Pharmacogenomics of mycophenolic acid in kidney transplantation: Contribution of immune response-related genes

Abstract Mycophenolic acid (MPA) inhibits IMPDH, involved in the guanosine nucleotides synthesis, and prevents DNA replication in immune cells. The repression of cell and humoral immunity by MPA induces allograft tolerance preventing acute rejection in solid organ transplantation. MPA is an effective and safe drug, but genetic and non-genetic factors have been implicated in the interindividual variability of drug response. Several studies have shown the impact of variants of pharmacokinetics or pharmacodynamics-related genes on MPA response in kidney transplantation. This review explored further the influence of genes involved in the immune response on clinical outcomes of kidney recipients on short- or long-term MPA treatment. Variants in genes related to T cell activation (CD28, CTL4, ICOS, PDPC1), pro-inflammatory cytokines (IL2, IL6, IL12A, IL12B, TNF, IFNG), immunomodulatory cytokines (IL4, IL10, TGFB1), and innate immune response (CD14, TLR2, TLR4) were shown to be associated with increased risk of acute rejection, graft function or survival, chronic graft nephropathy, viral infections or MPA-induced myelotoxicity. Some of the significant pharmacogenetic associations were confirmed by meta-analyses of kidney transplantation. These findings are suggestive that variants in immune response-related genes contribute to the variability of MPA response, and have potential application as biomarkers of acute rejection in kidney transplantation.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling.

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.

Pharmacogenomics Biomarker Discovery and Validation for Translation in Clinical Practice.

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome-disease and genome-drug interactions offers the opportunity to optimize tailored drug therapy. High-throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical-grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

Clinical Validation of a 106-SNV MALDI-ToF MS Pharmacogenomic Panel.

BACKGROUND: Laboratorians have the opportunity to help minimize the frequency of adverse drug reactions by implementing pharmacogenomic testing and alerting care providers to possible patient/drug incompatibilities before drug treatment is initiated. Methods combining PCR with MALDI-ToF MS have allowed for sensitive, economical, and multiplexed pharmacogenomic testing results to be delivered in a timely fashion. METHOD: This study evaluated the analytical performance of the Agena Biosciences iPLEX® PGx 74 panel and a custom iPLEX panel on a MassARRAY MALDI-TOF MS instrument in a clinical laboratory setting. Collectively, these panels evaluate 112 SNVs across 34 genes implicated in drug response. Using commercially available samples (Coriell Biorepository) and in-house extracted DNA, we determined ideal reaction conditions and assessed accuracy, precision, and robustness. RESULTS: Following protocol optimization, the Agena PGx74 and custom panels demonstrated 100% concordance with the 1000 Genomes Project Database and clinically validated hydrolysis probe genotyping assays. 100% concordance was also observed in all assessments of assay precision when appropriate QC metrics were applied. CONCLUSIONS: Significant development time was required to optimize sample preparation and instrumental analysis and 3 assays were removed due to inconsistent performance. Following modification of the manufacturer's protocol and instituting manual review of each assay plate, the Agena PGx74 and custom panel constitute a cost-effective, robust, and accurate method for clinical identification of 106 SNVs involved in drug response.

Pharmacogenetics: Role of Single Nucleotide Polymorphisms.

Genome sequencing methods have basically similar algorithms, although they show a few differences between the platforms. The human genome contains approximately three billion base pairs, and this amount is huge and therefore impossible to sequence at one step. However, this amount is not a problem for developed technology. Researchers break DNA into small random pieces and then sequence and reassemble. Library preparation, sequencing, bioinformatic approaches and reporting. High-quality library preparation is critical and the most important part of the next-generation sequencing workflow. Successful sequencing directly requires high-quality libraries. Sequencing is second step and all high-throughput sequencing approaches are generally based on conventional Sanger sequencing. After preparation of library and sequencing, later steps are completely computer-based (in silico) approaches called as bioinformatics.

