Journal articles: '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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Justad, Hanna, Ylva Askfors, Tero Shemeikka, Marine L. Andersson, and Tora Hammar. "Patients’ Use and Perceptions of a Drug-Drug Interaction Database: A Survey of Janusmed Interactions." Pharmacy 9, no. 1 (January 19, 2021): 23. http://dx.doi.org/10.3390/pharmacy9010023.

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Janusmed interactions is a drug-drug interactions (DDI) database available online for healthcare professionals (HCP) at all levels of the healthcare system including pharmacies. The database is aimed at HCP but is also open to the public for free, for those individuals who register for a personal account. The aim of this study was to investigate why and how patients use the database Janusmed interactions, how they perceive content and usability, and how they would react if they found an interaction. A web-based questionnaire was sent by email to all users who had registered for Janusmed interactions as a “patient” (n = 3219). A total of 406 patients completed the survey (response rate 12.6%). The study shows that there is an interest among patients to use a DDI database to check their own or a relative’s medication. The respondents found the database easy to use and perceive they understand the information aimed at HCP. Most patients stated they would talk to their HCP if they found an interaction and not adjust their treatment by themselves. However, the respondents in this study are actively searching for information and seem to have high health literacy. Thus, the findings are not generalizable for the general population.

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Talwar, Puneet, Renu Gupta, Suman Kushwaha, Rachna Agarwal, Luciano Saso, Shrikant Kukreti, and Ritushree Kukreti. "Viral Induced Oxidative and Inflammatory Response in Alzheimer’s Disease Pathogenesis with Identification of Potential Drug Candidates: A Systematic Review using Systems Biology Approach." Current Neuropharmacology 17, no. 4 (March 12, 2019): 352–65. http://dx.doi.org/10.2174/1570159x16666180419124508.

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Alzheimer’s disease (AD) is genetically complex with multifactorial etiology. Here, we aim to identify the potential viral pathogens leading to aberrant inflammatory and oxidative stress response in AD along with potential drug candidates using systems biology approach. We retrieved protein interactions of amyloid precursor protein (APP) and tau protein (MAPT) from NCBI and genes for oxidative stress from NetAge, for inflammation from NetAge and InnateDB databases. Genes implicated in aging were retrieved from GenAge database and two GEO expression datasets. These genes were individually used to create protein-protein interaction network using STRING database (score≥0.7). The interactions of candidate genes with known viruses were mapped using virhostnet v2.0 database. Drug molecules targeting candidate genes were retrieved using the Drug- Gene Interaction Database (DGIdb). Data mining resulted in 2095 APP, 116 MAPT, 214 oxidative stress, 1269 inflammatory genes. After STRING PPIN analysis, 404 APP, 109 MAPT, 204 oxidative stress and 1014 inflammation related high confidence proteins were identified. The overlap among all datasets yielded eight common markers (AKT1, GSK3B, APP, APOE, EGFR, PIN1, CASP8 and SNCA). These genes showed association with hepatitis C virus (HCV), Epstein– Barr virus (EBV), human herpes virus 8 and Human papillomavirus (HPV). Further, screening of drugs targeting candidate genes, and possessing anti-inflammatory property, antiviral activity along with a suggested role in AD pathophysiology yielded 12 potential drug candidates. Our study demonstrated the role of viral etiology in AD pathogenesis by elucidating interaction of oxidative stress and inflammation causing candidate genes with common viruses along with the identification of potential AD drug candidates.

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Li, Andrew, Stephanie Zhao, and Tomasz Z. Jodlowski. "How Up-to-Date is Your Drug-Drug Interaction Database?" Annals of Pharmacotherapy 45, no. 12 (November 29, 2011): 1591–92. http://dx.doi.org/10.1345/aph.1q444.

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Freshour, Sharon L., Susanna Kiwala, Kelsy C. Cotto, Adam C. Coffman, Joshua F. McMichael, Jonathan J. Song, Malachi Griffith, Obi L. Griffith, and Alex H. Wagner. "Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts." Nucleic Acids Research 49, no. D1 (November 25, 2020): D1144—D1151. http://dx.doi.org/10.1093/nar/gkaa1084.

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Abstract The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

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Yin, Jiayi, Fengcheng Li, Ying Zhou, Minjie Mou, Yinjing Lu, Kangli Chen, Jia Xue, et al. "INTEDE: interactome of drug-metabolizing enzymes." Nucleic Acids Research 49, no. D1 (October 12, 2020): D1233—D1243. http://dx.doi.org/10.1093/nar/gkaa755.

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Abstract Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome–DME interaction (MICBIO), xenobiotics–DME interaction (XEOTIC) and host protein–DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/

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Vecchia, Stefano, Elena Orlandi, Corrado Confalonieri, Enrico Damonti, Alessandra Riva, Alessia Sartori, and Luigi Cavanna. "Prevalence study on potential drug–drug interaction in cancer patients in Piacenza hospital’s Onco-Haematology department." Journal of Oncology Pharmacy Practice 24, no. 7 (July 16, 2017): 490–93. http://dx.doi.org/10.1177/1078155217717324.

