PGMD: a comprehensive manually curated pharmacogenomic database

Pharmacogenomic Study of Selected Genes Affecting Amlodipine Blood Pressure Response in Patients with Hypertension

Introduction

Despite the availability of various antihypertensive medications, the response to these medications varies among individuals. Understanding how individual genetic variations affect drugs treatment outcomes is a key area of focus in precision medicine. This study investigated the correlation between single nucleotide polymorphisms (SNPs) in selected genes (CACNA1C, CACNA1D, ABCB1, ACE, ADBR2, and NOS1AP) and the blood pressure (BP) control by amlodipine.

Methods

Four hundred individuals of Pashtun ethnicity undergoing amlodipine treatment for hypertension were included in the present study and divided into the controlled (BP less than 140/90 mmHg) and uncontrolled (BP greater than 140/90 mmHg) hypertension groups. Blood samples (3 mL) were collected from each participant, and DNA was extracted using the Kit method. Ten SNPs in amlodipine pharmacogenes were selected and genotyped using real-time PCR with the TaqMan® system. Logistic regression model was used to determine the association between SNPs and the amlodipine BP response.

Results

Notable association were observed between SNP rs2239050/CACNA1C and amlodipine blood pressure response, with GG genotype carriers demonstrating a better response (P=0.004) than individuals carrying CC or CG genotypes. SNP rs312481/CACNA1D also exhibited a positive pharmacogenetic association, Individuals with the GG genotype showing a considerable reduction in BP (P=0.021) compared to participants with AA or GA genotypes. In case of SNP rs429/ACE individuals carrying TA genotype were less likely to achieve BP control (P=0.002) than AA genotype carriers.

Conclusion

Our finding suggests that the SNPs rs2239050/CACNA1C, rs312481/CACNA1D and rs429/ACE influence amlodipine blood pressure response in patients with hypertension. It is recommended that prior knowledge of amlodipine associated pharmacogenetic variants is important that could improve its treatment outcomes in hypertensive patients.

Personalized Medicine Using Cutting Edge Technologies for Genetic Epilepsies

Epilepsy is the most common chronic neurologic disorder in the world, affecting 1-2% of the population. Besides, 30% of epilepsy patients are drug-resistant. Genomic mutations seem to play a key role in its etiology and knowledge of strong effect mutations in protein structures might improve prediction and the development of efficacious drugs to treat epilepsy. Several genetic association studies have been undertaken to examine the effect of a range of candidate genes for resistance. Although, few studies have explored the effect of the mutations into protein structure and biophysics in the epilepsy field. Much work remains to be done, but the plans made for exciting developments will hold therapeutic potential for patients with drug-resistance. In summary, we provide a critical review of the perspectives for the development of individualized medicine for epilepsy based on genetic polymorphisms/mutations in light of core elements such as transcriptomics, structural biology, disease model, pharmacogenomics and pharmacokinetics in a manner to improve the success of trial designs of antiepileptic drugs.

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

Contemporary pharmacogenetic assays in view of the PharmGKB database

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.

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.

Pharmacogenomics and big genomic data: from lab to clinic and back again

The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation and Retrieval

Pharmacogenomics holds promise as a critical component of precision medicine. Yet, the use of pharmacogenomics in routine clinical care is minimal, partly due to the lack of efficient and effective use of existing evidence. This paper describes the design, development, implementation and evaluation of a knowledge-based system that fulfills three critical features: a) providing clinically relevant evidence, b) applying an evidence-based approach, and c) using semantically computable formalism, to facilitate efficient evidence assessment to support timely decisions on adoption of pharmacogenomics in clinical care. To illustrate functionality, the system was piloted in the context of clopidogrel and warfarin pharmacogenomics. In contrast to existing pharmacogenomics knowledge bases, the developed system is the first to exploit the expressivity and reasoning power of logic-based representation formalism to enable unambiguous expression and automatic retrieval of pharmacogenomics evidence to support systematic review with meta-analysis.

