Pharmacogenomics: the genetics of variable drug responses

Polymorphism in INSR Locus Modifies Risk of Atrial Fibrillation in Patients on Thyroid Hormone Replacement Therapy

Aims

Atrial fibrillation (AF) is a risk for patients receiving thyroid hormone replacement therapy. No published work has focused on pharmacogenetics relevant to thyroid dysfunction and AF risk. We aimed to assess the effect of L-thyroxine on AF risk stratified by a variation in a candidate gene.

Methods and Results

A retrospective follow-up study was done among European Caucasian patients from the Genetics of Diabetes Audit and Research in Tayside Scotland cohort (Scotland, United Kingdom). Linked data on biochemistry, prescribing, hospital admissions, demographics, and genetic biobank were used to ascertain patients on L-thyroxine and diagnosis of AF. A GWAS-identified insulin receptor-INSR locus (rs4804416) was the candidate gene. Cox survival models and sensitivity analyses by taking competing risk of death into account were used. Replication was performed in additional sample (The Genetics of Scottish Health Research register, GoSHARE), and meta-analyses across the results of the study and replication cohorts were done. We analyzed 962 exposed to L-thyroxine and 5,840 unexposed patients who were rs4804416 genotyped. The rarer G/G genotype was present in 18% of the study population. The total follow-up was up to 20 years, and there was a significant increased AF risk for patients homozygous carriers of the G allele exposed to L-thyroxine (RHR = 2.35, P = 1.6e-02). The adjusted increased risk was highest within the first 3 years of exposure (RHR = 9.10, P = 8.5e-04). Sensitivity analysis yielded similar results. Effects were replicated in GoSHARE (n = 3,190).

Conclusion

Homozygous G/G genotype at the INSR locus (rs4804416) is associated with an increased risk of AF in patients on L-thyroxine, independent of serum of free thyroxine and thyroid-stimulating hormone serum concentrations.

Fast screening of covariates in population models empowered by machine learning

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

Pharmacogenetics Update on Biologic Therapy in Psoriasis

Background and objectives: Psoriasis is a chronic immune-mediated skin disease caused by several complex factors, both environmental and genetic, many of which are still not fully understood. Nowadays, several groups of biological drugs are being used for psoriasis treatment. Although these therapies are very effective, they show significant variability in efficacy among individuals. Therefore, there is a need for biomarkers to predict treatment outcomes in order to guide personalized therapeutic decisions. Pharmacogenetics is the study of variations in DNA sequences related to drug response. Materials and Methods: In this article, we review pharmacogenetics studies on the treatment of moderate-to-severe psoriasis focusing on anti-interleukin (IL) 12/23 (ustekinumab) and anti-IL17 drugs (secukinumab and ixekizumab), as well as recent studies concerning anti-TNF drugs. Results: Several polymorphisms have been studied over the years in reference to anti-TNF drugs; some of the most recent studies included the performance of a genome-wide association study (GWAS) and pharmacogenetics studies focused on the optimization of a treatment regimen. Various polymorphisms in different genes have been related to ustekinumab response; among them, the most commonly studied is the HLA-C*06:02 allele. Conclusions: Although not confirmed in some studies, most studies have shown that patients carrying this allele present a significantly higher response rate to ustekinumab. Some polymorphisms have been studied in patients treated with anti-IL17 drugs, mostly related to secukinumab; however, up to now, no association has been found between any of these polymorphisms and response. Nevertheless, further studies involving larger cohorts are needed in order to confirm these results before the implementation of this biomarker in clinical practice.

Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization

Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician's ability to understand the obtained results in a standardized way for each particular patient. The review aims to lead the reader through the general conception of PGx and related issues of PGx testing efficiency, personal data security, and health safety at a current clinical level.

Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders

Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

The Association of 3-Hydroxy-3-Methylglutaryl-CoA Reductase, Apolipoprotein E, and Solute Carrier Organic Anion Genetic Variants with Atorvastatin Response among Jordanian Patients with Type 2 Diabetes

Atorvastatin is commonly used among type 2 diabetic (DM2) patients at the University of Jordan Hospital to prevent cardiovascular complication. However, we noticed that there is a wide inter-individual variation in the efficacy and toxicity of atorvastatin. This study aimed to find out the effects of major genetic variants in 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR), Apolipoprotein E (APOE), and Solute Carrier Organic Anion (SLCO1B1) genes on atorvastatin response among DM2 patients. A sample of 139 DM2 patients on 20 mg of atorvastatin was included in this study. The lipid and glycemic profile and the levels of hepatic enzymes alanine aminotransferase (ALT) and aspartate transaminase were recorded before and after 3 months of atorvastatin treatment. Additionally, the genetic variants HMGCR rs17244841,APOE rs7412 and rs429357, and SLCO1B1 rs2306283 and rs11045818 were genotyped using an Applied Biosystems DNA sequencing method (ABI3730×1). We found that atorvastatin reduced total cholesterol and low-density lipoprotein (LDL) more significantly (p-value < 0.05) in patients with wild genotype than variant alleles APOE rs7412C > T and SLCO1B1 rs2306283A > G. Furthermore, the ALT level was elevated significantly (p-value < 0.05) by 27% in patients with heterozygous SLCO1B1 rs11045818 G/A genotype, while it was not elevated among wild genotype carriers. Additionally, atorvastatin reduced total cholesterol more significantly (p-value < 0.05) in patients with SLCO1B1 rs2306283A and rs11045818G haplotypes and increased ALT levels by 27% (p-value < 0.05) in patients with SLCO1B1 rs2306283G and rs11045818A haplotypes. In conclusion, it was found in this study that APOE rs7412, SLCO1B1 rs2306283, and rs11045818 genotypes can be considered as potential genetic biomarkers of atorvastatin response among DM2 patients of Jordanian Arabic origin. Further clinical studies with larger sample numbers are needed to confirm these findings.

