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Journal of Food and Drug Analysis Apr 2018There is a continued predisposition of concurrent use of drugs and botanical products. Consumers often self-administer botanical products without informing their health... (Review)
Review
There is a continued predisposition of concurrent use of drugs and botanical products. Consumers often self-administer botanical products without informing their health care providers. The perceived safety of botanical products with lack of knowledge of the interaction potential poses a challenge for providers and both efficacy and safety concerns for patients. Botanical-drug combinations can produce untoward effects when botanical constituents modulate drug metabolizing enzymes and/or transporters impacting the systemic or tissue exposure of concomitant drugs. Examples of pertinent scientific literature evaluating the interaction potential of commonly used botanicals in the US are discussed. Current methodologies that can be applied to advance our efforts in predicting drug interaction liability is presented. This review also highlights the regulatory science viewpoint on botanical-drug interactions and labeling implications.
Topics: Drug Labeling; Drugs, Chinese Herbal; Herb-Drug Interactions; Humans; Pharmaceutical Preparations; Pharmacology
PubMed: 29703380
DOI: 10.1016/j.jfda.2018.01.013 -
Drug Metabolism and Disposition: the... Jun 2023Combined oral contraceptives (COCs) are widely used in women of reproductive age in the United States. Metabolism plays an important role in the elimination of estrogens... (Review)
Review
Combined oral contraceptives (COCs) are widely used in women of reproductive age in the United States. Metabolism plays an important role in the elimination of estrogens and progestins contained in COCs. It is unavoidable that a woman using COCs may need to take another drug to treat a disease. If the concurrently used drug induces enzymes responsible for the metabolism of progestins and/or estrogens, unintended pregnancy or irregular bleeding may occur. If the concurrent drug inhibits the metabolism of these exogenous hormones, there may be an increased safety risk such as thrombosis. Therefore, for an investigational drug intended to be used in women with reproductive potential, evaluating its effects on the pharmacokinetics of COCs is important to determine if additional labeling is necessary for managing drug-drug interactions (DDIs) between the concomitant product and the COCs. It is challenging to determine when this clinical drug interaction study is needed, whether an observed exposure change of progestin/estrogen is clinically meaningful, and if the results of a clinical drug interaction study with one COC can predict exposure changes of unstudied COCs to inform labeling. In this review, we summarize the current understanding of metabolic pathways of estrogens and progestins contained in commonly used COCs and known interactions of these COCs as victim drugs and we discuss possible mechanisms of interactions for unexpected results. We also discuss recent advances, knowledge gaps, and future perspectives on this important topic. The review will enhance the understanding of DDIs with COCs and improve the safe and effective use of COCs. SIGNIFICANCE STATEMENT: This minireview provides an overview of the metabolic pathways of ethinyl estradiol and progestins contained in commonly used combined oral contraceptives (COCs) and significant drug interactions of these COCs as victims. It also discusses recent advances, knowledge gaps, future perspectives, and potential mechanisms for unexpected results of clinical drug interaction studies of COCs. This minireview will help the reader understand considerations when evaluating the drug interaction potential with COCs for drugs that are expected to be used concurrently.
Topics: Female; Humans; Contraceptives, Oral, Combined; Progestins; Ethinyl Estradiol; Estrogens; Drug Interactions
PubMed: 36963837
DOI: 10.1124/dmd.122.000854 -
CPT: Pharmacometrics & Systems... Dec 2022The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population... (Review)
Review
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system-specific parameters. Machine learning has the potential to be utilized for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine-learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically-based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine-learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug-drug interaction risk assessment across the stages of drug discovery and development.
Topics: Humans; Models, Biological; Drug Interactions; Machine Learning; Drug Discovery; Pharmacokinetics
PubMed: 36176050
DOI: 10.1002/psp4.12870 -
Journal of Pharmacokinetics and... Feb 2017We present competitive and uncompetitive drug-drug interaction (DDI) with target mediated drug disposition (TMDD) equations and investigate their pharmacokinetic DDI...
