Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions

Background. Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. Results. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.IU -Indiana Universit

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Binase and other microbial RNases as potential anticancer agents

Some RNases possess preferential cytotoxicity against malignant cells. The best known of these RNases, onconase, was isolated from frog oocytes; and is in clinical trials as anticancer therapy. Here we propose an alternative platform for anticancer therapy based on T1 RNases of microbial origin, in particular binase from Bacillus Intermedius and RNase Sa from Streptomyces aureofaciens. We discuss their advantages and the most promising directions of research for their potential clinical applications. © 2008 Wiley Periodicals, Inc

A Model for the Development of the Rhizobial and Arbuscular Mycorrhizal Symbioses in Legumes and Its Use to Understand the Roles of Ethylene in the Establishment of these two Symbioses

We propose a model depicting the development of nodulation and arbuscular mycorrhizae. Both processes are dissected into many steps, using Pisum sativum L. nodulation mutants as a guideline. For nodulation, we distinguish two main developmental programs, one epidermal and one cortical. Whereas Nod factors alone affect the cortical program, bacteria are required to trigger the epidermal events. We propose that the two programs of the rhizobial symbiosis evolved separately and that, over time, they came to function together. The distinction between these two programs does not exist for arbuscular mycorrhizae development despite events occurring in both root tissues. Mutations that affect both symbioses are restricted to the epidermal program. We propose here sites of action and potential roles for ethylene during the formation of the two symbioses with a specific hypothesis for nodule organogenesis. Assuming the epidermis does not make ethylene, the microsymbionts probably first encounter a regulatory level of ethylene at the epidermis–outermost cortical cell layer interface. Depending on the hormone concentrations there, infection will either progress or be blocked. In the former case, ethylene affects the cortex cytoskeleton, allowing reorganization that facilitates infection; in the latter case, ethylene acts on several enzymes that interfere with infection thread growth, causing it to abort. Throughout this review, the difficulty of generalizing the roles of ethylene is emphasized and numerous examples are given to demonstrate the diversity that exists in plants

Differential gene expression profiles are dependent upon method of peripheral blood collection and RNA isolation

<p>Abstract</p> <p>Background</p> <p>RNA isolation and purification steps greatly influence the results of gene expression profiling. There are two commercially available products for whole blood RNA collection, PAXgene™ and Tempus™ blood collection tubes, and each comes with their own RNA purification method. In both systems the blood is immediately lysed when collected into the tube and RNA stabilized using proprietary reagents. Both systems enable minimal blood handling procedures thus minimizing the risk of inducing changes in gene expression through blood handling or processing. Because the RNA purification steps could influence the total RNA pool, we examined the impact of RNA isolation, using the PAXgene™ or Tempus™ method, on gene expression profiles.</p> <p>Results</p> <p>Using microarrays as readout of RNA from stimulated whole blood we found a common set of expressed transcripts in RNA samples from either PAXgene™ or Tempus™. However, we also found several to be uniquely expressed depending on the type of collection tube, suggesting that RNA purification methods impact results of differential gene expression profiling. Specifically, transcripts for several known PHA-inducible genes, including IFNγ, IL13, IL2, IL3, and IL4 were found to be upregulated in stimulated vs. control samples when RNA was isolated using the ABI Tempus™ method, but not using the PAXgene™ method (p < 0.01, FDR corrected). Sequenom Quantiative Gene Expression (QGE) (SanDiego, CA) measures confirmed IL2, IL4 and IFNγ up-regulation in Tempus™ purified RNA from PHA stimulated cells while only IL2 was up-regulated using PAXgene™ purified (p < 0.05).</p> <p>Conclusion</p> <p>Here, we demonstrate that peripheral blood RNA isolation methods can critically impact differential expression results, particularly in the clinical setting where fold-change differences are typically small and there is inherent variability within biological cohorts. A modified method based upon the Tempus™ system was found to provide high yield, good post-hybridization array quality, low variability in expression measures and was shown to produce differential expression results consistent with the predicted immunologic effects of PHA stimulation.</p

Allosteric Modulation of the HIV-1 gp120-gp41 Association Site by Adjacent gp120 Variable Region 1 (V1) N-Glycans Linked to Neutralization Sensitivity

The HIV-1 gp120-gp41 complex, which mediates viral fusion and cellular entry, undergoes rapid evolution within its external glycan shield to enable escape from neutralizing antibody (NAb). Understanding how conserved protein determinants retain functionality in the context of such evolution is important for their evaluation and exploitation as potential drug and/ or vaccine targets. In this study, we examined how the conserved gp120-gp41 association site, formed by the N- and Cterminal segments of gp120 and the disulfide-bonded region (DSR) of gp41, adapts to glycan changes that are linked to neutralization sensitivity. To this end, a DSR mutant virus (K601D) with defective gp120-association was sequentially passaged in peripheral blood mononuclear cells to select suppressor mutations. We reasoned that the locations of suppressors point to structural elements that are functionally linked to the gp120-gp41 association site. In culture 1, gp120 association and viral replication was restored by loss of the conserved glycan at Asn136 in V1 (T138N mutation) inconjunction with the L494I substitution in C5 within the association site. In culture 2, replication was restored with deletion of the N139INN sequence, which ablates the overlapping Asn141-Asn142-Ser-Ser potential N-linked glycosylation sequons inV1, in conjunction with D601N in the DSR. The 136 and 142 glycan mutations appeared to exert their suppressive effects by altering the dependence of gp120-gp41 interactions on the DSR residues, Leu593, Trp596 and Lys601. The 136 and/or 142glycan mutations increased the sensitivity of HIV-1 pseudovirions to the glycan-dependent NAbs 2G12 and PG16, and also pooled IgG obtained from HIV-1-infected individuals. Thus adjacent V1 glycans allosterically modulate the distal gp120-gp41 association site. We propose that this represents a mechanism for functional adaptation of the gp120-gp41 association site to an evolving glycan shield in a setting of NAb selection

A.N. Kolmogorov’s defence of Mendelism

In 1939 N.I. Ermolaeva published the results of an experiment which repeated parts of Mendel’s classical experiments. On the basis of her experiment she concluded that Mendel’s principle that self-pollination of hybrid plants gave rise to segregation proportions 3:1 was false. The great probability theorist A.N. Kolmogorov reviewed Ermolaeva’s data using a test, now referred to as Kolmogorov’s, or Kolmogorov-Smirnov, test, which he had proposed in 1933. He found, contrary to Ermolaeva, that her results clearly confirmed Mendel’s principle. This paper shows that there were methodological flaws in Kolmogorov’s statistical analysis and presents a substantially adjusted approach, which confirms his conclusions. Some historical commentary on the Lysenko-era background is given, to illuminate the relationship of the disciplines of genetics and statistics in the struggle against the prevailing politically-correct pseudoscience in the Soviet Union. There is a Brazilian connection through the person of Th. Dobzhansky