Mapping the knowledge structure and trends of epilepsy genetics over the past decade: A co-word analysis based on medical subject headings terms

INTRODUCTION

Over the past 10 years, epilepsy genetics has made dramatic progress. This study aimed to analyze the knowledge structure and the advancement of epilepsy genetics over the past decade based on co-word analysis of medical subject headings (MeSH) terms.

METHODS

Scientific publications focusing on epilepsy genetics from the PubMed database (January 2009-December 2018) were retrieved. Bibliometric information was analyzed quantitatively using Bibliographic Item Co-Occurrence Matrix Builder (BICOMB) software. A knowledge social network analysis and publication trend based on the high-frequency MeSH terms was built using VOSviewer.

RESULTS

According to the search strategy, a total of 5185 papers were included. Among all the extracted MeSH terms, 86 high-frequency MeSH terms were identified. Hot spots were clustered into 5 categories including: "ion channel diseases," "beyond ion channel diseases," "experimental research & epigenetics," "single nucleotide polymorphism & pharmacogenetics," and "genetic techniques". "Epilepsy," "mutation," and "seizures," were located at the center of the knowledge network. "Ion channel diseases" are typically in the most prominent position of epilepsy genetics research. "Beyond ion channel diseases" and "genetic techniques," however, have gradually grown into research cores and trends, such as "intellectual disability," "infantile spasms," "phenotype," "exome," " deoxyribonucleic acid (DNA) copy number variations," and "application of next-generation sequencing." While ion channel genes such as "SCN1A," "KCNQ2," "SCN2A," "SCN8A" accounted for nearly half of epilepsy genes in MeSH terms, a number of additional beyond ion channel genes like "CDKL5," "STXBP1," "PCDH19," "PRRT2," "LGI1," "ALDH7A1," "MECP2," "EPM2A," "ARX," "SLC2A1," and more were becoming increasingly popular. In contrast, gene therapies, treatment outcome, and genotype-phenotype correlations were still in their early stages of research.

CONCLUSION

This co-word analysis provides an overview of epilepsy genetics research over the past decade. The 5 research categories display publication hot spots and trends in epilepsy genetics research which could consequently supply some direction for geneticists and epileptologists when launching new projects.

Protocol for the development of the STrengthening the Reporting Of Pharmacogenetic Studies (STROPS) guideline: checklist of items for reporting pharmacogenetic studies

INTRODUCTION

Large sample sizes are often required to detect statistically significant associations between pharmacogenetic markers and treatment response. Meta-analysis may be performed to synthesise data from several studies, increasing sample size and consequently power to detect significant genetic effects. However, performing robust synthesis of data from pharmacogenetic studies is often challenging due to poor reporting of key data in study reports. There is currently no guideline for the reporting of pharmacogenetic studies. The aim of this project is to develop the STrengthening the Reporting Of Pharmacogenetic Studies (STROPS) guideline. The STROPS guideline will facilitate the conduct of high-quality meta-analyses and thus improve the power to detect genetic associations.

METHODS AND ANALYSIS

We will establish a preliminary checklist of reporting items to be considered for inclusion in the guideline. We will then conduct a Delphi survey of key stakeholder groups to gain consensus opinion on which reporting items to include in the final guideline. The Delphi survey will consist of two rounds: the first round will invite participants to score items from the preliminary checklist and to suggest additional relevant items; the second round will provide feedback from the previous round and invite participants to re-score the items. Following the second round, we will summarise the distribution of scores for each item, stratified by stakeholder group. The Steering Committee for the project and representatives from the key stakeholder groups will meet to consider the results of the Delphi survey and to finalise the list of reporting items. We will then draft, pilot-test and publish the STROPS reporting guideline and accompanying explanatory document.

ETHICS AND DISSEMINATION

The University of Liverpool Ethics Committee has confirmed ethical approval for this study (reference: 3586). Dissemination activities will include presenting the reporting guideline at conferences relevant to pharmacogenetic research.

