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1
Rekka, Eleni A., Panos N. Kourounakis, and Maria Pantelidou. "Xenobiotic Metabolising Enzymes: Impact on Pathologic Conditions, Drug Interactions and Drug Design." Current Topics in Medicinal Chemistry 19, no. 4 (April 11, 2019): 276–91. http://dx.doi.org/10.2174/1568026619666190129122727.
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Background: The biotransformation of xenobiotics is a homeostatic defensive response of the body against bioactive invaders. Xenobiotic metabolizing enzymes, important for the metabolism, elimination and detoxification of exogenous agents, are found in most tissues and organs and are distinguished into phase I and phase II enzymes, as well as phase III transporters. The cytochrome P450 superfamily of enzymes plays a major role in the biotransformation of most xenobiotics as well as in the metabolism of important endogenous substrates such as steroids and fatty acids. The activity and the potential toxicity of numerous drugs are strongly influenced by their biotransformation, mainly accomplished by the cytochrome P450 enzymes, one of the most versatile enzyme systems. Objective: In this review, considering the importance of drug metabolising enzymes in health and disease, some of our previous research results are presented, which, combined with newer findings, may assist in the elucidation of xenobiotic metabolism and in the development of more efficient drugs. Conclusion: Study of drug metabolism is of major importance for the development of drugs and provides insight into the control of human health. This review is an effort towards this direction and may find useful applications in related medical interventions or help in the development of more efficient drugs.
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Anderson, Gail D. "A Mechanistic Approach to Antiepileptic Drug Interactions." Annals of Pharmacotherapy 32, no. 5 (May 1998): 554–63. http://dx.doi.org/10.1345/aph.17332.
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OBJECTIVE: To describe the primary types of antiepileptic drug (AED) interactions by using a mechanistic approach. DATA SOURCES: A literature search was performed using MEDLINE and bibliographies of recent review articles and published abstracts. DISCUSSION: AEDs are associated with a wide range of drug interactions, including hepatic enzyme induction and inhibition and protein-binding displacement. Hepatic induction by AEDs affects the metabolism of a limited number of drugs with low therapeutic indices. Anticipation of induction interactions and careful clinical monitoring may alleviate potential problems. Most commonly used AEDs are eliminated through hepatic metabolism catalyzed by the cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT) enzymes. Phenytoin, phenobarbital, and carbamazepine induce CYP and UGT enzymes. Lamotrigine is a weak inducer of UGT. Valproate is a broad-spectrum inhibitor of UGT enzymes, epoxide hydrolase, and CYP2C enzymes. Felbamate induces CYP3A4, but inhibits CYP2C19 substrates. Topiramate inhibits only CYP2C19 substrates. Ethosuximide, gabapentin, tiagabine, and vigabatrin are neither inducers nor inhibitors of drug metabolism. Hepatic enzyme inhibition usually occurs because of competition at the enzyme site. Knowledge of the specific metabolic enzymes involved in the metabolism of AEDs allows clinicians to predict potential interactions. CONCLUSIONS: By understanding the mechanisms of drug interactions, the pharmacist can play a key role in patient care by anticipating and preventing AED drug interactions.
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Klomp, Florian, Christoph Wenzel, Marek Drozdzik, and Stefan Oswald. "Drug–Drug Interactions Involving Intestinal and Hepatic CYP1A Enzymes." Pharmaceutics 12, no. 12 (December 11, 2020): 1201. http://dx.doi.org/10.3390/pharmaceutics12121201.
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Cytochrome P450 (CYP) 1A enzymes are considerably expressed in the human intestine and liver and involved in the biotransformation of about 10% of marketed drugs. Despite this doubtless clinical relevance, CYP1A1 and CYP1A2 are still somewhat underestimated in terms of unwanted side effects and drug–drug interactions of their respective substrates. In contrast to this, many frequently prescribed drugs that are subjected to extensive CYP1A-mediated metabolism show a narrow therapeutic index and serious adverse drug reactions. Consequently, those drugs are vulnerable to any kind of inhibition or induction in the expression and function of CYP1A. However, available in vitro data are not necessarily predictive for the occurrence of clinically relevant drug–drug interactions. Thus, this review aims to provide an up-to-date summary on the expression, regulation, function, and drug–drug interactions of CYP1A enzymes in humans.
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Thomas, Roger E. "Optimising Seniors’ Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug–Drug and Drug–Drug–Gene Interactions." Journal of Personalized Medicine 10, no. 3 (August 11, 2020): 84. http://dx.doi.org/10.3390/jpm10030084.
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Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.
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Xie, Zhang, Zhang, and Yuan. "Metabolism, Transport and Drug–Drug Interactions of Silymarin." Molecules 24, no. 20 (October 14, 2019): 3693. http://dx.doi.org/10.3390/molecules24203693.
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Silymarin, the extract of milk thistle, and its major active flavonolignan silybin, are common products widely used in the phytotherapy of liver diseases. They also have promising effects in protecting the pancreas, kidney, myocardium, and the central nervous system. However, inconsistent results are noted in the different clinical studies due to the low bioavailability of silymarin. Extensive studies were conducted to explore the metabolism and transport of silymarin/silybin as well as the impact of its consumption on the pharmacokinetics of other clinical drugs. Here, we aimed to summarize and highlight the current knowledge of the metabolism and transport of silymarin. It was concluded that the major efflux transporters of silybin are multidrug resistance-associated protein (MRP2) and breast cancer resistance protein (BCRP) based on results from the transporter-overexpressing cell lines and MRP2-deficient (TR-) rats. Nevertheless, compounds that inhibit the efflux transporters MRP2 and BCRP can enhance the absorption and activity of silybin. Although silymarin does inhibit certain drug-metabolizing enzymes and drug transporters, such effects are unlikely to manifest in clinical settings. Overall, silymarin is a safe and well-tolerated phytomedicine.
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Rao Gajula, Siva Nageswara, Gangireddy Navitha Reddy, Dannarm Srinivas Reddy, and Rajesh Sonti. "Pharmacokinetic drug–drug interactions: an insight into recent US FDA-approved drugs for prostate cancer." Bioanalysis 12, no. 22 (November 2020): 1647–64. http://dx.doi.org/10.4155/bio-2020-0242.
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Pharmacokinetic drug–drug interaction is a significant safety and efficiency concern as it results in considerable concentration changes. Drug–drug interactions are a substantial concern in anticancer drugs that possess a narrow therapeutic index. These interactions remain as the principal regulatory obstacle that can lead to termination in the preclinical stage, restrictions in the prescription, dosage adjustments or withdrawal of the drugs from the market. Drug metabolizing enzymes or transporters mediate the majority of clinically relevant drug interactions. Cancer diagnosed aged patients use multiple medications and are more prone to significant drug–drug interactions. This review provides detailed information on clinically relevant drug–drug interactions resulting from drug metabolism by enzymes and transporters with a particular emphasis on recent FDA approved antiprostate cancer drugs.
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Bachmann, Kenneth A., and Jeffrey D. Lewis. "Predicting Inhibitory Drug—Drug Interactions and Evaluating Drug Interaction Reports Using Inhibition Constants." Annals of Pharmacotherapy 39, no. 6 (June 2005): 1064–72. http://dx.doi.org/10.1345/aph.1e508.
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OBJECTIVE: To review the use of inhibitory constants (Ki) determined from in vitro experiments in the prediction of the significance of inhibitory drug—drug interactions (DDIs). DATA SOURCES: Searches of MEDLINE (1966—August 2004) and manual review of journals, conference proceedings, reference textbooks, and Web sites were performed using the key search terms cytochrome P450, drug—drug interaction, inhibition constant, and Ki. STUDY SELECTION AND DATA EXTRACTION: All articles identified from the data sources were evaluated, and information deemed relevant was included for this review. DATA SYNTHESIS: The cytochrome P450 isoenzymes factor prominently in the explanation of numerous DDIs. Although the regulation of these enzymes by one drug can affect the pharmacokinetics of other drugs, the consequences may not necessarily be significant either in terms of pharmacokinetic or clinical outcomes. Yet, many DDI monographs originate as unconfirmed case reports that implicate the influence of one drug on the CYP-mediated metabolism of another, and these often uncorroborated mechanisms can eventually become regarded as dogma. One consequence of this process is the overprediction of potentially important DDIs. The pharmaceutical industry, Food and Drug Administration, and pharmaceutical scientists have developed a strategy for predicting the significance of inhibitory DDIs at the earliest possible stages of drug development based on a new chemical entity's Ki value, determined in vitro. CONCLUSIONS: We suggest that the use of Ki values of drugs purported to behave as CYP inhibitors be incorporated in the assessment of case reports that ascribe DDIs to inhibition of metabolism of one drug by another.
