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PubMed2005

Interactions of mammalian cytochrome P450, NADPH-cytochrome P450 reductase, and cytochrome b(5) enzymes.

An immobilized system was developed to detect interactions of human cytochromes P450 (P450) with the accessory proteins NADPH-P450 reductase and cytochrome b(5) (b(5)) using an enzyme-linked affinity approach. Purified enzymes were first bound to wells of a polystyrene plate, and biotinylated partner enzymes were added and bound. A streptavidin-peroxidase complex was added, and protein-protein binding was monitored by measuring peroxidase activity of the bound biotinylated proteins. In a model study, we examined protein-protein interactions of Pseudomonas putida putidaredoxin (Pdx) and putidaredoxin reductase (PdR). A linear relationship (r(2)=0.96) was observed for binding of PdR-biotin to immobilized Pdx compared with binding of Pdx-biotin to immobilized PdR (the estimated K(d) value for the Pdx.PdR complex was 0.054muM). Human P450 2A6 interacted strongly with NADPH-P450 reductase; the K(d) values (with the reductase) ranged between 0.005 and 0.1muM for P450s 2C19, 2D6, and 3A4. Relatively weak interaction was found between holo-b(5) or apo-b(5) (devoid of heme) with NADPH-P450 reductase. Among the rat, rabbit, and human P450 1A2 enzymes, the rat enzyme showed the tightest interaction with b(5), although no increases in 7-ethoxyresorufin O-deethylation activities were observed with any of the P450 1A2 enzymes. Human P450s 2A6, 2D6, 2E1, and 3A4 interacted well with b(5), with P450 3A4 yielding the lowest K(d) values followed by P450s 2A6 and 2D6. No appreciable increases in interaction between human P450s with b(5) or NADPH-P450 reductase were observed when typical substrates for the P450s were included. We also found that NADPH-P450 reductase did not cause changes in the P450.substrate K(d) values estimated from substrate-induced UV-visible spectral changes with rabbit P450 1A2 or human P450 2A6, 2D6, or 3A4. Collectively, the results show direct and tight interactions between P450 enzymes and the accessory proteins NADPH-P450 reductase and b(5), with different affinities, and that ligand binding to mammalian P450s did not lead to increased interaction between P450s and the reductase.

Coenzymes↗
PubMed2005

Comparison of P-glycoprotein-mediated drug-digoxin interactions in Caco-2 with human and rodent intestine: relevance to in vivo prediction.

Inhibition of P-glycoprotein (PGP) resulting from the co-administration of substrate drugs represents a potential source of drug-drug interactions. Although in vitro screens can readily identify such interactions, the accuracy with which they mimic interactions in tissues or their value in predicting interactions in vivo is unresolved. This was addressed for the model PGP substrate digoxin by comparing the modulation of its permeability across Caco-2 cells and ex vivo human and rodent intestine by drugs for which pharmacokinetic data on interactions with digoxin in man is available. All five compounds (talinolol, omeprazole, verapamil, quinidine, cyclosporin) dose-dependently increased absorptive (A-B) digoxin permeability with maximal increases of 2.2-4.5-fold across Caco-2. Quantitatively similar increases were observed in ex vivo human and mouse intestine and studies in mdr1a(-/-) intestine confirmed that these interactions are mediated solely by PGP. In vitro changes in digoxin permeability were qualitative indicators of the increase in digoxin C(max) for these compounds in man, although accounting for the luminal drug concentrations expected for a given oral dose was a critical consideration. Based on a limited dataset these data suggest that Caco-2 accurately mimics intestinal digoxin interactions and may be useful in predicting the threshold dose at which interactions become clinically significant. Further studies across a wider range of drugs are needed to determine the broader applicability of in vitro data for quantitative prediction of clinical drug interactions.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed1997

In vitro studies on the interactions of beta2-adrenoceptor agonists, methylxanthines, Ca2+-channel blockers, K+-channel openers and other airway smooth muscle relaxants in isolated guinea-pig trachea.

