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British Journal of Clinical Pharmacology Jul 2023Midostaurin is often prescribed with azole antifungals in patients with leukaemia, either for aspergillosis prophylaxis or treatment. Midostaurin is extensively...
Midostaurin is often prescribed with azole antifungals in patients with leukaemia, either for aspergillosis prophylaxis or treatment. Midostaurin is extensively metabolized by cytochrome (CYP) 3A4. In addition, it inhibits and induces various CYPs at therapeutic concentrations. Thus, midostaurin is associated with a high potential for drug-drug interactions (DDIs), both as a substrate (victim) and as a perpetrator. However, data on midostaurin as a perpetrator of DDIs are scarce, as most pharmacokinetic studies have focused on midostaurin as a victim drug. We report a clinically relevant bidirectional DDI between midostaurin and voriconazole during induction treatment. A 49-year-old woman with acute myeloid leukaemia developed invasive pulmonary aspergillosis after induction chemotherapy. She was treated with voriconazole at standard dosage. Six days after starting midostaurin, she developed visual hallucinations with a concurrent sharp increase in voriconazole blood concentration (C 10.3 mg L , target C 1-5 mg L ). Neurotoxicity was considered to be related to voriconazole overexposure. The concentration of midostaurin was concomitantly six-fold above the average expected level, but without safety issues. Midostaurin was stopped and the dosage of voriconazole was adjusted with therapeutic drug monitoring. The evolution was favourable, with quick resolution and no recurrence of visual hallucinations. To our knowledge, this is the first case suggesting that midostaurin and voriconazole reciprocally inhibit each other's metabolism, leading to increased exposure of both. This case highlights the knowledge gap regarding drug-drug interactions between midostaurin and azole antifungals. Close clinical and therapeutic drug monitoring is advised in such cases.
Topics: Female; Humans; Middle Aged; Voriconazole; Antifungal Agents; Drug Interactions; Leukemia, Myeloid, Acute; Hallucinations
PubMed: 37050863
DOI: 10.1111/bcp.15743 -
BMC Bioinformatics Aug 2022Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention...
BACKGROUND
Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations.
RESULTS
Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets.
CONCLUSIONS
Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions.
Topics: Attention; Data Mining; Drug Interactions; Molecular Structure; Neural Networks, Computer
PubMed: 35965308
DOI: 10.1186/s12859-022-04876-8 -
Bioinformatics (Oxford, England) Jul 2021Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively...
MOTIVATION
Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information.
RESULTS
We evaluated our approach on the DDIExtraction 2013 shared task dataset. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information and molecular structure information are complementary and their effective combination is essential for the improvement.
AVAILABILITY AND IMPLEMENTATION
Our code is available at https://github.com/tticoin/DESC_MOL-DDIE.
Topics: Data Mining; Drug Interactions; Molecular Structure; Pharmaceutical Preparations; Publications
PubMed: 33098410
DOI: 10.1093/bioinformatics/btaa907 -
BMC Bioinformatics Dec 2022Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in...
BACKGROUND
Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs). As the number of biotechnology drugs (BioDs) increases, so makes the threat of interactions between SMDs and BioDs. However, few computational methods are available to predict their interactions.
RESULTS
Considering the structural specificity and relational complexity of SMDs and BioDs, a novel multi-modal representation learning method called Multi-SBI is proposed to predict their interactions. First, multi-modal features are used to adequately represent the heterogeneous structure and complex relationships of SMDs and BioDs. Second, an undersampling method based on Positive-unlabeled learning (PU-sampling) is introduced to obtain negative samples with high confidence from the unlabeled data set. Finally, both learned representations of SMD and BioD are fed into DNN classifiers to predict their interaction events. In addition, we also conduct a retrospective analysis.
