[Pharmacogenetic testing in acute and chronic pain: a preliminary study]

Background. Pain is one of the most common symptoms that weighs on life's quality and health expenditure. In a reality in which increasingly personalized therapies are needed, the early use of genetic tests that highlight the individual response to analgesic drugs could be a valuable help in clinical practice helping to reduce response times, to achieve a good level of analgesia and to reduce the risk of side effects and adverse events. The study aims to confront the clinical response to analgesic drugs with the result of pharmacogenetic testing in patients with persistent pain. Methods. This preliminary study compares the genetic results of pharmacological effectiveness and tolerability analyzed with a Pharmacogenetic Test with the results obtained in clinical practice in 5 patients suffering from acute and chronic pain. Results. Regarding the genetic results of the 5 samples analyzed, 2 reports were found to be completely comparable to what found in clinical practice, while 3 reports showed that the profile of tolerability and effectiveness were partially discordant. Conclusions. In light of the data, not completely overlapping with results observed in clinical practice, further studies would be appropriate in order to acquire more information on the use of the PGT in clinical practice.

MASS SPECTROMETRY-BASED PERSONALIZED DRUG THERAPY

Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost-effective, and easy to be used high-throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS-based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix-assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI-TOF-MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS-based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS-based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Advances in Genomics for Drug Development

Drug development (target identification, advancing drug leads to candidates for preclinical and clinical studies) can be facilitated by genetic and genomic knowledge. Here, we review the contribution of population genomics to target identification, the value of bulk and single cell gene expression analysis for understanding the biological relevance of a drug target, and genome-wide CRISPR editing for the prioritization of drug targets. In genomics, we discuss the different scope of genome-wide association studies using genotyping arrays, versus exome and whole genome sequencing. In transcriptomics, we discuss the information from drug perturbation and the selection of biomarkers. For CRISPR screens, we discuss target discovery, mechanism of action and the concept of gene to drug mapping. Harnessing genetic support increases the probability of drug developability and approval.

Identifying key transcription factors for pharmacogenetic studies of antipsychotics induced extrapyramidal symptoms

INTRODUCTION

We explore the transcription factors involved in the molecular mechanism of antipsychotic (AP)-induced acute extrapyramidalsymptoms (EPS) in order to identify new candidate genes for pharmacogenetic studies.

METHODS

Protein-protein interaction (PPI) networks previously created from three pharmacogenomic models (in vitro, animal, and peripheral blood inhumans) were used to, by means of several bioinformatic tools; identify key transcription factors (TFs) that regulate each network. Once the TFs wereidentified, SNPs disrupting the binding sites (TFBS) of these TFs in the genes of each network were selected for genotyping. Finally, SNP-basedassociations with EPS were analyzed in a sample of 356 psychiatric patients receiving AP.

RESULTS

Our analysis identified 33 TFs expressed in the striatum, and 125 SNPs disrupting TFBS in 50 genes of our initial networks. Two SNPs (rs938112,rs2987902) in two genes (LSMAP and ABL1) were significantly associated with AP induced EPS (p < 0.001). These SNPs disrupt TFBS regulated byPOU2F1.

CONCLUSION

Our results highlight the possible role of the disruption of TFBS by SNPs in the pharmacological response to AP.

[Pharmacogenetics in daily practice]

With the exception of a few medical specialties, the implementation of pharmacogenetic tests in daily practice has thus far been limited. The Royal Dutch Pharmacists Association (KNMP) has developed pharmacogenetics-based therapeutic doserecommendations for 80 medicinal product combinations on the basis of a systematic literature review. Genotyping of patients can take place on a reactive or pre-emptive basis; the advantage of pre-emptive genotyping is that it provides genetic information the moment a medicinal product is prescribed. Clinical decision support software is crucial to implement pharmacogenetics into daily practice.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Pharmacogenetic-Whole blood and intracellular pharmacokinetic-Pharmacodynamic (PG-PK2-PD) relationship of tacrolimus in liver transplant recipients

