Drug vector representation: a tool for drug similarity analysis.

DrugMatrix is a valuable toxicogenomic dataset, which provides in vivo transcriptome data corresponding to hundreds of chemical drugs. However, the relationships between drugs and how those drugs affect the biological process are still unknown. The high dimensionality of the microarray data hinders its application. The aims of this study are to (1) represent the transcriptome data by lower-dimensional vectors, (2) compare drug similarity, (3) represent drug combinations by adding vectors and (4) infer drug mechanism of action (MoA) and genotoxicity features. We borrowed the latent semantic analysis (LSA) technique from natural language processing to represent treatments (drugs with multiple concentrations and time points) by dense vectors, each dimension of which is an orthogonal biological feature. The gProfiler enrichment tool was used for the 100-dimensional vector feature annotation. The similarity between treatments vectors was calculated by the cosine function. Adding vectors may represent drug combinations, treatment times or treatment doses that are not presented in the original data. Drug-drug interaction pairs had a higher similarity than random drug pairs in the hepatocyte data. The vector features helped to reveal the MoA. Differential feature expression was also implicated for genotoxic and non-genotoxic carcinogens. An easy-to-use Web tool was developed by Shiny Web application framework for the exploration of treatment similarities and drug combinations (https://bioinformatics.fafu.edu.cn/drugmatrix/). We represented treatments by vectors and provided a tool that is useful for hypothesis generation in toxicogenomic, such as drug similarity, drug repurposing, combination therapy and MoA.

Challenges with Forecasting Budget Impact: A Case Study of Six ICER Reports.

BACKGROUND: Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact. OBJECTIVE: We examined how different modeling assumptions influenced the results of 6 BIMs from the Institute for Clinical and Economic Review (ICER). STUDY DESIGN: Retrospective analysis of pharmaceutical sales data. METHODS: From ICER reports issued before 2016, we collected estimates of 3 BIM outputs: aggregate therapy cost (ie, cost to treat the patient population with a particular therapy), therapy uptake, and price. We compared these against real-world estimates that we generated using drug sales data. We considered 2 classes of BIM estimates: those forecasting future uptake of new agents, which assumed "unmanaged uptake," and those describing the contemporaneous market state (ie, estimates of current, managed uptake and budget impact for compounds already on the market). RESULTS: Differences between ICER's estimates and our own were largest for forecasted studies. Here, ICER's uptake estimates exceeded real-world estimates by factors ranging from 7.4 (sacubitril/valsartan) to 54 (hepatitis C treatments). The "unmanaged uptake" assumption (removed from ICER's approach in 2017) yields large deviations between BIM estimates and real-world consumption. Nevertheless, in some cases, ICER's BIMs that relied on current market estimates also deviated substantially from real-world sales data. CONCLUSIONS: This study highlights challenges with forecasting budget impact. In particular, assumptions about uptake and data source selection can greatly influence the accuracy of results.

Drug knowledge bases and their applications in biomedical informatics research.

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

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

INTRODUCTION: Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. OBJECTIVES: Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. METHODS: A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. RESULTS: There was moderate agreement between ADRs in social media and traditional sources. "Local and injection site reactions" was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter-fatigue-ranked fifth and seventh in FAERS and DIDs. CONCLUSION: Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources.

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

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

Improving pharmaceutical innovation by building a more comprehensive database on drug development and use.

New drugs and biologics have had a tremendous impact on the treatment of many diseases. However, available measures suggest that pharmaceutical innovation has remained relatively flat, despite substantial growth in research and development spending. We review recent literature on pharmaceutical innovation to identify limitations in measuring and assessing innovation, and we describe the framework and collaborative approach we are using to develop more comprehensive, publicly available metrics for innovation. Our research teams at the Brookings Institution and Deerfield Institute are collaborating with experts from multiple areas of drug development and regulatory review to identify and collect comprehensive data elements related to key development and regulatory characteristics for each new molecular entity approved over the past several decades in the United States and the European Union. Subsequent phases of our effort will add data on downstream product use and patient outcomes and will also include drugs that have failed or been abandoned in development. Such a database will enable researchers to better analyze the drivers of drug innovation, trends in the output of new medicines, and the effect of policy efforts designed to improve innovation.

