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At least 1,603 records · Page 89Linked to original sources
PubMed2000

Changes of synaptotagmin interaction with t-SNARE proteins in vitro after calcium/calmodulin-dependent phosphorylation.

The regulation of multiple phases of the life cycle of synaptic vesicles is carried out by a complex series of protein-protein interactions. According to the SNARE hypothesis the core of these interactions is a heterotrimeric complex formed by syntaxin, SNAP-25, and VAMP-synaptobrevin. Other proteins interacting with the core of the SNARE complex, such as voltage-activated calcium channels and synaptotagmin (a putative calcium sensor), are considered crucial for the calcium dependence of release and also molecular mediators of synaptic plasticity. Here the interaction of synaptotagmin with SNARE proteins was studied in immunoprecipitated native complexes, and the effects of previous phosphorylation-dephosphorylation on this interaction were analyzed. It is surprising that the interaction of synaptotagmin with syntaxin and SNAP-25 in native complexes was not found to be calcium-dependent. However, previous incubation under dephosphorylating conditions decreased the synaptotagmin-syntaxin interaction. Stimulation of Ca2+/calmodulin-dependent protein kinase II, which endogenously phosphorylates synaptotagmin in synaptic vesicles, increased the interaction of syntaxin and SNAP-25 with synaptotagmin (particularly when measured in the presence of calcium), as well as increasing the binding of the kinase itself. These results suggest that calcium decreases synaptotagmin-t-SNARE interactions after dephosphorylation and increases them after phosphorylation. Overall, these results imply a phosphorylation-dephosphorylation balance in regulation of the synaptotagmin-t-SNARE interaction and suggest a role for protein phosphorylation in the modulation of calcium sensitivity in transmitter release.

Animals↗
PubMed2003

Drug-drug interactions of beta-adrenoceptor blockers.

Patients with cardiovascular diseases are often treated by concurrent multiple drug therapy. It is therefore plausible that with an increasing number of drugs the risk of drug interactions increases. Such interactions can be either pharmacodynamic (and are due to the mechanism of the administered drugs) or they can be pharmacokinetic (resulting in a reduction or enhancement of drug elimination). Pharmacokinetic interactions can be either due to interactions at the level of drug metabolizing enzymes (most important cytochrome P450 (CYP) enzymes) or interactions at the level of drug transporter proteins (for example P-glycoprotein (MDR1)). It is important to distinguish between both mechanisms because interactions at transporter proteins can be attributed to those drugs that are not enzymatically metabolized. The scope of this article is to give an overview on clinically relevant interactions of the four beta-blockers widely used in the therapy of cardiovascular diseases namely atenolol (CAS 29122-68-7), bisoprolol (CAS 66722-44-9), metoprolol (CAS 37350-58-6) (each beta-1 selective), and carvedilol (CAS 72956-09-3) (beta-1 and beta-2 nonselective). Among these beta-blockers atenolol is mainly eliminated by renal excretion, bisoprolol is in part excreted as parent compound via the renal route (50%), the other 50% are hepatically metabolised, whereas metoprolol and carvedilol are metabolised by CYP2D6. In addition, evidence is accumulating that carvedilol is a substrate for P-glycoprotein. For these four beta-blockers various pharmacodynamic and pharmacokinetic interactions have been demonstrated. Such interactions that result in an altered pharmacokinetics are mainly observed with those beta-blockers that are excreted via metabolism (metoprolol and carvedilol). Accordingly these drugs have a higher potential for drug interactions. However, it should be emphasized that, in general, beta-blockers are well tolerated safe drugs with a large therapeutic index.

Adrenergic beta-Antagonists↗
PubMed2005

Assessing in vitro combinations of antifungal drugs against yeasts and filamentous fungi: comparison of different drug interaction models.

