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Journal articles on the topic 'DDI interaction'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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 (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)ci

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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 (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 p

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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 respo

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Hu, Fang-Qi, and Ju-Kui Xue. "Breathing dynamics of a trapped impurity in a dipolar Bose gas." Modern Physics Letters B 28, no. 22 (2014): 1450185. http://dx.doi.org/10.1142/s0217984914501851.

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With the consideration of impurity-bosons coupling and dipole–dipole interactions (DDI), we study the breathing dynamics of a harmonically trapped impurity interacting with a separately trapped background of dipolar Bose gas. By using the variational approach, the breathing equations, the breathing frequencies and the effective potentials governing the breathing dynamics of the impurity in dipolar gas are obtained. The effects of DDI, impurity-bosons interaction and external trapping potentials on breathing dynamics of impurity are discussed. We find that, because of the anisotropic and long-r

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Rajeev Kumar Bhaskar. "High-Order Multimodal Interaction Network for Efficient Prediction of Drug-Drug Interaction." Communications on Applied Nonlinear Analysis 32, no. 2s (2024): 113–20. http://dx.doi.org/10.52783/cana.v32.2256.

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The pharmaceutical industry places a lot of focus on preventing drug-drug interactions (DDIs). Medication interaction detection has been the primary emphasis of machine learning-based DDI prediction methods. Since studies have shown that DDIs can cause different future occurrences, it is more beneficial to predict DDI connected events while examining the mechanism behind combination medicine consumption or adverse reactions. The areas of medication development and illness diagnostics are seeing increased usage of a developing approach that predicts DDIs-associated occurrences. We examine poten

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Moura, Cristiano Soares, Francisco Assis Acurcio, and Najara Oliveira Belo. "Drug-Drug Interactions Associated with Length of Stay and Cost of Hospitalization." Journal of Pharmacy & Pharmaceutical Sciences 12, no. 3 (2009): 266. http://dx.doi.org/10.18433/j35c7z.

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Purpose To evaluate the prevalence of drug-drug interactions (DDI) in prescriptions of hospitalized patients and to identify risk factors associated. Methods A retrospective cross-sectional analysis of prescription data and medical records from a public hospital in Brazil was conducted to identify potential DDI. An inappropriate drug combination was identified and classified with a standard drug interaction source. The main diagnoses were classified with Charlson Comorbidity Index (CCI). Sex, age, polypharmacy and length of stay, among other variables, were correlated with the frequency of pot

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Swathi Mirthika, G. L., and B. Sivakumar. "A Systematic Review on Drug Interaction Prediction Using Various Methods to Reduce Adverse Effects." International Journal of Information Systems and Social Change 14, no. 1 (2023): 1–8. http://dx.doi.org/10.4018/ijissc.329233.

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Interaction prediction between the drugs is a preeminent task. Drug - drug interaction (DDI) causes serious effects to human life. The adverse effect can result in death when the interaction is not known. Predicting all DDI is a challenging mission as it requires much time. Health care professionals and care givers may not be aware of all potential drug interactions. Many studies have been carried out to predict the DDI in meticulous way. Drug banks play the major role in providing information about the drugs; through drug banks we could predict the adverse effect while using two or more drugs

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Sudsakorn, Sirimas, Praveen Bahadduri, Jennifer Fretland, and Chuang Lu. "2020 FDA Drug-drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists." Current Drug Metabolism 21, no. 6 (2020): 403–26. http://dx.doi.org/10.2174/1389200221666200620210522.

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Background: In January 2020, the US FDA published two final guidelines, one entitled “In vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry” and the other entitled “Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry”. These were updated from the 2017 draft in vitro and clinical DDI guidance. Methods: This study is aimed to provide an analysis of the updates along with a comparison of the DDI guidelines published by the European Medicines Agency (EMA)

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9

Liu, Shengyu, Buzhou Tang, Qingcai Chen, and Xiaolong Wang. "Drug-Drug Interaction Extraction via Convolutional Neural Networks." Computational and Mathematical Methods in Medicine 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/6918381.

