Search

Aims: The aim of this study was to determine if there are discrepancies among various agency-approved labels for the same active ingredient and where the labels approved by the Turkish Medicines and Medical Devices Agency (TMMDA) stand regarding the inclusion of PGx and discuss these ethical implications. Background: The efficacy and safety of drugs can be improved by rational prescription and personalization of medicine for each patient. Pharmacogenomics information (PGx) in Drug Labels (DL) is one of the important tools for the personalization of medications because genetic differences may affect both drug efficacy and safety. Providing adequate PGx to patients has ethical implications. Objective: The study aims to evaluate PGx in the DLs approved by TMMDA and other national agencies provided by the Pharmacogenomics Knowledgebase. Methods: DL annotations from the Pharmacogenomics Knowledgebase and DLs approved by the TMMDA were analyzed according to information and action levels, which are “testing required”, “testing recommended”, “actionable”, and “informative”. Results: There are 381 drugs listed in PharmGKB drug label annotations with pharmacogenomics information, and 278 of these have biomarkers. A total of 242 (63.5%) drugs are approved and available in Turkey. Of these, 207 (85.5%) contain the same information as in or similar to that in the labels approved by the other agencies. The presence and level of information varied among the DLs approved by different agencies. The inconsistencies may have an important effect on the efficacy and the safety of drugs. Conclusion: These findings suggest a need for the standardization of PGx information globally because it may not only affect the efficacy and safety of medications but also essential ethical rules regarding patient rights by violating not sufficiently sharing all available information.

Genetics play an important role in opioid use disorder (OUD); however, few specific gene variants have been identified. Therefore, there is a need to further understand the pharmacogenomics influences on the pharmacodynamics of opioids. The Pharmacogenomics Knowledgebase (PharmGKB), a database that links genetic variation and drug interaction in the body, was queried to identify polymorphisms associated with heroin dependence in the context of opioid related disorders/OUD. Eight genes with 22 variants were identified as linked to increased risk of heroin dependence, with three genes and variants linked to decreased risk, although the level of evidence was moderate to low. Therefore, continued exploration of biomarker influences on OUD, reward pathways and other contributing circuitries is necessary to understand the true impact of genetics on OUD before integration into clinical guidelines.

Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant‐drug pairs, shows relevant findings from the curated literature, and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning an LOE becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes, including study size, reported P value, and whether the variant annotation supports or fails to find an association. Clinical guidelines or US Food and Drug Administration (FDA)‐approved drug labels which give variant‐specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate an LOE. Overall, the system increases transparency, consistency, and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.

Next-generation sequencing technology has afforded the discovery of many novel variants that are of significance to inheritable pharmacogenomics (PGx) traits but a large proportion of them have unknown consequences. These include missense variants resulting in single amino acid substitutions in cytochrome P450 (CYP) proteins that can impair enzyme function, leading to altered drug efficacy and toxicity. While most unknown variants are rare, an overlooked minority are variants that are collectively rare but enriched in specific populations. Here, we analyzed sequence variation data in 141,456 individuals from across eight study populations in gnomAD for 38 CYP genes to identify such variants in addition to common variants. By further comparison with data from two PGx-specific databases (PharmVar and PharmGKB) and ClinVar, we identified 234 missense variants in 35 CYP genes, of which 107 were unknown to these databases. Most unknown variants (n = 83) were population-specific common variants and several (n = 7) were found in important CYP pharmacogenes (CYP2D6, CYP4F2, and CYP2C19). Overall, 29% (n = 31) of 107 unknown variants were predicted to affect CYP enzyme function although further biochemical characterization is necessary. These variants may elucidate part of the unexplained interpopulation differences observed in drug response.

