PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge

The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop

The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as 'novel' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.

Implementation of pharmacogenomics: Where are we now?

Pharmacogenomics (PGx), examining the effect of genetic variation on interpatient variation in drug disposition and response, has been widely studied for several decades. However, as cost, as well as turnaround time associated with PGx testing, has significantly improved, the use of PGx in the clinical setting has been gaining momentum. Nevertheless, challenges have emerged in the broader clinical implementation of PGx. In this review, we will outline current models of PGx delivery and methodologies of evaluation, and discuss clinically relevant PGx tests and associated medications. Additionally, we will describe our approach for the broad implementation of pre-emptive DPYD genotyping in patients taking fluoropyrimidines in Ontario, Canada, as an example of clinically actionable PGx testing with sufficient clinical evidence of patient benefit that can become a new standard of patient care. We will highlight challenges associated with PGx testing, including a lack of diversity in PGx studies as well as general limitations that impact the broad adoption of PGx testing. Lastly, we examine the future of PGx, discussing new clinical targets, methodologies and analysis approaches.

Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

Background

Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers.

Results

We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves.

Conclusion

The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies

Introduction

Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases.

Methods

To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources.

Results and discussion

In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

Genetic Variability of CYP4F2, CYP2D6, CYP2E1, and ACE in the Chinese Yi Population

Genetic polymorphisms of very important pharmacogenes (VIP) are a significant factor contributing to inter-individual variability in drug therapy. The purpose of this study was to identify significantly different loci in the Yi population and to enrich their pharmacogenomic information. 54 VIP variants were selected from the Pharmacogenomics Knowledge Base (PharmGKB) and genotyped in 200 Yi individuals. Then, we compared their genotype distribution between the Yi population and the other 26 populations using the χ2 test. Compared with the other 26 populations, the genotype frequencies of 4 single nucleotide polymorphisms (SNPs), rs2108622 (CYP4F2), rs1065852 (CYP2D6), rs2070676 (CYP2E1), and rs4291 (ACE), had significant differences in the Yi population. For example, the TT genotype frequency of rs2108622 (8.1%) was higher than that of African populations, and the AA genotype frequency of rs1065852 (27.3%) was higher than that of other populations except East Asians. We also found that the Yi populations differed the least from East Asians and the most from Africans. Furthermore, the differences in these variants might be related to the effectiveness and toxicity risk of using warfarin, iloperidone, cisplatin cyclophosphamide, and other drugs in the Yi population. Our data complement the pharmacogenomic information of the Yi population and provide theoretical guidance for their personalized treatment.

QPGx-CARES: Qatar pharmacogenetics clinical applications and research enhancement strategies

Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks

Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.

Material basis and molecular mechanisms of Chaihuang Qingyi Huoxue Granule in the treatment of acute pancreatitis based on network pharmacology and molecular docking-based strategy

Objectives

This study aimed to analyze active compounds and signaling pathways of CH applying network pharmacology methods, and to additionally verify the molecular mechanism of CH in treating AP.

Materials and methods

Network pharmacology and molecular docking were firstly used to identify the active components of CH and its potential targets in the treatment of AP. The pancreaticobiliary duct was retrogradely injected with sodium taurocholate (3.5%) to create an acute pancreatitis (AP) model in rats. Histological examination, enzyme-linked immunosorbent assay, Western blot and TUNEL staining were used to determine the pathway and mechanism of action of CH in AP.

Results

Network pharmacological analysis identified 168 active compounds and 276 target proteins. In addition, there were 2060 targets associated with AP, and CH had 177 targets in common with AP. These shared targets, including STAT3, IL6, MYC, CDKN1A, AKT1, MAPK1, MAPK3, MAPK14, HSP90AA1, HIF1A, ESR1, TP53, FOS, and RELA, were recognized as core targets. Furthermore, we filtered out 5252 entries from the Gene Ontology(GO) and 186 signaling pathways from the Kyoto Encyclopedia of Genes and Genomes(KEGG). Enrichment and network analyses of protein-protein interactions predicted that CH significantly affected the PI3K/AKT signaling pathway, which played a critical role in programmed cell death. The core components and key targets showed strong binding activity based on molecular docking results. Subsequently, experimental validation demonstrated that CH inhibited the phosphorylation of PI3K and AKT in pancreatic tissues, promoted the apoptosis of pancreatic acinar cells, and further alleviated inflammation and histopathological damage to the pancreas in AP rats.