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Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extrac

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Sari, Flori R., Saiful Anwar, Risahmawati, Marita Fadhilah, and Fika Ekayanti. "A Clinical-Based Drug Interaction Alert (CIDIA) System for Preventing Drug Interaction and Its Associated Factors at Rural Primary Care Centres." Bangladesh Journal of Medical Science 22, no. 3 (2023): 667–75. http://dx.doi.org/10.3329/bjms.v22i3.66962.

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Objectives: Drug-drug interaction (DDI) occurs following the prescription of more than one drug. DDI and its associated factors in Indonesia’s country’s primary care have not been reported. Materials and Methods: Through this descriptive cross-sectional study, we analysed the DDI incidence using the Clinical-Based Drug Interaction Alert (CIDIA) alert system. Purposive research was carried out by analysing prescriptions (n=2410) from nine primary health cares. Results: CIDIA alert system detected 7.5% DDI incidence in all prescriptions, categorized as mild (63%), moderate (36%) and

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Bordbar, G. H., and F. Pouresmaeeli. "Microscopic analysis of homogeneous electron gas by considering dipole–dipole interaction." Modern Physics Letters B 31, no. 35 (2017): 1750334. http://dx.doi.org/10.1142/s0217984917503341.

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Implying perturbation theory, the impact of the dipole–dipole interaction (DDI) on the thermodynamic properties of a homogeneous electron gas at zero temperature is investigated. Through the second quantization formalism, the analytic expressions for the ground state energy and the DDI energy are obtained. In this paper, the DDI energy has similarities with the previous works done by others. We show that its general behavior depends on density and the total angular momentum. Especially, it is found that the DDI energy has a highly state-dependent behavior. With the growth of density, the magni

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Boyce, Richard D., Carol Collins, Marc Clayton, John Kloke, and John R. Horn. "Inhibitory Metabolic Drug Interactions with Newer Psychotropic Drugs: Inclusion in Package Inserts and Influences of Concurrence in Drug Interaction Screening Software." Annals of Pharmacotherapy 46, no. 10 (2012): 1287–98. http://dx.doi.org/10.1345/aph.1r150.

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Background: Food and Drug Administration (FDA) regulations mandate that package inserts (Pls) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect. Objective: To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved Pls and what influence the source of DDI information has on agreement between 3 DDI screening programs. Methods: The scientific literature and Pls were reviewed to determine all drug pairs for whic

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Sun, Zhaoyue, Jiazheng Li, Gabriele Pergola, and Yulan He. "ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25228–36. https://doi.org/10.1609/aaai.v39i24.34709.

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Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and develop

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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 (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 subscript

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Pruvost, Alain, Eugènia Negredo, Henri Benech, et al. "Measurement of Intracellular Didanosine and Tenofovir Phosphorylated Metabolites and Possible Interaction of the Two Drugs in Human Immunodeficiency Virus-Infected Patients." Antimicrobial Agents and Chemotherapy 49, no. 5 (2005): 1907–14. http://dx.doi.org/10.1128/aac.49.5.1907-1914.2005.

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ABSTRACT Recent work has demonstrated the existence of a systemic interaction between didanosine (ddI) and tenofovir disoproxyl fumarate (TDF) that leads to a significant increase in plasma ddI levels when coadministered with TDF (40 to 50% increase). These two drugs are, respectively, nucleoside and nucleotide analogues of adenosine and efficiently inhibit the human immunodeficiency virus (HIV) reverse transcriptase when transformed to their triphosphate moieties in the intracellular (IC) medium (ddA-TP and TFV-DP, respectively). Since ddI and TDF partly share the same IC metabolic pathway le

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Peixoto de Miranda, Érique José F., Thamy Takahashi, Felipe Iwamoto, et al. "Drug–Drug Interactions of 257 Antineoplastic and Supportive Care Agents With 7 Anticoagulants: A Comprehensive Review of Interactions and Mechanisms." Clinical and Applied Thrombosis/Hemostasis 26 (January 1, 2020): 107602962093632. http://dx.doi.org/10.1177/1076029620936325.