Opioids: The Painful Public Health Reality

The analgesic, sedative, antidepressant, euphoriant, intoxicating, and addictive properties of opium and its synthetic derivatives are well known and have been known for centuries. Hence, the current major public health problems due to excessive availability should be no surprise. What is unprecedented in the United States, and emerging elsewhere, is the extent of the profound consequences and complexity of addressing this public health crisis.

Estimated nationwide impact of implementing a preemptive pharmacogenetic panel approach to guide drug prescribing in primary care in The Netherlands

BACKGROUND

Pharmacogenetics (PGx) is currently implemented in hospitals to optimize therapy with high-risk drugs. However, many drugs with dosing recommendations from the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium are used in primary care. Actionable phenotypes for the genes covered in these guidelines are common with estimates ranging from 85 to 95% of the population carrying at least one actionable phenotype. The goal of this study was to estimate the clinical impact of implementation of an upfront panel-based pharmacogenetic screening for eight genes related to drugs used in primary care for 2016.

METHODS

For this study, dispensing data concerning first prescription for the period January 1-December 31, 2016, were combined with frequency data obtained in the "Implementation of Pharmacogenetics into Primary Care Project" (IP3) study to estimate the occurrence of actionable gene-drug pairs in daily practice in community pharmacies.

RESULTS

In 23.6% of all new prescriptions of 45 drugs (n = 856,002 new prescriptions/year), an actionable gene-drug interaction (GDI) was present according to the guidelines of the Dutch Pharmacogenetics Working Group. More importantly, these GDIs would result in a dose adjustment or switch to another drug in 5.4% of all new prescriptions.

CONCLUSIONS

Consequently, with an anticipated near future where healthcare professionals will be regularly confronted with PGx test results, adjusting pharmacotherapy based on this information will become a routine task in healthcare.

On the utilization of polygenic risk scores for therapeutic targeting

The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

Community pharmacists' educational needs for implementing clinical pharmacogenomic services

OBJECTIVES

Pharmacist leadership and knowledge of pharmacogenomics is critical to the acceleration and enhancement of clinical pharmacogenomic services. This study aims for a qualitative description of community pharmacists' pharmacogenomic educational needs when implementing clinical pharmacogenomic services at community pharmacies.

METHODS

Pharmacists practicing at Rite Aid Pharmacy locations in the Greater Pittsburgh Area were recruited to participate in this qualitative analysis. Pharmacists from pharmacy locations offering pharmacogenomic testing and robust patient care services were eligible to participate in a semistructured, audio-recorded interview. The semistructured interview covered 4 domains crafted by the investigative team: (1) previous knowledge of pharmacogenomics; (2) implementation resources; (3) workflow adaptation; and (4) learning preferences. Interviews were transcribed verbatim and independently coded by 2 researchers. A thematic analysis by the investigative team followed. Supporting quotes were selected to illustrate each theme.

RESULTS

Eleven pharmacists from 9 unique pharmacy locations participated in this study. The average length of practice as a community pharmacist was 12 years (range, 1.5-31 years). Pharmacist's pharmacogenomic educational needs were categorized into 5 key themes: (1) enriched pharmacogenomic education and training; (2) active learning to build confidence in using pharmacogenomic data in practice; (3) robust and reputable clinical resources to effectively implement pharmacogenomic services; (4) team-based approach throughout implementation; (5) readily accessible network of pharmacogenomic experts.

CONCLUSION

This study describes the educational needs and preferences of community pharmacists for the successful provision of clinical pharmacogenomic services in community pharmacies. Pharmacists recognized their needs for enriched knowledge and instruction, practice applying pharmacogenomic principles with team-based approaches, robust clinical resources, and access to pharmacogenomic experts. This deeper understanding of pharmacist needs for pharmacogenomic education could help to accelerate and enhance the clinical implementation of pharmacogenomic services led by community pharmacists.