Conclusion

Apoptosis of pancreatic acinar cells can be enhanced and the inflammatory response can be reduced through the modulation of the PI3K/AKT signaling pathway, resulting in the amelioration of pancreatic disease.

Implementation of whole-exome sequencing for pharmacogenomics profiling and exploring its potential clinical utilities

Whole-exome sequencing (WES) is widely used in clinical settings; however, the exploration of its use in pharmacogenomic analysis remains limited. Our study compared the variant callings for 28 core absorption, distribution, metabolism and elimination genes by WES and array-based technology using clinical trials samples. The results revealed that WES had a positive predictive value of 0.71-0.92 and a sensitivity of single-nucleotide variants between 0.68 and 0.95, compared with array-based technology, for the variants in the commonly targeted regions of the WES and PhamacoScan™ assay. Besides the common variants detected by both assays, WES identified 200-300 exclusive variants per sample, totalling 55 annotated exclusive variants, including important modulators of metabolism such as rs2032582 (ABCB1) and rs72547527 (SULT1A1). This study highlights the potential clinical advantages of using WES to identify a wider range of genetic variations and enabling precision medicine.

PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service

OBJECTIVES

 Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study.

METHODS

 We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview.

RESULTS

 Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice.

CONCLUSION

 Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods

Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.

Results

Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.

Discussion

The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Pharmacogenomics and non-genetic factors affecting drug response in autism spectrum disorder in Thai and other populations: current evidence and future implications

Autism spectrum disorder (ASD) may affect family and social life profoundly. Although there is no selective pharmacotherapy for ASD, the Food and Drug Administration (FDA) has recommended risperidone/aripiprazole to treat the associated symptoms of ASD, such as agitation/irritability. Strong associations of some pharmacokinetic/pharmacodynamic gene variants, e.g., CYP2D6 and DRD2, with risperidone-induced hyperprolactinemia have been found in children with ASD, but such strong genetic associations have not been found directly for aripiprazole in ASD. In addition to pharmacogenomic (PGx) factors, drug-drug interactions (DDIs) and possibly cumulative effects of DDIs and PGx may affect the safety or effectiveness of risperidone/aripiprazole, which should be assessed in future clinical studies in children with ASD. Reimbursement, knowledge, and education of healthcare professionals are the key obstacles preventing the successful implementation of ASD pharmacogenomics into routine clinical practice. The preparation of national and international PGx-based dosing guidelines for risperidone/aripiprazole based on robust evidence may advance precision medicine for ASD.

Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools.

METHODS

The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes.

RESULTS

There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping.

CONCLUSIONS

The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.

Topical drug delivery strategies for enhancing drug effectiveness by skin barriers, drug delivery systems and individualized dosing

Topical drug delivery is widely used in various diseases because of the advantages of not passing through the gastrointestinal tract, avoiding gastrointestinal irritation and hepatic first-pass effect, and reaching the lesion directly to reduce unnecessary adverse reactions. The skin helps the organism to defend itself against a huge majority of external aggressions and is one of the most important lines of defense of the body. However, the skin's strong barrier ability is also a huge obstacle to the effectiveness of topical medications. Allowing the bioactive, composition in a drug to pass through the stratum corneum barrier as needed to reach the target site is the most essential need for the bioactive, composition to exert its therapeutic effect. The state of the skin barrier, the choice of delivery system for the bioactive, composition, and individualized disease detection and dosing planning influence the effectiveness of topical medications. Nowadays, enhancing transdermal absorption of topically applied drugs is the hottest research area. However, enhancing transdermal absorption of drugs is not the first choice to improve the effectiveness of all drugs. Excessive transdermal absorption enhances topical drug accumulation at non-target sites and the occurrence of adverse reactions. This paper introduces topical drug delivery strategies to improve drug effectiveness from three perspectives: skin barrier, drug delivery system and individualized drug delivery, describes the current status and shortcomings of topical drug research, and provides new directions and ideas for topical drug research.

