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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., (© 2024 John Wiley & Sons Ltd.)

Objective: To investigate the potential pharmacological value of extracts from honeysuckle on patients with mild coronavirus disease 2019 (COVID-19) infection., Methods: The active components and targets of honeysuckle were screened by Traditional Chinese Medicine Database and Analysis Platform (TCMSP). SwissADME and pkCSM databases predict pharmacokinetics of ingredients. The Gene Expression Omnibus (GEO) database collected transcriptome data for mild COVID-19. Data quality control, differentially expressed gene (DEG) identification, enrichment analysis, and correlation analysis were implemented by R toolkit. CIBERSORT evaluated the infiltration of 22 immune cells., Results: The seven active ingredients of honeysuckle had good oral absorption and medicinal properties. Both the active ingredient targets of honeysuckle and differentially expressed genes of mild COVID-19 were significantly enriched in immune signaling pathways. There were five overlapping immunosignature genes, among which RELA and MAP3K7 expressions were statistically significant ( P < 0.05). Finally, immune cell infiltration and correlation analysis showed that RELA, MAP3K7, and natural killer (NK) cell are with highly positive correlation and highly negatively correlated with hematopoietic stem cells., Conclusion: Our analysis suggested that honeysuckle extract had a safe and effective protective effect against mild COVID-19 by regulating a complex molecular network. The main mechanism was related to the proportion of infiltration between NK cells and hematopoietic stem cells., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Huijuan Li et al.)

Background: In response to the US opioid epidemic, the Centers for Disease Control and Prevention updated their guideline on prescription opioids for chronic pain management in March 2016. The aim of this study was to provide detailed analysis of trends in opioid claims among cancer patients in the United States during 2013-2018., Methods: We analyzed pharmaceutical dispensing data from Symphony Health's Integrated Dataverse database, which covers approximately 80% of the US population. We examined annual trends in dispensed opioids in cancer patients during 2013-2018. We examined quarterly trends of the prevalence, mean number of days, and dose (stated as morphine milligram equivalents) of opioid dispensing in cancer patients., Results: Dispensing records of an average of over 3.7 million cancer patients contributed to the study annually in 2013-2018. The annual prevalence of opioid dispensing claims declined from 40.2% in 2013 to 34.5% in 2018. Annual declines occurred across cancer sites, and particularly among patients with metastatic cancer (decline of 19.8%), breast cancer (18.2%), and lung cancer (13.8%). By quarter, the prevalence of opioid claims declined statistically significantly from 26.6% in Q1 2013 to 21.2% in Q4 2018; this decline was more pronounced after Q3 2016 (2-sided P  =   .004). Both quarterly trends in mean days and morphine milligram equivalents of opioids supplied showed a gradual decline from 2013 to 2018, with a slightly larger decline after 2016., Conclusions: We observed a decline in opioid use among cancer patients, particularly after 2016, coinciding with the publication of the Centers for Disease Control and Prevention's guideline on prescription opioids for chronic pain management., (Published by Oxford University Press 2021. This work is written by US Government employees and is in the public domain in the US.)

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 β 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.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

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.

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., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

When patented, brand-name antibiotics lose market exclusivity, generics typically enter the market at lower prices, which may increase consumption of the drug. To examine the effect of generic market entry on antibiotic consumption in the United States, we conducted an interrupted time series analysis of the change in the number of prescriptions per month for antibiotics for which at least one generic entered the US market between 2000 and 2012. Data were acquired from the IQVIA Xponent database. Thirteen antibiotics were analyzed. Here, we show that one year after generic entry, the number of prescriptions increased for five antibiotics (5 to 406%)-aztreonam, cefpodoxime, ciprofloxacin, levofloxacin, ofloxacin-and decreased for one drug: cefdinir. These changes were sustained two years after. Cefprozil, cefuroxime axetil and clarithromycin had significant increases in trend, but no significant level changes. No consistent pattern for antibiotic use following generic entry in the United States was observed.

