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PubMed1997

Management of drug interactions in patients with HIV.

OBJECTIVE: To provide a complication of relevant information on drug interactions to assist healthcare practitioners in managing complex HIV-related pharmacotherapy. DATA SOURCES: Information was retrieved via a MEDLINE search (January 1966-December 1996) using MeSH headings "human immunodeficiency virus," "drug interactions," and names of medications commonly prescribed for the management of HIV infection and related opportunistic infections. Abstracts of international and national conferences, review articles, textbooks, and references of all articles were also searched. STUDY SELECTION AND DATA EXTRACTION: All literature on pharmacokinetic or pharmacodynamic interactions was considered for inclusion. Pertinent information, as assessed by the authors, was selected and summarized for discussion. DATA SYNTHESIS: Drug disposition and/or pharmacologic effect may be affected either by HIV-related physiologic changes or by the presence of concomitant drug therapy. Modifications in drug selection, dosage, dosing regimen, or route of administration may be needed to avoid or manage drug-disease, drug-drug, or drug-food interactions. Management options may depend on the mechanism and the clinical significance of the interaction, the availability of therapeutic alternatives, patient convenience, and cost restrictions. In the absence of specific data, consideration of pharmacokinetic and pharmacodynamic characteristics to assist practitioners in predicting the likelihood of possible interactions was included. RESULTS: A comprehensive table of clinically significant drug interactions is provided. Drug interaction principles and practical management strategies are also discussed. CONCLUSIONS: The potential for drug interactions is extremely common, given the increasing complexity of managing patients infected with HIV. To avoid compromising therapeutic efficacy or increasing drug toxicity, practitioners need to be aware of potential interactions and are encouraged to use a systematic approach when managing patient drug therapy.

Drug Interactions↗
PubMed2004

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level.

BACKGROUND: Whether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue. A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets. DISCUSSION: The number of interactions per protein, as measured by some protein interaction data sets, shows no correlation with evolutionary rate. Other data sets, however, do reveal a relationship. Furthermore, even when experimental biases of these data sets are taken into account, a real correlation between number of interactions and evolutionary rate appears to exist. SUMMARY: A strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies. The extremely low agreement between different protein interaction data sets indicates that interaction data are still of low coverage and/or quality. These limitations may explain why some data sets reveal no correlation with evolutionary rates.

Evolution, Molecular↗
PubMed2006

Comparative analysis of Saccharomyces cerevisiae WW domains and their interacting proteins.

BACKGROUND: The WW domain is found in a large number of eukaryotic proteins implicated in a variety of cellular processes. WW domains bind proline-rich protein and peptide ligands, but the protein interaction partners of many WW domain-containing proteins in Saccharomyces cerevisiae are largely unknown. RESULTS: We used protein microarray technology to generate a protein interaction map for 12 of the 13 WW domains present in proteins of the yeast S. cerevisiae. We observed 587 interactions between these 12 domains and 207 proteins, most of which have not previously been described. We analyzed the representation of functional annotations within the network, identifying enrichments for proteins with peroxisomal localization, as well as for proteins involved in protein turnover and cofactor biosynthesis. We compared orthologs of the interacting proteins to identify conserved motifs known to mediate WW domain interactions, and found substantial evidence for the structural conservation of such binding motifs throughout the yeast lineages. The comparative approach also revealed that several of the WW domain-containing proteins themselves have evolutionarily conserved WW domain binding sites, suggesting a functional role for inter- or intramolecular association between proteins that harbor WW domains. On the basis of these results, we propose a model for the tuning of interactions between WW domains and their protein interaction partners. CONCLUSION: Protein microarrays provide an appealing alternative to existing techniques for the construction of protein interaction networks. Here we built a network composed of WW domain-protein interactions that illuminates novel features of WW domain-containing proteins and their protein interaction partners.

Amino Acid Motifs↗
PubMed2006

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

BACKGROUND: The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. RESULTS: We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases. CONCLUSION: Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

Computational Biology↗
PubMed1997

Evidence for ligand-dependent intramolecular folding of the AF-2 domain in vitamin D receptor-activated transcription and coactivator interaction.

