This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
A large number of medications are prescribed in pediatric clinics and this leads to the development of drug-drug interactions (DDI) that may complicate the course of the disease. The aim of the study was to identify the prevalence of potential drug-drug interactions, to categorize main drug classes involved in severe drug-drug interactions and to highlight clinically relevant DDIs in a pediatric population.
Warnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards.
Research for ontology evaluation is scarce. If biomedical ontological datasets and knowledgebases are to be widely used, there needs to be quality control and evaluation for the content and structure of the ontology. This paper introduces how to effectively utilize a semiotic-inspired approach to ontology evaluation, specifically towards drug-related ontologies hosted on the National Center for Biomedical Ontology BioPortal.
Tuberculosis remains the leading cause of death among people living with HIV. Rifapentine is increasingly used to treat active disease or prevent reactivation, in both cases given either as weekly or daily therapy. However, rifapentine is an inducer of CYP3A4, potentially interacting with antiretrovirals like rilpivirine. This in silico study investigates the drug-drug interaction (DDI) magnitude between daily oral rilpivirine 25 mg with either daily 600 mg or weekly 900 mg rifapentine. A physiologically based pharmacokinetic (PBPK) model was built in Simbiology (Matlab R2018a) to simulate the drug-drug interaction. The simulated PK parameters from the PBPK model were verified against reported clinical data for rilpivirine and rifapentine separately, daily rifapentine with midazolam, and weekly rifapentine with doravirine. The simulations of concomitant administration of rifapentine with rilpivirine at steady-state lead to a maximum decrease on AUC0-24 and Ctrough by 83% and 92% on day 5 for the daily rifapentine regimen and 68% and 92% for the weekly regimen on day 3. In the weekly regimen, prior to the following dose, AUC0-24 and Ctrough were still reduced by 47% and 53%. In both simulations, the induction effect ceased 2 weeks after the interruption of rifapentine's treatment. A daily double dose of rilpivirine after initiating rifapentine 900 mg weekly was simulated but failed to compensate the drug-drug interaction. The drug-drug interaction model suggested a significant decrease on rilpivirine exposure which is unlikely to be corrected by dose increment, thus coadministration should be avoided.
Detection of cellular changes at single-cell level has a great potential for biomedical and biopharmaceutical applications. Raman spectroscopy is an important tool for single-cell molecular imaging analysis. Raman spectroscopy can provide time-resolved information of the selected biomolecular distributions inside a single cell without the need of chemical labeling. In this study, we monitored the cellular responses to antineoplastic drug at a single cell basis with Raman spectroscopy. We demonstrated that single nuclei Raman spectroscopy has the ability to detect and identify nuclear changes related to cytotoxicity at lower concentrations and in shorter time span than conventional cell based assays. Thus, this strategy of using Raman spectroscopy of single, isolated nuclei may be very valuable for rapid and sensitive detection of cellular changes in response to chemotherapeutic agents.
Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers.
Lemborexant is approved for treating insomnia and is under investigation for treating irregular sleep-wake rhythm disorder. Based on in vitro drug-drug interaction (DDI) characteristics, phase 1, open-label DDI studies were conducted to evaluate lemborexant's cytochrome P450 3A (CYP3A) and CYP2B6 interaction potential. Interactions between lemborexant 10 mg and strong and moderate CYP3A inhibitors (itraconazole and fluconazole), a strong CYP3A inducer (rifampin), and CYP3A (midazolam) and CYP2B6 substrates (bupropion) were evaluated. Coadministration of lemborexant with itraconazole or fluconazole resulted in 1.4- to 1.6-fold and 3.7- to 4-fold increases in lemborexant maximum observed concentration (Cmax ) and area under the concentration-time curve from zero time extrapolated to infinity (AUC0-inf ), respectively. Coadministration of lemborexant with rifampin resulted in >90% decreases in lemborexant Cmax and AUC0-inf . Midazolam exposure was not affected. Coadministration of lemborexant with bupropion resulted in 49.9% and 45.5% decreases in S-bupropion Cmax and AUC0-inf , respectively.Comparison of estimated exposures for patients in phase 3 trials who were/were not receiving concomitant weak CYP3A inhibitors substantiated the DDI pharmacokinetic findings. Lemborexant was generally well tolerated in the phase 1 studies. In summary, lemborexant does not affect the pharmacokinetics of CYP3A substrates and has potential to induce CYP2B6. Consistent with in vitro findings, moderate and strong CYP3A inhibitors and inducers affected the pharmacokinetics of lemborexant; hence, patients taking lemborexant 5 or 10 mg should avoid coadministration with moderate and strong CYP3A inhibitors and inducers.
Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P-glycoprotein (P-gp), organic cation transporter-2 (OCT2), and multidrug and toxin extrusion protein-1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug-drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration-time curve [AUC] and approximate 20% maximum plasma drug concentration [Cmax ] increase). The model underpredicted rifampin induction by 100% (approximate 6.7-fold decrease in AUC and approximate 2.6-fold decrease in Cmax in the DDI study), presumably reflecting non-CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates. The worst-case scenario DDI simulation for pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P-gp or OCT2/MATE1 substrates.
BalestraWeb is an online server that allows users to instantly make predictions about the potential occurrence of interactions between any given drug-target pair, or predict the most likely interaction partners of any drug or target listed in the DrugBank. It also permits users to identify most similar drugs or most similar targets based on their interaction patterns. Outputs help to develop hypotheses about drug repurposing as well as potential side effects.
Diminazene aceturate is a trypanocide with unwanted toxicity and limited efficacy. It was reasoned that conjugating diminazene aceturate to functionalized nanoparticle would lower untoward toxicity while improving selectivity and therapeutic efficacy. Silver and gold nanoparticles were evaluated for their capacities to serve as carriers for diminazene aceturate. The silver and gold nanoparticles were synthesized, functionalized and coupled to diminazene aceturate following established protocols. The nanoparticle conjugates were characterized. The free diminazene aceturate and drug conjugated nanoparticles were subsequently evaluated for cytotoxicity in vitro. The characterizations by transmission electron microscopy or UV/Vis spectroscopy revealed that conjugation of diminazene aceturate to silver or gold nanoparticles was successful. Evaluation for cytotoxic actions in vitro demonstrated no significance difference between free diminazene aceturate and the conjugates. Our data suggest that surface modified metal nanoparticles could be optimized for drug delivery systems.
Treatment of Parkinson's disease (PD) includes the use of monoamine oxidase-B (MAO-B) inhibitor drugs. In this work we have evaluated the possible gamma-decanolactone (GD) effect in vitro to inhibit the A and B isoforms of human monoamine oxidase (hMAO) enzyme and their citotoxicity in human hepatoma cell line (HepG2). Also, binding studies to A1, A2A A2B and A3 adenosine receptors were performed. A docking study of gamma-decanolactone has been carried out with the molecular targets of MAO-A and MAO-B isoforms. The physicochemical properties and ability to cross physiological barriers, as the blood brain barrier (BBB), was elucidated by computational studies. The in vivo assays, the rota-rod test, body temperature assessment and open field test were performed in reserpinized mice (1.5 mg/kg, i.p.; 18:00 before) to evaluate the effect of gamma-decanolactone (300 mg/kg), alone or associated with Levodopa plus Benserazide (LD + BZ, 100:25 mg/kg, i.p.). Gamma-decanolactone inhibited preferentially the MAO-B in a reversible manner, with an inhibitory concentration of 50% (IC50) 55.95 ± 9.06 μM. It was shown to be a safe drug since only at the highest concentration decreased the viability of HepG2 cells. It also does not bind to adenosine receptors investigated in this study. The molecular docking study show that the gamma-decanolactone ligand adopts a relatively compact conformation in the active site of hMAO-B, while we note an extended conformation of gamma-decanolactone ligand in the hMAO-A isoform. The physicochemical properties obtained, and the theoretical models utilized for the evaluation of ability to cross the BBB, predict a good gamma-decanolactone bioavailability and access to the central nervous system (CNS). In the in vivo studies, gamma-decanolactone partially reversed the ataxia of the reserpinized mice at 01:00 h and 01:30 h post-administration. Concomitant treatment of gamma-decanolactone with LD + BZ, at 01:30 h showed a potentiation of the reversibility of ataxia and facilitated the reversal of hypothermia caused by reserpine for all measured times (P <0.01 vs vehicle), except at 24:00 h, but not reversed the hypokinesia in the open field test. In summary, the results herein obtained and in conjunction with previous studies, suggest that gamma-decanolactone could be a drug with potential utility as antiparkinsonian drug.
