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PubMed2003

Potentially significant drug interactions of class III antiarrhythmic drugs.

Class III antiarrhythmic drugs, especially amiodarone (a broad-spectrum antiarrhythmic agent), have gained popularity for use in clinical practice in recent years. Other class III antiarrhythmic drugs include bretylium, dofetilide, ibutilide and sotalol. These agents are effective for the management of various types of cardiac arrhythmias both atrial and ventricular in origin. Class III antiarrhythmic drugs may interact with other drugs by two major processes: pharmacodynamic and pharmacokinetic interactions. The pharmacodynamic interaction occurs when the pharmacological effects of the object drug are stimulated or inhibited by the precipitant drug. Pharmacokinetic interactions can result from the interference of drug absorption, metabolism and/or elimination of the object drug by the precipitant drug. Among the class III antiarrhythmic drugs, amiodarone has been reported to be involved in a significant number of drug interactions. It is mainly metabolised by cytochrome P450 (CYP)3A4 and it is a potent inhibitor of CYP1A2, 2C9, 2D6 and 3A4. In addition, amiodarone may interact with other drugs (such as digoxin) via the inhibition of the P-glycoprotein membrane transporter system, a recently described pharmacokinetic mechanism of drug interactions. Bretylium is not metabolised; it is excreted unchanged in the urine. Therefore the interactions between bretylium and other drugs (including other antiarrhythmic drugs) is primarily through the pharmacodynamic mechanism. Dofetilide is metabolised by CYP3A4 and excreted by the renal cation transport system. Drugs that inhibit CYP3A4 (such as erythromycin) and/or the renal transport system (such as triamterene) may interact with dofetilide. It appears that the potential for pharmacokinetic interactions between ibutilide and other drugs is low. This is because ibutilide is not metabolised by CYP3A4 or CYP2D6. However, ibutilide may significantly interact with other drugs by a pharmacodynamic mechanism. Sotalol is primarily excreted unchanged in the urine. The potential for drug interactions due to hepatic enzyme induction or inhibition appears to be less likely. However, a number of drugs (such as digoxin) have been reported to interact with sotalol pharmacodynamically. If concurrent use of a class III antiarrhythmic agent and another drug cannot be avoided or no published studies for that particular drug interaction are available, caution should be exercised and close monitoring of the patient should be performed in order to avoid or minimise the risks associated with a possible adverse drug interaction.

Amiodarone↗
PubMed2006

Drug-herb interactions: eliminating toxicity with hard drug design.

By searching the literatures, it was found that a total of 32 drugs interacting with herbal medicines in humans. These drugs mainly include anticoagulants (warfarin, aspirin and phenprocoumon), sedatives and antidepressants (midazolam, alprazolam and amitriptyline), oral contraceptives, anti-HIV agents (indinavir, ritonavir and saquinavir), cardiovascular drug (digoxin), immunosuppressants (cyclosporine and tacrolimus) and anticancer drugs (imatinib and irinotecan). Most of them are substrates for cytochrome P450s (CYPs) and/or P-glycoprotein (PgP) and many of which have narrow therapeutic indices. However, several drugs including acetaminophen, carbamazepine, mycophenolic acid, and pravastatin did not interact with herbs. Both pharmacokinetic (e.g. induction of hepatic CYPs and intestinal PgP) and/or pharmacodynamic mechanisms (e.g. synergistic or antagonistic interaction on the same drug target) may be involved in drug-herb interactions, leading of altered drug clearance, response and toxicity. Toxicity arising from drug-herb interactions may be minor, moderate, or even fatal, depending on a number of factors associated with the patients, herbs and drugs. Predicting drug-herb interactions, timely identification of drugs that interact with herbs, and therapeutic drug monitoring may minimize toxic drug-herb interactions. It is likely to predict pharmacokinetic herb-drug interactions by following the pharmacokinetic principles and using proper models that are used for predicting drug-drug interactions. Identification of drugs that interact with herbs can be incorporated into the early stages of drug development. A fourth approach for circumventing toxicity arising from drug-herb interactions is proper design of drugs with minimal potential for herbal interaction. So-called "hard drugs" that are not metabolized by CYPs and not transported by PgP are believed not to interact with herbs due to their unique pharmacokinetic properties. More studies are needed and new approached are required to minimize toxicity arising from drug-herb interactions.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2000

Drug-drug interactions in medical patients: effects of in-hospital treatment and relation to multiple drug use.

