-
Journal of Veterinary Internal Medicine Jan 2015Drug-drug interactions can cause unanticipated patient morbidity and mortality. The consequences of drug-drug interactions can be especially severe when anticancer drugs... (Review)
Review
Drug-drug interactions can cause unanticipated patient morbidity and mortality. The consequences of drug-drug interactions can be especially severe when anticancer drugs are involved because of their narrow therapeutic index. Veterinary clinicians have traditionally been taught that drug-drug interactions result from alterations in drug metabolism, renal excretion or protein binding. More recently, drug-drug interactions resulting from inhibition of P-glycoprotein-mediated drug transport have been identified in both human and veterinary patients. Many drugs commonly used in veterinary patients are capable of inhibiting P-glycoprotein function and thereby causing an interaction that results in severe chemotherapeutic drug toxicity. The intent of this review is to describe the mechanism and clinical implications of drug-drug interactions involving P-glycoprotein and anticancer drugs. Equipped with this information, veterinarians can prevent serious drug-drug interactions by selecting alternate drugs or adjusting the dose of interacting drugs.
Topics: ATP Binding Cassette Transporter, Subfamily B, Member 1; Animals; Antineoplastic Agents; Drug Interactions; Humans; Neoplasms
PubMed: 25619511
DOI: 10.1111/jvim.12525 -
British Journal of Clinical Pharmacology 1977As both the tricyclic antidepressives and the monoamine oxidase (MAO) inhibitors were discovered accidently, their pharmacology has been established on an empirical... (Review)
Review
As both the tricyclic antidepressives and the monoamine oxidase (MAO) inhibitors were discovered accidently, their pharmacology has been established on an empirical basis only. There are no convincing models of depression in the normal human. The classification of depression remains controversial; also the relationship between clinical depression, depressive reactions and pessimistic personality traits is unresolved. Antidepressive effects are demonstrable in lugubrious normals. Pharmacokinetic and drug interaction data are important in the usage of a drug and are available from studies in normals. Secondary psychotropic effects such as sedation can be readily studied both in normals and in patients. The antidepressives produce characteristic changes in the electroencephalogram (EEG). Unwanted effects are often related to autonomic actions which can be readily quantified, salivation being most extensively studied. Psychological impairment is dose related in normals but lessens over a course of treatment in patients. Biochemical measures have been little explored despite their obvious relevance. MAO activity in platelets is diminished by MAO inhibitors. Tricyclic drugs inhibit the uptake of amines into neurones and platelets, and this inhibition can be assessed in various ways. Drug experiments in normal subjects provide data which complement, amplify and elucidate observations in patients without ever substituting for them.
Topics: Antidepressive Agents; Drug Interactions; Humans
PubMed: 334215
DOI: 10.1111/j.1365-2125.1977.tb05740.x -
Bioinformatics (Oxford, England) Dec 2022Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical...
MOTIVATION
Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions.
RESULTS
To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction.
AVAILABILITYAND IMPLEMENTATION
The source code and data are available at https://github.com/Azra3lzz/LaGAT.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Drug Interactions; Semantics; Data Accuracy; Databases, Factual; Software
PubMed: 36271850
DOI: 10.1093/bioinformatics/btac682 -
IET Systems Biology Aug 2020A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem,...
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.
Topics: Computational Biology; Deep Learning; Drug Interactions
PubMed: 32737279
DOI: 10.1049/iet-syb.2019.0116 -
European Journal of Drug Metabolism and... May 2022Felcisetrag (previously TAK-954 or TD-8954) is a highly selective and potent 5-HT receptor agonist in clinical development for prophylaxis and treatment of postoperative...
BACKGROUND AND OBJECTIVE
Felcisetrag (previously TAK-954 or TD-8954) is a highly selective and potent 5-HT receptor agonist in clinical development for prophylaxis and treatment of postoperative gastrointestinal dysfunction (POGD). The rat, dog, and human absorption, distribution, metabolism, and excretion (ADME) properties of felcisetrag were investigated.
METHODS
The metabolism and victim and perpetrator drug interaction potentials towards cytochrome P450s (CYP) and transporters were determined using in vitro models. The excretion, metabolite profile, and pharmacokinetics were determined during unlabeled and radiolabeled ADME studies in rat and dog for comparison with human. Due to a low clinical dose (0.5 mg) and radioactivity (~ 1.5 μCi), a combination of liquid scintillation counting and accelerator mass spectrometry was used for analysis of samples in this study.