Voluntary drug ingestion with benzodiazepine represent today the most frequent method of attempt of autolysis. One must note the difficulties the doctor may find in front such problems to judge the reliability of interviews made in such difficult conditions. Residual disturbances of superior functions, more precisely of vigilance during the period with follow the suicidal action must not be overlooked. Thanks to a clinical scale easily used the residual disturbances have been put in evidence on a sample of 20 subjects who had been admitted with this aim in view in a university ward specialized in psychiatric emergencies. The possibility of continuity within middle range care must allow an improvement of minimum care of such pathologies.
Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.
Drug-drug interactions (DDIs) can affect both treatment efficacy and toxicity. We used Drug-PIN® (Personalized Interactions Network) software in colorectal cancer (CRC) patients to evaluate drug-drug-gene interactions (DDGIs), defined as the combination of DDIs and individual genetic polymorphisms. Inclusion criteria were: (i) stage II-IV CRC; (ii) ECOG PS (Performance status sec. Eastern coperative oncology group) ≤2; (iii) ≥5 concomitant drugs; and (iv) adequate renal, hepatic, and bone marrow function. The Drug-PIN® system analyzes interactions between active and/or pro-drug forms by integrating biochemical, demographic, and genomic data from 110 SNPs. We selected DDI, DrugPin1, and DrugPin2 scores, resulting from concomitant medication interactions, concomitant medications, and SNP profiles, and DrugPin1 added to chemotherapy drugs, respectively. Thirty-four patients, taking a median of seven concomitant medications, were included. The median DrugPin1 and DrugPin2 scores were 42.6 and 77.7, respectively. In 13 patients, the DrugPin2 score was two-fold higher than the DrugPin1 score, with 7 (54%) of these patients experiencing severe toxicity that required hospitalization. On chi-squared testing for any toxicity, a doubled DrugPin2 score (p = 0.001) was significantly related to G3-G4 toxicity. Drug-PIN® software may prevent severe adverse events, decrease hospitalizations, and improve survival in cancer patients.
Tucatinib is approved for treatment of human epidermal growth factor receptor 2-positive metastatic breast cancer. Understanding potential drug-drug interactions (DDIs) informs proper dosing when co-administering tucatinib with other therapies. The aim of this study was to evaluate DDIs between tucatinib and metabolizing enzymes and transporters in healthy volunteers.
Background and objective: Background and objective: Drug repurposing has been considered a cost-effective and reduced-risk strategy for developing new drugs. Little is known and documented regarding the efficiency of repurposing strategies in drug development. The objective of this article is to assess the extent and meaning of this process in the CNS area. Methods: In order to identify repurposed drugs that target the CNS, an extensive search was performed. For each identified case, its initial and target indication, development status and the type of repurposing strategy (repositioning, reformulation or both) was recorded. Results: One hundred and eighteen source products were identified. They were repurposed (mainly repositioned) 203 times with 81 products repurposed once and 38 products repurposed twice or more. The highest number of source drugs originated from the CNS area. Alzheimer's disease was targeted most often. Half of the new indications were approved. Regarding repurposing within the CNS area, epilepsy, schizophrenia and depression were the richest sources of repurposed drugs. Conclusions: Repurposing drugs into CNS is an efficient and very active drug development method, exemplified by the considerable number of new indications that have been found via this strategy, with approximately half of the target indications currently under development.
Traditionally, safety evaluation at the early stage of drug discovery research has been done using in silico, in vitro, and in vivo systems in this order because of limitations on the amount of compounds available and the throughput ability of the assay systems. While these in vitro assays are very effective tools for detecting particular tissue-specific toxicity phenotypes, it is difficult to detect toxicity based on complex mechanisms involving multiple organs and tissues. Therefore, the development of novel high throughput in vivo evaluation systems has been expected for a long time. The zebrafish (Danio rerio) is a vertebrate with many attractive characteristics for use in drug discovery, such as a small size, transparency, gene and protein similarity with mammals (80% or more), and ease of genetic modification to establish human disease models. Actually, in recent years, the zebrafish has attracted interest as a novel experimental animal. In this article, the author summarized the features of zebrafish that make it a suitable laboratory animal, and introduced and discussed the applications of zebrafish to preclinical toxicity testing, including evaluations of teratogenicity, hepatotoxicity, and nephrotoxicity based on morphological findings, evaluation of cardiotoxicity using functional endpoints, and assessment of seizure and drug abuse liability.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count: