Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data.

One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.

Standardization Consensus of a Hospital Drug Database: An Efficient Tool.

BACKGROUND: Although the electronic prescribing software is the same for all hospitals of a regional health service, each has its own drug database, which it is responsible for maintaining. The aim of this study was to develop a consensus to standardize the hospital drug database of the electronic prescribing software, and to apply this tool to the electronic prescribing system of an oncology outpatient clinic of a Spanish tertiary-level hospital. Additionally, we sought to analyze the impact of the implemented actions on the health care services provided. METHODS: This was a prospective study carried out over a period of 15 months by a group of pharmacists representing all Organizational Integrated Management Systems of a regional health service, and coordinated by the General Subdirectorate of Pharmaceuticals. RESULTS: A total of 500 drugs and 500 active pharmaceutical ingredients included in the hospital drug database were standardized to implement the electronic prescribing system in the oncology outpatient clinic. The implementation of such standardization process yielded a 70% decrease in medication errors. In the satisfaction survey concerning the usefulness of the tall-man letters implemented in the electronic prescribing system, the interviewed doctors reported the highest levels of satisfaction. CONCLUSIONS: The creation of consensus documents to standardize the hospital drug database served to unify the information available in the regional hospital pharmacy services of an autonomous community. In addition, the implementation of the electronic prescribing system in the oncology outpatient clinic of a tertiary-level hospital resulted in a decrease in the number of medication errors.

Drug databases and their contributions to drug repurposing.

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab.

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.

Assessing the value of Western Cape Provincial Government health administrative data and electronic pharmacy records in ascertaining medicine use during pregnancy.

BACKGROUND: In African settings, where there is a high disease burden, there is a need to improve the science of documenting and analysing accurate information regarding medicine exposures in women immediately before and during pregnancy to assess the extent of use and safety in pregnant women and their unborn children. OBJECTIVES: To compare evidence of medicine use during pregnancy, as documented in paper-based clinical records (maternity case records (MCRs)) against electronic health information resources (Provincial Health Data Centre (PHDC)) and assess the level of concordance between the two as part of baseline investigations before piloting a provincial pregnancy exposure registry and birth defect surveillance system. The PHDC consolidates electronic clinical and pharmacy data. METHODS: A folder review of completed pregnancies between November 2013 and January 2016 was conducted on randomly selected MCRs from midwife-run obstetric units and a secondary maternity hospital in Cape Town, South Africa. Medication exposures in the MCR were captured and compared with a customised PHDC data extract. The type and timing of drug exposures were compared. Total exposures were compiled from all data sources. RESULTS: Two hundred and six MCRs from three facilities were sampled: 83 women had documented antiretroviral therapy (ART) exposure; all but 1 (1%) had been recorded in the PHDC extract. There was no evidence of ART use in the MCRs of 4 (5%) cases, despite evidence in the PHDC. There were imprecise drug names in the MCRs of 14 (17%) ART patients, discordant dates of onset between the MCRs and PHDC extracts in 10/83 (12%) and inaccurate medicine names and incorrect dates in 1 (1%) case each. Nine of 10 (90%) women who were administered antituberculosis medication were recorded in the PHDC extract. Ten of 21 (48%) isoniazid preventive therapy treatments appeared in the MCRs and PHDC; 9 (42%) in the PHDC only and 2 (10%) in the MCRs only. Half (n=18/36) of all antibiotic use was reflected only in the MCRs, while 13/36 (36%) appeared only in the PHDC extract. In the former cases, antibiotics used for treatment of sexually transmitted infections and urinary tract infections were dispensed from ward stock and not captured electronically. Antibiotics reflected only in the PHDC were either dispensed at a referral facility or before the first recorded antenatal clinic visit. Folic acid and iron were mostly documented in the MCR only (n=79/99 (80%) and n=107/128 (84%), respectively). However, analgesics and antihistamines more often appeared in the PHDC extract only (n=11/16 (73%) and n=5/5 (100%), respectively). CONCLUSIONS: The PHDC extract provided a better and more complete reflection of chronic drug exposures compared with the MCRs, especially when women sought care at facilities other than the antenatal care unit where they first attended, or when exposures occurred before the initial antenatal visit. The exception was antibiotics dispensed from ward stock to treat sexually transmitted and urinary tract infections.

