Bioinformatics In Pharmacogenomics And Individualized Drug Response Prediction – Characterization and phylogenomics of East Asian beech plastomes with special emphasis on Fagus multinervisi on Uleung Island, Korea

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Bioinformatics In Pharmacogenomics And Individualized Drug Response Prediction

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Predicting Patient Response With Models Trained On Cell Lines And Patient Derived Xenografts By Nonlinear Transfer Learning

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Pdf) Knowledge Guided Prioritization Of Genes Determinant Of Drug Response Using Progeni (2016)

Victoria Rollinson Victoria Rollinson Scilit Preprints.org Google Scholar *, † , Richard Turner Richard Turner Scilit Preprints.org Google Scholar † and Mounir Pirmohamed Mounir Pirmohamed Scilit Preprints.org Google Scholar

Wolfson Center for Personalized Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GL, UK

Received: 15.10.2020 / Revised: 11.02.2020 / Approved: 11.04.2020 / Published: 11.12.2020

Pharmacoepitranscriptomic Landscape Revealing M6a Modification Could Be A Drug Effect Biomarker For Cancer Treatment: Molecular Therapy

(This article is part of a Pharmacogenomics special issue on Factors Influencing Interindividual Variability in Drug Response: From Discovery to Implementation)

Most of the prescribing and dispensing of medications occurs in primary care. Pharmacogenomics (PGx) is the study and clinical application of the role of genetic variation in drug response. A growing body of evidence suggests that PGx may improve the safety and/or efficacy of several drugs commonly prescribed in primary care. However, adoption of PGx has generally been limited to a relatively few academic hospital centers, with little adoption in primary care. Despite this, many primary care providers are optimistic about the role of PGx in their future practice. The proliferation of direct-to-consumer genetic testing and PGx research in primary care portends the likely gradual adoption of PGx in primary care and highlights the changes required for optimal translation. This article reviews the potential use of PGx in primary care and discusses current barriers to implementation. The evidence base for several drug-gene pairs relevant to primary care is outlined, with a focus on antidepressants, codeine and tramadol, statins, clopidogrel, warfarin, metoprolol, and allopurinol. The purpose of this review is to provide both a general introduction to PGx and a more in-depth overview of factors related to primary care.

It is increasingly recognized that people react differently to drugs. In some cases, these differences in response can be clinically significant, leading to treatment failure or adverse drug reactions (ADRs). The etiology of this interindividual variation is complex and depends on many factors, including individual characteristics (eg, age, sex, body mass index), clinical factors (eg, renal or hepatic impairment; concomitant treatment), environmental exposures (eg , and genetics.

A Landscape Of Pharmacogenomic Interactions In Cancer

Pharmacogenomics (PGx) is the study of the influence of genetic variation on drug response [1] with the goal of improving the efficacy and safety of current and future treatments. Specifically, it aims to facilitate a move away from the standard empirical trial-and-error approach that exists today to a more layered and precise prescription paradigm.

It is estimated that there are 19,000 to 21,000 protein-coding genes in the human genome [2] . Many types of genetic variation can occur in these genes, including single nucleotide polymorphisms (SNPs), indels (small insertions/deletions), and larger structural rearrangements; of these, SNPs are the most common. Pharmacokinetics (PK) describes “what the body does to the drug” and pharmacodynamics (PD) “what the drug does to the body.” Genomic variations in genes involved in drug absorption, distribution, metabolism, and elimination (eg, drug-metabolizing enzymes or transporters) can alter the PK profile of drugs, affecting systemic exposure and leading to altered drug response (g .ie affecting his next PD). Alternatively, genomic variation in genes that modulate a drug’s PD (eg, its on- and off-target therapeutic sites) may directly influence drug response. Importantly, in both cases, altered response to a drug can weaken its efficacy or impair tolerability/safety (Figure 1).

The clinical and economic consequences of adverse drug reactions are significant, and are estimated to account for 6.5% of hospitalizations [3]. Interestingly, PGx guidelines are now available for several drugs that frequently cause hospitalization, such as warfarin, antiplatelet agents, and opioid analgesics [4]. In addition to serious side effects leading to hospitalization, poor drug tolerability (eg, due to mild side effects) is also known to be associated with poorer adherence [5], increasing the likelihood of reduced efficacy and drug wastage.

