Actionable pharmacogenetic variants in Hong Kong Chinese exome sequencing data and projected prescription impact in the Hong Kong population

Preemptive pharmacogenetic testing has the potential to improve drug dosing by providing point-of-care patient genotype information. Nonetheless, its implementation in the Chinese population is limited by the lack of population-wide data. In this study, secondary analysis of exome sequencing data was conducted to study pharmacogenomics in 1116 Hong Kong Chinese. We aimed to identify the spectrum of actionable pharmacogenetic variants and rare, predicted deleterious variants that are potentially actionable in Hong Kong Chinese, and to estimate the proportion of dispensed drugs that may potentially benefit from genotype-guided prescription. The projected preemptive pharmacogenetic testing prescription impact was evaluated based on the patient prescription data of the public healthcare system in 2019, serving 7.5 million people. Twenty-nine actionable pharmacogenetic variants/ alleles were identified in our cohort. Nearly all (99.6%) subjects carried at least one actionable pharmacogenetic variant, whereas 93.5% of subjects harbored at least one rare deleterious pharmacogenetic variant. Based on the prescription data in 2019, 13.4% of the Hong Kong population was prescribed with drugs with pharmacogenetic clinical practice guideline recommendations. The total expenditure on actionable drugs was 33,520,000 USD, and it was estimated that 8,219,000 USD (24.5%) worth of drugs were prescribed to patients with an implicated actionable phenotype. Secondary use of exome sequencing data for pharmacogenetic analysis is feasible, and preemptive pharmacogenetic testing has the potential to support prescription decisions in the Hong Kong Chinese population.

Pharmacogenetic testing provides relevant drug phenotype information to guide personalized drug prescription, which potentially improves drug efficacy and prevent adverse drug reactions. However, its implementation in the Chinese population is limited by the lack of Chinese-specific pharmacogenetics data. In this study, we studied the spectrum of 133 actionable pharmacogenetic variants and rare deleterious variants in 108 pharmacogenes using an exome sequencing consisting of 1116 Hong Kong Chinese subjects. It was found that nearly all individuals carried at least one actionable pharmacogenetic variant and one rare, predicted deleterious pharmacogenetic variant. In addition, we projected the potential prescription impact of actionable pharmacogenetic variants using prescription data of the Hong Kong's public healthcare system. We estimated that one-seventh of the Hong Kong population received at least one of the 36 drugs with clinical pharmacogenetics guideline recommendations. The findings demonstrated the potential of pharmacogenetic testing in improving patient care and resource allocation in Chinese. The cohort dataset also supports clinical implementation of pharmacogenetics in the Chinese population.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

[Recommendations for performing pharmacogenetic tests in drug monographs in Canada, France and the United States]

INTRODUCTION

Pharmacogenetics represents an opportunity in pharmaceutical practice. There are many documentary resources to support the pharmacist's work in this area.

OBJECTIVE

To compare the recommendations for carrying out pharmacogenetic tests from a documentary source in three countries: the United States, Canada and United France.

METHOD

This is a cross-sectional descriptive study. Based on the recommendations of the Clinical Pharmacogenetics Implementation Consortium type A (the highest threshold justifying the use of a pharmacogenetic test), we identified the drug-gene pairs (23 pairs). The proposed pairs involve a total of 47 separate international nonproprietary names and 18 genes. For each drug-gene pair, we consulted three open access documentary sources (one for each target country), namely the pharmaceutical products database (DPD) for Canada, the product characteristic summary (SPC) for France and the Micromedex® monograph (IBM, Truven Health Analytics, MI, USA) for the United States. The study was conducted in September 2019.

RESULTS

About a third of the drug-gene pairs are explicitly mentioned by the gene to be targeted and by the test suggested in the documentary sources consulted. Of the 23 pairs identified by the CPIC, thirteen pairs contain "consistent" recommendations between the three documentary sources.

CONCLUSION

There is great heterogeneity regarding the recommendations for pharmacogenetic tests from three documentary sources used by pharmacists to monitor drug therapy in the United States, Canada and France. There is an urgent need to standardize the requirements for nomenclature, description and the need to use pharmacogenetic tests to ensure proper use of drugs and these tests in the clinic.

Hsp70 Gene Polymorphisms Are Associated With Disease Susceptibility and HRQOL Improvement in Chinese Han Population With Systemic Lupus Erythematosus

OBJECTIVES

The aim of this study is to investigate whether heat shock protein 70 (Hsp70) gene polymorphisms are implicated in systemic lupus erythematous (SLE) susceptibility, the efficacy of glucocorticoids (GCs) treatment, and improvement of health-related quality of life.

METHODS

A total of 499 SLE patients and 499 controls were included in a case-control study, and 468 SLE patients treated with GCs for 12 weeks were involved in a follow-up study. Patients who completed the 12-week follow-up were divided into GCs-sensitive and GCs-insensitive group by using the SLE disease activity index. The SF-36 was used to evaluate the health-related quality of life of SLE patients, and genotyping was performed by improved multiplex ligation detection reaction.

RESULTS

rs2075800 was associated with SLE susceptibility (adjusted odds ratio [ORadj], 1.437; 95% confidence interval [CI], 1.113-1.855; Padj = 0.005; PBH = 0.020 by dominant model; ORadj, 1.602; 95% CI, 1.072-2.395; Padj = 0.022; PBH = 0.029 by TT vs CC model; ORadj = 1.396; 95% CI = 1.067-1.826; Padj = 0.015; PBH = 0.029 by TC vs CC model). In the follow-up study, rs2075799 was associated with the improvement in mental health (p = 0.004, PBH = 0.044), but we failed to find any association between the efficacy of GCs and Hsp70 gene polymorphisms.

CONCLUSIONS

Hsp70 gene polymorphisms may be associated with susceptibility to SLE and improvement of mental health in Chinese Han population.

