Non-adherence and non-persistence with antiretroviral treatment in Belgium: A real-world evaluation using a pharmacy database, 2018-2021.

PURPOSE: To evaluate the real-world rates of non-adherence and non-persistence to antiretroviral therapy (ART) among treatment-naïve adult patients with HIV after a 12-month follow-up period in Belgium. METHODS: A retrospective analysis of longitudinal pharmacy claims was conducted using the Pharmanet database from January 1, 2018, to December 31, 2021. Non-adherence was assessed over 12 months and reported as the proportion of days covered below the 80% threshold. Non-persistence was defined as the first 90-day gap in treatment between the two types of ART dispensed. Poisson regression with robust standard error and Cox proportional hazard models were used to assess the factors associated with non-adherence and non-persistence, respectively. RESULTS: Overall, 2999 patients were initiated on ART between 2018 and 2021. After a 12-month follow-up, the proportions of non-adherence and non-persistence were 35.6% and 15.9%, respectively in 2018, and decreased to 18.7% and 6.8%, respectively in 2021. Non-adherence was higher among women, Brussels residents, and those receiving multiple-tablet regimens (MTRs). Similarly, the prevalence of non-persistence was higher among women and MTR recipients. CONCLUSION: Among treatment-naïve adults with HIV in Belgium, non-adherence, and non-persistence to ART showed improvement over the study period but remained at high levels. Disparities were observed by sex and between geographical regions. Prioritizing strategies targeting women in Brussels and facilitating the transition from MTRs to single-tablet regimens should be emphasized optimize adherence to ART in Belgium.

Scopy: an integrated negative design python library for desirable HTS/VS database design.

BACKGROUND: High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION: Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

DockCoV2: a drug database against SARS-CoV-2.

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

High hepatitis C treatment uptake among people with recent drug dependence in New South Wales, Australia.

BACKGROUND & AIMS: High HCV treatment uptake among people at most risk of transmission is essential to achieve elimination. We aimed to characterise subpopulations of people with HCV based on drug dependence, to estimate direct-acting antiviral (DAA) uptake in an unrestricted treatment era, and to evaluate factors associated with treatment uptake among people with recent drug dependence. METHODS: HCV notifications in New South Wales, Australia (1995-2017) were linked to opioid agonist therapy (OAT), hospitalisations, incarcerations, HIV notifications, deaths, and prescription databases. Drug dependence was defined as hospitalisation due to injectable drugs or receipt of OAT, with indicators in 2016-2018 considered recent. Records were weighted to account for spontaneous clearance. Logistic regression was used to analyse factors associated with treatment uptake among those with recent drug dependence. RESULTS: 57,467 people were estimated to have chronic HCV throughout the DAA era. Treatment uptake was highest among those with recent (47%), compared to those with distant (38%), and no (33%) drug dependence. Among those with recent drug dependence, treatment was more likely among those with HIV (adjusted odds ratio [aOR] 1.71; 95% CI 1.24-2.36), recent incarceration (aOR 1.10; 95% CI 1.01-1.19), and history of alcohol use disorder (aOR 1.22; 95% CI 1.13-1.31). Treatment was less likely among women (aOR 0.78; 95% CI 0.72-0.84), patients of Indigenous ethnicity (aOR 0.75; 95% CI 0.69-0.81), foreign-born individuals (aOR 0.86; 95% CI 0.78-0.96), those with outer-metropolitan notifications (aOR 0.90; 95% CI 0.82-0.98), HBV coinfection (aOR 0.69; 95% CI 0.59-0.80), and >1 recent hospitalisation (aOR: 0.91; 95% CI 0.84-0.98). CONCLUSIONS: These data provide evidence of high DAA uptake among people with recent drug dependence, including those who are incarcerated. Enhancing this encouraging initial uptake among high-risk populations will be essential to achieve HCV elimination. LAY SUMMARY: To facilitate HCV elimination, those at highest risk of infection and transmission are a treatment priority. This study shows the successes of Australia's universal provision of DAA therapy in reducing the barriers to treatment which have historically persisted among people who inject drugs. Despite higher DAA therapy uptake among those with recent drug dependence, gaps remain. Strategies which aim to reduce marginalisation and increase treatment uptake to ensure equitable HCV elimination must be advanced.

