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Annual Review of Pharmacology and... Jan 2018Over 70% of Americans take some form of dietary supplement every day, and the supplement industry is currently big business, with a gross of over $28 billion. However,... (Review)
Review
Over 70% of Americans take some form of dietary supplement every day, and the supplement industry is currently big business, with a gross of over $28 billion. However, unlike either foods or drugs, supplements do not need to be registered or approved by the US Food and Drug Administration (FDA) prior to production or sales. Under the Dietary Supplement Health and Education Act of 1994, the FDA is restricted to adverse report monitoring postmarketing. Despite widespread consumption, there is limited evidence of health benefits related to nutraceutical or supplement use in well-nourished adults. In contrast, a small number of these products have the potential to produce significant toxicity. In addition, patients often do not disclose supplement use to their physicians. Therefore, the risk of adverse drug-supplement interactions is significant. An overview of the major supplement and nutraceutical classes is presented here, together with known toxic effects and the potential for drug interactions.
Topics: Animals; Dietary Supplements; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Humans; United States; United States Food and Drug Administration
PubMed: 28992429
DOI: 10.1146/annurev-pharmtox-010617-052844 -
Journal of Pharmacokinetics and... Apr 2019Here we characterize and summarize the pharmacokinetic changes for metabolized drugs when drug-drug interactions and pharmacogenomic variance are observed. Following... (Review)
Review
Here we characterize and summarize the pharmacokinetic changes for metabolized drugs when drug-drug interactions and pharmacogenomic variance are observed. Following multiple dosing to steady-state, oral systemic concentration-time curves appear to follow a one-compartment body model, with a shorter rate limiting half-life, often significantly shorter than the single dose terminal half-life. This simplified disposition model at steady-state allows comparisons of measurable parameters (i.e., area under the curve, half-life, maximum concentration and time to maximum concentration) following drug interaction or pharmacogenomic variant studies to be utilized to characterize whether a drug is low versus high hepatic extraction ratio, even without intravenous dosing. The characteristics of drugs based on the ratios of area under the curve, maximum concentration and half-life are identified with recognition that volume of distribution is essentially unchanged for drug interaction and pharmacogenomic variant studies where only metabolic outcomes are changed and transporters are not significantly involved. Comparison of maximum concentration changes following single dose interaction and pharmacogenomic variance studies may also identify the significance of intestinal first pass changes. The irrelevance of protein binding changes on pharmacodynamic outcomes following oral and intravenous dosing of low hepatic extraction ratio drugs, versus its relevance for high hepatic extraction ratio drugs is re-emphasized.
Topics: Area Under Curve; Drug Interactions; Half-Life; Humans; Metabolic Clearance Rate; Pharmaceutical Preparations; Pharmacogenetics
PubMed: 30911879
DOI: 10.1007/s10928-019-09626-7 -
Drug Safety Apr 2014Proton pump inhibitors (PPIs) are used extensively for the treatment of gastric acid-related disorders, often over the long term, which raises the potential for... (Review)
Review
Proton pump inhibitors (PPIs) are used extensively for the treatment of gastric acid-related disorders, often over the long term, which raises the potential for clinically significant drug interactions in patients receiving concomitant medications. These drug-drug interactions have been previously reviewed. However, the current knowledge is likely to have advanced, so a thorough review of the literature published since 2006 was conducted. This identified new studies of drug interactions that are modulated by gastric pH. These studies showed the effect of a PPI-induced increase in intragastric pH on mycophenolate mofetil pharmacokinetics, which were characterised by a decrease in the maximum exposure and availability of mycophenolic acid, at least at early time points. Post-2006 data were also available outlining the altered pharmacokinetics of protease inhibitors with concomitant PPI exposure. New data for the more recently marketed dexlansoprazole suggest it has no impact on the pharmacokinetics of diazepam, phenytoin, theophylline and warfarin. The CYP2C19-mediated interaction that seems to exist between clopidogrel and omeprazole or esomeprazole has been shown to be clinically important in research published since the 2006 review; this effect is not seen as a class effect of PPIs. Finally, data suggest that coadministration of PPIs with methotrexate may affect methotrexate pharmacokinetics, although the mechanism of interaction is not well understood. As was shown in the previous review, individual PPIs differ in their propensities to interact with other drugs and the extent to which their interaction profiles have been defined. The interaction profiles of omeprazole and pantoprazole sodium (pantoprazole-Na) have been studied most extensively. Several studies have shown that omeprazole carries a considerable potential for drug interactions because of its high affinity for CYP2C19 and moderate affinity for CYP3A4. In contrast, pantoprazole-Na appears to have lower potential for interactions with other medications. Lansoprazole and rabeprazole also seem to have a weaker potential for interactions than omeprazole, although their interaction profiles, along with those of esomeprazole and dexlansoprazole, have been less extensively investigated. Only a few drug interactions involving PPIs are of clinical significance. Nonetheless, the potential for drug interactions should be considered when choosing a PPI to manage gastric acid-related disorders. This is particularly relevant for elderly patients taking multiple medications, or for those receiving a concomitant medication with a narrow therapeutic index.
