Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus.

BACKGROUND: Angioedema is a rare but potentially life-threatening adverse drug reaction in patients receiving angiotensin-converting enzyme inhibitors (ACEis). Research suggests that susceptibility to ACEi-induced angioedema (ACEi-AE) involves both genetic and nongenetic risk factors. Genome- and exome-wide studies of ACEi-AE have identified the first genetic risk loci. However, understanding of the underlying pathophysiology remains limited. OBJECTIVE: We sought to identify further genetic factors of ACEi-AE to eventually gain a deeper understanding of its pathophysiology. METHODS: By combining data from 8 cohorts, a genome-wide association study meta-analysis was performed in more than 1000 European patients with ACEi-AE. Secondary bioinformatic analyses were conducted to fine-map associated loci, identify relevant genes and pathways, and assess the genetic overlap between ACEi-AE and other traits. Finally, an exploratory cross-ancestry analysis was performed to assess shared genetic factors in European and African-American patients with ACEi-AE. RESULTS: Three genome-wide significant risk loci were identified. One of these, located on chromosome 20q11.22, has not been implicated previously in ACEi-AE. Integrative secondary analyses highlighted previously reported genes (BDKRB2 [bradykinin receptor B2] and F5 [coagulation factor 5]) as well as biologically plausible novel candidate genes (PROCR [protein C receptor] and EDEM2 [endoplasmic reticulum degradation enhancing alpha-mannosidase like protein 2]). Lead variants at the risk loci were found with similar effect sizes and directions in an African-American cohort. CONCLUSIONS: The present results contributed to a deeper understanding of the pathophysiology of ACEi-AE by (1) providing further evidence for the involvement of bradykinin signaling and coagulation pathways and (2) suggesting, for the first time, the involvement of the fibrinolysis pathway in this adverse drug reaction. An exploratory cross-ancestry comparison implicated the relevance of the associated risk loci across diverse ancestries.

Fine-mapping genomic loci refines bipolar disorder risk genes.

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes.

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Genome-wide analysis toward the epigenetic aetiology of myelodysplastic syndrome disease progression and pharmacoepigenomic basis of hypomethylating agents drug treatment response.

Myelodysplastic syndromes (MDS) consist of a group of hematological malignancies characterized by ineffective hematopoiesis, cytogenetic abnormalities, and often a high risk of transformation to acute myeloid leukemia (AML). So far, there have been only a very limited number of studies assessing the epigenetics component contributing to the pathophysiology of these disorders, but not a single study assessing this at a genome-wide level. Here, we implemented a generic high throughput epigenomics approach, using methylated DNA sequencing (MeD-seq) of LpnPI digested fragments to identify potential epigenomic targets associated with MDS subtypes. Our results highlighted that PCDHG and ZNF gene families harbor potential epigenomic targets, which have been shown to be differentially methylated in a variety of comparisons between different MDS subtypes. Specifically, CpG islands, transcription start sites and post-transcriptional start sites within ZNF124, ZNF497 and PCDHG family are differentially methylated with fold change above 3,5. Overall, these findings highlight important aspects of the epigenomic component of MDS syndromes pathogenesis and the pharmacoepigenomic basis to the hypomethylating agents drug treatment response, while this generic high throughput whole epigenome sequencing approach could be readily implemented to other genetic diseases with a strong epigenetic component.

Discovery of new drug indications for COVID-19: A drug repurposing approach.

MOTIVATION: The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research. METHOD: Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs. RESULT: Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.

Development of an optimized and generic cost-utility model for analyzing genome-guided treatment data.

Economic evaluation is an integral component of informed public health decision-making in personalized medicine. However, performing economic evaluation assessments often requires specialized knowledge, expertise, and significant resources. To this end, developing generic models can significantly assist towards providing the necessary evidence for the cost-effectiveness of genome-guided therapeutic interventions, compared to the traditional drug treatment modalities. Here, we report a generic cost-utility analysis model, developed in R, which encompasses essential economic evaluation steps. Specifically, critical steps towards a comprehensive deterministic and probabilistic sensitivity analysis were incorporated in our model, while also providing an easy-to-use graphical user interface, which allows even non-experts in the field to produce a fully comprehensive cost-utility analysis report. To further demonstrate the model's reproducibility, two sets of data were assessed, one stemming from in-house clinical data and one based on previously published data. By implementing the generic model presented herein, we show that the model produces results in complete concordance with the traditionally performed cost-utility analysis for both datasets. Overall, this work demonstrates the potential of generic models to provide useful economic evidence for personalized medicine interventions.

