Development and Usage Patterns of a Home-Grown Drug Information Tool.

Drug information tools help avoid medication errors, a common cause of avoidable harm in health care systems. We sought to describe the design, development process and architecture of an electronic drug information tool, as well as its overall use by health professionals. We developed a tool that can be accessed by all health professionals in a tertiary level university hospital. The functionalities of eDrugs are organized into two main parts: Drug Summary sheet, and Prescription Simulator. Most users accessed eDrugs to use the Drug summary sheet. Clinical information and antimicrobial drugs were the most accessed drug information and drug group. The analysis of log data provides insights into the information priorities of health professionals.

Neonatal Drug Formularies-A Global Scope.

Neonatal drug information (DI) is essential for safe and effective pharmacotherapy in (pre)term neonates. Such information is usually absent from drug labels, making formularies a crucial part of the neonatal clinician's toolbox. Several formularies exist worldwide, but they have never been fully mapped or compared for content, structure and workflow. The objective of this review was to identify neonatal formularies, explore (dis)similarities, and raise awareness of their existence. Neonatal formularies were identified through self-acquaintance, experts and structured search. A questionnaire was sent to all identified formularies to provide details on formulary function. An original extraction tool was employed to collect DI from the formularies on the 10 most commonly used drugs in pre(term) neonates. Eight different neonatal formularies were identified worldwide (Europe, USA, Australia-New Zealand, Middle East). Six responded to the questionnaire and were compared for structure and content. Each formulary has its own workflow, monograph template and style, and update routine. Focus on certain aspects of DI also varies, as well as the type of initiative and funding. Clinicians should be aware of the various formularies available and their differences in characteristics and content to use them properly for the benefit of their patients.

EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers.

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

Opioid Prescription Monitoring in Preoperative and Postoperative Sacroiliac Joint Fusion Patients.

INTRODUCTION: An estimated 15%-25% of patients with chronic low back pain may in fact suffer from sacroiliac (SI) joint dysfunction. SI joint fusion has become a common treatment option for the management of SI joint dysfunction. However, little is known about opioid use prior to and after surgical treatment in this patient population. METHODS: The medical records of 62 patients treated with SI joint fusion at our institution were reviewed in this retrospective study. The Colorado Prescription Drug Monitoring Program (CPDMP) was accessed to gather opioid prescription information for these patients. Only those patients who had received an opioid prescription within 3 months prior to their surgery were included in the study. Patients who had SI joint fusion but underwent another surgical procedure during the 12-month follow-up period were excluded from analysis. Preoperative (6 and 3 months) and postoperative (3, 6, 9, and 12 months) mean morphine milligram equivalents (MME) were collected from the CPDMP database for each patient. Patient demographic and medical comorbidity data were also documented to identify any correlations or potential risk factors for chronic opioid prescribing. Visual analog scale (VAS), Oswestry Disability Index (ODI), and Denver SI Joint Questionnaire (DSIJQ) scores were recorded for each patient to assess clinical outcomes. RESULTS: At 3 months prior to surgery, patients were prescribed an average of 47.2 mean MME/d. At no point postoperatively did the quantity of opioids, measured in MME/d, change significantly from the 3-month preoperative prescription quantities. There was no significant difference in the quantity of opioids received by men vs women, in patients with vs without anxiety and/or depression, or in younger vs older patients. Low body mass index was correlated with decreased opioid prescriptions at 6 months postoperative but became statistically insignificant again by 9 months postoperative.Significant improvements in VAS scores were recorded for all postoperative clinical evaluation timepoints (at 6 weeks and 3, 6, and 12 months) and compared to preoperative scores. By 12 months, VAS scores had decreased from 6.2 to 3.9 (P < 0.001). This change is not only statistically significant but also meets the criteria for minimum clinically important difference in scores. Both the ODI and DSIJQ patient-reported outcomes scores also showed significant improvements at 12 months after surgery (ODI: 48.9 preoperative vs 24.6 postoperative, P = 0.02; DSIJQ: 53.2 preoperative vs 17.4 postoperative, P = 0.014). The ODI improvement also met the minimum clinically important difference criteria. By 6 months postoperatively, there was no significant correlation in VAS or ODI and opioid use. There was no significant correlation between the DSIJQ scores and the daily dose of opioids at any point postoperatively. CONCLUSION: Quantity of opioid prescriptions received by patients with SI joint pain did not change significantly from 3 months preoperatively to any point postoperatively despite significant improvements in all patient-reported outcome measures. This discordance between long-term opioid requirements and positive clinical outcomes is concerning and warrants further investigation.

Curation of an international drug proprietary names dataset.