Pharmacogenomic and Pharmacotranscriptomic Profiling of Childhood Acute Lymphoblastic Leukemia: Paving the Way to Personalized Treatment

Personalized medicine is focused on research disciplines which contribute to the individualization of therapy, like pharmacogenomics and pharmacotranscriptomics. Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. It is one of the pediatric malignancies with the highest cure rate, but still a lethal outcome due to therapy accounts for 1%⁻3% of deaths. Further improvement of treatment protocols is needed through the implementation of pharmacogenomics and pharmacotranscriptomics. Emerging high-throughput technologies, including microarrays and next-generation sequencing, have provided an enormous amount of molecular data with the potential to be implemented in childhood ALL treatment protocols. In the current review, we summarized the contribution of these novel technologies to the pharmacogenomics and pharmacotranscriptomics of childhood ALL. We have presented data on molecular markers responsible for the efficacy, side effects, and toxicity of the drugs commonly used for childhood ALL treatment, i.e., glucocorticoids, vincristine, asparaginase, anthracyclines, thiopurines, and methotrexate. Big data was generated using high-throughput technologies, but their implementation in clinical practice is poor. Research efforts should be focused on data analysis and designing prediction models using machine learning algorithms. Bioinformatics tools and the implementation of artificial i Lack of association of the CEP72 rs924607 TT genotype with intelligence are expected to open the door wide for personalized medicine in the clinical practice of childhood ALL.

Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

The antibiotic resistance epidemic has created a pressing need to understand factors that influence antibiotic efficacy. An often-overlooked factor in the search for new treatments is the pathogen environment. Understanding the differences in pathogen sensitivity to antibiotics in lab conditions versus inside the host is necessary for translating new discoveries into the clinic. Hence, we experimentally measured the sensitivity of E. coli to drugs and drug combinations in different metabolic conditions. Our data revealed that the environment dramatically changes treatment potency. Each antibiotic class was affected uniquely by each metabolic condition. The large number of metabolic conditions inside the host greatly complicates the identification of effective therapies. To address this challenge, we present a computational approach called MAGENTA that accurately predicted efficacy of antibiotic regimens in different conditions, which we confirmed experimentally. Furthermore, we show that MAGENTA can be applied to other bacterial pathogens such as A. baumannii and M. tuberculosis without the need for generating expensive data in each organism. MAGENTA accurately predicted efficacy in the pathogen A. baumannii using data from E. coli by identifying genes that are common between the two bacteria. Our study revealed the significant yet predictable impact of environment on drug combination potency.

Towards precision medicine; a new biomedical cosmology

Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new 'medical cosmology' in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive 'right' to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego.

Pharmacogenetic testing in oncology: a Brazilian perspective

Pharmacogenetics, a major component of individualized or precision medicine, relies on human genetic diversity. The remarkable developments in sequencing technologies have revealed that the number of genetic variants modulating drug action is much higher than previously thought and that a true personalized prediction of drug response requires attention to rare mutations (minor allele frequency, MAF<1%) in addition to polymorphisms (MAF>1%) in pharmacogenes. This has major implications for the conceptual development and clinical implementation of pharmacogenetics. Drugs used in cancer treatment have been major targets of pharmacogenetics studies, encompassing both germline polymorphisms and somatic variants in the tumor genome. The present overview, however, has a narrower scope and is focused on germline cancer pharmacogenetics, more specifically, on drug/gene pairs for which pharmacogenetics-informed prescription guidelines have been published by the Clinical Pharmacogenetics Implementation Consortium and/or the Dutch Pharmacogenetic Working Group, namely, thiopurines/TPMT, fluoropyrimidines/UGT1A1, irinotecan/UGT1A1 and tamoxifen/CYP2D6. I begin by reviewing the general principles of pharmacogenetics-informed prescription, pharmacogenetics testing and the perceived barriers to the adoption of routine pharmacogenetics testing in clinical practice. Then, I highlight aspects of the pharmacogenetics testing of the selected drug-gene pairs and finally present pharmacogenetics data from Brazilian studies pertinent to these drug-gene pairs. I conclude with the notion that pharmacogenetics testing has the potential to greatly benefit patients by enabling precision medicine applied to drug therapy, ensuring better efficacy and reducing the risk of adverse effects.