Generally, bioequivalence (BE) studies of drug products for pediatric patients are conducted in adults due to ethical reasons. Given the lack of direct BE assessment in pediatric populations, the aim of this work is to develop a database of BE and relative bioavailability (relative BA) studies conducted in pediatric populations and to enable the identification of risk factors associated with certain drug substances or products that may lead to failed BE or different pharmacokinetic (PK) parameters in relative BA studies in pediatrics. A literature search from 1965 to 2020 was conducted in PubMed, Cochrane Library, and Google Scholar to identify BE studies conducted in pediatric populations and relative BA studies conducted in pediatric populations. Overall, 79 studies covering 37 active pharmaceutical ingredients (APIs) were included in the database: 4 bioequivalence studies with data that passed BE evaluations; 2 studies showed bioinequivalence results; 34 relative BA studies showing comparable PK parameters, and 39 relative BA studies showing differences in PK parameters between test and reference products. Based on the above studies, common putative risk factors associated with differences in relative bioavailability (DRBA) in pediatric populations include age-related absorption effects, high inter-individual variability, and poor study design. A database containing 79 clinical studies on BE or relative BA in pediatrics has been developed. Putative risk factors associated with DRBA in pediatric populations are summarized.

Background: Heart failure (HF) poses a substantial global health burden, particularly in patients with chronic obstructive pulmonary disease (COPD). The objective of this study was to validate an electronic medical record-based definition of HF in patients with COPD in primary care practices in the province of British Columbia, Canada., Methods: We conducted a cross-sectional retrospective chart review from Sept. 1, 2018, to Dec. 31, 2018, for a cohort of patients from primary care practices in BC whose physicians were recruited through the BC node of the Canadian Primary Care Sentinel Surveillance Network. Heart failure case definitions were developed by combining diagnostic codes, medication information and laboratory values available in primary care electronic medical records. These were compared with HF diagnoses identified through detailed chart review as the gold standard. Sensitivity, specificity, negative (NPV) and positive predictive values (PPV) were calculated for each definition., Results: Charts of 311 patients with COPD were reviewed, of whom 72 (23.2%) had HF. Five categories of definitions were constructed, all of which had appropriate sensitivity, specificity and NPV. The optimal case definition consisted of 1 HF billing code or a specific combination of medications for HF. This definition had an excellent specificity (93.3%, 95% confidence interval [CI] 89.4%-96.1%), sensitivity (90.3%, 95% CI 81.0%-96.0%), PPV (80.2%, 95% CI 69.9%-88.3%) and NPV (97.0%, 95% CI 93.8%-98.8%)., Interpretation: This comprehensive case definition improves upon previous primary care HF definitions to include medication codes and laboratory data, along with previously used billing codes. A case definition for HF was derived and validated and can be used with data from electronic medical records to identify HF in patients with COPD in primary care accurately., Competing Interests: Competing interests: None declared., (© 2021 Joule Inc. or its licensors.)

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors., Competing Interests: The authors declare no conflict of interest, financial, or otherwise., (Copyright © 2021 Junlin Zhou et al.)

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., Competing Interests: Conflict of interest GD reports grants from Gilead, Abbvie, Merck, and Bristol-Myers Squibb, personal fees from Gilead, Abbvie, and Merck, and non-financial support from Gilead, Abbvie, and Merck, outside the submitted work. JGrebely reports grants from Merck, grants from Cepheid, during the conduct of the study; grants and personal fees from Abbvie, grants and personal fees from Gilead Sciences, grants and personal fees from Merck, grants and personal fees from Cepheid, grants from Hologic, grants from Indivior, outside the submitted work. ML reports grants from Gilead, ViiV Healthcare and Janssen outside the submitted work. MK reports receiving grants from Boehringer Ingelheim, Hologic, Merck, Roche Molecular Systems, and Siemens Healthcare Diagnostics outside of the submitted work. JGeorge reports funding from MSD, Abbvie, BMS, Pharmaxis, Norvartis, Cincera, and Bayer outside the submitted work. BH reports grants from NSW Health and Cancer Council NSW. LD has received investigator-initiated untied educational grants for studies of opioid medications in Australia from Indivior, Mundipharma and Seqirus. All remaining authors have no potential conflicts to declare. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2020 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