Non-parametric and parametric approaches of two competing zero-interaction theories--the Loewe additivity and the Bliss independence - were evaluated for analyzing the in vitro interactions of various antifungal drugs. Fifty-one data sets, derived from three drug combinations, tested in triplicate against 17 clinical yeast and mold isolates with a two-dimensional checkerboard microdilution technique, were selected to span from strong synergy to strong antagonism. These were analyzed with the standard FIC index model and modern concentration-effect response surface models: the fully parametric model developed by Greco et al. and the 3-D analysis developed by Prichard et al. The FIC index model is subjective, sensitive to experimental errors and resulted in approximated results and variable conclusions depending on the MIC endpoints determined and interpretation endpoints used. By using the MIC-2 endpoint (lowest drug concentration showing 50% of growth) for calculating the FIC indices, problems due to trailing phenomena were reduced and weak interactions could be detected; higher levels of reproducibility and agreement with the other models were achieved using the MIC-0 and MIC-1 (lowest drug concentration showing 10 and 25% of growth, respectively). High reproducibility was achieved in interpreting the FIC indices when the cutoffs of 0.25 and 4 (for single experiments) and the cutoff of 1 (for replicates) were used for defining the limits of additivity/indifference. Although the fully parametric Greco model did not describe precisely the entire response surface of all antifungal drug interactions, it was able to differentiate synergistic from non-synergistic interactions with a non-unit, reproducible, concentration-independent interaction parameter, including its uncertainty, without requiring replication. The Bliss independence based models resulted in mosaics of synergistic and antagonistic combinations, raising questions about the concentration-dependent nature of antifungal drug interaction. The sum of all statistically significant interactions were used as a summary interaction parameter for the entire response surface, concluding synergy or antagonism when it was positive or negative, respectively. The cutoffs of 100% and 200% were used to distinguish weak and moderate interactions, respectively in 12-16 x 8-12 checkerboard formats. Semi-parametric approaches need particular care as experimental errors are not eliminated from the entire response surface.

Antifungal Agents↗
PubMed2006

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.

Algorithms↗
PubMed2004

ICBS: a database of interactions between protein chains mediated by beta-sheet formation.

MOTIVATION: Interchain beta-sheet (ICBS) interactions occur widely in protein quaternary structures, interactions between proteins and protein aggregation. These interactions play a central role in many biological processes and in diseases ranging from AIDS and cancer to anthrax and Alzheimer's. RESULTS: We have created a comprehensive database of ICBS interactions that is updated on a weekly basis and allows entries to be sorted and searched by relevance and other criteria through a simple Web interface. We derive a simple ICBS index to quantify the relative contributions of the beta-ladders in the overall interchain interaction and compute first- and second-order statistics regarding amino acid composition and pairing at different relative positions in the beta-strands. Analysis of the database reveals a 15.8% prevalence of significant ICBS interactions, the majority of which involve the formation of antiparallel beta-sheets and many of which involve the formation of dimers and oligomers. The frequencies of amino acids in ICBS interfaces are similar to those in intrachain beta-sheet interfaces. A full range of non-covalent interactions between side chains complement the hydrogen-bonding interactions between the main chains. Polar amino acids pair preferentially with polar amino acids and non-polar amino acids pair preferentially with non-polar amino acids among antiparallel (i, j) pairs. We anticipate that the statistics and insights gained from the database will guide the development of agents that control interchain beta-sheet interactions and that the database will help identify new protein interactions and targets for these agents. AVAILABILITY: The database is available at: http://www.igb.uci.edu/servers/icbs/

Binding Sites↗
PubMed2006

Finding the evidence for protein-protein interactions from PubMed abstracts.

MOTIVATION: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. RESULTS: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. AVAILABILITY: Contact authors.

Abstracting and Indexing↗
PubMed2006

A lock-and-key model for protein-protein interactions.

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).

Algorithms↗
PubMed2003

Subunit interactions in the assembly of Saccharomyces cerevisiae DNA polymerase alpha.