A ligand-dependent transcriptional activation domain (AF-2) exists in region E of the nuclear receptors. This highly conserved domain may contact several coactivators that are putatively involved in nuclear receptor-mediated transcription. In this study, a panel of vitamin D receptor (VDR) AF-2 mutants was created to examine the importance of several conserved residues in VDR-activated transcription. Two AF-2 mutants (L417S and E420Q) exhibited normal ligand binding, heterodimerization with retinoid X receptor, and vitamin D-responsive element interaction, but they were transcriptionally inactive in a VDR-responsive reporter gene assay. All AF-2 mutations that abolished VDR-mediated transactivation also eliminated interactions between VDR and several putative coactivator proteins including suppressor of gal1 (SUG1), steroid hormone receptor coactivator-1 (SRC-1), or receptor interacting protein (RIP140), suggesting that coactivator interaction is important for AF-2-mediated transcription. In support of this concept, the minimal AF-2 domain [VDR(408-427)] fused to the gal4 DNA binding domain was sufficient to mediate transactivation as well as interaction with putative coactivators. Introducing the L417S and E420Q mutations into the minimal AF-2 domain abolished this autonomous transactivation and coactivator interactions. Finally, we demonstrate that the minimal AF-2 domain interacted with an AF-2 deletion mutant of the VDR in a 1,25-(OH)2D3-dependent manner, suggesting a ligand-induced intramolecular folding of the VDR AF-2 domain. The L417S mutant of this domain disrupted the interaction with VDR ligand-binding domain, while the E420Q mutant did not affect this interaction. These studies suggest that the conserved AF-2 motif may mediate transactivation through ligand-dependent intermolecular interaction with coactivators and through ligand-induced intramolecular contacts with the VDR ligand-binding domain itself.

Adaptor Proteins, Signal Transducing↗
PubMed2002

Interactions between recreational drugs and antiretroviral agents.

OBJECTIVE: To summarize existing data regarding potential interactions between recreational drugs and drugs commonly used in the management of HIV-positive patients. DATA SOURCES: Information was obtained via a MEDLINE search (1966-August 2002) using the MeSH headings human immunodeficiency virus, drug interactions, cytochrome P450, medication names commonly prescribed for the management of HIV and related opportunistic infections, and names of commonly used recreational drugs. Abstracts of national and international conferences, review articles, textbooks, and references of all articles were also reviewed. STUDY SELECTION AND DATA EXTRACTION: Literature on pharmacokinetic interactions was considered for inclusion. Pertinent information was selected and summarized for discussion. In the absence of specific data, prediction of potential clinically significant interactions was based on pharmacokinetic and pharmacodynamic properties. RESULTS: All protease inhibitors (PIs) and nonnucleoside reverse transcriptase inhibitors are substrates and potent inhibitors or inducers of the cytochrome P450 system. Many classes of recreational drugs, including benzodiazepines, amphetamines, and opioids, are also metabolized by the liver and can potentially interact with antiretrovirals. Controlled interaction studies are often not available, but clinically significant interactions have been observed in a number of case reports. Overdoses secondary to interactions between the "rave" drugs methylenedioxymethamphetamine (MDMA) or gamma-hydroxybutyrate (GHB) and PIs have been reported. PIs, particularly ritonavir, may also inhibit metabolism of amphetamines, ketamine, lysergic acid diethylmide (LSD), and phencyclidine (PCP). Case series and pharmacokinetic studies suggest that nevirapine and efavirenz induce methadone metabolism, which may lead to symptoms of opiate withdrawal. A similar interaction may exist between methadone and the PIs ritonavir and nelfinavir, although the data are less consistent. Opiate metabolism can be inhibited or induced by concomitant PIs, and patients should be monitored for signs of toxicity and/or loss of analgesia. PIs should not be coadministered with midazolam and triazolam, since prolonged sedation may occur. CONCLUSIONS: Interactions between agents commonly prescribed for patients with HIV and recreational drugs can occur, and may be associated with serious clinical consequences. Clinicians should encourage open dialog with their patients on this topic, to avoid compromising antiretroviral efficacy and increasing the risk of drug toxicity.

