The genetics of drug efficacy: opportunities and challenges

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype—that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.

Variations in Dysbindin-1 are associated with cognitive response to antipsychotic drug treatment

Antipsychotics are the most widely used medications for the treatment of schizophrenia spectrum disorders. While such drugs generally ameliorate positive symptoms, clinical responses are highly variable in terms of negative symptoms and cognitive impairments. However, predictors of individual responses have been elusive. Here, we report a pharmacogenetic interaction related to a core cognitive dysfunction in patients with schizophrenia. We show that genetic variations reducing dysbindin-1 expression can identify individuals whose executive functions respond better to antipsychotic drugs, both in humans and in mice. Multilevel ex vivo and in vivo analyses in postmortem human brains and genetically modified mice demonstrate that such interaction between antipsychotics and dysbindin-1 is mediated by an imbalance between the short and long isoforms of dopamine D2 receptors, leading to enhanced presynaptic D2 function within the prefrontal cortex. These findings reveal one of the pharmacodynamic mechanisms underlying individual cognitive response to treatment in patients with schizophrenia, suggesting a potential approach for improving the use of antipsychotic drugs.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications

The problem of the genetics of related phenotypes is often addressed by analyzing adjusted-model traits, but such traits warrant cautious interpretation. Here, we adopt a joint view of adiposity traits in ~322,154 subjects (GIANT consortium). We classify 159 signals associated with body mass index (BMI), waist-to-hip ratio (WHR), or WHR adjusted for BMI (WHRadjBMI) at P < 5 × 10-8, into four classes based on the direction of their effects on BMI and WHR. Our classes help differentiate adiposity genetics with respect to anthropometry, fat depots, and metabolic health. Class-specific Mendelian randomization reveals that variants associated with both WHR-decrease and BMI increase are linked to metabolically rather favorable adiposity through beneficial hip fat. Class-specific enrichment analyses implicate digestive systems as a pathway in adiposity genetics. Our results demonstrate that WHRadjBMI variants capture relevant effects of "unexpected fat distribution given the BMI" and that a joint view of the genetics underlying related phenotypes can inform on important biology.

Human CCL3L1 copy number variation, gene expression, and the role of the CCL3L1-CCR5 axis in lung function

Background: The CCL3L1-CCR5 signaling axis is important in a number of inflammatory responses, including macrophage function, and T-cell-dependent immune responses. Small molecule CCR5 antagonists exist, including the approved antiretroviral drug maraviroc, and therapeutic monoclonal antibodies are in development. Repositioning of drugs and targets into new disease areas can accelerate the availability of new therapies and substantially reduce costs. As it has been shown that drug targets with genetic evidence supporting their involvement in the disease are more likely to be successful in clinical development, using genetic association studies to identify new target repurposing opportunities could be fruitful. Here we investigate the potential of perturbation of the CCL3L1-CCR5 axis as treatment for respiratory disease. Europeans typically carry between 0 and 5 copies of CCL3L1 and this multi-allelic variation is not detected by widely used genome-wide single nucleotide polymorphism studies.  Methods: We directly measured the complex structural variation of CCL3L1 using the Paralogue Ratio Test and imputed (with validation) CCR5del32 genotypes in 5,000 individuals from UK Biobank, selected from the extremes of the lung function distribution, and analysed DNA and RNAseq data for CCL3L1 from the 1000 Genomes Project. Results: We confirmed the gene dosage effect of CCL3L1 copy number on CCL3L1 mRNA expression levels.  We found no evidence for association of CCL3L1 copy number or CCR5del32 genotype with lung function. Conclusions: These results suggest that repositioning CCR5 antagonists is unlikely to be successful for the treatment of airflow obstruction.

Combining hiPSCs and Human Genetics: Major Applications in Drug Development

Merging iPSC models and human genetic research has opened up new avenues in understanding disease mechanisms and target biology, which facilitate exciting translation of this research to many areas of drug development. We highlight recent applications of these combined disciplines and discuss remaining challenges and potential solutions.