Identification and functional validation of novel pharmacogenomic variants using a next-generation sequencing-based approach for clinical pharmacogenomics.

Inter-individual variability in pharmacokinetics and drug response is heavily influenced by single-nucleotide variants (SNVs) and copy-number variations (CNVs) in genes with importance for drug disposition. Nowadays, a plethora of studies implement next generation sequencing to capture rare and novel pharmacogenomic (PGx) variants that influence drug response. To address these issues, we present a comprehensive end-to-end analysis workflow, beginning from targeted PGx panel re-sequencing to in silico analysis pipelines and in vitro validation assays. Specifically, we show that novel pharmacogenetic missense variants that are predicted or putatively predicted to be functionally deleterious, significantly alter protein activity levels of CYP2D6 and CYP2C19 proteins. We further demonstrate that variant priorization pipelines tailored with functional in vitro validation assays provide supporting evidence for the deleterious effect of novel PGx variants. The proposed workflow could provide the basis for integrating next-generation sequencing for PGx testing into routine clinical practice.

The ethnogeographic variability of genetic factors underlying G6PD deficiency.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency caused by genetic variants in the G6PD gene, constitutes the most common enzymopathy worldwide, affecting approximately 5% of the global population. While carriers are mostly asymptomatic, they are at substantial risk of acute hemolytic anemia upon certain infections or exposure to various medications. As such, information about G6PD activity status in a given patient can constitute an important parameter to guide clinical decision-making. Here, we leveraged whole genome sequencing data from 142,069 unrelated individuals across seven human populations to provide a global comprehensive map of G6PD variability. By integrating established functional classifications with stringent computational predictions using 13 partly orthogonal algorithms for uncharacterized and novel variants, we reveal the large extent of ethnogeographic variability in G6PD deficiency and highlight its population-specific genetic composition. Overall, estimated disease prevalence in males ranged between 12.2% in Africans, 2.7-3.5% across Asia and 2.1% in Middle Easterners to < 0.3% in Europeans, Finnish and Amish. In Africans, the major deficient alleles were A-202A/376 G (minor allele frequency 11.6%) and A-968 C/376 G (0.5%). In contrast, G6PD deficiency in Middle Easterners was primarily due to the Mediterranean allele (1.3%) and the population-specific Cairo variant (0.4%). In South Asia, the most prevalent deficient alleles were Mediterranean (1.7%), Kerala (1.1%), Gond (0.9%) and Orissa (0.2%), whereas in East Asian populations the Canton (1.1%), Kaiping (0.7%) and Viangchan (0.3%) variants were predominant. Combined, our analyses provide a large dataset of G6PD variability across major ethnogeographic groups and can instruct population-specific genotyping strategies to optimize genetically guided therapeutic interventions.

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants.

BACKGROUND: The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility. METHODS: This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. RESULTS: All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function." CONCLUSION: Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

Pharmacogenomics variants are associated with BMI differences between individuals with bipolar and other psychiatric disorders.

Aim: Regardless of the plethora of next-generation sequencing studies in the field of pharmacogenomics (PGx), the potential effect of covariate variables on PGx response within deeply phenotyped cohorts remains unexplored. Materials & methods: We explored with advanced statistical methods the potential influence of BMI, as a covariate variable, on PGx response in a Greek cohort with psychiatric disorders. Results: Nine PGx variants within UGT1A6, SLC22A4, GSTP1, CYP4B1, CES1, SLC29A3 and DPYD were associated with altered BMI in different psychiatric disorder groups. Carriers of rs2070959 (UGT1A6), rs199861210 (SLC29A3) and rs2297595 (DPYD) were also characterized by significant changes in the mean BMI, depending on the presence of psychiatric disorders. Conclusion: Specific PGx variants are significantly associated with BMI in a Greek cohort with psychiatric disorders.

Economic evaluation in psychiatric pharmacogenomics: a systematic review.