CONCLUSIONS
Our proposed multi-modal representation learning method can extract drug features more comprehensively in heterogeneous drugs. In addition, PU-sampling can effectively reduce the noise in the sampling procedure. Our proposed method significantly outperforms other state-of-the-art drug interaction prediction methods. In a retrospective analysis of DrugBank 5.1.0, 14 out of the 20 predictions with the highest confidence were validated in the latest version of DrugBank 5.1.8, demonstrating that Multi-SBI is a valuable tool for predicting new drug interactions through effectively extracting and learning heterogeneous drug features.
Topics: Retrospective Studies; Drug Interactions
PubMed: 36575376
DOI: 10.1186/s12859-022-05101-2 -
British Journal of Clinical Pharmacology Sep 2020The use of complementary and alternative medicine at least once during or after cancer treatment has increased over the past years from an estimated 25% in the 1970s and...
The use of complementary and alternative medicine at least once during or after cancer treatment has increased over the past years from an estimated 25% in the 1970s and 1980s to more than 32% in the 1990s and to 49% after 2000. The risk of herb-drug interaction is therefore increasingly recognized as a public health problem. To the best of our knowledge, we report here the first case of interaction between ginger and anticancer drug, with serious consequences for the patient. There is an urgent need regarding complementary and alternative medicine: Both clinicians and patients should be aware of the potential interactions between herbs and prescribed drugs.
Topics: Antineoplastic Agents; Crizotinib; Zingiber officinale; Herb-Drug Interactions; Humans
PubMed: 30701569
DOI: 10.1111/bcp.13862 -
Nucleic Acids Research Jan 2022Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information...
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.
Topics: Clinical Decision-Making; Databases, Factual; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Humans; Patient Safety; Software
PubMed: 34634800
DOI: 10.1093/nar/gkab880 -
British Journal of Clinical Pharmacology Sep 2022The aim of this study was to explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in...
AIMS
The aim of this study was to explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in terms of: (1) interacting drug pairs; (2) severity rating; (3) evidence rating; and (4) clinical management recommendations.
METHODS
We extracted information from the British National Formulary (BNF), Thesaurus and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs in terms of DDI. We annotated clinical management recommendations either manually, where possible, or through application of a machine learning algorithm.
RESULTS
The DIRs contained 51 481 (BNF), 38 037 (Thesaurus) and 65 446 (Micromedex) drug pairs involved in DDIs. The number of common DDIs across the three DIRs was 6970 (13.54% of BNF, 18.32% of Thesaurus and 10.65% of Micromedex). Micromedex and Thesaurus overall showed higher levels of similarity in their severity ratings, while the BNF agreed more with Micromedex on the critical severity ratings and with Thesaurus on the least significant ones. Evidence rating agreement between BNF and Micromedex was generally poor. Variation in clinical management recommendations was also identified, with some categories (i.e., Monitor and Adjust dose) showing higher levels of agreement compared to others (i.e., Use with caution, Wash-out, Modify administration).
CONCLUSIONS
There is considerable variation in the DDIs included in the examined DIRs, together with variability in categorisation of severity and clinical advice given. DDIs labelled as critical were more likely to appear in multiple DIRs. Such variability in information could have deleterious consequences for patient safety, and there is a need for harmonisation and standardisation.
Topics: Drug Interactions; Humans; Pharmaceutical Preparations
PubMed: 35362214
DOI: 10.1111/bcp.15341 -
Molecules (Basel, Switzerland) Sep 2021Membrane transporters play an important role in the absorption, distribution, metabolism, and excretion of xenobiotic substrates, as well as endogenous compounds. The... (Review)
Review
Membrane transporters play an important role in the absorption, distribution, metabolism, and excretion of xenobiotic substrates, as well as endogenous compounds. The evaluation of transporter-mediated drug-drug interactions (DDIs) is an important consideration during the drug development process and can guide the safe use of polypharmacy regimens in clinical practice. In recent years, several endogenous substrates of drug transporters have been identified as potential biomarkers for predicting changes in drug transport function and the potential for DDIs associated with drug candidates in early phases of drug development. These biomarker-driven investigations have been applied in both preclinical and clinical studies and proposed as a predictive strategy that can be supplanted in order to conduct prospective DDIs trials. Here we provide an overview of this rapidly emerging field, with particular emphasis on endogenous biomarkers recently proposed for clinically relevant uptake transporters.