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/., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Qiang-Huo-Sheng-Shi decoction (QHSSD), a classic traditional Chinese herbal formula, which has been reported to be effective in rheumatoid arthritis (RA) and osteoarthritis (OA). However, the concurrent targeting mechanism of how the aforementioned formula is valid in the two distinct diseases OA and RA, which represents the homotherapy-for-heteropathy principle in traditional Chinese medicine (TCM), have not yet been clarified. In the present study, network pharmacology was adopted to analyze the potential molecular mechanism, and therapeutic effective components of QHSSD on both OA and RA. A total of 153 active ingredients in QHSSD were identified, 142 of which associated with 59 potential targets for the two diseases were identified. By constructing the protein-protein interaction network and the compound-target-disease network, 72 compounds and 10 proteins were obtained as the hub targets of QHSSD against OA and RA. The hub genes of ESR1, PTGS2, PPARG, IL1B, TNF, MMP2, IL6, CYP3A4, MAPK8, and ALB were mainly involved in osteoclast differentiation, the NF-κB and TNF signaling pathways. Moreover, molecular docking results showed that the screened active compounds had a high affinity for the hub genes. This study provides new insight into the molecular mechanisms behind how QHSSD presents homotherapy-for-heteropathy therapeutic efficacy in both OA and RA. For the first time, a two-disease model was linked with a TCM formula using network pharmacology to identify the key active components and understand the common mechanisms of its multi-pathway regulation. This study will inspire more innovative and important studies on the modern research of TCM formulas., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Regulatory agencies around the world have been using flexible requirements for approval of new drugs, especially for cancer drugs. The US Food and Drug Administration (FDA) is mostly the first agency to approve new drugs worldwide, mainly due to the faster terms of the accelerated pathway and breakthrough therapy designation. Surrogate endpoints and preliminary data (e.g. single-arm and phase 2 studies) are used for these new approvals, however larger effect sizes are expected. We aim to compare FDA Accelerated vs Regular Pathway approvals and Breakthrough therapy designations (BTD) for lung cancer treatments between 2006 and 2018 regarding study design, sample size, outcome measures and effect size. We assessed the FDA database to collect data from studies that formed the basis of approvals of new drugs or indications for lung cancer spanning from 2006 to 2018. We found that accelerated pathway approvals are based on significantly more single-arm studies with small sample sizes and surrogate primary endpoints. However, effect size was not different between the pathways. A large proportion of studies used to support regular pathway approvals also showed these characteristics that are related to low quality and uncertain evidence. Compared to other approvals, BTD were more frequently based on single-arm studies. There was no significant difference in use of surrogate endpoints or sample size. 44% of BTD were based on studies demonstrating large effect sizes, proportionally more than approvals not receiving this designation. In conclusion, based on the indicators of evidence quality we extracted, criteria's for granting accelerated approval and breakthrough therapy designation seen not clear. Faster approvals are in the majority full of uncertainties which should be viewed with caution and the patient have to be communicated to allow shared decision making. Post-marketing validation is essential., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2020 Haiyan Liang et al.)

Traditional Chinese Medicine (TCM) is widely used in the treatment of Mycoplasma pneumoniae Pneumonia (MPP) in East Asia. However, our current understanding of the underlying molecular mechanism remains dispersive and promiscuous. In this study, a systematic pharmacological approach combined with literature data mining was applied for drug similarity evaluation, drug half-life evaluation, oral bioavailability prediction, drug target exploration, Gene Ontology (GO) analysis, KEGG pathway enrichment and network construction, thus providing the rationale for its clinical performance. Five mostly studied herbs, including Ephedra Herba, Amygdalus communis Vas, Platycodon grandiforus, Licorice and Scutellariae Radix, were selected from the literature. Total ninety-three active ingredients, which are expected to be the effective components for MPP treatment, were screened out. Interrelationship between active compounds, drug targets and signaling pathways were analyzed to reveal the therapeutic effect of TCM in detail. Of importance, we found that TNF, β2AR and PTGS2 play pivotal role in TCM mediated MPP inhibition. And mechanistically, epithelial apoptosis (defensive barrier function), GPCR signaling (symptom amelioration) and immune pathways (innate signaling and adaptive Th17 response) are critically involved. Our work, achieved through systematic pharmacology and data mining, enlarges the knowledge of TCM in MPP therapy, and could provide valuable insights for further drug discovery studies., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020. Published by Elsevier Masson SAS.)

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Li Wang et al.)

