Drug interaction potential of resveratrol

Resveratrol is a naturally occurring polyphenol that is often used as a food supplement. Many positive health effects, including cardio protection, tumor suppression, and immune modulation, are associated with the intake of resveratrol. Resveratrol is well tolerated in healthy subjects without any comedication. However, supplemental doses of resveratrol in the range of 1 g/day or above by far exceed the natural intake through food. Whether resveratrol-drug interactions can be harmful in patients taking additional medications remains unknown. Recent in vivo studies and clinical trials indicate a possible drug-drug interaction potential using high-dosage formulations. In this review, the known in vitro and in vivo effects of resveratrol on various cytochrome P450 (CYP) isoenzymes are summarized. They are discussed in relation to clinically relevant plasma concentrations in humans. We conclude that resveratrol may lead to interactions with various CYPs, especially when taken in high doses. Aside from systemic CYP inhibition, intestinal interactions must also be considered. They can potentially lead to reduced first-pass metabolism, resulting in higher systemic exposure to certain coadministrated CYP substrates. Therefore, patients who ingest high doses of this food supplement combined with additional medications may be at risk of experiencing clinically relevant drug-drug interactions.

Characterizing bupropion metabolism, pharmacokinetics and drug-drug interaction liability

Thesis (Ph.D.)--University of Washington, 2016-06

Evaluation of three brands of drug interaction software for use in intensive care units

Objective To evaluate drug interaction software programs and determine their accuracy in identifying drug-drug interactions that may occur in intensive care units. Setting The study was developed in Brazil. Method Drug interaction software programs were identified through a bibliographic search in PUBMED and in LILACS (database related to the health sciences published in Latin American and Caribbean countries). The programs` sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting drug-drug interactions. The accuracy of the software programs identified was determined using 100 clinically important interactions and 100 clinically unimportant ones. Stockley`s Drug Interactions 8th edition was employed as the gold standard in the identification of drug-drug interaction. Main outcome Sensitivity, specificity, positive and negative predictive values. Results The programs studied were: Drug Interaction Checker (DIC), Drug-Reax (DR), and Lexi-Interact (LI). DR displayed the highest sensitivity (0.88) and DIC showed the lowest (0.69). A close similarity was observed among the programs regarding specificity (0.88-0.92) and positive predictive values (0.88-0.89). The DIC had the lowest negative predictive value (0.75) and DR the highest (0.91). Conclusion The DR and LI programs displayed appropriate sensitivity and specificity for identifying drug-drug interactions of interest in intensive care units. Drug interaction software programs help pharmacists and health care teams in the prevention and recognition of drug-drug interactions and optimize safety and quality of care delivered in intensive care units.

Risk of drug interaction: combination of antidepressants and other drugs

OBJECTIVE: To assess the frequency of combination of antidepressants with other drugs and risk of drug interactions in the setting public hospital units in Brazil. METHODS: Prescriptions of all patients admitted to a public hospital from November 1996 to February 1997 were surveyed from the hospital's data processing center in São Paulo, Brazil. A manual search of case notes of all patients admitted to the psychiatric unit from January 1993 to December 1995 and all patients registered in the affective disorders outpatient clinic in December 1996 was carried out. Patients taking any antidepressant were identified and concomitant use of drugs was checked. By means of a software program (Micromedex®) drug interactions were identified. RESULTS: Out of 6,844 patients admitted to the hospital, 63 (0.9%) used antidepressants and 16 (25.3%) were at risk of drug interaction. Out of 311 patients in the psychiatric unit, 63 (20.2%) used antidepressants and 13 of them (20.6%) were at risk. Out of 87 patients in the affective disorders outpatient clinic, 43 (49.4%) took antidepressants and 7 (16.2%) were at risk. In general, the use of antidepressants was recorded in 169 patients and 36 (21.3%) were at risk of drug interactions. Twenty different forms of combinations at risk of drug interactions were identified: four were classified as mild, 15 moderate and one severe interaction. CONCLUSION: In the hospital general units the number of drug interactions per patient was higher than in the psychiatric unit; and prescription for depression was lower than expected.

Pharmacokinetic drug interaction potential of risperidone with cytochrome p450 isozymes as assessed by the dextromethorphan, the caffeine, and the mephenytoin test.