A Novel Heterogeneous Network Based Method For Drug Response Prediction In Cancer Cell Lines

It has been estimated that more than 98% of people have at least one pharmacogenetic variant [6]. It is important to note that the majority of prescribing and dispensing occurs in primary care, with recent studies in the US and UK showing that more than 60% of primary care patients are prescribed PGx-recommended drugs [7, 8]

The Dutch Pharmacogenetics Working Group (DPWG) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) are the two most widely recognized expert groups involved in the development of clinical guidelines for PGx. CPIC and DPWG therapeutic guidelines, as well as those of other groups such as the Canadian Pharmacogenomics Network for Drug Safety (CPNDS), are curated and supported by the Pharmacogenomics Database (https://www.pharmgkb.org/) and are accessible to health professionals and others. access it online for free. To date, PGx associations with clinical practice PGx guidelines have been developed for slightly more than 80 drugs [9]. Almost two-thirds of these functional drug-gene associations involve genes for drug-metabolizing enzymes, of which approximately 80% are genes encoding cytochrome P450 (CYP) enzymes. A small number of functional drug-gene associations contain driver genes, and slightly less than a third contain genes that affect drug PD (~5% off-target, ~26% off-target, almost one-third last human leukocyte antigen (HLA) genes) . The US Food and Drug Administration (FDA) also evaluated drug-gene associations and determined that 47 drugs have sufficient evidence for PGx therapeutic recommendations and 16 PGx drugs potentially affect clinical safety or response [10]; there is considerable overlap between drug-gene associations reviewed by guideline committees and the FDA.

Table 1 provides a list (although not exhaustive) of drugs commonly prescribed in primary care according to currently available PGx guidelines.

Clinical Trial In A Dish

Despite the increasing availability of guidelines, the implementation of PGx in primary care has been slow. However, a nationwide study in the Netherlands concluded that one in 19 new primary care prescriptions could have been changed if PGx data had been available [11]. In the UK and many other countries, PGx testing is largely relegated to secondary and tertiary care (eg HLA B*57:01 abacavir testing). Table 2 provides a summary, although not exhaustive, of recent and large interventional studies evaluating the value of PGx in primary care.

A recent analysis identified the following pharmacogenes most commonly associated with primary care prescriptions in England: CYP2D6, CYP2C19 and SLCO1B1 (organic anion transporter family member 1B1), followed by CYP2C9, VKORC1 (vitamin K epoxide reductase complex), CYP4F2. and HLA-B (8). Therefore, the following sections of this review will provide an overview of the role of PGx in primary care in the prescribing and monitoring of specific drugs associated with these genes. Between June and October 2020, drug literature searches were conducted.

The number of antidepressants prescribed for various indications in UK primary care is increasing every year [21]. Although these drugs are effective treatments, their success rates vary [22] and up to 50% of patients do not respond to treatment. Some of this heterogeneity in response may be due to variation in PGx.

Pdf) A Performance Evaluation Of Drug Response Prediction Models For Individual Drugs

In the United Kingdom, selective serotonin reuptake inhibitors (SSRIs) account for more than 50% of antidepressants in primary care [23] ; Also used are tricyclic antidepressants (TCAs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and atypical antidepressants (eg, mirtazapine).

There are 57 putatively functional CYP genes in the human genome, of which about 12 are involved in the biotransformation of 70-80 percent of all drugs used in clinical practice [24]. Most antidepressants are partially metabolized by the CYP enzyme system, and CYP2D6 and CYP2C19 [25] are widely considered to be the enzymes most involved in the biotransformation of antidepressants.

CYP2D6 is highly polymorphic and more than 100 allelic variants have been reported [ 26 ]. Because of the number of possible diplotypes in a population, the translation of genotype into phenotype

Towards Precision Medicine: Interrogating The Human Genome To Identify Drug Pathways Associated With Potentially Functional, Population Differentiated Polymorphisms

Phd in genomics and bioinformatics, primary and secondary databases in bioinformatics, concepts in bioinformatics and genomics, masters in computational biology and bioinformatics, pharmacogenetics pharmacogenomics and individualized medicine, ms in bioinformatics and computational biology, protein structure prediction bioinformatics, emergency preparedness and response in the workplace, local and global alignment in bioinformatics, motifs and patterns in bioinformatics, big data in bioinformatics and health informatics, difference between homology and similarity in bioinformatics