OBJECTIVES: Adverse drug reactions (ADRs) are a major cause of hospital admissions, thereby leading to significant medical and economical problems. Drug interactions contribute to a major part of ADRs, especially in elderly patients and in patients under polymedication. As a peculiarity of the German health care system the general practitioner is less involved in patient care during hospital stay than in other countries. Consequently, changes in medication at the transition point from out-patient to in-patient care and back may contribute to drug-related problems. PATIENTS: In the present study we investigated potential interactions in 169 consecutive patients with the diagnosis CHD (coronary heart disease) or COLD (chronic obstructive lung disease) who were admitted to the University Hospitals of Hannover and Magdeburg. METHODS: For each patient, potential interactions between prescribed drugs at admission, at discharge, and 3 months after discharge were assessed by using drug interaction data bases. RESULTS: We found that the number of drugs taken per patient as well as the number of interactions per patient are higher during hospitalization than before admission (pre-admission), and fall back after the hospital stay (post-discharge), but not to the pre-admission level. The number of potential interactions was significantly correlated in a polynomial manner to the number of drugs taken by each patient. The number of patients without a potential interaction was 44.4% pre-admission, 39.6% at discharge, and 39.1% post-discharge. Patients with potential interactions had a mean of 2.8, 2.7, and 2.4 interactions at each of the time points. Drug classes mainly involved with potential interactions were kaliuretic diuretics (recorded by 43.3% at discharge), ACE inhibitors (30.3%), anticoagulants and aggregation inhibitors (20.4%) and digitalis glycosides (14.7%). Regarding the frequency of the interaction categories, 68-70% of the potential interactions demand clinical attention, while 1-2% are life-threatening. 17-19% may result in therapeutic benefit; 10-12% are without clinical relevance. CONCLUSIONS: The risk of drug interactions increases exponentially with the number of drugs given to a patient. Although we have no data on the fraction of interactions that became clinically manifest, our study indicates that prescribing fewer drugs can reduce the risk of suffering from sickness secondary to drug interactions. Taking this risk into account may help to improve the quality of drug treatment and to save costs.

Adult↗
PubMed1998

Assessing pharmacokinetic and pharmacodynamic interactions in clinical trials of antiepileptic drugs.

