New Approach for Targeted Treatment of Mild COVID-19 by Honeysuckle through Network Pharmacology Analysis.

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.

Trends in Opioid Use Among Cancer Patients in the United States: 2013-2018.

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.

Comparison of FDA accelerated vs regular pathway approvals for lung cancer treatments between 2006 and 2018.

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.

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

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

A systematic approach to orient the human protein-protein interaction network.

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.

Metatox - Web application for generation of metabolic pathways and toxicity estimation.

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.

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets.

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.

Perioperative cardiac arrests - A subanalysis of the anesthesia -related cardiac arrests and associated mortality.

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.

Drug-drug interaction potential in men treated with enzalutamide: Mind the gap.

AIMS: Metastatic castration-resistant prostate cancer (mCRPC) patients are generally older patients with several co-morbidities and are therefore at increased risk of complications due to drug-drug interactions (DDIs). We assessed the prevalence of potential DDIs in a cohort of mCRPC patients treated with enzalutamide. METHODS: We conducted a retrospective review of pharmacy records to retrieve individual drug histories of mCRPC patients who started enzalutamide therapy in a tertiary care setting. Potential DDIs were analysed using two international drug interaction compendia: Lexicomp® and Micromedex® , and the Dutch drug database. Two potential pharmacodynamic DDIs were analysed. RESULTS: A total of 105 records were evaluated for potential DDIs with enzalutamide. Of 205 different co-medications, 56 were flagged by at least one of the three compendia: Lexicomp, Micromedex and the Dutch drug database flagged for potential DDIs in 85%, 54% and 32%, respectively. Eighty-five per cent of DDIs were classified as major. The median number of co-medications per patient was 11 (range 1-26). The median (range) number of interactions per patient was 4 (0-10), 1 (0-5) and 0 (0-2) for Lexicomp, Micromedex and the Dutch drug database, respectively. In 23% and 45% of all patients, a potential DDI was found with PPIs and CNS depressants, respectively. CONCLUSIONS: A high prevalence of potential DDIs was found. The inclusion and grading of potential DDIs was highly variable between the three drug interaction compendia. Physicians, nurses and pharmacists should be aware of this potential problem, which might require intensive monitoring or alternative treatment strategies to prevent suboptimal treatment of the co-morbidities in patients treated with enzalutamide.

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action.

A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.

Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis.

PURPOSE: Drug interactions are a concern in oncology with the shift toward oral antineoplastics (OAs). Using electronic databases to screen for drug interactions with OAs is a common practice. There is little literature to guide clinicians on the reliability of these systems with OAs. The primary objective of this study was to explore the sensitivity of commonly available drug interaction databases in detecting drug interactions with OAs. METHODS: A list of 20 drug interactions with OAs was developed by two Board-certified oncology pharmacists. The list included multiple types of drug interactions. The sensitivity in detecting these interactions by MicroMedex, Facts & Comparisons, Lexi-Interact, and Epocrates were evaluated. These databases were chosen based on their local availability and widespread use in practice. Drugs.com was evaluated as a surrogate for a patient-accessible drug interaction database. The Cochran Q test was used to assess the sensitivity distribution across the five groups. RESULTS: Lexi-Interact and Drugs.com had a sensitivity of 95% for the 20 tested drug interaction pairs. Epocrates had a sensitivity of 90%, and both Micromedex and Facts & Comparisons had a sensitivity of 70%. There was a statistically significant difference ( P = .016) in the distribution across the databases in detecting clinically significant drug interactions. CONCLUSION: Commonly used databases for identifying drug interactions with oral antineoplastics vary significantly in their sensitivity. Clinicians should not rely on a single database and should consider using multiple resources as well as sound clinical judgment. Further work is needed in this area.

INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION.

We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets. We apply our method to AML data from TCGA and validate the predicted drug targets using known targets. To benchmark our patient-specific approach, we compare the personalized setting predictions to those of the conventional setting. Our predicted drug targets are highly enriched with known targets from DrugBank and COSMIC (p < 10(-5) outperforming the non-personalized predictions. Finally, we focus on the largest AML patient subgroup (~30%) which is characterized by an FLT3 mutation, and utilize our prediction score to rank patient sensitivity to inhibition of each predicted target, reproducing previous findings of in-vitro experiments.

Toward appropriate criteria in medication adherence assessment in older persons: Position Paper.

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

Efectos del tratamiento cubano para la retinosis pigmentaria sobre vasos coroideos / Effects of the Cuban treatment for retinitis pigmentosa on the choroidal vessels

Objetivo: describir los efectos sobre los vasos coroideos generados por la primera aplicación del tratamiento multiterapéutico cubano en pacientes con retinosis pigmentaria. Métodos: se realizó una investigación descriptiva longitudinal prospectiva, donde se seleccionaron 32 pacientes con retinosis pigmentaria, a quienes se les aplicó el tratamiento multiterapéutico cubano para esta enfermedad. Se utilizó un video angiógrafo de Heidelberg tipo 2 para realizar oftalmoscopia confocal por barrido láser infrarrojo, para adquirir y procesar imágenes de la capa media de vasos coroideos antes del tratamiento, 15 días y un año después de realizar este. El análisis de los resultados se realizó mediante Statistica 6.0 y SPSS 15.0 sobre Windows. Resultados: se observaron aumentos significativos de los diámetros vasculares en los cuadrantes temporales inferiores. En los temporales superiores hubo disminución no significativa; en los nasales inferiores se observaron aumentos significativos, y en los nasales superiores disminución significativa. Conclusión: después de aplicar el tratamiento multiterapéutico cubano para la retinosis pigmentaria, aumentan de forma duradera los diámetros de los vasos coroideos de la capa media solamente en el cuadrante temporal inferior(AU)