Human CCL3L1 copy number variation, gene expression, and the role of the CCL3L1-CCR5 axis in lung function

Background: The CCL3L1-CCR5 signaling axis is important in a number of inflammatory responses, including macrophage function, and T-cell-dependent immune responses. Small molecule CCR5 antagonists exist, including the approved antiretroviral drug maraviroc, and therapeutic monoclonal antibodies are in development. Repositioning of drugs and targets into new disease areas can accelerate the availability of new therapies and substantially reduce costs. As it has been shown that drug targets with genetic evidence supporting their involvement in the disease are more likely to be successful in clinical development, using genetic association studies to identify new target repurposing opportunities could be fruitful. Here we investigate the potential of perturbation of the CCL3L1-CCR5 axis as treatment for respiratory disease. Europeans typically carry between 0 and 5 copies of CCL3L1 and this multi-allelic variation is not detected by widely used genome-wide single nucleotide polymorphism studies.  Methods: We directly measured the complex structural variation of CCL3L1 using the Paralogue Ratio Test and imputed (with validation) CCR5del32 genotypes in 5,000 individuals from UK Biobank, selected from the extremes of the lung function distribution, and analysed DNA and RNAseq data for CCL3L1 from the 1000 Genomes Project. Results: We confirmed the gene dosage effect of CCL3L1 copy number on CCL3L1 mRNA expression levels.  We found no evidence for association of CCL3L1 copy number or CCR5del32 genotype with lung function. Conclusions: These results suggest that repositioning CCR5 antagonists is unlikely to be successful for the treatment of airflow obstruction.

Whole-Transcriptome Sequencing: a Powerful Tool for Vascular Tissue Engineering and Endothelial Mechanobiology

Among applicable high-throughput techniques in cardiovascular biology, whole-transcriptome sequencing is of particular use. By utilizing RNA that is isolated from virtually all cells and tissues, the entire transcriptome can be evaluated. In comparison with other high-throughput approaches, RNA sequencing is characterized by a relatively low-cost and large data output, which permits a comprehensive analysis of spatiotemporal variation in the gene expression profile. Both shear stress and cyclic strain exert hemodynamic force upon the arterial endothelium and are considered to be crucial determinants of endothelial physiology. Laminar blood flow results in a high shear stress that promotes atheroresistant endothelial phenotype, while a turbulent, oscillatory flow yields a pathologically low shear stress that disturbs endothelial homeostasis, making respective arterial segments prone to atherosclerosis. Severe atherosclerosis significantly impairs blood supply to the organs and frequently requires bypass surgery or an arterial replacement surgery that requires tissue-engineered vascular grafts. To provide insight into patterns of gene expression in endothelial cells in native or bioartificial arteries under different biomechanical conditions, this article discusses applications of whole-transcriptome sequencing in endothelial mechanobiology and vascular tissue engineering.

Applications of pharmacogenomics in regulatory science: a product life cycle review

With rapid developments of pharmacogenomics (PGx) and regulatory science, it is important to understand the current PGx integration in product life cycle, impact on clinical practice thus far and opportunities ahead. We conducted a cross-sectional review on PGx-related regulatory documents and implementation guidelines in the United States and Europe. Our review found that although PGx-related guidance in both markets span across the entire product life cycle, the scope of implementation guidelines varies across two continents. Approximately one-third of Food and Drug Administration (FDA)-approved drugs with PGx information in drug labels and half of the European labels posted on PharmGKB website contain recommendations on genetic testing. The drugs affected 19 and 15 World Health Organization Anatomical Therapeutic Chemical drug classes (fourth level) in the United States and Europe, respectively, with protein kinase inhibitors (13 drugs in the United States and 16 drugs in Europe) being most prevalent. Topics of emerging interest were novel technologies, adaptive design in clinical trial and sample collection.