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Chadwick, Ben, Derek G. Waller, and J. Guy Edwards. "Potentially hazardous drug interactions with psychotropics." Advances in Psychiatric Treatment 11, no. 6 (November 2005): 440–49. http://dx.doi.org/10.1192/apt.11.6.440.
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Of the many interactions with psychotropic drugs, a minority are potentially hazardous. Most interactions are pharmacodynamic, resulting from augmented or antagonistic actions at a receptor or from different mechanisms in the same tissue. Most important pharmacokinetic interactions are due to effects on metabolism or renal excretion. The major enzymes involved in metabolism belong to the cytochrome P450 (CYP) system. Genetic variation in the CYP system produces people who are ‘poor’, ‘extensive’ or ‘ultra-rapid’ drug metabolisers. Hazardous interactions more often result from enzyme inhibition, but the probability of interaction depends on the initial level of enzyme activity and the availability of alternative metabolic routes for elimination of the drug. There is currently interest in interactions involving uridine diphosphate glucuronosyltransferases and the P-glycoprotein cell transport system, but their importance for psychotropics has yet to be defined. The most serious interactions with psychotropics result in profound sedation, central nervous system toxicity, large changes in blood pressure, ventricular arrhythmias, an increased risk of dangerous side-effects or a decreased therapeutic effect of one of the interacting drugs.
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Nemeroff, Charles B., Sheldon H. Preskorn, and C. Lindsay DeVane. "Antidepressant Drug-Drug Interactions: Clinical Relevance and Risk Management." CNS Spectrums 12, S7 (2007): 1–16. http://dx.doi.org/10.1017/s1092852900026043.
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AbstractMultiple medication use is a common phenomenon, especially in patients with comorbid conditions and those treated with psychiatric drugs such as antidepressants. Combination treatment may result in potentially harmful drug-drug interactions (DDIs). Results of DDIs range from nuisance side effects to serious adverse consequences. DDIs may also result in improved efficacy. Augmentation strategies, for example, are intentional therapeutic DDIs. Pharmacokinetic DDIs occur when a second drug alters the absorption, distribution, metabolism, or clearance of the first drug. Research has concentrated on the relative effects of antidepressants on cytochrome P450 enzymes and, more recently, on drug transporters as potential mediators of clinically important pharmacokinetic DDIs. The most common, clinically relevant pharmacokinetic DDIs involve alteration in oxidative drug metabolism. Pharmacodynamic DDIs occur when the effects of a second drug quantitatively or qualitatively alters those of the first drug. Pharmacodynamic DDIs are not typically studied in vivo because of the potential for a serious adverse effect. All antidepressants can interact pharmacodynamically with certain other drugs. The risk of harmful DDIs can be reduced by recognizing variables that affect dose-concentration-effect relationships. It is important for physicians to weigh the risks and benefits of potential DDIs against the risks that accompany timid or ineffective disease treatment.
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Yin, Jiayi, Fengcheng Li, Ying Zhou, Minjie Mou, Yinjing Lu, Kangli Chen, Jia Xue, et al. "INTEDE: interactome of drug-metabolizing enzymes." Nucleic Acids Research 49, no. D1 (October 12, 2020): D1233—D1243. http://dx.doi.org/10.1093/nar/gkaa755.
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Abstract Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome–DME interaction (MICBIO), xenobiotics–DME interaction (XEOTIC) and host protein–DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/
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Kumar, Neeraj, Heerak Chugh, Damini Sood, Snigdha Singh, Aarushi Singh, Amar Deep Awasthi, Ravi Tomar, Vartika Tomar, and Ramesh Chandra. "Biology of Heme: Drug Interactions and Adverse Drug Reactions with CYP450." Current Topics in Medicinal Chemistry 18, no. 23 (January 10, 2019): 2042–55. http://dx.doi.org/10.2174/1568026619666181129124638.
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Heme is central to functions of many biologically important enzymes (hemoproteins). It is an assembly of four porphyrin rings joined through methylene bridges with a central Fe (II). Heme is present in all cells, and its synthesis and degradation balance its amount in the cell. The deregulations of heme networks and incorporation in hemoproteins lead to pathogenic state. This article addresses the detailed structure, biosynthesis, degradation, and transportation associated afflictions to heme. The article is followed by its roles in various diseased conditions where it is produced mainly as the cause of increased hemolysis. It manifests the symptoms in diseases as it is a pro-oxidant, pro-inflammatory and pro-hemolytic agent. We have also discussed the genetic defects that tampered with the biosynthesis, degradation, and transportation of heme. In addition, a brief about the largest hemoprotein group of enzymes- Cytochrome P450 (CYP450) has been discussed with its roles in drug metabolism.
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Pang, K. Sandy, H. Benson Peng, and Keumhan Noh. "The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug–Drug Interactions." Pharmaceutics 12, no. 4 (April 1, 2020): 312. http://dx.doi.org/10.3390/pharmaceutics12040312.
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The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (FI) and liver (FH) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).
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Pisani, F., E. Perucca, and R. Di Perri. "Clinically Relevant Anti-Epileptic Drug Interactions." Journal of International Medical Research 18, no. 1 (January 1990): 1–15. http://dx.doi.org/10.1177/030006059001800102.
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Anti-epileptic drugs frequently interact due to pharmacokinetic features (induction or inhibition of metabolism, production of active metabolites, low therapeutic indices) and the need for prolonged treatment with possible addition of other drugs to treat concomitant diseases. The most important pharmacokinetic interactions are those that inhibit phenytoin, carbamazepine and phenobarbitone metabolism and thus increase their toxicity. Drugs inhibiting metabolism include antibiotic macrolides, chloramphenicol, isoniazide, some sulphonamides, propoxyphene, cimetidine, valproic acid and sulthiame. Anti-epileptic drugs can induce hepatic microsomal enzymes and, therefore, may increase metabolism of corticosteroids, oral contraceptives, oral anticoagulants, cardiovascular agents, antibiotics, chemotherapeutic agents, psychotropic drugs and non-opiate analgesics, thereby reducing their efficacy. Advantageous pharmacodynamic interactions include synergism of ethosuximide plus valproic acid and of carbamazepine plus valproic acid. A pharmacodynamic mechanism may be responsible for the reduced sensitivity of chronically treated epileptics to some neuromuscular blockers.
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Bahri, Hafid, Abdelkader Douaoui, Moufida Gharbi, and Djamila Amroun. "Update on the drug-food interactions." Batna Journal of Medical Sciences (BJMS) 1, no. 2 (December 31, 2014): 100–106. http://dx.doi.org/10.48087/bjmstf.2014.1211.
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Drug interactions are a major public health problem, which partly attributed to some 10,000 deaths/year in Canada. Besides the interactions between two drugs, drug interactions are also due to the effect of other substances such as foods or nutrients. The drug-food interaction will be pharmacokinetic (affecting the absorption, distribution, metabolism, and elimination) or pharmacodynamic interaction. It is in the intestine that food may have the greatest impact with mainly a change in the amount of drugs absorbed that may be clinically significant for some drugs with narrow therapeutic index (cyclosporine, phenytoin, theophylline, etc.). The absorption of the drug in the presence of food will be determined by the particular physicochemical properties of the drug but also by the impact of food on one of the parameters determining the absorption such as: modified gastric acidity and emptying, the fat content of the food, the use of common transport between the drug and nutrients, chemical reactions between elements and drugs. Fasting situations or malnutrition can affect the distribution of drugs by increasing the free drug fraction, involving sometimes the risk of overdose. Diet affects drug metabolism by changing the activity of cytochrome P450. Most often is described the increase by grapefruit juice (enzyme inhibitor) of plasma concentrations of some drugs (cyclosporine, some statins, and calcium antagonists). Other foods (garlic, smoked meats and fish, caffeine) may increase metabolism. Diet can influence two stages of renal clearance (glomerular filtration - tubular reabsorption) by modifying urine pH or renal clearance. Pharmacodynamic interactions are also monitored, especially foods rich in vitamin k or tyramine with antivitamins K or MAOIs. Finally, health professionals must mobilize against these interactions, including through patient information.