Use of pharmacogenetic data to guide individualized opioid prescribing after surgery

BACKGROUND

Despite the current strategies aimed at avoiding opioid overprescription by implementing institutional guidelines, the use of opioids after surgical procedures remains highly variable. It is well known that opioids are activated by the cytochrome p450 CYP2D6 enzyme to exert pharmacologic effect. Individual variation in CYP2D6 activity affects drug metabolism, and genotyping can be performed to predict an individual's ability to metabolize CYP2D6 substrates. We postulate that the pharmacogenomic identification of patients with different opioid metabolism capacity may allow for the individualization of postsurgical opioid prescription.

METHODS

This study was generated by the unison of data from 2 prior initiatives taking place at our Institution. In the first study, patients undergoing 1 of 25 elective surgical procedures were prospectively identified as part of a quality initiative and surveyed by phone 21 to 35 days after hospital discharge to complete a 29-question survey regarding opioid utilization and pain experience. Additional chart abstraction was conducted to obtain prescribing data and pain scores during the hospitalization. The second study was the Mayo Clinic Right Drug, Right Dose, Right Time study protocol, in which 5 pharmacogenes, including CYP2D6, were genotyped for 1,000 Mayo Clinic Biobank participants. The goal of this study was to implement preemptive pharmacogenomics in an academic health care setting and to generate data for further pharmacogenomic research. Patients were classified by their predicted CYP2D6 activity based on their CYP2D6 genotype.

RESULTS

Of the 2,486 patients with prospective opioid utilization data, 21 had pharmacogenetic data available and were included in the analysis. These patients were classified according to their activity as opioid metabolizers, with 10 patients (48%) classified as intermediate, 4 patients (19%) as intermediate to normal, and 7 patients (33%) as normal or extensive. Compared with the intermediate to normal and intermediate phenotypes, normal or extensive patients had the highest percentages of preoperative opioid naivety and recorded pain scores throughout the surgical experience. The percentage of unused opioids for intermediate, intermediate to normal, and normal or extensive categories was 79%, 63%, and 46%, respectively. Moreover, of the 14 patients declaring the highest level of satisfaction for their pain control after discharge, 60% belonged to intermediate, 100% to intermediate to normal, and 57% to the normal or extensive group.

CONCLUSION

This study outlines a possible correlation between genetically controlled metabolism and opioid requirements after surgery. In this setting, an increased CYP2D6 enzymatic activity was associated to a greater opioid consumption, lesser amount of unused opioids, and a lower satisfaction level from opioid prescription.

A comparative study of the depth, breadth, and perception of pharmacogenomics instruction in a subgroup of US pharmacy curricula

INTRODUCTION

This study was designed to assess the depth, breadth, and perception of pharmacogenomics education in pharmacy curricula in the United States (US).

METHODS

A modified, online questionnaire from previous studies was sent to all accredited US schools and colleges of pharmacy. The survey covered three distinct areas related to the schools' educational environments, the depth and the extent of pharmacogenomics core competencies and topics taught, and the institutions' perceptions of the importance of pharmacogenomics in the curriculum and future plans for expanded pharmacogenomics education. Multiple approaches were used to increase the response rate, and results were analyzed using descriptive statistics.

RESULTS

Of the 133 eligible programs, 32 participated in the survey. Six invalid surveys were excluded from our study, resulting in a 19.6% response rate. Results revealed that all responding schools included pharmacogenomics in the curriculum. Interestingly, 76.9% of the respondents believed pharmacists do not have the appropriate knowledge of pharmacogenomics. However, only 30.7% indicated that their programs planned to expand pharmacogenomics in their curriculum.

CONCLUSIONS

The responding schools all included some pharmacogenomics in their curriculum. However, the depth and the extent of pharmacogenomics topics covered varied. Respondents perceived that pharmacists today do not possess the appropriate level of pharmacogenomics knowledge. Despite this, there is limited emphasis on expanding pharmacogenomics instruction in the responding schools' curriculums.

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.

An expanded pharmacogenomics warfarin dosing table with utility in generalised dosing guidance

Pharmacogenomics (PGx) guided warfarin dosing, using a comprehensive dosing algorithm, is expected to improve dose optimisation and lower the risk of adverse drug reactions. As a complementary tool, a simple genotype-dosing table, such as in the US Food and Drug Administration (FDA) Coumadin drug label, may be utilised for general risk assessment of likely over- or under-anticoagulation on a standard dose of warfarin. This tool may be used as part of the clinical decision support for the interpretation of genetic data, serving as a first step in the anticoagulation therapy decision making process. Here we used a publicly available warfarin dosing calculator (www.warfarindosing.org) to create an expanded gene-based warfarin dosing table, the CPMC-WD table that includes nine genetic variants in CYP2C9, VKORC1, and CYP4F2. Using two datasets, a European American cohort (EUA, n=73) and the Quebec Warfarin Cohort (QWC, n=769), we show that the CPMC-WD table more accurately predicts therapeutic dose than the FDA table (51 % vs 33 %, respectively, in the EUA, McNemar's two-sided p=0.02; 52 % vs 37 % in the QWC, p<1×10(-6)). It also outperforms both the standard of care 5 mg/day dosing (51 % vs 34 % in the EUA, p=0.04; 52 % vs 31 % in the QWC, p<1×10(-6)) as well as a clinical-only algorithm (51 % vs 38 % in the EUA, trend p=0.11; 52 % vs 45 % in the QWC, p=0.003). This table offers a valuable update to the PGx dosing guideline in the drug label.

Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study

We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical Bayesian approach to model individual effect estimates from maximal likelihood estimation (MLE) in a region jointly and shrinks them toward the regional effect. Unlike many methods that model a fixed selection scheme, MEA capitalizes on local multiplicity independent of selection. We compared mean square errors (MSEs) in simulated HGSs from naive MLE, MEA and a conditional likelihood adjustment (CLA) method that model threshold selection bias. We observed that MEA effectively reduced MSE from MLE on null effects with or without selection, and had a clear advantage over CLA on extreme MLE estimates from null effects under lenient threshold selection in small samples, which are common among 'top' associations from a pharmacogenetics HGS.