Phenome-wide association study using research participants' self-reported data provides insight into the Th17 and IL-17 pathway

A phenome-wide association study of variants in genes in the Th17 and IL-17 pathway was performed using self-reported phenotypes and genetic data from 521,000 research participants of 23andMe. Results replicated known associations with similar effect sizes for autoimmune traits illustrating self-reported traits can be a surrogate for clinically assessed conditions. Novel associations controlling for a false discovery rate of 5% included the association of the variant encoding p.Ile684Ser in TYK2 with increased risk of tonsillectomy, strep throat occurrences and teen acne, the variant encoding p.Arg381Gln in IL23R with a decrease in dandruff frequency, the variant encoding p.Asp10Asn in TRAF3IP2 with risk of male-pattern balding, and the RORC regulatory variant (rs4845604) with protection from allergies. This approach enabled rapid assessment of association with a wide variety of traits and investigation of traits with limited reported associations to overlay meaningful phenotypic context on the range of conditions being considered for drugs targeting this pathway.

Predicting ligand-dependent tumors from multi-dimensional signaling features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.

Pharmacogenetic meta-analysis of baseline risk factors, pharmacodynamic, efficacy and tolerability endpoints from two large global cardiovascular outcomes trials for darapladib

Darapladib, a lipoprotein-associated phospholipase A2 (Lp-PLA₂) inhibitor, failed to demonstrate efficacy for the primary endpoints in two large phase III cardiovascular outcomes trials, one in stable coronary heart disease patients (STABILITY) and one in acute coronary syndrome (SOLID-TIMI 52). No major safety signals were observed but tolerability issues of diarrhea and odor were common (up to 13%). We hypothesized that genetic variants associated with Lp-PLA₂ activity may influence efficacy and tolerability and therefore performed a comprehensive pharmacogenetic analysis of both trials. We genotyped patients within the STABILITY and SOLID-TIMI 52 trials who provided a DNA sample and consent (n = 13,577 and 10,404 respectively, representing 86% and 82% of the trial participants) using genome-wide arrays with exome content and performed imputation using a 1000 Genomes reference panel. We investigated baseline and change from baseline in Lp-PLA₂ activity, two efficacy endpoints (major coronary events and myocardial infarction) as well as tolerability parameters at genome-wide and candidate gene level using a meta-analytic approach. We replicated associations of published loci on baseline Lp-PLA₂ activity (APOE, CELSR2, LPA, PLA2G7, LDLR and SCARB1) and identified three novel loci (TOMM5, FRMD5 and LPL) using the GWAS-significance threshold P≤5E-08. Review of the PLA2G7 gene (encoding Lp-PLA₂) within these datasets identified V279F null allele carriers as well as three other rare exonic null alleles within various ethnic groups, however none of these variants nor any other loci associated with Lp-PLA₂ activity at baseline were associated with any of the drug response endpoints. The analysis of darapladib efficacy endpoints, despite low power, identified six low frequency loci with main genotype effect (though with borderline imputation scores) and one common locus (minor allele frequency 0.24) with genotype by treatment interaction effect passing the GWAS-significance threshold. This locus conferred risk in placebo subjects, hazard ratio (HR) 1.22 with 95% confidence interval (CI) 1.11–1.33, but was protective in darapladib subjects, HR 0.79 (95% CI 0.71–0.88). No major loci for tolerability were found. Thus, genetic analysis confirmed and extended the influence of lipoprotein loci on Lp-PLA₂ levels, identified some novel null alleles in the PLA2G7 gene, and only identified one potentially efficacious subgroup within these two large clinical trials.

The emerging science of precision medicine and pharmacogenomics for Parkinson's disease

Current therapies for Parkinson's disease are problematic because they are symptomatic and have adverse effects. New drugs have failed in clinical trials because of inadequate efficacy. At the core of the problem is trying to make one drug work for all Parkinson's disease patients, when we know this premise is wrong because (1) Parkinson's disease is not a single disease, and (2) no two individuals have the same biological makeup. Precision medicine is the goal to strive for, but we are only at the beginning stages of building the infrastructure for one of the most complex projects in the history of science, and it will be a long time before Parkinson's disease reaps the benefits. Pharmacogenomics, a cornerstone of precision medicine, has already proven successful for many conditions and could also propel drug discovery and improve treatment for Parkinson's disease. To make progress in the pharmacogenomics of Parkinson's disease, we need to change course from small inconclusive candidate gene studies to large-scale rigorously planned genome-wide studies that capture the nuclear genome and the microbiome. Pharmacogenomic studies must use homogenous subtypes of Parkinson's disease or apply the brute force of statistical power to overcome heterogeneity, which will require large sample sizes achievable only via internet-based methods and electronic databases. Large-scale pharmacogenomic studies, together with biomarker discovery efforts, will yield the knowledge necessary to design clinical trials with precision to alleviate confounding by disease heterogeneity and interindividual variability in drug response, two of the major impediments to successful drug discovery and effective treatment. © 2017 International Parkinson and Movement Disorder Society.