Methods to analyze big data in pharmacogenomics research

The scale and scope of pharmacogenomics research continues to expand as the cost and efficiency of molecular data generation techniques advance. These new technologies give rise to enormous opportunity for the identification of important genetic and genomic factors important for drug treatment response. With this opportunity come significant challenges. Most of these can be categorized as 'big data' issues, facing not only pharmacogenomics, but other fields in the life sciences as well. In this review, we describe some of the analysis techniques and tools being implemented for genetic/genomic discovery in pharmacogenomics.

Network Reconstruction Reveals that Valproic Acid Activates Neurogenic Transcriptional Programs in Adult Brain Following Traumatic Injury

Objectives

To determine the mechanism of action of valproic acid (VPA) in the adult central nervous system (CNS) following traumatic brain injury (TBI) and hemorrhagic shock (HS).

Methods

Data were analyzed from different sources, including experiments in a porcine model, data from postmortem human brain, published studies, public and commercial databases.

Results

The transcriptional program in the CNS following TBI, HS, and VPA treatment includes activation of regulatory pathways that enhance neurogenesis and suppress gliogenesis. Genes which encode the transcription factors (TFs) that specify neuronal cell fate, including MEF2D, MYT1L, NEUROD1, PAX6 and TBR1, and their target genes, are induced by VPA. VPA represses genes responsible for oligodendrogenesis, maintenance of white matter, T-cell activation, angiogenesis, and endothelial cell proliferation, adhesion and chemotaxis. NEUROD1 has regulatory interactions with 38% of the genes regulated by VPA in a swine model of TBI and HS in adult brain. Hi-C spatial mapping of a VPA pharmacogenomic SNP in the GRIN2B gene shows it is part of a transcriptional hub that contacts 12 genes that mediate chromatin-mediated neurogenesis and neuroplasticity.

Conclusions

Following TBI and HS, this study shows that VPA administration acts in the adult brain through differential activation of TFs responsible for neurogenesis, genes responsible for neuroplasticity, and repression of TFs that specify oligodendrocyte cell fate, endothelial cell chemotaxis and angiogenesis.

Short title: Mechanism of action of valproic acid in traumatic brain injury

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-017-2130-6) contains supplementary material, which is available to authorized users.

Establishing and validating regulatory regions for variant annotation and expression analysis

BACKGROUND

The regulatory effect of inherited or de novo genetic variants occurring in promoters as well as in transcribed or even coding gene regions is gaining greater recognition as a contributing factor to disease processes in addition to mutations affecting protein functionality. Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis.

RESULTS

In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding.

CONCLUSIONS

We have confirmed the validity of TRANSFAC's approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants.

Virtual Pharmacist: A Platform for Pharmacogenomics

We present Virtual Pharmacist, a web-based platform that takes common types of high-throughput data, namely microarray SNP genotyping data, FASTQ and Variant Call Format (VCF) files as inputs, and reports potential drug responses in terms of efficacy, dosage and toxicity at one glance. Batch submission facilitates multivariate analysis or data mining of targeted groups. Individual analysis consists of a report that is readily comprehensible to patients and practioners who have basic knowledge in pharmacology, a table that summarizes variants and potential affected drug response according to the US Food and Drug Administration pharmacogenomic biomarker labeled drug list and PharmGKB, and visualization of a gene-drug-target network. Group analysis provides the distribution of the variants and potential affected drug response of a target group, a sample-gene variant count table, and a sample-drug count table. Our analysis of genomes from the 1000 Genome Project underlines the potentially differential drug responses among different human populations. Even within the same population, the findings from Watson's genome highlight the importance of personalized medicine. Virtual Pharmacist can be accessed freely at http://www.sustc-genome.org.cn/vp or installed as a local web server. The codes and documentation are available at the GitHub repository (https://github.com/VirtualPharmacist/vp). Administrators can download the source codes to customize access settings for further development.