An understanding of the pharmacokinetic and pharmacodynamic properties of a drug is a basic requirement for its clinical use. The investigations of these properties and their timing are fairly clearly defined in the drug development process. Without fundamental knowledge of the pharmacokinetics and pharmacodynamics of a drug, a physician could not use it appropriately, nor would a regulatory agency be likely to approve its use. Information about the interactions of a new antiepileptic drug with other antiepileptic drugs also aids a physician and is required, in varying degrees, by regulatory agencies. The amount of this information that is needed depends, in part, on the class of drug and the population for which the agent is intended. Because antiepileptic drugs are often used as polytherapy and generally are developed first for this use, their interaction potential must be part of the thought process in their development. The correct time to obtain this information, however, is not clearly defined. The analytic methodology to investigate the pharmacokinetic profile of an NCE exists and is fairly sophisticated. This methodology has enabled the development of study designs to investigate pharmacokinetic interactions. Because the plasma concentration of an antiepileptic drug may be increased or decreased as a result of pharmacokinetic interactions with concomitant antiepileptic drugs, it is of great importance to know about the specific interaction potential of an antiepileptic drug early in its development. Recent studies have confirmed the importance of investigating pharmacokinetic interactions in phase I before proceeding into phases II and III. Without this information, study results are often difficult to interpret; with this knowledge, study designs can be modified to minimize the confounding effect. A methodology exists to investigate the pharmacodynamic effects of an antiepileptic drug at receptors in cell cultures and in animal models of seizures; however, no procedure has been established to evaluate the short-term or immediate clinical pharmacodynamic effect of an antiepileptic drug, as has been done for other classes of drugs and other diseases. The clinical effect that is sought in trials with antiepileptic drugs is a reduction in seizures with little toxicity. The methodology to investigate the effect of seizure reduction over time has been used repeatedly with minor variations in the development of all the new antiepileptic drugs. However, no study has evaluated the effect of pharmacodynamic interactions among antiepileptic drugs on seizure reduction. Some studies have purported to show an interaction effect on adverse events, and assumptions are made about pharmacodynamic interactions. Although the information regarding pharmacodynamic interactions is important and existing trial designs could evaluate this, there has been no perceived need to carry out such trials. This information is less accessible than pharmacokinetic interaction information. Moreover, pharmacodynamic interactions, as opposed to pharmacokinetic interactions, are probably unidirectional and lead only to increased effects. Although it would be preferable to have this knowledge, an antiepileptic drug can be used effectively without it; over time, the information about pharmacodynamics will be inferred. Thus, conducting pharmacokinetic interaction studies with antiepileptic drugs early in their development as part of phase I is essential, whereas obtaining pharmacodynamic interaction information can be deferred.

Anticonvulsants↗
PubMed1996

How Hofmeister ion interactions affect protein stability.

Model compound studies in the literature show how Hofmeister ion interactions affect protein stability. Although model compound results are typically obtained as salting-out constants, they can be used to find out how the interactions affect protein stability. The null point in the Hofmeister series, which divides protein denaturants from stabilizers, arises from opposite interactions with different classes of groups: Hofmeister ions salt out nonpolar groups and salt in the peptide group. Theories of how Hofmeister ion interactions work need to begin by explaining the mechanisms of these two classes of interactions. Salting-out nonpolar groups has been explained by the cavity model, but its use is controversial. When applied to model compound data, the cavity model 1) uses surface tension increments to predict the observed values of the salting-out constants, within a factor of 3, and 2) predicts that the salting-out constant should increase with the number of carbon atoms in the aliphatic side chain of an amino acid, as observed. The mechanism of interaction between Hofmeister ions and the peptide group is not well understood, and it is controversial whether this interaction is ion-specific, or whether it is nonspecific and the apparent specificity resides in interactions with nearby nonpolar groups. A nonspecific salting-in interaction is known to occur between simple ions and dipolar molecules; it depends on ionic strength, not on position in the Hofmeister series. A theory by Kirkwood predicts the strength of this interaction and indicates that it depends on the first power of the ionic strength. Ions interact with proteins in various ways besides the Hofmeister ion interactions discussed here, especially by charge interactions. Much of what is known about these interactions comes from studies by Serge Timasheff and his co-workers. A general model, suitable for analyzing diverse ion-protein interactions, is provided by the two-domain model of Record and co-workers.

Drug Stability↗
PubMed1978

Nonspecific interactions of Escherichia coli RNA polymerase with native and denatured DNA: differences in the binding behavior of core and holoenzyme.