Eukaryotic DNA polymerase (pol) alpha is a complex of four subunits. The subunits in the yeast Saccharomyces cerevisiae are: 167, 79, 62 and 48 kDa polypeptides. The p79 subunit has no known enzymatic functions, but it is essential for growth and chromosomal DNA replication. We have analyzed the interaction between the subunits of yeast pol alpha, particularly p167 and p79, using a yeast two-hybrid screen and deletion analysis. We have identified the interaction sites in each of these two subunits leading to p167.p79 complex formation, and correlated our results with the available genetic data. A detailed two-hybrid analysis, using the p79 gene as the bait and a yeast genomic DNA library, identified two independent groups of positive clones. One group that displayed strong positive interaction (delta1) with p79 represented a fusion of the p167 open reading frame at 3502 bp (Ile1168), and the second group, displaying weak positive interaction (delta2) with p79, had a fusion at 3697 bp (Asn1233) with the DNA-binding domain of the yeast Gal4 transcription factor. A detailed deletion analysis of the downstream region indicated the existence of two subdomains that interact with p79. Subdomain I encompasses a 65 amino acid segment between Ile1168 and Phe1232, and subdomain II is a 25 amino acid segment between Glu1259 and Leu1283. Deletion and two-hybrid interaction analysis of the p79 subunit of pol alpha revealed a complementary region with two subdomains: a 67 amino acid segment between Asn189 and Gln255 (I) and a 68 amino acid segment between Glu256 and Met323 (II). The p79 subdomains I and II appeared to interact with the p167 subdomains I and II, respectively. Analysis of interaction between p62 and various deletion clones of p167 did not result in an unambiguous and stable positive interaction in the two-hybrid screen between these two subunits. A strong interaction between p167 and p62 would probably require the presence of either p79 or p48 in the complex.

Amino Acid Sequence↗
PubMed2006

APID: Agile Protein Interaction DataAnalyzer.

Agile Protein Interaction DataAnalyzer (APID) is an interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform main currently known information about protein-protein interactions demonstrated by specific small-scale or large-scale experimental methods. At present, the application includes information coming from five main source databases enclosing an unified sever to explore >35 000 different proteins and 111 000 different proven interactions. The web includes search tools to query and browse upon the data, allowing selection of the interaction pairs based in calculated parameters that weight and qualify the reliability of each given protein interaction. Such parameters are for the 'proteins': connectivity, cluster coefficient, Gene Ontology (GO) functional environment, GO environment enrichment; and for the 'interactions': number of methods, GO overlapping, iPfam domain-domain interaction. APID also includes a graphic interactive tool to visualize selected sub-networks and to navigate on them or along the whole interaction network. The application is available open access at http://bioinfow.dep.usal.es/apid/.

Computational Biology↗
PubMed2025

Structure-informed theoretical modeling defines principles governing avidity in bivalent protein interactions.

In signaling cascades, where domain-motif interactions tend to interact with relatively low affinity (allowing for reversibility), signaling proteins often encode multiple domains or motifs, which present the possibility of avidity - drastically increasing the interaction strength and duration as a result of multivalent binding. However, given the large combinatorial space, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. While certain multivalent interactions have been shown to be necessary in immune receptor recruitment of partners, bivalent recruitment of tandem SH2 domains more broadly is poorly understood. Theoretical modeling suggests that maximum avidity occurs with closely spaced or flexibly linked phosphotyrosine sites, combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity. Surprisingly, despite sequence diversity, structure-based analysis showed remarkably conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears to be sufficiently accurate to predict and rank order high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

BLI↗
PubMed2005

Finding biologically relevant protein domain interactions: conserved binding mode analysis.

Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.

Algorithms↗
PubMed2004

Evaluation of clinical pharmacist interventions on drug interactions in outpatient pharmaceutical HIV-care.