The basic experimental data of traditional Chinese medicine are generally obtained by high-performance liquid chromatography and mass spectrometry. The data often show the characteristics of high dimensionality and few samples, and there are many irrelevant features and redundant features in the data, which bring challenges to the in-depth exploration of Chinese medicine material information. A hybrid feature selection method based on iterative approximate Markov blanket (CI&#95;AMB) is proposed in the paper. The method uses the maximum information coefficient to measure the correlation between features and target variables and achieves the purpose of filtering irrelevant features according to the evaluation criteria, firstly. The iterative approximation Markov blanket strategy analyzes the redundancy between features and implements the elimination of redundant features and then selects an effective feature subset finally. Comparative experiments using traditional Chinese medicine material basic experimental data and UCI's multiple public datasets show that the new method has a better advantage to select a small number of highly explanatory features, compared with Lasso, XGBoost, and the classic approximate Markov blanket method., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Canyi Huang et al.)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2019 John Wiley & Sons, Ltd.)

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., (© 2019 Royal Australian and New Zealand College of Ophthalmologists.)

Background: Bridging studies are mandatory in the EU and USA if the reference biological product used in the biosimilar comparability exercise is foreign sourced. However, it has been argued that the duplication of bridging studies may limit biosimilar development., Objective: The aim of the study was to explore whether it is necessary to conduct pharmacokinetic (PK)/pharmacodynamic (PD) bridging studies for biosimilars. This study examines similarities and differences between EU- and US-licensed reference biological products, based on literature-reported PK and/or PD data., Methods: We searched PubMed, Drugs@FDA, and European Medicines Agency (EMA) databases to identify biosimilar bridging studies designed to evaluate similarities between EU- and US-licensed reference biological products. PK and/or PD parameters were retrieved; the ratio of the parameter value of the EU-licensed product to that of the US-licensed product and its corresponding 90% confidence intervals (CIs) were calculated. Similarity was declared if the 90% CIs for the ratios of the PK or PD parameters were within the range of 80-125%., Results: Thirty-one bridging studies were identified for 11 biosimilars, including adalimumab (n = 10), bevacizumab (n = 4), epoetin alfa (n = 1), etanercept (n = 2), filgrastim (n = 1), infliximab (n = 3), insulin glargine (n = 1), insulin lispro (n = 1), PEGfilgrastim (n = 2), rituximab (n = 2), and trastuzumab (n = 4). Most studies showed PK and/or PD similarities between the EU- and US-licensed reference biological products. However, among the 31 studies, only three studies (accounting for two biologics, PEGfilgrastim and adalimumab) showed dissimilarity between the EU and US reference products. Although one bridging study on PEGfilgrastim (Sandoz) indicated dissimilar PKs (maximum observed plasma concentration [C max ] and area under the concentration-time curve [AUC]) between the reference products, the other study (Mylan) demonstrated similar PK. Moreover, two of ten studies involving adalimumab failed to demonstrate similarities between the reference products. However, for both cases, PK similarities were later confirmed in the follow-up bridging studies with larger sample sizes., Conclusion: Our analysis reveals that, in most cases, the reference biological products originated from the EU and those from the USA are almost indistinguishable in terms of PK/PD properties. Additional in vivo bridging studies between reference products from different global regions may not be required if similar physicochemical and structural properties are evident in vitro.

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., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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

Background: Prescribing errors and medication related harm may be common in patients with mental illness. However, there has been limited research focusing on the development and application of prescribing safety indicators (PSIs) for this population., Objective: Identify potential PSIs related to mental health (MH) medications and conditions., Methods: Seven electronic databases were searched (from 1990 to February 2019), including the bibliographies of included studies and of relevant review articles. Studies that developed, validated or updated a set of explicit medication-specific indicators or criteria that measured prescribing safety or quality were included, irrespective of whether they contained MH indicators or not. Studies were screened to extract all MH related indicators before two MH clinical pharmacists screened them to select potential PSIs based on established criteria. All indicators were categorised into prescribing problems and medication categories., Results: 79 unique studies were included, 70 of which contained at least one MH related indicator. No studies were identified that focused on development of PSIs for patients with mental illness. A total of 1386 MH indicators were identified (average 20 (SD = 25.1) per study); 245 of these were considered potential PSIs. Among PSIs the most common prescribing problem was 'Potentially inappropriate prescribing considering diagnoses or conditions' (n = 91, 37.1%) and the lowest was 'omission' (n = 5, 2.0%). 'Antidepressant' was the most common PSI medication category (n = 85, 34.7%)., Conclusion: This is the first systematic review to identify a comprehensive list of MH related potential PSIs. This list should undergo further validation and could be used as a foundation for the development of new suites of PSIs applicable to patients with mental illness., Competing Interests: The authors have declared that no competing interests exist.

