Drug Repurposing Based on Drug–Drug Interaction

Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug–drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10 835 drug–drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug–drug interaction data.     

Predicting Drug–Drug Interactions: An FDA Perspective

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity’s drug–drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.     

Indolealkylamines: Biotransformations and Potential Drug–Drug Interactions

Indolealkylamine (IAA) drugs are 5-hydroxytryptamine (5-HT or serotonin) analogs that mainly act on the serotonin system. Some IAAs are clinically utilized for antimigraine therapy, whereas other substances are notable as drugs of abuse. In the clinical evaluation of antimigraine triptan drugs, studies on their biotransformations and pharmacokinetics would facilitate the understanding and prevention of unwanted drug-drug interactions (DDIs). A stable, principal metabolite of an IAA drug of abuse could serve as a useful biomarker in assessing intoxication of the IAA substance. Studies on the metabolism of IAA drugs of abuse including lysergic acid amides, tryptamine derivatives and beta-carbolines are therefore emerging. An important role for polymorphic cytochrome P450 2D6 (CYP2D6) in the metabolism of IAA drugs of abuse has been revealed by recent studies, suggesting that variations in IAA metabolism, pharmaco- or toxicokinetics and dynamics can arise from distinct CYP2D6 status, and CYP2D6 polymorphism may represent an additional risk factor in the use of these IAA drugs. Furthermore, DDIs with IAA agents could occur additively at the pharmaco/toxicokinetic and dynamic levels, leading to severe or even fatal serotonin toxicity. In this review, the metabolism and potential DDIs of these therapeutic and abused IAA drugs are described.     

An In Silico Transwell Device for the Study of Drug Transport and Drug–Drug Interactions

PURPOSE: Validate and exemplify a discrete, componentized, in silico, transwell device (ISTD) capable of mimicking the in vitro passive transport properties of compounds through cell monolayers. Verify its use for studying drug-drug interactions. METHODS: We used the synthetic modeling method. Specialized software components represented spatial and functional features including cell components, semi-porous tight junctions, and metabolizing enzymes. Mobile components represented drugs. Experiments were conducted and analyzed as done in vitro. RESULTS: Verification experiments provided data analogous to those in the literature. ISTD parameters were tuned to simulate and match in vitro urea transport data; the objects representing tight junction (effective radius of 6.66 A) occupied 0.066% of the surface area. That ISTD was then tuned to simulate pH-dependent, in vitro alfentanil transport properties. The resulting ISTD predicted the passive transport properties of 14 additional compounds, individually and all together in one in silico experiment. The function of a two-site enzymatic component was cross-validated with a kinetic model and then experimentally validated against in vitro benzyloxyresorufin metabolism data. Those components were used to exemplify drug-drug interaction studies. CONCLUSIONS: The ISTD is an example of a new class of simulation models capable of realistically representing complex drug transport and drug-drug interaction phenomena.     

Microdosing Cocktail Assay Development for Drug–Drug Interaction Studies

Methodology for analysis of a microdosing drug cocktail designed to evaluate the contribution of drug transporters and drug metabolizing enzymes to disposition was developed using liquid chromatography–mass spectrometry–based detection. Fast and sensitive methods were developed and qualified for the quantification of statins (pitavastatin, pitavastain lactone, rosuvastatin, atorvastatin, 2-hydroxy, and 4-hydroxy atorvastatin), midazolam, and dabigatran in human plasma. Chromatographic separation was accomplished using reversed-phase liquid chromatography or hydrophilic interaction liquid chromatography with gradient elution and detection by tandem mass spectrometry in the positive ionization mode using electrospray ionization. The lower limit of quantitation (LLOQ) for the statins assay was 1 pg/mL for the 6 analytes with a linear range from 1 to 1000 pg/mL processing 250 μL plasma sample. The midazolam assay LLOQ was 0.5 pg/mL with a linear range of 0.5 to 1000 pg/mL. For the dabigatran assay, the LLOQ was 10 pg/mL with a linear range of 10 to 5000 pg/mL processing 100 μL plasma sample. The intraday and interday precision and accuracy of the assays were within acceptable ranges, and the assays were successfully applied to support a study where a microdose cocktail was dosed to healthy human subjects for simultaneous assessment of clinical drug-drug interactions mediated by major drug transporters and CYP3A.     

