Relevant bibliographies by topics / Drug interaction database

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Drug interaction database'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Drug interaction database"

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Yap, Kevin Yi-Lwern, Alexandre Chan, and Keung Chui Wai. "Opinions on Drug Interaction Sources in Anticancer Treatments and Parameters for an Oncology-Specific Database by Pharmacy Practitioners in Asia." Health Services Insights 3 (January 2010): HSI.S3679. http://dx.doi.org/10.4137/hsi.s3679.

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Cancer patients undergoing chemotherapy are particularly susceptible to drug-drug interactions (DDIs). Practitioners should keep themselves updated with the most current DDI information, particularly involving new anticancer drugs (ACDs). Databases can be useful to obtain up-to-date DDI information in a timely and efficient manner. Our objective was to investigate the DDI information sources of pharmacy practitioners in Asia and their views on the usefulness of an oncology-specific database for ACD interactions. A qualitative, cross-sectional survey was done to collect information on the respondents' practice characteristics, sources of DDI information and parameters useful in an ACD interaction database. Response rate was 49%. Electronic databases (70%), drug interaction textbooks (69%) and drug compendia (64%) were most commonly used. Majority (93%) indicated that a database catering towards ACD interactions was useful. Essential parameters that should be included in the database were the mechanism and severity of the detected interaction, and the presence of a management plan (98% each). This study has improved our understanding on the usefulness of various DDI information sources for ACD interactions among pharmacy practitioners in Asia. An oncology-specific DDI database targeting ACD interactions is definitely attractive for clinical practice.
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Monteith, Scott, Tasha Glenn, Michael Gitlin, and Michael Bauer. "Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs." Pharmacopsychiatry 53, no. 05 (April 30, 2020): 220–27. http://dx.doi.org/10.1055/a-1156-4193.

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Abstract Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder. Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability. Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight. Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.
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Bossaer, John B., and Christan M. Thomas. "Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis." Journal of Oncology Practice 13, no. 3 (March 2017): e217-e222. http://dx.doi.org/10.1200/jop.2016.016212.

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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.
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Noguchi, Yoshihiro, Tomoya Tachi, and Hitomi Teramachi. "Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database." Pharmaceutics 12, no. 8 (August 12, 2020): 762. http://dx.doi.org/10.3390/pharmaceutics12080762.

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Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.
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Ikeda, T. "DRUG INTERACTION WORKING GROUP: HAB DATABASE." Drug Metabolism and Pharmacokinetics 14, supplement (1999): 148–49. http://dx.doi.org/10.2133/dmpk.14.supplement_148.

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IKEDA, Toshihiko. "Drug Interaction Working Group. HAB Database." Drug Metabolism and Pharmacokinetics 15, no. 3 (2000): 255–59. http://dx.doi.org/10.2133/dmpk.15.255.

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Armahizer, Michael J., Sandra L. Kane-Gill, Pamela L. Smithburger, Ananth M. Anthes, and Amy L. Seybert. "Comparing Drug-Drug Interaction Severity Ratings between Bedside Clinicians and Proprietary Databases." ISRN Critical Care 2013 (November 26, 2013): 1–6. http://dx.doi.org/10.5402/2013/347346.

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Purpose. The purpose of this project was to compare DDI severity for clinician opinion in the context of the patient’s clinical status to the severity of proprietary databases. Methods. This was a single-center, prospective evaluation of DDIs at a large, tertiary care academic medical center in a 10-bed cardiac intensive care unit (CCU). A pharmacist identified DDIs using two proprietary databases. The physicians and pharmacists caring for the patients evaluated the DDIs for severity while incorporating their clinical knowledge of the patient. Results. A total of 61 patients were included in the evaluation and experienced 769 DDIs. The most common DDIs included: aspirin/clopidogrel, aspirin/insulin, and aspirin/furosemide. Pharmacists ranked the DDIs identically 73.8% of the time, compared to the physicians who agreed 42.2% of the time. Pharmacists agreed with the more severe proprietary database scores for 14.8% of DDIs versus physicians at 7.3%. Overall, clinicians agreed with the proprietary database 20.6% of the time while clinicians ranked the DDIs lower than the database 77.3% of the time. Conclusions. Proprietary DDI databases generally label DDIs with a higher severity rating than bedside clinicians. Developing a DDI knowledgebase for CDSS requires consideration of the severity information source and should include the clinician.
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Niazi-Ali, Saarah, Graham T. Atherton, Marcin Walczak, and David W. Denning. "Drug–drug interaction database for safe prescribing of systemic antifungal agents." Therapeutic Advances in Infectious Disease 8 (January 2021): 204993612110106. http://dx.doi.org/10.1177/20499361211010605.