The Reactome Pathway Knowledgebase 2024

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics

BACKGROUND

This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.

METHODS

Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging.

RESULTS

Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99).

CONCLUSION

While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact

The implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.

PharmVar GeneFocus: SLCO1B1

The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.

The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery

Complex disorders, such as depression, remain a mystery for scientists. Although genetic factors are considered important for the prediction of one's vulnerability, it is hard to estimate the exact risk for a patient to develop depression, based only on one category of vulnerability criteria. Genetic factors also regulate drug metabolism, and when they are identified in a specific combination, may result in increased drug resistance. A proper understanding of the genetic basis of depression assists in the development of novel promising medications and effective disorder management schemes. This review aims to analyze the recent literature focusing on the correlation between specific genes and the occurrence of depression. Moreover, certain aspects targeting a high drug resistance identified among patients suffering from major depressive disorder were highlighted in this manuscript. An expected direction of future drug discovery campaigns was also discussed.

Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans

Expression quantitative locus (eQTL) studies have paved the way in identifying genetic variation impacting gene expression levels. African Americans (AAs) are disproportionately underrepresented in eQTL studies, resulting in a lack of power to identify population-specific regulatory variants especially related to drug response. Specific drugs are known to affect the biosynthesis of drug metabolism enzymes as well as other genes. We used drug perturbation in cultured primary hepatocytes derived from AAs to determine the effect of drug treatment on eQTL mapping and to identify the drug response eQTLs (reQTLs) that show altered effect size following drug treatment. Whole-genome genotyping (Illumina MEGA array) and RNA sequencing were performed on 60 primary hepatocyte cultures after treatment with six drugs (Rifampin, Phenytoin, Carbamazepine, Dexamethasone, Phenobarbital, and Omeprazole) and at baseline (no treatment). eQTLs were mapped by treatment and jointly with Meta-Tissue. We found varying transcriptional changes across different drug treatments and identified Nrf2 as a potential general transcriptional regulator. We jointly mapped eQTLs with gene expression data across all drug treatments and baseline, which increased our power to detect eQTLs by 2.7-fold. We also identified 2,988 reQTLs (eQTLs with altered effect size after drug treatment). reQTLs were more likely to overlap transcription factor binding sites, and we uncovered reQTLs for drug metabolizing genes such as CYP3A5. Our results provide insights into the genetic regulation of gene expression in hepatocytes through drug perturbation and provide insight into SNPs that effect the liver's ability to respond to transcription upregulation.

Pharmacogenomics in Asians: Differences and similarities with other human populations

INTRODUCTION

Various pharmacogenomic (PGx) variants differ widely in different ethnicities. and clinical outcomes associated with these variants may also be substantially varied. Literature was searched in different databases, i.e. PubMed, ScienceDirect, Web of Science, and PharmGKB, from inception to 30 June 2022 for this review.