OBJECTIVE: To evaluate the usefulness of intervention in drug interactions of antiretroviral drugs with coadministered agents by a clinical pharmacist in outpatient HIV-treatment. METHODS: The study design included two intervention arms (A and B), which were both preceded by a control observation period. In arm A, a complete list of the currently used drugs, extracted from pharmacy records was provided to the treating physician. In arm B the same list was provided but with a notification when a drug interaction was present and an advice how to handle this. The infectious disease specialist obtained the information before the patient's visit to the outpatient clinic (time point 0). Three months prior (time point -3) and 3 months after (time point +3) the intervention, pharmacy records were also screened for drug interactions. The number of drug interactions (total and per patient) was determined at the three different time points (-3, 0, +3). In addition, drug interactions encountered at time points -3 and 0 were checked for their presence at time points 0 and +3, respectively, for both intervention arms. RESULTS: Arms A and B included 115 and 105 patients, respectively. Patient characteristics of both intervention arms were similar at time point 0. The number of interactions and the number of patients with interactions were similar in both intervention arms at time point 0. There were 42 and 40 potential drug interactions in 30 and 24 patients in arms A and B, respectively. The reduction in the number of interactions per patient over time and after intervention was small but significant, and was equal in both intervention arms. The advice of the clinical pharmacist had thus no additional value. CONCLUSION: Both interventions were effective in reducing the number of drug interactions per patient. The advice of a clinical pharmacist was, however, redundant in the studied setting.

Adult↗
PubMed2005

Clinical drug interactions in outpatients of a university hospital in Thailand.

BACKGROUND: A clinical event is likely to occur in patients receiving a pair of drugs, that have the potential to cause an interaction. The occurrence of a clinical drug-drug interaction in outpatients of university hospitals in Thailand is unknown. PURPOSE: To investigate the occurrence of a clinical event associated with drug-drug interactions in outpatients at a Thai university hospital. METHODS: A case-control study was established. The case was a sample group, randomly selected from a 1-year sample of outpatient prescriptions containing 'significance-1' potential drug-drug interactions, whereas the control was from the same year but with no potential drug interactions. Medical records of the cases and the controls were reviewed for an adverse event (AE) using a newly developed review form. The odds ratio of occurrence of the AE between the cases and the controls was determined. The AE was assessed for its possibility of being caused from a drug-drug interaction. RESULTS: The most common specific AE in both the cases and the controls was cough. An unplanned revisit to outpatient department or emergency room was found to be the most common general AE. The odds ratio of the occurrence of an AE in the cases, compared with the controls, was 1.495 (95% CI: 0.917-2.438). The possibility that the AEs resulted from drug interactions in the case group was nine 'probable' patients and 15 'possible' patients, whereas that in the control group was eight 'possible' patients. The most common interacting drug pair was isoniazid-rifampin with an increase in serum hepatic enzymes as the corresponding AE. CONCLUSIONS: Despite outpatients receiving drug pairs with a high potential for adverse interactions, the rate of occurrence of clinical drug interaction events was low.

Adult↗
PubMed1982

Polymedicine: known and unknown drug interactions.

Despite more than 15 years of extensive scrutiny, adverse drug interactions, especially in the elderly, remain a problem for the clinician. A two-pronged approach is suggested to contend with drug interactions: detection or avoidance of known drug interactions; and predictions and characterization of hitherto unknown interactions. The former approach is aided by critical reference sources on the subject and the use of drug interaction screening aids such as drug interactions alert lists or rotating drug discs. Computers are becoming increasingly important not only for the detection of potential drug interactions but also for the delineation of drug therapy options. Ultimately, computers can be used to predict interactions on the basis of physiochemical, pharmacokinetic, and pharmacodynamic data. For the present, extensive epidemiologic research programs can serve to ferret out interactions. Limited accessibility to such program data bases mandates a high index of suspicion in prepared and well-informed observers if the drug interaction problem is to be dealt with.

Documentation↗
PubMed2002

In vitro interaction of flucytosine combined with amphotericin B or fluconazole against thirty-five yeast isolates determined by both the fractional inhibitory concentration index and the response surface approach.