Clinical Pharmacology: A Comprehensive Drug Reference.

This column discusses the point-of-care tool Clinical Pharmacology. This review is primarily intended for newer health sciences librarians who are learning about drug references and clinical decision-making support systems or health sciences librarians making collection development decisions, although any librarian will find this review useful. A sample search will be provided to highlight the database's unique features as well as a comparison to other resources.

Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.

PURPOSE: To evaluate the performance of delirium-identification algorithms in administrative claims and drug utilization data. METHODS: We used data from a prospective study of 184 older adults who underwent aortic valve replacement at a single academic medical center to evaluate the following delirium-identification algorithms: (1) International Classification of Diseases (ICD) diagnosis codes for delirium; (2) antipsychotics use; (3) either ICD diagnosis codes or antipsychotics use; and (4) both ICD diagnosis codes and antipsychotics use. These algorithms were evaluated against a validated bedside assessment, the Confusion Assessment Method, and a validated delirium severity scale, the CAM-S. RESULTS: Delirium occurred in 66 patients (36%), of which 14 (21%) had hyperactive or mixed features and 15 (23%) had severe delirium. ICD diagnosis codes for delirium were present in 15 patients (8%). Antipsychotics were used in 13 patients (7%). ICD diagnosis codes alone and antipsychotics use alone had comparable sensitivity (18% vs. 18%) and specificity (98% vs. 99%). Defining delirium using either ICD diagnosis codes or antipsychotics use, sensitivity improved to 30% with little change in specificity (97%). This algorithm showed higher sensitivity for hyperactive or mixed delirium (64%) and severe delirium (73%). Requiring both ICD diagnosis codes and antipsychotics use resulted in perfect specificity but low sensitivity (6%). CONCLUSION: Delirium-identification algorithms in claims data have low sensitivity and high specificity. Defining delirium using ICD diagnosis codes or antipsychotics use performs better than considering either type of information alone. This information should inform the design and interpretation of claims-based comparative effectiveness and safety research. Copyright © 2017 John Wiley & Sons, Ltd.

Building a drug development database: challenges in reliable data availability.

CONTEXT: Policy and legislative efforts to improve the biomedical innovation process must rely on a detailed and thorough analysis of drug development and industry output. OBJECTIVE: As part of our efforts to build a publicly-available database on the characteristics of drug development, we present work undertaken to test methods for compiling data from public sources. These initial steps are designed to explore challenges in data extraction, completeness and reliability. Specifically, filing dates for Investigational New Drugs (IND) applications with the U.S. Food and Drug Administration (FDA) were chosen as the initial objective data element to be collected. MATERIALS AND METHODS: FDA's Drugs@FDA database and the Federal Register (FR) were used to collect IND dates for the 587 New Molecular Entities (NMEs) approved between 1994 and 2014. When available, the following data were captured: approval date, IND number, IND date and source of information. RESULTS: At least one IND date was available for 445 (75.8%) of the 587 NMEs. The Drugs@FDA database provided IND dates for 303 (51.6%) NMEs and the FR contributed with 297 (50.6%) IND dates. Out of the 445 NMEs for which an IND date was obtained, 274 (61.6%) had more than one date reported. DISCUSSION: Key finding of this paper is a considerable inconsistency in reliably available or reported data elements, in this particular case, IND application filing dates as assembled from publicly-available sources. CONCLUSION: Our team will continue to focus on finding ways to collect relevant information to measure impact of drug innovation.

The prevalence of major potential drug-drug interactions at a University health centre pharmacy in Jamaica / Prevalencia de interacciones potenciales medicamento-medicamento en una farmacia de centro de salud universitario en Jamaica

Objective: To identify major potential drug-drug interactions (DDIs) on prescriptions filled at the University Health Centre Pharmacy, Mona Campus, Jamaica. Methods: This investigation utilised a cross-sectional analysis on all prescriptions with more than one drug that were filled at the Health Centre Pharmacy between November 2012 and February 2013. Potential DDIs were identified using the online Drug Interactions Checker database of Drugs.com. Results: During the period of the study, a total of 2814 prescriptions were analysed for potential DDIs. The prevalence of potential DDIs found during the study period was 49.82%. Major potential DDIs accounted for 4.7 % of the total number of interactions detected, while moderate potential DDIs and minor potential DDIs were 80.8 % and 14.5 % respectively. The three most frequently occurring major potential DDIs were amlodipine and simvastatin (n=46), amiloride and losartan (n=27) and amiloride and lisinopril (n=16). Conclusion: This study has highlighted the need for educational initiatives to ensure that physicians and pharmacists collaborate in an effort to minimise the risks to the patients. These interactions are avoidable for the most part, as the use of online tools can facilitate the selection of therapeutic alternatives or guide decisions for closer patient monitoring and thus reduce the risks of adverse events (AU)