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Petric, Zvonimir, Irena Žuntar, Predrag Putnik, and Danijela Bursać Kovačević. "Food–Drug Interactions with Fruit Juices." Foods 10, no. 1 (December 24, 2020): 33. http://dx.doi.org/10.3390/foods10010033.
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Fruit juices contain a large number of phytochemicals that, in combination with certain drugs, can cause food–drug interactions that can be clinically significant and lead to adverse events. The mechanisms behind such interactions are in most cases related to phytochemical interference with the activity of cytochrome P450 metabolizing enzymes (CYPs) or drug transporters. Moreover, alterations in their activity can have a clinical relevance if systemic exposure to the drug is decreased or increased, meaning that the pharmacological drug effects are suboptimal, or the drug will cause toxicity. In general, the common pharmacokinetic parameters found to be altered in food–drug interactions regarding fruit juices are the area under the concentration–time curve, bioavailability, and maximum plasma concentration. In most cases, the results from the drug interaction studies with fruit juices provide only limited information due to the small number of subjects, which are also healthy volunteers. Moreover, drug interactions with fruit juices are challenging to predict due to the unknown amounts of the specific phytochemicals responsible for the interaction, as well as due to the inter-individual variability of drug metabolism, among others. Therefore, this work aims to raise awareness about possible pharmacological interactions with fruit juices.
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Watkins, Vish S., Ron E. Polk, and Jennifer L. Stotka. "Drug Interactions of Macrolides: Emphasis on Dirithromycin." Annals of Pharmacotherapy 31, no. 3 (March 1997): 349–56. http://dx.doi.org/10.1177/106002809703100314.
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Objective To describe the drug interactions of dirithromycin, a new macrolide, and to compare them with those of other macrolides. Data Sources A literature search was performed using MEDLINE to identify articles published between January 1980 and July 1995 concerning the drug interactions of macrolides. Published abstracts were also examined. All studies using dirithromycin were performed under the sponsorship of Eli Lilly and Company. Data Synthesis Erythromycin, the first macrolide discovered, is metabolized by the cytochrome P450 enzyme system. By decreasing their metabolism, erythromycin can interact with other drugs metabolized by the cytochrome P450 enzymes. The lack of such interactions would be a desirable feature in a newer macrolide. We describe studies performed to detect any interactions of dirithromycin with cyclosporine, theophylline, terfenadine, warfarin, and ethinyl estradiol. The studies showed that dirithromycin, like azithromycin, is much less likely to cause the interactions detected with clarithromycin and erythromycin. A review of the literature showed differences among macrolides in their abilities to inhibit cytochrome P450 enzymes and, thus, to cause drug–drug interactions. Erythromycin and clarithromycin inhibit cytochrome P450 enzymes, and have been implicated in clinically significant interactions. Azithromycin and dirithromycin neither inhibit cytochrome P450 enzymes nor are implicated in clinically significant drug–drug interactions. Conclusions Dirithromycin, a new macrolide, does not inhibit the cytochrome P450 enzyme system. The concomitant use of dirithromycin with cyclosporine, theophylline, terfenadine, warfarin, or ethinyl estradiol was studied in pharmacokinetic and pharmacodynamic studies. In vitro, dirithromycin did not bind cytochrome P450. In healthy subjects, erythromycin increases the clearance of cyclosporine by 51%, whereas dirithromycin causes no significant changes in the pharmacokinetics of cyclosporine. In kidney transplant recipients, administration of dirithromycin was associated with a significant (p < 0.003) decrease of 17.4% in the clearance of cyclosporine. In patients taking low-dose estradiol, the administration of dirithromycin caused a significant (p < 0.03) increase of 9.9% in the clearance of ethinyl estradiol; escape ovulation did not occur. Unlike erythromycin and clarithromycin, dirithromycin had no significant effects on the pharmacokinetics of theophylline, terfenadine, or warfarin. The alterations typical of drug interactions that are based on inhibition of the cytochrome P450 system occurring with erythromycin and clarithromycin were not observed with dirithromycin.
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Vaishnavi S, Balaji S, Ramesh M, Mothi S N, Swamy V H T, and Srirama B R. "Drug – Drug Interactions Between Newer Anti- Retroviral Drugs And Anti Epileptics - A Review." International Journal of Research in Pharmaceutical Sciences 11, no. 3 (July 6, 2020): 2963–67. http://dx.doi.org/10.26452/ijrps.v11i3.2386.
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Drug – Drug Interactions (DDIs) are the leading cause of drug toxicity and emergence of drug resistance, ultimately leading to increased burden in People Living with Human Immunodeficiency Virus (PLHIV). On an average 55 % of people on Anti Retroviral Therapy (ARVs) are co-administered with Anti Epileptic Drugs (AEDs). The introduction of newer anti-retroviral drugs such as dolutegravir, bictegravir, emtricitabine, doravirine are proven to have less side effects, high tolerability and effective decrease in the viral load, but the risk of DDIs still stands to be high. This review briefly describes about the pharmacokinetic properties of dolutegravir, bictegravir, emtricitabine, doravirine, mechanism of interaction between the above mentioned ARVs and AEDs, effect of DDIs on ARVs, effect of DDIs on interacting AEDs, outcome of DDIs and possible management of DDIs. The pharmacokinetic type of DDIs was observed between the ARVs and AEDs. The majority of DDIs were found affecting the metabolism and the absorption of the drugs. UGT1A1, CYP 3A are the two important classes of metabolic enzymes involved in the DDIs and p- glycoprotein (P-gp) is the transporter involved in the DDIs affecting the absorption. Significant interactions have been found in between the above mentioned newer ARV’s with carbamazepine, oxcarbazepine, phenytoin and phenobarbitol.
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Kocis, Paul T., and Kent E. Vrana. "Delta-9-Tetrahydrocannabinol and Cannabidiol Drug-Drug Interactions." Medical Cannabis and Cannabinoids 3, no. 1 (July 7, 2020): 61–73. http://dx.doi.org/10.1159/000507998.
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Although prescribing information (PI) is often the initial source of information when identifying potential drug-drug interactions, it may only provide a limited number of exemplars or only reference a class of medications without providing any specific medication examples. In the case of medical cannabis and medicinal cannabinoids, this is further complicated by the fact that the increased therapeutic use of marijuana extracts and cannabidiol oil will not have regulatory agency approved PI. The objective of this study was to provide a detailed and comprehensive drug-drug interaction list that is aligned with cannabinoid manufacturer PI. The cannabinoid drug-drug interaction information is listed in this article and online supplementary material as a PRECIPITANT (cannabinoid) medication that either INHIBITS/INDUCES the metabolism or competes for the same SUBSTRATE target (metabolic enzyme) of an OBJECT (OTHER) medication. In addition to a comprehensive list of drug-drug interactions, we also provide a list of 57 prescription medications displaying a narrow therapeutic index that are potentially impacted by concomitant cannabinoid use (whether through prescription use of cannabinoid medications or therapeutic/recreational use of cannabis and its extracts).
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Ahmane, Amel, Hocine Gacem, Karim Boulesbiaat, and Meriem Boullelli. "Pharmacokinetic interactions: from mechanisms to clinical relevance." Batna Journal of Medical Sciences (BJMS) 1, no. 2 (December 31, 2014): 85–95. http://dx.doi.org/10.48087/bjmstf.2014.1209.
