Validation of novel identification algorithms for major adverse cardiovascular events in a Japanese claims database.

Predicting clinical outcomes can be difficult, particularly for life-threatening events with a low incidence that require numerous clinical cases. Our aim was to develop and validate novel algorithms to identify major adverse cardiovascular events (MACEs) from claims databases. We developed algorithms based on the data available in the claims database International Classification of Diseases, Tenth Revision (ICD-10), drug prescriptions, and medical procedures. We also employed data from the claims database of Jichi Medical University Hospital, Japan, for the period between October 2012 and September 2014. In total, we randomly extracted 100 potential acute myocardial infarction cases and 200 potential stroke cases (ischemic and hemorrhagic stroke were analyzed separately) based on ICD-10 diagnosis. An independent committee reviewed the corresponding clinical data to provide definitive diagnoses for the extracted cases. We then assessed the algorithms' accuracy using positive predictive values (PPVs) and apparent sensitivities. The PPVs of acute myocardial infarction, ischemic stroke, and hemorrhagic stroke were low only by diagnosis (81.6% [95% CI 72.5-88.7]; 31.0% [95% CI 22.8-40.3]; and 45.5% [95% CI 34.1-57.2], respectively); however, the PPVs were elevated after adding the prescription and procedure data (87.0% [95% CI 78.3-93.1]; 44.4% [95% CI 32.7-56.6]; and 46.1% [95% CI 34.5-57.9], respectively). When we added event-specific prescription and procedure data to the algorithms, the PPVs for each event increased to 70%-98%, with apparent sensitivities exceeding 50%. Algorithms that rely on ICD-10 diagnosis in combination with data on specific drugs and medical procedures appear to be valid for identifying MACEs in Japanese claims databases.

Differential impact of diabetes mellitus on antiplatelet effects of prasugrel and clopidogrel.

BACKGROUND: Although prasugrel exerts stronger antiplatelet effects compared with clopidogrel, the factors affecting platelet reactivity under prasugrel have not been fully determined. This study aimed to find the novel mechanistic differences between two thienopyridines and identify the factor that influence platelet reactivity to each drug. METHODS: Forty patients with stable angina who underwent elective percutaneous coronary intervention were randomly assigned to receive either prasugrel (20 mg) or clopidogrel (300 mg) as a loading dose. Platelet function (light transmission, laser light scattering, and vasodilator-stimulated phosphoprotein phosphorylation) and plasma active metabolite levels were measured after the loading dose. RESULTS: Prasugrel consistently inhibited adenosine diphosphate receptor P2Y12 signalling to abolish amplification of platelet aggregation. Prasugrel abolished even small platelet aggregates composed of less than 100 platelets. On the other hand, clopidogrel inhibited large aggregates but increased small and medium platelet aggregates. Diabetes was the only independent variable for determining antiplatelet effects and active metabolite concentration of prasugrel, but not clopidogrel. Sleep-disordered breathing was significantly correlated with platelet reactivity in patients who had clopidogrel. CONCLUSIONS: Prasugrel efficiently abolishes residual P2Y12 signalling that causes small platelet aggregates, but these small aggregates are not inhibited by clopidogrel. Considering the differential effect of diabetes on antiplatelet effects between these two drugs, the pharmacokinetics of prasugrel, other than cytochrome P450 metabolism, might be affected by diabetes. TRIAL REGISTRATION: UMIN-CTR UMIN000017624, retrospectively registered 21 May 2015.

Long sleep duration: a nonconventional indicator of arterial stiffness in Japanese at high risk of cardiovascular disease: the J-HOP study.

Long and short sleep durations were reported as independently associated with hypertension, aortic stiffness, and cardiovascular disease. High-sensitivity C-reactive protein (hs-CRP) was shown to be associated with increased aortic stiffness. Here, we investigated the relationship between self-reported sleep duration and pulse wave velocity (PWV) in the elderly at high risk of cardiovascular disease. We also investigated whether hs-CRP moderates this relationship. Among 4310 patients with ≥1 cardiovascular risks recruited for the Japan Morning Surge-Home Blood Pressure Study, a questionnaire including items on daily sleep duration was completed. We measured the brachial-ankle PWV (baPWV) and hs-CRP levels in 2304 of these patients (mean age 64.7 years, 49.6% males). In accord with the patients' sleep duration (<6 hours, ≥6 to <8 hours, and ≥8 hours per night), significant associations between sleep duration and the PWV were observed (1594 vs. 1644 vs. 1763 cm/s, P < .0001). In the multiple regression analysis adjusted for age, body mass index, total cholesterol, HbA1c and clinic systolic blood pressure, long sleep duration (≥8 hours per night) (P < .05), and log hs-CRP (P < .05) were significantly positively associated with PWV when the patients with 6- to 8-hour sleep duration were defined as a reference group. Significant interactions of long sleep duration by age and that by antihypertensive medication for baPWV were observed. The effect of long sleep on PWV was greatest in the oldest age group. Long sleep duration and hs-CRP were significant indicators of increased baPWV in this elderly high-risk Japanese population. Age and antihypertensive medication use were significant modulators of the relationship between long sleep duration and arterial stiffness.

Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants.

Chemical genomics research has revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale. We also address the question of how to select plausible negative noninteraction pairs based on a statistical approach in order to develop reliable prediction models for GPCR-ligand interactions. The key interaction sites estimated by our approach can be of great use not only for screening of active compounds but also for modification of active compounds with the aim of improving activity or selectivity.

CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions.

With advancements in high-throughput technologies and open availability of bioassay data, computational methods to generate models, that zoom out from a single protein with a focused ligand set to a larger and more comprehensive description of compound-protein interactions and furthermore demonstrate subsequent translational validity in prospective experiments, are of prime importance. In this article, we discuss some of the new benefits and challenges of the emerging computational chemogenomics paradigm, particularly with respect to compound-protein interaction. Examples of experimentally validated computational predictions and recent trends in molecular feature extraction are presented. In addition, analyses of cross-family interactions are considered. We also discuss the expected role of computational chemogenomics in contributing to increasingly expansive network-level modeling and screening projects.

Dissecting kinase profiling data to predict activity and understand cross-reactivity of kinase inhibitors.

The development of selective and multitargeted kinase inhibitors has received much attention, because cross-reactivity with unintended targets may cause toxic side effects, while it can also give rise to efficacious multitargeted drugs. Here we describe a deconvolution approach to dissecting kinase profiling data in order to gain knowledge about cross-reactivity of inhibitors from large-scale profiling data. This approach not only enables activity predictions of given compounds on a kinome-wide scale, but also allows to extract residue-fragment pairs that are associated with activity. We demonstrate its effectiveness using a large-scale public chemogenomics data set and also apply our proposed model to a recently published bioactivity data set. We further illustrate that the preference of given compounds for kinases of interest is better understood by residue-fragment pairs, which could provide both biological and chemical insights into cross-reactivity.

Platinum agent-induced hypersensitivity reactions: data mining of the public version of the FDA adverse event reporting system, AERS.

OBJECTIVE: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to confirm the platinum agent-associated mild, severe, and lethal hypersensitivity reactions. METHODS: Authorized pharmacovigilance tools were used for quantitative signal detection, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Excess2, given by the multi-item gamma Poisson Shrinker algorithm, was used to evaluate the effects of dexamethasone and diphenhydramine on oxaliplatin-induced hypersensitivity reactions. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, carboplatin and oxaliplatin proved to cause mild, severe, and lethal hypersensitivity reactions, whereas cisplatin did not. Dexamethasone affected oxaliplatin-induced mild hypersensitivity reactions, but had lesser effects on severe and lethal reactions. The effects of diphenhydramine were not confirmed. CONCLUSION: The FDA's adverse event reporting system, AERS, with optimized data mining tools is useful to authorize potential associations between platinum agents and hypersensitivity reactions.

Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.

Cross-target view to feature selection: identification of molecular interaction features in ligand-target space.

There is growing interest in computational chemogenomics, which aims to identify all possible ligands of all target families using in silico prediction models. In particular, kernel methods provide a means of integrating compounds and proteins in a principled manner and enable the exploration of ligand-target binding on a genomic scale. To better understand the link between ligands and targets, it is of fundamental interest to identify molecular interaction features that contribute to prediction of ligand-target binding. To this end, we describe a feature selection approach based on kernel dimensionality reduction (KDR) that works in a ligand-target space defined by kernels. We further propose an efficient algorithm to overcome a computational bottleneck and thereby provide a useful general approach to feature selection for chemogenomics. Our experiment on cytochrome P450 (CYP) enzymes has shown that the algorithm is capable of identifying predictive features, as well as prioritizing features that are indicative of ligand preference for a given target family. We further illustrate its applicability on the mutation data of HIV protease by identifying influential mutated positions within protease variants. These results suggest that our approach has the potential to uncover the molecular basis for ligand selectivity and off-target effects.

Laplacian linear discriminant analysis approach to unsupervised feature selection.

Until recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis. In this paper, we address the problem of unsupervised feature selection. First, we extend Laplacian linear discriminant analysis (LLDA) to unsupervised cases. Second, we propose a novel algorithm for computing LLDA, which is efficient in the case of high dimensionality and small sample size as in microarray data. Finally, an unsupervised feature selection method, called LLDA-based Recursive Feature Elimination (LLDA-RFE), is proposed. We apply LLDA-RFE to several public data sets of cancer microarrays and compare its performance with those of Laplacian score and SVD-entropy, two state-of-the-art unsupervised methods, and with that of Fisher score, a supervised filter method. Our results demonstrate that LLDA-RFE outperforms Laplacian score and shows favorable performance against SVD-entropy. It performs even better than Fisher score for some of the data sets, despite the fact that LLDA-RFE is fully unsupervised.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

BACKGROUND: DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database. RESULTS: GEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories. CONCLUSION: GEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.

GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of Chemical Genomics research to easily retrieve such information from either biological or chemical starting points. GLIDA includes a variety of similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their conserved molecular recognition patterns and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. This article provides a summary of the GLIDA database and user facilities, and describes recent improvements to database design, data contents, ligand classification programs, similarity search options and graphical interfaces. GLIDA is publicly available at http://pharminfo.pharm.kyoto-u.ac.jp/services/glida/. We hope that it will prove very useful for Chemical Genomics research and GPCR-related drug discovery.

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.

BACKGROUND: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. RESULTS: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC-RFE). The MMC-RFE algorithms naturally extend to multi-class cases. The performance of MMC-RFE was extensively compared with that of SVM-RFE using nine cancer microarray datasets, including four multi-class datasets. CONCLUSION: Our extensive comparison has demonstrated that for binary-class datasets MMC-RFE tends to show intermediate performance between hard-margin SVM-RFE and SVM-RFE with a properly chosen soft-margin parameter. Notably, MMC-RFE achieves significantly better performance with a smaller number of genes than SVM-RFE for multi-class datasets. The results suggest that MMC-RFE is less sensitive to noise and outliers due to the use of average margin, and thus may be useful for biomarker discovery from noisy data.

Multiclass molecular cancer classification by kernel subspace methods with effective kernel parameter selection.

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods. However, there exist few studies on the application of other kernel methods in the literature. We apply a kernel subspace (KS) method to multiclass cancer classification problems, and assess its validity by comparing it with multiclass SVMs. Our comparative study using seven multiclass cancer datasets demonstrates that the KS method has high performance that is comparable to multiclass SVMs. Furthermore, we propose an effective criterion for kernel parameter selection, which is shown to be useful for the computation of the KS method.