenomics testing program among members of a commercial and Medicare client of a pharmacy benefits manager.

BACKGROUND: Although the field of pharmacogenomics (PGx) has existed for decades, use of pharmacogenomic information by providers to optimize medication therapy for patients has had relatively slow adoption. There are many factors that have contributed to the slow adoption of PGx testing, but it is partially due to a lack of coverage by payers. If PGx testing is covered by payers, frequently only testing of a specific gene is covered, rather than a panel of many genes. As a result, little is known about how coverage of a panel-based PGx test will affect a member's medication therapy. OBJECTIVES: To determine how giving providers specific medication optimization recommendations, based on results of a panel-based PGx test, impacted members' medication regimens. METHODS: Pharmacy claims data were retrospectively reviewed for this exploratory study. Members who participated in PGx testing were in the intervention group and members who chose not to participate in the PGx testing, but who were eligible to participate, were in the control group. PGx test results, including suggested medication changes, were mailed to providers. To determine if providers adopted the suggested medication changes, pharmacy claims data were analyzed retrospectively for the 4-month period preceding and following the date from which recommendations were provided to prescribers. RESULTS: Of the 101 members included in the analysis, 50 were in the intervention group and 51 were in the control group. In the intervention group, members were taking in a total of 352 medications; 165 of the medications had PGx guidance. Based on the PGx test results, 62 of these medications (37.6%) had recommendations. Of members who received PGx testing, 76% had at least 1 recommended change. When pharmacist recommendations were made, a change was made to the medication 27% of the time. There was a statistically significant difference between the number of medication changes in the PGx group and the control group (P = 0.024). CONCLUSIONS: Recommendations based on PGx testing can lead to changes in medications and an optimized medication regimen for members. DISCLOSURES: The authors have no conflicts to disclose that may present a potential conflict of interest.