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Background Cancer patients can be a human model of potential drug interactions. Usually they receive a large number of different medications, including antineoplastic agents, drugs for comorbid illness and medication for supportive care, however information about these interactions are fragmented and poor. Objective We assessed a prospective study to evaluate the prevalence of drug interaction among patients hospitalized in the Onco-Haematology department, Hospital of Piacenza. Methods Data on drugs administered for cancer, comorbidities, or supportive care were collected from different computerized prescription software in use in the department; we compared them with a database to focus on the co-administration of drugs. A literature review was performed to identify major potential drug interaction and to classify them by level of severity and by strengths of scientific evidence. Results In this study 284 cancer patients were enrolled; patients had taken an average of seven drugs on each day of therapy plus chemotherapeutic agents, we identified 67 potential drug interactions. At least 53 patients had one potential drug interaction. Of all potential drug interactions 63 were classified as moderate severity and only four as major. In 55 cases chemotherapeutic agents were involved in possible interactions with supportive care drugs, meanwhile in 12 cases the potential drug interactions were between supportive care drugs. Conclusions In our centre, thanks to a computerized prescription software, integrated with caution alarm in case of possible interaction, we had a lower rate of potential drug interactions than the one from literature. It is possible to improve the software integrating the alarm with the potential drug interactions between chemotherapy agents and supportive care drugs.

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Bahar, Muh Akbar, Pauline Lanting, Jens H. J. Bos, Rolf H. Sijmons, Eelko Hak, and Bob Wilffert. "Impact of Drug-Gene-Interaction, Drug-Drug-Interaction, and Drug-Drug-Gene-Interaction on (es)Citalopram Therapy: The PharmLines Initiative." Journal of Personalized Medicine 10, no. 4 (November 28, 2020): 256. http://dx.doi.org/10.3390/jpm10040256.

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We explored the association between CYP2C19/3A4 mediated drug-gene-interaction (DGI), drug-drug-interaction (DDI) and drug-drug-gene-interaction (DDGI) and (es)citalopram dispensing course. A cohort study was conducted among adult Caucasians from the Lifelines cohort (167,729 participants) and linked dispensing data from the IADB.nl database as part of the PharmLines Initiative. Exposure groups were categorized into (es)citalopram starters with DGI, DDI and DDGI. The primary outcome was drug switching and/or dose adjustment, and the secondary was early discontinuation after the start of (es)citalopram. Logistic regression modeling was applied to estimate adjusted odd ratios with their confidence interval. We identified 316 (es)citalopram starters with complete CYP2C19/3A4 genetic information. The CYP2C19 IM/PM and CYP3A4 NM combination increased risks of switching and/or dose reduction (OR: 2.75, 95% CI: 1.03–7.29). The higher effect size was achieved by the CYP2C19 IM/PM and CYP3A4 IM combination (OR: 4.38, 95% CI: 1.22–15.69). CYP2C19/3A4 mediated DDIs and DDGIs showed trends towards increased risks of switching and/or dose reduction. In conclusion, a DGI involving predicted decreased CYP2C19 function increases the need for (es)citalopram switching and/or dose reduction which might be enhanced by co-presence of predicted decreased CYP3A4 function. For DDI and DDGI, no conclusions can be drawn from the results.

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Ordak, Michal, Tadeusz Nasierowski, Elzbieta Muszynska, and Magdalena Bujalska-Zadrozny. "Increasing the Effectiveness of Pharmacotherapy in Psychiatry by Using a Pharmacological Interaction Database." Journal of Clinical Medicine 10, no. 10 (May 18, 2021): 2185. http://dx.doi.org/10.3390/jcm10102185.

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Recent studies have shown that the knowledge of pharmacological interaction databases in global psychiatry is negligible. The frequency of hospitalizations in the case of patients taking new psychoactive substances along with other drugs continues to increase, very often resulting in the need for polypharmacotherapy. The aim of our research was to make members of the worldwide psychiatric community aware of the need to use a pharmacological interaction database in their daily work. The study involved 2146 psychiatrists from around the world. Participants were primarily contacted through the LinkedIn Recruiter website. The surveyed psychiatrists answered 5 questions concerning case reports of patients taking new psychoactive substances along with other drugs. The questions were answered twice, i.e., before and after using the Medscape drug interaction database. The mean percentage of correct answers given by the group of psychiatrists who were studied separately in six individual continents turned out to be statistically significantly higher after using the pharmacological interaction database (p < 0.001). This also applies to providing correct answers separately, i.e., to each of the five questions asked concerning individual case reports (p < 0.001). Before using the drug interaction database, only 14.1% of psychiatrists stated that they knew and used this type of database (p < 0.001). In the second stage of the study, a statistically significant majority of subjects stated that they were interested in using the pharmacological interaction database from that moment on (p < 0.001) and expressed the opinion that it could be effective in everyday work (p < 0.001). Using a pharmacological interaction database in psychiatry can contribute to the effectiveness of pharmacotherapy.

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Fiorini, Giulia, John Martin Bland, Elizabeth Hughes, Valentina Castelli, and Dino Vaira. "A Systematic Review on Drugs Absorption Modifications after Eradication in Helicobacter pylori Positive Patients undergoing Replacement Therapy." Journal of Gastrointestinal and Liver Diseases 24, no. 1 (March 1, 2015): 95–100. http://dx.doi.org/10.15403/jgld.2014.1121.fio.