Pharmacodynamic interactions in vitro between different types of airway smooth muscle relaxants were systematically and quantitatively evaluated by using a new methodological technique. Relaxant concentration-effect curves for terbutaline, theophylline, cromakalim, sodium nitroprusside and isradipine were obtained in isolated guinea-pig trachea contracted by histamine (1 microM). The effects of three different fixed concentrations of each airway smooth muscle relaxant were initially attained and concentration-effect curves for combinations with increasing concentrations of either one of the other relaxants were produced. Based on pharmacodynamic parameters obtained by non-linear regression analysis of experimental data for the relaxants alone theoretical concentration-effect curves for predicted additive interaction were constructed by using the isobolic method. Synergistic (over-additive) interaction was defined as existing when data points and derived pharmacodynamic parameters obtained with combinations of the relaxants showed statistically significant deviation from the predicted additive interaction curve and its functional parameters. Significant synergistic interaction with terbutaline was found for both theophylline (70 or 200 microM), cromakalim (0.1, 0.3 or 1 microM), sodium nitroprusside (30 or 100 nM) and isradipine (1, 3 or 10 nM). Theophylline showed synergistic interaction with cromakalim (0.1, 0.3 or 1 microM), sodium nitroprusside (10 nM) and isradipine (1, 3 or 10 nM). Interactions between cromakalim and sodium nitroprusside (10, 30 or 100 nM) were also synergistic, whereas cromakalim and isradipine (1, 3 or 10 nM) produced only additive interaction. Possible mechanisms underlying the interactions are discussed on basis of existing knowledge with special regards to phosphodiesterase isoenzymes, K+ and Ca2+ channels.

Adrenergic beta-Agonists↗
PubMed1999

Drug interactions with itraconazole, fluconazole, and terbinafine and their management.

A drug interaction develops when the effect of a drug is increased or decreased or when a new effect is produced by the prior, concurrent, or subsequent administration of the other. Before prescribing a drug, it is important to obtain a thorough drug history of the prescription and nonprescription medications taken by the patient. The nonprescription medications may include items such as nutritional supplements and herbal medications. The risk of side effects is an inevitable consequence of drug use. The frequency of adverse reactions is increased in those patients receiving multiple medications. Drug interactions reported in animal or in vitro studies may not necessarily develop in humans. When drug interactions are observed with a particular agent, it cannot be automatically assumed that all closely related drugs will necessarily produce the same interaction. However, caution is advised until sufficient experience accrues. The prescriber should not overestimate or underestimate the potential for a given drug interaction on the basis of personal experience alone. Drug interactions will not necessarily occur in every patient who is given a particular combination of drugs known to produce an interaction. For a clinically significant drug interaction to be manifest, several other factors may be relevant other than just using the two drugs. In many instances drug interactions can be predicted and therefore avoided if the pharmacodynamic effects, the pharmacokinetic properties, and the mechanisms of action of the 2 drugs in question are known. In the case of contraindicated drugs, it may be possible to use an alternative agent.

Animals↗
PubMed2004

Comparison of C-H...pi and hydrophobic interactions in a beta-hairpin peptide: impact on stability and specificity.

We have examined the impact of C-H...pi and hydrophobic interactions in the diagonal position of a beta-hairpin peptide through comparison of the interaction of Phe, Trp, or Cha (cyclohexylalanine) with Lys or Nle (norleucine). NMR studies, including NOESY and chemical shift perturbation studies, of the Lys side chain indicates that Lys interacts in a specific geometry with Phe or Trp through the polarized C epsilon. In contrast, Nle does not interact in a specific manner with the diagonal aromatic residue. Thermal denaturation provides additional support that Lys and Nle interact in fundamentally different manners. Folding of the peptide with a diagonal Trp...Lys interaction was found to be enthalpically driven, whereas the peptide with a diagonal Trp...Nle interaction displayed cold denaturation, as did the control peptide with a diagonal Cha...Nle interaction, indicating different driving forces for interaction of Lys and Nle with Trp. These findings have significant implications for specificity in protein folding and de novo protein design.

Anisotropy↗
PubMed2005

Interactions between volatile and nonvolatile coffee components. 1. Screening of nonvolatile components.