RESULTS
The ADME properties, including metabolite profile, for felcisetrag are generally conserved across species. Felcisetrag is primarily cleared through renal excretion (0.443) and metabolism in humans (0.420), with intact parent as the predominant species in circulation. There are multiple metabolites, each representing < 10% of the circulating radioactivity, confirming no metabolites in safety testing (MIST) liabilities. Metabolites were also detected in animals. The potential for major CYP- and transporter-based drug-drug interaction (DDI) of felcisetrag as a victim or perpetrator is considered to be low.
CONCLUSIONS
Felcisetrag is primarily cleared in humans through renal excretion. Although the metabolism of felcisetrag is primarily through CYP3A, the potential for clinically relevant DDI as a victim is significantly reduced as metabolism plays a minor role in the overall clearance.
Topics: Animals; Cytochrome P-450 Enzyme System; Dogs; Drug Interactions; Humans; Rats; Serotonin
PubMed: 35157234
DOI: 10.1007/s13318-021-00751-8 -
Bioinformatics (Oxford, England) Jan 2023Most of the conventional deep neural network-based methods for drug-drug interaction (DDI) extraction consider only context information around drug mentions in the text....
MOTIVATION
Most of the conventional deep neural network-based methods for drug-drug interaction (DDI) extraction consider only context information around drug mentions in the text. However, human experts use heterogeneous background knowledge about drugs to comprehend pharmaceutical papers and extract relationships between drugs. Therefore, we propose a novel method that simultaneously considers various heterogeneous information for DDI extraction from the literature.
RESULTS
We first construct drug representations by conducting the link prediction task on a heterogeneous pharmaceutical knowledge graph (KG) dataset. We then effectively combine the text information of input sentences in the corpus and the information on drugs in the heterogeneous KG (HKG) dataset. Finally, we evaluate our DDI extraction method on the DDIExtraction-2013 shared task dataset. In the experiment, integrating heterogeneous drug information significantly improves the DDI extraction performance, and we achieved an F-score of 85.40%, which results in state-of-the-art performance. We evaluated our method on the DrugProt dataset and improved the performance significantly, achieving an F-score of 77.9%. Further analysis showed that each type of node in the HKG contributes to the performance improvement of DDI extraction, indicating the importance of considering multiple pieces of information.
AVAILABILITY AND IMPLEMENTATION
Our code is available at https://github.com/tticoin/HKG-DDIE.git.
Topics: Humans; Pattern Recognition, Automated; Data Mining; Drug Interactions; Neural Networks, Computer; Pharmaceutical Preparations
PubMed: 36416141
DOI: 10.1093/bioinformatics/btac754 -
Scientific Reports Sep 2019Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting...
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD .
Topics: Algorithms; Computational Biology; Drug Interactions; Machine Learning; Neural Networks, Computer
PubMed: 31541145
DOI: 10.1038/s41598-019-50121-3 -
The American Journal of Emergency... Feb 2016CYP450 polymorphisms result in variable rates of drug metabolism. CYP drug-drug interactions can contribute to altered drug effectiveness and safety. (Observational Study)
Observational Study
BACKGROUND
CYP450 polymorphisms result in variable rates of drug metabolism. CYP drug-drug interactions can contribute to altered drug effectiveness and safety.
STUDY OBJECTIVES
The primary objective was to determine the percentage of emergency department (ED) patients with cytochrome 2C19 (CYP2C19) drug-drug interactions. The secondary objective was to determine the prevalence of CYP2C19 polymorphisms in a US ED population.
METHODS
We conducted a prospective observational study in an urban academic ED with 72,000 annual visits. Drug ingestion histories for the 48 hours preceding ED visit were obtained; each drug was coded as CYP2C19 substrate, inhibitor, inducer, or not CYP2C19 dependent. Ten percent of patients were randomized to undergo CYP2C19 genotyping using the Roche Amplichip.
RESULTS
A total of 502 patients were included; 61% were female, 65% were white, and median age was 39 years (interquartile range, 22-53). One hundred thirty-one (26.1%) patients had taken at least 1 CYP2C19-dependent home drug. Eighteen (13.7%) patients who were already taking a CYP2C19-dependent drug were given or prescribed a CYP2C19-dependent drug while in the ED. Among the 53 patients genotyped, 52 (98%) were extensive metabolizers and 1 was a poor metabolizer.