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Background & Aims: Helicobacter pylori (H. pylori) infection has been suggested as a cause of impaired drug absorption. This infection leads to alteration of the gastric acid secretion that may change the conformational characteristics of drugs and their intestinal absorption leading to uncertainties about the dose to administer and the therapeutic results. A systematic review was undertaken to clarify the implications of drug absorption during the administration of replacement therapies.Methods: Electronic databases such as MEDLINE/Pubmed, EMBASE and The Cochrane Library [which includes Cochrane Database of Systematic Review (CDSR), the Cochrane Central Register of Controlled Trials (CENTRAL), the Database of Abstract of Reviews of Effect (DARE)] were searched. Grey literature databases (e.g. the International clinical trials registry platform, Trials Register, Clinical Trials.gov, Controlled Trials and TrialsCentral), Theses database, Government publication and LILACS database were also searched. No language restriction was applied.Results: Infection and altered drug absorption were evaluated in patients under replacement therapies with iron, thyroxin and L-dopa. In all, seven studies included an improvement in drug absorption after eradication and an existing inverse correlation between the grade of gastric inflammation and indices of drug absorption were noticed.Conclusion: This systematic review confirmed the presence of an interaction between infection and drug absorption of orally administered replacement therapies. Gastric acid reduction and subsequent alteration of drug composition seem to lead this mechanism. Clinicians should be aware of this possible interaction when starting a replacement therapy in patients and when evaluating poor clinical response.

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Sarvesh, Sabarathinam, Preethi L, Haripritha Meganathan, M. Arjun Gokulan, Dhivya Dhanasekaran, Nila Ganamurali, and Rahul Radhakrishnan. "HCIP: An Online database for prediction CYP450 Enzyme Inhibition potential of bioactive compounds." Journal of Drug Delivery and Therapeutics 11, no. 2 (April 1, 2021): 253–55. http://dx.doi.org/10.22270/jddt.v11i2.4637.

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Background: Concomitant administration of herbal medicine and conventional may lead to severe metabolism-oriented herb-drug interactions. However, detecting herb-drug interaction is expensive and higher time-consuming. Several computer-aided techniques have been proposed in recent years to predict drug interactions. However, most of the methods cannot predict herb-drug interactions effectively. Methods: Canonical SMILES of bioactive compounds was gathered from the PubChem online database, and its inhibition details were gathered PKCSM from the webserver. Results: By searching the bioactive compound name in the search bar of “The Herb-CYP450 Enzyme Inhibition Predictor online database” (HCIP- http://hcip.in/), it will provide the liver enzyme inhibition profile of the selected bioactive compound. For example; Guggulsterone: CYP3A4 inhibitor. Conclusion: The Herb-CYP450 Enzyme Inhibition Predictor online database is very peculiar and easy to determine the inhibition profile of the targeted bioactive compound. Keywords: CYP450; Enzyme inhibition; Bioactive Compounds; Online database; Herb-Drug Interaction

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Schreyer, Adrian, and Tom Blundell. "CREDO: A Protein-Ligand Interaction Database for Drug Discovery." Chemical Biology & Drug Design 73, no. 2 (February 2009): 157–67. http://dx.doi.org/10.1111/j.1747-0285.2008.00762.x.

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Yang, Changsop, Youngeun Kim, Icktae Kim, Chul Kim, Sangjun Yea, and Miyoung Song. "Developing a Database of Herb-Drug Interaction on Hypertension." Integrative Medicine Research 4, no. 1 (May 2015): 142. http://dx.doi.org/10.1016/j.imr.2015.04.275.

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Andersson, M. L., Y. Böttiger, J. D. Lindh, B. Wettermark, and B. Eiermann. "Impact of the drug-drug interaction database SFINX on prevalence of potentially serious drug-drug interactions in primary health care." European Journal of Clinical Pharmacology 69, no. 3 (July 1, 2012): 565–71. http://dx.doi.org/10.1007/s00228-012-1338-y.

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Böttiger, Ylva, Kari Laine, Marine L. Andersson, Tuomas Korhonen, Björn Molin, Marie-Louise Ovesjö, Tuire Tirkkonen, Anders Rane, Lars L. Gustafsson, and Birgit Eiermann. "SFINX—a drug-drug interaction database designed for clinical decision support systems." European Journal of Clinical Pharmacology 65, no. 6 (February 11, 2009): 627–33. http://dx.doi.org/10.1007/s00228-008-0612-5.

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Kibrom, Samson, Zelalem Tilahun, and Solomon Assefa Huluka. "Potential Drug-Drug Interactionsamong Adult Patients Admitted to MedicalWards at a Tertiary Teaching Hospital inEthiopia." Journal of Drug Delivery and Therapeutics 8, no. 5-s (October 1, 2018): 348–54. http://dx.doi.org/10.22270/jddt.v8i5-s.2056.

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Abstract Introduction: A Drug-drug interaction (DDI) is a decrease or increase in the pharmacological or clinical response to the administration of two or more drugs that are different from the anticipated response they initiate when individually administered. Objectives: To assess the prevalence and factors associated with potential DDIs among adult inpatients admitted to the medical wards of a tertiary teaching Hospital in Ethiopia. Methods: A retrospective cross-sectional study design was employed on adult patients who were admitted to the medical ward in one year period. A total of 384patients’ medical records were checked for a possible DDI using Micromedex DrugReax® drug interaction database and analyzed consecutively using SPSS version 20.0. Results: Among 384 adult patients enrolled in the study, 209 (54.4%) of them had medications with at least one potential DDI in their prescriptions. Of the 209 potential DDI, 26.3% were with a minimum of one major potential DDI. The median number of potential DDI per patient was 2.2. Overall, 296 potential DDI were identified in the current study. Among 296 identified potential drug-drug interactions, most of the interaction (49.7%) had good documentation. The number of medication prescribed per patient showed a significant (p< 0.001) association with the occurrence of potential DDIs. Conclusion: More than half of the patients’ prescription contains potentially interacting medications. This study, additionally, revealed that there is a significant association between potential DDIs and number of medications prescribed per patient. Key words: Drug-drug interactions, pharmacokinetic interaction, pharmacodynamic interaction, internal medicine

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Cui, Zhijie, Hong Kang, Kailin Tang, Qi Liu, Zhiwei Cao, and Ruixin Zhu. "Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation." BioMed Research International 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/657159.