Objetivo: Identificar interacciones potenciales medicamento-medicamento (DDI) en las prescripciones atendidas en la farmacia del centro de salud de Universitario del campus de Mona, Jamaica. Métodos: Esta investigación utilizó un análisis transversal de todas las prescripciones con más de un medicamento que fueron atendidas en el centro de salud universitario entre noviembre de 2012 y febrero de 2013. Las DDI potenciales se identificaban en el Drug Interactions Checker de la base de datos Drugs.com. Resultados: Durante el periodo de estudio, se analizaron a la busca de DDI un total de 2.814 prescripciones. La prevalencia de DDI potenciales encontrada durante el estudio fue del 49,82%. Las DDI major potenciales totalizaron el 4,7% del total de interaciones detectadas, mientras que las moderadas y minor fueron el 80,8% y el 14,5%, respectivamente. Las tres DDI potenciales major que aparecieron más frecuentemente fueron amlodipina y simvastatina (n=46), amilorida y losartan (n=27), y amilorida y lisinopril (n=16). Conclusión: Este estudio ha remarcado la necesidad de iniciativas educativas para asegurar que los médicos y los farmacéuticos colaboren en el esfuerzo de minimizar los riesgos de los pacientes. Estas interacciones eran evitables en su mayor parte, ya que el uso de herramientas online puede facilitar la selección de tratamientos alternativos o guiar decisiones para monitorizar más de cerca a los pacientes, y así reducir el riesgo de eventos adversos (AU)

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review.

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.

Auditing of SNOMED CT's Hierarchical Structure using the National Drug File - Reference Terminology.

With the ongoing development in the field of Medical Informatics, the availability of cross-references and the consistency of coverage between terminologies become critical requirements for clinical decision support. In this paper, we examine the possibility of developing a framework that highlights and exposes hierarchical incompatibilities between different medical terminologies in order to facilitate the process of achieving a sufficient level of consistency between terminologies. For the purpose of this research, we are working with the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT) and the National Drug File--Reference Terminology (NDF-RT)--a clinical terminology focused on drugs. For discovery of inconsistencies we built an automated tool.

Drug interactions involving antiepileptic drugs: assessment of the consistency among three drug compendia and FDA-approved labels.

Interactions of antiepileptic drugs (AEDs) with other substances may lead to adverse effects and treatment failure. To avoid such interactions, clinicians often rely on drug interaction compendia. Our objective was to compare the concordance for twenty-two AEDs among three drug interaction compendia (Micromedex, Lexi-Interact, and Clinical Pharmacology) and the US Food and Drug Administration-approved product labels. For each AED, the overall concordance among data sources regarding existence of interactions and their classification was poor, with less than twenty percent of interactions listed in all four sources. Concordance among the three drug compendia decreased with the fraction of the drug excreted unchanged and was greater for established inducers of hepatic drug-metabolizing enzymes than for the drugs that are not inducers (R-square=0.83, P<0.01). For interactions classified as contraindications, major, and severe, concordance among the four data sources was, in most cases, less than 30%. Prescribers should be aware of the differences between drug interaction sources of information for both older AEDs and newer AEDs, in particular for those AEDs which are not involved in hepatic enzyme-mediated interactions.

[MedDRA and its applications in statistical analysis of adverse events].

Safety assessment in clinical trials is dependent on an in-depth analysis of the adverse events to a great extent. However, there are difficulties in summary classification, data management and statistical analysis of the adverse events because of the different expressions on the same adverse events caused by regional, linguistic, ethnic, cultural and other differences. In order to ensure the normative expressions, it's necessary to standardize the terms in recording the adverse events. MedDRA (medical dictionary for regulatory activities) has been widely recommended and applied in the world as a powerful support for the adverse events reporting in clinical trials. In this paper, the development history, applicable scope, hierarchy structure, encoding term selection and standardized query strategies of the MedDRA is introduced. Furthermore, the practical process of adverse events encoding with MedDRA is proposed. Finally, the framework of statistical analysis about adverse events is discussed.

Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup's mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug-HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

Many approved drugs have bioactive analogs with different target annotations.

Close structural relationships between approved drugs and bioactive compounds were systematically assessed using matched molecular pairs. For structural analogs of drugs, target information was assembled from ChEMBL and compared to drug targets reported in DrugBank. For many drugs, multiple analogs were identified that were active against different targets. Some of these additional targets were closely related to known drug targets while others were not. Surprising discrepancies between reported drug targets and targets of close structural analogs were often observed. On one hand, the results suggest that hypotheses concerning alternative drug targets can often be formulated on the basis of close structural relationships to bioactive compounds that are easily detectable. It is conceivable that such obvious structure-target relationships are frequently not considered (or might be overlooked) when compounds are developed with a focus on a primary target and a few related (or undesired) ones. On the other hand, our findings also raise questions concerning database content and drug repositioning efforts.

Standardizing drug adverse event reporting data.

Normalizing data in the Adverse Event Reporting System (AERS), an FDA database, would improve the mining capacity of AERS for drug safety signal detection. In this study, we aim to normalize AERS and build a publicly available normalized Adverse drug events (ADE) data source.he drug information in AERS is normalized to RxNorm, a standard terminology source for medication. Drug class information is then obtained from the National Drug File - Reference Terminology (NDF-RT). Adverse drug events (ADE) are aggregated through mapping with the PT (Preferred Term) and SOC (System Organ Class) codes of MedDRA. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). The AERS-DM could provide more perspectives to mine AERS database for drug safety signal detection and could be used by research community in the data mining field.

Online drug databases: a new method to assess and compare inclusion of clinically relevant information.

BACKGROUND: Evidence-Based Practice requires health care decisions to be based on the best available evidence. The model "Information Mastery" proposes that clinicians should use sources of information that have previously evaluated relevance and validity, provided at the point of care. Drug databases (DB) allow easy and fast access to information and have the benefit of more frequent content updates. Relevant information, in the context of drug therapy, is that which supports safe and effective use of medicines. Accordingly, the European Guideline on the Summary of Product Characteristics (EG-SmPC) was used as a standard to evaluate the inclusion of relevant information contents in DB. OBJECTIVE: To develop and test a method to evaluate relevancy of DB contents, by assessing the inclusion of information items deemed relevant for effective and safe drug use. METHOD: Hierarchical organisation and selection of the principles defined in the EGSmPC; definition of criteria to assess inclusion of selected information items; creation of a categorisation and quantification system that allows score calculation; calculation of relative differences (RD) of scores for comparison with an "ideal" database, defined as the one that achieves the best quantification possible for each of the information items; pilot test on a sample of 9 drug databases, using 10 drugs frequently associated in literature with morbidity-mortality and also being widely consumed in Portugal. Main outcome measure Calculate individual and global scores for clinically relevant information items of drug monographs in databases, using the categorisation and quantification system created. RESULTS: A--Method development: selection of sections, subsections, relevant information items and corresponding requisites; system to categorise and quantify their inclusion; score and RD calculation procedure. B--Pilot test: calculated scores for the 9 databases; globally, all databases evaluated significantly differed from the "ideal" database; some DB performed better but performance was inconsistent at subsections level, within the same DB. CONCLUSION: The method developed allows quantification of the inclusion of relevant information items in DB and comparison with an "ideal database". It is necessary to consult diverse DB in order to find all the relevant information needed to support clinical drug use.

Scientific competency questions as the basis for semantically enriched open pharmacological space development.

Molecular information systems play an important part in modern data-driven drug discovery. They do not only support decision making but also enable new discoveries via association and inference. In this review, we outline the scientific requirements identified by the Innovative Medicines Initiative (IMI) Open PHACTS consortium for the design of an open pharmacological space (OPS) information system. The focus of this work is the integration of compound-target-pathway-disease/phenotype data for public and industrial drug discovery research. Typical scientific competency questions provided by the consortium members will be analyzed based on the underlying data concepts and associations needed to answer the questions. Publicly available data sources used to target these questions as well as the need for and potential of semantic web-based technology will be presented.