AREAS COVERED

Certain PGx variants were distinctly varied in Asian populations compared to the other human populations, e.g. CYP2C19*2,*3,*17; CYP2C9*2,*3; CYP2D6*4,*5,*10,*41; UGT1A1*6,*28; HLA-B*15:02, HLA-B*15:21, HLA-B*58:01, and HLA-A*31:01. However, certain other variants do not vary greatly between Asian and other ethnicities, e.g. CYP3A5*3; ABCB1, and SLCO1B1*5. As evident in this review, the risk of major adverse cardiovascular events (MACE) was much stronger in Asian patients taking clopidogrel and who inherited the CYP2C19 loss-of-function alleles, e.g. CYP2C19*2 and*3, when compared to the western/Caucasian patients. Additionally, the risk of carbamazepine-induced severe cutaneous adverse drug reactions (SCARs) for the patients inheriting HLA-B*15:02 and HLA-B*15:21 alleles varied significantly between Asian and other ethnicities. In contrast, both Caucasian and Asian patients inheriting the SLCO1B1*5 variant possessed a similar magnitude of muscle toxicity, i.e. myopathy.

EXPERT OPINION

Asian countries should take measures toward expanding PGx research, as well as initiatives for the purposes of obtaining clinical benefits from this newly evolving and economically viable treatment model.

Pharmacology Mechanism of Polygonum Bistorta in Treating Ulcerative Colitis Based on Network Pharmacology

Aim. Ulcerative colitis (UC) is a refractory gastrointestinal disease. The study aimed to expound the mechanism of Polygonum bistorta (PB) in treating UC by network pharmacology, molecular docking, and experiment verification. Methods. The compositions and targets of PB and UC-associated targets were obtained by searching the websites and the literature. The potential mechanism of PB in the treatment of UC was predicted by protein-protein interaction network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecule docking was performed by AutoDock. In vitro experiments explored the mechanism of quercetin (Que), the main active composition of PB, in treating UC. Results. Six compositions, 139 PB targets, and 934 UC-associated targets were obtained. 93 overlapping targets between PB and UC were identified, and 18 of them were the core targets. 467 biological processes, 10 cell components, and 30 molecular functions were obtained by GO analysis. 102 pathways were enriched through KEGG analysis. Among them, the IL-17 signaling pathway had high importance. The core targets FOS, JUN, IL-1β, CCL2, CXCL8, and MMP9 could dock with Que successfully. Act1, TRAF6, FOS, and JUN were identified by KEGG as the key proteins of the IL-17 signaling pathway. The expressions of the abovementioned proteins were increased in Caco-2 cells stimulated by Dextran sulfate sodium and decreased after being treated by Que. Conclusion. PB might treat UC by downregulating the IL-17 signaling pathway. It is worth doing further research on PB treating UC in vivo.

Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population

BACKGROUND

Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population.

METHODS

A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles.

RESULTS

We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68).

CONCLUSIONS

Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

Integrated plasma pharmacochemistry and network pharmacology to explore the mechanism of Gerberae Piloselloidis Herba in treatment of allergic asthma

ETHNOPHARMACOLOGICAL RELEVANCE

Gerberae Piloselloidis Herba (GPH), a commonly used traditional medicine in China, is derived from Gerbera piloselloides (Linn.) Cass. It is featured by its special bioactivities as antitussive, expectorant, anti-asthma, anti-bacterial, anti-tumor, uterine analgesia, and immunity-enhancing. With a long history of medication in ethnic minority areas in China, it is often used as an effective treatment for cough and sore throat as well as allergic asthma. Although our previous investigation also has discovered GPH performed effective treatment on allergic asthma, its underlying mechanism remains unclear.

AIM OF THE STUDY

This research aims to reveal the pharmacological mechanism of GPH in the treatment for allergic asthma through combination of plasma pharmacology and network pharmacology.

MATERIALS AND METHODS

Firstly, the components of GPH in blood samples were identified using UHPLC- Q-Orbitrap HRMS. An interaction network of "compound-target-disease" was constructed based on the compounds confirmed in blood and on their corresponding targets of allergic asthma acquired from disease gene databases, predicting the possible biological targets and potential signal pathways of GPH with the network pharmacology analysis. Then, a molecular docking between the blood ingredients and the core targets was carried out using the Autodock Vina software. Subsequently, after establishing a mouse model with allergic asthma induced by ovalbumin (OVA), the effect of GPH on allergic asthma was evaluated by analyzing a series of indicators including behavior, lung pathological changes, inflammatory factors in serum and bronchoalveolar lavage fluid (BALF). Finally, the key pathway and targets predicted by network pharmacology and molecular docking were further verified using Western blot analysis.

RESULTS

Eleven chemical constituents (such as arbutin, neochlorogenic acid, chlorogenic acid, etc.) were identified through the analysis of plasma samples, on which basis a total of 142 genes intersecting GPH and allergic asthma were collected by network pharmacology. After performing enrichment analysis of these genes in gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG), it was found that arbutin-related targets mainly focused on phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt) signal pathway, while luteolin and marmesin -related targets tended to locate at Interleukin-17 (IL-17) signal pathway. Meanwhile, the findings of molecular docking suggested that such components as arbutin, luteolin and marmesin entering into blood had good binding with the core targets related to PI3K/Akt and IL-17 pathways. In addition, GPH improved the OVA-induced asthma symptoms, the alveolar septa thickening and the infiltration of inflammatory cell around bronchi and bronchioles as well as reduced the levels of IgE, IL-8 and TNF-α in serum or BALF. Furthermore, GPH could inhibit the phosphorylation level of Akt and the expression of PI3K, an efficacy supported by the findings by way of Western blot which suggests that GPH in the treatment of allergic asthma was linked to PI3K/Akt signal pathway.

CONCLUSION

In this study, a comprehensive strategy to combine the UPLC-Q-Orbitrap HRMS with network pharmacology was employed to clarify the mechanism of GPH against allergic asthma, a finding where GPH may inhibit PI3K/Akt signal pathway to protect mice from OVA-induced allergic asthma. This study provides a deeper understanding of the pharmacological mechanism of GPH in treatment of asthma, offering a scientific reference for further research and clinical application of GPH in terms of allergic asthma.

Qingfei Jiedu decoction inhibits PD-L1 expression in lung adenocarcinoma based on network pharmacology analysis, molecular docking and experimental verification

Objective: We aim at investigating the molecular mechanisms through which the Qingfei Jiedu decoction (QFJDD) regulates PD-L1 expression in lung adenocarcinoma (LUAD).

Methods: Bioactive compounds and targets of QFJDD were screened from TCMSP, BATMAN-TCM, and literature. Then, GeneCard, OMIM, PharmGKB, Therapeutic Target, and DrugBank databases were used to identify LUAD-related genes. The protein-protein interaction (PPI) network was constructed using overlapping targets of bioactive compounds in LUAD with the Cytoscape software and STRING database. The potential functions and pathways in which the hub genes were enriched by GO, KEGG, and DAVID pathway analyses. Molecular docking of bioactive compounds and key genes was executed via AutoDock Vina. Qualitative and quantitative analyses of QFJDD were performed using UPLC-Q-TOF-MS and UPLC. Expressions of key genes were determined by qRT-PCR, immunoreactivity score (IRS) of PD-L1 was assessed by immunohistochemistry (IHC), while the CD8⁺PD-1⁺T% derived from spleen tissues of Lewis lung cancer (LLC) bearing-mice was calculated using flow cytometry (FCM).

Results: A total of 53 bioactive compounds and 288 targets of QFJDD as well as 8151 LUAD associated genes were obtained. Further, six bioactive compounds, including quercetin, luteolin, kaempferol, wogonin, baicalein, and acacetin, and 22 hub genes were identified. The GO analysis showed that the hub genes were mainly enriched in DNA or RNA transcription. KEGG and DAVID pathway analyses revealed that 20 hub genes were primarily enriched in virus, cancer, immune, endocrine, and cardiovascular pathways. The EGFR, JUN, RELA, HIF1A, NFKBIA, AKT1, MAPK1, and MAPK14 hub genes were identified as key genes in PD-L1 expression and PD-1 checkpoint pathway. Moreover, ideal affinity and regions were identified between core compounds and key genes. Notably, QFJDD downregulated EGFR, JUN, RELA, HIF1A, NFKBIA, and CD274 expressions (p < 0.05), while it upregulated AKT1 and MAPK1 (p < 0.05) levels in A549 cells. The PD-L1 IRS of LLC tissue in the QFJDD high dose (H_d) group was lower than model group (p < 0.01). CD8⁺PD-1⁺T% was higher in the QFJDD H_d group than in normal and model groups (p < 0.05).