Mapping genetic vulnerabilities reveals BTK as a novel therapeutic target in oesophageal cancer

OBJECTIVE

Oesophageal cancer is the seventh most common cause of cancer-related death worldwide. Disease relapse is frequent and treatment options are limited.

DESIGN

To identify new biomarker-defined therapeutic approaches for patients with oesophageal cancer, we integrated the genomic profiles of 17 oesophageal tumour-derived cell lines with drug sensitivity data from small molecule inhibitor profiling, identifying drug sensitivity effects associated with cancer driver gene alterations. We also interrogated recently described RNA interference screen data for these tumour cell lines to identify candidate genetic dependencies or vulnerabilities that could be exploited as therapeutic targets.

RESULTS

By integrating the genomic features of oesophageal tumour cell lines with siRNA and drug screening data, we identified a series of candidate targets in oesophageal cancer, including a sensitivity to inhibition of the kinase BTK in MYC amplified oesophageal tumour cell lines. We found that this genetic dependency could be elicited with the clinical BTK/ERBB2 kinase inhibitor, ibrutinib. In both MYC and ERBB2 amplified tumour cells, ibrutinib downregulated ERK-mediated signal transduction, cMYC Ser-62 phosphorylation and levels of MYC protein, and elicited G1 cell cycle arrest and apoptosis, suggesting that this drug could be used to treat biomarker-selected groups of patients with oesophageal cancer.

CONCLUSIONS

BTK represents a novel candidate therapeutic target in oesophageal cancer that can be targeted with ibrutinib. On the basis of this work, a proof-of-concept phase II clinical trial evaluating the efficacy of ibrutinib in patients with MYC and/or ERBB2 amplified advanced oesophageal cancer is currently underway (NCT02884453).

TRIAL REGISTRATION NUMBER

NCT02884453; Pre-results.

Expression levels of selected genes can predict individual rheumatoid arthritis patient response to tumor necrosis factor alpha blocker treatment

OBJECTIVES

Rheumatoid arthritis (RA) patients have many therapeutic options; however, tools to predict individual patient response are limited. The Genefron personal diagnostic kit, developed by analyzing large datasets, utilizes selected interferon stimulated gene expressions to predict treatment response. This study evaluates the kit's prediction accuracy of individual RA patients' response to tumor necrosis alpha (TNFα) blockers.

METHODS

A retrospective analysis was performed on RA patients reported in published datasets. A prospective analysis assessed RA patients, before and 3 months after starting a TNFα blocker. Clinical response was evaluated according to EULAR response criteria. Blood samples were obtained before starting treatment and were analyzed utilizing the kit which measures expression levels of selected genes by quantitative real time polymerase chain reaction (PCR). ROC analysis was applied to the published datasets and the prospective data.

RESULTS

The Genefron kit analysis of retrospective data predicted the response to a TNFα blocker in 53 of 61 RA patients (86.8% accuracy). In the prospective analysis, the kit predicted the response in 16 of 18 patients (89% accuracy) achieving a EULAR moderate response, and in 15 of 18 patients achieving a EULAR good response (83.3% accuracy). ROC analysis applied to the two published datasets yielded an AUC of 0.89. ROC analysis applied to the prospective data yielded an AUC of 0.83 (sensitivity - 100%, specificity - 75%) The statistical power obtained in the prospective study was .9.

CONCLUSION

The diagnostic kit predicted the response to TNFα blockers in a high percentage of patients assessed retrospectively or prospectively. This personal kit may guide selection of a suitable biological drug for the individual RA patient.

Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing

The accrual and analysis of genomic sequencing data have identified specific genetic variants that are associated with major depressive disorder. Moreover, substantial investigations have been devoted to identifying gene-drug interactions that affect the response to antidepressant medications by modulating their pharmacokinetic or pharmacodynamic properties. Despite these advances, individual responses to antidepressants, as well as the unpredictability of adverse side effects, leave clinicians with an imprecise prescribing strategy that often relies on trial and error. These limitations have spawned several combinatorial pharmacogenetic testing products that are marketed to physicians. Typically, combinatorial pharmacogenetic decision support tools use algorithms to integrate multiple genetic variants and assemble the results into an easily interpretable report to guide prescribing of antidepressants and other psychotropic medications. The authors review the evidence base for several combinatorial pharmacogenetic decision support tools whose potential utility has been evaluated in clinical settings. They find that, at present, there are insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice, although there are clinical situations in which the technology may be informative, particularly in predicting side effects.

Genotype-Based Recall Studies in Complex Cardiometabolic Traits

In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.

Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome

The development of new knowledge around the genetic determinants of variable drug action has naturally raised the question of how this new knowledge can be used to improve the outcome of drug therapy. Two broad approaches have been taken: a point-of-care approach in which genotyping for specific variant(s) is undertaken at the time of drug prescription, and a preemptive approach in which multiple genetic variants are typed in an individual patient and the information archived for later use when a drug with a "pharmacogenetic story" is prescribed. This review addresses the current state of implementation, the rationale for these approaches, and barriers that must be overcome. Benefits to pharmacogenetic testing are only now being defined and will be discussed.

Genetic Variants in HSD17B3, SMAD3, and IPO11 Impact Circulating Lipids in Response to Fenofibrate in Individuals With Type 2 Diabetes

Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin-treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed-up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P < 5 × 10-6 ). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q < 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q < 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor-beta (TGF-β) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D.

Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10-8), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis.

Precision Medicine Approaches and the Health of Populations: Study Design Concerns and Considerations

Biomedical advances in the past decade have aimed to capitalize on two movements that have dominated the research conversation: precision medicine and the ascent of big data. These emerging shifts have resulted in growing confidence that we can better characterize health, predict who will get ill and with what, develop new treatments which exploit genetic, metabolic, and other vulnerabilities in cancers and infectious agents, and tailor some of these treatments to match characteristics of the individual patient and their specific disease. However, we suggest that there are important cautions. Weaknesses in the data and the methods used to study them raise three potential concerns. First, any data collected, and analysis attempted, will have limited utility absent internal validity, unless fundamental issues of accurate and consistent measurement can be addressed. Second, lack of attention to external validity limits generalizability beyond the narrow (even if large) samples in hand, so that the utility of inference that can emerge from these approaches remains limited. Third, the proposed approaches seldom include consideration of ubiquitous forces that can determine whether observed associations are truly attributable to the innovation or to other, unmeasured forces. This essay discusses these limitations and explores how they can influence inference drawn from big data precision medicine science.

A New Zealand platform to enable genetic investigation of adverse drug reactions

A multitude of factors can affect drug response in individuals. It is now well established that variations in genes, especially those coding for drug metabolising enzymes, can alter the pharmacokinetic and/or pharmacodynamic profile of a drug, impacting on efficacy and often resulting in drug-induced toxicity. The UDRUGS study is an initiative from the Carney Centre for Pharmacogenomics to biobank DNA and store associated clinical data from patients who have suffered rare and/or serious adverse drug reactions (ADRs). The aim is to provide a genetic explanation of drug-induced ADRs using methods ranging from Sanger sequencing to whole exome and whole genome sequencing. Participants for the UDRUGS study are recruited from various sources, mainly via referral through clinicians working in Canterbury District Health Board, but also from district health boards across New Zealand. Participants have also self-referred to us from word-of-mouth communication between participants. We have recruited various ADRs across most drug classes. Where possible, we have conducted genetic analyses in single or a cohort of cases to identify known and novel genetic association(s) to offer an explanation to why the ADR occurred. Any genetic results relevant to the ADR are communicated back to the referring clinician and/or participant. In conclusion, we have developed a programme for studying the genetic basis of severe, rare or unusual ADR cases resulting from pharmacological treatment. Genomic analyses could eventually identify most genetic variants that predispose to ADRs, enabling a priori detection of such variants with high throughput DNA tests.