CYP17A1 polymorphism c.-362T>C predicts clinical outcome in metastatic castration-resistance prostate cancer patients treated with abiraterone

BACKGROUND

Abiraterone became a standard hormonal therapy for patients with metastatic castration-resistance prostate cancer (mCRPC). However, patients may experience primary resistance to treatment. To date, few predictive biomarkers of efficacy have been identified. Our aim was to investigate the association between the single nucleotide polymorphism (SNP) c.-362T>C in the CYP17A1 gene, and clinical outcome in mCRPC patients treated with abiraterone.

PATIENTS AND METHODS

mCRPC patients candidate to receive abiraterone were enrolled in the present retrospective pharmacogenetic study. Based on a literature selection, CYP17A1 rs2486758 (c.-362T > C) was selected and analysed by real-time PCR on genomic DNA extracted from whole blood. Univariate analysis was performed to test the association between the SNP and treatment-related clinical outcomes.

RESULTS

Sixty mCRPC patients were enrolled in the present study. Patients carrying the mutant CYP17A1 c.-362CT/CC genotypes showed a shorter median progression-free survival (PFS) and prostate-specific antigen-PFS (PSA-PFS) compared to patients carrying the TT genotype (10.7 vs 14.2 months and 8 vs 16 months, respectively; p = 0.04). No association between the selected SNP and the overall survival was found.

CONCLUSIONS

These findings suggest an association between CYP17A1 c.-362T>C polymorphism and poorer clinical outcome with abiraterone for mCRPC patients. However, further validations on larger cohort of patients are needed to confirm its role as a predictive biomarker for abiraterone resistance.

Major CYP450 polymorphism Among Saudi Patients

BACKGROUND

Cytochrome P450 (CYP) contributes to a huge collection of medicinal products' Phase I metabolization. We aimed to summarize and investigate the current evidence regarding the frequency of CYP2D6, CYP2C9, CYP2C19, MDR1 in Saudi Arabia.

METHODS

A computerized search in four databases was done using the relevant keywords. Screening process was done in two steps; title and abstract screening and full-text screening. Data of demographic and characteristics of included studies and patients was extracted and tabulated.

RESULTS

Ten studies were eligible for our criteria and were included in this systematic review. Age of participants ranged between 17-65 years. Only two subjects showed PM phenotype of CYP2C19 in Saudi population. The most frequent alleles were CYP2C19*1 (62.9%), CYP2C19*2 (11.2%-32%), and CYP2C19*17 (25.7%). The CYP2C19m1 was observed in 97 cases of extensive metabolizing (EM) phenotype CYP2C19. Concerning the CYP2C9, the most frequent alleles were CYP2C9*1 and CYP2C9*2, and the most frequent genotype was CYP2C9*1*1. The CYP2D6*41 allele and C1236T MDR1 were the most frequent allele in this population.

CONCLUSION

The current evidence suggests that Saudi Arabians resembled European in the frequency of CYP2C19, Caucasians in both the incidence of CYP2C9 and CYP2C19m1 and absence of CYP2C19m2. The CYP2D6*41 allele frequency in Saudi Arabians is relatively high. We recommend a further research to evaluate the basic and clinical relevance of gene polymorphism in such ethnicity.

Using pharmacogenetics to structure individual pain management protocols in total knee arthroplasty

Aims

The purpose of this study was to use pharmacogenetics to determine the frequency of genetic variants in our total knee arthroplasty (TKA) patients that could affect postoperative pain medications. Pharmacogenetic testing evaluates patient DNA to determine if a drug is expected to have a normal clinical effect, heightened effect, or no effect at all on the patient. It also predicts whether patients are likely to experience side effects from medicine. We further sought to determine if changing the multimodal programme based on these results would improve pain control or reduce side effects.

Methods

In this pilot study, buccal samples were collected from 31 primary TKA patients. Pharmacogenetics testing examined genetic variants in genes OPRM1, CYP1A2, CYP2B6, CYP2C19, CYP3A4, CYP2C9, and CYP2D6. These genes affect the pharmacodynamics and pharmacokinetics of non-steroidal anti-inflammatory drugs and opioids. We examined the frequency of genetic variants to any of the medications we prescribed including celecoxib, hydrocodone, and tramadol. Patients were randomized to one of two groups: the control group received the standard postoperative pain regimen, and the study group received a customized regimen based on the pharmacogenetic results. For the first ten postoperative days, patients recorded pain scores, medication, and side effects.