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The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR’s agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

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Monteiro, Camila Ribeiro de Arruda, Jean Henri Maselli Schoueri, Debora Terra Cardial, Lívia de Castro Linhares, Karine Corcione Turke, Lia Vineyard Steuer, Levy Werneck de Almeida Menezes, et al. "Evaluation of the systemic and therapeutic repercussions caused by drug interactions in oncology patients." Revista da Associação Médica Brasileira 65, no. 5 (May 2019): 611–17. http://dx.doi.org/10.1590/1806-9282.65.5.611.

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SUMMARY INTRODUCTION: Drug interaction is an important cause of global morbidity. It is of particular importance in cancer patients since they are often in use of polypharmacy, related to interactions between the drugs and the chemotherapeutics used. OBJECTIVE: To evaluate the drug interaction between chemotherapy and other drugs in cancer patients. METHODS: a cross-sectional study carried out in the outpatient oncology department of a public tertiary hospital. Two hundred thirty-five patients were included, and the drugs they were using were identified. Using the MedScape and Epocrates database, we evaluated the interactions between medications and chemotherapy by defining their frequency and dividing their severity from interaction into mild, close monitoring necessity and severe. RESULTS: 161 patients had some drug interaction. We identified 9 types of mild interactions, 23 types of interactions with close monitoring necessity, and 2 types of serious interactions. The most frequent interactions were between fluorouracil and leucovorin (32 cases) and cyclophosphamide and doxorubicin (19 cases). Serious interactions were between aspirin and pemetrexed; and leucovorin and Bactrim. CONCLUSION: In the present study, drug interactions were frequent, including serious interactions with a potential increase in morbidity and mortality. Thus, it is necessary for oncologists to draw up a therapeutic plan considering potential interactions between prescribed chemotherapy and current medications in use by patients.

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Schjøtt, Pernille, Martina Šutovská, and Jan Schjøtt. "The Possibility of Therapeutic Drug Monitoring of the Most Important Interactions in Nursing Homes." Current Clinical Pharmacology 14, no. 2 (October 25, 2019): 152–56. http://dx.doi.org/10.2174/1574884714666181224144722.

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Background:Therapeutic drug monitoring is a relevant tool in drug treatment of elderly patients. The aim of this study was to assess the possibility of therapeutic drug monitoring of the most important potential interactions in nursing homes.Methods:A material of prescribed drugs to 446 patients in three nursing homes in Bergen, Norway from a single day in March 2016 was analysed. Clinically relevant drug interactions (pharmacodynamic or pharmacokinetic) were identified and classified with Stockley`s Interaction Alerts. The most important interaction among several in each patient were ranked by recommended action > severity > evidence according to Stockley`s. The possibility of therapeutic drug monitoring of drug combinations involved in the most important interactions was retrieved from a database of all laboratories performing clinical pharmacology in Norway (the Pharmacology Portal).Results:Two or more drugs were used by 443 (99.3%) of 446 patients. Three-hundred and eightyfour patients (86.1%) had > 1 interaction. About 95% of the most important interactions were pharmacodynamic. In 280 (72.9%) of these interactions, Stockley`s recommended adjust dose or monitoring. Among the 384 most important interactions, 93% involved one drug and 41% involved two drugs available for therapeutic drug monitoring.Conclusion:In this pilot study, therapeutic drug monitoring was possible in the majority of the most important interactions in Norwegian nursing homes. This option is of importance since adjust dose or monitoring were frequently recommended actions associated with these interactions.

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Chaudhary, Shrijana Kumari, Naresh Manadhar, and Laxman Adhikari. "Polypharmacy and potential drug-drug interactions among medications prescribed to chronic kidney disease patients." Janaki Medical College Journal of Medical Science 9, no. 1 (July 8, 2021): 25–32. http://dx.doi.org/10.3126/jmcjms.v9i1.38047.

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Background and Objectives: Chronic kidney disease is a major systemic condition. Presence of comorbid conditions with the deteriorating renal function, lead them to use multiple drugs. Polypharmacy is common among chronic kidney disease. The possibility of drug interaction rises when a patients concurrently receive more than one drug and the chances increase with the number of drugs taken, which may be associated with increased morbidity, mortality, length of hospital stay and health-care cost. The aim of this study was to assess the polypharmacy and pattern of drug- drug interactions in chronic kidney disease patients attending OPD and ward of nephrology unit in Kathmandu Medical College teaching hospital. Material and Methods: This was a prospective cross sectional study conducted among 143 chronic kidney disease diagnosed patients in Kathmandu Medical College Teaching Hospital. The Lexi-comp database was used to evaluate patient’s medications for potential drug-drug interactions. Results: Chronic kidney disease was predominant among male (65.7%) than the female (34.3%). The most common age group was 41-60yrs followed by 61-80 yrs. The mean age of the patients was 54.38 ± 16.43 years. Chronic kidney disease was associated with multiple co-morbid conditions. The most common comorbid conditions were hypertension 52 (36. 4%) and hypertension and diabetes both in 42 (29.4%). A total of 143 prescriptions were included in this study. Average number of drugs per prescription was 6.1. Almost 5-8 medicines per prescription were observed among 95(65.73%) patients. A total of 837 medicines were prescribed. A total number of 206 potential drug-drug interactions were observed among 143 patients. Depending upon the risk rating categorize, the most common were, risk rating C 178( 86.4%) and the most frequent drug interaction was between amlodipine and calcium carbonate 65 (45.45%) . Conclusion: The prevalence of potential drug-drug interaction is high among chronic kidney disease patients. About 63% of interactions have moderate severity. The safest approach to avoid potentials drug-drug interaction is the implementation of appropriate guidelines, detailed and rationalize knowledge of drugs and to utilize available drug-drug interaction software to avoid harmful drug-drug interaction among chronic kidney disease patients.