KNOWLEDGE DRIVEN BINNING AND PHEWAS ANALYSIS IN MARSHFIELD PERSONALIZED MEDICINE RESEARCH PROJECT USING BIOBIN

Next-generation sequencing technology has presented an opportunity for rare variant discovery and association of these variants with disease. To address the challenges of rare variant analysis, multiple statistical methods have been developed for combining rare variants to increase statistical power for detecting associations. BioBin is an automated tool that expands on collapsing/binning methods by performing multi-level variant aggregation with a flexible, biologically informed binning strategy using an internal biorepository, the Library of Knowledge (LOKI). The databases within LOKI provide variant details, regional annotations and pathway interactions which can be used to generate bins of biologically-related variants, thereby increasing the power of any subsequent statistical test. In this study, we expand the framework of BioBin to incorporate statistical tests, including a dispersion-based test, SKAT, thereby providing the option of performing a unified collapsing and statistical rare variant analysis in one tool. Extensive simulation studies performed on gene-coding regions showed a Bin-KAT analysis to have greater power than BioBin-regression in all simulated conditions, including variants influencing the phenotype in the same direction, a scenario where burden tests often retain greater power. The use of Madsen- Browning variant weighting increased power in the burden analysis to that equitable with Bin-KAT; but overall Bin-KAT retained equivalent or higher power under all conditions. Bin-KAT was applied to a study of 82 pharmacogenes sequenced in the Marshfield Personalized Medicine Research Project (PMRP). We looked for association of these genes with 9 different phenotypes extracted from the electronic health record. This study demonstrates that Bin-KAT is a powerful tool for the identification of genes harboring low frequency variants for complex phenotypes.

THE CHALLENGES IN USING ELECTRONIC HEALTH RECORDS FOR PHARMACOGENOMICS AND PRECISION MEDICINE RESEARCH

Access and utilization of electronic health records with extensive medication lists and genetic profiles is rapidly advancing discoveries in pharmacogenomics. In this study, we analyzed ~116,000 variants on the Illumina Metabochip for response to antihypertensive and lipid lowering medications in African American adults from BioVU, the Vanderbilt University Medical Center's biorepository linked to de-identified electronic health records. Our study population included individuals who were prescribed an antihypertensive or lipid lowering medication, and who had both pre- and post-medication blood pressure or low-density lipoprotein cholesterol (LDL-C) measurements, respectively. Among those with pre- and post-medication systolic and diastolic blood pressure measurements (n=2,268), the average change in systolic and diastolic blood pressure was -0.6 mg Hg and -0.8 mm Hg, respectively. Among those with pre- and post-medication LDL-C measurements (n=1,244), the average change in LDL-C was -26.3 mg/dL. SNPs were tested for an association with change and percent change in blood pressure or blood levels of LDL-C. After adjustment for multiple testing, we did not observe any significant associations, and we were not able to replicate previously reported associations, such as in APOE and LPA, from the literature. The present study illustrates the benefits and challenges with using electronic health records linked to biorepositories for pharmacogenomic studies.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

Adoption of a clinical pharmacogenomics implementation program during outpatient care--initial results of the University of Chicago "1,200 Patients Project"

Pharmacogenomic testing is viewed as an integral part of precision medicine. To achieve this, we originated The 1,200 Patients Project which offers broad, preemptive pharmacogenomic testing to patients at our institution. We analyzed enrollment, genotype, and encounter-level data from the first year of implementation to assess utility of providing pharmacogenomic results. Results were delivered via a genomic prescribing system (GPS) in the form of traffic lights: green (favorable), yellow (caution), and red (high risk). Additional supporting information was provided as a virtual pharmacogenomic consult, including citation to relevant publications. Currently, 812 patients have participated, representing 90% of those approached; 608 have been successfully genotyped across a custom array. A total of 268 clinic encounters have occurred at which results were accessible via the GPS. At 86% of visits, physicians accessed the GPS, receiving 367 result signals for medications patients were taking: 57% green lights, 41% yellow lights, and 1.4% red lights. Physician click frequencies to obtain clinical details about alerts varied according to color severity (100% of red were clicked, 72% yellow, 20% green). For 85% of visits, clinical pharmacogenomic information was available for at least one drug the patient was taking, suggesting relevance of the delivered information. We successfully implemented an individualized health care model of preemptive pharmacogenomic testing, delivering results along with pharmacogenomic decision support. Patient interest was robust, physician adoption of information was high, and results were routinely utilized. Ongoing examination of a larger number of clinic encounters and inclusion of more physicians and patients is warranted.

Combining heterogenous data for prediction of disease related and pharmacogenes

Identifying genetic variants that affect drug response or play a role in disease is an important task for clinicians and researchers. Before individual variants can be explored efficiently for effect on drug response or disease relationships, specific candidate genes must be identified. While many methods rank candidate genes through the use of sequence features and network topology, only a few exploit the information contained in the biomedical literature. In this work, we train and test a classifier on known pharmacogenes from PharmGKB and present a classifier that predicts pharmacogenes on a genome-wide scale using only Gene Ontology annotations and simple features mined from the biomedical literature. Performance of F=0.86, AUC=0.860 is achieved. The top 10 predicted genes are analyzed. Additionally, a set of enriched pharmacogenic Gene Ontology concepts is produced.

The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity

Computational efficiency is important for learning algorithms operating in the "large p, small n" setting. In computational biology, the analysis of data sets containing tens of thousands of features ("large p"), but only a few hundred samples ("small n"), is nowadays routine, and regularized regression approaches such as ridge-regression, lasso, and elastic-net are popular choices. In this paper we propose a novel and highly efficient Bayesian inference method for fitting ridge-regression. Our method is fully analytical, and bypasses the need for expensive tuning parameter optimization, via cross-validation, by employing Bayesian model averaging over the grid of tuning parameters. Additional computational efficiency is achieved by adopting the singular value decomposition reparametrization of the ridge-regression model, replacing computationally expensive inversions of large p × p matrices by efficient inversions of small and diagonal n × n matrices. We show in simulation studies and in the analysis of two large cancer cell line data panels that our algorithm achieves slightly better predictive performance than cross-validated ridge-regression while requiring only a fraction of the computation time. Furthermore, in comparisons based on the cell line data sets, our algorithm systematically out-performs the lasso in both predictive performance and computation time, and shows equivalent predictive performance, but considerably smaller computation time, than the elastic-net.

Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging Web ontology language (OWL) and cheminformatics approaches

Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. Since it does not require costly experiments that have a high risk of failure, it has attracted increasing interest from diverse fields such as biomedical, pharmaceutical, and informatics areas. In this study, we used pharmacogenomics data generated from pharmacogenomics studies, applied informatics and Semantic Web technologies to address the drug repositioning problem. Specifically, we explored PharmGKB to identify pharmacogenomics related associations as pharmacogenomics profiles for US Food and Drug Administration (FDA) approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations, which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs.

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data

Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datasets have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.

Utilization of an EMR-biorepository to identify the genetic predictors of calcineurin-inhibitor toxicity in heart transplant recipients

Calcineurin-inhibitors CI are immunosuppressive agents prescribed to patients after solid organ transplant to prevent rejection. Although these drugs have been transformative for allograft survival, long-term use is complicated by side effects including nephrotoxicity. Given the narrow therapeutic index of CI, therapeutic drug monitoring is used to prevent acute rejection from underdosing and acute toxicity from overdosing, but drug monitoring does not alleviate long-term side effects. Patients on calcineurin-inhibitors for long periods almost universally experience declines in renal function, and a subpopulation of transplant recipients ultimately develop chronic kidney disease that may progress to end stage renal disease attributable to calcineurin inhibitor toxicity (CNIT). Pharmacogenomics has the potential to identify patients who are at high risk for developing advanced chronic kidney disease caused by CNIT and providing them with existing alternate immunosuppressive therapy. In this study we utilized BioVU, Vanderbilt University Medical Center's DNA biorepository linked to de-identified electronic medical records to identify a cohort of 115 heart transplant recipients prescribed calcineurin-inhibitors to identify genetic risk factors for CNIT We identified 37 cases of nephrotoxicity in our cohort, defining nephrotoxicity as a monthly median estimated glomerular filtration rate (eGFR)<30 mL/min/1.73 m2 at least six months post-transplant for at least three consecutive months. All heart transplant patients were genotyped on the Illumina ADME Core Panel, a pharmacogenomic genotyping platform that assays 184 variants across 34 genes. In Cox regression analysis adjusting for age at transplant, pre-transplant chronic kidney disease, pre-transplant diabetes, and the three most significant principal components (PCAs), we did not identify any markers that met our multiple-testing threshold. As a secondary analysis we also modeled post-transplant eGFR directly with linear mixed models adjusted for age at transplant, cyclosporine use, median BMI, and the three most significant principal components. While no SNPs met our threshold for significance, a SNP previously identified in genetic studies of the dosing of tacrolimus CYP34A rs776746, replicated in an adjusted analysis at an uncorrected p-value of 0.02 (coeff(S.E.)=14.60(6.41)). While larger independent studies will be required to further validate this finding, this study underscores the EMRs usefulness as a resource for longitudinal pharmacogenetic study designs.

Genetic association studies of copy-number variation: should assignment of copy number states precede testing?

Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (> 12-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.

Ranking gene-drug relationships in biomedical literature using Latent Dirichlet Allocation

Drug responses vary greatly among individuals due to human genetic variations, which is known as pharmacogenomics (PGx). Much of the PGx knowledge has been embedded in biomedical literature and there is a growing interest to develop text mining approaches to extract such knowledge. In this paper, we present a study to rank candidate gene-drug relations using Latent Dirichlet Allocation (LDA) model. Our approach consists of three steps: 1) recognize gene and drug entities in MEDLINE abstracts; 2) extract candidate gene-drug pairs based on different levels of co-occurrence, including abstract level, sentence level, and phrase level; and 3) rank candidate gene-drug pairs using multiple different methods including term frequency, Chi-square test, Mutual Information (MI), a reported Kullback-Leibler (KL) distance based on topics derived from LDA (LDA-KL), and a newly defined probabilistic KL distance based on LDA (LDA-PKL). We systematically evaluated these methods by using a gold standard data set of gene-drug relations derived from PharmGKB. Our results showed that the proposed LDA-PKL method achieved better Mean Average Precision (MAP) than any other methods, suggesting its promising uses for ranking and detecting PGx relations.

Interpretome: a freely available, modular, and secure personal genome interpretation engine

The decreasing cost of genotyping and genome sequencing has ushered in an era of genomic personalized medicine. More than 100,000 individuals have been genotyped by direct-to-consumer genetic testing services, which offer a glimpse into the interpretation and exploration of a personal genome. However, these interpretations, which require extensive manual curation, are subject to the preferences of the company and are not customizable by the individual. Academic institutions teaching personalized medicine, as well as genetic hobbyists, may prefer to customize their analysis and have full control over the content and method of interpretation. We present the Interpretome, a system for private genome interpretation, which contains all genotype information in client-side interpretation scripts, supported by server-side databases. We provide state-of-the-art analyses for teaching clinical implications of personal genomics, including disease risk assessment and pharmacogenomics. Additionally, we have implemented client-side algorithms for ancestry inference, demonstrating the power of these methods without excessive computation. Finally, the modular nature of the system allows for plugin capabilities for custom analyses. This system will allow for personal genome exploration without compromising privacy, facilitating hands-on courses in genomics and personalized medicine.