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Data on drug–drug interactions (DDI) of antineoplastic drugs with anticoagulants is scarce. We aim to evaluate factors associated with DDI of antineoplastic and supportive care drugs with anticoagulants resulting in modification of pharmacokinetics of these last mentioned. A literature review on DDI databases and summaries of products characteristics (SmPC) was done. Drug–drug interactions of 257 antineoplastic and supportive care drugs with direct oral anticoagulants (DOACs), warfarin, enoxaparin, or fondaparinux were categorized as no clinically significant expected DDI, potentially weak DDI

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Rinner, Christoph, Wilfried Grossmann, Simone Katja Sauter, Michael Wolzt, and Walter Gall. "Effects of Shared Electronic Health Record Systems on Drug-Drug Interaction and Duplication Warning Detection." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/380497.

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Shared electronic health records (EHRs) systems can offer a complete medication overview of the prescriptions of different health care providers. We use health claims data of more than 1 million Austrians in 2006 and 2007 with 27 million prescriptions to estimate the effect of shared EHR systems on drug-drug interaction (DDI) and duplication warnings detection and prevention. The Austria Codex and the ATC/DDD information were used as a knowledge base to detect possible DDIs. DDIs are categorized as severe, moderate, and minor interactions. In comparison to the current situation where only DDIs

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Marok, Fatima Zahra, Laura Maria Fuhr, Nina Hanke, Dominik Selzer, and Thorsten Lehr. "Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network." Pharmaceutics 13, no. 3 (2021): 331. http://dx.doi.org/10.3390/pharmaceutics13030331.

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The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug–drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug–gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD,

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Peabody, John, Mary Tran, David Paculdo, Joshua Schrecker, Czarlota Valdenor, and Elaine Jeter. "Clinical Utility of Definitive Drug–Drug Interaction Testing in Primary Care." Journal of Clinical Medicine 7, no. 11 (2018): 384. http://dx.doi.org/10.3390/jcm7110384.

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Drug–drug interactions (DDIs) are a leading cause of morbidity and mortality. New tools are needed to improve identification and treatment of DDIs. We conducted a randomized controlled trial to assess the clinical utility of a new test to identify DDIs and improve their management. Primary care physicians (PCPs) cared for simulated patients presenting with DDI symptoms from commonly prescribed medications and other ingestants. All physicians, in either control or one of two intervention groups, cared for six patients over two rounds of assessment. Intervention physicians were educated on the D

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Perwirani, Resia, and Ika Puspitasari. "Perancangan Clinical Decision Support System (CDSS) untuk Drug Drug Interaction (DDI) pada e-Prescription." JURNAL MANAJEMEN DAN PELAYANAN FARMASI (Journal of Management and Pharmacy Practice) 12, no. 4 (2023): 198. http://dx.doi.org/10.22146/jmpf.74506.

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Not all drugs side-effect that occur can be avoided, but those caused by drug-drug interactions (DDI) are among the most likely to be prevented and managed due to their predictability. The increasing number of drugs co-prescribed, affects the potential for drug interactions exponentially. Clinical Decision Support System (CDSS) is a promising strategy to prevent patient safety risks caused by drug interactions. This study aims to design a CDSS for DDI on e-Prescription. This research is qualitative study with action research design. The research was carried out at Digital Health Innovation Stu

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Wang, Ji-Guo, and Ju-Kui Xue. "The coherence of boson–fermion mixed system." Modern Physics Letters B 28, no. 07 (2014): 1450057. http://dx.doi.org/10.1142/s0217984914500572.

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In this paper, we investigate the quantum coherence of Bose–Fermi mixtures of ultracold dipolar particles trapped in a three-site Bose–Fermi–Hubbard model. We show that the interplay between the boson–boson on-site interaction, boson–fermion on-site interaction, and boson–fermion inter-site dipole–dipole interaction (DDI) results in interesting coherence characters. When boson–boson on-site interaction is present, the resonance character of coherence against both boson–fermion DDI and boson–fermion on-site interaction emerges, the coherence of the system can be enhanced at certain values of bo

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Zhang, Ran, Xuezhi Wang, Guannan Liu, Pengyang Wang, Yuanchun Zhou, and Pengfei Wang. "Motif-Oriented Representation Learning with Topology Refinement for Drug-Drug Interaction Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 1102–10. https://doi.org/10.1609/aaai.v39i1.32097.

