Web resources for pharmacogenomics

Molecular and modular intricacies of precision oncology

Precision medicine is revolutionizing the world in combating different disease modalities, including cancer. The concept of personalized treatments is not new, but modeling it into a reality has faced various limitations. The last decade has seen significant improvements in incorporating several novel tools, scientific innovations and governmental support in precision oncology. However, the socio-economic factors and risk-benefit analyses are important considerations. This mini review includes a summary of some commendable milestones, which are not just a series of successes, but also a cautious outlook to the challenges and practical implications of the advancing techno-medical era.

ursaPGx: a new R package to annotate pharmacogenetic star alleles using phased whole-genome sequencing data

Long-read sequencing technologies offer new opportunities to generate high-confidence phased whole-genome sequencing data for robust pharmacogenetic annotation. Here, we describe a new user-friendly R package, ursaPGx, designed to accept multi-sample phased whole-genome sequencing data VCF input files and output star allele annotations for pharmacogenes annotated in PharmVar.

Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool ( www.coriell.org/StarAllele/Search ). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

Genetic analysis of pharmacogenomic VIP variants in the Wa population from Yunnan Province of China

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-00999-8.

Genetic Findings as the Potential Basis of Personalized Pharmacotherapy in Phelan-McDermid Syndrome

Phelan-McDermid syndrome (PMS) is a genetic disorder often characterized by autism or autistic-like behavior. Most cases are associated with haploinsufficiency of the SHANK3 gene resulting from deletion of the gene at 22q13.3 or from a pathogenic variant in the gene. Treatment of PMS often targets SHANK3, yet deletion size varies from <50 kb to >9 Mb, potentially encompassing dozens of genes and disrupting regulatory elements altering gene expression, inferring the potential for multiple therapeutic targets. Repurposed drugs have been used in clinical trials investigating therapies for PMS: insulin-like growth factor 1 (IGF-1) for its effect on social and aberrant behaviors, intranasal insulin for improvements in cognitive and social ability, and lithium for reversing regression and stabilizing behavior. The pharmacogenomics of PMS is complicated by the CYP2D6 enzyme which metabolizes antidepressants and antipsychotics often used for treatment. The gene coding for CYP2D6 maps to 22q13.2 and is lost in individuals with deletions larger than 8 Mb. Because PMS has diverse neurological and medical symptoms, many concurrent medications may be prescribed, increasing the risk for adverse drug reactions. At present, there is no single best treatment for PMS. Approaches to therapy are necessarily complex and must target variable behavioral and physical symptoms of PMS.

Pharmacogenomics of Cognitive Dysfunction and Neuropsychiatric Disorders in Dementia

Symptomatic interventions for patients with dementia involve anti-dementia drugs to improve cognition, psychotropic drugs for the treatment of behavioral disorders (BDs), and different categories of drugs for concomitant disorders. Demented patients may take >6-10 drugs/day with the consequent risk for drug-drug interactions and adverse drug reactions (ADRs >80%) which accelerate cognitive decline. The pharmacoepigenetic machinery is integrated by pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes redundantly and promiscuously regulated by epigenetic mechanisms. CYP2D6, CYP2C9, CYP2C19, and CYP3A4/5 geno-phenotypes are involved in the metabolism of over 90% of drugs currently used in patients with dementia, and only 20% of the population is an extensive metabolizer for this tetragenic cluster. ADRs associated with anti-dementia drugs, antipsychotics, antidepressants, anxiolytics, hypnotics, sedatives, and antiepileptic drugs can be minimized by means of pharmacogenetic screening prior to treatment. These drugs are substrates, inhibitors, or inducers of 58, 37, and 42 enzyme/protein gene products, respectively, and are transported by 40 different protein transporters. APOE is the reference gene in most pharmacogenetic studies. APOE-3 carriers are the best responders and APOE-4 carriers are the worst responders; likewise, CYP2D6-normal metabolizers are the best responders and CYP2D6-poor metabolizers are the worst responders. The incorporation of pharmacogenomic strategies for a personalized treatment in dementia is an effective option to optimize limited therapeutic resources and to reduce unwanted side-effects.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

Population-scale genomics-Enabling precision public health

The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.

Clinical Pharmacogenomics: Applications in Nephrology

Pharmacogenomics is a tool for practitioners to provide precision pharmacotherapy using genomics. All providers are likely to encounter genomic data in practice with the expectation that they are able to successfully apply it to patient care. Pharmacogenomics tests for genetic variations in genes that are responsible for drug metabolism, transport, and targets of drug action. Variations can increase the risk for drug toxicity or poor efficacy. Pharmacogenomics can, therefore, be used to help select the best medication or aid in dosing. Nephrologists routinely treat cardiovascular disease and manage patients after kidney transplantation, two situations for which there are several high-evidence clinical recommendations for commonly used anticoagulants, antiplatelets, statins, and transplant medications. Successful use of pharmacogenomics in practice requires that providers are familiar with how to access and use pharmacogenomics resources. Similarly, clinical decision making related to whether to use existing data, whether to order testing, and if data should be used in practice is needed to deliver precision medicine. Pharmacogenomics is applicable to virtually every medical specialty, and nephrologists are well positioned to be implementation leaders.

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Integrating pharmacogenetic testing into primary care

Introduction

Pharmacogenetic (PGx) testing has greatly expanded due to enhanced understanding of the role of genes in drug response and advances in DNA-based testing technology development. As many primary care visits result in a prescription, the use of PGx testing may be particularly beneficial in this setting. However, integration of PGx testing may be limited as no uniform approach to delivery of tests has been established and providers are ill-prepared to integrate PGx testing into routine care.

Areas covered

In this paper, the readiness of primary care practitioners are reviewed as well as strategies to address these barriers based on published research and ongoing activities on education and implementation of PGx testing.

Expert Commentary

Widespread integration of PGx testing will warrant continued education and point-of-care decisional support. Primary care providers may also benefit from consultation services or team-based care with laboratory medicine specialists, pharmacists, and genetic counselors.

Genetic polymorphisms of pharmacogenomic VIP variants in Li nationality of southern China

OBJECTIVES

The present study aimed to screen members of the Li nationality in southern China for genotype frequencies of VIP variants and to determine differences between the Li ethnicity and global human population samples in HapMap.

METHODS

In this study, we genotyped 77 very important pharmacogenetic (VIP) variants selected from the pharmacogenomics knowledge base (PharmGKB) in members of the Li population and compared our data with other eleven populations from the HapMap data set.

RESULTS

Our results showed that VDR rs1540339, VKORC1 rs9934438, and MTHFR rs1801133 were most different in Li compared with most of the eleven populations from the HapMap data set. Furthermore, population structure and F-statistics (Fst) analysis also showed differences between the Li and other HapMap populations, and the results suggest that the Li are most genetically similar to the CHD population, and the least similar to the YRI in HapMap.

CONCLUSIONS

The findings of our study complement the pharmacogenomics database with information on members of the Li ethnicity and provide a stronger scientific basis for safer drug administration, which may help clinicians to predict individual drug responses, thereby avoiding the risk of adverse effects and optimizing efficacy in this population.