Discovery and explanation of drug-drug interactions via text mining

Drug-drug interactions (DDIs) can occur when two drugs interact with the same gene product. Most available information about gene-drug relationships is contained within the scientific literature, but is dispersed over a large number of publications, with thousands of new publications added each month. In this setting, automated text mining is an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. In previous work, we have shown that gene-drug interactions can be extracted from Medline abstracts with high fidelity - we extract not only the genes and drugs, but also the type of relationship expressed in individual sentences (e.g. metabolize, inhibit, activate and many others). We normalize these relationships and map them to a standardized ontology. In this work, we hypothesize that we can combine these normalized gene-drug relationships, drawn from a very broad and diverse literature, to infer DDIs. Using a training set of established DDIs, we have trained a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product. The classifier recognizes the combinations of relationships, drugs and genes that are most associated with the gold standard DDIs, correctly identifying 79.8% of assertions relating interacting drug pairs and 78.9% of assertions relating noninteracting drug pairs. Most significantly, because our text processing method captures the semantics of individual gene-drug relationships, we can construct mechanistic pharmacological explanations for the newly-proposed DDIs. We show how our classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.

The extraction of pharmacogenetic and pharmacogenomic relations--a case study using PharmGKB

In this paper, we report on adapting the JREX relation extraction engine, originally developed For the elicitation of protein-protein interaction relations, to the domains of pharmacogenetics and pharmacogenomics. We propose an intrinsic and an extrinsic evaluation scenario which is based on knowledge contained in the PharmGKB knowledge base. Porting JREX yields favorable results in the range of 80% F-score for Gene-Disease, Gene-Drug, and Drug-Disease relations.

Linking PharmGKB to phenotype studies and animal models of disease for drug repurposing

The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype-disease association, combine them with drug-gene associations available from the PharmGKB database, and infer novel associations between drugs and diseases. We evaluate and quantify our results based on our method's capability to reproduce known drug-disease associations. We find and discuss evidence that levonorgestrel, tretinoin and estradiol are associated with cystic fibrosis (p < 2.65 · 10(-6), p < 0.002 and p < 0.031, Wilcoxon signed-rank test, Bonferroni correction) and that ibuprofen may be active in chronic lymphocytic leukemia (p < 2.63 · 10(-23), Wilcoxon signed-rank test, Bonferroni correction). To enable access to our results, we implement a web server and make our raw data freely available. Our results are the first steps in implementing an integrated system for the analysis and prediction of drug-disease associations for rare and orphan diseases for which the molecular basis is not known.

Integrating VA's NDF-RT drug terminology with PharmGKB: preliminary results

Biomedical terminology and vocabulary standards play an important role in enabling consistent, comparable, and meaningful sharing of data within and across institutional boundaries, as well as ensuring semantic interoperability. The Veterans Affairs (VA) National Drug File Reference Terminology (NDF-RT) is a federally recommended standardized terminology resource encompassing medications, ingredients, and a hierarchy for high-level drug classes. In this study, we investigate the drug-disease relationships in NDF-RT and determine how PharmGKB can be leveraged to augment NDF-RT, and vice-versa. Our preliminary results indicate that with additional curation and analyses, information contained in both knowledge resources can be mutually integrated.

Ontology-based knowledge discovery in pharmacogenomics

One current challenge in biomedicine is to analyze large amounts of complex biological data for extracting domain knowledge. This work holds on the use of knowledge-based techniques such as knowledge discovery (KD) and knowledge representation (KR) in pharmacogenomics, where knowledge units represent genotype-phenotype relationships in the context of a given treatment. An objective is to design knowledge base (KB, here also mentioned as an ontology) and then to use it in the KD process itself. A method is proposed for dealing with two main tasks: (1) building a KB from heterogeneous data related to genotype, phenotype, and treatment, and (2) applying KD techniques on knowledge assertions for extracting genotype-phenotype relationships. An application was carried out on a clinical trial concerned with the variability of drug response to montelukast treatment. Genotype-genotype and genotype-phenotype associations were retrieved together with new associations, allowing the extension of the initial KB. This experiment shows the potential of KR and KD processes, especially for designing KB, checking KB consistency, and reasoning for problem solving.

Extraction of genotype-phenotype-drug relationships from text: from entity recognition to bioinformatics application

Advances in concept recognition and natural language parsing have led to the development of various tools that enable the identification of biomedical entities and relationships between them in text. The aim of the Genotype-Phenotype-Drug Relationship Extraction from Text workshop (or GPD-Rx workshop) is to examine the current state of art and discuss the next steps for making the extraction of relationships between biomedical entities integral to the curation and knowledge management workflow in Pharmacogenomics. The workshop will focus particularly on the extraction of Genotype-Phenotype, Genotype-Drug, and Phenotype-Drug relationships that are of interest to Pharmacogenomics. Extracting and structuring such text-mined relationships is a key to support the evaluation and the validation of multiple hypotheses that emerge from high throughput translational studies spanning multiple measurement modalities. In order to advance this agenda, it is essential that existing relationship extraction methods be compared to one another and that a community wide benchmark corpus emerges; against which future methods can be compared. The workshop aims to bring together researchers working on the automatic or semi-automatic extraction of relationships between biomedical entities from research literature in order to identify the key groups interested in creating such a benchmark.

The role of 5-HTTLPR polymorphism in antidepressant-associated mania in bipolar disorder

BACKGROUND

The occurrence of mania during antidepressant treatment is a key issue in the clinical management of bipolar disorder (BD). The serotonin transporter gene is a candidate to be associated with antidepressant-associated mania (AAM) in some patients. This gene has a polymorphism within the promoter region (5-HTTLPR) with two allelic forms, the long (L) and the short (S) variants.

METHODS

We performed a case-control study to compare 5-HTTLPR genotype and allelic frequencies between 43 patients with a DSM-IV diagnosis of BD, with at least one manic/hypomanic episode associated with treatment with proserotonergic antidepressants (AAM+) and 69 unrelated, matched bipolar patients, who had been exposed to proserotonergic antidepressants without development of manic symptoms (AAM-(*)). Furthermore, we performed this comparison between a subgroup of 23 AAM+ patients that, when they presented AAM, were not using mood stabilizer (AAM+(*)) and 25 AAM- patients who used antidepressant without the concomitant use of a mood stabilizer (AAM-(*)). 5-HTTLPR genotyping was performed using PCR.