Topics: Animals; Biological Transport; Drug Interactions; Humans; Membrane Transport Proteins
PubMed: 34576971
DOI: 10.3390/molecules26185500 -
Epilepsy Research Jul 2020Brivaracetam is an antiepileptic drug (AED) indicated for the treatment of focal seizures, with improved safety and tolerability vs first-generation AEDs. Brivaracetam... (Review)
Review
Brivaracetam is an antiepileptic drug (AED) indicated for the treatment of focal seizures, with improved safety and tolerability vs first-generation AEDs. Brivaracetam binds with high affinity to synaptic vesicle protein 2A in the brain, which confers its antiseizure activity. Brivaracetam is rapidly absorbed and extensively biotransformed, and exhibits linear and dose-proportional pharmacokinetics at therapeutic doses. Brivaracetam does not interact with most metabolizing enzymes and drug transporters, and therefore does not interfere with drugs that use these metabolic routes. The favorable pharmacokinetic profile of brivaracetam and lack of clinically relevant drug-drug interactions with commonly prescribed AEDs or oral contraceptives allows administration without dose adjustment, and avoids potential untoward events from decreased efficacy of an AED or oral contraceptive due to a drug-drug interaction. Few agents have been reported to affect the pharmacokinetics of brivaracetam. The strong enzyme-inducing AEDs carbamazepine, phenytoin and phenobarbital/primidone have been shown to moderately lower brivaracetam plasma concentrations, with no adjustment of brivaracetam dose needed. Dose adjustment should be considered when brivaracetam is coadministered with the more potent CYP inducer, rifampin. Additionally, caution should be used when adding or ending treatment with the strong enzyme inducer, St. John's wort. In summary, brivaracetam (50-200 mg/day) has a favorable pharmacokinetic profile and is associated with few clinically relevant drug-drug interactions.
Topics: Anticonvulsants; Brain; Carbamazepine; Drug Interactions; Humans; Pyrrolidinones; Seizures
PubMed: 32361205
DOI: 10.1016/j.eplepsyres.2020.106327 -
Pharmacology 2011Although there is a growing impact of psychiatric and depressive disorders in cancer patients, literature on the idiosyncrasies of antidepressants (ADs) used in those... (Review)
Review
BACKGROUND AND OBJECTIVES
Although there is a growing impact of psychiatric and depressive disorders in cancer patients, literature on the idiosyncrasies of antidepressants (ADs) used in those conditions and their interactions with antineoplastic agents (ANs) is scarce. Sharing the same biotransformation pathways enhances the risk of drug interaction between ADs and ANs, specifically when compounds are inducers, inhibitors or substrates of cytochrome P450 (CYP 450). In cancer patients, such drug interactions may result in less efficacy of the drug and/or increase of their side effects. Therefore, the choice of AD should be cautious (safe and effective) and well supported. The main purpose of this review was to analyze the individual pharmacokinetic properties of the most used ADs and ANs in order to summarize the risk of possible drug interactions between them, anticipating the consequences of their coadministration.
METHODS
The authors reviewed books and PubMed online articles published in the last 6 years.
RESULTS
Most of the ANs are subject to transformation by CYP 450 3A4 and their coadministration with ADs, that have inhibitory properties of this CYP isoform, such as fluoxetine, sertraline, paroxetine and fluvoxamine, may result in the loss of the AN's efficacy or higher toxicity.
CONCLUSION
Among the ADs, escitalopram, citalopram, venlafaxine, mirtazapine and milnacipran stand out for their weak CYP 450 inhibitory potential and their safety profile in those patients.
Topics: Animals; Antidepressive Agents; Antineoplastic Agents; Cytochrome P-450 Enzyme Inhibitors; Cytochrome P-450 Enzyme System; Depressive Disorder; Drug Interactions; Humans; Neoplasms
PubMed: 22123153
DOI: 10.1159/000334738