Napretkom genetičke tehnologije, genomike i bioinformatike znanje medicinske genetike postaje sve nužnije i korisnije u svakodnevnoj praksi studenata, liječnika, drugih stručnjaka i zdravstvenih djelatnika. Za dobro funkcioniranje multidisciplinarnog tima koji uključuje medicinske genetičare i druge stručnjake važno je poznavanje vokabulara medicinske genetike te alata za dijagnostiku. Upravo su glavni alati za dijagnostiku genetičkih poremećaja dijagnostičke i edukativne baze podataka genetičkih bolesti. Većina takvih baza slobodno je dostupna na internetu, a pružaju relevante informacije temeljene na dokazima. Genetičke baze podataka imaju dvije osnovne funkcije, edukativnu i dijagnostičku. Svrha ovog rada je dati pregled edukativnih i dijagnostičkih elektroničkih baza podataka s ciljem omogućavanja njihovog lakšeg korištenja te uputiti čitatelja gdje i na koji način potražiti informacije o genetičkim poremećajima. Poznavanje genetičkih baza podataka, njihovo razumijevanje i korištenje ima nevjerojatnu vrijednost za današnju i buduću medicinsku praksu. U prvom dijelu rada prikazan je pregled i upute za korištenje najznačajnijih edukativnih elektroničkih genetičkih baza podataka, uključujući OMIM, GeneReviews, Orphanet te PharmGKB. U drugom dijelu rada slijedi kratkih pregled najčešće korištenih dijagnostičkih elektroničkih genetičkih baza podataka, uključujući Human Phenotype Ontology, Face2Gene, ClinVar, gnomAD te Decipher., With the advancement of genetic technology, genomics and bioinformatics, knowledge of medical genetics is becoming more and more necessary and useful in the daily practice of students, doctors, health professionals and others. Knowledge of medical genetics vocabulary and diagnostic tools is important for the proper functioning of a multidisciplinary team that includes medical geneticists and other experts. Diagnostic and educational databases of genetic diseases are the main tools for diagnosing genetic disorders. Most such databases are freely available online and provide relevant evidence-based information. Genetic databases have two basic functions, educational and diagnostic. The purpose of this paper is to provide an overview of educational and diagnostic electronic databases to facilitate their use and instruct the reader where and how to look for information about genetic disorders. Knowledge of genetic databases, their understanding and use is of incredible value for current and future medical practice. The first part of this paper presents an overview and instructions for using the most important educational electronic genetic databases, including OMIM, GeneReviews, Orphanet and PharmGKB. The second part of the paper follows a brief overview of the most used diagnostic electronic genetic databases, including Human Phenotype Ontology, Face2Gene, ClinVar, gnomAD, and DECIPHER.

There is limited evidence to support pharmacogenetic (PGx) testing in children. We conducted a retrospective review of PGx testing among 452 patients at an academic children's hospital to determine the potential utility of PGx in diseases of childhood and to identify targets for future pediatric pharmacogenetic research. An actionable gene-drug pair associated with the 28 genes tested (Clinical Pharmacogenetics Implementation Consortium (CPIC) level A or B, Pharmacogenomics Knowledge Base (PharmGKB) level 1A or B, or US Food and Drug Administration (FDA) recommendation and a PharmGKB level) was present in 98.7% of patients. We identified 203 actionable gene-drug-diagnosis groups based on the indications for each actionable drug listed in Lexicomp. Among patients with an actionable gene-drug-diagnosis group, 49.3% had a diagnosis where the drug was a therapeutic option and PGx could be used to guide treatment selection. Among patients with an associated diagnosis, 30.9% had a prescription for the actionable drug allowing PGx guided dosing. Three genes (CYP2C19, CYP2D6, and CYP3A5) accounted for all the gene-drug-diagnosis groups with matching diagnoses and prescriptions. The most common gene-drug-diagnosis groups with matching diagnoses and prescriptions were CYP2C19-citalopram-escitalopram-depression 3.3% of patients tested; CYP2C19-dexlansoprazole-gastritis-esophagitis 3.1%; CYP2C19-omeprazole-gastritis-esophagitis 2.4%; CYP2D6-atomoxetine-attention deficit hyperactivity disorder 2.2%; and CYP2C19-citalopram-escitalopram-obsessive-compulsive disorder 1.5%. PGx could be used to guide selection of current treatment options or medication dosing in almost half (48.7%) of pediatric patients tested. Mood disorders and gastritis/esophagitis are promising targets for future study of PGx testing because of the high prevalence of these diagnoses and associated actionable gene-drug pairs in the pediatric population.

Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n = 93) were classified as “actionable PGx”. Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group “nervous system”. Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.