RESULTS

No significant differences were found between AAM+ and AAM-. Within the subgroups, our results show that S-carriers (LS+SS Genotypes) are more prone to make a manic/hypomanic episode associated with antidepressant (P=0.017).

LIMITATIONS

Our study is retrospective.

CONCLUSIONS

The 5-HTTLPR polymorphism may be considered a predictor of abnormal response to antidepressant in patients with BP, but this action is influenced by the presence of a mood stabilizer. Such observations reinforce that a correct diagnosis of bipolarity before the beginning of the treatment is essential, mainly for S-carriers patients.

Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks

Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.

Querying parse tree database of Medline text to synthesize user-specific biomolecular networks

Curated biological knowledge of interactions and pathways is largely available from various databases, and network synthesis is a popular method to gain insight into the data. However, such data from curated databases presents a single view of the knowledge to the biologists, and it may not be suitable to researchers' specific needs. On the other hand, Medline abstracts are publicly accessible and encode the necessary information to synthesize different kinds of biological networks. In this paper, we propose a new paradigm in synthesizing biomolecular networks by allowing biologists to create their own networks through queries to a specialized database of Medline abstracts. With this approach, users can specify precisely what kind of information they want in the resulting networks. We demonstrate the feasibility of our approach in the synthesis of gene-drug, gene-disease and protein-protein interaction networks. We show that our approach is capable of synthesizing these networks with high precision and even finds relations that have yet to be curated in public databases. In addition, we demonstrate a scenario of recovering a drug-related pathway using our approach.

Gamma-glutamyl carboxylase (GGCX) tagSNPs have limited utility for predicting warfarin maintenance dose

BACKGROUND

The pharmacogenetic factors contributing to warfarin dosing are of great interest to clinicians, and may have utility in the management of at-risk patients prescribed warfarin. Gamma-glutamyl carboxylase (GGCX), in its role as a key component of the vitamin K cycle, is a potential candidate gene associated with warfarin treatment.

OBJECTIVE

To identify single nucleotide polymorphisms (SNPs) and correlated GGCX tagSNPs and test for association with warfarin maintenance dose.

PATIENTS/METHODS

A small discovery population of European-descent individuals (n = 23) were resequenced for GGCX SNPs. Polymorphisms identified with > 5% minor allele frequency (MAF) were genotyped in a larger clinical population of 186 European patients. Univariate, multivariate and haplotype-based linear regression were used to assess the impact of GGCX SNPs on warfarin dose.

RESULTS

We identified 37 SNPs in GGCX, of which 21 were present at > 5% MAF. These SNPs were binned, based on linkage disequilibrium, and six informative tagSNPs were identified. A single polymorphism at position 12970 (rs11676382; C/G-11%/89%) was associated with a warfarin maintenance dose across all analysis methods. GGCX-12970 explained 2% of the total variance in warfarin dose, in contrast to 21 and 8%, respectively, for VKORC1 and CYP2C9.

CONCLUSIONS

The GGCX-12970 SNP had a small, but significant effect, on warfarin maintenance dose. Other polymorphisms in GGCX previously associated with warfarin dose were not confirmed in this study, suggesting that the effects of GGCX are potentially population/treatment-dependent and will not have broad utility for determining warfarin dosing.

Estimating a Positive False Discovery Rate for Variable Selection in Pharmacogenetic Studies

Selecting predictors to optimize the outcome prediction is an important statistical method. However, it usually ignores the false positives in the selected predictors. In this paper, we develop a positive false discovery rate (pFDR) estimate for a conventional step-wise forward variable selection procedure. We propose two views of a variable selection process, an overall and an individual test. An interesting feature of the overall test is that its power of selecting non-null predictors increases with the proportion of non-null predictors among all candidate predictors. Data analysis is illustrated with a pharmacogenetics example.

Prävention unerwünschter Arzneimittelwirkungen bei älteren Patienten

Adverse drug reactions are among the most common adverse events and a significant cause of preventable morbidity and mortality. As multimorbidity and polypharmacy are frequent in this population, the elderly are at special risk for adverse drug events, although the calendar age has not been proved as independent risk factor in this context. In particular falls and delirium are clinically significant and typical adverse drug events in the elderly. In this review mechanisms and factors which determine adverse drug re actions are described, and possible strategies for an effective prevention are given. This covers pharmacokinetic, pharmacogenetic and pharmacodynamic aspects as well as factors influencing individual adherence to drug therapy. A significant portion of adverse drug reaction may be prevented by a thorough indication and prudent monitoring of pharmacotherapy. Also adherence to pharmacotherapy may be improved by tailored and individual means referring to the patient's needs and expectancies. In the elderly functional limitations such as reduced cognitive abilities, reduced visual acuity and impaired dexterity determine an ineffective pharmacotherapy and medication errors. Hereby these functional limitations are significant predictors of adverse drug events in the context of self-management of pharmacotherapy. Testing of functional abilities as provided in the geriatric assessment is helpful to identify these factors. Among altered pharmacokinetic factors in the elderly, reduced renal function is most important to avoid overdosage. Although a precise measurement of renal function is not possible in a bed-side manner, an estimation of actual renal function utilizing estimation-formulas should always take place.

Case-only analysis of treatment-covariate interactions in clinical trials

Differential effectiveness of treatments across subgroups defined by pretreatment variables are of increasing interest, particularly in the expanding research field of pharmacogenomics. When the pretreatment variable is difficult to obtain or expensive to measure, but can be assessed at the end of the study using stored samples, nested case-control and case-cohort methods can be used to reduce costs in large efficacy trials with rare outcomes. Case-only methods are even more efficient, and reliable under a range of circumstances.

Extracting semantic predications from Medline citations for pharmacogenomics

We describe a natural language processing system (Enhanced SemRep) to identify core assertions on pharmacogenomics in Medline citations. Extracted information is represented as semantic predications covering a range of relations relevant to this domain. The specific relations addressed by the system provide greater precision than that achievable with methods that rely on entity co-occurrence. The development of Enhanced SemRep is based on the adaptation of an existing system and crucially depends on domain knowledge in the Unified Medical Language System. We provide a preliminary evaluation (55% recall and 73% precision) and discuss the potential of this system in assisting both clinical practice and scientific investigation.

An algorithm for model construction and its applications to pharmacogenomic studies

A model depicts the relationship between clinical phenotypes and genotypes on a set of genetic polymorphisms. After the model is constructed and validated, it may be used to predict clinical phenotypes such as traits of complex diseases. A pharmacogenomic model is used to predict the efficacies or adverse drug reactions of a medication. The construction of a model is a challenging task. This is because a single-locus polymorphism does not contain enough information to stratify patients in general, given the complex biological mechanisms involved. An exhaustive search for the correct combination of genotypes across multiple loci is, however, computationally infeasible. We are, thus, motivated to propose a novel algorithm for the construction of models using the multiple single-nucleotide polymorphism (SNP) information in diplotype forms. This algorithm utilizes the techniques of genetic algorithms and Boolean algebra (GABA). The proposed algorithm is tested on simulated data, as well as real genotype datasets of chronic hepatitis C patients treated with interferon-combined therapy. A model for predicting the treatment efficacy is constructed and validated. The results showed that the proposed algorithm is very effective in deriving models comprising multiple SNPs.

Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies

In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. MDR is a nonparametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.

EUDRAGENE: European collaboration to establish a case–control DNA collection for studying the genetic basis of adverse drug reactions

Type B adverse drug reactions (ADRs) are often serious, limit the usefulness of drugs that are otherwise effective, and increase the risks of drug development as they often lead to postmarketing withdrawal. There is evidence that susceptibility to at least some Type B ADRs is under strong genetic influence. Identifying genes in which variation influences susceptibility has obvious practical value for genetic testing and might also make it easier to screen molecules likely to cause ADRs at an early stage of the drug development process. Research in this area is hampered by the lack of a resource in which to study genetic determinants of susceptibility to Type B ADRs. As serious Type B ADRs are rare, case–control designs are the most frequently-used approach. The EUDRAGENE collaboration seeks to develop a resource using an international collaboration. This will provide a basis for adverse drug susceptibility genome association-wide studies using tag single nucleotide polymorphisms, or a direct approach using putative functional polymorphisms.

Assessing performance of prediction rules in machine learning

Introduction: An important goal in machine learning is to assess the degree to which prediction rules are robust and replicable, since these rules are used for decision making and for planning follow-up studies. This requires an estimate of a prediction rule's true error rate, a statistic that can be estimated by resampling data. However, there are many possible approaches depending upon whether we draw observations with or without replacement, or sample once, repeatedly, or not at all, and the pros and cons of each are often unclear. This study illustrates and compares different methods for estimating true error with the aim of providing practical guidance to users of machine learning techniques. Methods: We conducted Monte Carlo simulation studies using four different error estimators: bootstrap, split sample, resubstitution and a direct estimate of true error. Here, 'split sample' refers to a single random partition of the data into a pair of training and test samples, a popular scheme. We used stochastic gradient boosting as a learning algorithm, and considered data from two studies for which the underlying data mechanism was known to be complex: a library of 6000 tripeptide substrates collected for analysis of proteasome inhibition as part of anticancer drug design, and a cardiovascular study involving 600 subjects receiving antiplatelet treatment for acute coronary syndrome. Results: There were important differences in the performance of the various error estimators examined. Error estimators for split sample and resubstitution, while being the most transparent in action and the simplest to apply, did not quantify the performance of prediction rules as accurately as the bootstrap. This was true for both types of study data, despite their highly different nature. Conclusions: The robustness and reliability of decisions based on analysis of genomics data could, in many cases, be improved by following best practices for prediction error estimation. For this, techniques such as bootstrap should be considered.

Clinical genomics data standards for pharmacogenetics and pharmacogenomics

This special report concerns a talk on data standards given at a workshop entitled 'An International Perspective on Pharmacogenetics: The Intersections between Innovation, Regulation and Health Delivery', which was held by the Organization for Economic Co-operation and Development (OECD) on October 17-19, 2005, in Rome, Italy. The worlds of healthcare and life sciences (HCLS) are extremely fragmented in terms of their underlying information technology, making it difficult to semantically exchange information between disparate entities. While we have reached the point where functional interoperability is ubiquitous, we are still far from achieving true semantic interoperability where a receiving system can use incoming data as though it was created internally. The critical enablers of semantic interoperability are information standards dedicated to HCLS data, spanning all the way from biological research data to clinical research and clinical trials, and finally to healthcare clinical data. The challenge lies in integrating various data standards based on predetermined goals, thereby improving the quality of care provided to patients.

Application of SPLINDID, a semiparametric, model-based method for pharmacogenomic modeling of mRNA dynamics

PURPOSE

This study was conducted to evaluate the applicability of SPLINDID, a semiparametric, model-based approach for obtaining transcription rates from the pharmacodynamics of mRNA expression.

METHODS

A nonparametric exponential cubic spline function was used to obtain the transcription rate profile and the dynamics of mRNA expression was fitted using compartmental approaches. The transcription rate profile and mRNA degradation parameter was estimated using maximum likelihood method of ADAPT II software.

RESULTS

Data sets containing noise for mRNA levels were simulated for four diverse pharmaceutically relevant conditions: receptor nonlinearity, a model in which the variant mRNAs differing in mRNA degradation constants were transcribed and for a minimal model of the cell cycle. SPLINDID was able to fit the data sets and accurately recapitulate the transcription rate profiles normalized to the mRNA degradation rate constants. The model was also challenged using experimental data containing time profiles of cell-cycle-regulated genes.

CONCLUSIONS

The SPLINDID approach is flexible in capturing complicated/complex mRNA profiles that are encountered in many experimental data sets.