We have investigated the nonspecific interactions of Escherichia coli RNA polymerase core and holoenzyme with double-stranded (ds) and single-stranded (ss) DNA. Binding constants for these interactions as functions of such solution variables as monovalent and/or divalent cation concentration, temperature, or pH were determined by the method of deHaseth et a. [deHaseth, P.L., Gross, C.A., Burgess, R.R. and Record, M.T. (1977), Biochemistry 16, 4777--4783] from analysis of the elution of the proteins from small columns containing immobilized DNA. This technique, although as yet empirical, has been demonstrated to yield accurate binding constants fot the nonspecific interation of lac repressor with ds DNA. We find that observed binding constants (Kobsd) are extraordinarily sensitive functions of the monovalent cation concentration for the interactions of both core and holoenzyme with ds DNA. In the absence of divalent cations, the derivatives --(d log Kobsd/d log [Na+]) are 11 +/- 2 for the holo--ds DNA interaction and 21 +/- 3 for the core--ds DNA interaction. Consequently, approximately 11 and 21 low-molecular-weight ions are released, iin the thermodynamic sense, in the formation of the holo--ds and core--ds complexes, respectively (Record, M.T., Jr., Lohman, T.M., and deHaseth, P.L. (1976), J. Mol. Biol. 107, 145--158; Record, M.T., Jr., Anderson, C.F., and Lohman, T.M. (1978), Q. Rev. Biophys., in press). Ion release is a thermodynamic driving force for these nonspecific interactions and causes the stability of the complexes to increase very substantially with a reduction in monovalent ion concnetration. Possible molecular models which account for the different salt sensitivities of the holo--ds and core--ds complexes are discussed. Effects of the competitive ligand Mg2+ on these interactions are also examined. Substantial ion release (approximately 18 monovalent ions) also accompanies the interaction of either holo or core polymerase with ss DNA. Over the range of ion concentrations investigated the holo--ss interaction is substantially stronger than the core--ss interaction; furthermore, we conclude that the interactions of polymerase with ss DNA are, in general, stronger than the nonspecific interations of the enzyme with ds DNA. It is likely that the nonspecific interactions of RNA polymerase with DNA have physiological relevance. Not only is it plausible to assume that the same regions of the protein are involved in both specific and nonspecific interactions, but in addition nonspecific interactions of RNA polymerase and DNA may play role in determining the availability of this protein, in both the thermodynamic and the kinetic sense, for promoter binding and RNA chain initiation [von Hippel. P.H., Revzin, A., Gross, C.A., and Wang, A.C. (1974), Proc. Natl. Acad. Sci U.S.A. 71, 4808--4812]. Consequently, the strong dependences of the nonspecific interactions of RNA polymerase on ionic conditions suggest the possibility of a modulating role of ion concentrations in the control of transcription.

DNA↗
PubMed2002

A heterodimerizing leucine zipper coiled coil system for examining the specificity of a position interactions: amino acids I, V, L, N, A, and K.

We use a heterodimerizing leucine zipper system to examine the contribution of the interhelical a-a' interaction to dimer stability for six amino acids (A, V, L, I, K, and N). Circular dichroism (CD) spectroscopy monitored the thermal denaturation of 36 heterodimers that generate six homotypic and 30 heterotypic a-a' interactions. Isoleucine (I-I) is the most stable homotypic a-a' interaction, being 9.2 kcal/mol per dimer more stable than the A-A interaction and 4.0 kcal/mol per dimer more stable than either the L-L or V-V interaction, and 7.0 kcal/mol per dimer more stable than the N-N interaction. Only lysine was less stable than alanine. An alanine-based double-mutant thermodynamic cycle calculated coupling energies between the a and a' positions in the heterodimer. The aliphatic amino acids L, V, and I prefer to form homotypic interactions with coupling energies of -0.6 to -0.9 kcal/mol per dimer, but the heterotypic aliphatic interactions have positive coupling energies of <1.0 kcal/mol per dimer. The asparagine homotypic interaction has a coupling energy of -0.5 kcal/mol per dimer, while heterotypic interactions with the aliphatic amino acids produce coupling energies ranging from 2.6 to 4.9 kcal/mol per dimer. The homotypic K-K interaction is 2.9 kcal/mol per dimer less stable than the A-A interaction, but the coupling energy is only 0.3 kcal/mol per dimer. Heterotypic interactions with lysine and either asparagine or aliphatic amino acids produce similar coupling energies ranging from -0.2 to -0.7 kcal/mol per dimer. Thus, of the amino acids that were examined, asparagine contributes the most to dimerization specificity because of the large positive coupling energies in heterotypic interactions with the aliphatic amino acids which results in the N-N homotypic interaction.