The latest developments in precision medicine allow the modulation of therapeutic approaches in different pathologies on the basis of the specific molecular characterization of the patient. This review of the literature coupled with in silico analysis was to provide a selected screening of interactions between single-nucleotide polymorphisms (SNPs) and drugs (repurposed, investigational, and biological agents) showing efficacy and toxicityin counteracting Covid-19 infection. In silico analysis of genetic variants related to each drug was performed on such databases as PharmGKB, Ensembl Genome Browser, www.drugs.com, and SNPedia, with an extensive literature review of papers (to May 10, 2020) on Covid-19 treatments using Medline, Embase, International Pharmaceutical Abstracts, PharmGKB, and Google Scholar. The clinical relevance of SNPs, known as both drug targets and markers, considering genetic variations with known drug responses, and the therapeutic consequences are discussed. In the context of clinical treatment of Covid-19, including infection prevention, control measures, and supportive care, this review highlights the importance of a personalized approach in the final selection of therapy, which is probably essential in the management of the Covid-19 pandemic.

Background This study used a network pharmacology approach to elucidate the molecular mechanism governing the effect of Radix Achyranthis Bidentatae (RAB) on osteoarthritis (OA). Methods Based on oral bioavailability and drug-likeness, the main active components of RAB were screened via the Traditional Chinese Medicine Systems Pharmacology platform. The GeneCard, OMIM, PharmGkb, Therapeutic Targets database, and DrugBank database were used to establish a database of osteoarthritis targets. The interactive active network map of “ingredient-target” was constructed with Cytoscape software (Version 3.7.1). The protein-protein interaction network was constructed with the STRING database, and the related protein interaction relationship was analysed. GO biological function analysis and KEGG enrichment analysis for core targets were performed. Finally, docking of the active components with the core target was carried out. Results Sixteen active components of RAB were obtained, and 63 potential targets for OA were identified. Network analysis results indicate that these targets are primarily involved in regulating biological processes, such as cell metabolism, apoptosis, and cell proliferation. Pathways involved in the treatment of osteoarthritis include virus-related signalling pathways, apoptosis signalling pathways, IL-17 signalling pathways, and PI3K/AKT signalling pathways. Conclusion RAB has the characteristics of being multi-system, multi-component and multi-target. Possible mechanisms of action for RAB include regulating the immune and inflammatory responses, reducing chondrocyte apoptosis, and protecting the joint synovial membrane and cartilage to control disease development. The active ingredients in RAB, such as sterols and flavonoids, exhibit strong potential as candidate drugs for the treatment of osteoarthritis.

Objective This study aimed to explore the mechanisms of Hippophae fructus oil (HFO) in the treatment of tympanic membrane (TM) perforation through network pharmacology‐based identification. Methods The compounds and related targets of HFO were extracted from the TCMSP database, and disease information was obtained from the OMIM, GeneCards, PharmGkb, TTD, and DrugBank databases. A Venn diagram was generated to show the common targets of HFO and TM, and GO and KEGG analyses were performed to explore the potential biological processes and signaling pathways. The PPI network and core gene subnetwork were constructed using the STRING database and Cytoscape software. A molecular docking analysis was also conducted to simulate the combination of compounds and gene proteins. Results A total of 33 compounds and their related targets were obtained from the TCMSP database. After screening the 393 TM‐related targets, 21 compounds and 22 gene proteins were selected to establish the network diagram. GO and KEGG enrichment analyses revealed that HFO may promote TM healing by influencing cellular oxidative stress and related signaling pathways. A critical subnetwork was obtained by analyzing the PPI network with nine core genes: CASP3, MMP2, IL1B, TP53, EGFR, CXCL8, ESR1, PTGS2, and IL6. In addition, a molecular docking analysis revealed that quercetin strongly binds the core proteins. Conclusion According to the analysis, HFO can be utilized to repair perforations by influencing cellular oxidative stress. Quercetin is one of the active compounds that potentially plays an important role in TM regeneration by influencing 17 gene proteins., The mechanism of hippophae fructus oil in treatment of tympanic membrane perforations was explored by the technology of network pharmacology and molecular docking. Hippophae fructus oil may repair perforations by influencing the regulation of cellular oxidative stress, and the quercetin may be one of the possibly effective compounds in tympanic membrane repair. This study provided a novel idea to explore new drugs in tympanic membrane repair.