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Drug-Drug Interaction (DDI) prediction has attracted considerable attention in designing multi-drug combination strategies and avoiding adverse reactions. Notably, Artificial Intelligence (AI)-driven DDI prediction methods have emerged as a pivotal research paradigm. However, most AI-driven DDI prediction methods fall short in exploring intra-molecular motifs, and heavily rely on the overly idealized assumption of the complete inter-molecular topology, limiting their expressive capacities. To this end, we propose a Motif-Oriented representation learning with TOpology Refinement for DDI predict

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Zhang, Tianyi, Sheila M. Gephart, Vignesh Subbian, et al. "Barriers to Adoption of Tailored Drug–Drug Interaction Clinical Decision Support." Applied Clinical Informatics 14, no. 04 (2023): 779–88. http://dx.doi.org/10.1055/s-0043-1772686.

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Abstract Objective Despite the benefits of the tailored drug–drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts. Methods We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Pr

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Stader, Felix, Hannah Kinvig, Manuel Battegay, et al. "Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications." Antimicrobial Agents and Chemotherapy 62, no. 7 (2018): e00717-18. http://dx.doi.org/10.1128/aac.00717-18.

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ABSTRACTDespite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from

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Xiong, Zhankun, Shichao Liu, Feng Huang, et al. "Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5339–47. http://dx.doi.org/10.1609/aaai.v37i4.25665.

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Drug-drug interactions (DDIs) could lead to various unexpected adverse consequences, so-called DDI events. Predicting DDI events can reduce the potential risk of combinatorial therapy and improve the safety of medication use, and has attracted much attention in the deep learning community. Recently, graph neural network (GNN)-based models have aroused broad interest and achieved satisfactory results in the DDI event prediction. Most existing GNN-based models ignore either drug structural information or drug interactive information, but both aspects of information are important for DDI event pr

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Lu, Yin, Aditya Ramachandra, Minh Pham, Yi-Cheng Tu, and Feng Cheng. "CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature." Molecules 24, no. 6 (2019): 1081. http://dx.doi.org/10.3390/molecules24061081.

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Drug-drug interaction (DDI) is becoming a serious issue in clinical pharmacy as the use of multiple medications is more common. The PubMed database is one of the biggest literature resources for DDI studies. It contains over 150,000 journal articles related to DDI and is still expanding at a rapid pace. The extraction of DDI-related information, including compounds and proteins from PubMed, is an essential step for DDI research. In this paper, we introduce a tool, CuDDI (compute unified device architecture-based DDI searching), for identification of DDI-related terms (including compounds and p

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Marok, Fatima Zahra, Jan-Georg Wojtyniak, Laura Maria Fuhr, et al. "A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug–Drug Interaction Perpetrators." Pharmaceutics 15, no. 2 (2023): 679. http://dx.doi.org/10.3390/pharmaceutics15020679.

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The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing’s syndrome, is prone to drug–food interactions (DFIs) and is well known for its strong drug–drug interaction (DDI) potential. Some of ketoconazole’s potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole’s metabolites on its DDI potential. The parent–

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Megawati, Megawati, Nurul Izza AR., and Wahdini Wahdini. "Description of Poly Pharmacy Prescribed Drug Interactions on One Pharmacy at Bumi Tamalanrea Permai Makassar." Jurnal Farmasi Sandi Karsa 8, no. 1 (2022): 17–23. http://dx.doi.org/10.36060/jfs.v8i1.101.

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Research has been carried out on the description of the interaction of prescription drugs in polypharmacy at one of the pharmacies in Bumi Tamalanrea Permai Makassar. The purpose of this study was to determine the description and number of prescription drug interactions in polypharmacy at one of the pharmacies in Bumi Tamalanrea Permai Makassar. This research was conducted descriptively and data collection was carried out prospectively. The results showed that there were 43 prescriptions (out of a total of 284 prescriptions) that had DDI potential, 5 prescriptions that had DDI potential in the

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Feng, Yue-Hua, and Shao-Wu Zhang. "Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs." Molecules 27, no. 9 (2022): 3004. http://dx.doi.org/10.3390/molecules27093004.