Alanine↗
PubMed2002

Interrogating protein interaction networks through structural biology.

Protein-protein interactions are central to most biological processes. Although much recent effort has been put into methods to identify interacting partners, there has been a limited focus on how these interactions compare with those known from three-dimensional (3D) structures. Because comparison of protein interactions often involves considering homologous, but not identical, proteins, a key issue is whether proteins that are homologous to an interacting pair will interact in the same way, or interact at all. Accordingly, we describe a method to test putative interactions on complexes of known 3D structure. Given a 3D complex and alignments of homologues of the interacting proteins, we assess the fit of any possible interacting pair on the complex by using empirical potentials. For studies of interacting protein families that show different specificities, the method provides a ranking of interacting pairs useful for prioritizing experiments. We evaluate the method on interacting families of proteins with multiple complex structures. We then consider the fibroblast growth factor/receptor system and explore the intersection between complexes of known structure and interactions proposed between yeast proteins by methods such as two-hybrids. We provide confirmation for several interactions, in addition to suggesting molecular details of how they occur.

Amino Acid Sequence↗
PubMed2002

Describing biological protein interactions in terms of protein states and state transitions: the LiveDIP database.

Biological protein-protein interactions differ from the more general class of physical interactions; in a biological interaction, both proteins must be in their proper states (e.g. covalently modified state, conformational state, cellular location state, etc.). Also in every biological interaction, one or both interacting molecules undergo a transition to a new state. This regulation of protein states through protein-protein interactions underlies many dynamic biological processes inside cells. Therefore, understanding biological interactions requires information on protein states. Toward this goal, DIP (the Database of Interacting Proteins) has been expanded to LiveDIP, which describes protein interactions by protein states and state transitions. This additional level of characterization permits a more complete picture of the protein-protein interaction networks and is crucial to an integrated understanding of genome-scale biology. The search tools provided by LiveDIP, Pathfinder, and Batch Search allow users to assemble biological pathways from all the protein-protein interactions collated from the scientific literature in LiveDIP. Tools have also been developed to integrate the protein-protein interaction networks of LiveDIP with large scale genomic data such as microarray data. An example of these tools applied to analyzing the pheromone response pathway in yeast suggests that the pathway functions in the context of a complex protein-protein interaction network. Seven of the eleven proteins involved in signal transduction are under negative or positive regulation of up to five other proteins through biological protein-protein interactions. During pheromone response, the mRNA expression levels of these signaling proteins exhibit different time course profiles. There is no simple correlation between changes in transcription levels and the signal intensity. This points to the importance of proteomic studies to understand how cells modulate and integrate signals. Integrating large scale, yeast two-hybrid data with mRNA expression data suggests biological interactions that may participate in pheromone response. These examples illustrate how LiveDIP provides data and tools for biological pathway discovery and pathway analysis.

Databases, Protein↗
PubMed1999

Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: implications for the protein docking problem.