This study is the first of two publications that investigate the phenomena of coffee nonvolatiles interacting with coffee volatile compounds. The purpose was to identify which coffee nonvolatile(s) are responsible for the interactions observed between nonvolatile coffee brew constituents and thiols, sulfides, pyrroles, and diketones. The overall interaction of these compounds with coffee brews prepared with green coffee beans roasted at three different roasting levels (light, medium, and dark), purified nonvolatiles, and medium roasted coffee brew fractions (1% solids after 1 or 24 h) was measured using a headspace solid-phase microextraction technique. The dark roasted coffee brew was slightly more reactive toward the selected compounds than the light roasted coffee brew. Selected pure coffee constituents, such as caffeine, trigonelline, arabinogalactans, chlorogenic acid, and caffeic acid, showed few interactions with the coffee volatiles. Upon fractionation of medium roasted coffee brew by solid-phase extraction, dialysis, size exclusion chromatography, or anion exchange chromatography, characterization of each fraction, evaluation of the interactions with the aromas, and correlation between the chemical composition of the fractions and the magnitude of the interactions, the following general conclusions were drawn. (1) Low molecular weight and positively charged melanoidins present significant interactions. (2) Strong correlations were shown between the melanoidin and protein/peptide content, on one hand, and the extent of interactions, on the other hand (R = 0.83-0.98, depending on the volatile compound). (3) Chlorogenic acids and carbohydrates play a secondary role, because only weak correlations with the interactions were found in complex matrixes.

Carbohydrates↗
PubMed2005

Detergent binding as a sensor of hydrophobicity and polar interactions in the binding cavities of proteins.

To evaluate the role of hydrophobic and electrostatic or other polar interactions for protein-ligand binding, we studied the interaction of human serum albumin (HSA) and beta-lactoglobulin with various aliphatic (C10-C14) cationic and zwitterionic detergents. We find that cationic detergents, at levels that do not cause unfolding, interact with a single site on beta-lactoglobulin and with two primary and five to six secondary sites on HSA with an affinity that is approximately the same as that with which zwitterionic (dimethylamineoxide) detergents interact, suggesting the absence of significant electrostatic interactions in the high-affinity binding of these compounds. The binding affinity for all of the groups of compounds was dependent upon hydrocarbon chain length, suggesting the predominant role of hydrophobic forces, supported by polar interactions at the protein surface. A distinct correlation between the binding energy and the propensity for micelle formation within the group of cationic or noncharged (nonionic and zwitterionic) detergents indicated that the critical micellar concentration (CMC) for each of these detergent groups, rather than the absolute length of the hydrocarbon chain, can be used to compare their hydrophobicities during their interaction with protein. Intrinsic fluorescence data suggest that the two primary binding sites on serum albumin for the zwitterionic and cationic compounds are located in the C-terminal part of the albumin molecule, possibly in the Sudlow II binding region. Comparisons with previous binding data on anionic amphiphiles emphasize the important contribution of ion bond formation and other polar interactions in the binding of fatty acids and dodecyl sulfate (SDS) by HSA but not by beta-lactoglobulin. Electrostatic interactions by cationic detergents played a significant role in destabilizing the protein structure at high binding levels, with beta-lactoglobulin being more susceptible to unfolding than HSA. Zwitterionic detergents, in contrast to the cationic detergents, had no tendency to unfold the proteins at high concentrations.

Amino Acid Sequence↗
PubMed2003

Computational approaches to protein-protein interaction.

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.

Algorithms↗
PubMed2000

Psychotropic interactions with warfarin.

OBJECTIVE: Improving knowledge about the cytochrome p450 system means that potential drug interactions can be predicted. Interactions involving warfarin may be thus avoidable. As many patients who have suffered from a stroke or other thromboembolic events may also develop psychiatric disorder, knowledge about possible interactions with psychotropics is essential for prescribers. METHOD: A Medline and hand search of published literature was complemented by contacting manufacturers. RESULTS: The antidepressants citalopram, nefazodone and sertraline have relatively low interaction potential with warfarin; fluoxetine and fluvoxamine relatively high. Carbamazepine appears to reduce warfarin's anticoagulant effect. Other antipsychotics, antidepressants and anxiolytics have only a theoretical risk of interaction. Lithium, gabapentin, sulpiride and amisulpride are predominantly renally excreted and so are not likely to interact with warfarin. CONCLUSION: Many psychotropics are involved in predictable interactions with warfarin. Drugs with a known low interaction potential should be chosen instead of those known or predicted to interact.

Anticoagulants↗
PubMed2002

Beta-lactamase protein fragment complementation assays as in vivo and in vitro sensors of protein protein interactions.