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Introduction: A drug–drug interaction (DDI) describes the influence of one drug upon another or the change in a drug’s effect on the body when the drug is taken together with a second drug. A DDI can delay, decrease or enhance absorption or metabolism of either drug. Several antifungal agents have a large number of potentially deleterious DDIs. Methods: The antifungal drug interactions database https://antifungalinteractions.org/was first launched in 2012 and is updated regularly. It is available as web and app versions to allow information on potential drug interactions with antifungals with a version for patients and another for health professionals. A new and updated database and interface with apps was created in 2019. This allows clinicians and patients to rapidly check for DDIs. The database is fully referenced to allow the user to access further information if needed. Currently DDIs for fluconazole, itraconazole, voriconazole, posaconazole, isavuconazole, terbinafine, amphotericin B, caspofungin, micafungin and anidulafungin are cross-referenced against 2398 other licensed drugs, a total of nearly 17,000 potential DDIs. Results: The database records 541 potentially severe DDIs, 1129 moderate and 1015 mild DDIs, a total of 2685 (15.9%). Conclusion: As the online database and apps are free to use, we hope that widespread acceptance and usage will reduce medical misadventure and iatrogenic harm from unconsidered DDIs.
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Kunz, Meik, Chunguang Liang, Santosh Nilla, Alexander Cecil, and Thomas Dandekar. "The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development." Database 2016 (2016): baw041. http://dx.doi.org/10.1093/database/baw041.

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Monteith, Scott, and Tasha Glenn. "A comparison of potential psychiatric drug interactions from six drug interaction database programs." Psychiatry Research 275 (May 2019): 366–72. http://dx.doi.org/10.1016/j.psychres.2019.03.041.

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Dissertations / Theses on the topic "Drug interaction database"

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Bossaer, John B., and Christan M. Thomas. "Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/2328.

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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.
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Bossaer, John B., and Christian Thomas. "Drug Interaction Database Sensitivity with Oral Antineoplastics: An Exploratory Analysis." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/2339.

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Björn, Niklas. "Database processing for identification of concomitant drug frequencies in a forensic material positive for antidepressant drugs." Thesis, Linköpings universitet, Institutionen för medicin och hälsa, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-107575.

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This article presents a study conducted on data containing drug concentrations. The data was obtained from femoral venous blood samples collected at medico legal autopsies in Sweden. Cases positive for antidepressant drugs were scrutinized and divided in to two groups for 15 antidepressant drugs: B‑cases, where the cause of death was intoxication with more than one drug detected in the blood sample. C‑cases, where the cause of death was NOT intoxication and at least one drug (the antidepressant) was detected in the blood sample. This data was then processed to find frequencies of concomitant drugs taken together with the antidepressant drugs. Frequencies of the most common concomitant drugs were then compared between B-cases and C-cases for each antidepressant drug. This revealed that the drugs dextropropoxyphene, ethanol, codeine, flunitrazepam, paracetamol, propiomazine and alimemazine were signifcantly more common as concomitant drugs in B-cases (intoxications) than in C‑cases (non‑intoxications). With regards to unknown interactions the most interesting combinations were: Propiomazine with mirtazapine, venlafaxine, citalopram or fluoxetine; Paracetamol with paroxetine; Flunitrazepam with mirtazapine, venlafaxine or citalopram; Codeine with mirtazapine or sertraline. These combinations should be further investigated.
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Bossaer, John B., and Kanishka Chakraborty. "Drug Interaction Between Idelalisib and Diazepam Resulting in Altered Mental Status and Respiratory Failure." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/2325.

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In recent years, several new oral anticancer drugs have been approved, many via an accelerated approval process. These new agents have the potential for drug interactions, but lack of familiarity with these drugs by clinicians may increase the risk for drug interactions. We describe an interaction between the new anticancer agent idelalisib (CYP 3A4 inhibitor) and diazepam (CYP 3A4 substrate) that resulted in altered mental status and type II respiratory failure resulting in hospitalization. After discontinuation of both agents, the patient recovered quickly. Idelalisib was reinitiated after discharge. Lorazepam was substituted for diazepam since it is not metabolized via CYP 3A4. Both agents were tolerated well thereafter. This interaction was only flagged by two of four commonly used drug interaction databases. Clinicians should exercise caution with initiating new oral anticancer agents and consider the potential for drug interactions without solely relying on drug interaction databases.
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Eskens, D., and A. Gardner. "Specificity and Sensitivity of Drug Interaction Databases to Detect Meaningful QTc Interactions with Oral Antineoplastics." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etsu-works/7800.

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Clayborn, Jordan, Moses Holleyman, and John B. Bossaer. "Reliability of Drug Information Databases in Identifying Drug-drug Interactions with Oral Antineoplastic Agents." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/2349.

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Hsin, Kun-Yi. "Development and use of databases for ligand-protein interaction studies." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3974.

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This project applies structure-activity relationship (SAR), structure-based and database mining approaches to study ligand-protein interactions. To support these studies, we have developed a relational database system called EDinburgh University Ligand Selection System (EDULISS 2.0) which stores the structure-data files of +5.5 million commercially available small molecules (+4.0 million are recognised as unique) and over 1,500 various calculated molecular properties (descriptors) for each compound. A user-friendly web-based interface for EDULISS 2.0 has been established and is available at http://eduliss.bch.ed.ac.uk/. We have utilised PubChem bioassay data from an NMR based screen assay for a human FKBP12 protein (PubChem AID: 608). A prediction model using a Logistic Regression approach was constructed to relate the assay result with a series of molecular descriptors. The model reveals 38 descriptors which are found to be good predictors. These are mainly 3D-based descriptors, however, the presence of some predictive functional groups is also found to give a positive contribution to the binding interaction. The application of a neural network technique called Self Organising Maps (SOMs) succeeded in visualising the similarity of the PubChem compounds based on the 38 descriptors and clustering the 36 % of active compounds (16 out of 44) in a cluster and discriminating them from 95 % of inactive compounds. We have developed a molecular descriptor called the Atomic Characteristic Distance (ACD) to profile the distribution of specified atom types in a compound. ACD has been implemented as a pharmacophore searching tool within EDULISS 2.0. A structure-based screen succeeded in finding inhibitors for pyruvate kinase and the ligand-protein complexes have been successfully crystallised. This study also discusses the interaction of metal-binding sites in metalloproteins. We developed a database system and web-based interface to store and apply geometrical information of these metal sites. The programme is called MEtal Sites in Proteins at Edinburgh UniverSity (MESPEUS; http://eduliss.bch.ed.ac.uk/MESPEUS/). MESPEUS is an exceptionally versatile tool for the collation and abstraction of data on a wide range of structural questions. As an example we carried out a survey using this database indicating that the most common protein types which contain Mg-OATP-phosphate site are transferases and the most common pattern is linkage through the β- and γ-phosphate groups.
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Wang, Chen. "High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5509.

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Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.
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Blaskowsky, Jeffrey, Adam Odeh, Tyler Stuntz, and Ali McBride. "Drug Therapy Interactions with New Oral Anticoagulants in Oncology Patients: a Retrospective Database Analysis 2013 - 2015." The University of Arizona, 2016. http://hdl.handle.net/10150/613993.

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Class of 2016 Abstract
Objectives: To identify common and serious drug-drug interactions involving novel anticoagulant drugs in cancer patients. Subjects: 60 patients who were treated at the Banner University of Arizona Cancer Center between November 1, 2013 and April 1, 2015 with rivaroxaban, dabigatran, or apixaban. Methods: A retrospective chart review was performed for patients who received a NOAC (novel oral anticoagulant) to determine if a medication regimen contained a drug-drug interaction involving the NOAC. Results: When analyzing the DDIs involving rivaroxaban, dabigatran, and apixaban, Micromedex® detected a total of 123 interactions, compared to Lexicomp®, which detected 111 interactions. When using Lexicomp®, there were 59 (32%) instances of no detected interactions, 19 (10%) moderate interactions, 27 (15%) major interactions, and 65 (36%) contraindicated DDIs with rivaroxaban. When using Micromedex®, there were 47 (26%) instances where no interaction was detected, 4 (2%) moderate interactions, and 119 (65%) major interactions, and no interactions were classified as contraindicated with rivaroxaban. Lexicomp® detected 3 (50%) interactions as major, and found no DDIs in 3 (50%) instances for dabigatran, and detected 1 (7%) moderate, 2 (14%) major and 6 (43%) contraindicated interactions for apixaban. Micromedex® detected 3 (50%) interactions as major, and found no DDIs in 3 (50%) instances for dabigatran, and detected 12 (86%) of interactions as major and found no DDIs in 2 (14%) instances for apixaban. Conclusions: There was significant variation in DDI detection between current literature4,5 and the drug information databases, Lexicomp® and Micromedex®, however most interactions detected were major or contraindicated.
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Kumar, Vivek. "Computational Prediction of Protein-Protein Interactions on the Proteomic Scale Using Bayesian Ensemble of Multiple Feature Databases." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1322489637.