Conclusion: QFJDD downregulates PD-L1 expression and increases CD8⁺PD-1⁺T% via regulating HIF-1, EGFR, JUN and NFκB signaling pathways. Therefore, QFJDD is a potential treatment option for LUAD.

Genetic polymorphisms of pharmacogenomic VIP variants in the Lahu population from Yunnan Province

BACKGROUND

Pharmacogenomics has been widely used to study the very important pharmacogenetic (VIP) variants among populations, but information on pharmacogenomics in the Lahu population is limited. The purpose of this study was to determine the differences in the distribution of VIP variants between the Lahu and the other 26 populations.

METHODS

We genotyped 55 VIP variants of 27 genes in the Lahu population from the PharmGKB database. χ² test was used to compare the genotype and allele frequencies between the Lahu and the other 26 populations from the 1000 Genomes Project.

RESULTS

The genotype and allele frequencies of single nucleotide polymorphisms (SNPs) on rs20417 (PTGS2), rs776746 (CYP3A5), rs2115819 (ALOX5), and rs3093105 (CYP4F2) were considerably different in the Lahu population compared with those in the other 26 populations. Besides, based on the PharmGKB database, we identified several VIP variants that may alter the drug metabolism of aspirin (PTGS2), tacrolimus (CYP3A5), montelukast (ALOX5), and vitamin E (CYP4F2).

CONCLUSION

The results show that there are significant differences in the genotype frequency distribution between the Lahu and the other 26 populations. Our study supplements the pharmacogenomics information of the Lahu population and provides a theoretical basis for individualized medicine in Lahu.

Analysis of pharmacogenomic very important pharmacogenomic variants: CYP3A5, ACE, PTGS2 and NAT2 genes in Chinese Bai population

Aim: Our study aimed to screen the genotype frequencies of very important pharmacogenomic (VIP) mutations and identify their differences between Bai and other populations. Materials & methods: We selected 66 VIP variants from PharmGKB (www.pharmgkb.org/) for genotyping. χ2 test was used to identify differences in loci between these populations and Fst values of Bai and the other 26 populations were analyzed. Results: Our study showed that the frequencies of SNPs of CYP3A5, ACE, PTGS2 and NAT2 differed significantly from those of the other 26 populations. At the same time, we found that some VIP variants may affect the metabolism of drugs and the genetic relationship between the Bai population and East Asian populations was found to be the closest. Conclusion: By comparing the genotype frequencies of different populations, the loci with significant differences were identified and discussed, providing a theoretical basis for individualized drug use in the Bai ethnic population.

Nation-Wide Survey Assessing the Knowledge and Attitudes of Romanian Pharmacists Concerning Pharmacogenetics

Background: Pharmacogenetics (PGx) is an important component of personalized medicine that has the potential to improve medicines’ effectiveness and safety. However, despite progress in technology and availability, PGx testing application into patient-care in Eastern Europe countries, has been slow.

Objectives: Our aim was to describe knowledge and attitudes of Romanian pharmacists concerning PGx, and identify potential factors limiting PGx implementation.

Method: An anonymous, web-based questionnaire was distributed to Romanian pharmacists registered in the National Pharmacists’ Association (NPA) via an official e-mail sent by NPA representatives.

Results: A total of 1,058 pharmacists completed the questionnaires, resulting in a response rate of 7.6%. Pharmacists were predominantly female (90.1%), younger than 49 years (87.5%) and mostly worked in community pharmacies (80.2%). Most pharmacists (64.8%) had a knowledge score between 30 and 49 points out of 60, and (75.4%) had attitude scores between 9 and 7 out of 10. Attitude and knowledge scores positively correlated.