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Among the various types of known drug interactions, those involving pharmacokinetic processes are more complex and dangerous. From digestive pH changes to plasma protein binding and induction or inhibition phenomena; current data used to define, with precision, the sites of interaction. The enzymes involved in metabolism, the transporters involved in tissue distribution and excretion of drugs, and nuclear receptors that regulate the expression of these enzymes and transporters are keys determinants that should be defined for each drug. The clinical relevance of a pharmacokinetic interaction is related to the magnitude of changes in drug concentrations and pharmacological properties of these. Good knowledge of the pharmacokinetic properties of drugs and the mechanisms involved in the genesis of these interactions is, then, needed to prevent and avoid theme.
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Remmel, Rory P., and Brian Burchell. "Validation and use of cloned, expressed human drug-metabolizing enzymes in heterologous cells for analysis of drug metabolism and drug-drug interactions." Biochemical Pharmacology 46, no. 4 (August 1993): 559–66. http://dx.doi.org/10.1016/0006-2952(93)90538-8.
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Urichuk, Liana, Trevor Prior, Serdar Dursun, and Glen Baker. "Metabolism of Atypical Antipsychotics: Involvement of Cytochrome P450 Enzymes and Relevance for Drug-Drug Interactions." Current Drug Metabolism 9, no. 5 (June 1, 2008): 410–18. http://dx.doi.org/10.2174/138920008784746373.
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Fuhr, Uwe, Chih-hsuan Hsin, Xia Li, Wafaâ Jabrane, and Fritz Sörgel. "Assessment of Pharmacokinetic Drug–Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited." Annual Review of Pharmacology and Toxicology 59, no. 1 (January 6, 2019): 507–36. http://dx.doi.org/10.1146/annurev-pharmtox-010818-021909.
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Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug–drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.
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Gómez-Lechón, María José, Teresa Donato, Xavier Ponsoda, and José V. Castell. "Human Hepatic Cell Cultures: In Vitro and In Vivo Drug Metabolism." Alternatives to Laboratory Animals 31, no. 3 (May 2003): 257–65. http://dx.doi.org/10.1177/026119290303100307.
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Drug metabolism is the major determinant of drug clearance, and the factor most frequently responsible for inter-individual differences in drug pharmacokinetics. The expression of drug metabolising enzymes shows significant interspecies differences, and variability among human individuals (polymorphic or inducible enzymes) makes the accurate prediction of the metabolism of a new compound in humans difficult. Several key issues need to be addressed at the early stages of drug development to improve drug candidate selection: a) how fast the compound will be metabolised; b) what metabolites will be formed (metabolic profile); c) which enzymes are involved and to what extent; and d) whether drug metabolism will be affected directly (drug-drug interactions) or indirectly (enzyme induction) by the administered compound. Drug metabolism studies are routinely performed in laboratory animals, but they are not sufficiently accurate to predict the metabolic profiles of drugs in humans. Many of these issues can now be addressed by the use of relevant human in vitro models, which speed up the selection of new candidate drugs. Human hepatocytes are the closest in vitro model to the human liver, and they are the only model which can produce a metabolic profile of a drug which is very similar to that found in vivo. However, the use of human hepatocytes is restricted, because limited access to suitable tissue samples prevents their use in high throughput screening systems. The pharmaceutical industry has made great efforts to develop fast and reliable in vitro models to overcome these drawbacks. Comparative studies on liver microsomes and cells from animal species, including humans, are very useful for demonstrating species differences in the metabolic profile of given drug candidates, and are of great value in the judicious and justifiable selection of animal species for later pharmacokinetic and toxicological studies. Cytochrome P450 (CYP)-engineered cells (or microsomes from CYP-engineered cells, for example, Supersomes™) have made the identification of the CYPs involved in the metabolism of a drug candidate more straightforward and much easier. However, the screening of compounds acting as potential CYP inducers can only be conducted in cellular systems fully capable of transcribing and translating CYP genes.
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Hakkola, Jukka, Janne Hukkanen, Miia Turpeinen, and Olavi Pelkonen. "Inhibition and induction of CYP enzymes in humans: an update." Archives of Toxicology 94, no. 11 (October 27, 2020): 3671–722. http://dx.doi.org/10.1007/s00204-020-02936-7.
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Abstract The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.
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Pakkir Maideen, Naina Mohamed, Gobinath Manavalan, and Kumar Balasubramanian. "Drug interactions of meglitinide antidiabetics involving CYP enzymes and OATP1B1 transporter." Therapeutic Advances in Endocrinology and Metabolism 9, no. 8 (April 6, 2018): 259–68. http://dx.doi.org/10.1177/2042018818767220.
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Meglitinides such as repaglinide and nateglinide are useful to treat type 2 diabetes patients who follow a flexible lifestyle. They are short-acting insulin secretagogues and are associated with less risk of hypoglycemia, weight gain and chronic hyperinsulinemia compared with sulfonylureas. Meglitinides are the substrates of cytochrome P450 (CYP) enzymes and organic anion transporting polypeptide 1B1 (OATP1B1 transporter) and the coadministration of the drugs affecting them will result in pharmacokinetic drug interactions. This article focuses on the drug interactions of meglitinides involving CYP enzymes and OATP1B1 transporter. To prevent the risk of hypoglycemic episodes, prescribers and pharmacists must be aware of the adverse drug interactions of meglitinides.
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D'Arcy, P. F. "Drug Interactions with Oral Contraceptives." Drug Intelligence & Clinical Pharmacy 20, no. 5 (May 1986): 353–62. http://dx.doi.org/10.1177/106002808602000504.
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In the very rare cases where a pregnancy occurs during oral contraceptive use, the blame is usually laid against the patient for having forgotten to take the pill. Evidence has started to accumulate to suggest that neither the patient nor the pill is at fault in some contraceptive failures. It may be because the patient is taking other medicines and these may be preventing the pill from suppressing ovulation. Most drug interactions reducing or negating contraceptive activity are due to concomitant use of drugs having microsomal enzyme-inducing activity (e.g., some antibiotics, especially rifampicin, and anticonvulsants, including phenobarbital, Phenytoin, and primidone. Other antibiotics (e.g., tetracycline) may also interact by interruption of the enterohepatic circulation of contraceptive steroids. Less well appreciated, oral contraceptive steroids may themselves modify the metabolism and pharmacological activity of various other drugs (e.g., anticoagulants, benzodiazepines, β-blockers, caffeine, corticosteroids, and tricyclic antidepressants); in this respect the oral contraceptives are acting as enzyme inhibitors. Contraceptive steroids may also interact with drugs that cause enzyme inhibition and this delays the metabolism of the hormonal agents. Interactions of this type would be expected to potentiate the action of the contraceptive steroids. It is suggested that the effects of such interaction might be presented in terms of increased incidence of side effects, including water retention, diabetogenic effects, hypertension, and an increased risk of thromboembolic disorders. The spectrum of interactions with oral contraceptives is presented in three tables.
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Rendic, Slobodan, and Frederick Peter Guengerich. "Metabolism and Interactions of Chloroquine and Hydroxychloroquine with Human Cytochrome P450 Enzymes and Drug Transporters." Current Drug Metabolism 21, no. 14 (December 30, 2020): 1127–35. http://dx.doi.org/10.2174/1389200221999201208211537.