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

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., (© 2019 John Wiley & Sons, Ltd.)

Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp). By utilizing adverse drug reactions (ADRs) as the intermediate, we constructed a heterogeneous health network containing drugs, diseases, and ADRs, and developed path-based heterogeneous network mining approaches for drug repositioning. Additionally, we investigated on how the data sources affect the performance on drug repositioning. Experiment results showed that combining both PharmKGB and MedHelp identified 479 repositioning drugs, which are more than the repositioning drugs discovered by other alternatives. In addition, 31% of the 479 of the discovered repositioning drugs were supported by evidence from PubMed., (Copyright © 2019 Elsevier B.V. All rights reserved.)

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.

Background: Osteoporotic fractures are a major cause of morbidity and mortality. It is recognized that persistence with medication is crucial to reach optimal clinical outcomes. We aimed to estimate the persistence level to weekly and monthly oral bisphosphonates (OBP) in women with postmenopausal osteoporosis (PMO) over 24 months from therapy initiation in a population-based setting., Methods: Prospective observational cohort study of PMO women ≥50 years initiating OBP recruited through community pharmacies. Data were collected at baseline during face-to-face interviews. Follow-up included pharmacy records (refill dates and medication possession; cohort 1) and telephone-surveys for patients who agreed to be interviewed (cohort 2). Patients were classified as persistent if they refilled their prescription within 30 days after exhausting the time covered by their previous supply. Log-rank tests were used to compare Kaplan-Meier curves of time to non-persistence., Results: Of 427 women recruited with a mean age of 65.0 years, 380 (89%) agreed to be interviewed (cohort 2). Over 24-months of follow-up, 3.4% (95% CI: [2.0%; 5.6%]) of all subjects were persistent to OBP based on pharmacy records. Analysis combining both self-reported information and pharmacy records (cohort 2) showed a persistence estimate of 20.0% (95% CI: [16.1%; 24.2%]). Lower persistence was associated with more frequent OBP dosing and living alone. The most common reason for treatment discontinuation was self-reported adverse events (27.6%)., Conclusions: Results indicate a low level of persistence with OBP. Barriers and reasons leading to discontinuation of anti-PMO therapies should be proactively addressed to promote persistence and improve fracture protection.

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., (© 2019 John Wiley & Sons, Ltd.)

Aim: Appropriateness of anti-drug antibody (ADA) assay is critical for immunogenicity assessment of biopharmaceuticals. Although cut point setting in ADA assay has a large impact on the results, a standard statistical approach for its setting has not been well established. Methodology: In this multi-laboratory study, to elucidate factors influencing the cut point setting, we compared the statistical approaches and calculated cut points for multiple datasets of ADA assays using the individual procedure employed at each laboratory. Conclusion: We showed that outlier exclusion, false-positive rate and investigating data distribution have the greatest impact on both screening and confirmatory cut points. Our results would be useful for industry researchers and regulators engaged in immunogenicity assessment of biopharmaceuticals.

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic's biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite - it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.

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.

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.

Objectives: The Division of Drug Information (DDI) within the U.S. Food and Drug Administration's (FDA's) Center for Drug Evaluation and Research receives telephone calls, e-mails, and letters from patients and industry. The consistently high number of contacts about drugs in the fluoroquinolone class indicates an opportunity to increase awareness about the drugs' evolving safety profile., Data Sources: Database used by DDI to summarize inquiries., Summary: Inquiries about fluoroquinolones peaked in 2016 and were highest after FDA Drug Safety Communications that were issued that year. Among the first fluoroquinolone, ciprofloxacin, was approved in 1987. Since then, the FDA has required class-wide labeling changes reflecting postmarket benefit-risk assessments of oral and injectable (systemic) fluoroquinolones, including changes in 1990, 2008, 2013, 2016, and 2018., Conclusion: Pharmacists have a role that is more critical than ever to ensure the safety of patients who are prescribed a systemic fluoroquinolone. Pharmacists should stay informed on this class of drugs, be prepared to suggest alternate therapies, counsel patients on potential benefits and risks, and work with patients to submit Medwatch reports to the FDA if adverse events occur., (Published by Elsevier Inc.)