Anti-HIV Agents↗
PubMed1993

Adverse drug interactions with nonsteroidal anti-inflammatory drugs (NSAIDs). Recognition, management and avoidance.

The prevalence and incidence of adverse drug interactions involving nonsteroidal anti-inflammatory drugs (NSAIDs) remains unknown. To identify those proposed drug interactions of greatest clinical significance, it is appropriate to focus on interactions between commonly used and/or commonly coprescribed drugs, interactions for which there are numerous well documented case reports in reputable journals, interactions validated by well designed in vivo human studies and those affecting high-risk drugs and/or high-risk patients. While most interactions between NSAIDs and other drugs are pharmacokinetic, NSAID-related pharmacodynamic interactions may be considerably more important in the clinical context, and prescriber ignorance is likely to be a major determinant of many adverse drug interactions. Prescribing NSAIDs is relatively contraindicated for patients on oral anticoagulants due to the risk of haemorrhage, and for patients taking high-dose methotrexate due to the dangers of bone marrow toxicity, renal failure and hepatic dysfunction. Combination NSAID therapy cannot be justified as toxicity may be increased without any improvement in efficacy. Where lithium or anti-hypertensives are coprescribed with NSAIDs, close monitoring is mandatory for lithium toxicity and hypertension, respectively, and aspirin (acetylsalicylic acid) or sulindac are preferred. Phenytoin or oral hypoglycaemic agents may be administered with NSAIDs other than pyrazoles and salicylates provided that patients are monitored carefully at the initiation and cessation of NSAID treatment. Digoxin, aminoglycosides and probenecid may be coprescribed with NSAIDs, but close monitoring is required, particularly for high-risk patients such as the elderly. Indomethacin and triamterene should be avoided due to the risk of renal failure. High dose aspirin should be replaced by naproxen in patients on valproic acid (sodium valproate) and care is required when corticosteroids are administered to patients taking salicylates long term in high dosage. Interactions between NSAIDs and antacids or cholestyramine are generally avoidable. Adverse drug interactions involving NSAIDs may be limited by rational prescribing and by careful monitoring, particularly for high-risk patients, drugs and therapy periods.

Animals↗
PubMed1995

Antihyperglycaemic agents. Drug interactions of clinical importance.

Non-insulin-dependent (type 2) diabetes mellitus (NIDDM) affects middle-aged or elderly people who frequently have several other concomitant diseases, especially obesity, hypertension, dyslipidaemias, coronary insufficiency, heart failure and arthropathies. Thus, polymedication is the rule in this population, and the risk of drug interactions is important, particularly in elderly patients. The present review is restricted to the interactions of other drugs with antihyperglycaemic compounds, and will not consider the mirror image, i.e. the interactions of antihyperglycaemic agents with other drugs. Oral antihyperglycaemic agents include sulphonylureas, biguanides--essentially metformin since the withdrawn of phenformin and buformin--and alpha-glucosidase inhibitors, acarbose being the only representative on the market. These drugs can be used alone or in combination to obtain better metabolic control, sometimes with insulin. Drug interactions with antihyperglycaemic agents can be divided into pharmacokinetic and pharmacodynamic interactions. Most pharmacokinetic studies concern sulphonylureas, whose action may be enhanced by numerous other drugs, thus increasing the risk of hypoglycaemia. Such an effect may result essentially from protein binding displacement, inhibition of hepatic metabolism and reduction of renal clearance. Reduction of the hypoglycaemic activity of sulphonylureas due to pharmacokinetic interactions with other drugs appears to be much less frequent. Drug interactions leading to an increase in plasma metformin concentrations, mainly by reducing the renal excretion or the hepatic metabolism of the biguanide, should be avoided to limit the risk of hyperlactaemia. Owing to its mode of action, pharmacokinetic interferences with acarbose are limited to the gastrointestinal tract, but have not been extensively studied yet. Pharmacodynamic interactions are quite numerous and may result in a potentiation of the hypoglycaemic action or, conversely, in a deterioration of blood glucose control. Such interactions may be observed whatever the type of antidiabetic treatment. They result from the intrinsic properties of the coprescribed drug on insulin secretion and action, or on a key step of carbohydrate metabolism. Finally, a combination of 2 to 3 antihyperglycaemic agents is common for treating patients with NIDDM to benefit from the synergistic effect of compounds acting on different sites of carbohydrate metabolism. Possible pharmacokinetic interactions between alpha-glucosidase inhibitors and classical antidiabetic oral agents should be better studied in the diabetic population.