Nowadays, many relevant drug-gene associations have been discovered, but pharmacogenomics (PGx)-guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard health care. To address current challenges, this systematic review provides an update regarding previously published studies, which assessed the cost-effectiveness of PGx testing for the prescription of antidepressants and antipsychotics. From a total of 1159 studies initially identified by literature database querying, and after manual assessment and curation of all of them, a mere 18 studies met our inclusion criteria. Of the 18 studies evaluations, 16 studies (88.89%) drew conclusions in favor of PGx testing, of which 9 (50%) genome-guided interventions were cost-effective and 7 (38.9%) were less costly compared to standard treatment based on cost analysis. More precisely, supportive evidence exists for CYP2D6 and CYP2C19 drug-gene associations and for combinatorial PGx panels, but evidence is limited for many other drug-gene combinations. Amongst the limitations of the field are the unclear explanation of perspective and cost inputs, as well as the underreporting of study design elements, which can influence though the economic evaluation. Overall, the findings of this article demonstrate that although there is growing evidence on the cost-effectiveness of genome-guided interventions in psychiatric diseases, there is still a need for performing additional research on economic evaluations of PGx implementation with an emphasis on psychiatric disorders.

Genome-based therapeutic interventions for ß-type hemoglobinopathies.

For decades, various strategies have been proposed to solve the enigma of hemoglobinopathies, especially severe cases. However, most of them seem to be lagging in terms of effectiveness and safety. So far, the most prevalent and promising treatment options for patients with ß-types hemoglobinopathies, among others, predominantly include drug treatment and gene therapy. Despite the significant improvements of such interventions to the patient's quality of life, a variable response has been demonstrated among different groups of patients and populations. This is essentially due to the complexity of the disease and other genetic factors. In recent years, a more in-depth understanding of the molecular basis of the ß-type hemoglobinopathies has led to significant upgrades to the current technologies, as well as the addition of new ones attempting to elucidate these barriers. Therefore, the purpose of this article is to shed light on pharmacogenomics, gene addition, and genome editing technologies, and consequently, their potential use as direct and indirect genome-based interventions, in different strategies, referring to drug and gene therapy. Furthermore, all the latest progress, updates, and scientific achievements for patients with ß-type hemoglobinopathies will be described in detail.

Ethics and equity in rare disease research and healthcare.

Rapid advances in next-generation sequencing technology, particularly whole exome sequencing and whole genome sequencing, have greatly affected our understanding of genetic variation underlying rare genetic diseases. Herein, we describe ethical principles of guiding consent and sharing of genomics research data. We also discuss ethical dilemmas in rare diseases research and patient recruitment policies and address bioethical and societal aspects influencing the ethical framework for genetic testing. Moreover, we focus on addressing ethical issues surrounding research in low- and middle-income countries. Overall, this perspective aims to address key aspects and issues for building proper ethical frameworks, when conducting research involving genomics data with a particular emphasis on rare diseases and genetics testing.

Attitudes and Awareness Toward Pharmacogenomics and Personalized Medicine Adoption Among Health Sciences Trainees: Experience from Greece and Lessons for Europe.

The increasing incorporation of genomics in clinical practice underscores the need to improve genomics knowledge and familiarity among future health care providers. To this end, it is important to consider both the "push" and the "pull" factors that shape or determine the transition of new personalized medicine (PM) discoveries to clinical practice. One of the pull factors involves the attitudes, values, and education of the user communities such as patients, physicians, and scientists who are poised to use the PM diagnostics. Among the push factors are often health scientists who contribute to PM science and development efforts. Seen in this light, health sciences trainees represent both the push and pull factors, not to mention the next generation of stakeholders and innovation actors who will make PM a reality in mainstream medical practice in the future. Τhis study aimed at investigating and comparing awareness and attitudes (ethical and other) on pharmacogenomics (PGx) and PM adoption among undergraduate students from the school of health sciences and other students. A convenience sample was used in this survey in two groups of students: 205 students from the School of Health Sciences and 141 students from other schools (e.g., biology, computer engineering, and business administration) of the University of Patras, Greece, and mostly at undergraduate education level. We observed that despite the relatively low level of awareness about genetics, PGx, and relevant notions, both groups of students were very optimistic about the genetic testing usefulness and professed their positive anticipations about PGx on disease management. Thus, health sciences students and those in other faculties appeared to be avid proponents of genetics testing and in favor of public endorsement of the concepts of individually tailored medicine. This case study in Greece is one of the first studies on perceptions and attitudes toward PGx testing and PM in Southern Europe. Of importance, the study informs the prospects and challenges on the push and pull factors of PM innovation while offering potential lessons for future PM curriculum needs in health sciences in other countries in Europe.