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Hachad, Houda, Isabelle Ragueneau-Majlessi, and René H. Levy. "A useful tool for drug interaction evaluation: The University of Washington Metabolism and Transport Drug Interaction Database." Human Genomics 5, no. 1 (2010): 61. http://dx.doi.org/10.1186/1479-7364-5-1-61.

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OHSHIMA, TAEYUKI, KENICHI MORITA, KIKUKO SAKAI, KENJIRO KOGA, and SUSUMU KAWASHIMA. "Database of Simultaneous-use and Drug-interaction Information between Switch-OTC-drug and Prescription Drug." Japanese Journal of Hospital Pharmacy 23, no. 1 (1997): 91–96. http://dx.doi.org/10.5649/jjphcs1975.23.91.

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Marandi, T., L. Orav, A. Kändmaa, and S. Nahkur. "Drug-drug interaction database SFINX – first results from north Estonia Medical Centre, Estonia." Clinical Therapeutics 37, no. 8 (August 2015): e5-e6. http://dx.doi.org/10.1016/j.clinthera.2015.05.026.

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Muthaiyan, Mathavan, Leimarembi Devi Naorem, Vassavi Seenappa, Shilpa Sri Pushan, and Amouda Venkatesan. "Ebolabase: Zaire ebolavirus-human protein interaction database for drug-repurposing." International Journal of Biological Macromolecules 182 (July 2021): 1384–91. http://dx.doi.org/10.1016/j.ijbiomac.2021.04.184.

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Oreagba, IA, SO Usman, KA Oshikoya, AA Akinyede, EO Agbaje, O. Opanuga, and SA Akanmu. "CLINICALLY SIGNIFICANT DRUG-DRUG INTERACTION IN A LARGE ANTIRETROVIRAL TREATMENT CENTRE IN LAGOS, NIGERIA." Journal of Population Therapeutics and Clinical Pharmacology 26, no. 1 (January 22, 2019): e1-e19. http://dx.doi.org/10.22374/1710-6222.26.1.1.

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Background An important cause of treatment failure to antiretroviral therapy (ART) is the potential interaction between the antiretroviral (ARV) drugs and concomitant drugs (CD) used for the treatment of opportunistic infections and comorbid ailments in HIV-infected patients. Objectives The study evaluated potential Clinically Significant Drug Interactions (CSDIs) occurring between recommended ART regimens and their CD. Method This study was carried out in a large HIV treatment centre supported by AIDS Preventive initiative in Nigeria (APIN) clinic in a teaching hospital in Lagos, Nigeria, caring for over 20,000 registered patients. Electronic Medical Records (EMRs) of 500 patients, who received treatment between 2005 and 2015, were selected using systematic random sampling, reviewed retrospectively, and evaluated for potential CSDIs using Liverpool HIV Pharmacology Database and other databases for drug-drug interaction check. Results Majority of patients, 421 (84%) prescribed CDs were at risk of CSDIs, of which 410 (82%) were moderate and frequently involved co-trimoxazole + combinations of Nucleoside Reverse Transcriptase Inhibitors (NRTIs) such as zidovudine (or stavudine) /lamivudine 386 (77.2%) and Non-nucleoside Reverse Transcriptase Inhibitors (NNRTIs) or Protease Inhibitors (PIs) + artemisinin-based combination therapies (ACTs) 296 (59.2%). Age (p=0.13), sex (p=0.32) and baseline CD4+ cell counts (p=0.20) were not significantly associated with CSDIs. The interactions, however, were significantly associated with the development of antiretroviral treatment failure (p <0.001) which occurred in nearly a third 139 (27.8%) of the patients. Conclusion There is a high prevalence of CSDIs between ART and CDs, most of which were categorized as moderate. Further studies are required to evaluate the pharmacokinetic and clinical relevance of these interactions.

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Huang, Qiao, Si-Jia Zhai, Xing-Wei Liao, Yu-Chao Liu, and Shi-Hua Yin. "Gene Expression, Network Analysis, and Drug Discovery of Neurofibromatosis Type 2-Associated Vestibular Schwannomas Based on Bioinformatics Analysis." Journal of Oncology 2020 (July 15, 2020): 1–9. http://dx.doi.org/10.1155/2020/5976465.

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Neurofibromatosis Type 2- (NF2-) associated vestibular schwannomas (VSs) are histologically benign tumors. This study aimed to determine disease-related genes, pathways, and potential therapeutic drugs associated with NF2-VSs using the bioinformatics method. Microarray data of GSE108524 were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened using GEO2R. The functional enrichment and pathway enrichment of DEGs were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG). Furthermore, the STRING and Cytoscape were used to analyze the protein-protein interaction (PPI) network of all differentially expressed genes and identify hub genes. Finally, the enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in NF2-associated VSs. A total of 542 DEGs were identified, including 13 upregulated and 329 downregulated genes, which were mainly enriched in terms of focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction, Toll-like receptor signaling pathway, Rap1 signaling pathway, and regulation of actin cytoskeleton. 28 hub genes were identified based on the subset of PPI network, and 31 drugs were selected based on the Drug-Gene Interaction database. Drug discovery using bioinformatics methods facilitates the identification of existing or potential therapeutic drugs to improve NF2 treatment.