A drug dataset containing international proprietary names is essential for researchers investigating different drugs from different countries worldwide. However, many websites on the internet offer free access for a single drug searching service to identify international drug trade names, but not for a list of drugs to be searched and identified. Therefore, it will be problematic if the researcher has a list of hundreds or thousands of drug trade names to be identified. In this project, we have created an International Drug Dictionary (IDD) by curating collected drug lists from open access websites belonging to official drug regulatory agencies, official healthcare systems, or recognized scientific bodies from 44 countries around the world in addition to the European public assessment reports (EPAR) and the DRUGBANK vocabulary published in the public domain. Researchers interested in pharmacovigilance, pharmacoepidemiology, or pharmacoeconomics can benefit from this dataset, especially when identifying lists of proprietary drug names, particularly of multi-national origin. To enhance its adaptability, we also mapped the IDD to the standardized drug vocabulary RxNorm. The IDD can also be used as a tool for mapping international drug trade names to RxNorm. Each drug entity in the IDD mapped to a unique identification number for each entity called Atom Unique Identifier (RXAUI) from RxNorm.

A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

BACKGROUND: The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures. OBJECTIVE: The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures. METHODS: A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found. CONCLUSIONS: Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.

BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. RESULTS: We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. CONCLUSIONS: Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.

Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information.

We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in the form of textual pieces of information which have not been targeted in the conventional graph completion tasks. To investigate the utility of textual information for knowledge graph completion, we generate embeddings from textual descriptions given to heterogeneous items, such as drugs and proteins, while learning knowledge graph embeddings. We evaluate the obtained graph embeddings on the link prediction task for knowledge graph completion, which can be used for drug discovery and repurposing. We also compare the results with existing methods and discuss the utility of the textual information.

An estimation of patients at potential risk for drug-induced sexual dysfunction using pharmacy dispensing data.

BACKGROUND AND OBJECTIVES: Adverse drug reactions on sexual functioning (sADRs) may seriously decrease a person's quality of life. A multitude of diseases and drugs are known risk factors for sexual dysfunction. To inform patients better about these potential effects, more insight is needed on the estimated number of patients at high risk for sADRs and their characteristics. METHODS: This cross-sectional study estimated the number of patients in the Netherlands who were dispensed drugs with a potential very high risk (>10%) or high risk (1-10%) for sADRs as registered in the Summary of Product Characteristics, the official drug information text in Europe. RESULTS: In April 2019, 2.06% of the inhabitants of the Netherlands received drugs with >10% risk for sADRs and 7.76% with 1-10% risk. The majority of these patients had at least one additional risk factor for decreased sexual function such as high age or depression. Almost half of the patients were identified with two or more morbidities influencing sexual functioning. Paroxetine, sertraline and spironolactone were the most dispensed drugs with a potential >10% risk for sADRs. One-third of their first dispenses were not followed by a second dispense, with a higher risk of discontinuation for a decreasing number of morbidities. CONCLUSION: About 1 in 11 inhabitants of the Netherlands was dispensed a drug with a potential high risk for sADRs, often with other risk factors for sexual complaints. Further research is needed whether these users actually experience sADRs, to understand its impact on multimorbid patients and to provide alternatives if needed.

Antidiabetic medication use in commercially insured children and adolescents in the United States from 2004 to 2019.

AIM: To describe the patterns of non-insulin antidiabetic medication use, initiation and adherence in the paediatric population. METHODS: We conducted a descriptive study of non-insulin antidiabetic medication use in children and adolescents (aged 10-18 years) using real-world data from a nationwide US commercial claims database (January 2004-September 2019). Trends in the prevalence of non-insulin antidiabetic medication use overall and by class were evaluated. Among new users of non-insulin antidiabetic agents, medication adherence was examined using group-based trajectory models. RESULTS: In a cohort of more than 1 million paediatric patients, the prevalence of any non-insulin antidiabetic medication use was 75.7 per 100 000 patients in 2004 and more than doubled to 162.0 per 100 000 in 2019. Biguanides (metformin) was by far the most widely used medication class. The use of newer classes was low (<10 per 100 000), but there was an uptake in the use of glucagon-like peptide-1 receptor agonists after liraglutide received paediatric approval in 2019. Medication adherence was poor during the 18 months after treatment initiation: 79.6% of initiators experienced an early treatment interruption (median time to interruption: 90 days among metformin monotherapy initiators) and 21% of initiators did not return for a prescription refill after the first month. CONCLUSIONS: There was a substantial increase in non-insulin antidiabetic medication use among commercially insured paediatric patients from 2004 to 2019. Nearly all patients were treated with metformin, while the use of newer agents remained low. Despite the increase in medication use, short treatment episodes were observed, even among patients with a diagnosis of type 2 diabetes, raising concern over poor adherence.