Economic Evaluation of Implementing a Novel Pharmacogenomic Test (IDgenetix®) to Guide Treatment of Patients with Depression and/or Anxiety

BACKGROUND

The response to therapeutics varies widely in patients with depression and anxiety, making selection of an optimal treatment choice challenging. IDgenetix^®, a novel pharmacogenomic test, has been shown to improve outcomes by predicting the likelihood of response to different psychotherapeutic medications.

OBJECTIVE

The objective of this study was to estimate the cost effectiveness of implementing a novel pharmacogenomic test (IDgenetix^®) to guide treatment choices in patients with depression and/or anxiety compared with treatment as usual from the US societal perspective.

METHODS

We developed a discrete event simulation to compare clinical events, quality-adjusted life-years, and costs of the two treatment strategies. Target patients had a Hamilton Rating Scale for Depression Score ≥ 20 and/or a Hamilton Rating Scale for Anxiety score ≥ 18 at baseline. Remission, response, and no response were simulated based on the observed rates in the IDgenetix^® randomized controlled trial. Quality-adjusted life-years and direct and indirect costs attributable to depression and anxiety were estimated and compared over a 3-year time horizon. We conducted extensive deterministic and probabilistic sensitivity analyses to assess the robustness of the results.

RESULTS

The model predicted cumulative remission rates of 78 and 66% in IDgenetix^® and treatment as usual groups, respectively. Estimated discounted quality-adjusted life-years were 2.09 and 1.94 per patient for IDgenetix^® and treatment as usual, respectively, which resulted in 0.15 incremental quality-adjusted life-years (95% credible interval 0.04-0.28). The total costs after accounting for a US$2000 test cost were US$14,124 for IDgenetix^® compared with US$14,659 for treatment as usual, suggesting a US$535 (95% credible interval - 2902 to 1692) cost saving per patient in the IDgenetix^® group. Incremental quality-adjusted life-year gain (0.49) and cost savings (US$6800) were substantially larger in patients with severe depression (Hamilton Rating Scale for Depression score ≥ 25).

CONCLUSION

Using the IDgenetix^® test to guide the treatment of patients with depression and anxiety may be a dominant strategy, as it improves quality-adjusted life-years and decreases overall costs over a 3-year time horizon.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Prostate cancer immunotherapy, particularly in combination with androgen deprivation or radiation treatment. Customized pharmacogenomic approaches to overcome immunotherapy cancer resistance

Conventional therapeutic approaches for advanced prostate cancer - such as androgen deprivation, chemotherapy, radiation - come up often against lack of effectiveness because of possible arising of correlative cancer cell resistance and/or inadequate anti-tumor immune conditions. Whence the timeliness of resorting to immune-based treatment strategies including either therapeutic vaccination-based active immunotherapy or anti-tumor monoclonal antibody-mediated passive immunotherapy. Particularly attractive, as for research studies and clinical applications, results to be the cytotoxic T-lymphocyte check point blockade by the use of anti-CTLA-4 and PD-1 monoclonal antibodies, particularly when combined with androgen deprivation therapy or radiation. Unlike afore said immune check point inhibitors, both cell-based (by the use of prostate specific antigen carriers autologous dendritic cells or even whole cancer cells) and recombinant viral vector vaccines are able to induce immune-mediated focused killing of specific antigen-presenting prostate cancer cells. Such vaccines, either used alone or concurrently/sequentially combined with above-mentioned conventional therapies, led to generally reach, in the field of various clinical trials, reasonable results particularly as regards the patient's overall survival. Adoptive trasferred T-cells, as adoptive T-cell passive immunotherapy, and monoclonal antibodies against specific antigen-endowed prostate cancer cells can improve immune micro-environmental conditions. On the basis of a preliminary survey about various immunotherapy strategies, are here also outlined their effects when combined with androgen deprivation therapy or radiation. What's more, as regard the immune-based treatment effectiveness, it has to be pointed out that suitable personalized epigenetic/gene profile-achieved pharmacogenomic approaches to target identified gene aberrations, may lead to overcome - as well as for conventional therapies - possible prostate cancer resistance to immunotherapy.