Topics: Cytochrome P-450 CYP2C19; Cytochrome P-450 CYP3A; Drug Interactions; Humans; Proton Pump Inhibitors
PubMed: 24550106
DOI: 10.1007/s40264-014-0144-0 -
European Journal of Pharmaceutical... Jun 2019The simultaneous intake of food and drugs can have a strong impact on drug release, absorption, distribution, metabolism and/or elimination and consequently, on the... (Review)
Review
The simultaneous intake of food and drugs can have a strong impact on drug release, absorption, distribution, metabolism and/or elimination and consequently, on the efficacy and safety of pharmacotherapy. As such, food-drug interactions are one of the main challenges in oral drug administration. Whereas pharmacokinetic (PK) food-drug interactions can have a variety of causes, pharmacodynamic (PD) food-drug interactions occur due to specific pharmacological interactions between a drug and particular drinks or food. In recent years, extensive efforts were made to elucidate the mechanisms that drive pharmacokinetic food-drug interactions. Their occurrence depends mainly on the properties of the drug substance, the formulation and a multitude of physiological factors. Every intake of food or drink changes the physiological conditions in the human gastrointestinal tract. Therefore, a precise understanding of how different foods and drinks affect the processes of drug absorption, distribution, metabolism and/or elimination as well as formulation performance is important in order to be able to predict and avoid such interactions. Furthermore, it must be considered that beverages such as milk, grapefruit juice and alcohol can also lead to specific food-drug interactions. In this regard, the growing use of food supplements and functional food requires urgent attention in oral pharmacotherapy. Recently, a new consortium in Understanding Gastrointestinal Absorption-related Processes (UNGAP) was established through COST, a funding organisation of the European Union supporting translational research across Europe. In this review of the UNGAP Working group "Food-Drug Interface", the different mechanisms that can lead to pharmacokinetic food-drug interactions are discussed and summarised from different expert perspectives.
Topics: Administration, Oral; Biological Availability; Drug Liberation; Europe; Food-Drug Interactions; Gastrointestinal Absorption; Gastrointestinal Tract; Humans; Intestinal Absorption; Pharmacokinetics
PubMed: 30974173
DOI: 10.1016/j.ejps.2019.04.003 -
Classification of drugs for evaluating drug interaction in drug development and clinical management.Drug Metabolism and Pharmacokinetics Dec 2021During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro... (Review)
Review
During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro studies. The obtained information should be provided efficiently to medical experts through package inserts and various information materials after the drug's launch. A recently updated Japanese guideline presents general procedures that are considered scientifically valid at the present moment. In this review, we aim to highlight the viewpoints of the Japanese guideline and enumerate drugs that were involved or are anticipated to be involved in evident pharmacokinetic drug interactions and classify them by their clearance pathway and potential intensity based on systematic reviews of the literature. The classification would be informative for designing clinical studies during the development stage, and the appropriate management of drug interactions in clinical practice.
Topics: Drug Development; Drug Interactions; Pharmaceutical Preparations
PubMed: 34666290
DOI: 10.1016/j.dmpk.2021.100414 -
Database : the Journal of Biological... May 2022The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on...
The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug-nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug-interaction research experience and four annotators with less drug-interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus.
Topics: Data Mining; Databases, Factual; Drug Interactions; Humans
PubMed: 35616099
DOI: 10.1093/database/baac031 -
Drug Metabolism and Disposition: the... Nov 2015Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of... (Review)
Review
Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.
Topics: Animals; Computer Simulation; Drug Interactions; Humans; Models, Biological; Pharmaceutical Preparations; Pharmacokinetics
PubMed: 26296709
DOI: 10.1124/dmd.115.065920 -
Methods in Molecular Biology (Clifton,... 2014In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are... (Review)
Review
In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.
Topics: Animals; Biological Ontologies; Data Curation; Data Mining; Drug Interactions; Humans
PubMed: 24788261
DOI: 10.1007/978-1-4939-0709-0_4 -
Journal of Clinical Pharmacology Jun 2021Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in... (Review)
Review
Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.
Topics: Child; Computer Simulation; Drug Interactions; Humans; Models, Biological; Pediatrics; Pharmaceutical Preparations; Pharmacokinetics
PubMed: 34185913
DOI: 10.1002/jcph.1881 -
Briefings in Bioinformatics Nov 2023In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the... (Review)
Review
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
Topics: Humans; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Databases, Factual; Computer Simulation; Drug Combinations
PubMed: 38113076
DOI: 10.1093/bib/bbad445