Administration, Oral↗
PubMed1998

Systemic antifungal agents. Drug interactions of clinical significance.

There are 3 main classes of systemic antifungals: the polyene macrolides (e.g. amphotericin B), the azoles (e.g. the imidazoles ketoconazole and miconazole and the triazoles itraconazole and fluconazole) and the allylamines (e.g. terbinafine). Other systemic antifungals include griseofulvin and flucytosine. Most drug-drug interactions involving systemic antifungals have negative consequences. The interactions of amphotericin B, flucytosine, griseofulvin, terbinafine and azole antifungals can be divided into the following categories: (i) additive dangerous interactions; (ii) modifications of antifungal kinetics by other drugs; and (iii) modifications of the kinetics of other drugs by antifungals. Amphotericin B and flucytosine mainly interact with other agents pharmacodynamically. Clinically important drug interactions with amphotericin B cause nephrotoxicity, hypokalaemia and blood dyscrasias. The most important drug interaction of flucytosine occurs with myelotoxic agents. Hypokalaemia can precipitate the long QT syndrome, as well as potentially lethal ventricular arrhythmias like torsade de pointes. Synergism is likely to occur when either QT interval-modifying drugs (e.g. terfenadine and astemizole) and drugs that induce hypokalaemia (e.g. amphotericin B) are coadministered. Induction and inhibition of cytochrome P450 enzymes at hepatic and extrahepatic sites are the mechanisms that underlie the most serious pharmacokinetic drug interactions of the azole antifungals. These agents have been shown to notably decrease the catabolism of numerous drugs: histamine H1 receptor antagonists, warfarin, cyclosporin, tacrolimus, digoxin, felodipine, lovastatin, midazolam, triazolam, methylprednisolone, glibenclamide (glyburide), phenytoin, rifabutin, ritonavir, saquinavir, nevirapine and nortriptyline. Non-antifungal drugs like carbamazepine, phenobarbital (phenobarbitone), phenytoin and rifampicin (rifampin) can induce the metabolism of azole antifungals. The bioavailability of ketoconazole and itraconazole is also reduced by drugs that increase gastric pH, such as H2 receptor antagonists, proton pump inhibitors, sucralfate and didanosine. Griseofulvin is an enzymatic inducer of coumarin-like drugs and estrogens, whereas terbinafine seems to have a low potential for drug interactions. Despite important advances in our understanding of the mechanisms underlying pharmacokinetic drug interactions during the 1990s, at this time they still remain difficult to predict in terms of magnitude in individual patients. This is because of the large interindividual and intraindividual variations in the catalytic activity of those metabolising enzymes that can either be induced or inhibited by various drugs. Notwithstanding these variations, increasing clinical experience is allowing pharmacokinetic interactions to be used to advantage in order to improve the tolerability of some drugs, as recently exemplified by the use of a fixed combination of ketoconazole and cyclosporin.

Antifungal Agents↗
PubMed2002

Clinically significant interactions with drugs used in the treatment of tuberculosis.