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Background:: In clinical practice, chloroquine and hydroxychloroquine are often co-administered with other drugs in the treatment of malaria, chronic inflammatory diseases, and COVID-19. Therefore, their metabolic properties and the effects on the activity of cytochrome P450 (P450, CYP) enzymes and drug transporters should be considered when developing the most efficient treatments for patients. Methods:: Scientific literature on the interactions of chloroquine and hydroxychloroquine with human P450 enzymes and drug transporters, was searched using PUBMED.Gov (https://pubmed.ncbi.nlm.nih.gov/) and the ADME database (https://life-science.kyushu.fujitsu.com/admedb/). Results:: Chloroquine and hydroxychloroquine are metabolized by P450 1A2, 2C8, 2C19, 2D6, and 3A4/5 in vitro and by P450s 2C8 and 3A4/5 in vivo by N-deethylation. Chloroquine effectively inhibited P450 2D6 in vitro; however, in vivo inhibition was not apparent except in individuals with limited P450 2D6 activity. Chloroquine is both an inhibitor and inducer of the transporter MRP1 and is also a substrate of the Mate and MRP1 transport systems. Hydroxychloroquine also inhibited P450 2D6 and the transporter OATP1A2. Conclusions:: Chloroquine caused a statistically significant decrease in P450 2D6 activity in vitro and in vivo, also inhibiting its own metabolism by the enzyme. The inhibition indicates a potential for clinical drug-drug interactions when taken with other drugs that are predominant substrates of the P450 2D6. When chloroquine and hydroxychloroquine are used clinically with other drugs, substrates of P450 2D6 enzyme, attention should be given to substrate-specific metabolism by P450 2D6 alleles present in individuals taking the drugs.
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de Mendonça Lima, C. A. "Drug Interactions with Antidementia Drugs: Clinical Consequences." European Psychiatry 24, S1 (January 2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)70476-6.
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Alzheimer" disease (AD) is a major public health problem, and it is at the origin of a significant burden: 15% of direct costs in dementia are attributed to pharmacological treatment. Persons with dementia often have comorbidities and receive multiple medications. Both factors increase the risk of drug-drug interactions (DDIs) which can result in adverse drug reactions (ADRs). In a study, a total of 1058 spontaneous reports were identified that involved cholinesterase inhibitors (ChEIs) in the French Pharmacovigilance Database; 35.5% contained at least one DDI; 118 of them (31.4%) were the cause of ADRs. Pharmacodynamic interactions play a far greater role than pharmacokinetic interactions in the significance of DDIs. Some known interactions with ChEIs are:1.atropinic drugs aggravate cognitive disorders;2.combinations of ChEIs and antipsychotics are associated with an increased risk of extrapyramidal adverse effects;3.combining ChEIs with drugs that reduce the heart rate, depress cardiac conduction, or induce torsades de pointes increases the risk of arrhythmias and cardiac conduction disorders.Recent studies suggest that the therapeutic response in Alzheimer"s disease is genotype specific, depending on genes associated with AD pathogenesis and/or genes responsible for drug metabolism. APOEe4/e4 genotype carriers are the poorest responders to treatments. Some ChEIs are metabolized via CYP-related enzymes and can interact with other drugs that are substrates, inhibitors or inducers of the CYP system. Health professionals should be aware of the potential adverse effects of ChEIs, including the possible DDIs and antagonist effects with other drugs.
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Pai, Manjunath P., Danielle M. Graci, and Guy W. Amsden. "Macrolide Drug Interactions: An Update." Annals of Pharmacotherapy 34, no. 4 (April 2000): 495–513. http://dx.doi.org/10.1345/aph.19138.
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OBJECTIVE: To describe the current drug interaction profiles for the commonly used macrolides in the US and Europe, and to comment on the clinical impact of these interactions. DATA SOURCES: A MEDLINE search (1975–1998) was performed to identify all pertinent studies, review articles, and case reports. When appropriate information was not available in the literature, data were obtained from the product manufacturers. STUDY SELECTION: All available data were reviewed to provide an unbiased account of possible drug interactions. DATA EXTRACTION: Data for some of the interactions were not available from the literature, but were available from abstracts or company-supplied materials. Although the data were not always explicit, the best attempt was made to deliver pertinent information that clinical practitioners would need to formulate practice opinions. When more in-depth information was supplied in the form of a review or study report, a thorough explanation of pertinent methodology was supplied. DATA SYNTHESIS: Several clinically significant drug interactions have been identified since the approval of erythromycin. These interactions usually were related to the inhibition of the cytochrome P450 enzyme systems, which are responsible for the metabolism of many drugs. The decreased metabolism by the macrolides has in some instances resulted in potentially severe adverse events. The development and marketing of newer macrolides are hoped to improve the drug interaction profile associated with this class. However, this has produced variable success. Some of the newer macrolides demonstrated an interaction profile similar to that of erythromycin; others have shown improved profiles. The most success in avoiding drug interactions related to the inhibition of cytochrome P450 has been through the development of the azalide subclass, of which azithromycin is the first and only to be marketed. Azithromycin has not been demonstrated to inhibit the cytochrome P450 system in studies using a human liver microsome model, and to date has produced none of the classic drug interactions characteristic of the macrolides. CONCLUSIONS: Most of the available data regarding macrolide drug interactions are from studies in healthy volunteers and case reports. These data suggest that clarithromycin appears to have an interaction profile similar to that of erythromycin. Given this similarity, it is important to consider the interaction profile of clarithromycin when using erythromycin. This is especially necessary as funds for further studies of a medication available in generic form (e.g., erythromycin) are limited. Azithromycin has produced few clinically significant interactions with any agent cleared through the cytochrome P450 enzyme system. Although the available data are promising, the final test should come from studies conducted in patients who are taking potentially interacting compounds on a chronic basis.
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Nikolic, Bozana, and Miroslav Savic. "Hierarchy of evidence in interpretation of clinical significance of drug interactions." Medical review 65, no. 1-2 (2012): 45–49. http://dx.doi.org/10.2298/mpns1202045n.
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Introduction. Since drug interactions may result in serious adverse effects or failure of therapy, it is of huge importance that health professionals base their decisions about drug prescription, dispensing and administration on reliable research evidence, taking into account the hierarchy of data sources for evaluation. Clinical Significance of Potential Interactions - Information Sources. The sources of data regarding drug interactions are numerous, beginning with various drug reference books. However, they are far from uniformity in the way of choosing and presenting putative clinically relevant interactions. Clinical Significance of Potential Interactions - Interpretation of Information. The difficulties in interpretation of drug interactions are illustrated through the analysis of a published example involving assessment made by two different groups of health professionals. Systematic Evaluation of Drug-Drug Interaction. The potential for interactions is mainly investigated before marketing a drug. Generally, the in vitro, followed by in vivo studies are to be performed. The major metabolic pathways involved in the metabolism of a new molecular entity, as well as the potential of induction of human enzymes involved in drug metabolism are to be examined. In the field of interaction research it is possible to make use of the population pharmacokinetic studies as well as of the pharmacodynamic assessment, and also the postregistration monitoring of the reported adverse reactions and other literature data. Conclusion. In vitro and in vivo drug metabolism and transport studies should be conducted to elucidate the mechanisms and potential for drug-drug interactions. The assessment of their clinical significance should be based on well-defined and validated exposure-response data.
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Cott, Jerry M. "Herb-Drug Interactions: Focus on Pharmacokinetics." CNS Spectrums 6, no. 10 (October 2001): 827–32. http://dx.doi.org/10.1017/s1092852900001644.
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ABSTRACTRecent literature regarding drug-drug, herb-drug, and food-drug interactions must not be ignored; nor can they always be taken at face value. Studies have shown that St. John's wort (SJW)(Hypericum perforatum) can reduce plasma levels of indinavir, cyclosporin, digoxin, and possibly other drugs as well. Current knowledge regarding the metabolism of these medications suggests that the cytochrome P450 (CYP) drug metabolizing enzyme systems cannot account for all these effects. It has been reported that the P-glycoprotein (Pgp) transmembrane pump is also induced by SJW. Medications that are substrates of both CYP 3A4 a Pgp are of particular concern and may pose special interaction risks when combined with certain foods or botanical products such as SJW.
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Liu, Mou-Ze, Yue-Li Zhang, Mei-Zi Zeng, Fa-Zhong He, Zhi-Ying Luo, Jian-Quan Luo, Jia-Gen Wen, Xiao-Ping Chen, Hong-Hao Zhou, and Wei Zhang. "Pharmacogenomics and Herb-Drug Interactions: Merge of Future and Tradition." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/321091.