Background. Coronary heart disease (CHD) seriously affects human health, and its pathogenesis is closely related to atherosclerosis. The Huzhang (the root of Polygonum cuspidatum)–Shanzha (the fruit of Crataegus sp.), a classic herb pair, has been widely used for the treatment of CHD. In recent years, Huzhang–Shanzha herb pair (HSHP) was found to have a wide range of effects in CHD; however, its therapeutic specific mechanisms remain to be further explored. The aim of this study was to elucidate the molecular mechanism of HSHP in the treatment of CHD using a network pharmacology analysis approach. Methods. The Batman-TCM database was used to explore bioactive compounds and corresponding targets of HSHP. CHD disease targets were extracted from Genecards, OMIM, PharmGkb, TTD, and DrugBank databases. Then, the protein-protein interaction (PPI) network was constructed using the STRING web platform and Cytoscape software. GO functional and KEGG pathway enrichment analyses were carried out on the Metascape web platform. Finally, molecular docking of the active components was assessed to verify the potential targets of HSHP to treat CHD by the AutoDock Vina and PyMOL software. Results. Totally, 243 active components and 2459 corresponding targets of LDP were screened out. Eighty-five common targets of HSHP and CHD were identified. The results of the network analysis showed that resveratrol, anthranone, emodin, and ursolic acid could be defined as four therapeutic components. TNF, ESR1, NFКB1, PPARG, INS, TP53, NFКBIA, AR, PIK3R1, PIK3CA, PTGS2, and NR3C1 might be the 12 key targets. These targets were mainly involved in the regulation of biological processes, such as inflammatory responses and lipid metabolism. Enrichment analysis showed that the identified genes were mainly involved in fluid shear force, insulin resistance (IR), inflammation, and lipid metabolism pathways to contribute to CHD. This suggests that resveratrol, anthranone, emodin, and ursolic acid from HSHP can be the main therapeutic components of atherosclerosis. Conclusion. Using network pharmacology, we provide new clues on the potential mechanism of action of HSHP in the treatment of CHD, which may be closely related to the fluid shear force, lipid metabolism, and inflammatory response.

GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (1) Quality assurance of the genetic data; (2) Qualitative assessment of genetic characteristics; (3) Quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (4) Query of third-party variant databases (e.g., ClinVAR and PharmGKB); and (5) Quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2,504 genomes made publicly available through the 1,000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported.

Introduction. Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC). Methods. The Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. The Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. Results. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. The hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) showed good binding activities to five of the six hub gene targets. Discussion. The active ingredients of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. This may be a potential mechanism of XJD in the treatment of UC.

The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to variation in drug response. Our focus includes not only pharmacogenomic information useful for clinical implementation (e.g., drug dosing guidelines and annotated drug labels), but also information to catalyze scientific research and drug discovery (e.g., variant-drug annotations and drug-centered pathways). As of April 2021, the annotated content of PharmGKB spans 715 drugs, 1761 genes, 227 diseases, 165 clinical guidelines, and 784 drug labels. We have manually curated data from more than 9000 published papers to generate the content of PharmGKB. Recently, we have also implemented an automated natural language processing (NLP) tool to broaden our coverage of the pharmacogenomic literature. This article contains a basic protocol describing how to navigate the PharmGKB website to retrieve information on how genes and genetic variations affect drug efficacy and toxicity. It also includes a protocol on how to use PharmGKB to facilitate interpretation of findings for a pharmacogenomic variant genotype or metabolizer phenotype. PharmGKB is freely available at http://www.pharmgkb.org. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Navigating the homepage of PharmGKB and searching by drug Basic Protocol 2: Using PharmGKB to facilitate interpretation of pharmacogenomic variant genotypes or metabolizer phenotypes.

HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196,325 high-quality variants with 5.93% being novel, and 25,472 variants were found to be unique in HKG compared to other Chinese populations (CHN). PCA illustrates the uniqueness of HKG in CHN, and IBD analysis revealed that it is related mostly to southern Chinese with a similar effective population size. An admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.

Biomedical Entity Explorer (BEE) is a web server that can search for biomedical entities from a database of six biomedical entity types (gene, miRNA, drug, disease, single nucleotide polymorphism [SNP], pathway) and their gene associations. The search results can be explored using intersections, unions, and negations. BEE has integrated biomedical entities from 16 databases (Ensemble, PharmGKB, Genetic Home Reference, Tarbase, Mirbase, NCI Thesaurus, DisGeNET, Linked life data, UMLS, GSEA MsigDB, Reactome, KEGG, Gene Ontology, HGVD, SNPedia, and dbSNP) based on their gene associations and built a database with their synonyms, descriptions, and links containing individual details. Users can enter the keyword of one or more entities and select the type of entity for which they want to know the relationship for and by using set operations such as union, negation, and intersection, they can navigate the search results more clearly. We believe that BEE will not only be useful for biologists querying for complex associations between entities, but can also be a good starting point for general users searching for biomedical entities. BEE is accessible at (http://bike-bee.snu.ac.kr).