Clinically significant interactions occurring during antituberculous chemotherapy principally involve rifampicin (rifampin), isoniazid and the fluoroquinolones. Such interactions between the antituberculous drugs and coadministered agents are definitely much more important than among antituberculous drugs themselves. These can be associated with consequences even amounting to therapeutic failure or toxicity. Most of the interactions are pharmacokinetic rather than pharmacodynamic in nature. The cytochrome P450 isoform enzymes are responsible for many interactions (especially those involving rifampicin and isoniazid) during drug biotransformation (metabolism) in the liver and/or intestine. Generally, rifampicin is an enzyme inducer and isoniazid acts as an inhibitor. The agents interacting significantly with rifampicin include anticoagulants, anticonvulsants, anti-infectives, cardiovascular therapeutics, contraceptives, glucocorticoids, immunosuppressants, psychotropics, sulphonylureas and theophyllines. Isoniazid interacts principally with anticonvulsants, theophylline, benzodiapines, paracetamol (acetaminophen) and some food. Fluoroquinolones can have absorption disturbance due to a variety of agents, especially the metal cations. Other important interactions of fluoroquinolones result from their enzyme inhibiting potential or pharmacodynamic mechanisms. Geriatric and immunocompromised patients are particularly at risk of drug interactions during treatment of their tuberculosis. Among the latter, patients who are HIV infected constitute the most important group. This is largely because of the advent of new antiretroviral agents such as the HIV protease inhibitors and the non-nucleoside reverse transcriptase inhibitors in the armamenterium of therapy. Compounding the complexity of drug interactions, underlying medical diseases per se may also contribute to or aggravate the scenario. It is imperative for clinicians to be on the alert when treating tuberculosis in patients with difficult co-morbidity requiring polypharmacy. With advancement of knowledge and expertise, it is hoped that therapeutic drug monitoring as a new paradigm of care can enable better management of these drug interactions.

AIDS-Related Opportunistic Infections↗
PubMed2005

Drug interactions of clinical importance with antihyperglycaemic agents: an update.

Because management of type 2 diabetes mellitus usually involves combined pharmacological therapy to obtain adequate glucose control and treatment of concurrent pathologies (especially dyslipidaemia and arterial hypertension), drug-drug interactions must be carefully considered with antihyperglycaemic drugs. Additive glucose-lowering effects have been extensively reported when combining sulphonylureas (or the new insulin secretagogues, meglitinide derivatives, i.e. nateglinide and repaglinide) with metformin, sulphonylureas (or meglitinide derivatives) with thiazolidinediones (also called glitazones) and the biguanide compound metformin with thiazolidinediones. Interest in combining alpha-glucosidase inhibitors with either sulphonylureas (or meglitinide derivatives), metformin or thiazolidinediones has also been demonstrated. These combinations result in lower glycosylated haemoglobin (HbA(1c)), fasting glucose and postprandial glucose levels than with either monotherapy. Even if modest pharmacokinetic interferences have been reported with some combinations, they do not appear to have important clinical consequences. No significant adverse effects, except a higher risk of hypoglycaemic episodes that may be attributed to better glycaemic control, occur with any combination. Challenging the classical dual therapy with sulphonylurea plus metformin, there is a recent trend to use alternative dual combinations (sulphonylurea plus thiazolidinedione or metformin plus thiazolidinedione). In addition, triple therapy with the addition of a thiazolidinedione to the metformin-sulphonylurea combination has been recently evaluated and allows glucose targets to be reached before insulin therapy is considered. This triple therapy appears to be safe, with no deleterious drug-drug interactions being reported so far.Potential interferences may also occur between glucose-lowering agents and other drugs, and such drug-drug interactions may have important clinical implications. Relevant pharmacological agents are those that are widely coadministered in diabetic patients (e.g. lipid-lowering agents, antihypertensive agents); those that have a narrow efficacy/toxicity ratio (e.g. digoxin, warfarin); or those that are known to induce (rifampicin [rifampin]) or inhibit (fluconazole) the cytochrome P450 (CYP) system. Metformin is currently a key compound in the pharmacological management of type 2 diabetes, used either alone or in combination with other antihyperglycaemics. There are no clinically relevant metabolic interactions with metformin, because this compound is not metabolised and does not inhibit the metabolism of other drugs. In contrast, sulphonylureas, meglitinide derivatives and thiazolidinediones are extensively metabolised in the liver via the CYP system and thus, may be subject to drug-drug metabolic interactions. Many HMG-CoA reductase inhibitors (statins) are also metabolised via the CYP system. Even if modest pharmacokinetic interactions may occur, it is not clear whether drug-drug interactions between oral antihyperglycaemic agents and statins may have clinical consequences regarding both efficacy and safety. In contrast, a marked pharmacokinetic interference has been reported between gemfibrozil and repaglinide and, to a lesser extent, between gemfibrozil and rosiglitazone. This leads to a drastic increase in plasma concentrations of each antihyperglycaemic agent when they are coadministered with the fibric acid derivative, and an increased risk of adverse effects. Some antihypertensive agents may favour hypoglycaemic episodes when co-prescribed with sulphonylureas or meglitinide derivatives, especially ACE inhibitors, but this effect seems to result from a pharmacodynamic drug-drug interaction rather than from a pharmacokinetic drug-drug interaction. No, or only modest, interferences have been described with glucose-lowering agents and other pharmacological compounds such as digoxin or warfarin. The effects of inducers or inhibitors of CYP isoenzymes on the metabolism and pharmacokinetics of the glucose-lowering agents of each pharmacological class has been tested. Significantly increased (with CYP inhibitors) or decreased (with CYP inducers) plasma levels of sulphonylureas, meglitinide derivatives and thiazolidinediones have been reported in healthy volunteers, and these pharmacokinetic changes may lead to enhanced or reduced glucose-lowering action, and thus hypoglycaemia or worsening of metabolic control, respectively. In addition, some case reports have evidenced potential drug-drug interactions with various antihyperglycaemic agents that are usually associated with a higher risk of hypoglycaemia.