Farmacogenética del metotrexato. Un paso adelante / A step forward in methotrexate pharmacogenetics

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Farmacogenética del metotrexato / Pharmacogenetics of methotrexate

El metotrexato es el fármaco de elección en primera línea de tratamiento para pacientes con artritis reumatoide. En las últimas décadas ha habido un gran interés para identificar marcadores farmacogenéticos que puedan predecir con exactitud y fiabilidad la eficacia y toxicidad del metotrexato. Diferentes estudios han sugerido que los genes que participan en la vía metabólica del ácido fólico (MTHFR, DHFR, ATIC y TS) y el transporte intra y extraceluar del metotrexato (RFC1/SLC19A1, ABCB1, FPGS y GGH) pueden tener un potencial predictor de respuesta y toxicidad en el uso de metotrexato (AU)

Methotrexate is the drug of choice in the first-line treatment of patients with rheumatoid arthritis. In the last few decades, there has been strong interest in identifying pharmacogenetic markers that could accurately and reliably predict the effectiveness and toxicity of methotrexate. Several studies have suggested that the genes involved in the metabolic pathway of folic acid (MTHFR, DHFR, ATIC and TS) and the intraand extracellular methotrexate transport (RFC1/SLC19A1, ABCB1, FPGS and GGH) could play a role in predicting response to methotrexate, as well the toxicity of the drug (AU)

A fully integrated and automated microsystem for rapid pharmacogenetic typing of multiple warfarin-related single-nucleotide polymorphisms.

A fully integrated and automated microsystem consisting of low-cost, disposable plastic chips for DNA extraction and PCR amplification combined with a reusable glass capillary array electrophoresis chip in a modular-based format was successfully developed for warfarin pharmacogenetic testing. DNA extraction was performed by adopting a filter paper-based method, followed by "in situ" PCR that was carried out directly in the same reaction chamber of the chip without elution. PCR products were then co-injected with sizing standards into separation channels for detection using a novel injection electrode. The entire process was automatically conducted on a custom-made compact control and detection instrument. The limit of detection of the microsystem for the singleplex amplification of amelogenin was determined to be 0.625 ng of standard K562 DNA and 0.3 µL of human whole blood. A two-color multiplex allele-specific PCR assay for detecting the warfarin-related single-nucleotide polymorphisms (SNPs) 6853 (-1639G>A) and 6484 (1173C>T) in the VKORC1 gene and the *3 SNP (1075A>C) in the CYP2C9 gene was developed and used for validation studies. The fully automated genetic analysis was completed in two hours with a minimum requirement of 0.5 µL of input blood. Samples from patients with different genotypes were all accurately analyzed. In addition, both dried bloodstains and oral swabs were successfully processed by the microsystem with a simple modification to the DNA extraction and amplification chip. The successful development and operation of this microsystem establish the feasibility of rapid warfarin pharmacogenetic testing in routine clinical practice.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data.

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

The AmpliChip: A Review of its Analytic and Clinical Validity and Clinical Utility.

BACKGROUND: In 2005, the FDA approved the Roche AmpliChip™ for clinical application. The AmpliChip is a microarray chip that has the capability to play an important role in clinical pharmacogenetics. OBJECTIVE: Because of the possible influence the AmpliChip may have on patient medication management, the purpose of the review is to address the available evidence for the AmpliChip's overall performance at three key levels: analytic validity (genotyping accuracy, and prediction of the phenotype from the genotype) and clinical utility. DATA SOURCES: We searched Medline, Embase and PubMed for studies of the AmpliChip. Limits were English language and 2005 (the year of FDA approval) and onwards, and we corresponded with authors for further papers of interest. RESULTS: 17 articles provided data for analysis in this review: 4 involving genotype accuracy, 7 involving genotype to phenotype prediction and 9 involving clinical utility. CONCLUSION: There is limited literature comparing AmpliChip results to gold standard tests and test-retest reliability when assessing genotype accuracy. Also, there is limited literature on the accuracy of AmpliChip predictions of phenotypes from genotypes and minimal evidence with appropriately powered studies whether the AmpliChip genotype to phenotype predictions result in clinical benefit. At all three levels there is significant evidence that the AmpliChip has the potential to be a robust clinical tool. However, more and adequately powered studies are required to determine fully whether the AmpliChip is a clinically effective tool.

Metabolomics as a tool for drug discovery and personalised medicine. A review.

Studying the effects of drugs on the metabolome constitutes a huge part of the metabolomics discipline. Whether the approach is associated with drug discovery (altered pathways due to the disease that provide future targets and information into the mechanism of action or resistance, etc.) or pharmacometabolomics (studying the outcome of treatment), there have been many aspiring published articles in this area. With specific experimental design, including fingerprinting analysis with different analytical platforms in a non-targeted way, the approach is advancing towards the discovery of markers for the implication of personalised medicine, while also providing information that could help to improve the efficacy and reduce the side effects associated with a treatment. In this review, the evolution of pharmacometabolomics from other areas of drug efficacy metabolomics studies is explored.