Conclusion: Despite performing fairly well on general questions regarding PGx, Romanian pharmacists may lack in-depth knowledge, which can affect their readiness to discuss PGx information with patients or other healthcare professionals. High pricing was considered an important impediment in PGx implementation.

Drug Metabolism for the Identification of Clinical Biomarkers in Breast Cancer

Breast cancer is classified into four major molecular subtypes, and is considered a heterogenous disease. The risk profiles and treatment of breast cancer differ according to these subtypes. Early detection dramatically improves the prospects of successful treatment, resulting in a reduction in overall mortality rates. However, almost 30% of women primarily diagnosed with the early-stage disease will eventually develop metastasis or resistance to chemotherapies. Immunotherapies are among the most promising cancer treatment options; however, long-term clinical benefit has only been observed in a small subset of responding patients. The current strategies for diagnosis and treatment rely heavily on histopathological examination and molecular diagnosis, disregarding the tumor microenvironment and microbiome involving cancer cells. In this review, we aim to praise the use of pharmacogenomics and pharmacomicrobiomics as a strategy to identify potential biomarkers for guiding and monitoring therapy in real-time. The finding of these biomarkers can be performed by studying the metabolism of drugs, more specifically, immunometabolism, and its relationship with the microbiome, without neglecting the information provided by genetics. A larger understanding of cancer biology has the potential to improve patient care, enable clinical decisions, and deliver personalized medicine.

Do Genetic Polymorphisms Affect Fetal Hemoglobin (HbF) Levels in Patients With Sickle Cell Anemia Treated With Hydroxyurea? A Systematic Review and Pathway Analysis

Hydroxyurea has long been used for the treatment of sickle cell anemia (SCA), and its clinical effectiveness is related to the induction of fetal hemoglobin (HbF), a major modifier of SCA phenotypes. However, there is substantial variability in response to hydroxyurea among patients with SCA. While some patients show an increase in HbF levels and an ameliorated clinical condition under low doses of hydroxyurea, other patients present a poor effect or even develop toxicity. However, the effects of genetic polymorphisms on increasing HbF levels in response to hydroxyurea in patients with SCA (Hb SS) have been less explored. Therefore, we performed a systematic review to assess whether single-nucleotide polymorphisms (SNPs) affect HbF levels in patients with SCA treated with hydroxyurea. Moreover, we performed pathway analysis using the set of genes with SNPs found to be associated with changes in HbF levels in response to hydroxyurea among the included studies. The systematic literature search was conducted on Medline/PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, and Web of Science. Seven cohort studies were included following our inclusion and exclusion criteria. From the 728 genetic polymorphisms examined in the included studies, 50 different SNPs of 17 genes were found to be associated with HbF changes in patients with SCA treated with hydroxyurea, which are known to affect baseline HbF but are not restricted to them. Enrichment analysis of this gene set revealed reactome pathways with the lowest adjusted p-values and highest combined scores related to VEGF ligand–receptor interactions (R-HSA-194313; R-HSA-195399) and the urea cycle (R-HSA-70635). Pharmacogenetic studies of response to hydroxyurea therapy in patients with SCA are still scarce and markedly heterogeneous regarding candidate genes and SNPs examined for association with HbF changes and outcomes, suggesting that further studies are needed. The reviewed findings highlighted that similar to baseline HbF, changes in HbF levels upon hydroxyurea therapy are likely to be regulated by multiple loci. There is evidence that SNPs in intron 2 of BCL11A affect HbF changes in response to hydroxyurea therapy, a potential application that might improve the clinical management of SCA.

Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=208790).

Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis

BACKGROUND

Patients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis.

METHODS

The disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein-protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD.

RESULTS

This study identified 495 risk genes for DKD complicated with IBD. After constructing a protein-protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C-C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product-receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway.

CONCLUSION

There is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.