Objective. The purpose of our research is to systematically explore the multiple mechanisms of Hemerocallis fulva Flowers (HF) on depressive disorder (DD). Methods. The components of HF were searched from the literature. The targets of components were obtained from PharmMapper. After that, Cytoscape software was used to build a component-target network. The targets of DD were collected from DisGeNET, PharmGKB, TTD, and OMIM. Protein-protein interactions (PPIs) among the DD targets were executed to screen the key targets. Afterward, the GO and KEGG pathway enrichment analysis were performed by the KOBAS database. A compound-target-KEGG pathway network was built to analyze the key compounds and targets. Finally, the potential active substances and targets were validated by molecular docking. Results. A total of 55 active compounds in HF, 646 compound-related targets, and 527 DD-related targets were identified from public databases. After treated with PPI, 219 key targets of DD were acquired. The gene enrichment analysis suggested that HF probably benefits DD patients by modulating pathways related to the nervous system, endocrine system, amino acid metabolism, and signal transduction. The network analysis showed the critical components and targets of HF on DD. Results of molecular docking increased the reliability of this study. Conclusions. It predicted and verified the pharmacological and molecular mechanism of HF against DD from a holistic perspective, which will also lay a foundation for further experimental research and rational clinical application of DD.

In 2009, the Clinical Pharmacogenetics Implementation Consortium (CPIC; www.cpicpgx.org), a shared project between Pharmacogenomics Knowledge Base (PharmGKB, http://www.pharmgkb.org) and the National Institutes of Health (NIH), was created to provide freely available, evidence-based, peer-reviewed, and updated pharmacogenetic clinical practice guidelines. To date, CPIC has published 23 guidelines (of which 11 have been updated), covering 19 genes and 46 drugs across several therapeutic areas. CPIC also now provides additional resources to facilitate the implementation of pharmacogenetics into routine clinical practice and the electronic health record. Furthermore, since its inception, CPIC’s interactions with other resources, databases, websites and genomic communities have grown. This purpose of this paper is to highlight the progress of CPIC over the past 10 years.

To analyze the effective components of Chinese medicine (CM) contained in Chaihu Shugan Powder (柴胡疏肝散, CSP) in the treatment of depressive disorders and to predict its anti-depressant mechanism by network pharmacology. Absorption, distribution, metabolism, excretion, and toxicity calculation method was used to screen the active components of CSP. Traditional Chinese Medicine System Pharmacological Database Analysis Platform and text mining tool (GoPuMed database) were used to predict and screen the active ingredients of CSP and anti-depressive targets. Through Genetic Association Database, Therapeutic Target Database, and PharmGkb database targets for depression were obtained. Cytoscape3.2.1 software was used to establish a network map of the active ingredients-targets of CSP, and to analyze gene function and metabolic pathways through Database for Annotation, Visualization and Integrated Discovery and the Omicshare database. The 121 active ingredients and 15 depression-related targets which were screened from the database can exert antidepressant effects by improving the neural plasticity, growth, transfer condition and gene expression of neuronal cell, and the raise of the expression of gap junction protein. The 15 targets passed 14 metabolic pathways, mainly involved in the regulation of neurotransmitters (5-hydroxytryptamine, dopamine and epinephrine), inflammatory mediator regulation of TRP channels, calcium signaling pathway, cyclic adenosine monophosphate signaling pathway and neuroactive ligand-receptor interaction and other signal channels to exert anti-depressant effects. This article reveals the possible mechanism of CSP in the treatment of depression through network pharmacology research, and lays a foundation for further target studies.