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied. Recent works achieved promising results using machine learning. The presented work focuses on machine learning methods that use drug profiles for making predictions and use features from multiple data sources. We argue that despite promising results, existing works have limitations, especially regarding flexibility in experimenting with different data sets and/or predictive models. We suggest to address these limitations by generalization of the key principles used by the state of the art. Namely, we explore effects of: (1) using knowledge graphs-machine-readable interlinked representations of biomedical knowledge-as a convenient uniform representation of heterogeneous data; and (2) casting ADR prediction as a multi-label ranking problem. We present a specific way of using knowledge graphs to generate different feature sets and demonstrate favourable performance of selected off-the-shelf multi-label learning models in comparison with existing works. Our experiments suggest better suitability of certain multi-label learning methods for applications where ranking is preferred. The presented approach can be easily extended to other feature sources or machine learning methods, making it flexible for experiments tuned toward specific requirements of end users. Our work also provides a clearly defined and reproducible baseline for any future related experiments.

Web-based drug repurposing tools: a survey.

Drug repurposing (a.k.a. drug repositioning) is the search for new indications or molecular targets distinct from a drug's putative activity, pharmacological effect or binding specificities. With the ever-increasing rates of termination of drugs in clinical trials, drug repositioning has risen as one of the effective solutions against the risk of drug failures. Repositioning finds a way to reverse the grim but real trend that Eroom's law portends for the pharmaceutical and biotech industry, and drug discovery in general. Further, the advent of high-throughput technologies to explore biological systems has enabled the generation of zeta bytes of data and a massive collection of databases that store them. Computational analytics and mining are frequently used as effective tools to explore this byzantine series of biological and biomedical data. However, advanced computational tools are often difficult to understand or use, thereby limiting their accessibility to scientists without a strong computational background. Hence it is of great importance to build user-friendly interfaces to extend the user-base beyond computational scientists, to include life scientists who may have deeper chemical and biological insights. This survey is focused on systematically presenting the available Web-based tools that aid in repositioning drugs.

Drug knowledge bases and their applications in biomedical informatics research.

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.

Major Cardiac-Psychiatric Drug-Drug Interactions: a Systematic Review of the Consistency of Drug Databases.

PURPOSE: Major depressive disorder (MDD) and anxiety disorders (AD) are both highly prevalent among individuals with arrhythmia, ischemic heart disease, heart failure, hypertension, and dyslipidemia. There should be increased support for MDD and AD diagnosis and treatment in individuals with cardiac diseases, because treatment rates have been low. However, cardiac-psychiatric drug interaction can make pharmacologic treatment challenging. METHODS: The objective of the present systematic review was to investigate cardiac-psychiatric drug interactions in three different widely used pharmacological databases (Micromedex, Up to Date, and ClinicalKey). RESULTS: Among 4914 cardiac-psychiatric drug combinations, 293 significant interactions were found (6.0%). When a problematic interaction is detected, it may be easier to find an alternative cardiac medication (32.6% presented some interaction) than a psychiatric one (76.9%). Antiarrhythmics are the major class of concern. The most common problems produced by these interactions are related to cardiotoxicity (QT prolongation, torsades de pointes, cardiac arrest), increased exposure of cytochrome P450 2D6 (CYP2D6) substrates, or reduced renal clearance of organic cation transporter 2 (OCT2) substrates and include hypertensive crisis, increased risk of bleeding, myopathy, and/or rhabdomyolysis. CONCLUSION: Unfortunately, there is considerable inconsistency among the databases searched, such that a clinician's discretion and clinical experience remain invaluable tools for the management of patients with comorbidities present in psychiatric and cardiac disorders. The possibility of an interaction should be considered. With a multidisciplinary approach, particularly involving a pharmacist, the prescriber should be alerted to the possibility of an interaction. MDD and AD pharmacologic treatment in cardiac patients could be implemented safely both by cardiologists and psychiatrists. TRIAL REGISTRATION: PROSPERO Systematic Review Registration Number: CRD42018100424.