Results

Genetic variants involving one or more medications in the multimodal pain protocol occurred in 13 of the 31 patients (42%). In total, eight patients (26%) had variants affecting more than one of the medications. For the 25 patients who recorded pain and medication logs, the mean pain levels and morphine equivalents (MEQs) consumed in the first ten days were higher in the control group than in the custom-guided group (p = 0.019 for pain and p = 0.655 for MEQ).

Conclusion

Overall, 42% of patients had a variant involving one of the pain medications prescribed in our perioperative pain program for TKA. Ongoing research will help determine if using these data to modify a patient’s medication will improve outcomes. Cite this article: Bone Joint J 2020;102-B(6 Supple A):73–78.

Discovery of novel hepatocyte eQTLs in African Americans

African Americans (AAs) are disproportionately affected by metabolic diseases and adverse drug events, with limited publicly available genomic and transcriptomic data to advance the knowledge of the molecular underpinnings or genetic associations to these diseases or drug response phenotypes. To fill this gap, we obtained 60 primary hepatocyte cultures from AA liver donors for genome-wide mapping of expression quantitative trait loci (eQTL) using LAMatrix. We identified 277 eGenes and 19,770 eQTLs, of which 67 eGenes and 7,415 eQTLs are not observed in the Genotype-Tissue Expression Project (GTEx) liver eQTL analysis. Of the eGenes found in GTEx only 25 share the same lead eQTL. These AA-specific eQTLs are less correlated to GTEx eQTLs. in effect sizes and have larger Fst values compared to eQTLs found in both cohorts (overlapping eQTLs). We assessed the overlap between GWAS variants and their tagging variants with AA hepatocyte eQTLs and demonstrated that AA hepatocyte eQTLs can decrease the number of potential causal variants at GWAS loci. Additionally, we identified 75,002 exon QTLs of which 48.8% are not eQTLs in AA hepatocytes. Our analysis provides the first comprehensive characterization of AA hepatocyte eQTLs and highlights the unique discoveries that are made possible due to the increased genetic diversity within the African ancestry genome.

A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study

INTRODUCTION

The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint.

METHODS

Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment.

RESULTS

Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models.

DISCUSSION

Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Precision Medicine in Non-Communicable Diseases

The increase in life expectancy during the 20th century ranks as one of society's greatest achievements, with massive growth in the numbers and proportion of the elderly, virtually occurring in every country of the world. The burden of chronic diseases is one of the main consequences of this phenomenon, severely hampering the quality of life of elderly people and challenging the efficiency and sustainability of healthcare systems. Non-communicable diseases (NCDs) are considered a global emergency responsible for over 70% of deaths worldwide. NCDs are also the basis for complex and multifactorial diseases such as hypertension, diabetes, and obesity. The epidemics of NCDs are a consequence of a complex interaction between health, economic growth, and development. This interaction includes the individual genome, the microbiome, the metabolome, the immune status, and environmental factors such as nutritional and chemical exposure. To counteract NCDs, it is therefore essential to develop an innovative, personalized, preventative, early care model through the integration of different molecular profiles of individuals to identify both the critical biomarkers of NCD susceptibility and to discover novel therapeutic targets.

Mouse Systems Genetics as a Prelude to Precision Medicine

Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.

Pharmacogenomics Instruction Depth, Extent, and Perception in US Medical Curricula

INTRODUCTION

This descriptive study aimed to evaluate the depth, extent, and perception of pharmacogenomics instruction in schools and colleges of medicine in the United States. Changes in medical pharmacogenomics instruction over the past decade were also assessed by comparing our results with those of a previous study.

METHODS

An electronic survey was emailed to all accredited allopathic and osteopathic medical schools across the US using Qualtrics online survey software. Multiple email reminders were sent to increase the response rate.

RESULTS

Of 151 targeted eligible medical schools across the United States, 22 responded to the survey. One invalid response was excluded, resulting in a response rate of 13.9%. Of responding schools, 85.7% cover pharmacogenomics in their curriculum, mainly in the second year, however, none teach pharmacogenomics as a stand-alone course. The depth and the extent of pharmacogenomics coverage varied among responding programs. Although 66.7% of respondents believe that neither physicians nor other health care professionals possess appropriate knowledge in pharmacogenomics, only 23.8% plan to increase pharmacogenomics instruction in their curricula in the near future.

CONCLUSIONS

Most medical schools surveyed include some pharmacogenomics instruction in their curricula, although the depth and the extent of the instruction varies. Most respondents believe that physicians and other health care professionals today do not possess an appropriate level of knowledge in pharmacogenomics; however, few institutions report short-term plans to increase pharmacogenomics instruction. Pharmacogenomics plays a significant role in personalized medicine; greater efforts by medical school decision-makers are needed to improve the level of pharmacogenomics instruction in medical curricula.

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

Knowledge And Attitudes Of Pharmacy Students Towards Pharmacogenomics Among Universities In Jordan And West Bank Of Palestine

BACKGROUND

Testing by pharmacogenomics (PGx) aims to reduce the side-effects of medicines and to optimize therapy.

AIM

To ascertain the knowledge and attitudes towards PGx among pharmacy students in Jordan and West Bank of Palestine (WBP).