Two published case reports showed that addition of risperidone (1 and 2 mg/d) to a clozapine treatment resulted in a strong increase of clozapine plasma levels. As clozapine is metabolized by cytochrome P450 isozymes, a study was initiated to assess the in vivo interaction potential of risperidone on various cytochrome P450 isozymes. Eight patients were phenotyped with dextromethorphan (CYP2D6), mephenytoin (CYP2C19), and caffeine (CYP1A2) before and after the introduction of risperidone. Before risperidone, all eight patients were phenotyped as being extensive metabolizers of CYP2D6 and CYP2C19. Risperidone at dosages between 2 and 6 mg/d does not appear to significantly inhibit CYP1A2 and CYP2C19 in vivo (median plasma paraxanthine/caffeine ratios before and after risperidone: 0.65, 0.69; p = 0.89; median urinary (S)/(R) mephenytoin ratios before and after risperidone:0.11, 0.12; p = 0.75). Although dextromethorphan metabolic ratio is significantly increased by risperidone (median urinary dextromethorphan/dextrorphan ratios before and after risperidone: 0.010, 0.018; p = 0.042), risperidone can be considered a weak in vivo CYP2D6 inhibitor, as this increase is modest and none of the eight patients was changed from an extensive to a poor metabolizer. The reported increase of clozapine concentrations by risperidone can therefore not be explained by an inhibition of CYP1A2, CYP2D6, CYP2C19 or by any combination of the three.

Development of an enantioselective assay for simultaneous separation of venlafaxine and O-desmethylvenlafaxine by micellar electrokinetic chromatography-tandem mass spectrometry: Application to the analysis of drug-drug interaction.

To-date, there has been no effective chiral capillary electrophoresis-mass spectrometry (CE-MS) method reported for the simultaneous enantioseparation of the antidepressant drug, venlafaxine (VX) and its structurally-similar major metabolite, O-desmethylvenlafaxine (O-DVX). This is mainly due to the difficulty of identifying MS compatible chiral selector, which could provide both high enantioselectivity and sensitive MS detection. In this work, poly-sodium N-undecenoyl-L,L-leucylalaninate (poly-L,L-SULA) was employed as a chiral selector after screening several dipeptide polymeric chiral surfactants. Baseline separation of both O-DVX and VX enantiomers was achieved in 15min after optimizing the buffer pH, poly-L,L-SULA concentration, nebulizer pressure and separation voltage. Calibration curves in spiked plasma (recoveries higher than 80%) were linear over the concentration range 150-5000ng/mL for both VX and O-DVX. The limit of detection (LOD) was found to be as low as 30ng/mL and 21ng/mL for O-DVX and VX, respectively. This method was successfully applied to measure the plasma concentrations of human volunteers receiving VX or O-DVX orally when co-administered without and with indinivar therapy. The results suggest that micellar electrokinetic chromatography electrospray ionization-tandem mass spectrometry (MEKC-ESI-MS/MS) is an effective low cost alternative technique for the pharmacokinetics and pharmacodynamics studies of both O-DVX and VX enantiomers. The technique has potential to identify drug-drug interaction involving VX and O-DVX enantiomers while administering indinivar therapy.

Sequentially testing for a gene-drug interaction in a genomewide analysis

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs. Copyright (c) 2007 John Wiley & Sons, Ltd.

Drug interaction and location in liposomes: correlation with polar surface areas

An important step in liposome characterization is to determine the location of a drug within the liposome. This work thus investigated the interaction of dipalmitoylphosphatidylcholine liposomes with drugs of varied water solubility, polar surface area (PSA) and partition coefficient using high sensitivity differential scanning calorimetry. Lipophilic estradiol (ES) interacted strongest with the acyl chains of the lipid membrane, followed by the somewhat polar 5-fluorouracil (5-FU). Strongly hydrophilic mannitol (MAN) showed no evidence of interaction but water soluble polymers inulin (IN) and an antisense oligonucleotide (OLG), which have very high PSAs, interacted with the lipid head groups. Accordingly, the drugs could be classified as: hydrophilic ones situated in the aqueous core and which may interact with the head groups; those located at the water-bilayer interface with some degree of penetration into the lipid bilayer; those lipophilic drugs constrained within the bilayer. (c) 2004 Elsevier B.V. All rights reserved.

Solution calorimetry as a tool for investigating drug interaction with intestinal fluid

Solution calorimetry offers a reproducible technique for measuring the enthalpy of solution (ΔsolH) of a solute dissolving into a solvent. The ΔsolH of two solutes, propranolol HCl and mannitol were determined in simulated intestinal fluid (SIF) solutions designed to model the fed and fasted states within the gut, and in Hanks’ balanced salt solution (HBSS) of varying pH. The bile salt and lipid within the SIF solutions formed mixed micelles. Both solutes exhibited endothermic reactions in all solvents. The ΔsolH for propranolol HCl in the SIF solutions differed from those in the HBSS and was lower in the fed state than the fasted state SIF solution, revealing an interaction between propranolol and the micellar phase in both SIF solutions. In contrast, for mannitol the ΔsolH was constant in all solutions indicating minimal interaction between mannitol and the micellar phases of the SIF solutions. In this study, solution calorimetry proved to be a simple method for measuring the enthalpy associated with the dissolution of model drugs in complex biological media such as SIF solutions. In addition, the derived power–time curves allowed the time taken for the powdered solutes to form solutions to be estimated.

