Rapid eye movement sleep (REMS) has been linked with spatial and emotional memory consolidation. However, establishing direct causality between neural activity during REMS and memory consolidation has proven difficult because of the transient nature of REMS and significant caveats associated with REMS deprivation techniques. In mice, we optogenetically silenced medial septum γ-aminobutyric acid-releasing (MS(GABA)) neurons, allowing for temporally precise attenuation of the memory-associated theta rhythm during REMS without disturbing sleeping behavior. REMS-specific optogenetic silencing of MS(GABA) neurons selectively during a REMS critical window after learning erased subsequent novel object place recognition and impaired fear-conditioned contextual memory. Silencing MS(GABA) neurons for similar durations outside REMS episodes had no effect on memory. These results demonstrate that MS(GABA) neuronal activity specifically during REMS is required for normal memory consolidation.

Rapid-Eye Movement (REM) sleep correlates with neuronal activity in the brainstem, basal forebrain and lateral hypothalamus (LH). LH melanin-concentrating hormone (MCH)-expressing neurons are active during sleep, however, their action on REM sleep remains unclear. Using optogenetic tools in newly-generated Tg(Pmch-Cre) mice, we found that acute activation of MCH neurons (ChETA, SSFO) at the onset of REM sleep extended the duration of REM, but not non-REM sleep episode. In contrast, their acute silencing (eNpHR3.0, ArchT) reduced the frequency and amplitude of hippocampal theta rhythm, without affecting REM sleep duration. In vitro activation of MCH neuron terminals induced GABAA-mediated inhibitory post-synaptic currents (IPSCs) in wake-promoting histaminergic neurons of the tuberomammillary nucleus (TMN), while in vivo activation of MCH neuron terminals in TMN or medial septum also prolonged REM sleep episodes. Collectively, these results suggest that activation of MCH neurons maintains REM sleep, possibly through inhibition of arousal circuits in the mammalian brain.

Background Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. Objective To provide recommendations for systematic evaluation of evidence from the scientific literature, drug product labeling, and regulatory documents with respect to DDIs for clinical decision support. Methods A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 15 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. Results We developed expert-consensus answers to three key questions: 1) What is the best approach to evaluate DDI evidence?; 2) What evidence is required for a DDI to be applicable to an entire class of drugs?; and 3) How should a structured evaluation process be vetted and validated? Conclusion Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug information systems that implement these recommendations should be able to provide higher quality information about DDIs in drug compendia and clinical decision support tools.

Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009–2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40–64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

INTRODUCTION Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research. METHODS In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration. RESULTS Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ≥ 0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event. CONCLUSIONS Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.

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PCR amplification using COnsensus DEgenerate Hybrid Oligonucleotide Primers (CODEHOPs) has proven to be highly effective for identifying unknown pathogens and characterizing novel genes. We describe iCODEHOP; a new interactive web application that simplifies the process of designing and selecting CODEHOPs from multiply-aligned protein sequences. iCODEHOP intelligently guides the user through the degenerate primer design process including uploading sequences, creating a multiple alignment, deriving CODEHOPs and calculating their annealing temperatures. The user can quickly scan over an entire set of degenerate primers designed by the program to assess their relative quality and select individual primers for further analysis. The program displays phylogenetic information for input sequences and allows the user to easily design new primers from selected sequence sub-clades. It also allows the user to bias primer design to favor specific clades or sequences using sequence weights. iCODEHOP is freely available to all interested researchers at https://icodehop.cphi.washington.edu/i-codehop-context/Welcome.