Objective: to describe the effects on the choroidal vessels after the first application of the Cuban multi-therapeutic treatment for patients with retinitis pigmentosa. Methods: a prospective, longitudinal and descriptive study of 32 patients with retinitis pigmentosa, who had undergone the Cuban multi-therapeutic treatment for this disease. There was used Heidelberg Retinal Angiograph- 2 to perform infrared laser scanning confocal ophthalmoscopy in order to take and to process images from the medial layer of the choroidal vessels before, 15 days, and one year after treatment. The results were analyzed with Statistica 6.0 and SPSS 15.0 on Windows. Results: significant increases in vascular diameters of the lower temporal quadrants were observed whereas non-significant decrease occurred in the upper temporal quadrants. Additionally, the choroidal vascular diameters increased significantly in the lower nasal quadrants and decreased in a significant way in the upper nasal ones. Conclusions: the Cuban multi-therapeutic treatment for retinitis pigmentosa increases the diameter of choroidal vascular vessels in a permanent way just in the lower temporal quadrant(AU)

Insulin and other antidiabetic drugs and hepatocellular carcinoma risk: a nested case-control study based on Italian healthcare utilization databases.

PURPOSE: Insulin and other antidiabetic drugs may modulate hepatocellular carcinoma (HCC) risk in diabetics. METHODS: We have analyzed the role of various antidiabetic drugs on HCC in a nested case-control study using the healthcare utilization databases of the Lombardy Region in Italy. This included 190 diabetic subjects with a hospital discharge reporting a diagnosis of malignant HCC and 3772 diabetic control subjects matched to each case on sex, age, date at cohort entry, and duration of follow-up. RESULTS: Increased risks of HCC were found for use of insulin (odds ratio [OR] = 3.73, 95% confidence interval [CI] 2.52-5.51), sulfonylureas (OR = 1.39, 95%CI 0.98-1.99), and repaglinide (OR = 2.12, 95%CI 1.38-3.26), while a reduced risk was found for use of metformin (OR = 0.57, 95%CI 0.41-0.79). The risk of HCC increased with increasing duration of insulin use (OR = 2.52 for <1 year, 5.41 for 1-2 years, and 6.01 for ≥2 years; p for trend < 0.001), while no clear pattern with duration was observed for sulfonylureas, repaglinide, and metformin. CONCLUSION: Our study supports the evidence that patients with diabetes using metformin, and possibly other antidiabetic drugs that increase insulin sensibility, have a reduced risk of HCC, while those using insulin or drugs that increase circulating insulin, such as insulin secretagogues, have an increased risk. Whether these associations are causal, or influenced by different severity of diabetes and/or possible residual bias or misclassification, is still open to discussion.

Comparison of psychotropic prescriptions between oncology and cardiology inpatients: result from a pharmacy database in a teaching hospital in Malaysia.

OBJECTIVE: To examine the prescription rates in cancer patients of three common psychotropic drugs: anxiolytic/ hypnotic, antidepressant and antipsychotic. MATERIALS AND METHODS: In this retrospective cohort study, data were extracted from the pharmacy database of University Malaya Medical Center (UMMC) responsible for dispensing records of patients stored in the pharmacy's Medication Management and Use System (Ascribe). We analyzed the use of psychotropics in patients from the oncology ward and cardiology from 2008 to 2012. Odds ratios (ORs) were adjusted for age, gender and ethnicity. RESULTS: A total of 3,345 oncology patients and 8,980 cardiology patients were included. Oncology patients were significantly more often prescribed psychotropic drugs (adjusted OR: anxiolytic/hypnotic=5.55 (CI: 4.64-6.63); antidepressants=6.08 (CI: 4.83-7.64) and antipsychotics=5.41 (CI: 4.17-7.02). Non-Malay female cancer patients were at significantly higher risk of anxiolytic/hypnotic use. CONCLUSIONS: Psychotropic drugs prescription is common in cancer patients. Anxiolytic/hypnotic prescription rates are significantly higher in non-Malay female patients in Malaysia.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

[Surveillance of antibiotic consumption : clarification of the "definition of data on the nature and extent of antibiotic consumption in hospitals according to § 23 paragraph 4 sentence 2 of the IfSG"]. / Antibiotika-Verbrauchs-Surveillance : Ausführungen und Erläuterungen zur Bekanntmachung "Festlegung der Daten zu Art und Umfang des Antibiotika-Verbrauchs in Krankenhäusern nach § 23 Abs. 4 Satz 2 IfSG"

According to § 23 paragraph 4 of the German Infection Prevention Act (IfSG; July 2011), hospitals and clinics for ambulatory surgery are obliged to establish a continuous monitoring system of antibiotic consumption. This is aimed at contributing to an optimization of antibiotic prescription practices in order to confine the development and spread of resistant pathogens. The general requirements (restricted to hospitals) on the method and extent of data collection are provided by the national public health institution after discussion with representatives of various professional societies (Robert Koch-Institut, Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 59, 2013). The article aims to clarify these specifications and to provide background details. In agreement with national and European surveillance systems, the Anatomical Therapeutic Chemical (ATC)/Defined Daily Dose (DDD) classification system recommended by the WHO should be used as reference standard. Antibiotic consumption should be expressed as the number of DDDs per 100 patient days and per 100 admissions. The categories of antimicrobials and hospital organizational units to be monitored and the time intervals in which analyses should be conducted are determined. Furthermore, various approaches of data assessment are described.

Innovating by developing new uses of already-approved drugs: trends in the marketing approval of supplemental indications.

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