Incorporation of Ag nanoparticles into membrane mimetic systems composed by phospholipid layer-by-layer (LbL) films to achieve surface-enhanced Raman scattering as a tool in drug interaction studies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The interaction between vitamin K nutriture and warfarin administration in patients with bacterial overgrowth due to atrophic gastritis

Atrophic gastritis patients have intestinal bacterial overgrowth which could produce menaquinones. The aim of this study was to evaluate the interaction between a diet low in phylloquinone and minidoses of warfarin in subjects with and without bacterial overgrowth. Subjects with atrophic gastritis (indicated by serum pepsinogen ratio) and healthy volunteers were studied while fed a restrictive phylloquinone diet and while receiving a minidose of warfarin. Coagulation times, serum osteocalcin, serum undercarboxylated osteocalcin, plasma phylloquinone, plasma K-epoxide, plasma undercarboxylated prothrombin (PIVKA)-II and urinary gamma-carboxyglutamic acid (Gla) were measured. At baseline, there were no differences between groups for any variable measured. Comparisons between baseline and post intervention in both groups, showed significant increases in circulating levels of K-epoxide, PIVKA II and undercarboxylated osteocalcin. However, no differences were observed when comparisons were made between groups. Our data do not support the hypothesis that bacterial synthesis of menaquinones in patients with bacterial overgrowth due to atrophic gastritis confers considerable resistance to the effect of warfarin.

Transfection of drug-specific T-cell receptors into hybridoma cells: tools to monitor drug interaction with T-cell receptors and evaluate cross-reactivity to related compounds

In the context of drug hypersensitivity, our group has recently proposed a new model based on the structural features of drugs (pharmacological interaction with immune receptors; p-i concept) to explain their recognition by T cells. According to this concept, even chemically inert drugs can stimulate T cells because certain drugs interact in a direct way with T-cell receptors (TCR) and possibly major histocompatibility complex molecules without the need for metabolism and covalent binding to a carrier. In this study, we investigated whether mouse T-cell hybridomas transfected with drug-specific human TCR can be used as an alternative to drug-specific T-cell clones (TCC). Indeed, they behaved like TCC and, in accordance with the p-i concept, the TCR recognize their specific drugs in a direct, processing-independent, and dose-dependent way. The presence of antigen-presenting cells was a prerequisite for interleukin-2 production by the TCR-transfected cells. The analysis of cross-reactivity confirmed the fine specificity of the TCR and also showed that TCR transfectants might provide a tool to evaluate the potential of new drugs to cause hypersensitivity due to cross-reactivity. Recombining the alpha- and beta-chains of sulfanilamide- and quinolone-specific TCR abrogated drug reactivity, suggesting that both original alpha- and beta-chains were involved in drug binding. The TCR-transfected hybridoma system showed that the recognition of two important classes of drugs (sulfanilamides and quinolones) by TCR occurred according to the p-i concept and provides an interesting tool to study drug-TCR interactions and their biological consequences and to evaluate the cross-reactivity potential of new drugs of the same class.

The well-being model: a new drug interaction model for positive and negative effects

BACKGROUND: Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account. METHODS: By extension of a previously published interaction model, the authors propose a method to study drug interactions considering also their side effects. A general outcome parameter identified as patient's well-being is defined by superposition of positive and negative effects. Well-being response surfaces are computed and analyzed for varying drugs pharmacodynamics and interaction types. In particular, the existence of multiple maxima and of optimal drug combinations is investigated for the combination of two drugs. RESULTS: Both drug pharmacodynamics and interaction type affect the well-being surface and the deriving optimal combinations. The effect of the interaction parameters can be explained in terms of synergy and antagonism and remains unchanged for varying pharmacodynamics. For all simulations performed for the combination of two drugs, the presence of more than one maximum was never observed. CONCLUSIONS: The model is consistent with clinical knowledge and supports previously published experimental results on optimal drug combinations. This new framework improves understanding of the characteristics of drug combinations used in clinical practice and can be used in clinical research to identify optimal drug dosing.

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.