We have previously described a strategy for detecting protein protein interactions based on protein interaction assisted folding of rationally designed fragments of enzymes. We call this strategy the protein fragment complementation assay (PCA). Here we describe PCAs based on the enzyme TEM-1 beta-lactamase (EC: 3.5.2.6), which include simple colorimetric in vitro assays using the cephalosporin nitrocefin and assays in intact cells using the fluorescent substrate CCF2/AM (ref. 6). Constitutive protein protein interactions of the GCN4 leucine zippers and of apoptotic proteins Bcl2 and Bad, and the homodimerization of Smad3, were tested in an in vitro assay using cell lysates. With the same in vitro assay, we also demonstrate interactions of protein kinase PKB with substrate Bad. The in vitro assay is facile and amenable to high-throughput modes of screening with signal-to-background ratios in the range of 10:1 to 250:1, which is superior to other PCAs developed to date. Furthermore, we show that the in vitro assay can be used for quantitative analysis of a small molecule induced protein interaction, the rapamycin-induced interaction of FKBP and yeast FRB (the FKBP-rapamycin binding domain of TOR (target of rapamycin)). The assay reproduces the known dissociation constant and number of sites for this interaction. The combination of in vitro colorimetric and in vivo fluorescence assays of beta-lactamase in mammalian cells suggests a wide variety of sensitive and high-throughput large-scale applications, including in vitro protein array analysis of protein protein or enzyme protein interactions and in vivo applications such as clonal selection for cells expressing interacting protein partners.

3-Phosphoinositide-Dependent Protein Kinases↗
PubMed2002

The Tol/Pal system function requires an interaction between the C-terminal domain of TolA and the N-terminal domain of TolB.

The Tol/Pal system of Escherichia coli is composed of the YbgC, TolQ, TolA, TolR, TolB, Pal and YbgF proteins. It is involved in maintaining the integrity of the outer membrane, and is required for the uptake of group A colicins and DNA of filamentous bacteriophages. To identify new interactions between the components of the Tol/Pal system and gain insight into the mechanism of colicin import, we performed a yeast two-hybrid screen using the different components of the Tol/Pal system and colicin A. Using this system, we confirmed the already known interactions and identified several new interactions. TolB dimerizes and the periplasmic domain of TolA interacts with YbgF and TolB. Our results indicate that the central domain of TolA (TolAII) is sufficient to interact with YbgF, that the C-terminal domain of TolA (TolAIII) is sufficient to interact with TolB, and that the amino terminal domain of TolB (D1) is sufficient to bind TolAIII. The TolA/TolB interaction was confirmed by cross-linking experiments on purified proteins. Moreover, we show that the interaction between TolA and TolB is required for the uptake of colicin A and for the membrane integrity. These results demonstrate that the TolA/TolB interaction allows the formation of a trans-envelope complex that brings the inner and outer membranes in close proximity.

Bacterial Outer Membrane Proteins↗
PubMed2001

Preventing drug interactions by online prescription screening in community pharmacies and medical practices.

BACKGROUND: Drug interactions have been shown to be preventable by computerized prescription entry and screening only in hospitals and not in community-based practice. METHODS: We retrospectively evaluated the effect of online prescription screening in community pharmacies and physician offices of one health maintenance organization, phased in during 3 consecutive 6-month periods in 1998 to 1999 (period I, system active only in 40% of pharmacies; period II, system active in 90% of pharmacies and 50% of physician offices; period III, system active in 95% of pharmacies and 90% of physician practices), on rates of prescriptions with-, patient exposure to-, and physician prescribing of-potential drug interactions. FINDINGS: Cumulative data included 775,186 patients given at least one prescription, by one or more of 5504 physicians, whose prescriptions were dispensed at 572 pharmacies. Dispensing of drug interaction prescriptions was reduced by 21.1% and by 67.5% in periods II and III compared with period I (odds ratio, 0.79; 95% confidence limit, 0.75-0.83 and odds ratio, 0.28; 95% confidence limit, 0.26-0.30, respectively). Patient exposure decreased only in those receiving 3 to 7 concurrent drugs (odds ratio, 0.80; 95% confidence limit, 0.71-0.90) with no reductions for patients who were given 2 drugs or 8 or more drugs. Only 19% to 25% of physicians wrote prescriptions for drugs that interact, but 85% of these repeated this pattern after being alerted. The proportion of prescriptions of drugs that interact that originated with a single prescriber, as opposed to 2 prescribers, decreased during the 3 periods from 0.81 to 0.74 and 0.69 (P <.001). INTERPRETATION: Computerized prescription entry and drug interaction screening in the community caused a 62.8% reduction in pharmacy-dispensed prescriptions with severe drug interactions and a 20% reduction in patient exposure to prescriptions with severe drug interactions; this reduction was negated by polypharmacy of 8 or more drugs. The effect of interaction alerts on physician prescribing patterns was limited.