Pharmacogenomics (PGx) decision support and return of results is an active area of precision medicine. One challenge of implementing PGx is extracting genomic variants and assigning haplotypes in order to apply prescribing recommendations and information from the Clinical Pharmacogenetics Implementation Consortium (CPIC), the US Food and Drug Administration (FDA), the Pharmacogenomics Knowledgebase (PharmGKB), etc. Pharmacogenomics Clinical Annotation Tool (PharmCAT) (i) extracts variants specified in guidelines from a genetic data set derived from sequencing or genotyping technologies, (ii) infers haplotypes and diplotypes, and (iii) generates a report containing genotype/diplotype-based annotations and guideline recommendations. We describe PharmCAT and a pilot validation project comparing results for 1000 Genomes Project sequences of Coriell samples with corresponding Genetic Testing Reference Materials Coordination Program (GeT-RM) sample characterization. PharmCAT was highly concordant with the GeT-RM data. PharmCAT is available in GitHub to evaluate, test, and report results back to the community. As precision medicine becomes more prevalent, our ability to consistently, accurately, and clearly define and report PGx annotations and prescribing recommendations is critical.

What is known and objective Statins are widely used worldwide in the prevention and treatment of coronary atherosclerotic heart disease and ischaemic stroke. However, in clinical application, statins have shown great individual differences in terms of the efficacy and safety, some of which are related to genetic factors. The purpose of this article was to summarize the recent advances about the correlation between gene polymorphisms and the efficacy/safety of statins. Methods We searched the databases including PharmGKB and PubMed (published before June 2019) using the keywords such as 'statin', 'gene polymorphism' and 'SNP' and obtained more than 100 articles. In this review, we described the clinical studies of genetic variants associated with both the efficacy and adverse reactions of statins. We also clarified the importance of taking pharmacogenetic variation into account to improve the clinical application of statins. Results and discussion The available data were collected and analysed to present the polymorphisms of candidate genes encoding the most promising proteins including SLCO1B1 (encoding uptake transporters); ABCB1, ABCC2, ABCG2 (encoding effluent transporter); APOE, APOA5 (encoding apolipoprotein); genes encoding cytochrome P450 enzyme system; KIF6, HMGCR, LDLR, LPA, PCSK9, COQ2, CETP, etc These genes were proved to be related to the pharmacodynamics and pharmacokinetics of statins, thus affecting the efficacy and safety. What is new and conclusion In this paper, the correlation between gene polymorphisms and the efficacy/safety of statins was summarized. The authors reached a consensus that the variants of the genes encoding uptake and effluent transporters have the most effect on the efficacy/safety of statins. It pointed out that it is desirable to do genetic testing of these transporter genes to reduce the incidence of myopathy or to achieve better outcomes before patients use statins, especially in the regions with high frequency of risk allele.

Pharmacogenetics (PGx) in clinical practice is a tool that the clinician can use to guide, in a personalized way, the most suitable treatment that will be administered to the patient. The objective of this review is to summarize in a practical and accessible rational way, the advances that currently exist for the application of PGx in colorectal cancer chemotherapy management through the study of the patients’ germline polymorphisms. To define the polymorphisms that can be applied, we rely on three fundamental cornerstones: the recommendations of drug regulatory agencies; the implementation guidelines prepared by expert consortia in PGx and information from clinical annotations (the drug/polymorphism relation) according to the scientific level of evidence assigned by PharmGKB experts.

With exome/genome sequencing (ES/GS) integrated into the practice of medicine, there is some potential for reporting incidental/secondary findings (IFs/SFs). The issue of IFs/SFs has been studied extensively over the last 4 years. In order to evaluate their implications in care organisation, we retrospectively evaluated, in a cohort of 700 consecutive probands, the frequency and burden of introducing the search for variants in a maximum list of 244 medically actionable genes (genes that predispose carriers to a preventable or treatable disease in childhood/adulthood and genes for genetic counselling issues). We also focused on the 59 PharmGKB class IA/IB pharmacogenetic variants. We also compared the results in different gene lists. We identified variants (likely) affecting protein function in genes for care in 26 cases (3.7%) and heterozygous variants in genes for genetic counselling in 29 cases (3.8%). Mean time for the 700 patients was about 6.3 min/patient for medically actionable genes and 1.3 min/patient for genes for genetic counselling, and a mean time of 37 min/patients for the reinterpreted variants. These results would lead to all 700 pre-test counselling sessions being longer, to 55 post-test genetic consultations and to 27 secondary specialised medical evaluations. ES also detected 42/59 pharmacogenetic variants or combinations of variants in the majority of cases. An extremely low metabolizer status in genes relevant for neurodevelopmental disorders (CYP2C9 and CYP2C19) was found in 57/700 cases. This study provides information regarding the need to anticipate the implementation of genomic medicine, notably the work overload at various steps of the process.