The protein-protein interaction energy of 12 nonhomologous serine protease-inhibitor and 15 antibody-antigen complexes is calculated using a molecular mechanics formalism and dissected in terms of the main-chain vs. side-chain contribution, nonrotameric side-chain contributions, and amino acid residue type involvement in the interface interaction. There are major differences in the interactions of the two types of protein-protein complex. Protease-inhibitor complexes interact predominantly through a main-chain-main-chain mechanism while antibody-antigen complexes interact predominantly through a side-chain-side-chain or a side-chain-main-chain mechanism. However, there is no simple correlation between the main-chain-main-chain interaction energy and the percentage of main-chain surface area buried on binding. The interaction energy is equally effected by the presence of nonrotameric side-chain conformations, which constitute approximately 20% of the interaction energy. The ability to reproduce the interface interaction energy of the crystal structure if original side-chain conformations are removed from the calculation is much greater in the protease-inhibitor complexes than the antibody-antigen complexes. The success of a rotameric model for protein-protein docking appears dependent on the extent of the main-chain-main-chain contribution to binding. Analysis of (1) residue type and (2) residue pair interactions at the interface show that antibody-antigen interactions are very restricted with over 70% of the antibody energy attributable to just six residue types (Tyr > Asp > Asn > Ser > Glu > Trp) in agreement with previous studies on residue propensity. However, it is found here that 50% of the antigen energy is attributable to just four residue types (Arg = Lys > Asn > Asp). On average just 12 residue pair interactions (6%) contribute over 40% of the favorable interaction energy in the antibody-antigen complexes, with charge-charge and charge/polar-tyrosine interactions being prominent. In contrast protease inhibitors use a diverse set of residue types and residue pair interactions.

Antigen-Antibody Complex↗
PubMed1991

Cell-cell interactions and the regulation of testis function.

Regulatory interactions have been shown to occur between all the testicular cell types considered. The paracrine factors mediating these interactions generally influence either cellular growth or differentiation. The regulation of cellular growth is essential in the developing testis and is required for the maintenance of spermatogenesis in the adult testis. The rapid rate of germinal cell proliferation and the continuous but slowed growth of the peritubular cells and Leydig cells requires the presence of specific growth factors in the adult. Therefore, cell-cell interactions have evolved that involve growth factors such as IGF, TGF-alpha, TGF-beta and NGF. Other growth factors such as FGF or less characterized components like the seminiferous growth factor (SGF) also may be involved in the paracrine regulation of testis cell growth. An alternate cellular parameter to cell growth to consider is the regulation of cellular function and differentiation. A number of endocrine agents and locally produced paracrine factors have been shown to control and maintain testis cell function and differentiation. Cell-cell interactions mediated by factors such as androgens, POMC peptides, and PModS are all primarily directed at the regulation of cellular differentiation. Therefore, the agents which mediate cell-cell interactions in the testis can generally be categorized into factors that regulate cell growth or those which influence cellular differentiation. The specific cell-cell interactions identified will likely be the first of a large number of cellular interactions yet to be investigated. Although a number of potentially important cell-cell interactions have been identified, future research will require the elucidation of the in vivo physiological significance of these interactions. The existence of different cell types and potential cell-cell interactions in a tissue implies that the actions of an endocrine agent on a tissue will not simply involve a single hormone and single cell. The endocrine regulation of testis function will have effects on cell-cell interactions and be affected by local cell-cell interactions. The ability of LH to influence Leydig cell androgen production promotes a cascade of interactions mediated through several cell types to maintain the process of spermatogenesis. FSH actions on Sertoli cells also promote cell-cell interactions that influence germinal cell development, peritubular myoid cell differentiation and Leydig cell function. Therefore, elucidation of the endocrine regulation of testis function requires an understanding of the local cell-cell interactions in the testis.

Animals↗
PubMed2001

Construction of optimum index to maximize overall response across countries in the presence of genotype x environment interaction.

Sire effect is partitioned into two parts: constant effect unaffected by environments and interaction effect specific to each environment and responsible for genotype x environment (GE) interaction. Response to selection for constant effect is the same across environments, thus increasing genetic stability, whereas response to selection for interaction effect would vary depending upon environments. The conventional measure of GE interaction based on genetic correlation (gammaG) considers both constant and interaction components even though the constant component plays no role in GE interaction. In contrast, the proposed measure of GE interaction based on interaction correlation (gammaI) deals only with interaction component responsible for GE interaction and thus indicates the intensity of GE interaction generated by responsible genes. Constant and interaction effects with different economic weights were combined into optimum index to improve both genetic stability and overall response across countries (countries represent environments). Optimum index was more efficient than the unpartitioned index which was more efficient than selection in a single country except when economic weights between constant and interaction effects were equal. Optimum index and unpartitioned index were the same when these economic weights were equal. The advantage of optimum index over the other selection methods increases as the intensity of GE interaction increases. When the relative economic weights are equivalent among countries and between constant and interaction effects, selection in a country with a larger sire variance is more effective than selection in a country with a smaller sire variance.