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Wang, Trezise, Ku, Lu, Hsu, and Hsu. "Effect of Pharmacist Intervention on a Population in Taiwan with High Healthcare Utilization and Excessive Polypharmacy." International Journal of Environmental Research and Public Health 16, no. 12 (June 21, 2019): 2208. http://dx.doi.org/10.3390/ijerph16122208.

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Patients with high healthcare utilization are at increased risk of polypharmacy and drug interactions. This study investigated the changes in the number of medications, drug interactions and interaction severity in high frequency outpatients with polypharmacy at hospitals and clinics in Taiwan after home pharmaceutical care, to understand the effectiveness of interventions by pharmacists. This was a retrospective observational study. Cases with excessive polypharmacy (10+ drugs) were selected from the Pharmaceutical Care Practice System database of the Taiwan Pharmacist Association in 2017. After the home care intervention, the number of drug types used decreased 1.89-fold (p < 0.001), and the number of medications fell 61.6%. The incidence of drug interaction was 93.82%. In an average case, the incidence of drug interaction after the pharmacist intervention decreased 0.6-fold (p < 0.001). The drug most commonly causing interactions was aspirin, followed by diclofenac; also common were three used in diabetes, two psycholeptics and two beta blockers. Among 22 cases of severe drug interaction, seven resulted in increased risk of extrapyramidal symptoms and neuroleptic malignant syndrome. By analyzing the relationship between the side effects of individual drugs and the pharmacokinetic Tmax, a sequential thermal zone model of adverse drug reactions can be established, the value of which could prompt physicians and pharmacists to intervene in order to prevent adverse events. It is concluded that home pharmaceutical care by pharmacists can significantly reduce the number of medications and interactions in patients with excessive polypharmacy and high healthcare utilization.

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Andersson, Marine L., Linda Björkhem-Bergman, and Jonatan D. Lindh. "Possible drug-drug interaction between quetiapine and lamotrigine - evidence from a Swedish TDM database." British Journal of Clinical Pharmacology 72, no. 1 (June 9, 2011): 153–56. http://dx.doi.org/10.1111/j.1365-2125.2011.03941.x.

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Andersson, Marine L., Ylva Böttiger, Pia Bastholm-Rahmner, Marie-Louise Ovesjö, Aniko Vég, and Birgit Eiermann. "Evaluation of usage patterns and user perception of the drug–drug interaction database SFINX." International Journal of Medical Informatics 84, no. 5 (May 2015): 327–33. http://dx.doi.org/10.1016/j.ijmedinf.2015.01.013.

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Islam, Sk Mazharul, Sk Md Mosaddek Hossain, and Sumanta Ray. "DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation." PLOS ONE 16, no. 2 (February 19, 2021): e0246920. http://dx.doi.org/10.1371/journal.pone.0246920.

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In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

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François, Liesbeth, Jonathan van Eyll, and Patrice Godard. "Dictionary of disease ontologies (DODO): a graph database to facilitate access and interaction with disease and phenotype ontologies." F1000Research 9 (August 7, 2020): 942. http://dx.doi.org/10.12688/f1000research.25144.1.

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The formal, hierarchical classification of diseases and phenotypes in ontologies facilitates the connection to various biomedical databases (drugs, drug targets, genetic variant, literature information...). Connecting these resources is complicated by the use of heterogeneous disease definitions, and differences in granularity and structure. Despite ongoing efforts on integration, two challenges remain: (1) no resource provides a complete mapping across the multitude of disease ontologies and (2) there is no software available to comprehensively explore and interact with disease ontologies. In this paper, the DODO (Dictionary of Disease Ontology) database and R package are presented. DODO aims to deal with these two challenges by constructing a meta-database incorporating information of different publicly available disease ontologies. Thanks to the graph implementation, DODO allows the identification of indirect cross-references by allowing some relationships to be transitive. The R package provides several functions to build and interact with disease networks or convert identifiers between ontologies. They specifically aim to facilitate the integration of information from life science databases without the need to harmonize these upfront. The workflow for local adaptation and extension of the DODO database and a docker image with a DODO database instance are available.

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Chasioti, Danai, Xiaohui Yao, Pengyue Zhang, Samuel Lerner, Sara K. Quinney, Xia Ning, Lang Li, and Li Shen. "Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database." IEEE Journal of Biomedical and Health Informatics 23, no. 5 (September 2019): 2156–63. http://dx.doi.org/10.1109/jbhi.2018.2874533.

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Hodge, Catherine, Fiona Marra, Catia Marzolini, Alison Boyle, Sara Gibbons, Marco Siccardi, David Burger, David Back, and Saye Khoo. "Drug interactions: a review of the unseen danger of experimental COVID-19 therapies." Journal of Antimicrobial Chemotherapy 75, no. 12 (August 4, 2020): 3417–24. http://dx.doi.org/10.1093/jac/dkaa340.