Aim: Six modern PGx assays were compared with the Pharmacogenomics Knowledge Base (PharmGKB) to determine the proportion of the currently known PGx genotypes that are assessed by these assays. Materials & methods: Investigated assays were ‘Ion AmpliSeq Pharmacogenomics’, ‘iPLEX PGx Pro’, ‘DMET Plus,’ ‘PharmcoScan,’ ‘Living DNA’ and ‘23andMe.’ Results: PharmGKB contains 3474 clinical annotations of which 75, 70 and 45% can be determined by PharmacoScan, Living DNA and 23andMe, respectively. The other assays are designed to test a specific subset of PGx variants. Conclusion: Assaying all known PGx variants would only comprise a minor fraction of the current assays’ capacity. Unfortunately, this is not achieved. Moreover, not necessarily the variants with the highest effects or the highest evidence are selected.

Aim: Genetic polymorphisms may influence the incidence of angiotensin-converting enzyme (ACE) inhibitor-induced cough. This study aims to investigate this association. Methods: Ten electronic databases and PharmGKB were systematically searched. Pooled odds ratio values and their 95% CI were used to assess the association, using the random-effects model. Results: A total of 26 studies were included in the review, 17 of them were included from two separated meta-analysis (ACE I/D or BDKRB2-58T/C). Significant association was found between ACE I/D I carriers (ACE gene insertion) and ACE inhibitor-induced cough, showing racial and age differences. Conclusion: This study demonstrated that ACE I/D but not BDKRB2-58T/C polymorphism could be a predictor for the risk of ACE inhibitor-induced cough, especially in east Asians and the aged.

Objective. Exploration of the underlying molecular mechanism of Jinchan Oral Liquid (JOL) in treating children with the respiratory syncytial virus (RSV) pneumonia to provide new evidence for the clinical application. Methods. The active components and target genes of JOL were screened by the TCMSP database. The targets of RSV pneumonia were obtained from the GeneCards, OMIM, DrugBank, and PharmGKB database. Then, we constructed the active component-target network and screened the core genes. The overlaps were screened for PPI network analysis, GO analysis, and KEGG analysis. Finally, result validation was performed by molecular docking. Results. According to the screening criteria of the ADME, 74 active compounds of JOL were obtained; after removing redundant targets, we selected 180 potential targets. By screening the online database, 893 RSV pneumonia-related targets were obtained. A total of 82 overlapping genes were chosen by looking for the intersection. The STRING online database was used to acquire PPI relationships, and 16 core genes were obtained. GO and KEGG analyses showed that the main pathways of JOL in treating RSV pneumonia include TNF signaling pathway and IL17 signaling pathway. The molecular docking results showed that the active compounds of JOL had a good affinity with the core genes. Conclusion. In this study, we preliminarily discussed the main active ingredients, related targets, and pathways of JOL and predicted the pharmacodynamic basis and the potential therapeutic mechanisms of RSV pneumonia. In summary, the network pharmacology strategy may be helpful for the discovery of multitarget drugs against complex diseases.

Background The variation of drug responses and target does among individuals is mostly determined by genes. With the development of pharmacogenetics and pharmacogenomics, the differences in drug response between different races seem to be mainly caused by the genetic diversity of pharmacodynamics and pharmacokinetics genes. Very important pharmacogenetic (VIP) variants mean that genes or variants play important and vital roles in drug response, which have been listed in pharmacogenomics databases, such as Pharmacogenomics Knowledge Base (PharmGKB). The information of Chinese ethnic minorities such as the Wa ethnic group is scarce. This study aimed to uncover the significantly different loci in the Wa population in Yunnan Province of China from the perspective of pharmacogenomics, to provide a theoretical basis for the future medication guidance, and to ultimately achieve the best treatment in the future. Results In this study, we recruited 200 unrelated healthy Wa adults from the Yunnan province of China, selected 52 VIP variants from the PharmGKB for genotyping. We also compared the genotype frequency and allele distribution of VIP variants between Wa population and the other 26 populations from the 1000 Genomes Project (http://www.1000Genomes.org/). Next, χ2 test was used to determine the significant points between these populations. The study results showed that compared with the other 26 population groups, five variants rs776746 (CYP3A5), rs4291 (ACE), rs3093105 (CYP4F2), rs1051298 (SLC19A1), and rs1065852 (CYP2D6) had higher frequencies in the Wa population. The genotype frequencies rs4291-TA, rs3093105-CA, rs1051298-AG and rs1065852-GA were higher than those of the other populations, and the allele distributions of rs4291-T and rs3093105-C were significantly different. Additionally, the difference between the Wa ethnic group and East Asian populations, such as CDX, CHB, and CHS, was the smallest. Conclusions Our research results show that there is a significant difference in the distribution of VIP variants between the Wa ethnic group and the other 26 populations. The study results will have an effect on supplementing the pharmacogenomics information for the Wa population and providing a theoretical basis for individualised medication for the Wa population.