Diabetes Mellitus, Type 2↗
PubMed1998

Drug interactions at the renal level. Implications for drug development.

The kidney plays a major role in the elimination of drugs. The purpose of this paper is to: (i) review the mechanisms of renal elimination; (ii) identify potential mechanisms for renal drug interactions; (iii) review in vitro and in vivo animal models for studying renal elimination mechanisms and identifying potential drug-drug interactions; (iv) review experimental designs used in identifying drug-drug interactions in humans with an emphasis on gaining information regarding the mechanism of the interaction; and (v) make recommendations regarding the potential for renal drug interactions in drug development. It is concluded that clinically significant drug interactions resulting in toxicity because of some mechanism at the renal level appear to be relatively rare and that in vitro screening should not be done on all drugs during drug development. Five potential mechanisms exist for drug interactions at the renal level: (i) a displacement of bound drug resulting in an increase in drug excretion via an increase in glomerular filtration; (ii) competition at a tubular secretion site resulting in a decrease in drug excretion; (iii) competition at the tubular reabsorption site resulting in an increase in drug excretion; (iv) a change in urinary pH and/or flow that may increase or decrease drug excretion depending on the pKa of the drug; and (v) inhibition of renal drug metabolism. The most well known renal drug interaction is competitive inhibition of tubular secretion, ultimately leading to an increase in plasma drug concentration. Only when renal clearance is a major contributor to the total clearance (> 30%) and plasma concentrations are greater than the Michaelis-Menten transport constant does the potential exist for clinically significant renal drug-drug interactions because only then does nonlinear pharmacokinetics become evident. The potential for drug interactions is small when renal clearance is less than 20 to 30% of the total clearance and/or when plasma concentrations are less than the Michaelis-Menten transport constant, unless the drug has a narrow therapeutic window.

Animals↗
PubMed2003

Drug interactions between antiretroviral drugs and comedicated agents.