Genetics of coronary artery disease: discovery, biology and clinical translation

Coronary artery disease is the leading global cause of mortality. Long recognized to be heritable, recent advances have started to unravel the genetic architecture of the disease. Common variant association studies have linked approximately 60 genetic loci to coronary risk. Large-scale gene sequencing efforts and functional studies have facilitated a better understanding of causal risk factors, elucidated underlying biology and informed the development of new therapeutics. Moving forwards, genetic testing could enable precision medicine approaches by identifying subgroups of patients at increased risk of coronary artery disease or those with a specific driving pathophysiology in whom a therapeutic or preventive approach would be most useful.

Precision reproductive medicine: multigene panel testing for infertility risk assessment

The concept of precision medicine relies on a thorough understanding of the consequences of unique features of individual patients, such as environmental exposures and genetic profiles. A key component of implementing individualized care in this paradigm will be improved assessment of genetic risk. Compared with single gene tests, multigene panel testing-which has recently become commercially available for female infertility-offers the possibility of a more comprehensive and efficient risk evaluation. However, as the use of multigene panel testing for breast cancer risk has shown, this approach must be used judiciously to ensure its usefulness in a clinical setting. Key challenges which have been encountered in oncology include the interpretation of gene variants of questionable clinical effect and a lack of evidence to guide management after variants are identified. In this review, the core concepts of multigene panel testing for risk assessment are discussed, with careful attention to both its shortcomings as well as its potential for benefit in reproductive medicine.

Genetics of primary sclerosing cholangitis and pathophysiological implications

Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.

Epigenetic assays for chemical biology and drug discovery

The implication of epigenetic abnormalities in many diseases and the approval of a number of compounds that modulate specific epigenetic targets in a therapeutically relevant manner in cancer specifically confirms that some of these targets are druggable by small molecules. Furthermore, a number of compounds are currently in clinical trials for other diseases including cardiovascular, neurological and metabolic disorders. Despite these advances, the approved treatments for cancer only extend progression-free survival for a relatively short time and being associated with significant side effects. The current clinical trials involving the next generation of epigenetic drugs may address the disadvantages of the currently approved epigenetic drugs.

The identification of chemical starting points of many drugs often makes use of screening in vitro assays against libraries of synthetic or natural products. These assays can be biochemical (using purified protein) or cell-based (using for example, genetically modified, cancer cell lines or primary cells) and performed in microtiter plates, thus enabling a large number of samples to be tested. A considerable number of such assays are available to monitor epigenetic target activity, and this review provides an overview of drug discovery and chemical biology and describes assays that monitor activities of histone deacetylase, lysine-specific demethylase, histone methyltransferase, histone acetyltransferase and bromodomain. It is of critical importance that an appropriate assay is developed and comprehensively validated for a given drug target prior to screening in order to improve the probability of the compound progressing in the drug discovery value chain.