We present competitive and uncompetitive drug-drug interaction (DDI) with target mediated drug disposition (TMDD) equations and investigate their pharmacokinetic DDI properties. For application of TMDD models, quasi-equilibrium (QE) or quasi-steady state (QSS) approximations are necessary to reduce the number of parameters. To realize those approximations of DDI TMDD models, we derive an ordinary differential equation (ODE) representation formulated in free concentration and free receptor variables. This ODE formulation can be straightforward implemented in typical PKPD software without solving any non-linear equation system arising from the QE or QSS approximation of the rapid binding assumptions. This manuscript is the second in a series to introduce and investigate DDI TMDD models and to apply the QE or QSS approximation.
Topics: Binding, Competitive; Chemistry, Pharmaceutical; Dose-Response Relationship, Drug; Drug Interactions; Humans; Models, Biological; Pharmaceutical Preparations; Pharmacokinetics; Protein Binding; Receptors, Drug; Tissue Distribution
PubMed: 28074396
DOI: 10.1007/s10928-016-9502-0 -
Scientific Reports Sep 2022The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected...
The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.
Topics: Aged; Drug Interactions; Humans; Neural Networks, Computer
PubMed: 36114278
DOI: 10.1038/s41598-022-19999-4 -
PloS One 2022Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or...
Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.
Topics: Drug Interactions; Neural Networks, Computer; Pharmaceutical Preparations
PubMed: 36037188
DOI: 10.1371/journal.pone.0273764 -
American Journal of Health-system... Jun 2022Inaccurate and nonspecific medication alerts contribute to high override rates, alert fatigue, and ultimately patient harm. Drug-drug interaction (DDI) alerts often fail...
PURPOSE
Inaccurate and nonspecific medication alerts contribute to high override rates, alert fatigue, and ultimately patient harm. Drug-drug interaction (DDI) alerts often fail to account for factors that could reduce risk; further, drugs that trigger alerts are often inconsistently grouped into value sets. Toward improving the specificity of DDI alerts, the objectives of this study were to (1) highlight the inconsistency of drug value sets for triggering DDI alerts and (2) demonstrate a method of classifying factors that can be used to modify the risk of harm from a DDI.
METHODS
This was a proof-of-concept study focused on 15 well-known DDIs. Using 3 drug interaction references, we extracted 2 drug value sets and any available order- and patient-related factors for each DDI. Fleiss' kappa was used to measure the consistency of value sets among references. Risk-modifying factors were classified as order parameters (eg, route and dose) or patient characteristics (eg, comorbidities and laboratory results).
RESULTS
Seventeen value sets (56%) had nonsignificant agreement. Agreement among the remaining 13 value sets was on average moderate. Thirty-three factors that could reduce risk in 14 of 15 DDIs (93%) were identified. Most risk-modifying factors (67%) were classified as order parameters.
CONCLUSION
This study demonstrates the importance of increasing the consistency of drug value sets that trigger DDI alerts and how alert specificity and usefulness can be improved with risk-modifying factors obtained from drug references. It may be difficult to operationalize certain factors to reduce unnecessary alerts; however, factors can be used to support decisions by providing contextual information.
Topics: Data Collection; Decision Support Systems, Clinical; Drug Interactions; Humans; Medical Order Entry Systems; Risk Factors
PubMed: 35136935
DOI: 10.1093/ajhp/zxac045 -
Clinical Pharmacology and Therapeutics Apr 2020Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and... (Review)
Review
Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and link them to clinical consequences. Consequently, we developed information retrieval (IR) models to retrieve DDI and drug-gene interaction (DGI) evidence from 25 million PubMed abstracts and distinguish DDI evidence into in vitro pharmacokinetic (PK), clinical PK, and clinical pharmacodynamic (PD) studies for US Food and Drug Administration (FDA) approved and withdrawn drugs. Additionally, information extraction models were developed to extract DDI-pairs and DGI-pairs from the IR-retrieved abstracts. An overlapping analysis identified 986 unique DDI-pairs between all 3 types of evidence. Another 2,157 and 13,012 DDI-pairs and 3,173 DGI-pairs were identified from known clinical PK/PD DDI, clinical PD DDI, and DGI evidence, respectively. By integrating DDI and DGI evidence, we discovered 119 and 18 new pharmacogenetic hypotheses associated with CYP3A and CYP2D6, respectively. Some of these DGI evidence can also aid us in understanding DDI mechanisms.