The Innovative Canadian Pharmacogenomic Screening Initiative in Community Pharmacy (ICANPIC) study

OBJECTIVES

The safety and efficacy of medications can vary significantly between patients as a result of genetic variability. As genomic screening technologies become more widely available, pharmacists are ideally suited to use such tools to optimize medication therapy management. The objective of this study was to evaluate the feasibility of implementing personalized medication services into community pharmacy practice and to assess the number of drug therapy problems identified as a result of pharmacogenomic screening.

SETTING

The study was conducted in 2 busy urban community pharmacies, operating under the brand Shoppers Drug Mart, in Toronto, Ontario.

PRACTICE INNOVATION

Pharmacists offered pharmacogenomic screening as part of their professional services program. Eligible patients received a buccal swab followed by DNA analysis with the use of Pillcheck. Pillcheck is a genotyping assay that translates genomic data and generates a personalized evidence-based report that provides insight into patients' inherited drug metabolic profile. After receiving the report, pharmacists invited patients back to the clinic for interpretation of the results. Clinically significant drug therapy problems were identified and recommendations for medication optimization forwarded to the primary care physician.

RESULTS

One hundred patients were enrolled in the study. Average age was 56.7 years, and patients were taking a mean of 4.9 chronic medications. Pharmacists cited the most common reasons for testing as ineffective therapy (43.0%), to address an adverse reaction (32.6%), and to guide initiation of therapy (10.4%). An average of 1.3 drug therapy problems directly related to pharmacogenomic testing were identified per patient. Pharmacist recommendations included change in therapy (60.3%), dose adjustment (13.2%), discontinuation of a drug (4.4%), and increased monitoring (22.1%).

CONCLUSION

These results highlight the readiness of community pharmacists to adopt pharmacogenomic screening into practice and their ability to leverage this novel technology to positively affect medication therapy management. Community pharmacists are ideally suited to both offer personalized medication services and interpret genomic results.

Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies

BACKGROUND

An individual patient's response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task of identifying the genetic contribution is often complicated by response phenotypes that are based on imprecise or subjective clinical observations. Because the success of a pharmacogenetic study depends on the analysis of a heritable phenotype, it is important to identify phenotypes with a significant heritable component to ensure reliable and reproducible results in subsequent genetic association studies.

METHODS

We retrospectively analyzed data collected from 436 rheumatoid arthritis patients treated with golimumab during the phase III GO-FURTHER study. We investigated the reliability of several potential response outcomes after golimumab treatment. Using whole-genome sequencing of the clinical trial cohort, we estimated the heritability of each potential outcome measure. We further performed a longitudinal analysis of the clinical data to estimate variability of outcome measures over time and the degree to which each response metric could be confounded by placebo response.

RESULTS

We determined that the high degree of within-patient variation over time makes a single follow-up visit insufficient to assess an individual patient's response to golimumab treatment. We found that different potential response outcomes had varying degrees of heritability and that averaging across multiple follow-up visits yielded higher heritability estimates than single follow-up estimates. Importantly, we found that the change in swollen and tender joint counts were the most heritable outcome metrics we tested; however, we showed that they are also more likely to be confounded by a placebo response than objective phenotypes like the change in C-reactive protein levels.

CONCLUSIONS

Our rigorous approach to finding robust and heritable response phenotypes could be beneficial to all pharmacogenetic studies and may lead to more reliable and reproducible results.

TRIAL REGISTRATION

Clinicaltrials.gov NCT00973479 . Registered 4 September 2009.