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The worldwide using of herb products and the increasing potential herb-drug interaction issue has raised enthusiasm on discovering the underlying mechanisms. Previous review indicated that the interactions may be mediated by metabolism enzymes and transporters in pharmacokinetic pathways. On the other hand, an increasing number of studies found that genetic variations showed some influence on herb-drug interaction effects whereas these genetic factors did not draw much attention in history. We highlight that pharmacogenomics may involve the pharmacokinetic or pharmacodynamic pathways to affect herb-drug interaction. We are here to make an updated review focused on some common herb-drug interactions in association with genetic variations, with the aim to help safe use of herbal medicines in different individuals in the clinic.
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Zhang, Rui Xue, Ken Dong, Zhigao Wang, Ruimin Miao, Weijia Lu, and Xiao Yu Wu. "Nanoparticulate Drug Delivery Strategies to Address Intestinal Cytochrome P450 CYP3A4 Metabolism towards Personalized Medicine." Pharmaceutics 13, no. 8 (August 16, 2021): 1261. http://dx.doi.org/10.3390/pharmaceutics13081261.
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Drug dosing in clinical practice, which determines optimal efficacy, toxicity or ineffectiveness, is critical to patients’ outcomes. However, many orally administered therapeutic drugs are susceptible to biotransformation by a group of important oxidative enzymes, known as cytochrome P450s (CYPs). In particular, CYP3A4 is a low specificity isoenzyme of the CYPs family, which contributes to the metabolism of approximately 50% of all marketed drugs. Induction or inhibition of CYP3A4 activity results in the varied oral bioavailability and unwanted drug-drug, drug-food, and drug-herb interactions. This review explores the need for addressing intestinal CYP3A4 metabolism and investigates the opportunities to incorporate lipid-based oral drug delivery to enable precise dosing. A variety of lipid- and lipid-polymer hybrid-nanoparticles are highlighted to improve drug bioavailability. These drug carriers are designed to target different intestinal regions, including (1) local saturation or inhibition of CYP3A4 activity at duodenum and proximal jejunum; (2) CYP3A4 bypass via lymphatic absorption; (3) pH-responsive drug release or vitamin-B12 targeted cellular uptake in the distal intestine. Exploitation of lipidic nanosystems not only revives drugs removed from clinical practice due to serious drug-drug interactions, but also provide alternative approaches to reduce pharmacokinetic variability.
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Ojezele, M. O. "Microbiome: pharmacokinetics, pharmacodynamics and drug/xenobiotic interactions." African Journal of Clinical and Experimental Microbiology 21, no. 2 (February 17, 2020): 78–87. http://dx.doi.org/10.4314/ajcem.v21i2.1.
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The participation of microbiota in myriads of physiological, metabolic, genetic and immunological processes shows that they are a fundamental part of human existence and health maintenance. The efficiency of drugs’ absorption depends on solubility, stability, permeability and metabolic enzymes produced by the body and gut microbiota. Two major types of microbiota-drug interaction have been identified; direct and indirect. The use of antibiotics is a direct means of targeting intestinal microbes and short-term use of antibiotic can significantly alter the microbiome composition. It is noteworthy that not every microbial drug metabolism is of benefit to the host as some drugs can shut down microbial processes as observed in the co-administration of antiviral sorivudine with fluoropyridimide resulting in a toxic buildup of fluoropyridimide metabolites from blockade of host fluoropyridimide by the microbial-sorivudine metabolite. It has been reported that many classes of drugs and xenobiotics modify the gut microbiome composition which may be detrimental to human health. Microbiome-drug interaction may be beneficial or detrimental resulting in either treatment success or failure which is largely dependent on factors such as microbial enzymes, chemical composition of candidate drug, host immunity and the complex relationship that exists with the microbiome. The effects of microbiota on pharmacology of drugs and vice versa are discussed in this review.Keywords: microbiome; pharmacokinetic, pharmacodynamic, drug, xenobiotic English Title: Microbiome: pharmacocinétique, pharmacodynamique et interactions médicamenteuses/xénobiotiquesLa participation du microbiote à des myriades de processus physiologiques, métaboliques, génétiques et immunologiques montre qu’ils sont un élément fondamental de l’existence et du maintien de la santé de l’être humain. L’efficacité de l’absorption des médicaments dépend de la solubilité, de la stabilité, de la perméabilité et des enzymes métaboliques produites par le corps et le microbiote intestinal. Deux types principaux d’interaction microbiote-médicament ont été identifiés; direct et indirect. L'utilisation d'antibiotiques est un moyen direct de cibler les microbes intestinaux et une utilisation à court terme d'antibiotique peut modifier de manière significative la composition du microbiome. Il est à noter que tous les métabolismes de médicaments microbiens ne sont pas bénéfiques pour l'hôte, car certains médicaments peuvent arrêter les processus microbiens observés lors de l'administration concomitante d'antiviral sorivudine et de fluoropyridimide, ce qui entraîne une accumulation toxique de métabolites de fluoropyridimide résultant du blocage du fluoropyridimide par l'hôte. métabolite microbien-sorivudine. Il a été rapporté que de nombreuses classes de médicaments et de xénobiotiques modifiaient la composition du microbiome intestinal, ce qui pourrait nuire à la santé humaine. Une interaction médicamenteuse-microbiome peut être bénéfique ou préjudiciable, entraînant le succès ou l'échec du traitement, qui dépend en grande partie de facteurs tels que les enzymes microbiennes, la composition chimique du médicament candidat, l'immunité de l'hôte et la relation complexe qui existe avec le microbiome. Les effets du microbiote sur la pharmacologie des médicaments et inversement sont discutés dans cette revue.Mots-clés: microbiome; pharmacocinétique, pharmacodynamique, médicament, xénobiotique
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Roy, Harekrishna, and Sisir Nandi. "In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery." Current Pharmaceutical Design 25, no. 31 (November 14, 2019): 3292–305. http://dx.doi.org/10.2174/1381612825666190903155935.
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Background: Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly. Methods: To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status. Results: The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound. Conclusion: : It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound–dependent induction of drug-metabolizing enzymes.
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Di, Li. "The Impact of Carboxylesterases in Drug Metabolism and Pharmacokinetics." Current Drug Metabolism 20, no. 2 (April 30, 2019): 91–102. http://dx.doi.org/10.2174/1389200219666180821094502.
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Background:Carboxylesterases (CES) play a critical role in catalyzing hydrolysis of esters, amides, carbamates and thioesters, as well as bioconverting prodrugs and soft drugs. The unique tissue distribution of CES enzymes provides great opportunities to design prodrugs or soft drugs for tissue targeting. Marked species differences in CES tissue distribution and catalytic activity are particularly challenging in human translation.Methods:Review and summarization of CES fundamentals and applications in drug discovery and development.Results:Human CES1 is one of the most highly expressed drug metabolizing enzymes in the liver, while human intestine only expresses CES2. CES enzymes have moderate to high inter-individual variability and exhibit low to no expression in the fetus, but increase substantially during the first few months of life. The CES genes are highly polymorphic and some CES genetic variants show significant influence on metabolism and clinical outcome of certain drugs. Monkeys appear to be more predictive of human pharmacokinetics for CES substrates than other species. Low risk of clinical drug-drug interaction is anticipated for CES, although they should not be overlooked, particularly interaction with alcohols. CES enzymes are moderately inducible through a number of transcription factors and can be repressed by inflammatory cytokines.Conclusion:Although significant advances have been made in our understanding of CESs, in vitro - in vivo extrapolation of clearance is still in its infancy and further exploration is needed. In vitro and in vivo tools are continuously being developed to characterize CES substrates and inhibitors.
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Stader, Felix, Hannah Kinvig, Manuel Battegay, Saye Khoo, Andrew Owen, Marco Siccardi, and Catia Marzolini. "Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications." Antimicrobial Agents and Chemotherapy 62, no. 7 (April 23, 2018): e00717-18. http://dx.doi.org/10.1128/aac.00717-18.
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ABSTRACTDespite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n= 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.
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Bleasby, Kelly, Robert Houle, Michael Hafey, Meihong Lin, Jingjing Guo, Bing Lu, Rosa I. Sanchez, and Kerry L. Fillgrove. "Islatravir Is Not Expected to Be a Victim or Perpetrator of Drug-Drug Interactions via Major Drug-Metabolizing Enzymes or Transporters." Viruses 13, no. 8 (August 7, 2021): 1566. http://dx.doi.org/10.3390/v13081566.