Animals↗
PubMed2001

The potential use of SUISEKI as a protein interaction discovery tool.

Relevant information about protein interactions is stored in textual sources. This sources are commonly used not only as archives of what is already known but also as information for generating new knowledge, particularly to pose hypothesis about new possible interactions that can be inferred from the existing ones. This task is the more creative part of scientific work in experimental systems. We present a large-scale analysis for the prediction of new interactions based on the interaction network for the ones already known and detected automatically in the literature. During the last few years it has became clear that part of the information about protein interactions could be extracted with automatic tools, even if these tools are still far from perfect and key problems such as detection of protein names are not completely solved. We have developed a integrated automatic approach, called SUISEKI (System for Information Extraction on Interactions), able to extract protein interactions from collections of Medline abstracts. Previous experiments with the system have shown that it is able to extract almost 70% of the interactions present in relatively large text corpus, with an accuracy of approximately 80% (for the best defined interactions) that makes the system usable in real scenarios, both at the level of extraction of protein names and at the level of extracting interaction between them. With the analysis of the interaction map of Saccharomyces cerevisiae we show that interactions published in the years 2000/2001 frequently correspond to proteins or genes that were already very close in the interaction network deduced from the literature published before these years and that they are often connected to the same proteins. That is, discoveries are commonly done among highly connected entities. Some biologically relevant examples illustrate how interactions described in the year 2000 could have been proposed as reasonable working hypothesis with the information previously available in the automatically extracted network of interactions.

Cell Cycle↗
PubMed2005

Classification and occurrence of clinically significant drug interactions with irinotecan and oxaliplatin in patients with metastatic colorectal cancer.

BACKGROUND: Pharmacokinetic and pharmacodynamic drug interactions with cytotoxic drugs may significantly influence the efficacy and toxicity of chemotherapy. OBJECTIVE: The purpose of this study was to identify drug interactions with irinotecan and oxaliplatin reported in the literature, to assess their clinical significance, and to examine the occurrence of these interactions in patients with metastatic colorectal cancer treated with either irinotecan or oxaliplatin or both. METHODS: To obtain data on drug-drug interactions with irinotecan and oxaliplatin, a literature search of PubMed and EMBASE was conducted using the search terms irinotecan, oxaliplatin, and interactions (English-language studies only published between 1980 and August 2004). The interactions found were subsequently classified for documentation evidence and severity of clinical effect, according to a 5-level classification system of a standard reference text, by a study panel of medical oncologists and clinical pharmacists. Comedication of patients who were treated with irinotecan or oxaliplatin, or both, was then examined to determine the occurrence of clinically significant interactions. RESULTS: Ninety-eight patients (50 women, 48 men;mean age, 60 years) were included in the study. Seventeen interactions with irinotecan were found in the literature, and 11 were classified as clinically significant. Only 1 nonspecific, clinically significant interaction was identified for oxaliplatin. Irinotecan-treated patients received a mean of 8 different comedications and oxaliplatin-treated patients received a mean of 6. Apart from antiemetic and antidiarrheal drugs that were prescribed for treatment-related toxicities, only 1 patient appeared to be exposed to a possible clinically significant interaction (between irinotecan and phenytoin). CONCLUSIONS: Eleven of the 17 interactions with irinotecan that were found in the literature were classified as clinically significant versus 1 clinically significant interaction with oxaliplatin. The occurrence of these interactions in the study patients with metastatic colorectal cancer was low. For medication surveillance purposes, however, the significant interactions should be considered in clinical practice.

