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Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects ([Formula: see text]) and pharmacological information ([Formula: see text]), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, [Formula: see text] data from the STITCH database, [Formula: see text] from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.
Combination drug therapy is an efficient way to treat complicated diseases. Drug-drug interaction (DDI) is an important research topic in this therapy as patient safety is a problem when two or more drugs are taken at the same time. Traditionally, in vitro experiments and clinical trials are common ways to determine DDIs. However, these methods cannot meet the requirements of large-scale tests. It is an alternative way to develop computational methods for predicting DDIs. Although several previous methods have been proposed, they always need several types of drug information, limiting their applications. In this study, we proposed a simple computational method to predict DDIs. In this method, drugs were represented by their fingerprint features, which are most widely used in investigating drug-related problems. These features were refined by three models, including addition, subtraction, and Hadamard models, to generate the representation of DDIs. The powerful classification algorithm, random forest, was picked up to build the classifier. The results of two types of tenfold cross-validation on the classifier indicated good performance for discovering novel DDIs among known drugs and acceptable performance for identifying DDIs between known drugs and unknown drugs or among unknown drugs. Although the classifier adopted a sample scheme to represent DDIs, it was still superior to other methods, which adopted features generated by some advanced computer algorithms. Furthermore, a user-friendly web-server, named DDIPF (http://106.14.164.77:5004/DDIPF/), was developed to implement the classifier.
The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases.
Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage - a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) - a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting "contraindicated" DDIs (AUROC = 0.92) and less effective for "minor" DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.
COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.
In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power-including the prediction of 432 novel DDIs-our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.
Awareness of drug interactions involving opioids is critical for patient treatment as they are common therapeutics used in numerous care settings, including both chronic and disease-related pain. Not only do opioids have narrow therapeutic indexes and are extensively used, but they have the potential to cause severe toxicity. Opioids are the classical pain treatment for patients who suffer from moderate to severe pain. More importantly, opioids are often prescribed in combination with multiple other drugs, especially in patient populations who typically are prescribed a large drug regimen. This review focuses on the current knowledge of common opioid drug-drug interactions (DDIs), focusing specifically on hydrocodone, oxycodone, and morphine DDIs. The DDIs covered in this review include pharmacokinetic DDI arising from enzyme inhibition or induction, primarily due to inhibition of cytochrome p450 enzymes (CYPs). However, opioids such as morphine are metabolized by uridine-5'-diphosphoglucuronosyltransferases (UGTs), principally UGT2B7, and glucuronidation is another important pathway for opioid-drug interactions. This review also covers several pharmacodynamic DDI studies as well as the basics of CYP and UGT metabolism, including detailed opioid metabolism and the potential involvement of metabolizing enzyme gene variation in DDI. Based upon the current literature, further studies are needed to fully investigate and describe the DDI potential with opioids in pain and related disease settings to improve clinical outcomes for patients. SIGNIFICANCE STATEMENT: A review of the literature focusing on drug-drug interactions involving opioids is important because they can be toxic and potentially lethal, occurring through pharmacodynamic interactions as well as pharmacokinetic interactions occurring through inhibition or induction of drug metabolism.
Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug-drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug-drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug-drug interactions. Furthermore, we define several statistical metrics in the context of human protein-protein interaction networks and signaling pathways to measure the interaction intensity, interaction efficacy and action range between two drugs. Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein-protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. The unravelled mechanisms could provide biological insights into potential adverse drug reactions of co-prescribed drugs.
Classic psychedelics, including lysergic acid diethylamide (LSD), psilocybin, mescaline, N,N-dimethyltryptamine (DMT) and 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), are potent psychoactive substances that have been studied for their physiological and psychological effects. However, our understanding of the potential interactions and outcomes when using these substances in combination with other drugs is limited. This systematic review aims to provide a comprehensive overview of the current research on drug-drug interactions between classic psychedelics and other drugs in humans. We conducted a thorough literature search using multiple databases, including PubMed, PsycINFO, Web of Science and other sources to supplement our search for relevant studies. A total of 7102 records were screened, and studies involving human data describing potential interactions (as well as the lack thereof) between classic psychedelics and other drugs were included. In total, we identified 52 studies from 36 reports published before September 2, 2023, encompassing 32 studies on LSD, 10 on psilocybin, 4 on mescaline, 3 on DMT, 2 on 5-MeO-DMT and 1 on ayahuasca. These studies provide insights into the interactions between classic psychedelics and a range of drugs, including antidepressants, antipsychotics, anxiolytics, mood stabilisers, recreational drugs and others. The findings revealed various effects when psychedelics were combined with other drugs, including both attenuated and potentiated effects, as well as instances where no changes were observed. Except for a few case reports, no serious adverse drug events were described in the included studies. An in-depth discussion of the results is presented, along with an exploration of the potential molecular pathways that underlie the observed effects.