Pharmacogenomic data sample collection and storage: ethical issues and policy approaches

This perspective report will focus on the ethical, legal and social issues raised by pharmacogenomic research using large population-based databases. Access to databases established or developed at the level of whole populations or communities (e.g., the Estonian Genome Project, the UK Biobank, CARTaGENE, GenomEUtwin, and so on) will become increasingly important in pharmacogenomic research for the purpose of confirming associations between genetic variations and drug-related effects. The capacity of database creators and managers, along with that of researchers, to meet the ethical issues raised by such vast public projects will determine the integration of pharmacogenomics into mainstream clinical practice.

Learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data: does gene expression make a difference?

We address the problem of learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data. Extending the classical Quantitative Structure Activity Relationship paradigm, we investigate whether including gene expression data leads to a statistically significant improvement of prediction quality. Our analysis shows that the straightforward approach of including individual gene expression as features does not necessarily improve, but on the contrary, may degrade performance significantly. When gene expression information is aggregated, for instance by features representing the correlation with reference cell lines, performance can be improved significantly. Further improvements may be expected if the learning task is structured by grouping features and instances.

A bivariate functional mapping model for identifying haplotypes that control drug response for systolic and diastolic blood pressures

A bivariate functional mapping model has been proposed to detect haplotype-based DNA sequence variants that regulate the response curves of systolic and diastolic blood pressures (SBP and DBP) to a particular drug. This model capitalizes on the haplotype structure constructed by single nucleotide polymorphisms (SNPs) and incorporates the mathematical aspects of pharmacodynamic reactions into the estimation process, aimed to identify DNA sequence variants responsible for drug response. In this way, by estimating and testing the curve parameters that define drug response, many genetically and clinically meaningful hypotheses regarding the degree and pattern of the genetic control of SBP and DBP can be formulated, tested and disseminated. In a pharmacogenetic study composed of 107 subjects, our bivariate model has probed two haplotypes within the beta 2AR candidate gene that exert a significant effect on both SBP and DBP respond to dobutamine. With this candidate gene, two SNPs are genotyped, with allele Gly16 (G) and Arg16 (A) at codon 16 and alleles Glu27 (G) and Gln27 (C) at codon 27, respectively. The significant haplotypes are [AC] for SBP and [GG] for DBP. This model provides a powerful tool for elucidating the genetic variants of drug response and ultimately designing personalized medications based on each patient's genetic makeup.

Class prediction from time series gene expression profiles using dynamical systems kernels

We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters. A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For pharmacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.

Genotype-phenotype associations of cytochrome P450 3A4 and 3A5 polymorphism with midazolam clearance in vivo

The molecular basis for the wide interindividual variability of cytochrome P450 (CYP) 3A metabolic activity was studied in vivo at a genetic level. A single oral dose of midazolam was administered to 26 healthy subjects. The variability in midazolam oral clearance was 11-fold. No differences in midazolam oral clearance related to gender or ethnicity were observed. Selective sequencing of CYP3A4 and CYP3A5 genes revealed 18 single nucleotide polymorphisms (SNPs), including 8 novel CYP3A4 SNPs. Thirteen novel CYP3A4 haplotypes, 2 novel CYP3A5 haplotypes, and 1 major novel multigene haplotype ( CYP3A4*VI - CYP3A5*3A ) were also identified. No significant genotype-phenotype or haplotype-phenotype associations were found for any of the SNPs or haplotypes studied, including CYP3A4*1B , CYP3A5*3 , and CYP3A5*6 , even when ethnicity was considered. The only exceptions were the haplotype CYP3A4*VI and the multigene haplotype CYP3A4*VI - CYP3A5*3A . The carriers of the haplotype CYP3A4*VI had a 1.8-fold higher clearance of midazolam in black subjects (ANOVA on ranks, P = .028) compared with other individuals, and the carriers of the multigene haplotype CYP3A4*VI - CYP3A5*3A had a 1.7-fold higher clearance in the entire population (ANOVA on ranks, P = .012). In conclusion, these results indicate that the genetic variants identified so far in the CYP3A4 and CYP3A5 genes have only a limited impact on CYP3A-mediated drug metabolism in vivo.

Warfarin dosing and cytochrome P450 2C9 polymorphisms

Two cytochrome P450 2C9 (CYP2C9) polymorphisms, CYP2C9*2 and *3, metabolize warfarin inefficiently. We assessed the extent to which these polymorphisms explain very low warfarin dose requirements and hemorrhagic complications after excluding non-genetic determinants of warfarin dosing. In this retrospective observational study, 73 patients with stable warfarin doses for > or =1 month and International Normalized Ratios (INR) of 2.0-3.0 were enrolled from our Anticoagulation Clinic. Seventeen patients required < or =2 mg (low-dose), 41 required 4-6 mg (moderate-dose), and 15 required > or =10 mg (high-dose) of daily warfarin. CYP2C9 genotyping was assessed by PCR amplification and restriction enzyme digestion analysis of DNA isolated from circulating leukocytes. The CYP2C9 polymorphisms independently predicted low warfarin requirements after adjusting for Body Mass Index, age, acetaminophen use, and race (OR 24.80; 95% CI 3.83-160.78). At least one polymorphism was present in every patient requiring < or =1.5 mg of daily warfarin, and 88%, 37%, and 7% of the low-, moderate-, and high-dose groups, respectively. All homozygotes and compound-heterozygotes for the variant alleles were in the low-dose group. Rates of excessive (INR>6.0) anticoagulation (and bleeding) were 4.5 (6.0), 7.9 (7.9), and 14.7 (0) per 100 patient-years in the wild-types, heterozygotes, and compound heterozygotes/homozygotes, respectively. In conclusion, CYP2C9*2 or *3 compound heterozygotes and homozygotes have low warfarin requirements even after excluding liver disease, excessive alcohol or acetaminophen consumption, low body weight, advancing age, and drug interactions. These polymorphisms increase the rate of excessive anticoagulation, but this risk does not appear to be associated with higher bleeding rates when anticoagulation status is closely monitored.