Drug discovery strategies in the field of tumor energy metabolism: Limitations by metabolic flexibility and metabolic resistance to chemotherapy

The search for new drugs capable of blocking the metabolic vulnerabilities of human tumors has now entered the clinical evaluation stage, but several projects already failed in phase I or phase II. In particular, very promising in vitro studies could not be translated in vivo at preclinical stage and beyond. This was the case for most glycolysis inhibitors that demonstrated systemic toxicity. A more recent example is the inhibition of glutamine catabolism in lung adenocarcinoma that failed in vivo despite a strong addiction of several cancer cell lines to glutamine in vitro. Such contradictory findings raised several questions concerning the optimization of drug discovery strategies in the field of cancer metabolism. For instance, the cell culture models in 2D or 3D might already show strong limitations to mimic the tumor micro- and macro-environment. The microenvironment of tumors is composed of cancer cells of variegated metabolic profiles, supporting local metabolic exchanges and symbiosis, but also of immune cells and stroma that further interact with and reshape cancer cell metabolism. The macroenvironment includes the different tissues of the organism, capable of exchanging signals and fueling the tumor 'a distance'. Moreover, most metabolic targets were identified from their increased expression in tumor transcriptomic studies, or from targeted analyses looking at the metabolic impact of particular oncogenes or tumor suppressors on selected metabolic pathways. Still, very few targets were identified from in vivo analyses of tumor metabolism in patients because such studies are difficult and adequate imaging methods are only currently being developed for that purpose. For instance, perfusion of patients with [¹³C]-glucose allows deciphering the metabolomics of tumors and opens a new area in the search for effective targets. Metabolic imaging with positron emission tomography and other techniques that do not involve [¹³C] can also be used to evaluate tumor metabolism and to follow the efficiency of a treatment at a preclinical or clinical stage. Relevant descriptors of tumor metabolism are now required to better stratify patients for the development of personalized metabolic medicine. In this review, we discuss the current limitations in basic research and drug discovery in the field of cancer metabolism to foster the need for more clinically relevant target identification and validation. We discuss the design of adapted drug screening assays and compound efficacy evaluation methods for the discovery of innovative anti-cancer therapeutic approaches at the level of tumor energetics. This article is part of a Special Issue entitled Mitochondria in Cancer, edited by Giuseppe Gasparre, Rodrigue Rossignol and Pierre Sonveaux.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

Precision medicine, genomics and drug discovery

The hope for precision medicine has long been on the drug discovery horizon, well before the Human Genome Project gave it promise at the turn of the 21st century. In oncology, the concept has finally been realized and is now firmly embedded in ongoing drug discovery programs, and with many recent therapies involving some level of patient/disease stratification, including some highly personalized treatments. In addition, several drugs for rare diseases have been recently approved or are in late-stage clinical development, and new delivery modalities in cell and gene therapy and oligonucleotide approaches are yielding exciting new medicines for rare diseases of unmet need. For common complex diseases, however, the GWAS-driven advances in annotation of the genetic architecture over the past decade have not led to a concomitant shift in refined treatments. Similarly, attempts to disentangle treatment responders from non-responders via genetic predictors in pharmacogenetics studies have not met their anticipated success. It is possible that common diseases are simply lagging behind due to the inherent time lag with drug discovery, but it is also possible that their inherent multifactorial nature and their etiological and clinical heterogeneity will prove more resistant to refined treatment paradigms. The emergence of population-based resources in electronic health records, coupled with the rapid expansion of mobile devices and digital health may help to refine the measurement of phenotypic outcomes to match the exquisite detail emerging at the molecular level.

Challenges of development and implementation of point of care pharmacogenetic testing

INTRODUCTION

Just as technology was the underlying driver of the sequencing of the human genome and subsequent generation of volumes of genome sequence data from healthy and affected individuals, animal, plant, and microbial species alike, so too will technology revolutionize diagnostic testing. One area of intense interest is the use of genetic data to inform decisions regarding drug selection and drug dosing, known as pharmacogenetic (PGx) testing, to improve likelihood of successful treatment outcomes with minimal risks.

AREAS COVERED

This commentary will provide an overview of implementation research of PGx testing, the benefits of point-of-care (POC) testing and overview of POC testing platforms, available PGx tests, and barriers and facilitators to the development and integration of POC-PGx testing into clinical settings. Sources include the published literature, and databases from the Centers for Medicaid and Medicare Services, Food and Drug Administration. Expert commentary: The utilization of POC PGx testing may enable more routine test use, but the development and implementation of such tests will face some barriers before personalized medicine is available to every patient. In particular, provider training, availability of clinical decision supports, and connectivity will be key areas to facilitate routine use.