Drug–phytochemical interactions

Changes in dietary habits favouring diets rich in fruits and vegetables, and a meteoric rise in the consumption of dietary supplements and herbal products have substantially increased human exposure to phytochemicals. It is, therefore, not surprising that diet and herbal remedies can modulate drug-metabolising enzyme systems, such as cytochromes P450, leading to clinically relevant drug-phytochemical interactions. Phytochemicals have the potential to both elevate and suppress cytochrome P450 activity. Such effects are more likely to occur in the intestine, where high concentrations of phytochemicals may be achieved, and alteration in cytochrome P450 activity will influence, in particular, the fate of drugs that are subject to extensive first-pass metabolism as a result of intestinal cytochrome P450-mediated biotransformation. Moreover, it is becoming increasingly apparent that phytochemicals can also influence the pharmacological activity of drugs by modifying their absorption characteristics through interaction with drug transporters. Clearly, phytochemicals have the potential to alter the effectiveness of drugs, either impairing or exaggerating their pharmacological activity.     

Drug Information Systems,Early Warning,and New Drug Trends: Can Drug Monitoring Systems Become More Sensitive to Emerging Trends in Drug Consumption?

Drug Information Systems (DIS) are called upon to provide an early warning of emerging trends in drug use. However, little theoretical attention is directed toward exploring conceptual issues in this area. In this paper a typology of existing DIS is offered. Among the features that distinguish DIS are their structure (human network or organization systems) and the range of information sources used. Indicators of drug use can be placed on a continuum of sensitivity ranging from leading edge indicators to lagged indicators. Sensitivity implies volatility as sensitive indicators also react to fluctuations that do not become trends. DIS conventionally are largely reliant upon lagged indicators. What is required are DIS that combine a critical information processing function with the ongoing systematic collection of data from a range of data sources. [Translations are provided in the International Abstracts Section of this issue.]     

Drug–Drug Interaction Studies: Regulatory Guidance and An Industry Perspective

Recently, the US Food and Drug Administration and European Medicines Agency have issued new guidance for industry on drug interaction studies, which outline comprehensive recommendations on a broad range of in vitro and in vivo studies to evaluate drug–drug interaction (DDI) potential. This paper aims to provide an overview of these new recommendations and an in-depth scientifically based perspective on issues surrounding some of the recommended approaches in emerging areas, particularly, transporters and complex DDIs. We present a number of theoretical considerations and several case examples to demonstrate complexities in applying (1) the proposed transporter decision trees and associated criteria for studying a broad spectrum of transporters to derive actionable information and (2) the recommended model-based approaches at an early stage of drug development to prospectively predict DDIs involving time-dependent inhibition and mixed inhibition/induction of drug metabolizing enzymes. We hope to convey the need for conducting DDI studies on a case-by-case basis using a holistic scientifically based interrogative approach and to communicate the need for additional research to fill in knowledge gaps in these areas where the science is rapidly evolving to better ensure the safety and efficacy of new therapeutic agents.     

Is low BMI Associated with Specific Drug Use Among Injecting Drug Users?

Body mass index (BMI) of a sample of people who regularly inject drugs (N = 781) was examined to gauge the impact of specific types of drug use. Cross-sectional interviews were undertaken in 2010 as part of a national monitoring program funded by the Australian Government. Latent class analysis identified three groups of drug users, with heroin users at 3.4 times the risk of being underweight compared with amphetamine users, and amphetamine users were at almost twice the odds of being obese compared with lower level morphine users. Nutrition should play a part in harm minimization.     

Drug–Drug Interaction Potential of Marketed Oncology Drugs: In Vitro Assessment of Time-Dependent Cytochrome P450 Inhibition, Reactive Metabolite Formation and Drug–Drug Interaction Prediction

Purpose

To evaluate 26 marketed oncology drugs for time-dependent inhibition (TDI) of cytochrome P450 (CYP) enzymes. Evaluate TDI-positive drugs for potential to generate reactive intermediates. Assess clinical drug–drug interaction (DDI) risk using static mechanistic models.

Methods

Human liver microsomes and CYP-specific probes were used to assess TDI in a dilution shift assay followed by generation of K_I and k_inact. Reactive metabolite trapping studies were performed with stable label probes. Static mechanistic model was used to predict DDI risk using a 1.25-fold AUC increase as a cut-off for positive DDI.