Evaluation of the Expression Profile of Antibiotic-Induced Thrombocytopenia Using the Japanese Adverse Drug Event Report Database.

Drug-induced thrombocytopenia (DITP) can be triggered by antibiotics; however, the details remain unclear. Here, we evaluated the expression profiles of DITP using the Japanese Adverse Drug Event Report (JADER) database. We analyzed reports of DITP between April 2004 and January 2021 from the JADER database. The reporting odds ratio (ROR) and 95% confidence interval (CI) were used to detect DITP signals. Factors thought to affect DITP, such as male sex and an age of at least 60 years, were added as covariates. We evaluated the time-to-onset profile and hazard type using the Weibull shape parameter. The JADER database contained 1,048,576 reports. Twelve of 60 antibiotics showed signals for DITP; the RORs (95% CIs) for ampicillin/sulbactam, ceftazidime, cefozopran, ciprofloxacin, fluconazole, fos-fluconazole, linezolid, pazufloxacin, piperacillin/tazobactam, teicoplanin, trimethoprim/sulfamethoxazole, and voriconazole were 1.75 (1.41-2.16), 1.77 (1.42-2.18), 1.35 (1.06-1.72), 2.56 (2.19-2.98), 1.93 (1.67-2.23), 2.08 (1.76-2.46), 5.29 (2.73-9.60), 1.92 (1.51-2.41), 1.54 (1.05-2.19), 1.47 (1.16-1.84), 1.92 (1.73-2.14), and 2.32 (1.59-3.30), respectively. In multiple logistic regression analysis, 7 and 6 antibiotics were detected for the factors age and male sex, respectively. The median times-to-onset of DITP for ciprofloxacin (oral treatment), fluconazole, linezolid, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole were 91, 91, 11.5, 10, and 9 days, respectively. Furthermore, the 95% CI of the Weibull shape parameter ß for these antibiotics was above and excluded 1, indicating that the antibiotics were the wear out failure type. We revealed the expression profiles of DITP following treatment with 12 antibiotics.

A review of network-based approaches to drug repositioning.

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Overlap matrix completion for predicting drug-associated indications.

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.

Diversifying chemical libraries with generative topographic mapping.

Generative topographic mapping was used to investigate the possibility to diversify the in-house compounds collection of Boehringer Ingelheim (BI). For this purpose, a 2D map covering the relevant chemical space was trained, and the BI compound library was compared to the Aldrich-Market Select (AMS) database of more than 8M purchasable compounds. In order to discover new (sub)structures, the "AutoZoom" tool was developed and applied in order to analyze chemotypes of molecules residing in heavily populated zones of a map and to extract the corresponding maximum common substructures. A set of 401K new structures from the AMS database was retrieved and checked for drug-likeness and biological activity.

Using Supervised Learning Methods to Develop a List of Prescription Medications of Greatest Concern during Pregnancy.

INTRODUCTION: Women and healthcare providers lack adequate information on medication safety during pregnancy. While resources describing fetal risk are available, information is provided in multiple locations, often with subjective assessments of available data. We developed a list of medications of greatest concern during pregnancy to help healthcare providers counsel reproductive-aged and pregnant women. METHODS: Prescription drug labels submitted to the U.S. Food and Drug Administration with information in the Teratogen Information System (TERIS) and/or Drugs in Pregnancy and Lactation by Briggs & Freeman were included (N = 1,186 medications; 766 from three data sources, 420 from two). We used two supervised learning methods ('support vector machine' and 'sentiment analysis') to create prediction models based on narrative descriptions of fetal risk. Two models were created per data source. Our final list included medications categorized as 'high' risk in at least four of six models (if three data sources) or three of four models (if two data sources). RESULTS: We classified 80 prescription medications as being of greatest concern during pregnancy; over half were antineoplastic agents (n = 24), angiotensin converting enzyme inhibitors (n = 10), angiotensin II receptor antagonists (n = 8), and anticonvulsants (n = 7). DISCUSSION: This evidence-based list could be a useful tool for healthcare providers counseling reproductive-aged and pregnant women about medication use during pregnancy. However, providers and patients may find it helpful to weigh the risks and benefits of any pharmacologic treatment for both pregnant women and the fetus when managing medical conditions before and during pregnancy.