Adolescent↗
PubMed1992

Prospective evaluation of adverse drug interactions in the emergency department.

STUDY OBJECTIVE: To determine the incidence and risk factors of clinically relevant adverse drug interactions occurring in emergency department patients. DESIGN: This report describes the drug interactions identified in an emergency population. Patients' drug regimens were evaluated prospectively at the time of the emergency evaluation. SETTING: University hospital ED. TYPE OF PARTICIPANTS: Convenience sample of 341 patients. INTERVENTIONS: Patients' medications on arrival at the ED (current medications) and medications initiated in the ED were entered into Hansten's computer-based drug interaction program to identify potential drug interactions. All potential drug interactions were brought to the attention of the attending emergency physician, whose subsequent actions were noted. Clinically relevant interactions were determined by a physician panel based on the ED attendings' actions, set criteria, and a review of hospital charts and four-week telephone follow-up of patients with potential drug interactions. MEASUREMENTS AND MAIN RESULTS: Three hundred forty patients were enrolled. One hundred thirty-five potential drug interactions were identified in 61 patients. Among these 135 potential drug interactions and 61 patients, we identified 20 clinically relevant interactions in 15 patients. The incidence of clinically relevant interactions was significantly higher (chi 2 = 3.95, P = .047) among current medication (9.7%) than medication added in the ED (3.1%). Clinically relevant interaction from both current and ED-initiated medication was associated with taking three or more medications on ED arrival (P = .016 and .045, respectively). Multiple regression showed age of 60 years or older to be the sole predictor of clinically relevant interaction among current medication (P = .05). CONCLUSION: Clinically relevant adverse drug interaction was significantly less from medication administered or prescribed in the ED than from current medication.

Age Factors↗
PubMed2004

Evaluation of personal digital assistant software for drug interactions.

PURPOSE: The accuracy, comprehensiveness, and ease of use of drug interaction software used with personal digital assistants (PDAs) were studied. METHODS: Each program was assessed for accuracy using 40 clinically important and 40 clinically unimportant drug interaction pairs. Accuracy was scored through the summation of software sensitivity, specificity, and positive and negative predictive values. The comprehensiveness of each program was determined by the number of components in the drug interaction monograph. Time needed to identify the management of five important drug interactions defined each program's ease of use. The aggregate scores for accuracy, comprehensiveness, and ease of use were calculated. RESULTS: Scoring 777 and 756 out of a possible 800 points, iFacts and Lexi-lnteract, respectively, provided the most competent, complete, use-friendly compendia for assessment of drug interactions. Mosby's Drug Consult and Mobile Micromedex ranked third and fourth, scoring 688 and 655 points, respectively, while ePocrates Rx v. 6.0 rated seventh, with a score of 559. All drug interaction resources suffer from limitations in the quality or relevance of evidence for the interaction, an absence of identifiable patient and medication risk factors, and a lack of standardization in assigning significance to the interaction. Consequently, clinicians must interpret the importance of the interaction based on all available evidence. Discussion of such evidence was available for only iFacts and Lexi-Interact. CONCLUSION: Both iFacts and Lexi-Interact excelled as PDA pharmacopoeia for assessing drug interactions. However, clinicians should understand the limitations of all current drug interaction resources and exercise vigilance in prevention and recognition of interactions relevant to their patients.

Computers, Handheld↗
PubMed2005

Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships.

MOTIVATION: Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein-protein interfaces to predict putative protein interaction pairs. RESULTS: We have obtained a large amount of putative protein-protein interaction (approximately 130,000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions.

Algorithms↗
PubMed2005

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast.

BACKGROUND: In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. RESULTS: We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). CONCLUSION: Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed).

Protein Interaction Mapping↗