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Abstract As global health services respond to the coronavirus pandemic, many prescribers are turning to experimental drugs. This review aims to assess the risk of drug–drug interactions in the severely ill COVID-19 patient. Experimental therapies were identified by searching ClinicalTrials.gov for ‘COVID-19’, ‘2019-nCoV’, ‘2019 novel coronavirus’ and ‘SARS-CoV-2’. The last search was performed on 30 June 2020. Herbal medicines, blood-derived products and in vitro studies were excluded. We identified comorbidities by searching PubMed for the MeSH terms ‘COVID-19’, ‘Comorbidity’ and ‘Epidemiological Factors’. Potential drug–drug interactions were evaluated according to known pharmacokinetics, overlapping toxicities and QT risk. Drug–drug interactions were graded GREEN and YELLOW: no clinically significant interaction; AMBER: caution; RED: serious risk. A total of 2378 records were retrieved from ClinicalTrials.gov, which yielded 249 drugs that met inclusion criteria. Thirteen primary compounds were screened against 512 comedications. A full database of these interactions is available at www.covid19-druginteractions.org. Experimental therapies for COVID-19 present a risk of drug–drug interactions, with lopinavir/ritonavir (10% RED, 41% AMBER; mainly a perpetrator of pharmacokinetic interactions but also risk of QT prolongation particularly when given with concomitant drugs that can prolong QT), chloroquine and hydroxychloroquine (both 7% RED and 27% AMBER, victims of some interactions due to metabolic profile but also perpetrators of QT prolongation) posing the greatest risk. With management, these risks can be mitigated. We have published a drug–drug interaction resource to facilitate medication review for the critically ill patient.

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Boff da Costa, Raquel, Marisa Boff Costa, Larisse Longo, Daniela Elisa Miotto, Gustavo Hirata Dellavia, Matheus Trucollo Michalczuk, and Mario Reis Álvares-da-Silva. "Direct antiviral agents for hepatitis C and drug interaction risk: A retrospective cohort study with real and simulated data on medication interaction, prevalence of comorbidities and comedications." PLOS ONE 16, no. 2 (February 12, 2021): e0245767. http://dx.doi.org/10.1371/journal.pone.0245767.

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Introduction and aim Comorbidities and comedication are common in patients with hepatitis C, which could result in a risk of drug-drug interaction. The objective of this study was to evaluate the prevalence of comorbidities, comedication and drug-drug interactions involving direct-acting antivirals in this population. Methods Comorbidities and comedications were evaluated in a retrospective cohort of hepatitis C patients. Drug-drug interactions were estimated in real life and with simulated data on comedications following drug regimens: telaprevir; elbasvir/grazoprevir, ombitasvir/paritaprevir/r/ritonavir (2D regimen), and sofosbuvir/simeprevir, sofosbuvir/daclatasvir, sofosbuvir/ledipasvir; 2D/dasabuvir (3D regimen); glecaprevir/pibrentasvir and sofosbuvir/velpatasvir/voxilaprevir. The interactions were evaluated according to the University of Liverpool database. Statistical analysis was performed by SPSS® 18.0. Results Data from 1433 patients with hepatitis C were evaluated. The mean patient age was 51.7 years (SD ± 10.7), and 50.6% were female. Direct-acting antivirals were prescribed for 345 (24.1%) patients, and a sustained virological response occurred in 264 (76.5%). The main comorbidities were systemic arterial hypertension [436 (30.4%)], diabetes mellitus [352 (24.6%)] and depression [130 (9.1%)]. The mean number of comorbidities was 1.52 (median [IQR] of 1.00 [1.00–2.00]). The mean number of comedications was 3.16 (median [IQR] of 3.00 [1.00–5.00]). A total of 12916 drug-drug interactions were found, of which 1.859 (14.4%) were high risk, with a mean of 1.29 ± 3.13 per patient. The 3D regimen, as well as glecaprevir/pibrentasvir and sofosbuvir/velpatasvir/voxilaprevir, presented the highest drug-drug interaction indexes. Conclusion Comorbidities and comedications are common in patients with hepatitis C, as are drug-drug interactions. Even when second generation drugs are used, the occurrence of drug-drug interactions still presents a significant risk.

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Thurnherr, Thomas, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel. "Genomic variant annotation workflow for clinical applications." F1000Research 5 (August 12, 2016): 1963. http://dx.doi.org/10.12688/f1000research.9357.1.

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Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different source databases and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, DGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

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Türei, Dénes, Diána Papp, Dávid Fazekas, László Földvári-Nagy, Dezső Módos, Katalin Lenti, Péter Csermely, and Tamás Korcsmáros. "NRF2-ome: An Integrated Web Resource to Discover Protein Interaction and Regulatory Networks of NRF2." Oxidative Medicine and Cellular Longevity 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/737591.

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NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2.

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Drozdoff, Loisa, Evelyn Klein, Matthias Kalder, Christine Brambs, Marion Kiechle, and Daniela Paepke. "Potential Interactions of Biologically Based Complementary Medicine in Gynecological Oncology." Integrative Cancer Therapies 18 (January 2019): 153473541984639. http://dx.doi.org/10.1177/1534735419846392.