Validation of drug prescription records for senior patients in Alberta's Tomorrow Project: Assessing agreement between two population-level administrative pharmaceutical databases in Alberta, Canada.

PURPOSE: To assess agreement between the Pharmaceutical Information Network (PIN), a newly implemented medication data repository in Alberta, Canada, and the Alberta Blue Cross (ABC) database, a long established database with medication records of all senior patients in Alberta. METHODS: PIN data (2008-2015) were cross-validated with ABC medication records for senior participants (older than 65 years old) in Alberta's Tomorrow Project (ATP), a longitudinal cohort study in Alberta. The completeness and accuracy of PIN were respectively calculated as the percentage of ABC records coexisting (concordant) in PIN and the percentage of concordant records having mutually agreeable information on drug quantity. Generalized linear models were used to examine potential association of PIN completeness and accuracy with sociodemographic factors. RESULTS: A total of 1 218 191 drug prescription records from 13 143 ATP participants were captured by PIN and ABC in 2008-2015, among which 91.6% were from PIN, 82.5% from ABC, and 74.2% coexisted in PIN and ABC. The overall completeness of PIN in capturing ABC medication records was 89.9%, with small variations (less than ±5%) across types of drugs. The completeness of PIN was improved on average by 1.3% annually over time (P < .001). PIN had 100% accuracy as defined by drug quantity data agreeable with ABC records. No significant associations were observed with age, sex, ethnicity, rural/urban areas, and socioeconomic status of the participants. CONCLUSIONS: Cross-validated with the ABC dataset, our study showed that irrespective of drug type, PIN has a fairly good completeness (approximately 90%) and accuracy (100%) in capturing the ABC claimed medications for senior patients in Alberta.

The utilization and challenges of Japan's MID-NET® medical information database network in postmarketing drug safety assessments: A summary of pilot pharmacoepidemiological studies.

PURPOSE: To examine the potential role of Medical Information Database Network (MID-NET® ), a newly established Japanese medical information database network, in postmarketing drug safety assessments through the characterization of its advantages and limitations in five pilot studies. METHODS: The pilot studies were designed to address three major objectives in postmarketing drug safety assessments, ie, the examination of actual drug utilization, the impact of safety-related regulatory actions, and drug-associated risks. The five studies were conducted on the following topics: (a) utilization of codeine-containing products and its relationship with respiratory depression, (b) impact of a Dear Healthcare Professional letter on hypocalcemia incidence associated with denosumab (Ranmark® ) use, (c) risk of acute myocardial infarction associated with antidiabetic drug use, (d) risk of glucose metabolism disorders associated with atypical antipsychotic drug use, and (e) prospective monitoring of abnormal laboratory test results during new drug prescriptions. RESULTS: The pilot studies were successfully conducted and demonstrated the value of MID-NET® in postmarketing drug safety assessments. In particular, the ability to utilize laboratory test results as objective clinical indicators is a major advantage of this database. Potential limitations include a relatively small sample size and a lack of patient-level data linkages among hospitals. CONCLUSIONS: MID-NET® was confirmed to be a valuable database for postmarketing drug safety assessments. The use of laboratory test results to define clinical outcomes may allow more objective and accurate analyses to be conducted. These studies furthered our understanding of the characteristics of MID-NET® , including its advantages and limitations.

A systematic review of methods for determining cross-sectional active medications using pharmacy databases.