The inconsistency of open pharmacogenomics datasets produced by different studies limits the usage of such datasets in many tasks, such as biomarker discovery. Investigation of multiple pharmacogenomics datasets confirmed that the pairwise sensitivity data correlation between drugs, or rows, across different studies (drug-wise) is relatively low, while the pairwise sensitivity data correlation between cell-lines, or columns, across different studies (cell-wise) is considerably strong. This common interesting observation across multiple pharmacogenomics datasets suggests the existence of subtle consistency among the different studies (i.e., strong cell-wise correlation). However, significant noises are also shown (i.e., weak drug-wise correlation) and have prevented researchers from comfortably using the data directly. Motivated by this observation, we propose a novel framework for addressing the inconsistency between large-scale pharmacogenomics data sets. Our method can significantly boost the drug-wise correlation and can be easily applied to re-summarized and normalized datasets proposed by others. We also investigate our algorithm based on many different criteria to demonstrate that the corrected datasets are not only consistent, but also biologically meaningful. Eventually, we propose to extend our main algorithm into a framework, so that in the future when more datasets become publicly available, our framework can hopefully offer a "ground-truth" guidance for references.

Drugs can produce intended therapeutic effects to treat different diseases. However, they may also cause side effects at the same time. For an approved drug, it is best to detect all side effects it can produce. Otherwise, it may bring great risks for pharmaceuticals companies as well as be harmful to human body. It is urgent to design quick and reliable identification methods to detect the side effects for a given drug. In this study, a binary classification model was proposed to predict drug side effects. Different from most previous methods, our model termed the pair of drug and side effect as a sample and convert the original problem to a binary classification problem. Based on the similarity idea, each pair was represented by five features, each of which was derived from a type of drug property. The strong machine learning algorithm, random forest, was adopted as the prediction engine. The ten-fold cross-validation on five datasets with different negative samples indicated that the proposed model yielded a good performance of Matthews correlation coefficient around 0.550 and AUC around 0.8492. In addition, we also analyzed the contribution of each drug property for construction of the model. The results indicated that drug similarity in fingerprint was most related to the prediction of drug side effects and all drug properties gave less or more contributions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Study Objective: To determine the incidence, risk factors, and predictors of survival of perioperative cardiac arrests (PCAs) occurring in patients who underwent non-cardiac and non-obstetric surgery from January 2008 to May 2015 at a tertiary hospital; determine the incidence and risk factors of anesthesia-related PCA., Design: Retrospective observational study., Setting: Operating room and postoperative recovery area., Patients: Sixty-two PCA cases from an anesthesia database of 122,289 anesthetics., Interventions: Each PCA was classified as anesthesia-related, partially anesthesia-related, or anesthesia unrelated. The main outcome variables were occurrence of PCA, survival at least 1 h after initial resuscitation and survival to hospital discharge. To determine the risk factors for PCA, for each patient who suffered a PCA, two other patients that underwent anesthesia on the same day and in the same operating suite were selected., Measurements: Three sets of variables were collected; patient-related, surgical procedure-related, and PCA-related., Main Results: The incidence of PCAs of all causes was 5.07 per 10,000 anesthetics, and the associated mortality was 2.9 per 10,000 anesthetics. The independent risk factors for occurrence were: ASA PS score higher than 3, diagnosed cardiac disease, and the use of vasopressors. Decreased survival was associated with: higher ASA PS score, urgent surgical procedures of a higher complexity, use of vasopressors, documented hypotension prior to PCA, and arrests due to bleeding. The incidence of anesthesia-related PCAs was 0.74 per 10,000 anesthetics, and the associated mortality was 0.08 per 10,000 anesthetics. The main causes of anesthesia-related PCAs were associated with medication and airway/ventilation, and the independent risk factors for occurrence were: ASA PS score higher than 3 and diagnosed cardiac disease., Conclusions: Most PCAs were not due to anesthesia-related causes, and anesthesia-related PCAs were associated with improved survival. Improvements in the management of high-risk patients, medication administration, and airway/ventilation management may result in better outcomes., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background and Objective: Drug-target interaction prediction plays an intrinsic role in the drug discovery process. Prediction of novel drugs and targets helps in identifying optimal drug therapies for various stringent diseases. Computational prediction of drug-target interactions can help to identify potential drug-target pairs and speed-up the process of drug repositioning. In our present, work we have focused on machine learning algorithms for predicting drug-target interactions from the pool of existing drug-target data. The key idea is to train the classifier using existing DTI so as to predict new or unknown DTI. However, there are various challenges such as class imbalance and high dimensional nature of data that need to be addressed before developing optimal drug-target interaction model., Methods: In this paper, we propose a bagging based ensemble framework named BE-DTI' for drug-target interaction prediction using dimensionality reduction and active learning to deal with class-imbalanced data. Active learning helps to improve under-sampling bagging based ensembles. Dimensionality reduction is used to deal with high dimensional data., Results: Results show that the proposed technique outperforms the other five competing methods in 10-fold cross-validation experiments in terms of AUC=0.927, Sensitivity=0.886, Specificity=0.864, and G-mean=0.874., Conclusion: Missing interactions and new interactions are predicted using the proposed framework. Some of the known interactions are removed from the original dataset and their interactions are recalculated to check the accuracy of the proposed framework. Moreover, validation of the proposed approach is performed using the external dataset. All these results show that structurally similar drugs tend to interact with similar targets., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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.

Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈D p →D s 〉, the prior medicine D p leads to an ADE for which the succeeding medicine D s is dispensed to treat. We propose a data-driven method which integrates (1) a constrained sequential pattern mining to uncover prescription sequences as potential signals of ADEs, (2) domain constraints to eliminate interference signals and (3) an adapted Self-Controlled Case Series model to evaluate the potential signals of ADEs. Despite ample prior works using Electronic Health Records (EHRs), our method utilises pure prescription data which does not contain additional information, e.g. symptoms or diagnoses as included in EHRs. To assess the performance of the proposed method, we apply it to a real-world dataset from the Pharmaceutical Benefits Scheme of Australia. The dataset contains over 50 million records covering approximately 2 million patients. The results demonstrate the effectiveness of our method in identifying both known ADEs and unknown yet suspicious ADEs with limited detection of false positive signals. Comparing to a recognised gold standard, our method successfully detects 67.4% of the positive adverse events while only 8.78% false positives exist., (Copyright © 2018 Elsevier Inc. All rights reserved.)

A database containing maximum daily doses of 1841 marketed oral drugs was used to examine the influence of physicochemical properties on dose and hepatotoxicity (drug induced liver injury, DILI). Drugs in the highest ∼20% dose range had significantly reduced mean lipophilicity and molecular weight, increased fractional surface area, increased % of acids, and decreased % of bases versus drugs in the lower ∼60% dose range. Drugs in the ∼20-40% dose range had intermediate mean properties, similar to the mean values for the full drug set. Drugs that are both large and highly lipophilic almost invariably do not have doses in the upper ∼20% range. The results show that oral druglike physicochemical properties are different according to these dose ranges, and this is consistent with maintenance of acceptable safety profiles as efficacious exposure increases. Verified DILI annotations from a compilation of >1000 approved drugs (Chen, M.; et al. Drug Discov. Today, 2016, 21, 648 ) were used. The drugs classified as "No DILI" ( n = 163) had significantly lower dose and lipophilicity, and higher Fsp3 (fraction of carbon atoms that are sp 3 hybridized) versus the "Most DILI" ( n = 163) drugs. The percentages of acids were reduced and bases increased in the "No DILI" versus the "Most DILI" groups. Drugs classified as "Less DILI" or "Ambiguous DILI" had intermediate mean values of dose, lipophilicity, Fsp3, and % acids and bases. The impact of lipophilicity and Fsp3 on DILI increases in the upper 20% versus the lower 80% dose range, and a simple decision tree model predicted "No DILI" versus "Most DILI" outcomes with 82% accuracy. The model correctly classified 19 of 22 drugs (86%) that failed in development due to human hepatotoxicity. Because many oral drugs lacking DILI annotations are predicted to be "Most DILI", the model is best used preclinically in conjunction with experimental DILI mitigation.