Interacciones farmacológicas en ancianos hospitalizados utilizando programas informaticos en linea / Drug interactions in hospitalized elderly using computer programs online

Introducción: Se denominan interacciones farmacológicas (IF) a las relaciones que se establecen entre los fármacos dentro del organismo, que pueden resultar en cambios en la eficacia y seguridad de los mismos, a veces con efectos desfavorables. La polifarmacia genera una inmensa cantidad de combinaciones de drogas que aumenta el riesgo de IF. Este crecimiento es exponencial. Existen softwares que permiten conocer los pares de drogas capaces de generar IF. Estudiamos uno de ellos: Interdrugs® "http://www.medicamentos-rothlin.com.ar/” para conocer su desempeño y utilizarlo para estudiar el riesgo de IF en pacientes ancianos hospitalizados. Materiales y Métodos: Estudio transversal. Se ingresaron pacientes hospitalizados mayores de 65 años. Se consignaron datos demográficos, motivos de ingreso, patologías crónicas y medicación utilizada durante las primeras 24 hs de hospitalización. Las prescripciones se analizaron con el software "Interdrugs®” para pesquizar IF. Fueron clasificadas en leves, moderadas y severas. Se estimaron las frecuencias de: motivos de ingreso, drogas prescriptas y severidad de las IF. Se correlacionaron y compararon las frecuencias de IF moderadas a severas con los pacientes sin riesgo de IF o con IF leves. Se utilizaron los programas estadísticos SPSS 17 y EpiDat 3.

ABSTRACT: Introduction: Drug interactions (DI) are the relationships established between drugs inside the body, which can result in changes in the efficacy and safety of themselves, sometimes with adverse effects. Polypharmacy generates an immense amount of drug combinations that makes DI more frequent. There are software that allow to know the pairs of drugs capable of generating DI. We studied: Interdrugs® http://www.medicamentosrothlin.com.ar/" for their performance and use it to study the risk of DI in a group of hospitalized older patients. Materials and Methods: A cross-sectional study. We included patients 65 years old or older admitted to common room in three hospitals at Cordoba.We recorded demographic data, reason for admission, diagnoses of chronic diseases and medication used during the first 24 hours of hospitalization. The prescriptions were analyzed with the software "Interdrugs®" to search for DI. These were classified as mild, moderate, or severe. Frequencies were estimated for admission reasons, drugs prescription and severity of DI. Were correlated and compared moderate to severe DI frequencies with patients who had no risk of DI or mild DI. We used SPSS 17 and EpiDat 3 for statistical analysis.

Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework.

Based on existing data and previous work, a series of studies is proposed as a basis toward a pragmatic early step in transforming toxicity testing. These studies were assembled into a data-driven framework that invokes successive tiers of testing with margin of exposure (MOE) as the primary metric. The first tier of the framework integrates data from high-throughput in vitro assays, in vitro-to-in vivo extrapolation (IVIVE) pharmacokinetic modeling, and exposure modeling. The in vitro assays are used to separate chemicals based on their relative selectivity in interacting with biological targets and identify the concentration at which these interactions occur. The IVIVE modeling converts in vitro concentrations into external dose for calculation of the point of departure (POD) and comparisons to human exposure estimates to yield a MOE. The second tier involves short-term in vivo studies, expanded pharmacokinetic evaluations, and refined human exposure estimates. The results from the second tier studies provide more accurate estimates of the POD and the MOE. The third tier contains the traditional animal studies currently used to assess chemical safety. In each tier, the POD for selective chemicals is based primarily on endpoints associated with a proposed mode of action, whereas the POD for nonselective chemicals is based on potential biological perturbation. Based on the MOE, a significant percentage of chemicals evaluated in the first 2 tiers could be eliminated from further testing. The framework provides a risk-based and animal-sparing approach to evaluate chemical safety, drawing broadly from previous experience but incorporating technological advances to increase efficiency.

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.