CONCLUSIONS
In a population of ED patients, more than a quarter had taken a CYP2C19-dependent drug in the preceding 48 hours, but few were given or prescribed another CYP2C19-dependent drug in the ED. On genotyping analysis, CYP2C19 polymorphisms were uncommon in our cohort. We conclude that changing prescribing practice due to CYP2C19 drug-drug interaction or genotype is unlikely to be useful in most US ED populations.
Topics: Adult; Cytochrome P-450 CYP2C19; Drug Interactions; Emergency Service, Hospital; Female; Genotype; Humans; Male; Middle Aged; Pharmacogenetics; Polymorphism, Genetic; Prevalence; Prospective Studies; United States
PubMed: 26639454
DOI: 10.1016/j.ajem.2015.10.055 -
Journal of the Medical Library... Jan 2019The research evaluated point-of-care drug interaction resources for scope, completeness, and consistency in drug-ethanol and drug-tobacco content.
OBJECTIVE
The research evaluated point-of-care drug interaction resources for scope, completeness, and consistency in drug-ethanol and drug-tobacco content.
METHODS
In a cross-sectional analysis, 2 independent reviewers extracted data for 108 clinically relevant interactions using 7 drug information resources (Clinical Pharmacology Drug Interaction Report, Facts & Comparisons eAnswers, Lexicomp Interactions, Micromedex Drug Interactions, and ). Scope (presence of an entry), completeness (content describing mechanism, clinical effects, severity, level of certainty, and course of action for each present interaction; up to 1 point per assessed item for a total possible score of 5 points), and consistency (similarity among resources) were evaluated.
RESULTS
Fifty-three drug-ethanol and 55 drug-tobacco interactions were analyzed. Drug-ethanol interaction entries were most commonly present in Lexicomp (84.9%), Clinical Pharmacology (83.0%), and (73.6%), compared to other resources (<0.05). Drug-tobacco interactions were more often covered in Micromedex (56.4%), (56.4%), Drug Interaction Facts (43.6%), and Clinical Pharmacology (41.8%) (<0.001). Overall completeness scores were higher for Lexicomp, Micromedex, and Facts & Comparisons (median 5/5 points, interquartile range [IQR] 5 to 5, <0.001) for drug-ethanol and for Micromedex (median 5/5 points, IQR 5 to 5, <0.05) for drug-tobacco, compared to other resources. and Micromedex were among the highest scoring resources for both drug-ethanol (73.7%, 68.6%) and drug-tobacco (75.0%, 32.3%) consistency.
CONCLUSIONS
Scope and completeness were high for drug-ethanol interactions, but low for drug-tobacco interactions. Consistency was highly variable across both interaction types.
Topics: Cross-Sectional Studies; Databases, Factual; Drug Information Services; Drug Interactions; Ethanol; Humans; Nicotiana
PubMed: 30598650
DOI: 10.5195/jmla.2019.549 -
Journal of Pharmacy & Pharmaceutical... 2017In vitro and in silico models of drug metabolism are utilized regularly in the drug research and development as tools for assessing pharmacokinetic variability and... (Review)
Review
In vitro and in silico models of drug metabolism are utilized regularly in the drug research and development as tools for assessing pharmacokinetic variability and drug-drug interaction risk. The use of in vitro and in silico predictive approaches offers advantages including guiding rational design of clinical drug-drug interaction studies, minimization of human risk in the clinical trials, as well as cost and time savings due to lesser attrition during compound development process. This article gives a review of some of the current in vitro and in silico methods used to characterize cytochrome P450(CYP)-mediated drug metabolism for estimating pharmacokinetic variability and the magnitude of drug-drug interactions. Examples demonstrating the predictive applicability of specific in vitro and in silico approaches are described. Commonly encountered confounding factors and sources of bias and error in these approaches are presented. With the advent of technological advancement in high throughput screening and computer power, the in vitro and in silico methods are becoming more efficient and reliable and will continue to contribute to the process of drug discovery, development and ultimately safer and more effective pharmacotherapy. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
Topics: Computer Simulation; Cytochrome P-450 Enzyme System; Drug Discovery; Drug Interactions; Humans; Models, Biological; Oxidation-Reduction; Pharmaceutical Preparations
PubMed: 29145931
DOI: 10.18433/J3434R