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Objective. The aim of this study was to assess the potential risks of interactions between biologically based complementary and alternative medication (BB-CAM) and conventional drugs during systemic therapy in breast and gynecological cancer patients by analyzing the actual CAM-drug combinations from individual patients’ records. Methods. From September 2014 to December 2014 and from February 2017 to May 2017, all patients (n = 717) undergoing systemic therapy at the Gynecologic Oncology Day Care Unit in the Gynecology and Obstetrics Department of the Technical University of Munich, Germany, were asked to participate in a questionnaire about all their medications. To assess the potential risk of CAM-drug interactions (CDIs), we initially utilized the Lexicomp drug interaction database. This assessment was then expanded with a systematic search of other digital databases, such as the National Center for Complementary and Integrative Health, Memorial Sloan Kettering Cancer Center, PubMed, and MEDLINE as well as the Cochrane Library. Results. Among 448 respondents, 74.1% reported using BB-CAM simultaneously with their systemic therapy. The assessment showed 1 patient with a potentially clinically relevant CDI, where the interaction was based on a self-medicated combination of Echinacea and cyclophosphamide. Furthermore, 81 patients (18.1%) were thought to have interactions because of a combination of BB-CAMs and cytochrome P450 3A4–metabolized anticancer drugs. Conclusions. Our data demonstrated high overall use of BB-CAMs by cancer patients undergoing systemic therapy. The analyses showed only 1 clinically relevant CDI.

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Nikolic, Bozana, Jovan Popovic, Mirjana Becarevic, and Dusica Rakic. "Exposure to potential drug-antimicrobial agent interactions in primary health care." Vojnosanitetski pregled 75, no. 8 (2018): 795–802. http://dx.doi.org/10.2298/vsp160930383n.

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Background/Aim. Drug-drug interactions involving antimicrobials present important and often unrecognized complications of pharmacotherapy which can be prevented. The aim of the present study was to identify the frequency and type of potential drug-antimicrobial agent interactions among outpatients and to define recommendations for their management. Methods. Crosssectional prescription database study was conducted. The analysis randomly included 823 patients who visited Health Center Novi Sad over 1-month period (November 1?30, 2011) and had prescribed ? 2 drugs where at least one drug was antimicrobial agent for systemic use. All interacting drug combinations involving antimicrobials were identified according to Drug Interaction Facts. Additionally, based on the compendium, potential interactions were classified into categories: pharmacological mechanisms, potential clinical outcomes and management advice. Results. Overall, 88 potential clinically significant drug-antimicrobial agent interactions were identified among 69 (8.4%) exposed outpatients [the mean age 61.7 years (SD ? 15.4); the mean number of prescribed drugs 7.5 (SD ? 2.9); 56.5% females]. The most common identified potential interacting pairs were benzodiazepines undergoing oxidative metabolism and clarithromycin or erythromycin, and aminophylline and ciprofloxacin. In 83.0% of all cases underlying mechanism was pharmacokinetic involving primary inhibition of metabolic pathways mediated by CYP3A4 and CYP1A2 isoenzymes. Excessive sedation (22.7%), cardiotoxicity (20.5%), miscellaneous aminophylline adverse effects (13.6%), and bleeding (10.2%) were the most frequently implicated potential clinical outcomes. Risk for adverse interactions could be managed by close monitoring of simultaneous administration of drugs (37.5%), different risk-modifyng strategies (31.8%), and avoiding combinations (30.7%). Conclusion. Among outpatients, there was common potential for clinically significant interactions involving antimicrobials. Information based on the results of the present study could be integrated in existing computerized physician order entry system in the Health Center as a form of clinical support.

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Sauter, S. K., L. M. Neuhofer, D. Edlinger, W. Grossmann, M. Wolzt, G. Endel, W. Gall, and C. Rinner. "Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication." Applied Clinical Informatics 05, no. 03 (2014): 603–11. http://dx.doi.org/10.4338/aci-2014-04-ra-0030.

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Summary Objective: The objective of this study is to estimate the amount of severe drug-drug interaction warnings per medical specialist group triggered by prescribed drugs of a patient before and after the introduction of a nationwide eMedication system in Austria planned for 2015. Methods: The estimations of interaction warnings are based on patients’ prescriptions of a single health care professional per patient, as well as all patients’ prescriptions from all visited health care professionals. We used a research database of the Main Association of Austrian Social Security Organizations that contains health claims data of the years 2006 and 2007. Results: The study cohort consists of about 1 million patients, with 26.4 million prescribed drugs from about 3,400 different health care professionals. The estimation of interaction warnings show a heterogeneous pattern of severe drug-drug-interaction warnings across medical specialist groups. Conclusion: During an eMedication implementation it must be taken into consideration that different medical specialist groups require customized support. Citation: Rinner C, Sauter SK, Neuhofer LM, Edlinger D, Grossmann W, Wolzt M, Endel G, Gall W. Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication. Appl Clin Inf 2014; 5: 603–611http://dx.doi.org/10.4338/ACI-04-RA-0030

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Deng, Yifan, Xinran Xu, Yang Qiu, Jingbo Xia, Wen Zhang, and Shichao Liu. "A multimodal deep learning framework for predicting drug–drug interaction events." Bioinformatics 36, no. 15 (May 14, 2020): 4316–22. http://dx.doi.org/10.1093/bioinformatics/btaa501.

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Abstract Motivation Drug–drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. Results In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug–drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision–recall curve of 0.9208. Availability and implementation The source code and data are available at https://github.com/YifanDengWHU/DDIMDL. Supplementary information Supplementary data are available at Bioinformatics online.

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Isoherranen, Nina, Houda Hachad, Catherine K. Yeung, and Rene H. Levy. "Qualitative Analysis of the Role of Metabolites in Inhibitory Drug−Drug Interactions: Literature Evaluation Based on the Metabolism and Transport Drug Interaction Database." Chemical Research in Toxicology 22, no. 2 (February 16, 2009): 294–98. http://dx.doi.org/10.1021/tx800491e.

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Journal articles on the topic 'Drug interaction database'

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