Discrepancies and similarities in the genome-informed guidance for psychiatric disorders amongst different regulatory bodies and research consortia using next generation sequencing-based clinical pharmacogenomics data.

Undoubtedly, pharmacogenomics (PGx) aims in optimizing drug treatment responses whilst also improving the patients' quality of life, either via a reduction of adverse drug reactions and/or an enhancement of drug treatment efficacy. To achieve this, PGx guidance is provided by the two major regulatory bodies in a worldwide level, specifically the U.S. Food and Drug Administration (FDA) and the European Medicine Agency (EMA), and occasionally some research consortia, such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) or the Dutch Pharmacogenomics Working Group (DPWG). However, so far, there is a limited number of studies focusing on the delineation of the similarities and more importantly, the discrepancies in the PGx guidance by the different regulatory bodies and consortia. Herein, we use real-life clinical PGx data to highlight such discrepancies and similarities for genome-guided interventions in psychiatric disorders, thus demonstrating the need for harmonization of the guidelines and recommendations. More precisely, we used the PharmCAT genome-informed drug treatment reports from 304 Greek individuals with psychiatric disorders in order to emphasize on the discrepancies in the PGx guidance/guidelines between FDA vs EMA and CPIC vs DPWG, respectively. For example, CYP2D6-pimozide pair is characterized as 'Testing Required' according to FDA and is accompanied by a DPWG PGx guideline, whilst no EMA or CPIC PGx guidance is found for this drug-gene pair. Moreover, discrepancies are observed regarding the type of PGx guidance for CYP2C19-doxepin pair, with 89 individuals from our study cohort requiring a dose prescribing change based on FDA, whilst only 5 individuals have to receive genome-guided treatment adjustment according to CPIC. To our knowledge, this is the first study, in which discrepancies regarding the type of PGx guidance and the number of actionable drug-gene pairs amongst FDA and EMA, as well as CPIC and DPWG, are brought to light with an emphasis on psychiatric disorders.

Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm).

Pharmacogenomics can enhance the outcome of treatment by adopting pharmacogenomic testing to maximize drug efficacy and lower the risk of serious adverse events. Next-generation sequencing (NGS) is a cost-effective technology for genotyping several pharmacogenomic loci at once, thereby increasing publicly available data. A panel of 100 pharmacogenes among Southeast Asian (SEA) populations was resequenced using the NGS platform under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm). Here, we present the frequencies of pharmacogenomic variants and the comparison of these pharmacogenomic variants among different SEA populations and other populations used as controls. We investigated the different types of pharmacogenomic variants, especially those that may have a functional impact. Our results provide substantial genetic variations at 100 pharmacogenomic loci among SEA populations that may contribute to interpopulation variability in drug response phenotypes. Correspondingly, this study provides basic information for further pharmacogenomic investigations in SEA populations.

Catalyzing clinical implementation of pharmacogenomics and personalized medicine interventions in Africa.

Pharmacogenomics is considered to be the low-hanging fruit in the tree of genomic medicine with numerous examples of its successful implementation in the clinic. In this perspective, we provide details about the potential clinical application of pharmacogenomics in African populations by using relevant drug cases and high-throughput genomics approaches; involving numerous countries and stakeholders; and most importantly exploiting the existing knowledge of respective large-scale initiatives. We emphasize on the necessity of constructing appropriate frameworks for government policies in African countries. We also provide input about different initiatives in the field of genomics medicine implementation in Africa, not only for their potential for synergy and collaboration among them, but also as models for replication in other regions worldwide, aiming for healthcare improvement.

What Do Students in Pharmacy and Medicine Think About Pharmacogenomics and Personalized Medicine Education? Awareness, Attitudes, and Perceptions in Malaysian Health Sciences.