METHODS

This cross-sectional study focused on pharmacy students from five universities in Jordan and WBP. Students were asked to answer an online survey comprising 30-closed ended questions measuring the knowledge and attitudes towards PGx.

RESULTS

The total number of respondents to the questionnaire was 466. Most (96.1%) respondents knew that genetic variations can affect the drug response. Most students stated that the total number of lectures mentioning PGx was fewer than three. Most (>80%) respondents answered that they knew that human genetics can affect the response, inter-individual variation, and ethnic variations in the drug response. However, their knowledge about US Food and Drug Administration recommendations regarding PGx testing of commonly used drugs was weak. Also, 60.3% of respondents stated that the information they received about PGx was insufficient. Most (>92.7%) students wished to know more about PGx and believed that PGx is helpful in choosing the appropriate drug.

CONCLUSION

Pharmacy students had fair knowledge and good attitudes towards PGx. These factors could aid application of PGx in clinical practice in Jordan and WBP.

A stepwise approach to implementing pharmacogenetic testing in the primary care setting

Pharmacogenetic testing can help identify primary care patients at increased risk for medication toxicity, poor response or treatment failure and inform drug therapy. While testing availability is increasing, providers are unprepared to routinely use pharmacogenetic testing for clinical decision-making. Practice-based resources are needed to overcome implementation barriers for pharmacogenetic testing in primary care.The NHGRI's IGNITE I Network (Implementing GeNomics In pracTicE; www.ignite-genomics.org) explored practice models, challenges and implementation barriers for clinical pharmacogenomics. Based on these experiences, we present a stepwise approach pharmacogenetic testing in primary care: patient identification; pharmacogenetic test ordering; interpretation and application of test results, and patient education. We present clinical factors to consider, test-ordering processes and resources, and provide guidance to apply test results and counsel patients. Practice-based resources such as this stepwise approach to clinical decision-making are important resources to equip primary care providers to use pharmacogenetic testing.

Gut Microbiome and Response to Cardiovascular Drugs

The gut microbiome is emerging as an important contributor to both cardiovascular disease risk and metabolism of xenobiotics. Alterations in the intestinal microbiota are associated with atherosclerosis, dyslipidemia, hypertension, and heart failure. The microbiota have the ability to metabolize medications, which can results in altered drug pharmacokinetics and pharmacodynamics or formation of toxic metabolites which can interfere with drug response. Early evidence suggests that the gut microbiome modulates response to statins and antihypertensive medications. In this review, we will highlight mechanisms by which the gut microbiome facilitates the biotransformation of drugs and impacts pharmacological efficacy. A better understanding of the complex interactions of the gut microbiome, host factors, and response to medications will be important for the development of novel precision therapeutics for targeting CVD.

A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping

We discuss a decision-theoretic approach to building a panel-based, preemptive genotyping program. The method is based on findings that a large percentage of patients are prescribed medications that are known to have pharmacogenetic associations, and over time, a substantial proportion are prescribed additional such medication. Preemptive genotyping facilitates genotype-guided therapy at the time medications are prescribed; panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach to identify patients with the highest anticipated benefit. First, we construct prediction models to estimate the risk of being prescribed one of the target medications using readily available clinical data. Second, we use literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individual’s genetic data in the electronic health record after an indication has occurred. Finally, we combine medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of preemptive genotyping. For each patient-clinic visit, we calculate this expected benefit across a range of medications and select patients with the highest expected benefit overall. We build a proof of concept implementation using a cohort of patients from a single academic medical center observed from July 2010 through December 2012. We then apply the results of our modeling strategy to show the extent to which we can improve clinical and economic outcomes in a cohort observed from January 2013 through December 2015.

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays

MOTIVATION

High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.

RESULTS

We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters.

AVAILABILITY AND IMPLEMENTATION

The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Copy number variation profiling in pharmacogenetics CYP-450 and GST genes in Colombian population

BACKGROUND

Copy Number variation (CNVs) in genes related to drug absorption, distribution, metabolism and excretion (ADME) are relevant in the interindividual variability of drug response. Studies of the CNVs in ADME genes in Latin America population are lacking. The objective of the study was to identify the genetic variability of CNVs in CYP-450 and GST genes in a subgroup of individuals of Colombian origin.

METHODS

Genomic DNA was isolated from 123 healthy individuals from a Colombian population. Multiplex Ligation-Dependent Probe Amplification (MLPA) was performed for the identification of CNVs in 40 genomic regions of 11 CYP-450 and 3 GST genes. The genetic variability, allelic and genotypic frequencies were analyzed.

RESULTS

We found that 13 out of 14 genes had CNVs: 5 (35.7%) exhibited deletions and duplications, while 8 (57.1%) presented either deletions or duplications.. 33.3% of individuals carried deletions and duplications while 49.6% had a unique type of CNV (deletion or duplication). The allelic frequencies of the CYP and GST genes were 0 to 47.6% (allele null), 0 to 17.5% (duplicated alleles) and 37 to 100% (normal alleles).

CONCLUSIONS

Our results describe, for the first time, the genomic profile of CNVs in a subgroup of Colombian population in GST and CYP-450 genes. GST genes indicated greater genetic variability than CYP-450 genes. The data obtained contributes to the knowledge of genetic profiles in Latin American subgroups. Although the clinical relevance of CNVs has not been fully established, it is a valuable source of pharmacogenetic variability data with potential involvement in the response to medications.