Results

Negative TDI across CYPs was observed for 13/26 drugs; the rest were time-dependent inhibitors of, predominantly, CYP3A. The k_inact/K_I ratios for 11 kinase inhibitors ranged from 0.7 to 42.2 ml/min/μmol. Stable label trapping agent–drug conjugates were observed for ten kinase inhibitors. DDI predictions gave no false negatives, one true negative, four false positives and three true positives. The magnitude of DDI was overestimated irrespective of the inhibitor concentration selected.

Conclusions

13/26 oncology drugs investigated showed TDI potential towards CYP3A, formation of reactive metabolites was also observed. An industry standard static mechanistic model gave no false negative predictions but did not capture the modest clinical DDI potential of kinase inhibitors.     

Relationships between Inmates’ Past Drug Practices and Current Drug Knowledge and Attitudes

A questionnaire used in the measurement of past drug practices and current drug knowledge and attitudes was completed by 200 inmates. Data analysis provided an examination of relationships between the variables. Significant positive correlations were recorded between drug knowledge and drug practices, and between drug knowledge and drug attitude scores. However, a significant negative relationship was noted between drug practices and drug attitude scores. Additional findings are presented regarding inmates’ demographic characteristics and drug variable measures. Implications are discussed for the development of drug education programs in jails and prisons.     

Pharmacokinetic Drug–Drug Interaction of the Novel Anticancer Agent E7070 and Acenocoumarol

E7070 is a novel sulfonamide anticancer agent that arrests cancer cells at the G1/S boundary of the cell cycle. Three patients receiving chronic therapy with the oral anticoagulant acenocoumarol experienced bleeding and/or a prolonged prothrombin time after treatment with E7070 at a dose of 700mg/m² given as a 1-h infusion. In vitro studies have shown that E7070 has the potential to inhibit several cytochrome P450 (CYP)-enzymes, including CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. The major enzyme involved in the metabolism of acenocoumarol in man is CYP2C9. This study was performed to investigate the interaction between E7070 and acenocoumarol.Blood samples were obtained from two patients receiving daily oral maintenance treatment with acenocoumarol both prior to and following treatment with E7070. In addition, we incubated acenocoumarol enantiomers with pooled human microsomes with and without E7070 and measured the in vitro plasma protein binding of acenocoumarol after incubation with E7070. Pharmacokinetic parameters of acenocoumarol were calculated by noncompartmental analysis and revealed that in both patients the area under the concentration–time curve up to 24h after the acenocoumarol administration was higher following E7070 (2.56 and 1.58h*mol/L) compared to the systemic exposure in the absence of E7070 (1.87 and 1.23h*mol/l). The formation of acenocoumarol metabolites was retarded by E7070 at already low concentrations (2.1M). The plasma protein binding of acenocoumarol was reduced at higher concentrations of E7070 (259M).These results indicate that E7070 may primarily interact with acenocoumarol by reducing its systemic clearance. Displacement of acenocoumarol's plasma protein binding by E7070 may also occur but to a minor extent. In the absence of careful monitoring this drug–drug interaction may result in hypoprothrombinemia and a hemorrhagic tendency.     

Drug–drug interaction with statins

3-hydroxy-3-methyl-glutaryl (HMG)-CoA reductase inhibitors (the so-called statins: atorvastatin, fluvastatin, pravastatin, lovastatin, rosuvastatin and simvastatin) are a well-established class of drugs in the treatment of hypercholesterolemia. Statin monotherapy is generally well tolerated, with a low frequency of adverse events. The most important adverse effects associated with statins are myopathy and an asymptomatic increase in hepatic transaminases, both of which occur infrequently. Since statins are prescribed on a long-term basis, possible interactions with other drugs deserve particular attention, as many patients will typically receive pharmacological therapy for concomitant conditions during the course of statin treatment. Moreover, a combination of therapy between statins and other classes of lipid-lowering agents (e.g., ezetimibe, fibrates, resins and nicotinic acid) is recommended for some patients by current guidelines. Therefore, the potential for drug–drug interactions emerges as a relevant factor in determining the safety profile of statins. This review summarizes the pharmacokinetic properties of statins and emphasizes their clinically relevant drug interactions.     

Drug regulations and GCP in China

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China Food Drug Administration征订启事

《中国食品药品监管》杂志是国家食品药品监督管理局的机关刊物,由国家食品药品监督管理局主管,中国医药报社主办。本刊自2011年1月起全面改版,秉承全心全意为食品药品监管系统服务的宗旨