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Islatravir (MK-8591) is a nucleoside reverse transcriptase translocation inhibitor in development for the treatment and prevention of HIV-1. The potential for islatravir to interact with commonly co-prescribed medications was studied in vitro. Elimination of islatravir is expected to be balanced between adenosine deaminase–mediated metabolism and renal excretion. Islatravir did not inhibit uridine diphosphate glucuronosyltransferase 1A1 or cytochrome p450 (CYP) enzymes CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, or 3A4, nor did it induce CYP1A2, 2B6, or 3A4. Islatravir did not inhibit hepatic transporters organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 1, bile salt export pump (BSEP), multidrug resistance-associated protein (MRP) 2, MRP3, or MRP4. Islatravir was neither a substrate nor a significant inhibitor of renal transporters organic anion transporter (OAT) 1, OAT3, OCT2, multidrug and toxin extrusion protein (MATE) 1, or MATE2K. Islatravir did not significantly inhibit P-glycoprotein and breast cancer resistance protein (BCRP); however, it was a substrate of BCRP, which is not expected to be of clinical significance. These findings suggest islatravir is unlikely to be the victim or perpetrator of drug-drug interactions with commonly co-prescribed medications, including statins, diuretics, anti-diabetic drugs, proton pump inhibitors, anticoagulants, benzodiazepines, and selective serotonin reuptake inhibitors.
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Zhang, Liyun, Xiaoqing Xu, Sara Badawy, Awais Ihsan, Zhenli Liu, Changqing Xie, Xu Wang, and Yanfei Tao. "A Review: Effects of Macrolides on CYP450 Enzymes." Current Drug Metabolism 21, no. 12 (December 30, 2020): 928–37. http://dx.doi.org/10.2174/1389200221666200817113920.
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: As a kind of haemoglobin, cytochrome P450 enzymes (CYP450) participate in the metabolism of many substances, including endogenous substances, exogenous substances and drugs. It is estimated that 60% of common prescription drugs require bioconversion through CYP450. The influence of macrolides on CYP450 contributes to the metabolism and drug-drug interactions (DDIs) of macrolides. At present, most studies on the effects of macrolides on CYP450 are focused on CYP3A, but a few exist on other enzymes and drug combinations, such as telithromycin, which can decrease the activity of hepatic CYP1A2 and CYP3A2. This article summarizes some published applications of the influence of macrolides on CYP450 and the DDIs of macrolides caused by CYP450. And the article may subsequently guide the rational use of drugs in clinical trials. To a certain extent, poisoning caused by adverse drug interactions can be avoided. Unreasonable use of macrolide antibiotics may enable the presence of residue of macrolide antibiotics in animal-origin food. It is unhealthy for people to eat food with macrolide antibiotic residues. So it is of great significance to guarantee food safety and protect the health of consumers by the rational use of macrolides. This review gives a detailed description of the influence of macrolides on CYP450 and the DDIs of macrolides caused by CYP450. Moreover, it offers a perspective for researchers to further explore in this area.
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Piedade, Rita, Stefanie Traub, Andreas Bitter, Andreas K. Nüssler, José P. Gil, Matthias Schwab, and Oliver Burk. "Carboxymefloquine, the Major Metabolite of the Antimalarial Drug Mefloquine, Induces Drug-Metabolizing Enzyme and Transporter Expression by Activation of Pregnane X Receptor." Antimicrobial Agents and Chemotherapy 59, no. 1 (October 13, 2014): 96–104. http://dx.doi.org/10.1128/aac.04140-14.
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ABSTRACTMalaria patients are frequently coinfected with HIV and mycobacteria causing tuberculosis, which increases the use of coadministered drugs and thereby enhances the risk of pharmacokinetic drug-drug interactions. Activation of the pregnane X receptor (PXR) by xenobiotics, which include many drugs, induces drug metabolism and transport, thereby resulting in possible attenuation or loss of the therapeutic responses to the drugs being coadministered. While several artemisinin-type antimalarial drugs have been shown to activate PXR, data on nonartemisinin-type antimalarials are still missing. Therefore, this study aimed to elucidate the potential of nonartemisinin antimalarial drugs and drug metabolites to activate PXR. We screened 16 clinically used antimalarial drugs and six major drug metabolites for binding to PXR using the two-hybrid PXR ligand binding domain assembly assay; this identified carboxymefloquine, the major and pharmacologically inactive metabolite of the antimalarial drug mefloquine, as a potential PXR ligand. Two-hybrid PXR-coactivator and -corepressor interaction assays and PXR-dependent promoter reporter gene assays confirmed carboxymefloquine to be a novel PXR agonist which specifically activated the human receptor. In the PXR-expressing intestinal LS174T cells and in primary human hepatocytes, carboxymefloquine induced the expression of drug-metabolizing enzymes and transporters on the mRNA and protein levels. The crucial role of PXR for the carboxymefloquine-dependent induction of gene expression was confirmed by small interfering RNA (siRNA)-mediated knockdown of the receptor. Thus, the clinical use of mefloquine may result in pharmacokinetic drug-drug interactions by means of its metabolite carboxymefloquine. Whether thesein vitrofindings are ofin vivorelevance has to be addressed in future clinical drug-drug interaction studies.
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Li, Junhao, Hanwen Du, Zengrui Wu, Haixia Su, Guixia Liu, Yun Tang, and Weihua Li. "Interactions of omeprazole-based analogues with cytochrome P450 2C19: a computational study." Molecular BioSystems 12, no. 6 (2016): 1913–21. http://dx.doi.org/10.1039/c6mb00139d.
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Dmitriev, Filimonov, Lagunin, Karasev, Pogodin, Rudik, and Poroikov. "Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation." Molecules 24, no. 21 (October 31, 2019): 3955. http://dx.doi.org/10.3390/molecules24213955.
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Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter‐mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.
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Elmassry, Moamen M., Sunghwan Kim, and Ben Busby. "Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes." PLOS ONE 16, no. 1 (January 7, 2021): e0244876. http://dx.doi.org/10.1371/journal.pone.0244876.
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Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.
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Cohen, Lawrence J., and C. Lindsay DeVane. "Clinical Implications of Antidepressant Pharmacokinetics and Pharmacogenetics." Annals of Pharmacotherapy 30, no. 12 (December 1996): 1471–80. http://dx.doi.org/10.1177/106002809603001216.
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OBJECTIVE: To review available data on pharmacokinetic and pharmacogenetic influences on the response to antidepressant therapy, analyze the mechanisms for and clinical significance of pharmacokinetic and pharmacogenetic differences, and explain the implications of pharmacokinetics and pharmacogenetics for patient care. DATA SOURCES: A MEDLINE search of English-language clinical studies, abstracts, and review articles on antidepressant pharmacokinetics, pharmacogenetics, and drug interactions was used to identify pertinent literature. DATA SYNTHESIS: The pharmacokinetic profiles of selected antidepressants are reviewed and the impact of hepatic microsomal enzymes on antidepressant metabolism is considered. How phenotypic differences influence the metabolism of antidepressant drug therapy is addressed. To evaluate the clinical implications of these pharmacokinetic and pharmacogenetic considerations, the findings of studies designed to elucidate drug interactions involving antidepressant agents are discussed. CONCLUSIONS: Differences in antidepressant plasma concentrations, and possibly safety, are caused by polymorphism in the genes that encode some of the cytochrome P450 isoenzymes that metabolize antidepressants. The isoenzymes 1A2, 2C9/19, 2D6, and 3A4 are the major enzymes that catalyze antidepressant metabolic reactions. Antidepressants can be either substrates or inhibitors of these enzymes, which also metabolize many other pharmacologic agents. Although the cytochrome enzymes that metabolize antidepressants have not been fully characterized, interaction profiles of the newer antidepressants are becoming more clearly defined. Determining patient phenotypes is not practical in the clinical setting, but an awareness of the possibility of genetic polymorphism in antidepressant metabolism may help explain therapeutic failure or toxicity, help predict the likelihood of drug interactions, and help clinicians better manage antidepressant drug therapy.