Farmacogenómica y el Profesional Farmacéutico. Qué es y por qué es importante para la profesión / Pharmacogenomics and the Professional Pharmacist. What it is and Why it is important for the Pharmacist

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Personalized medicine: risk prediction, targeted therapies and mobile health technology.

Personalized medicine is increasingly being employed across many areas of clinical practice, as genes associated with specific diseases are discovered and targeted therapies are developed. Mobile apps are also beginning to be used in medicine with the aim of providing a personalized approach to disease management. In some areas of medicine, patient-tailored risk prediction and treatment are applied routinely in the clinic, whereas in other fields, more work is required to translate scientific advances into individualized treatment. In this forum article, we asked specialists in oncology, neurology, endocrinology and mobile health technology to discuss where we are in terms of personalized medicine, and address their visions for the future and the challenges that remain in their respective fields.

Opening the gate for genomics data into clinical research: a use case in managing patients' DNA samples from the bench to drug development.

The use of human genetic polymorphism data in drug development is not a recent event. Typically, the detection of patients' genetic variations in drug-metabolizing enzymes has become common practice in clinical laboratories. What is new is the scale and diversity of genomics data that has entered into the drug research and development decision-making process. At least three concurrent events contribute to this paradigm shift: first the growing body of evidence that establishes that interindividual variation in both therapeutic response and adverse events are attributable to a genetic component; second the technological progress that enables the consistent and reproducible detection of human genomic quantities; third the expectation that the productivity of new drug development will be increased by identifying which patients would benefit from candidate therapies early in the clinical process. This influx of human genomics data into clinical laboratories requires some logistical adjustment in terms of data management. The major specifications of an information solution system intended for a clinical genomic laboratory are its compliance with regulatory procedures, regarding the handling of human genetic data and its subsequent integration into an existing clinical data management system from the hosting institution. The purpose of this article is to inform the community of the challenges in setting up a center for genomics data that ensures accurate, traceable and integrated data for laboratory management. This is by no means the only way to accomplish the same goal, and is simply presented as one way that Pfizer chose to solve these issues.

Characterization of 107 genomic DNA reference materials for CYP2D6, CYP2C19, CYP2C9, VKORC1, and UGT1A1: a GeT-RM and Association for Molecular Pathology collaborative project.

Pharmacogenetic testing is becoming more common; however, very few quality control and other reference materials that cover alleles commonly included in such assays are currently available. To address these needs, the Centers for Disease Control and Prevention's Genetic Testing Reference Material Coordination Program, in collaboration with members of the pharmacogenetic testing community and the Coriell Cell Repositories, have characterized a panel of 107 genomic DNA reference materials for five loci (CYP2D6, CYP2C19, CYP2C9, VKORC1, and UGT1A1) that are commonly included in pharmacogenetic testing panels and proficiency testing surveys. Genomic DNA from publicly available cell lines was sent to volunteer laboratories for genotyping. Each sample was tested in three to six laboratories using a variety of commercially available or laboratory-developed platforms. The results were consistent among laboratories, with differences in allele assignments largely related to the manufacturer's assay design and variable nomenclature, especially for CYP2D6. The alleles included in the assay platforms varied, but most were identified in the set of 107 DNA samples. Nine additional pharmacogenetic loci (CYP4F2, EPHX1, ABCB1, HLAB, KIF6, CYP3A4, CYP3A5, TPMT, and DPD) were also tested. These samples are publicly available from Coriell and will be useful for quality assurance, proficiency testing, test development, and research.

Biomarkers in the era of personalized medicine - a multiplexed SNP assay using capillary electrophoresis for assessing drug metabolism capacity.