Inconsistency in race and ethnic classification in pharmacogenetics studies and its potential clinical implications

Introduction

Racial and ethnic categories are frequently used in pharmacogenetics literature to stratify patients; however, these categories can be inconsistent across different studies. To address the ongoing debate on the applicability of traditional concepts of race and ethnicity in the context of precision medicine, we aimed to review the application of current racial and ethnic categories in pharmacogenetics and its potential impact on clinical care.

Methods

One hundred and three total pharmacogenetics papers involving the CYP2C9, CYP2C19, and CYP2D6 genes were analyzed for their country of origin, racial, and ethnic categories used, and allele frequency data. Correspondence between the major continental racial categories promulgated by National Institutes of Health (NIH) and those reported by the pharmacogenetics papers was evaluated.

Results

The racial and ethnic categories used in the papers we analyzed were highly heterogeneous. In total, we found 66 different racial and ethnic categories used which fall under the NIH race category “White”, 47 different racial and ethnic categories for “Asian”, and 62 different categories for “Black”. The number of categories used varied widely based on country of origin: Japan used the highest number of different categories for “White” with 17, Malaysia used the highest number for “Asian” with 24, and the US used the highest number for “Black” with 28. Significant variation in allele frequency between different ethnic subgroups was identified within 3 major continental racial categories.

Conclusion

Our analysis showed that racial and ethnic classification is highly inconsistent across different papers as well as between different countries. Evidence-based consensus is necessary for optimal use of self-identified race as well as geographical ancestry in pharmacogenetics. Common taxonomy of geographical ancestry which reflects specifics of particular countries and is accepted by the entire scientific community can facilitate reproducible pharmacogenetic research and clinical implementation of its results.

Receptor variants and the development of centrally acting medications

The progressive changes in research paradigms observed in the largest pharmaceutical companies and the burgeoning of biotechnology startups over the last 10 years have generated a need for outsourcing research facilities. In parallel, progress made in the fields of genomics, protein expression in recombinant systems, and electrophysiological recording methods have offered new possibilities for the development of contract research organizations (CROs). Successful partnering between pharmaceutical companies and CROs largely depends upon the competences and scientific quality on offer for the discovery of novel active molecules and targets. Thus, it is critical to review the knowledge in the field of neuroscience research, how genetic approaches are augmenting our knowledge, and how they can be applied in the translation from the identification of potential molecules up to the first clinical trials. Taking these together, it is apparent that CROs have an important role to play in the neuroscience of drug discovery.


Pharmacogenetics of Postoperative Nausea and Vomiting

Postoperative nausea and vomiting (PONV) remains one of the most common adverse effects of anesthesia, affecting up to 80% of high-risk patients within 24 hours after surgery. Patient-related factors, surgical procedure, and perioperative medications such as opioids determine a patient's risk for PONV. To prevent and manage PONV, ondansetron, a 5-hydroxytryptamine type 3 (5-HT₃) receptor antagonist, is frequently administered. Ondansetron is metabolized predominantly by hepatic cytochrome P450 (CYP2D6) enzymes, encoded by the CYP2D6 gene, whereas most of the effects of opioids are exerted at the opioid mu-1 receptor, encoded by the OPRM1 gene. Genetic polymorphisms of the CYP2D6 and OPRM1 genes may have a role in interindividual variation in the occurrence of PONV. Specifically, the occurrence of the G-allele produced by the OPRM1 A118G appears to be protective against PONV, whereas CYP2D6 ultrarapid metabolism increases the risk for PONV. The Clinical Pharmacogenetics Implementation Consortium guidelines provide CYP2D6-guided therapeutic recommendations for ondansetron. However, further studies are needed to investigate the role of genetic polymorphism in the occurrence of PONV and response to antiemetics.

Sleep Pharmacogenetics: The Promise of Precision Medicine

Pharmacogenetics is the branch of personalized medicine concerned with the variability in drug response occurring because of heredity. Advances in genetics research, and decreasing costs of gene sequencing, are promoting tremendous growth in pharmacogenetics in all areas of medicine, including sleep medicine. This article reviews the body of research indicating that there are genetic variations that affect the therapeutic actions and adverse effects of agents used for the treatment of sleep disorders to show the potential of pharmacogenetics to improve the clinical practice of sleep medicine.

Point mutation detection by economic HRM protocol primer design

Globally more than 100 million SNPs in populations. These variations approximately 4-5 million SNPs in a people genome, occur almost every 1000 nucleotides on average and present either unique or in many in individuals. They can act as genetic signs, associated with illness and respond to chemicals and drugs. SNPs occurrence within or near a gene play important role in disease throughout affecting gene task. Frequently many protocols have been used to study single nucleotide polymorphism (SNP) among human variants genome. Restriction fragment length polymorphism (RFLP), Amplification refractory mutation system PCR(ARMS-PCR), sequencing and SNaPshot assays considered familial methods. The potential risk of contamination after PCR is common due to further other steps. In this direction, a high resolution melting (HRM) real-time PCR method is an alternative, reducing the post-PCR transferring steps. uVariants is clarified as appropriate website for designing primers used for SNP recognition by easy and inexpensive protocol called HRM. The researchers can focus on the interest of reference SNP ID number, or "rs" ID to avoid loss time. In this article description how to uses uVariants website for primer design used in HRM technique.