Combination therapy could be of benefit for the treatment of invasive yeast infections. However, in vitro interaction studies are relatively scarce and the interpretation of the fractional inhibitory concentration (FIC) index can be contradictory due to various definitions used; not all information on the interaction study is used in the index, and different MIC end points exist for different classes of drugs. Fitting an interaction model to the whole response surface and estimation of an interaction coefficient alpha (IC(alpha)) would overcome these objections and has the additional advantage that confidence intervals of the interaction are obtained. The efficacy of flucytosine (5FC) in combination with amphotericin B (AB) and fluconazole (FCZ) was studied against 35 yeast isolates in triplicate (Candida albicans [n = 9], Candida glabrata [n = 9], Candida krusei [n = 9], and Cryptococcus neoformans [n = 8]) using a broth microdilution checkerboard method and measuring growth after 48 h by a spectrophotometer. The FIC index and IC(alpha) were determined, the latter by estimation from the response surface approach described by Greco et al. (W. R. Greco, G. Bravo, and J. C. Parsons, Pharmacol. Rev. 47:331-385, 1995) by using a computer program developed for that purpose. For the 5FC-FCZ combination, the interactions determined by the IC(alpha) generally were in concordance with the interactions determined by the FIC index, but large discrepancies were found between both methods for the 5FC-AB combination. These could mainly be explained by shortcomings in the FIC approach. The in vitro interaction of 5FC-AB demonstrated variable results depending on the tested Candida isolate. In general, the 5FC-FCZ combination was antagonistic against Candida species, but for some Candida isolates synergism was found. For C. neoformans the interaction for both combinations was highly dependent on the tested isolate and the method used. Response surface approach is an alternative method for determining the interaction between antifungal agents. By using this approach, some of the problems encountered with the FIC were overcome.

Algorithms↗
PubMed2005

[Antihypertensive drug-drug interactions].

A drug interaction is the quantitative or qualitative modification of the effect of a drug by the simultaneous or successive administration of a different one. Hypertensive patients, mainly the more elderly ones, frequently present concomitant diseases that require the administration of several medicines which facilitates the appearance of interactions. The lack of effectiveness of the antihypertensive treatment is a relatively frequent fact that sometimes is due to interactions of antihypertensive drugs with other treatments. It is difficult to determine the incidence of interactions, but it is related to the number of drugs administered simultaneously. Between 37 and 60% of hospital-admissions are treated with potentially dangerous drug associations and up to a 6% of fatal events are due to this circumstance. Among antihypertensive drugs, diuretics and angiotensin converting enzyme inhibitors are less affected by drug-interactions. Lipophilic beta-blockers agents may present some clinical relevant interactions, whereas calcium channel blockers, especially the non-dihydropiridinic ones, are implied in clinically relevant pharmacokinetic interactions. Among the angiotensin receptor blockers there are differences that would have to be considered when they are used in patients who receive other drugs. Although it is impossible for the doctor to remember all the clinical relevant interactions, it is important to bear in mind their existence and the possible mechanisms of production which can help to identify them and to contribute to their prevention. The most frequent interactions related with clinical problems are the pharmacokinetic ones, mainly those related to the metabolism through the cytochrome P450 system or the presystemic clearance by means of the P-glycoprotein. Enzymes of the cytochrome P450 system may present polymorphisms that can explain the individual differences in the response to drugs or the appearance of drug-interactions.

Antihypertensive Agents↗
PubMed1999

Assessment of the quality and quantity of drug-drug interaction studies in recent NDA submissions: study design and data analysis issues.

