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Nonadherence to medication regimens is a worldwide challenge; adherence rates range from 38 to 57 % in older populations with an average rate of less than 45 % and nonadherence contributes to adverse drug events, increased emergency visits and hospitalisations. Accurate measurement of medication adherence is important in terms of both research and clinical practice. However, the identification of an objective approach to measure nonadherence is still an ongoing challenge. The aim of this Position Paper is to describe the advantages and disadvantages of the known medication adherence tools (self-report, pill count, medication event monitoring system (MEMS) and electronic monitoring devices, therapeutic drug monitoring, pharmacy records based on pharmacy refill and pharmacy claims databases) to provide the appropriate criteria to assess medication adherence in older persons. To the best of our knowledge, no gold standard has been identified in adherence measurement and no single method is sufficiently reliable and accurate. A combination of methods appears to be the most suitable. Secondly, adherence assessment should always consider tools enabling polypharmacy adherence assessment. Moreover, it is increasingly evident that adherence, as a process, has to be assessed over time and not just at one evaluation time point (drug discontinuation). When cognitive deficits or functional impairments may impair reliability of adherence assessment, a comprehensive geriatric assessment should be performed and the caregiver involved. Finally, studies considering the possible implementation in clinical practice of adherence assessment tools validated in research are needed.

Much of the literature on trends and factors affecting biopharmaceutical innovation has focused overwhelmingly on the development and approval of never-before approved drugs and biologics. Little attention has been paid to new uses for already-approved compounds, which can be an important form of innovation. This paper aimed to determine and analyze recent trends in the number and type of new or modified US indication approvals for drugs and biologics. We also examine regulatory approval-phase times for new-use efficacy supplements and compare them to approval-phase times for original-use approvals over the same period. We developed a data set of efficacy supplements approved by the US Food and Drug Administration (FDA) from 1998 to 2011 that includes information on the type, approval-phase time (time from submission to the FDA of an application for marketing approval to approval of the application), and FDA therapeutic-significance rating for the approved application, which we obtained from an FDA Web site. This data set was merged with a Tufts Center for the Study of Drug Development (CSDD) data set of US new drug and biologics approvals. We developed descriptive statistics on trends in the number and type of new-use efficacy supplements, on US regulatory approval-phase times for the supplements, and on original new drug and biologics approvals over the study period and for the time from original- to new-use approval. The total number of new-use efficacy-supplement approvals did not exhibit a marked trend, but the number of new pediatric-indication approvals increased substantially. Approval-phase times for new-use supplements varied by therapeutic class and FDA therapeutic-significance rating. Mean approval-phase times were highest for central nervous system compounds (13.8 months) and lowest for antineoplastics (8.9 months). The mean time from original to supplement approval was substantially longer for new pediatric indications than for other new uses. Mean approval-phase time during the study period for applications that received a standard review rating from the FDA was substantially shorter for supplements compared to original uses, but the differences for applications that received a priority review rating from the FDA were negligible. Development of and regulatory approval for new uses of already-approved drugs and biologics is an important source of innovation by biopharmaceutical firms. Despite rising development costs, the output of new-use approvals has remained stable in recent years, driven largely by the pursuit of new pediatric indications. FDA approval-phase times have generally declined substantially for all types of applications since the mid-1990s following legislation that provided a new source of income for the agency. However, while the resources needed to review supplemental applications are likely lower in general than for original-use approvals, the approval-phase times for important new uses are no lower than for important original-use applications.

Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding herbal materials. We used an association rule algorithm combined with network analysis and found useful and informative relationships between the symptoms and medicines.

Introduction Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. Objectives Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. Methods A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. Results There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. Conclusion Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources.

Background Multiple databases provide ratings of drug-drug interactions. The ratings are often based on different criteria and lack background information on the decision making process. User acceptance of rating systems could be improved by providing a transparent decision path for each category. Methods We rated 200 randomly selected potential drug-drug interactions by a transparent decision model developed by our team. The cases were generated from ward round observations and physicians’ queries from an outpatient setting. We compared our ratings to those assigned by a senior clinical pharmacologist and by a standard interaction database, and thus validated the model. Results The decision model rated consistently with the standard database and the pharmacologist in 94 and 156 cases, respectively. In two cases the model decision required correction. Following removal of systematic model construction differences, the DM was fully consistent with other rating systems. Conclusion The decision model reproducibly rates interactions and elucidates systematic differences. We propose to supply validated decision paths alongside the interaction rating to improve comprehensibility and to enable physicians to interpret the ratings in a clinical context.

Purpose It is well documented that drug-drug interaction databases (DIDs) differ substantially with respect to classification of drug-drug interactions (DDIs). The aim of this study was to study online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation of the five most commonly used open access English language-based online DIDs and the three most commonly used subscription English language-based online DIDs in the literature. Methods We conducted a systematic literature search to identify the five most commonly used open access and the three most commonly used subscription DIDs in the medical literature. The following parameters were assessed for each of the databases: Ownership, classification of interactions, primary information sources, and staff qualification. We compared the overall proportion of yes/no answers from open access databases and subscription databases by Fisher’s exact test—both prior to and after requesting missing information. Results Among open access DIDs, 20/60 items could be verified from the webpage directly compared to 24/36 for the subscription DIDs ( p  = 0.0028). Following personal request, these numbers rose to 22/60 and 30/36, respectively ( p  < 0.0001). For items within the “classification of interaction” domain, proportions were 3/25 versus 11/15 available from the webpage ( P  = 0.0001) and 3/25 versus 15/15 ( p  < 0.0001) available upon personal request. Conclusion Available information on online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation varies substantially among various DIDs. Open access DIDs had a statistically lower score on parameters assessed.

The productivity of the pharmaceutical industry has been widely discussed in recent years, particularly with regard to concerns that substantial expenditures on research and development have failed to translate into approved drugs. Various analyses of this productivity challenge have focused on aspects such as attrition rates at particular clinical phases or the physicochemical properties of drug candidates, but relatively little attention has been paid to how the industry has performed from the standpoint of the choice of therapeutic mechanisms and their intended indications. This article examines what the pharmaceutical industry has achieved in this respect by analysing comprehensive industry-wide data on the mechanism-indication pairs that have been investigated during the past 20 years. Our findings indicate several points and trends that we hope will be useful in understanding and improving the productivity of the industry, including areas in which the industry has had substantial success or failure and the relative extent of novelty in completed and ongoing projects.

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network. The directions of most human protein-protein interactions (PPIs) remain unknown. Here, the authors use cancer genomic and drug response data to infer the direction of signal flow in the human PPI network and show that the directed network improves drug target and cancer driver gene prioritization.

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical 'omics' data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that 'omics' data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer's disease.