Aims

To describe uVariants and uDesign software, application and usefulness of HRM technique primer design in the genotyping SNPs among people and public health.

Accessibility and requirements

uVariants and uDesign are freely accessible at: https://www.dna.utah.edu/variants/;https://www.dna.utah.edu/udesign/app.php respectively.The network server supports the browsers: Chrome, Firefox, Torch, CoolNovo, 360 Browser, Internet Explorer, Opera, and Safari.

Clinical and Experimental Human Sleep-Wake Pharmacogenetics

Sleep and wakefulness are highly complex processes that are elegantly orchestrated by fine-tuned neurochemical changes among neuronal and non-neuronal ensembles, nuclei, and networks of the brain. Important neurotransmitters and neuromodulators regulating the circadian and homeostatic facets of sleep-wake physiology include melatonin, γ-aminobutyric acid, hypocretin, histamine, norepinephrine, serotonin, dopamine, and adenosine. Dysregulation of these neurochemical systems may cause sleep-wake disorders, which are commonly classified into insomnia disorder, parasomnias, circadian rhythm sleep-wake disorders, central disorders of hypersomnolence, sleep-related movement disorders, and sleep-related breathing disorders. Sleep-wake disorders can have far-reaching consequences on physical, mental, and social well-being and health and, thus, need be treated with effective and rational therapies. Apart from behavioral (e.g., cognitive behavioral therapy for insomnia), physiological (e.g., chronotherapy with bright light), and mechanical (e.g., continuous positive airway pressure treatment of obstructive sleep apnea) interventions, pharmacological treatments often are the first-line clinical option to improve disturbed sleep and wake states. Nevertheless, not all patients respond to pharmacotherapy in uniform and beneficial fashion, partly due to genetic differences. The improved understanding of the neurochemical mechanisms regulating sleep and wakefulness and the mode of action of sleep-wake therapeutics has provided a conceptual framework, to search for functional genetic variants modifying individual drug response phenotypes. This article will summarize the currently known genetic polymorphisms that modulate drug sensitivity and exposure, to partly determine individual responses to sleep-wake pharmacotherapy. In addition, a pharmacogenetic strategy will be outlined how based upon classical and opto-/chemogenetic strategies in animals, as well as human genetic associations, circuit mechanisms regulating sleep-wake functions in humans can be identified. As such, experimental human sleep-wake pharmacogenetics forms a bridge spanning basic research and clinical medicine and constitutes an essential step for the search and development of novel sleep-wake targets and therapeutics.

Consequences of CYP2D6 Copy-Number Variation for Pharmacogenomics in Psychiatry

Pharmacogenomics represents a potentially powerful enhancement to the current standard of care for psychiatric patients. However, a variety of biological and technical challenges must be addressed in order to provide adequate clinical decision support for personalized prescribing and dosing based on genomic data. This is particularly true in the case of CYP2D6, a key drug-metabolizing gene, which not only harbors multiple genetic variants known to affect enzyme function but also shows a broad range of copy-number and hybrid alleles in various patient populations. Here, we describe several challenges in the accurate measurement and interpretation of data from the CYP2D6 locus including the clinical consequences of increased copy number. We discuss best practices for overcoming these challenges and then explore various current and future applications of pharmacogenomic analysis of CYP2D6 in psychiatry.

Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives

In the past decades, many efforts have been made to individualize medical treatments, taking into account molecular profiles and the individual genetic background. The development of molecularly targeted drugs and immunotherapy have revolutionized medical treatments but the inter-patient variability in the anti-tumor drug pharmacokinetics (PK) and pharmacodynamics can be explained, at least in part, by genetic variations in genes encoding drug metabolizing enzymes and transporters (ADME) or in genes encoding drug receptors. Here, we focus on high-throughput technologies applied for PK screening for the identification of predictive biomarkers of efficacy or toxicity in cancer treatment, whose application in clinical practice could promote personalized treatments tailored on individual's genetic make-up. Pharmacogenomic tools have been implemented and the clinical utility of pharmacogenetic screening could increase safety in patients for the identification of drug metabolism-related biomarkers for a personalized medicine. Although pharmacogenomic studies were performed in adult cohorts, pharmacogenetic pediatric research has yielded promising results. Additionally, we discuss the current challenges and theoretical bases for the implementation of pharmacogenetic tests for translation in the clinical practice taking into account that pharmacogenomics platforms are discovery oriented and must open the way for the setting of robust tests suitable for daily practice.