This report investigates the quality and quantity of drug-drug interaction studies in recent new drug applications (NDAs). Eighty-nine studies contained in 14 NDAs submitted between December 1995 and November 1996 to the U.S. Food and Drug Administration (FDA) were reviewed. The results indicated that the median number of clinical drug-drug interaction studies per NDA was 6, almost double that of a 1994-1995 survey. In vitro metabolism data were present in 70% of the submissions. More than 50% of the submissions contained interaction studies using a battery of drugs (cimetidine, digoxin, or warfarin) without optimal use of the in vitro metabolism or in vivo mass balance data. Various study designs using a median number of 12 subjects were employed in the evaluation of drug-drug interactions. Some of the important study design factors such as dose size, dosing regimen, dosing duration, and timing of coadministration were considered, although not consistently, by the sponsors in their study design. Seventy-five percent of the studies used normal, healthy male subjects, and 25% used patients for whom the new molecular entities were intended. In 33% of the studies, female subjects were also recruited. Although the majority (80%) of the submissions still used p-values to determine the significance of drug interactions, 30% used a more relevant equivalence approach with 90% confidence intervals for key pharmacokinetic and/or pharmacodynamic parameter ratios to assess the extent of drug interactions. Overall, 82% of the studies concluded no interaction. Although population pharmacokinetic analysis can be a useful tool in studying drug-drug interactions, only 21% of the submissions used this approach. In summary, this assessment reveals that the quantity and quality of drug-drug interaction studies in NDAs have improved over the years. These improvements, as well as others that can be implemented, should result in more informative labeling and better patient care. FDA guidance for industry dealing with the design, analysis, and labeling language of in vivo metabolic drug-drug interactions has been developed to assist sponsors and FDA reviewers with these issues.

Clinical Trials as Topic↗
PubMed1997

Drug interactions of macrolides: emphasis on dirithromycin.

OBJECTIVE: To describe the drug interactions of dirithromycin, a new macrolide, and to compare them with those of other macrolides. DATA SOURCES: A literature search was performed using MEDLINE to identify articles published between January 1980 and July 1995 concerning the drug interactions of macrolides. Published abstracts were also examined. All studies using dirithromycin were performed under the sponsorship of Eli Lilly and Company. DATA SYNTHESIS: Erythromycin, the first macrolide discovered, is metabolized by the cytochrome P450 enzyme system. By decreasing their metabolism, erythromycin can interact with other drugs metabolized by the cytochrome P450 enzymes. The lack of such interactions would be a desirable feature in a newer macrolide. We describe studies performed to detect any interactions of dirithromycin with cyclosporine, theophylline, terfenadine, warfarin, and ethinyl estradiol. The studies showed that dirithromycin, like azithromycin, is much less likely to cause the interactions detected with clarithromycin and erythromycin. A review of the literature showed differences among macrolides in their abilities to inhibit cytochrome P450 enzymes and, thus, to cause drug-drug interactions. Erythromycin and clarithromycin inhibit cytochrome P450 enzymes, and have been implicated in clinically significant interactions. Azithromycin and dirithromycin neither inhibit cytochrome P450 enzymes nor are implicated in clinically significant drug-drug interactions. CONCLUSIONS: Dirithromycin, a new macrolide, does not inhibit the cytochrome P450 enzyme system. The concomitant use of dirithromycin with cyclosporine, theophylline, terfenadine, warfarin, or ethinyl estradiol was studied in pharmacokinetic and pharmacodynamic studies. In vitro, dirithromycin did not bind cytochrome P450. In healthy subjects, erythromycin increases the clearance of cyclosporine by 51%, whereas dirithromycin causes no significant changes in the pharmacokinetics of cyclosporine. In kidney transplant recipients, administration of dirithromycin was associated with a significant (p < 0.003) decrease of 17.4% in the clearance of cyclosporine. In patients taking low-dose estradiol, the administration of dirithromycin caused a significant (p < 0.03) increase of 9.9% in the clearance of ethinyl estradiol; escape ovulation did not occur. Unlike erythromycin and clarithromycin, dirithromycin had no significant effects on the pharmacokinetics of theophylline, terfenadine, or warfarin. The alterations typical of drug interactions that are based on inhibition of the cytochrome P450 system occurring with erythromycin and clarithromycin were not observed with dirithromycin.

Animals↗