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The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. Therefore, for polypharmacy safety it is crucial to identify DDIs and explore their underlying mechanisms. The detection of DDI in the wet lab is expensive and time-consuming, due to the need for experimental research over a large volume of drug combinations. Although many computational methods have been developed to predict DDIs, most of these are incapable of predicting potential DDIs between

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Payne, Thomas H., Lisa E. Hines, Raymond C. Chan, et al. "Recommendations to improve the usability of drug-drug interaction clinical decision support alerts." Journal of the American Medical Informatics Association 22, no. 6 (2015): 1243–50. http://dx.doi.org/10.1093/jamia/ocv011.

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Abstract Objective: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. Materials and Methods: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to impr

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Qiu, Jiayue, Xiao Yan, Yanan Tian, et al. "PTB-DDI: An Accurate and Simple Framework for Drug–Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model." International Journal of Molecular Sciences 25, no. 21 (2024): 11385. http://dx.doi.org/10.3390/ijms252111385.

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The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framew

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Biady, Yasmine, Teresa Lee, Lily Pham, et al. "Factors Influencing Health Care Professionals' Perceptions of Frequent Drug–Drug Interaction Alerts." ACI Open 08, no. 01 (2024): e25-e32. http://dx.doi.org/10.1055/s-0044-1782534.

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Abstract Background Drug–drug interactions (DDIs) remain a highly prevalent issue for patients in both community and hospital settings. Electronic medication management systems have implemented DDI alerts to mitigate DDI-related harm from occurring. Objectives The primary aim of this study was to explore factors that influence health care professionals' (hospital doctors, hospital pharmacists, general practitioners, and community pharmacists) perceptions and action taken by them in response to DDI alerts. Methods A qualitative study was conducted using semi-structured interviews between early

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Rafferty, PJ, EM McCarty, WW Dinsmore, et al. "P054 2017 update of drug interactions detected using electronic care records (ECR)." Sexually Transmitted Infections 93, Suppl 1 (2017): A35.1—A35. http://dx.doi.org/10.1136/sextrans-2017-053232.100.

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IntroductionIn 2014 the pharmacy team completed an interaction screen of all HIV patients on a boosted antiretroviral (ARV) regimen using then recently launched NIECR. We concluded that there was a need for primary and secondary care teams to screen and manage drug-drug interactions (DDI). 56 patients in 2014 required urgent clinical intervention.MethodsIn 2014 we reported on patients taking a boosted ARV regimen for DDI; we continued this work for all patients and this year we reviewed our interaction screening database, to assess the following: Interaction screen documented, Number of patien

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Helmons, Pieter J., Bas O. Suijkerbuijk, Prashant V. Nannan Panday, and Jos GW Kosterink. "Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis." Journal of the American Medical Informatics Association 22, no. 4 (2015): 764–72. http://dx.doi.org/10.1093/jamia/ocu010.

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Abstract Background Drug-drug interactions (DDIs) are very prevalent in hospitalized patients. Objectives To determine the number of DDI alerts, time saved, and time invested after suppressing clinically irrelevant alerts and adding clinical-decision support to relevant alerts. Materials and methods The most frequently occurring DDIs were evaluated for clinical relevance by a multidisciplinary expert panel. Pharmacist evaluation of relevant DDIs was facilitated using computerized decision support systems (CDSS). During Phase 1, only CDSS-assisted DDI checking was implemented. During Phase 2, C

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Kanacher, Tobias, Andreas Lindauer, Enrica Mezzalana, et al. "A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug–Drug–Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine." Pharmaceutics 12, no. 12 (2020): 1191. http://dx.doi.org/10.3390/pharmaceutics12121191.

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Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victim

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Yu, Jingjing, Nathalie Rioux, Iain Gardner, Katie Owens, and Isabelle Ragueneau-Majlessi. "Metabolite Measurement in Index Substrate Drug Interaction Studies: A Review of the Literature and Recent New Drug Application Reviews." Metabolites 14, no. 10 (2024): 522. http://dx.doi.org/10.3390/metabo14100522.