Adult↗
PubMed2005

An integrated mass spectrometry-based proteomic approach: quantitative analysis of tandem affinity-purified in vivo cross-linked protein complexes (QTAX) to decipher the 26 S proteasome-interacting network.

We developed an integrated proteomic approach to decipher in vivo protein-protein interactions and applied this strategy to globally map the 26 S proteasome interaction network in yeast. We termed this approach QTAX for quantitative analysis of tandem affinity purified in vivo cross-linked (X) protein complexes. For this work, in vivo formaldehyde cross-linking was used to freeze both stable and transient interactions occurring in intact cells prior to lysis. To isolate cross-linked protein complexes with high purification efficiency under fully denaturing conditions, a new tandem affinity tag consisting of a hexahistidine sequence and an in vivo biotinylation signal was adopted for affinity-based purification. Tandem affinity purification after in vivo cross-linking was combined with tandem mass spectrometry coupled with a quantitative SILAC (stable isotope labeling of amino acids in cell culture) strategy to carry out unambiguous protein identification and quantification of specific protein interactions. Using this method, we captured and identified the full composition of yeast 26 S proteasome complex as well as the two known ubiquitin receptors, Rad23 and Dsk2. Quantitative mass spectrometry analysis allowed us to distinguish specific proteasome-interacting proteins (PIPs) from background proteins and led to the identification of a total of 64 potential PIPs of which 42 are novel interactions. Among the 64 putative specific PIPs, there are ubiquitin pathway components, ubiquitinated substrates, chaperones, and transcription and translation regulators, demonstrating the efficacy of the developed approach in capturing in vivo protein interactions. The method offers an advanced technical approach to elucidate the dynamic protein interaction networks of the proteasome and can find a wide range of applications in the studies of other macromolecular protein complex interaction networks.

Amino Acid Sequence↗
PubMed2006

Identification of the ligands of protein interaction domains through a functional approach.

The identification of protein-protein interaction networks has often given important information about the functions of specific proteins and on the cross-talk among metabolic and regulatory pathways. The availability of entire genome sequences has rendered feasible the systematic screening of collections of proteins, often of unknown function, aimed to find the cognate ligands. Once identified by genetic and/or biochemical approaches, the interaction between two proteins should be validated in the physiologic environment. Herein we describe an experimental strategy to screen collections of protein-protein interaction domains to find and validate candidate interactors. The approach is based on the assumption that the overexpression in cultured cells of protein-protein interaction domains, isolated from the context of the whole protein, could titrate the endogenous ligand and, in turn, exert a dominant negative effect. The identification of the ligand could provide us with a tool to check the relevance of the interaction because the contemporary overexpression of the isolated domain and of its ligand could rescue the dominant negative phenotype. We explored this approach by analyzing the possible dominant negative effects on the cell cycle progression of a collection of phosphotyrosine binding (PTB) domains of human proteins. Of 47 PTB domains, we found that the overexpression of 10 of them significantly interfered with the cell cycle progression of NIH3T3 cells. Four of them were used as baits to identify the cognate interactors. Among these proteins, CARM1, interacting with the PTB domain of RabGAP1, and EF1alpha, interacting with RGS12, were able to rescue the block of the cell cycle induced by the isolated PTB domain of the partner protein, thus confirming in vivo the relevance of the interaction. These results suggest that the described approach can be used for the systematic screening of the ligands of various protein-protein interaction domains also by using different biological assays.

Adaptor Proteins, Signal Transducing↗
PubMed2000

Assessment of healthcare professionals' knowledge about warfarin-vitamin K drug-nutrient interactions.