Pharmacokinetic-Pharmacodynamic Model of Neutropenia in Patients With Myeloma Receiving High-Dose Melphalan for Autologous Stem Cell Transplant

High-dose melphalan (HDM) is part of the conditioning regimen in patients with multiple myeloma (MM) receiving autologous stem cell transplantation (ASCT). However, individual sensitivity to melphalan varies, and many patients experience severe toxicities. Prolonged severe neutropenia is one of the most severe toxicities and contributes to potentially life-threatening infections and failure of ASCT. Granulocyte-colony stimulating factor (G-CSF) is given to stimulate neutrophil proliferation after melphalan administration. The aim of this study was to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model capable of predicting neutrophil kinetics in individual patients with MM undergoing ASCT with high-dose melphalan and G-CSF administration. The extended PK/PD model incorporated several covariates, including G-CSF regimen, stem cell dose, hematocrit, sex, creatinine clearance, p53 fold change, and race. The resulting model explained portions of interindividual variability in melphalan exposure, therapeutic effect, and feedback regulation of G-CSF on neutrophils, thus enabling simulation of various doses and prediction of neutropenia duration.

Frequencies of poor metabolizer alleles of 12 pharmacogenomic actionable genes in Punjabi Sikhs of Indian Origin

Diversity in drug response is attributed to both genetic and non-genetic factors. However, there is paucity of pharmacogenetics information across ethnically and genetically diverse populations of India. Here, we have analyzed 21 SNPs from 12 pharmacogenomics genes in Punjabi Sikhs of Indian origin (N = 1,616), as part of the Sikh Diabetes Study (SDS). We compared the allele frequency of poor metabolism (PM) phenotype among Sikhs across other major global populations from the Exome Aggregation Consortium and 1000 Genomes. The PM phenotype of CYP1A2*1 F for slow metabolism of caffeine and carcinogens was significantly higher in Indians (SDS 42%, GIH [Gujarati] 51%, SAS [Pakistani] 45%) compared to Europeans 29% (p_genotype = 5.3E-05). Similarly, South Asians had a significantly higher frequency of CYP2C9*3 (12% SDS, 13% GIH, 11% SAS) vs. 7% in Europeans (p_genotype = <1.0E-05) and ‘T’ allele of CYP4F2 (36%) SDS, (43%) GIH, 40% (SAS) vs. (29%) in Europeans (p_genotype = <1.0E-05); both associated with a higher risk of bleeding with warfarin. All South Asians –the Sikhs (0.36), GIH (0.34), and SAS (0.36) had a higher frequency of the NAT2*6 allele (linked with slow acetylation of isoniazid) compared to Europeans (0.29). Additionally, the prevalence of the low activity ‘C’ allele of MTHFR (rs1801131) was highest in Sikhs compared to all other ethnic groups [SDS (44%), GIH (39%), SAS (42%) and European (32%) (p_genotype = <1.0E-05)]. SNPs in MTHFR affect metabolism of statins, 5-fluorouracil and methotrexate-based cancer drugs. These findings underscore the need for evaluation of other endogamous ethnic groups of India and beyond for establishing a global benchmark for pre-emptive genotyping in drug metabolizing genes before beginning therapeutic intervention.

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential

Aims

Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research.

Methods and results

We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health.

Conclusion

High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.

Targeting PLK1 overcomes T-DM1 resistance via CDK1-dependent phosphorylation and inactivation of Bcl-2/xL in HER2-positive breast cancer

Trastuzumab-refractory, HER2 (human epidermal growth factor receptor 2)-positive breast cancer is commonly treated with trastuzumab emtansine (T-DM1), an antibody-drug conjugate of trastuzumab and the microtubule-targeting agent, DM1. However, drug response reduces greatly over time due to acquisition of resistance whose molecular mechanisms are mostly unknown. Here, we uncovered a novel mechanism of resistance against T-DM1 by combining whole transcriptome sequencing (RNA-Seq), proteomics and a targeted small interfering RNA (siRNA) sensitization screen for molecular level analysis of acquired and de novo T-DM1-resistant models of HER2-overexpressing breast cancer. We identified Polo-like kinase 1 (PLK1), a mitotic kinase, as a resistance mediator whose genomic as well as pharmacological inhibition restored drug sensitivity. Both acquired and de novo resistant models exhibited synergistic growth inhibition upon combination of T-DM1 with a selective PLK1 inhibitor, volasertib, at a wide concentration range of the two drugs. Mechanistically, T-DM1 sensitization upon PLK1 inhibition with volasertib was initiated by a spindle assembly checkpoint (SAC)-dependent mitotic arrest, leading to caspase activation, followed by DNA damage through CDK1-dependent phosphorylation and inactivation of Bcl-2/xL. Furthermore, we showed that Ser70 phosphorylation of Bcl-2 directly regulates apoptosis by disrupting the binding to and sequestration of the pro-apoptotic protein Bim. Importantly, T-DM1 resistance signature or PLK1 expression correlated with cell cycle progression and DNA repair, and predicted a lower sensitivity to taxane/trastuzumab combination in HER2-positive breast cancer patients. Finally, volasertib in combination with T-DM1 greatly synergized in models of T-DM1 resistance in terms of growth inhibition both in three dimensional (3D) cell culture and in vivo. Altogether, our results provide promising pre-clinical evidence for potential testing of T-DM1/volasertib combination in T-DM1 refractory HER2-positive breast cancer patients for whom there is currently no treatment available.