Hypertension (HTN) is a persistent public health problem affecting approximately 1.3 billion individuals globally. Treatment-resistant hypertension (TRH) is defined as high blood pressure (BP) in a hypertensive patient that remains above goal despite use of ≥3 antihypertensive agents of different classes including a diuretic. Despite a plethora of treatment options available, only 31.0% of individuals have their HTN controlled. Interindividual genetic variability to drug response might explain this disappointing outcome because of genetic polymorphisms. Additionally, the poor knowledge of pathophysiological mechanisms underlying hypertensive disease and the long-term interaction of antihypertensive drugs with blood pressure control mechanisms further aggravates the problem. Furthermore, in Africa, there is a paucity of pharmacogenomic data on the treatment of resistant hypertension. Therefore, identification of genetic signals having the potential to predict the response of a drug for a given individual in an African population has been the subject of intensive investigation. In this review, we aim to systematically extract and discuss African evidence on the genetic variation, and pharmacogenomics towards the treatment of HTN. Furthermore, in silico methods are utilized to elucidate biological processes that will aid in identifying novel drug targets for the treatment of resistant hypertension in an African population. To provide an expanded view of genetic variants associated with the development of HTN, this study was performed using publicly available databases such as PubMed, Scopus, Web of Science, African Journal Online, PharmGKB searching for relevant papers between 1984 and 2020. A total of 2784 articles were reviewed, and only 42 studies were included following the inclusion criteria. Twenty studies reported associations with HTN and genes such as AGT (rs699), ACE (rs1799752), NOS3 (rs1799983), MTHFR (rs1801133), AGTR1 (rs5186), while twenty-two studies did not show any association within the African population. Thereafter, an in silico predictive approach was utilized to identify several genes including CLCNKB, CYPB11B2, SH2B2, STK9, and TBX5 which may act as potential drug targets because they are involved in pathways known to influence blood pressure. Next, co-expressed genes were identified as they are controlled by the same transcriptional regulatory program and may potentially be more effective as multiple drug targets in the treatment regimens for HTN. Genes belonging to the co-expressed gene cluster, ACE, AGT, AGTR1, AGTR2, and NOS3 as well as CSK and ADRG1 showed enrichment of G-protein-coupled receptor activity, the classical targets of drug discovery, which mediate cellular signaling processes. The latter is of importance, as the targeting of co-regulatory gene clusters will allow for the development of more effective HTN drug targets that could decrease the prevalence of both controlled and TRH.

Pharmacogenomics (PGx) is a key subset of precision medicine that relates genomic variation to individual response to pharmacotherapy. We assessed longitudinal trends in US FDA approval of new drugs labeled with PGx information. Drug labels containing PGx information were obtained from Drugs@FDA and guidelines from PharmGKB were used to compare the actionability of PGx information in drug labels across therapeutic areas. The annual proportion of new drug approvals with PGx labeling has increased by nearly threefold from 10.3% (n = 3) in 2000 to 28.2% (n = 11) in 2020. Inclusion of PGx information in drug labels has increased for all clinical areas over the last two decades but most prominently for cancer therapies, which comprise the largest proportion (75.5%) of biomarker–drug pairs for which PGx testing is required. Clinically actionable information was more frequently observed in biomarker–drug pairs associated with cancer drugs compared to those for other therapeutic areas (n = 92 (59.7%) vs. n = 62 (40.3%), p &lt, 0.0051). These results suggest that further evidence is needed to support the clinical adoption of pharmacogenomics in non-cancer therapeutic areas.