OBJECTIVE: Dietary vitamin K can interact with oral anticoagulant drugs and interfere with their therapeutic safety and efficacy. Therefore, knowledge about drug-nutrient interactions involving vitamin K possessed by physicians, pharmacists, dietitians and nurses practicing anticoagulant therapy was assessed. METHODS: Healthcare practitioners were surveyed using a 30-question, 98-item questionnaire on the most common and/or important food interactions with warfarin, drug interactions with warfarin and general drug-nutrient interactions involving vitamin K. The study sample included 160 randomly selected healthcare providers (40 physicians, pharmacists, dietitians and nurses) from 10 hospitals with 200 to 1000 beds from six Massachusetts regions. Random selection was conducted from a pool of selected healthcare providers practicing anticoagulant therapy who counsel patients receiving warfarin. RESULTS: All surveys were completed within three months of the start of the study, and all participants provided usable data for statistical analysis. The mean scores (+/- SD) on the overall test were 72.5+/-9.0 for pharmacists, 62.51+/-10.6 for physicians, 56.9+/-8.8 for dietitians and 50.2+/-9.3 for nurses, with 100 being a perfect score. Pharmacists scored significantly higher in the area of drug interactions (75.9+/-11.3, p<0.05). Dietitians scored higher in the area of food interactions (73.0+/-10.3). No significant differences between physicians and pharmacists were evident on general drug-nutrient interactions. While over 87% of the healthcare professionals correctly identified some common foods containing large amounts of vitamin K, such as broccoli and spinach, fewer than 25% were able to identify others such as pea soup, coleslaw and dill pickles. CONCLUSIONS: Although the healthcare professionals surveyed in this study appear to have demonstrated some proficiency in their respective areas of expertise, they exhibited less knowledge in others. Therefore, additional training and integration of knowledge and expertise about drug-nutrient interactions among healthcare professionals are essential to provide appropriate patient counseling and optimal therapeutic outcomes.

Anticoagulants↗
PubMed1999

Extrapolating in vitro data on drug metabolism to in vivo pharmacokinetics: evaluation of the pharmacokinetic interaction between amitriptyline and fluoxetine.

Recently, models have been proposed to extrapolate in vitro data on the influence of inhibitors on drug metabolism to in vivo decrement in drug clearance. Many factors influence drug clearance such as age, gender, habits, diet, environment, liver disease, heredity, and other drugs. In vitro investigation of hepatic cytochrome P450 activity has generally centered on genetic influences and interactions with other drugs. This group of enzymes is involved in many, although not all, drug interactions. The interaction of amitriptyline and fluoxetine is an example. Of the different in vitro paradigms, interaction studies utilizing human liver microsomal preparations have proved to be the most generally applicable for in vitro scaling models. Assuming Michaelis-Menten conditions and applying nonlinear regression, a hybrid inhibition constant (Ki) can be generated that allows classification of the inhibitory potency of an inhibitor toward a specific reaction. This constant is largely independent of the substrate concentration, but in vivo relevance is critically dependent on the inhibitor concentration in the site of metabolic activity, the liver cell cytosol. Many lipophilic drugs are extensively bound to plasma protein but, nonetheless, demonstrate extensive partitioning into liver tissue. This is not compatible with diffusion only of the unbound drug fraction into liver cells. The introduction of a partition factor, based on data from a number of possible sources, provided a reasonable basis for the scaling of in vitro data to in vivo conditions. Many interactions could be reconstructed or predicted with greater accuracy and clinical relevance for interactions such as terfenadine or midazolam and ketoconazole. Even for less marked interactions such as amitriptyline and fluoxetine, this model provides a forecast consistent with the clinically observed range of 22-45% reduction in oral clearance, although this interaction is complicated by the presence of two inhibitors, fluoxetine and norfluoxetine. The concept of in vitro-in vivo scaling is promising and might ultimately yield a fast and more cost-effective screening for drug interactions with reduced human drug exposure and risk.

Amitriptyline↗
PubMed2002

Update on natural product--drug interactions.

The interactions of natural products with drugs are discussed. Interactions between natural products and drugs are based on the same pharmacokinetic and pharmacodynamic principles as drug-drug interactions. Clinically important interactions appear to involve effects on drug metabolism via cytochrome P-450 isoenzymes, impairment of hepatic or renal function, and other possible mechanisms. Natural products that have been reported to interact with drugs in humans include coenzyme Q10, dong quai, ephedra, Ginkgo biloba, ginseng, glucosamine sulfate, ipriflavone, melatonin, and St. John's wort. In many cases, more research is needed to confirm these interactions and to determine whether other natural products may also interact with drugs. To effectively counsel patients about interactions involving natural products, pharmacists should be familiar with the most commonly used products and have access to information on more obscure products. In view of the less than stringent provisions of the Dietary Supplement Health and Education Act, pharmacists should consult reliable, independent sources of information on natural products rather than rely on literature provided by manufacturers. Pharmacists should recommend only those products that are manufactured to high quality-control standards. Natural products can interact with drugs and with other natural products by the same mechanisms as drugs.

Biological Factors↗
PubMed2003

Learning to predict protein-protein interactions from protein sequences.

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.

Algorithms↗