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Background/Objectives: Index substrates are used to understand the processes involved in pharmacokinetic (PK) drug–drug interactions (DDIs). The aim of this analysis is to review metabolite measurement in clinical DDI studies, focusing on index substrates for cytochrome P450 (CYP) enzymes, including CYP1A2 (caffeine), CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 ((S)-warfarin, flurbiprofen), CYP2C19 (omeprazole), CYP2D6 (desipramine, dextromethorphan, nebivolol), and CYP3A (midazolam, triazolam). Methods: All data used in this evaluation were obtained from the Certara Drug Interaction Data

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Yang, Guoquan, Juan Guo, and Suying Zhang. "Influence of the dipole–dipole interaction on the interference between Bose–Einstein condensates." International Journal of Modern Physics B 33, no. 07 (2019): 1950048. http://dx.doi.org/10.1142/s0217979219500486.

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We have investigated the interference of the dipolar Bose–Einstein condensates (DBECs) released from a double-well potential and studied the effects of dipole–dipole interaction (DDI) on the interference phenomena. We find that the DDI plays an important role in the interference process. When the effective polarization direction of the dipolar atoms is in the normal direction of the condensate plane, with the increasing of the strength of DDI, the visibility of fringes reduces and the width of fringes becomes larger. When the strength of DDI is fixed and the effective polarization direction of

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Naidoo, Panjasaram, and Manoranjenni Chetty. "Progress in the Consideration of Possible Sex Differences in Drug Interaction Studies." Current Drug Metabolism 20, no. 2 (2019): 114–23. http://dx.doi.org/10.2174/1389200220666181128160813.

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Background: Anecdotal evidence suggests that there may be sex differences in Drug-drug Interactions (DDI) involving specific drugs. Regulators have provided general guidance for the inclusion of females in clinical studies. Some clinical studies have reported sex differences in the Pharmacokinetics (PK) of CYP3A4 substrates, suggesting that DDI involving CYP3A4 substrates could potentially show sex differences. Objective: The aim of this review was to investigate whether recent prospective DDI studies have included both sexes and whether there was evidence for the presence or absence of sex di

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Marcath, Lauren A., Taylor D. Coe, Bruce G. Redman, and Daniel Louis Hertz. "Development of a drug-drug interaction screening tool for oncology clinical trial enrollment." Journal of Clinical Oncology 36, no. 30_suppl (2018): 315. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.315.

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315 Background: Screening drug-drug interactions (DDI) for subjects enrolling in oncology clinical trials is critical to ensuring patient safety and the validity of clinical trial data. We previously reported that DDI screening is not uniformly conducted when screening patients for enrollment into SWOG clinical trials and found that at the University of Michigan Rogel Cancer Center up to 24.2% of subjects enrolled in National Clinical Trial Network (NCTN) trials had a DDI. Screening tools aid in DDI reduction in clinical practice, but none have been created for clinical trial enrollment. Our o

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Morte-Romea, Elena, Pilar Luque-Gómez, Mercedes Arenere-Mendoza, et al. "Performance Assessment of Software to Detect and Assist Prescribers with Antimicrobial Drug Interactions: Are all of them Created Equal?" Antibiotics 9, no. 1 (2020): 19. http://dx.doi.org/10.3390/antibiotics9010019.

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Background: Detecting and managing antimicrobial drug interactions (ADIs) is one of the facets of prudent antimicrobial prescribing. Our aim is to compare the capability of several electronic drug–drug interaction (DDI) checkers to detect and report ADIs. Methods: Six electronic DDI checking platforms were evaluated: Drugs.com®, Medscape®, Epocrates®, Medimecum®, iDoctus®, and Guía IF®. Lexicomp® Drug Interactions was selected as the gold standard. Ten ADIs addressing different mechanisms were evaluated with every electronic DDI checker. For each ADI, we assessed five dimensions and calculated

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TIAN, WEI, BIN CHEN, and GUANG TONG. "CONTROLLING SINGLE-PHOTON TRANSPORT IN A ONE-DIMENSIONAL RESONATOR WAVEGUIDE BY INTERATOMIC DIPOLE–DIPOLE INTERACTION." International Journal of Modern Physics B 26, no. 32 (2012): 1250194. http://dx.doi.org/10.1142/s0217979212501949.