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Books on the topic "Drug interaction database"

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Faculty, Therapeutic Research. Natural Medicines Comprehensive Database. 2nd ed. Pharmacists Letter, 1999.

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Faculty, Therapeutic Research. Natural Medicines Comprehensive Database. Pharmacists Letter, 2000.

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M, Jellin Jeff, and Therapeutic Research Faculty, eds. Natural medicines comprehensive database. 5th ed. Stockton, CA: Therapeutic Research Faculty, 2003.

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M, Jellin Jeff, ed. Natural medicines comprehensive database. 9th ed. Stockton, CA: Therapeutic Research Faculty, 2007.

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Natural medicines comprehensive database: Consensus of current scientific information on herbal medicines and dietary supplements of practical importance to the health professional. 4th ed. Stockton, CA: Therapeutic Research Faculty, 2002.

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Ainsworth, Sean. Neonatal Formulary. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198840787.001.0001.

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Neonatal Formulary bridges a gap between a standard formulary (stating doses, indications, etc.) and a standard neonatal textbook by expanding information about the conditions for which each drug is used. Much of drug use during pregnancy, lactation, and in neonates and young infants is ‘off license’ (i.e. using licensed drugs but for an indication that is outside the licensed use—in many cases simply because the studies and the licensing application did not include data about neonatal use). The book offers information to allow practitioners to make informed choices whether to use such a drug or not by presenting data from published studies to support such a use. Part 1 concentrates on drug prescribing and drug administration, presenting general information on drug storage, drug licensing, and drug prescribing. It also explains to the reader why the metabolism of drugs differs in premature and sick infants and why the practice of extrapolating doses from adult studies is wrong. Patient safety, excipients, and therapies that affect drug metabolism (such as therapeutic hypothermia) are also covered. Part 2 consists of drug monographs for over 250 drugs that may find use in the neonatal population but which nonetheless may also find use outside the neonatal unit. Each monograph is divided into sections covering use, pharmacology, treatment, drug interactions, or other administration information, supply, and administration, and references. The monographs also contain links to Cochrane Database of Systematic Reviews and national guidelines supported by bodies such as the National Institute for Health and Care Excellence or the Royal Colleges. Part 3 contains brief notes on a range of additional drugs and groups of drugs that are often taken by mothers during pregnancy, labour, or during breast feeding where effects on either the fetus or infant can be seen. This information will help to provide safe and effective prescribing of drugs to all mothers and their babies.
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M, Jellin Jeff, Batz Forrest, Hitchens Katy, and Therapeutic Research Faculty, eds. Natural medicines comprehensive database: Consensus of current scientific information on herbal medicines and dietary supplements of practical importance to the health professional. 2nd ed. Stockton, CA: Therapeutic Research Faculty, 1999.

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M, Jellin Jeff, Batz Forrest, Hitchens Kathy, and Therapeutic Research Faculty, eds. Natural medicines comprehensive database: Consensus of current scientific information of practical and clinical importance to health professionals covering herbal medicines, dietary supplements, and other natural medicines. 3rd ed. Stockton, CA: Therapeutic Research Faculty, 2000.

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Florida. Agency for Health Care Administration., ed. Florida's Automated Interactive Medical Information System. [Tallahassee, Fla.]: The Agency, 1998.

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Book chapters on the topic "Drug interaction database"

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Wang, Dongsheng, Hongjie Fan, and Junfei Liu. "Drug-Drug Interaction Extraction via Attentive Capsule Network with an Improved Sliding-Margin Loss." In Database Systems for Advanced Applications, 612–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73197-7_41.