Tacrolimus (TAC) is the cornerstone of immunosuppressive therapy in liver transplantation. This study aimed at elucidating the interplay between pharmacogenetic determinants of TAC whole blood and intracellular exposures as well as the pharmacokinetic-pharmacodynamic relationship of TAC in both compartments. Complete pharmacokinetic profiles (Predose, and 20 min, 40 min, 1h, 2h, 3h, 4h, 6h, 8h, 12h post drug intake) of twice daily TAC in whole blood and peripheral blood mononuclear cells (PBMC) were collected in 32 liver transplanted patients in the first ten days post transplantation. A non-parametric population pharmacokinetic model was applied to explore TAC pharmacokinetics in blood and PBMC. Concurrently, calcineurin activity was measured in PBMC. Influence of donor and recipient genetic polymorphisms of ABCB1, CYP3A4 and CYP3A5 on TAC exposure was assessed. Recipient ABCB1 polymorphisms 1199G>A could influence TAC whole blood and intracellular exposure (p<0.05). No association was found between CYP3A4 or CYP3A5 genotypes and TAC whole blood or intracellular concentrations. Finally, intra-PBMC calcineurin activity appeared incompletely inhibited by TAC and less than 50% of patients were expected to achieve intracellular IC₅₀ concentration (100 pg/millions of cells) at therapeutic whole blood concentration (i.e.: 4–10 ng/mL). Together, these data suggest that personalized medicine regarding TAC therapy might be optimized by ABCB1 pharmacogenetic biomarkers and by monitoring intracellular concentration whereas the relationship between intracellular TAC exposure and pharmacodynamics biomarkers more specific than calcineurin activity should be further investigated.

Pharmacogenetic Markers: A Path toward Individualized HIV Therapy

INTRODUCTION Approximately 73% of persons with HIV who receive antiretroviral therapy in Cuba are in viral suppression. The non-response of the remaining 27% could be due to several factors including adverse drug reactions and HIV resistance to antiretroviral drugs, as well as social factors and idiosyncratic characteristics of each patient. Genetic information explains from 20% to 95% of a drug's effects and variations in response. Considering optimization of therapeutic efficacy in our country, genetic factors of the host should be identified. OBJECTIVE Identify polymorphisms affecting genetic variability of responses to antiretroviral drugs. EVIDENCE ACQUISITION A literature review was conducted (of original articles, published theses, clinical reports and bibliographic review studies, from 2000 to 2018, in Spanish and English listed in MEDLINE/PubMed, SciELO, LILACS, PharmGKB and Google Scholar) with the following key words: pharmacogenetics, human immunodeficiency virus, anti-retroviral agents, genetic polymorphism, genetic techniques, pharmacogenomic variants. DEVELOPMENT The review identified 77 relevant publications meeting specific quality criteria. A summary table was built with data collected on antiretroviral drugs, genes and proteins involved in polymorphic variations, their associated effects and relevant scientific references. Information was included on polymorphisms related to 12 antiretroviral drugs used in HIV therapy. Polymorphisms determine variations in proteins involved in drug transport and metabolism and in elements of immunity. Relevant pharmacogenetic biomarkers recognized by drug regulatory agencies were identified. CONCLUSIONS The study identified genetic variations (single-nucleotide polymorphisms) associated with 12 antiretroviral drugs. In most cases, no statistically significant causal association was found. Identifying polymorphic variations is a medium- and long-term objective that requires statistical support and adoption of strategies to optimize antiretroviral therapy. An approach combining plasma-level monitoring and pharmacogenetic analysis is recommended to optimize therapy for HIV patients. KEYWORDS Pharmacogenetics, HIV, anti-retroviral agents, antiretroviral therapy, genetic polymorphism, genetic techniques, pharmacogenomic variants.