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Controlling single-photon transport in a one-dimensional resonator waveguide can be realized by the interatomic dipole–dipole interaction (DDI). Our numerical results show that the effects of the DDI act as that of a positive detuning. Because of the DDI, the period of the transmission spectrum changes, and its amplitude increases for stronger DDI intensity. We also discuss the influences of the DDI on the transport in low-energy and high-energy regimes. Besides, a cooperation dissipation, induced by the two atoms coupling to a common reservoir, can lead to the increase of the atomic total dec

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Salman, Muhammad, Hafiz Suliman Munawar, Khalid Latif, Muhammad Waseem Akram, Sara Imran Khan, and Fahim Ullah. "Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare." Big Data and Cognitive Computing 6, no. 1 (2022): 30. http://dx.doi.org/10.3390/bdcc6010030.

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The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in

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Meslin, S., W. Zheng, R. Day, E. Tay, and M. Baysari. "Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation." Applied Clinical Informatics 09, no. 04 (2018): 849–55. http://dx.doi.org/10.1055/s-0038-1676039.

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Introduction Drug–drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review. Methods After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the ag

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Sun, Lei, Kun Mi, Yixuan Hou, et al. "Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications." Metabolites 13, no. 8 (2023): 897. http://dx.doi.org/10.3390/metabo13080897.

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Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug–drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a si

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Horn, John, and Stephen Ueng. "The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study." Annals of Pharmacotherapy 53, no. 11 (2019): 1087–92. http://dx.doi.org/10.1177/1060028019863419.

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Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts

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Yadav, Dinesh Kumar, Kadir Alam, Anil Kumar Sah, and Deependra Prasad Sarraf. "Utilization pattern of antibiotics and drug related problems in the orthopedic department at a tertiary care hospital: a prospective study." Journal of Drug Delivery and Therapeutics 10, no. 3 (2020): 24–30. http://dx.doi.org/10.22270/jddt.v10i3.4056.

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Background: Antibiotics are generally prescribed for both prophylactically and to treat ongoing infections in the orthopedic department. Assessment of prescribing pattern at regular interval is essential to avoid inappropriate use of drugs. Objectives: To know the utilization pattern of antibiotics and drug related problems like adverse drug reactions (ADR) and drug-drug interactions (DDI) in hospitalized patients. Materials and Methods: A cross-sectional study was conducted among hospitalized patients using WHO Anatomical Therapeutic Chemical/Defined Daily Dose (ATC/DDD) methodolo

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Ma, Mei, and Xiujuan Lei. "A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions." PLOS Computational Biology 19, no. 1 (2023): e1010812. http://dx.doi.org/10.1371/journal.pcbi.1010812.

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Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interacti

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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 (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 event

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McEvoy, Dustin S., Dean F. Sittig, Thu-Trang Hickman, et al. "Variation in high-priority drug-drug interaction alerts across institutions and electronic health records." Journal of the American Medical Informatics Association 24, no. 2 (2016): 331–38. http://dx.doi.org/10.1093/jamia/ocw114.

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Objective: The United States Office of the National Coordinator for Health Information Technology sponsored the development of a “high-priority” list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive). Materials and methods: We conducted evaluations of electronic health records (EHRs) at a convenience sample of health care organizations across the United Stat

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Hu, Haotian, Alex Jie Yang, Sanhong Deng, Dongbo Wang, Min Song, and Si Shen. "A Generative Drug–Drug Interaction Triplets Extraction Framework Based on Large Language Models." Proceedings of the Association for Information Science and Technology 60, no. 1 (2023): 980–82. http://dx.doi.org/10.1002/pra2.918.

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ABSTRACTDrug–Drug Interaction (DDI) may affect the activity and efficacy of drugs, potentially leading to diminished therapeutic effect or even serious side effects. Therefore, automatic recognition of drug entities and relations involved in DDI is of great significance for pharmaceutical and medical care. In this paper, we propose a generative DDI triplets extraction framework based on Large Language Models (LLMs). We comprehensively apply various training methods, such as In‐context learning, Instruction‐tuning, and Task‐tuning, to investigate the biomedical information extraction capabiliti

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