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Hachad, Houda, Isabelle Ragueneau-Majlessi, and René H. Levy. "Management of Drug Interactions of New Drugs in Multicenter Trials Using the Metabolism and Transport Drug Interaction Database©." In Enzyme- and Transporter-Based Drug-Drug Interactions, 371–86. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0840-7_15.

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Maeda, Kazuya, Yoshihisa Shitara, Toshiharu Horie, and Yuichi Sugiyama. "Web-Based Database as a Tool to Examine Drug–Drug Interactions Involving Transporters." In Enzyme- and Transporter-Based Drug-Drug Interactions, 387–412. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0840-7_16.

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Linnarsson, Rolf. "Drug Interactions in Primary Care — A Retrospective Database Study." In Medical Informatics Europe 1991, 195–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-93503-9_34.

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Cole, Jason C., Jos P. M. Lommerse, R. Scott Rowland, Robin Taylor, and Frank H. Allen. "Use of the Cambridge Structural Database to Study Non-Covalent Interactions: Towards a Knowledge Base of Intermolecular Interactions." In Structure-Based Drug Design, 113–24. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9028-0_11.

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Buza, Krisztian, and Ladislav Peska. "ALADIN: A New Approach for Drug–Target Interaction Prediction." In Machine Learning and Knowledge Discovery in Databases, 322–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_20.

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Kalyani, Duggineni, Naresh Babu Muppalaneni, Ch Ambedkar, and Kiran Kumar Reddi. "Identification of Drug Targets from Integrated Database of Diabetes Mellitus Genes Using Protein-Protein Interactions." In Application of Computational Intelligence to Biology, 83–86. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0391-2_8.

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"Development of a Metabolic Drug Interaction Database at the University of Washington." In Drug-Drug Interactions, 580–95. CRC Press, 2001. http://dx.doi.org/10.1201/b14003-17.

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Lalanne, Frédéric, Pierrick Bedouch, Cyril Simonnet, Vincent Depras, Georgeta Bordea, Romain Bourqui, Thierry Hamon, Frantz Thiessard, and Fleur Mougin. "Visualizing Food-Drug Interactions in the Thériaque Database." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210159.

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This paper presents a prototype for the visualization of food-drug interactions implemented in the MIAM project, whose objective is to develop methods for the extraction and representation of these interactions and to make them available in the Thériaque database. The prototype provides users with a graphical visualization showing the hierarchies of drugs and foods in front of each other and the links between them representing the existing interactions as well as additional details about them, including the number of articles reporting the interaction. The prototype is interactive in the following ways: hierarchies can be easily folded and unfolded, a filter can be applied to view only certain types of interactions, and details about a given interaction are displayed when the mouse is moved over the corresponding link. Future work includes proposing a version more suitable for non-health professional users and the representation of the food hierarchy based on a reference classification.
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Hachad, Houda, Isabelle Ragueneau-Majlessi, and René H. Levy. "Metabolism and Transport Drug Interaction Database: A Web-Based Research and Analysis Tool." In Drug-Drug Interactions, 567–80. CRC Press, 2019. http://dx.doi.org/10.1201/9780429131967-13.

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Conference papers on the topic "Drug interaction database"

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Zhang, Shijun, Heng-Yi Wu, Rohith Vanam, chien-WeiChiang, Lei Wang, and Lang Li. "Abstract 4400: Translational drug interaction database (TDID): A knowledgebase for drug interactions." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4400.

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Kathad, Umesh, Yuvanesh Vedaraju, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, and Arun Asaithambi. "Abstract 672: Establishment of a drug-tumor interaction database using Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM)." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-672.

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Kathad, Umesh, Yuvanesh Vedaraju, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, and Arun Asaithambi. "Abstract 672: Establishment of a drug-tumor interaction database using Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM)." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-672.

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Celebi, Remzi, Vahab Mostafapour, Erkan Yasar, Ozgur Gumus, and Oguz Dikenelli. "Prediction of Drug-Drug Interactions Using Pharmacological Similarities of Drugs." In 2015 26th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2015. http://dx.doi.org/10.1109/dexa.2015.23.