Data Fusion to Convert Drug Consumption Quantities into Defined Daily Doses.

Analysis of consumptions has proved a misuse of antibiotics, despite the existence of national requirements. To be able to compute drugs consumption quantities of highly heterogeneous drugs expressed in various doses units, the World Health Organization has defined a defined daily dose. A methodology has been also defined from previous work to compute manually the drugs consumptions in daily defined dose. We automated this methodology by using data fusion on data retrieved from different sources including a French public database and the World Health Organization website. Evaluation proved the efficiency of the approach, except for inconsistency cases. We identified these cases and proposed a solution to avoid them.

A Review of Drug Side Effect Identification Methods.

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study.

Because other coronaviruses enter the cells by binding to dipeptidyl-peptidase-4 (DPP-4), it has been speculated that DPP-4 inhibitors (DPP-4is) may exert an activity against severe acute respiratory syndrome coronavirus 2. In the absence of clinical trial results, we analysed epidemiological data to support or discard such a hypothesis. We retrieved information on exposure to DPP-4is among patients with type 2 diabetes (T2D) hospitalized for COVID-19 at an outbreak hospital in Italy. As a reference, we retrieved information on exposure to DPP-4is among matched patients with T2D in the same region. Of 403 hospitalized COVID-19 patients, 85 had T2D. The rate of exposure to DPP-4is was similar between T2D patients with COVID-19 (10.6%) and 14 857 matched patients in the region (8.8%), or 793 matched patients in the local outpatient clinic (15.4%), 8284 matched patients hospitalized for other reasons (8.5%), and when comparing 71 patients hospitalized for COVID-19 pneumonia (11.3%) with 351 matched patients with pneumonia of another aetiology (10.3%). T2D patients with COVID-19 who were on DPP-4is had a similar disease outcome as those who were not. In summary, we found no evidence that DPP-4is might affect hospitalization for COVID-19.

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.

Pharmacology and social media: Potentials and biases of web forums for drug mention analysis-case study of France.

The aim of this study is to analyze drug mentions in web forums to evaluate the utility of this data source for drug post-marketing studies. We automatically annotated over 60 million posts extracted from 21 French web forums. Drug mentions detected in this corpus were matched to drug names in a French drug database (Theriaque®). Our analysis showed that a high proportion of the most frequent drug mentions in the selected web forums correspond to drugs that are usually prescribed to young women, such as combined oral contraceptives. The most mentioned drugs in our corpus correlated weakly to the most prescribed drugs in France but seemed to be influenced by events widely reported in traditional media. In this article, we conclude that web forums have high potential for post-marketing drug-related studies, such as pharmacovigilance, and observation of drug utilization. However, the bias related to forum selection and the corresponding population representativeness should always be taken into account.

A Review of Recent Advances and Research on Drug Target Identification Methods.

BACKGROUND: From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue. METHODS: We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail. RESULTS: Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved. CONCLUSION: The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods.

Doctor shopping of opioid analgesics relative to benzodiazepines: A pharmacoepidemiological study among 11.7 million inhabitants in the French countries.

BACKGROUND: The abuse of prescription opioids and its subsequent consequences is an important public concern particularly in the USA. The literature on opioid analgesic abuse is scarce. OBJECTIVE: We assess the extent and risk of opioid analgesics abuse relative to benzodiazepines (BZD) using the doctor shopping method, taken into account the pharmacological characteristics (dosage, route of administration, extended or immediate release). METHODS: We used SNIIRAM database covering 11.7 million inhabitants. All individuals with at least one reimbursement for non-injectable opioid analgesic or BZD in 2013 were included. Opioids for mild to moderate pain and for moderately severe to severe pain were studied. The Doctor Shopping Quantity (DSQ) is the quantity obtained by overlapping prescriptions from several prescribers. The Doctor Shopping Indicator (DSI) is the DSQ divided by the total dispensed quantity. RESULTS: The strong opioid analgesics have the highest DSI (2.79%) versus 2.06% for BZD hypnotics. Flunitrazepam ranked first according to its DSI (13.2%), followed by morphine (4%), and zolpidem (2.2%). The three-strong opioids having the highest DSI were morphine, oxycodone and fentanyl (respectively 4%, 1.7% and 1.5%). The highest DSI was observed for the highest dosages of morphine (DSI = 8.4% for 200 mg) and oxycodone (DSI = 2.8% for 80 mg). The highest DSI for fentanyl was described with nasal and transmucosal forms (4.1% and 3.3% respectively). The highest DSI for morphine was described for extended-release (4.1%). CONCLUSION: There is a need to reinforce surveillance systems to track opioid misuse and to increase awareness of healthcare professionals.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes.

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. Foral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results.

Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.