This study reports on the attitudes and perceptions toward pharmacogenomics (PGx) and personalized medicine (PM) education among pharmacy and medical students in Malaysian health sciences. Importantly, the survey was developed through a codesign approach, with field pretesting/design with users before the actual survey, and based on collaboration between institutions in Greece and Malaysia. The study addressed eight key areas of interest to education in health sciences: (1) General awareness about genetics and PGx, (2) Attitude toward genetic testing usefulness, (3) Benefits of direct-to-consumer personal genome testing as a "diagnostic" tool, (4) Concerns (risks) about genetics, (5) Effectiveness of genetic testing in PM, (6) Benefits of PGx on disease management, (7) Benefits of PGx on drug management, and (8) Attitudes toward genetic testing public endorsement. We observed that Malaysian students appear aware of the term PGx, but there are areas of critical knowledge gap such as the need for greater familiarity with the concept of PGx implementation science, and the availability of genetic testing in clinical practice. This is one of the first studies on perceptions and attitudes toward PGx testing in Southeast Asia. The present findings provide a map of the views and perspectives of medicine and pharmacy students regarding PGx and implementation of PM in Malaysia and should assist toward facilitating the integration of genomics into the medical decision-making process. To this end, it is necessary to enhance collaboration between universities, health care institutions, and governing bodies to incorporate further training and additional education topics related to PGx and genetic testing. This is the first study that assesses the level of PGx and genomics knowledge of pharmacy and medicine students in Southeast Asia, Malaysia in particular, and thus paves the way to guide future global PGx implementation science.

A Novel Text-Mining Approach for Retrieving Pharmacogenomics Associations From the Literature.

Text mining in biomedical literature is an emerging field which has already been shown to have a variety of implementations in many research areas, including genetics, personalized medicine, and pharmacogenomics. In this study, we describe a novel text-mining approach for the extraction of pharmacogenomics associations. The code that was used toward this end was implemented using R programming language, either through custom scripts, where needed, or through utilizing functions from existing libraries. Articles (abstracts or full texts) that correspond to a specified query were extracted from PubMed, while concept annotations were derived by PubTator Central. Terms that denote a Mutation or a Gene as well as Chemical compound terms corresponding to drug compounds were normalized and the sentences containing the aforementioned terms were filtered and preprocessed to create appropriate training sets. Finally, after training and adequate hyperparameter tuning, four text classifiers were created and evaluated (FastText, Linear kernel SVMs, XGBoost, Lasso, and Elastic-Net Regularized Generalized Linear Models) with regard to their performance in identifying pharmacogenomics associations. Although further improvements are essential toward proper implementation of this text-mining approach in the clinical practice, our study stands as a comprehensive, simplified, and up-to-date approach for the identification and assessment of research articles enriched in clinically relevant pharmacogenomics relationships. Furthermore, this work highlights a series of challenges concerning the effective application of text mining in biomedical literature, whose resolution could substantially contribute to the further development of this field.

Development of Rapid Pharmacogenomic Testing Assay in a Mobile Molecular Biology Laboratory (2MoBiL).

Pharmacogenomics is rapidly assuming an integral part in modern health care. Still, its broad applicability relies on the feasibility of performing pharmacogenomic testing in all clinical settings, including in remote areas or resource-limited settings with budget restrictions. In this study, we describe the development and feasibility of rapid and reliable pharmacogenomics assays using a portable molecular biology laboratory, namely the 2MoBiL (Mobile Molecular Biology Laboratory). More precisely, we demonstrate that the genotyping of rs4149056, located within SLCO1B1, can be efficiently and reliably performed using the 2MoBiL portable laboratory and conventional benchtop laboratory equipment and a gold standard genotyping method (KASP assay) as directly comparable methodologies. Taking into account the compact size of 2MoBiL, which directly and positively impacts on its portability, and the high accuracy achieved, we conclude that the 2MoBiL-based genotyping method is warranted for further studies in clinical practices at remote areas and resource-limited as well as time-constrained planetary health settings. To contextualize the broader and potential future applications of 2MoBiL, we emphasize that genotyping of a limited set of clinically relevant single-nucleotide polymorphisms is often a common endpoint of genomics and pharmacogenomics discovery and translational research pipeline. Hence, rapid genotyping by 2MoBiL can be an essential catalyst for global implementation of pharmacogenomics and personalized medicine in the clinic. The Clinical Trial Registration number is NCT03093818.