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Junaid, E., ER Hermes-DeSantis, N. Bhalla, B. Cadman, and A. Eggleton. "5PSQ-137 When is a drug interaction not a drug interaction? Comparison of drug-drug interactions-checking databases between the UK and USA." In 24th EAHP Congress, 27th–29th March 2019, Barcelona, Spain. British Medical Journal Publishing Group, 2019. http://dx.doi.org/10.1136/ejhpharm-2019-eahpconf.570.

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Howerton, Brian M., and Michael G. Jones. "A Conventional Liner Acoustic/Drag Interaction Benchmark Database." In 23rd AIAA/CEAS Aeroacoustics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-4190.

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Heckmann, Klaus, Jürgen Sievers, and Fabian Weyermann. "Leak Rate Computation: Flow Resistance vs. Thermal-Hydraulic Aspect." In ASME 2018 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/pvp2018-84534.

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The computation of mass flow rates through crack-like defects in piping systems of light water reactors requires typically the description of two-phase flow conditions. The computed discharge rate depends on the crack opening area, the thermal-hydraulic modeling of the flow, and the flow resistance of the crack. Several models have been proposed to characterize the critical flow through crack-like defects. An evaluation of advantages and shortcomings of the different models with regard to the interaction of the three different parts (crack opening area, thermal-hydraulic modeling, flow resistance) has been performed. In this paper, the flow resistance modeling from several approaches is discussed, and compared with a database from eight different testing programs. Five different flow models are applied to analyze a database of more than 800 leak rate measurements for subcooled water from twelve different experimental programs. It is shown that the correct modeling of the flow resistance is crucial for a best estimate reproduction of the measured data. It turns out that generally, equilibrium models are about as good as non-equilibrium models. The data were processed with the GRS software WinLeck which includes different analytical approaches for the calculation of crack sizes and leak rates in piping components. The most reliable results within the model selection are produced by the CDR model (Critical Discharge Rate) of the ATHLET code (Analysis of Thermal-hydraulics of Leaks and Transients) developed by GRS. As a conclusion, the accurate modeling of form losses and frictional pressure losses for critical discharge flow rates through crack-like leaks are essential for a reliable prediction of flow rates. Uncertainties in leak rate computations results arise due to the lack of information about the flow geometry and its associated drag. The assumed flow resistance of a through-wall crack influences the computed leak rate as significant as the phase-change- and flow-models. The manifest difference between equilibrium models (Pana, Estorf) and non-equilibrium models (Henry, ATHLET-CDR) seems to be less significant than the pressure loss issue. One can conjecture that, for crack-like through-wall defects, friction effects play a more important role than non-equilibrium effects.
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Le Cunff, Ce´dric, Ste´phane Toumit, and Jean-Michel Heurtier. "A Simplified Approach to Estimate Wake Oscillations in Riser Arrays." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49208.

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In the offshore industry, modeling pipe vibrations due to current is important to predict structural fatigue life. In the case of Wake Induced Oscillations (WIO), clashing is also an issue during the design phase to be able to define enough clearance to prevent clashing. If not possible, it is then needed to estimate contact energy between pipes and ensure that clashing is acceptable. Wake induced oscillations are difficult to predict, involving Vortex-Induced Vibrations (VIV) at relatively high frequency and small displacement together with larger motion at lower frequency leading to potential contact. A hydrodynamic model is proposed to predict WIO of a flexible pipe in the wake of an upstream pipe. The structural displacement of the two pipes is computed with a classical finite element model. The pipes are linked thru two dimensional strips where the hydrodynamic loads are computed based on the pipe distance in the strip. Since both pipes are flexible, the upstream pipe is subjected to VIV while the downstream pipe is subjected to the mean wake created by the pipe, to VIV as well as WIO. Each effect is represented by a simplified model. A Blevins model is used to represent the quasi-static drag and lift forces on the downstream riser. The VIV on the upstream riser is computed with a Van der Pol oscillator model, and a similar model is used for the downstream riser with an added term to account for the upstream riser presence. Experimental results on two tandem jumpers are used to validate the approach in steady current and in current plus wave. The database has a large number of model tests with different initial gaps between the two jumpers. Dynamic response of the two pipes is measured thru accelerometers and tension sensors. Some of the configurations exhibit clashing and/or overlapping of the jumpers. Amplitude and spectra of vibrations are compared to the proposed model; the general characteristics of the interaction (contact, overlapping) are also addressed.
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