Topics: Data Mining; Drug Interactions; Humans; Knowledge Discovery; Pharmacogenetics; Translational Research, Biomedical; United States; United States Food and Drug Administration
PubMed: 31863452
DOI: 10.1002/cpt.1745 -
Drugs in R&D 2010Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major...
BACKGROUND AND OBJECTIVE
Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major concerns is the change of exposures, including the rate and extent of drug absorption, distribution, metabolism and elimination. To address the concerns, a common practice is to measure and report the differences between the exposure in the presence and in the absence of concomitant medication (COMED). The area under the plasma concentration versus time curve (AUC), maximum plasma concentration (C(max)) and time to reach the C(max) (t(max)) changes are usually measured in DDI studies. A usual observation is the different extents of changes among AUC, C(max) and t(max), which may raise concerns in certain therapeutic areas or some special agents. The objective of this study was to investigate the variation among changes of AUC, C(max) and t(max) in DDI studies, and its pharmacokinetic manifestation.
DATA SOURCES
Based on a list of DDI results from the literature, with the assumptions that the primary parameters of a drug of interest were altered during a DDI, two sets of simulated data were generated according to a single oral dose, one-compartment model. The first set including 24 cases with different half-lives and absorption constants (k(a)) considered the exposure changes upon independent variation of bioavailability (F), clearance (CL), volume of distribution (V(d)) and k(a) up to 50-fold increases or decreases. The second set considered the exposure changes with simultaneous variation of F, CL, V(d), and k(a) within 5-fold range (increase or decrease) for a case selected from the first set.
STUDY SELECTION, DATA EXTRACTION AND SYNTHESIS
Parameter fold changes (defined in a fashion showing fold increase or fold decreases, including CL fold change, F fold change, V(d) fold change and k(a) fold change) and exposure changes (AUC fold change, C(max) fold change, t(max) fold change and fold change difference [AUC fold change - C(max) fold change]) were used to generate plots demonstrating various relationships between parameter fold changes and exposure changes. Based on the observations that AUC was influenced by CL and F, C(max) was affected by all four parameters, t(max) was mainly determined by CL and k(a), F did little for t(max) and k(a) was unrelated to AUC, a chart was created for DDI pattern recognition.
CONCLUSION
An approach, named DDI pattern recognition, is proposed for didactical purposes. It provides a quick initial estimate for interpreting the DDI results based on the exposure changes. This approach entails the following stages: (i) performing a drug interaction study; (ii) calculating the exposure changes in the presence of COMED compared to those in the absence of COMED, and the fold change difference; (iii) selecting the parameter fold changes that may play important roles in a specific DDI, by estimating their possible ranges; and (iv) interpreting the DDI by integrating all the information available, such as the possible mechanism involved. A quicker and better understanding about the processes, which dominate a DDI, has been achieved using this approach by focusing on integration of all information available and mechanistic interpretation.
Topics: Computer Simulation; Drug Interactions; Humans; Models, Statistical; Pattern Recognition, Automated; Pharmaceutical Preparations; Pharmacokinetics
PubMed: 20509711
DOI: 10.2165/11537440-000000000-00000 -
European Heart Journal May 2019
Topics: Anticoagulants; Atrial Fibrillation; Drug Interactions; Humans; Levetiracetam; Stroke; Warfarin
PubMed: 30500876
DOI: 10.1093/eurheartj/ehy780