HIV-infected individuals usually receive a wide variety of drugs in addition to their antiretroviral drug regimen. Since both non-nucleoside reverse transcriptase inhibitors and protease inhibitors are extensively metabolised by the cytochrome P450 system, there is a considerable potential for pharmacokinetic drug interactions when they are administered concomitantly with other drugs metabolised via the same pathway. In addition, protease inhibitors are substrates as well as inhibitors of the drug transporter P-glycoprotein, which also can result in pharmacokinetic drug interactions. The nucleoside reverse transcriptase inhibitors are predominantly excreted by the renal system and may also give rise to interactions. This review will discuss the pharmacokinetics of the different classes of antiretroviral drugs and the mechanisms by which drug interactions can occur. Furthermore, a literature overview of drug interactions is given, including the following items when available: coadministered agent and dosage, type of study that is performed to study the drug interaction, the subjects involved and, if specified, the type of subjects (healthy volunteers, HIV-infected individuals, sex), antiretroviral drug(s) and dosage, interaction mechanism, the effect and if possible the magnitude of interaction, comments, advice on what to do when the interaction occurs or how to avoid it, and references. This discussion of the different mechanisms of drug interactions, and the accompanying overview of data, will assist in providing optimal care to HIV-infected patients.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2000

Sense and nonsense in the prediction of drug-drug interactions.

Drug interactions are always a major concern in medicine and within the pharmaceutical industry. Fatal drug interactions have been reported, and several prominent drugs have been withdrawn from the market because of serious adverse reactions related to drug interactions. Therefore, drug interactions represent not only a medical problem for clinicians, but also an economic loss for pharmaceutical companies. Today, many pharmaceutical companies are predicting potential interactions of new drug candidates in an attempt to minimize such losses and to more effectively safeguard the welfare of patients. Can in vivo drug interactions be predicted accurately from in vitro metabolic studies? Should the prediction be qualitative or quantitative? These are the fundamental questions that industrial drug metabolism scientists must confront daily. Prediction of in vivo drug interactions from in vitro metabolic data is highly controversial, because of the complexities of factors that are involved in drug interactions. Some scientists believe that quantitative prediction of drug interaction is possible, whereas others are less optimistic, and believe that quantitative prediction is extremely difficult, if not impossible. The purpose of this review is to present and discuss the technical problems inherent in estimating in vitro Ki values and in measuring inhibitor concentration at the active-site of enzymes. Theoretic considerations are briefly reviewed, and representative examples are drawn from literature to illustrate the sense and nonsense in predicting in vivo drug interactions.

Algorithms↗
PubMed1999

[Drug-food interactions].

Pharmacological interaction is identified among other aspects when the effects of a drug are significantly altered clinically by the presence of food. The most important clinical significance in the history of drug-food interactions occurred in the fifties, the vitamin B6 deficiency when administering the tuberculostatic isoniazide, but there is no doubt that the interaction in the sixties between drugs that were inhibitors of the enzyme mono-amine oxidase and the biogenic amines tyramine and histamine had an important clinical significance due to the increase in hypertension and deaths due to cerebro-vascular accidents. In the seventies the types of interactions were due to the chelation between tetracyclines and the calcium of milk products, influencing the bioavailability of the antibiotic, and from this point on, studies explore in depth the interaction of foods with the different steps in the pharmacokinetics of the drugs (absorption, distribution, metabolization, and excretion), and these have helped to explain the metabolization interactions of drugs like the clinical significance between grapefruit juice whose naringenine flavonoid is a powerful inhibitor of cytochrome 450, particularly the CYP3A4 family, and terfenadine (antihistamine), with an increase in the plasma levels of the drug and patient death due to ventricular arrhythmia. In the USA the Joint Commission on Accreditation of Health Care Organization (JCAHO) has recommended monitoring of the possible drug and food interactions since 1985, and recommends that patients be informed of this. Despite the recommendations of the JCAHO, few American hospitals have protocolized these interactions and even fewer comply with these, and according to some authors, this is due to the lack of motivation caused by the lack of clinical significance. In this chapter we will study the effect of foods on drugs in two aspects: at the pharamcokinetic level with the possible alterations in absorption, distribution, metabolization, and excretion, and at the pharamcodynamic level, by alterations in the action of the drug. Also, based on a study by Delgado, which we have changed somewhat, we will report how the drugs should be taken to avoid interactions with foods. As a conclusion, it is difficult to establish which are the relevant drug-food interactions, unless they have given clinical problems. The following are important: a) drugs with a narrow therapeutic margin in which an absorption problem or a metabolization problem may disrupt the plasma levels and the patient needs (digoxin, teophylline, cyclosporin, etc). And b) the drugs like some antibiotics that, because of their action mechanisms, need to maintain adequate plasma concentrations.