Pharmacogenomic genotypes define genetic ancestry in patients and enable population-specific genomic implementation

The importance of genetic ancestry characterization is increasing in genomic implementation efforts, and clinical pharmacogenomic guidelines are being published that include population-specific recommendations. Our aim was to test the ability of focused clinical pharmacogenomic SNP panels to estimate individual genetic ancestry (IGA) and implement population-specific pharmacogenomic clinical decision-support (CDS) tools. Principle components and STRUCTURE were utilized to assess differences in genetic composition and estimate IGA among 1572 individuals from 1000 Genomes, two independent cohorts of Caucasians and African Americans (AAs), plus a real-world validation population of patients undergoing pharmacogenomic genotyping. We found that clinical pharmacogenomic SNP panels accurately estimate IGA compared to genome-wide genotyping and identify AAs with ≥70 African ancestry (sensitivity >82%, specificity >80%, PPV >95%, NPV >47%). We also validated a new AA-specific warfarin dosing algorithm for patients with ≥70% African ancestry and implemented it at our institution as a novel CDS tool. Consideration of IGA to develop an institutional CDS tool was accomplished to enable population-specific pharmacogenomic guidance at the point-of-care. These capabilities were immediately applied for guidance of warfarin dosing in AAs versus Caucasians, but also provide a real-world model that can be extended to other populations and drugs as actionable genomic evidence accumulates.

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.

Recommendations for Clinical CYP2C9 Genotyping Allele Selection: A Joint Recommendation of the Association for Molecular Pathology and College of American Pathologists

The goals of the Association for Molecular Pathology Pharmacogenomics (PGx) Working Group of the Association for Molecular Pathology Clinical Practice Committee are to define the key attributes of PGx alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document provides recommendations for a minimum panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories when designing assays for CYP2C9 testing. The Working Group considered the functional impact of the variants, allele frequencies in different populations and ethnicities, the availability of reference materials, and other technical considerations for PGx testing when developing these recommendations. Our goal is to promote standardization of testing PGx genes and alleles across clinical laboratories. These recommendations are not to be interpreted as restrictive but to provide a reference guide. The current document will focus on CYP2C9 testing that can be applied to all CYP2C9-related medications. A separate recommendation on warfarin PGx testing is being developed to include recommendations on CYP2C9 alleles and additional warfarin sensitivity-associated genes and alleles.

Are There Different Evidence Thresholds for Genomic Versus Clinical Precision Medicine? A Value of Information-Based Framework Applied to Antiplatelet Drug Therapy

BACKGROUND

The threshold of sufficient evidence for adoption of clinically- and genomically-guided precision medicine (PM) has been unclear.

OBJECTIVE

To evaluate evidence thresholds for clinically guided PM versus genomically guided PM.

METHODS

We develop an "evidence threshold criterion" (ETC), which is the time-weighted difference between expected value of perfect information and incremental net health benefit minus the cost of research, and use it as a measure of evidence threshold that is proportional to the upper bound of disutility to a risk-averse decision maker for adopting a new intervention under decision uncertainty. A larger (more negative) ETC value indicates that only decision makers with low risk aversion would adopt new intervention. We evaluated the ETC plus cost of research (ETCc), assuming the same cost of research for both interventions, over time for a pharmacogenomic (PGx) testing intervention and avoidance of a drug-drug interaction (aDDI) intervention for acute coronary syndrome patients indicated for antiplatelet therapy. We then examined how the ETC may explain incongruous decision making across different national decision-making bodies.

RESULTS

The ETCc for PGx increased over time, whereas the ETCc for aDDI decreased to a negative value over time, indicating that decision makers with even low risk aversion will have doubts in adopting PGx, whereas decision makers who are highly risk-averse will continue to have doubts about adopting aDDI. National recommendation bodies appear to be consistent over time within their own decision making, but had different levels of risk aversion.

CONCLUSION

The ETC may be a useful metric for assessing policy makers' risk preferences and, in particular, understanding differences in policy recommendations for genomic versus clinical PM.

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.

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.