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Touw, D. J. "The developing role of pharmacogenetics in psychiatry." Acta Neuropsychiatrica 11, no. 2 (June 1999): 77–79. http://dx.doi.org/10.1017/s092427080003622x.
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A great interindividual variability exists in biological response to drugs. This variability is partly attributable to pharmacodynamic factors (drug - receptor interactions) and partly to pharmacokinetic factors. Drugs can be eliminated from the body by renal clearance, metabolism or both. Although every tissue has some ability to metabolise xenobiotics like drugs, the liver is the principal organ of biotransformation. Major metabolising enzymes are the cytochrome-P450 mono-oxygenases, epoxide hydrolase, glucuronosyl-transferase, acetyl-transferase, sulfo-transferase and xanthine oxidase. Some of these enzymes display in a subset of subjects a ‘normal’ activity and in another subset of subjects a reduced or a greatly increased activity. This altered activity may be genetically determined and is then called genetic polymorphism. Clinically relevant metabolic differences traditionally have been defined by their genotypie expression such as ‘poor’ and ‘extensive’ metaboliser. The recent developments of powerful methods for DNA (or genomic) analysis portends a revolutionary expansion of our understanding of physiology as well as pathology. Pharmacogenetics is the study of genetic variation underlying differential response to drugs. Genotyping may become a useful tool in optimising drug treatment. Another part of the genetic research is directed towards the discovery of genetic alterations leading to diseases. Once identified, these genetic alterations can become targets for drug treatment (e.g. gene therapy). Pharmaco-genomics applies the large-scale systematic approaches of genomics to speed the discovery of drug response markers, whether they act at the level of the drug target, drug metabolism or disease pathway. Table I gives some examples of genetic alterations that are identified together with their effects. Some of these examples will be briefly discussed here.
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Patel, Rahul, James Barker, and Amr ElShaer. "Pharmaceutical Excipients and Drug Metabolism: A Mini-Review." International Journal of Molecular Sciences 21, no. 21 (November 3, 2020): 8224. http://dx.doi.org/10.3390/ijms21218224.
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Conclusions from previously reported articles have revealed that many commonly used pharmaceutical excipients, known to be pharmacologically inert, show effects on drug transporters and/or metabolic enzymes. Thus, the pharmacokinetics (absorption, distribution, metabolism and elimination) of active pharmaceutical ingredients are possibly altered because of their transport and metabolism modulation from the incorporated excipients. The aim of this review is to present studies on the interaction of various commonly-used excipients on pre-systemic metabolism by CYP450 enzymes. Excipients such as surfactants, polymers, fatty acids and solvents are discussed. Based on all the reported outcomes, the most potent inhibitors were found to be surfactants and the least effective were organic solvents. However, there are many factors that can influence the inhibition of CYP450, for instance type of excipient, concentration of excipient, type of CYP450 isoenzyme, incubation condition, etc. Such evidence will be very useful in dosage form design, so that the right formulation can be designed to maximize drug bioavailability, especially for poorly bioavailable drugs.
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Nowak, Jan Krzysztof, Bartłomiej Bancerz, and Alicja Bartkowska-Śniatkowska. "CYP3A drug metabolism in the developmental age: recent advances." Journal of Medical Science 88, no. 1 (March 12, 2019): 58–61. http://dx.doi.org/10.20883/jms.2019.290.
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Background. The 3A subfamily of cytochrome P450 (CYP3A) accomplishes phase I metabolism for approximately half of the available medications. We aimed to review the recent advances in our understanding of CYP3A activity, which could apply to infants and toddlers.Material and Methods. A literature review.Results. The reviewed recent data cover: CYP3A7 expression and functions, changes of CYP3A4 function in the first two years of life, CYP3A intestinal metabolism and zonation, CYP3A metabolic programming, pediatric CYP3A pharmacogenetics, the impact of critical illness on CYP3A, phenotyping, and other clinical implications of a better comprehension of CYP3A biology.Conclusions. Although the knowledge of CYP3A enzymes has already changed pediatric practice, much more is to be expected in the upcoming years. The areas to watch include: endogenous markers for phenotyping, new CYP3A7 substrates and products, pharmacogenetic interactions with transporter genes for non‑immunomodulatory drugs, as well as interactions with microbiota and specific bioactive foodstuffs.
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Golfar, Yeganeh, and Ali Shayanfar. "Prediction of Biopharmaceutical Drug Disposition Classification System (BDDCS) by Structural Parameters." Journal of Pharmacy & Pharmaceutical Sciences 22 (June 23, 2019): 247–69. http://dx.doi.org/10.18433/jpps30271.
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Modeling of physicochemical and pharmacokinetic properties is important for the prediction and mechanism characterization in drug discovery and development. Biopharmaceutics Drug Disposition Classification System (BDDCS) is a four-class system based on solubility and metabolism. This system is employed to delineate the role of transporters in pharmacokinetics and their interaction with metabolizing enzymes. It further anticipates drug disposition and potential drug-drug interactions in the liver and intestine. According to BDDCS, drugs are classified into four groups in terms of the extent of metabolism and solubility (high and low). In this study, structural parameters of drugs were used to develop classification-based models for the prediction of BDDCS class. Reported BDDCS data of drugs were collected from the literature, and structural descriptors (Abraham solvation parameters and octanol–water partition coefficient (log P)) were calculated by ACD/Labs software. Data were divided into training and test sets. Classification-based models were then used to predict the class of each drug in BDDCS system using structural parameters and the validity of the established models was evaluated by an external test set. The results of this study showed that log P and Abraham solvation parameters are able to predict the class of solubility and metabolism in BDDCS system with good accuracy. Based on the developed methods for prediction solubility and metabolism class, BDDCS could be predicted in the correct with an acceptable accuracy. Structural properties of drugs, i.e. logP and Abraham solvation parameters (polarizability, hydrogen bonding acidity and basicity), are capable of estimating the class of solubility and metabolism with an acceptable accuracy.
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Banerjee, Priyanka, Mathias Dunkel, Emanuel Kemmler, and Robert Preissner. "SuperCYPsPred—a web server for the prediction of cytochrome activity." Nucleic Acids Research 48, W1 (March 17, 2020): W580—W585. http://dx.doi.org/10.1093/nar/gkaa166.
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Abstract Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug–drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs—published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.
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Fatunde, Olubadewa A., and Sherry-Ann Brown. "The Role of CYP450 Drug Metabolism in Precision Cardio-Oncology." International Journal of Molecular Sciences 21, no. 2 (January 17, 2020): 604. http://dx.doi.org/10.3390/ijms21020604.
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As many novel cancer therapies continue to emerge, the field of Cardio-Oncology (or onco-cardiology) has become crucial to prevent, monitor and treat cancer therapy-related cardiovascular toxicity. Furthermore, given the narrow therapeutic window of most cancer therapies, drug-drug interactions are prevalent in the cancer population. Consequently, there is an increased risk of affecting drug efficacy or predisposing individual patients to adverse side effects. Here we review the role of cytochrome P450 (CYP450) enzymes in the field of Cardio-Oncology. We highlight the importance of cardiac medications in preventive Cardio-Oncology for high-risk patients or in the management of cardiotoxicities during or following cancer treatment. Common interactions between Oncology and Cardiology drugs are catalogued, emphasizing the impact of differential metabolism of each substrate drug on unpredictable drug bioavailability and consequent inter-individual variability in treatment response or development of cardiovascular toxicity. This inter-individual variability in bioavailability and subsequent response can be further enhanced by genomic variants in CYP450, or by modifications of CYP450 gene, RNA or protein expression or function in various ‘omics’ related to precision medicine. Thus, we advocate for an individualized approach to each patient by a multidisciplinary team with clinical pharmacists evaluating a treatment plan tailored to a practice of precision Cardio-Oncology. This review may increase awareness of these key concepts in the rapidly evolving field of Cardio-Oncology.