Food-Drug Interactions↗
PubMed1992

Evaluation of six computerized drug interaction screening programs.

Six widely used computerized drug interaction screening programs were evaluated according to criteria developed by a panel of pharmacists. A panel of six pharmacists from various practice settings developed criteria that were used to evaluate six computerized drug interaction screening programs: Medicom Micro Plus, Medical Letter, S-O-A-P, Drug Interactions by Hansten, Drug Therapy Screening Systems (DTSS), and RxTriage. The criteria were that the programs be user friendly and efficient, provide guidance, and be relevant to the user's practice setting. Also, they should detect interactions, quickly alert the user, and guide the user to the appropriate action. The user should be able to set the level of clinical significance, screen only current drugs in a patient profile, and print out summaries. The panel selected nine known drug interactions to test the programs' knowledge bases. Only Medicom Micro Plus and DTSS detected all nine drug interactions; Medical Letter missed three. RxTriage and Drug Interactions received the highest ratings in user friendliness and efficiency, but both missed two interactions. RxTriage was rated most relevant to their practice by hospital and community pharmacists, and Drug Interactions was rated most relevant by faculty practitioners. S-O-A-P received low ratings overall. Of the six drug interaction screening programs evaluated, none was considered by the panel of pharmacists to be ideal.

Data Display↗
PubMed1991

Isobolographic analysis of analgesic interactions between intrathecally and intracerebroventricularly administered fentanyl, morphine and D-Ala2-D-Leu5-enkephalin in morphine-tolerant and nontolerant mice.

Concurrent administration of morphine sulfate i.c.v. and i.t. produces a multiplicative interaction for analgesia in the tail flick response in mice. This interaction decreases to an additive interaction in mice which are tolerant to s.c. morphine. To test the responses of opioids selective for mu or delta receptors, the present study examined the interactions between fentanyl citrate (mu agonist) and D-Ala2-D-Leu5 enkephalin (DADLE, a relatively selective delta agonist) administered i.c.v. and i.t. using the tail flick test in control and morphine pellet-implanted mice. A method was developed for assigning statistical significance to the resulting ED50 values when analyzed isobolographically. When fentanyl or DADLE was administered i.c.v. plus i.t., an additive interaction between sites occurred in control animals, which changed to an antagonistic interaction for fentanyl and a multiplicative interaction for DADLE after morphine pellet treatment. When morphine was given i.c.v. along with i.t. fentanyl or DADLE in control animals, multiplicative interactions occurred when equipotent doses were given. Thus, opioids which were more receptor-selective than morphine did not produce multiplicative interactions, but were multiplicative when given with morphine. These results suggest that activation of combinations of receptors (by morphine) was required for the multiplicative interaction. The supraspinal site involved mu receptors (which are not self-sufficient and require an additional component) and the spinal site involved mu or delta receptors. The use of isobolographic analysis required that the drugs, when administered concurrently at two sites, be given in a constant dose ratio.

Animals↗
PubMed1991

Drug interactions software programs.

The value of a computer application is usually dependent on the program's ability to store, rearrange, and retrieve information in a format that is useful to the computer user. Based on a list of drugs (input), drug interactions programs search their databases for possible interactions between each drug pair in the input list. An output list specifies pairs of drugs that potentially interact. Since each program uses its own information database, results may differ slightly among programs. In general, however, the screen menus, appearance of the programs, and the "extras" included in each program differ more than the programs' drug interactions output. The software programs tested are all easy to use. Each of the three drug interactions programs reviewed has unique features that may appeal to certain users. The Medical Letter Drug Interactions Program is the least costly and provides the most academic output, allowing users to view citations for each specific interaction. The PDR Drug Interactions and Side Effects software includes a side effects index that allows either searching of a drug, which produces a list of the drug's side effects, or searching of a specific side effect, which produces a list of drugs associated with that side effect. Drug Interactions III is the only program that allows users to add drug interactions to its database.

Databases, Factual↗