Pharmacogenetics is the study of genetic differences in an individual that leads to variability in drug response. Single-nucleotide polymorphisms (SNPs) prove to be important determinants in evaluating and predicting a patient's response to certain medications and risk of adverse events. The Cytochrome P450 2D6 (CYP2D6) gene is particularly important in the metabolism of many clinically prescribed drugs. In this study, we designed a multiplexed panel to interrogate 8 clinically relevant SNPs of CYP2D6 (*2 at C2856G, *2 at G4181C, *3, *4, *5,*6, *7, and *8). We PCR-amplified a 4.7 kB segment of the CYP2D6 locus containing all the SNPs of interest using genomic DNA extracted from whole blood. Using single base extension and capillary electrophoresis separation, peaks corresponding to the SNPs resolve within a 25-60 bp window. We subsequently analyzed 25 samples using this protocol and compared results to traditional DNA sequencing using an ABI 3730. All samples were 100% concordant between the two methods. This assay can be performed with <24 h turnaround time and minimal hands-on effort. This multiplex SNP panel can be used for interrogation of 8 SNPs within the 2D6 gene, with application to identifying poor metabolizers of 2d6. Patients harbouring SNPs in 2D6 could be triaged to alternative therapies in an effort to maximize therapeutic efficacy and reduce adverse drug reactions.

DMET microarray technology for pharmacogenomics-based personalized medicine.

Human genome sequence variation in the form of single nucleotide polymorphisms (SNPs) as well as more complex structural variation such as insertions, duplications, and deletions underlies each individual's response to drugs and thus the likelihood of experiencing an adverse drug reaction. The ongoing challenge of the field of pharmacogenetics is to further understand the relationship between genetic variation and differential drug responses, with the overarching goal being that this will lead to improvements in both the safety and efficacy of drugs. The Affymetrix DMET Plus Premier Pack (DMET stands for Drug Metabolizing Enzymes and Transporters) enables highly multiplexed genotyping of known polymorphisms in Absorption, Distribution, Metabolism, and Elimination (ADME)-related genes on a single array. The DMET Plus Panel interrogates markers in 225 genes that have documented functional significance in phase I and phase II drug metabolism enzymes as well as drug transporters. The power of the DMET Assay has previously been demonstrated with regard to several different drugs including warfarin and clopidogrel. In a research study using an earlier four-color version of the assay, it was demonstrated that warfarin dosing can be influenced by a cytochrome P450 (CYP) 4F2 variant. Additionally, the assay has been used to demonstrate that CYP2C19 variants with decreased enzyme activity led to lower levels of the active clopidogrel metabolite, resulting in a decreased inhibition of platelets and a higher rate of cardiovascular events when compared to noncarriers of the DNA variant. Thus, highly multiplexed SNP genotyping focused on ADME-related polymorphisms should enable research into development of safer drugs with greater efficacy.

Pharmacogenetic testing for clopidogrel using the rapid INFINITI analyzer: a dose-escalation study.

OBJECTIVES: Our aim was to assess whether a higher clopidogrel maintenance dose has a greater antiplatelet effect in CYP2C19*2 allele carriers compared with noncarriers. BACKGROUND: Clopidogrel is a prodrug that is biotransformed by the cytochrome P450 enzymes CYP2C19, 2C9, and 3A4, 2B6, 1A2. The CYPC219*2 loss of function variant has been associated with a reduced antiplatelet response to clopidogrel and a 3-fold risk of stent thrombosis. METHODS: Forty patients on standard maintenance dosage clopidogrel (75 mg), for 9.4 +/- 9.2 weeks, were enrolled into a dose escalation study. Platelet function was assessed at baseline and after 1 week of 150 mg once daily using the VerifyNow platelet function analyzer (Accumetrics Ltd., San Diego, California). Genomic DNA was hybridized to a BioFilmChip microarray on the INFINITI analyzer (AutoGenomics Inc., Carlsbad, California) and analyzed for the CYP19*2, *4, *17, and CYP2C9*2, *3 polymorphisms. RESULTS: Platelet inhibition increased over 1 week, mean +8.6 +/- 13.5% (p = 0.0003). Carriers of the CYP2C19*2 allele had significantly reduced platelet inhibition at baseline (median 18%, range 0% to 72%) compared with wildtype (wt) (median 59%, range 11% to 95%, p = 0.01) and at 1 week (p = 0.03). CYP2C19*2 allele carriers had an increase in platelet inhibition of (mean +9 +/- 11%, p = 0.03) and reduction in platelet reactivity (mean -26 +/- 38 platelet response unit, p = 0.04) with a higher dose. Together CYP2C19*2 and CYP2C9*3 loss of function carriers had a greater change in platelet inhibition with 150 mg daily than wt/wt (+10.9% vs. +0.7%, p = 0.04). CONCLUSIONS: Increasing the dose of clopidogrel in patients with nonresponder polymorphisms can increase antiplatelet response. Personalizing clopidogrel dosing using pharmacogenomics may be an effective method of optimizing treatment.