PURPOSE: Pharmacy dispensing databases are often used to identify patients' medications at a particular time point, for example to measure prescribing quality or the impact of medication use on clinical outcomes. We performed a systematic review of studies that examined methods to assess medications in use at a specific point in time. METHODS: Comprehensive literature search to identify studies that compared active medications identified using pharmacy databases to medications identified using nonautomated data sources. Two investigators independently reviewed abstracts and full-text material. RESULTS: Of 496 studies screened, 29 studies evaluating 50 comparisons met inclusion criteria. Twenty-nine comparisons evaluated fixed look-back period approaches, defining active medications as those filled in a specified period prior to the index date (range 84-730 days). Fourteen comparisons evaluated medication-on-hand approaches, defining active medications as those for which the most recent fill provided sufficient supply to last through the study index date. Sensitivity ranged from 48% to 93% for fixed look-back period approaches and 35% to 97% for medication-on-hand approaches. Interpretation of comparative performance of methods was limited by use of different reference sources, target medication classes, and databases across studies. In four studies with head-to-head comparisons of these methods, sensitivity of the medication-on-hand approach was a median of 7% lower than the corresponding fixed look-back approach. CONCLUSIONS: The reported accuracy of methods for identifying active medications using pharmacy databases differs greatly across studies. More direct comparisons of common approaches are needed to establish the accuracy of methods within and across populations, medication classes, and databases.

Fluoroquinolone consumption and Escherichia coli resistance in Japan: an ecological study.

BACKGROUND: The frequency of antimicrobial resistance has steadily increased worldwide, induced by inappropriate use of antibiotics in a variety of settings. We analyzed the ecological correlation between fluoroquinolone consumption and levofloxacin resistance in Escherichia coli in Japan. METHODS: We collected information on cases of E. coli resistant to levofloxacin in 2015-2016 in all 47 prefectures from the Japan Nosocomial Infections Surveillance system. Information on fluoroquinolone consumption was obtained from pharmaceutical sales data. To address potential confounding, we also collected information on the number of physicians, nurses, and medical facilities per 100,000 individuals. RESULTS: We identified higher fluoroquinolone consumption and higher resistance in western prefectures, and lower consumption and resistance in eastern prefectures. Multivariate analysis identified a positive correlation between fluoroquinolone consumption and levofloxacin resistance in both 2015 and 2016. CONCLUSIONS: Fluoroquinolone consumption and levofloxacin-resistant E. coli are potentially associated on a nationwide scale. The relationship between the two must be elucidated using additional studies with different epidemiological designs, so that any possible counter-measures, including alternative prescription, can be considered in the future.

Real-world assessment of topical glaucoma medication persistence rates based on national pharmaceutical claim data in a defined population.

IMPORTANCE: The rate and determinants of persistence to topical glaucoma medications are important for identifying patients at high risk of discontinuing medications and designing targeted approaches to improve persistence. BACKGROUND: To evaluate the rate and determinants of persistence to topical glaucoma medications among middle-aged and older Australian adults. DESIGN: Population-based cohort study. PARTICIPANTS: Participants in need of persistent topical glaucoma medications in the 45 and Up Study. METHODS: The 45 and Up Study is a large-scale population-based cohort study. Participants were classified as needing persistent topical glaucoma medications if at least three claims with related prescriptions were recorded. Persistence was defined as topical glaucoma medications were filled within 90 days. MAIN OUTCOME MEASURES: The rates and determinants of medication persistence at 2-year follow-up. RESULTS: A total of 12 899 patients requiring persistent topical glaucoma medications were identified. Among them, 9019 (69.9%) had persisted with their glaucoma medications for at least 2 years. Multiple logistic regression analysis documented significant effects of patient-related factors (gender, socioeconomic status, language spoken at home, lifestyle and comorbidities) and drug-related factors (total number and drug class) on the persistence rate. Those most at risk groups of non-persistence were those patients living in remote areas (odds ratio, OR: 0.59, 95% confidence interval, CI: 0.37-0.92), having family income over 70 000 AUD/year (OR: 0.53, 95% CI: 0.45-0.62), speaking other languages at home (OR: 0.61, 95% CI: 0.53-0.68), and using cholinergic classes of medications (OR: 0.55, 95% CI: 0.38-0.79). CONCLUSIONS AND RELEVANCE: Our data has shown a medium level of persistence to topical glaucoma medication among middle-aged and older Australian adults. However, efforts are still needed to improve the rate of persistence.