Baricitinib, an oral selective Janus kinase 1 and 2 inhibitor, undergoes active renal tubular secretion. Baricitinib was not predicted to inhibit hepatic and renal uptake and efflux drug transporters, based on the ratio of the unbound maximum eliminating-organ inlet concentration and the in vitro half-maximal inhibitory concentrations (IC50 ). In vitro, baricitinib was a substrate for organic anion transporter (OAT)3, multidrug and toxin extrusion protein (MATE)2-K, P-glycoprotein (P-gp), and breast cancer resistance protein (BCRP). Probenecid, a strong OAT3 inhibitor, increased the area under the concentration-time curve from time zero to infinity (AUC[0-∞] ) of baricitinib by twofold and decreased renal clearance to 69% of control in healthy subjects. Physiologically based pharmacokinetic (PBPK) modeling reproduced the renal clearance of baricitinib and the inhibitory effect of probenecid using the in vitro IC50 value of 4.4 μM. Using ibuprofen and diclofenac in vitro IC50 values of 4.4 and 3.8 μM toward OAT3, 1.2 and 1.0 AUC(0-∞) ratios of baricitinib were predicted. These predictions suggest clinically relevant drug-drug interactions (DDIs) with ibuprofen and diclofenac are unlikely.
Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.
Tenapanor (RDX5791, AZD1722) is an inhibitor of sodium/hydrogen exchanger isoform 3 in development for the treatment of constipation-predominant irritable bowel syndrome and the treatment of hyperphosphatemia in patients with chronic kidney disease on dialysis. We aimed to investigate whether tenapanor inhibits or induces cytochrome P450s (CYPs). In vitro experiments assessing the potential of tenapanor to affect various CYPs indicated that it could inhibit CYP3A4/5 (IC50 0.4-0.7 μM). An open-label, phase 1 clinical study (NCT02140268) evaluated the pharmacokinetics of the CYP3A4 substrate midazolam when administered with and without tenapanor. Healthy volunteers received a single oral dose of midazolam 7.5 mg on day 1 followed by tenapanor 15 mg twice daily on days 2 to 15, with an additional single 7.5-mg midazolam dose coadministered on day 15. Midazolam exposure was similar whether it was administered alone or with tenapanor (geometric least-squares mean ratio [90%CI] for [midazolam + tenapanor]/midazolam: area under the concentration-time curve, 107% [101% to 113%]; Cmax 104% [89.6% to 122%]). Findings were similar for metabolites of midazolam. These results indicate that tenapanor 15 mg twice daily does not have a clinically relevant impact on CYP3A4 in humans and suggest that tenapanor can be coadministered with CYP3A4-metabolized drugs without affecting their exposure.
Treatment options for chronic hepatitis C virus (HCV) infection have drastically changed since the development and licensing of new potent direct-acting antivirals (DAAs). The majority of DAAs are extensively metabolized by liver enzymes and have the ability to influence cytochrome P450 (CYP) enzymes. Additionally, these DAAs are both substrates and inhibitors of drug transporters, which makes the DAAs both possible victims or perpetrators of drug-drug interactions (DDIs). There is a high prevalence of mental illnesses such as depression or psychosis in HCV-infected patients; therefore, psychoactive medications are frequently co-administered with DAAs. The majority of these psychoactive medications are also metabolized by CYP enzymes but remarkably little information is available on DDIs between psychoactive medications and DAAs. Hence, the aim of this review is to provide an overview of the interaction mechanisms between DAAs and psychoactive agents. In addition, we describe evidenced-based interactions between DAAs and psychoactive drugs and identify safe options for the simultaneous treatment of mental illnesses and chronic HCV infection.
The accurate and timely detection of adverse drug-drug interactions (DDIs) during the postmarketing phase is an important yet complex task with potentially major clinical implications. The development of data mining methodologies that scan healthcare databases for drug safety signals requires appropriate reference sets for performance evaluation. Methodologies for establishing DDI reference sets are limited in the literature, while there is no publicly available resource simultaneously focusing on clinical relevance of DDIs and individual behaviour of interacting drugs. By automatically extracting and aggregating information from multiple clinical resources, we provide a scalable approach for generating a reference set for DDIs that could support research in postmarketing safety surveillance. CRESCENDDI contains 10,286 positive and 4,544 negative controls, covering 454 drugs and 179 adverse events mapped to RxNorm and MedDRA concepts, respectively. It also includes single drug information for the included drugs (i.e., adverse drug reactions, indications, and negative drug-event associations). We demonstrate usability of the resource by scanning a spontaneous reporting system database for signals of DDIs using traditional signal detection algorithms.
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.
Ginseng has been the subject of many experimental and clinical studies to uncover the diverse biological activities of its constituent compounds. It is a traditional medicine that has been used for its immunostimulatory, antithrombotic, antioxidative, anti-inflammatory, and anticancer effects. Ginseng may interact with concomitant medications and alter metabolism and/or drug transport, which may alter the known efficacy and safety of a drug; thus, the role of ginseng may be controversial when taken with other medications.
It has been recognized that significant transporter interactions result in volume of distribution changes in addition to potential changes in clearance. For drugs that are not clinically significant transporter substrates, it is expected that drug-drug interactions would not result in any changes in volume of distribution.
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