Treatment patterns following initiation of generic glatiramer acetate among patients with multiple sclerosis from two large real-world databases in the United States.

INTRODUCTION: To better understand treatment patterns in US patients with multiple sclerosis (MS) initiating generic glatiramer acetate (GA), this study examined adherence, discontinuation and switching patterns from generic follow-on glatiramer acetate (FOGA) therapy in real-world patient cohorts. METHODS: Retrospective analyses utilized data from two large US databases (administrative claims and linked electronic medical records). Eligible adult MS patients had ≥1 pharmacy claim for FOGA during the identification period; the first FOGA claim was the index date. All analyses were descriptive; proportion of days covered (PDC) was calculated as a measure of adherence to FOGA during the follow-up period. RESULTS: The first cohort consisted of 95 patients, with 93.6% having a branded GA claim for Copaxone during the baseline period. Half these patients (48.4%) had high adherence to FOGA therapy (PDC: 0.8-1.0). Fifty-five patients (57.9%) initially discontinued FOGA with a mean persistence of 112 days. Of those who discontinued, 7.3% had no subsequent disease-modifying therapy (DMT), 30.9% restarted FOGA and 61.8% did not restart FOGA. The second cohort consisted of 1957 patients, with 63.8% having a branded GA claim for Copaxone during the baseline period and 33.5% were treatment naïve. The majority of patients (61.9%) had high adherence to FOGA therapy. A total of 1597 patients (81.6%) initially discontinued FOGA with a mean persistence of 93 days. Of those who discontinued, 55.8% switched to another DMT, 16.7% restarted FOGA and 37.5% had no subsequent DMT. CONCLUSION: Adherence to FOGA therapy was reasonably high across cohorts; however, most patients discontinued their initial FOGA within four months of the index date and most switches from FOGA were to branded GA products.

Indazole-based ligands for estrogen-related receptor α as potential anti-diabetic agents.

Estrogen-related receptor α (ERRα) is an orphan nuclear receptor that has been functionally implicated in the regulation of energy homeostasis. Herein is described the development of indazole-based N-alkylthiazolidenediones, which function in biochemical assays as selective inverse agonists against this receptor. Series optimization provided several potent analogues that inhibited the recruitment of a co-activator peptide fragment in vitro (IC50s < 50 nM) and reduced fasted circulating insulin and triglyceride levels in a sub-chronic pre-diabetic rat model when administered orally (10 mg/kg). A multi-parametric optimization strategy led to the identification of 50 as an advanced lead, which was more extensively evaluated in additional diabetic models. Chronic oral administration of 50 in two murine models of obesity and insulin resistance improved glucose control and reduced circulating triglycerides with efficacies similar to that of rosiglitazone. Importantly, these effects were attained without the concomitant weight gain that is typically observed with the latter agent. Thus, these studies provide additional support for the development of such molecules for the potential treatment of metabolic diseases.

Interconnectivity of Disparate Nonclinical Data Silos for Drug Discovery and Development.

Pharmaceutical research and development generates enormous amounts of nonclinical and clinical data related to safety and efficacy, and the ability to manage and utilize these data is critical for discovering and developing new drugs. Information systems exist that store and analyze relationships among seemingly disparate data sets (ie, data silos); however, to fully utilize the potential of these informatics systems, it is necessary to define basic parameters about the data and to develop concepts regarding "interconnectivity," or relationships among disparate data sets. To explore these issues, the Nonclinical Data Interconnectivity Sub-Group was chartered: a component of the Non-Clinical Road-Map and Impacts on Implementation Working Group associated with the US FDA-PhUSE (Pharmaceutical Users Software Exchange) Computational Sciences initiative. As a starting point, the group defined the meaning of data interconnectivity. Nonclinical data types were then identified and challenges and opportunities for interconnectivity explored. Specific-use cases were identified to provide examples of the value for interconnecting data across disciplines or silos.

Identification of diaryl ether-based ligands for estrogen-related receptor α as potential antidiabetic agents.

Estrogen-related receptor α (ERRα) is an orphan nuclear receptor that has been functionally implicated in the regulation of energy homeostasis. Herein is described the development of diaryl ether based thiazolidenediones, which function as selective ligands against this receptor. Series optimization provided several potent analogues that inhibit the recruitment of a coactivator peptide fragment in in vitro biochemical assays (IC(50) < 150 nM) and cellular two-hybrid reporter assays against the ligand binding domain (IC(50) = 1-5 µM). A cocrystal structure of the ligand-binding domain of ERRα with lead compound 29 revealed the presence of a covalent interaction between the protein and ligand, which has been shown to be reversible. In diet-induced murine models of obesity and in an overt diabetic rat model, oral administration of 29 normalized insulin and circulating triglyceride levels, improved insulin sensitivity, and was body weight neutral. This provides the first demonstration of functional activities of an ERRα ligand in metabolic animal models.

IRINI: random group allocation of multiple prognostic factors.

Statistically sound experimental design in pharmacology studies ensures that the known prognostic factors, if any, are equally represented across investigational groups to avoid bias and imbalance which could render the experiment invalid or lead to false conclusions. Complete randomization can be effective to reduce bias in the created groups especially in large sample size situations. However, in small studies which involve only few treatment subjects, as in preclinical trials, there is a high chance of imbalance. The effects of this imbalance may be removed through covariate analysis or prevented with stratified randomization, however small studies limit the number of covariates to be analyzed this way. The problem is accentuated when there are multiple baseline covariates with varying scales and magnitudes to be considered in the randomization, and creating a balanced solution becomes a combinatorial challenge. Our method, IRINI, uses an optimization technique to achieve treatment to subject group allocation across multiple prognostic factors concurrently. It ensures that the created groups are equal in size and statistically comparable in terms of mean and variance. This method is a novel application of genetic algorithms to solve the allocation problem and simultaneously ensure quality, speed of the results and randomness of the process. Results from preclinical trials demonstrate the effectiveness of the method.

Experimental validation of predicted mammalian erythroid cis-regulatory modules.

Multiple alignments of genome sequences are helpful guides to functional analysis, but predicting cis-regulatory modules (CRMs) accurately from such alignments remains an elusive goal. We predict CRMs for mammalian genes expressed in red blood cells by combining two properties gleaned from aligned, noncoding genome sequences: a positive regulatory potential (RP) score, which detects similarity to patterns in alignments distinctive for regulatory regions, and conservation of a binding site motif for the essential erythroid transcription factor GATA-1. Within eight target loci, we tested 75 noncoding segments by reporter gene assays in transiently transfected human K562 cells and/or after site-directed integration into murine erythroleukemia cells. Segments with a high RP score and a conserved exact match to the binding site consensus are validated at a good rate (50%-100%, with rates increasing at higher RP), whereas segments with lower RP scores or nonconsensus binding motifs tend to be inactive. Active DNA segments were shown to be occupied by GATA-1 protein by chromatin immunoprecipitation, whereas sites predicted to be inactive were not occupied. We verify four previously known erythroid CRMs and identify 28 novel ones. Thus, high RP in combination with another feature of a CRM, such as a conserved transcription factor binding site, is a good predictor of functional CRMs. Genome-wide predictions based on RP and a large set of well-defined transcription factor binding sites are available through servers at http://www.bx.psu.edu/.

Clustering of diverse genomic data using information fusion.

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.

Kullback-Leibler clustering of continuous wavelet transform measures of heart rate variability.

Power spectral analysis of beat-to-beat heart rate variability (HRV) has provided a useful means of understanding the interplay between autonomic and cardiovascular functionality. Despite their utility, commonly employed frequency-domain techniques are limited in their prerequisite for stationary signals and their inability to account for temporal changes in the power spectral and/or frequency properties of signals. The purpose of this study is to develop an algorithm that utilizes continuous wavelet transform (CWT) parameters as inputs to a Kohonen self-organizing map (SOM), providing a method of clustering subjects with similar wavelet transform signatures. Continuous interbeat-intervals were recorded (Portapres monitor at 200 Hz) during a perception of affect test in 79 African-American volunteers (ages 21-83), where after a 5-min baseline, participants evaluated emotional expressions in sentences and pictures of faces, followed by a 5-min recovery. Individual HRV biosignals from each session were pre-processed (artifact replacement and signal resampling at 2 Hz) and a CWT was applied (db9 wavelet basis function over 32 scales). Standard deviations of resulting wavelet coefficients at each scale were calculated, normalized, and used as inputs into a SOM with Kullback-Leibler divergence as the dissimilarity measure used for clustering. Differences in subject demographics between two final clusters were assessed via two-independent-groups t-tests or chi-square or Fisher's exact tests of contingency tables. Significant differences were found for age, initial systolic blood pressure, smoking status, and mean s.d. of coefficients in the high frequency band (0.15-0.4 Hz). These findings may have clinical significance and the developed algorithm provides an alternative means of analyzing HRV data originating from populations with complex covariates.

An information theoretic approach for analyzing temporal patterns of gene expression.

MOTIVATION: Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS: The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. AVAILABILITY: Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).