Pharmacogenetics and Pharmacogenomics in Moderate-to-Severe Psoriasis

Pharmacogenetics is the study of variations in DNA sequence related to drug response. Moreover, the evolution of biotechnology and the sequencing of human DNA have allowed the creation of pharmacogenomics, a branch of genetics that analyzes human genes, the RNAs and proteins encoded by them, and the inter-and intra-individual variations in expression and function in relation to drug response. Pharmacogenetics and pharmacogenomics are being used to search for biomarkers that can predict response to systemic treatments, including those for moderate-to-severe psoriasis. Psoriasis is a chronic inflammatory disease with an autoimmune contribution. Although its etiology remains unknown, genetic, epigenetic, and environmental factors play a role in its development. Diverse systemic and biologic therapies are used to treat moderate-to-severe psoriasis. However, these treatments are not curative, and patients exhibit a wide range of responses to them. Moderate-to-severe psoriasis is usually treated with systemic immunomodulators such as acitretin, ciclosporin, and methotrexate. Anti-tumor necrosis factor (TNF) drugs (adalimumab, etanercept, or infliximab) are the first-line treatment for patients resistant to conventional systemic therapies. Although these therapies are very efficient, around 30-50% of patients have inadequate response. Ustekinumab is a monoclonal antibody that targets interleukin (IL)-12 and IL-23 and is used for moderate-to-severe psoriasis. New drugs (apremilast, brodalumab, guselkumab, ixekizumab, and secukinumab) have recently been approved for psoriasis. However, response rates to systemic treatments for moderate-to-severe psoriasis range from 35 to 80%, so it is necessary to identify non-invasive biomarkers that could help predict treatment outcomes of these therapies and individualize care for patients with psoriasis. These biomarkers could improve patient quality of life and reduce health costs and potential side effects. Pharmacogenetic studies have identified potential biomarkers for response to biologic treatments for moderate-to-severe psoriasis. These biomarkers need to be validated in clinical trials involving large cohorts of patients before they can be translated to the clinic. We review pharmacogenetics and pharmacogenomics studies for the treatment of moderate-to-severe plaque psoriasis.

Perspective on the Genetic Response to Antiparasitics: A Review Article

BACKGROUND

Drugs' pharmacokinetics and pharmacodynamics can be affected by diverse genetic variations, within which simple nucleotide polymorphisms (SNPs) are the most common. Genetic variability is one of the factors that could explain questions like why a given drug does not have the desired effect or why do adverse drug reactions arise.

METHODS

In this retrospective observational study, literature search limits were set within PubMed database as well as the epidemiological bulletins published by the Mexican Ministry of Health, from Jan 1st 2001 to Mar 31st 2017 (16 years).

RESULTS

Metabolism of antiparasitic drugs and their interindividual responses are mainly modified by variations in cytochrome P450 enzymes. These enzymes show high frequencies of polymorphic variability thus affecting the expression of CYP2C, CYP2A, CYP2A6, CYP2D6, CYP2E6 and CYP2A6 isoforms. Research in this field opens the door to new personalized treatment approaches in medicine.

CONCLUSION

Clinical and pharmacological utility yield by applying pharmacogenetics to antiparasitic treatments is not intended as a mean to improve the prescription process, but to select or exclude patients that could present adverse drug reactions as well as to evaluate genetic alterations which result in a diversity of responses, ultimately seeking to provide a more effective and safe treatment; therefore choosing a proper dose for the appropriate patient and the optimal treatment duration. Furthermore, pharmacogenetics assists in the development of vaccines. In other words, the aim of this discipline is to find therapeutic targets allowing personalized treatments.

European healthcare systems readiness to shift from ‘one-size fits all’ to personalized medicine

Personalized medicine (PM) is no longer an abstract healthcare approach. It has become a reality over the last years and is already successfully applied in the various medical fields. Although there are success stories of implementing PM, there are still many more opportunities to further implement and make full use of the potential of PM. We assessed the system readiness of healthcare systems in Europe to shift from the predominant ‘one size fits all’ healthcare approach to PM. We conclude that European healthcare systems are only partially ready for PM. Key challenges such as integration of big data, health literacy, reimbursement and regulatory issues need to be overcome in order to strengthen the implementation and uptake of PM.

Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Genotyping NUDT15 can predict the dose reduction of 6-MP for children with acute lymphoblastic leukemia especially at a preschool age

The pharmacokinetics among children has been altered dynamically. The difference between children and adults is caused by immaturity in things such as metabolic enzymes and transport proteins. The periods when these alterations happen vary from a few days to some years after birth. We hypothesized that the effect of gene polymorphisms associated with the dose of medicine could be influenced by age. In this study, we analyzed 51 patients with childhood acute lymphoblastic leukemia (ALL) retrospectively. We examined the associations between the polymorphism in NUDT15 and clinical data, especially the dose of 6-mercaptopurine (6-MP). Ten of the patients were heterozygous for the variant allele in NUDT15. In patients under 7 years old with NUDT15 variant allele, the average administered dose of 6-MP was lower than that for the patients homozygous for the wild-type allele (P=0.04). Genotyping of NUDT15 could be a beneficial to estimate the tolerated dose of 6-MP for patients with childhood ALL, especially at a preschool age in Japan. Furthermore, the analysis with stratification by age might be useful in pharmacogenomics among children.

Clinical association between pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual's response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

Role of genomics in eliminating health disparities

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