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Today much is known about cytochrome P450 (P450) enzymes and their catalytic specificity, but the range of reactions catalyzed by each still continues to surprise. Historically, P450s had been considered to be involved in either the metabolism of xenobiotics or endogenous chemicals, in the former case playing a generally protective role and in the latter case a defined physiological role. However, the line of demarcation is sometimes blurred. It is difficult to be completely specific in drug design, and some P450s involved in the metabolism of steroids and vitamins can be off-targets. In a number of cases, drugs have been developed that act on some of those P450s as primary targets, e.g., steroid aromatase inhibitors. Several of the P450s involved in the metabolism of endogenous substrates are less specific than once thought and oxidize several related structures. Some of the P450s that primarily oxidize endogenous chemicals have been shown to oxidize xenobiotic chemicals, even in a bioactivation mode.
Simvastatin is among the most commonly used prescription medications for cholesterol reduction. A single coding single-nucleotide polymorphism, rs4149056T>C, in SLCO1B1 increases systemic exposure to simvastatin and the risk of muscle toxicity. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for simvastatin based on SLCO1B1 genotype. This article is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium guideline for SLCO1B1 and simvastatin-induced myopathy.
Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.
OBJECTIVE - Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials.
METHODS - Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively.
RESULTS - The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources.
CONCLUSIONS - Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.
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Methamphetamine (METH) exposure results in dopaminergic neurotoxicity in striatal regions of the brain, an effect that has been linked to an increased risk of Parkinson's disease. Various aspects of neuroinflammation, including astrogliosis, are believed to be contributory factors in METH neurotoxicity. METH interacts with sigma receptors at physiologically relevant concentrations and treatment with sigma receptor antagonists has been shown to mitigate METH-induced neurotoxicity in rodent models. Whether these compounds alter the responses of glial cells within the central nervous system to METH however has yet to be determined. Therefore, the purpose of the current study was to determine whether the sigma receptor antagonist, SN79, mitigates METH-induced striatal reactive astrogliosis. Male, Swiss Webster mice treated with a neurotoxic regimen of METH exhibited time-dependent increases in striatal gfap mRNA and concomitant increases in GFAP protein, indicative of astrogliosis. This is the first report that similar to other neurotoxicants that induce astrogliosis through the activation of JAK2/STAT3 signaling by stimulating gp-130-linked cytokine signaling resulting from neuroinflammation, METH treatment also increases astrocytic oncostatin m receptor (OSMR) expression and the phosphorylation of STAT3 (Tyr-705) in vivo. Pretreatment with SN79 blocked METH-induced increases in OSMR, STAT3 phosphorylation and astrocyte activation within the striatum. Additionally, METH treatment resulted in striatal cellular degeneration as measured by Fluoro-Jade B, an effect that was mitigated by SN79. The current study provides evidence that sigma receptor antagonists attenuate METH-induced astrocyte activation through a pathway believed to be shared by various neurotoxicants.
Copyright © 2014 Elsevier Inc. All rights reserved.
OBJECTIVES - An efavirenz-based antiretroviral therapy (ART) regimen is preferred for children more than 3 years of age with tuberculosis. However, rifampin, a key component of antituberculosis therapy, induces CYP2B6. An increased dose of efavirenz is recommended in adults weighing more than 50 kg who require rifampin, but there is scant information in children being treated for tuberculosis.
DESIGN - Plasma efavirenz concentrations were compared in 40 children during concomitant treatment for tuberculosis and HIV-1, after stopping rifampicin, and in a control group of children without tuberculosis. Associations with antituberculosis treatment, metabolizer genotype (based on CYP2B6 516G→T, 983T→C, and 15582C→T), weight, and time after dose were evaluated.
RESULTS - Compared to children with extensive metabolizer genotypes, efavirenz concentrations were increased 1.42-fold (95% confidence interval, CI 0.94–2.15) and 2.85-fold (95% CI 1.80–4.52) in children with intermediate and slow metabolizer genotypes, respectively. Concomitant antituberculosis treatment increased efavirenz concentrations 1.49-fold (95% CI 1.10–2.01) in children with slow metabolizer genotypes, but did not affect efavirenz concentrations in extensive or intermediate metabolizer genotypes. After adjustment for dose/kg, each kilogram of weight was associated with a 2.8% (95% CI 0.9–4.7) decrease in efavirenz concentrations. Despite higher milligram per kilogram doses, a higher proportion of children in the lowest weight band (10–13.9 kg) had efavirenz concentrations less than 1.0 mg/l than larger children.
CONCLUSION - Antituberculosis treatment was not associated with reduced efavirenz concentrations in children, which does not support increased efavirenz doses. Children with slow metabolizer genotype have increased efavirenz concentrations during antituberculosis treatment, likely due to isoniazid inhibiting enzymes involved in accessory metabolic pathways for efavirenz.
Antipsychotic drugs are medications commonly for schizophrenia (SCZ) treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC) classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the "alimentary tract and metabolism" category while SCZ atypical drugs had significant interactions with drugs of the categories "nervous system" and "antiinfectives for systemic uses." This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.
Allosteric modulation of G protein-coupled receptors has gained considerable attention in the drug discovery arena because it opens avenues to achieve greater selectivity over orthosteric ligands. We recently identified a series of positive allosteric modulators (PAMs) of metabotropic glutamate receptor 5 (mGlu(5)) for the treatment of schizophrenia that exhibited robust heterotropic activation of CYP3A4 enzymatic activity. The prototypical compound from this series, 5-(4-fluorobenzyl)-2-((3-fluorophenoxy)methyl)-4,5,6,7-tetrahydropyrazolo[1,5-a]pyrazine (VU0448187), was found to activate CYP3A4 to >100% of its baseline intrinsic midazolam (MDZ) hydroxylase activity in vitro; activation was CYP3A substrate specific and mGlu(5) PAM dependent. Additional studies revealed the concentration-dependence of CYP3A activation by VU0448187 in multispecies hepatic and intestinal microsomes and hepatocytes, as well as a diminished effect observed in the presence of ketoconazole. Kinetic analyses of the effect of VU0448187 on MDZ metabolism in recombinant P450 or human liver microsomes resulted in a significant increase in V(max) (minimal change in K(m)) and required the presence of cytochrome b5. The atypical kinetics translated in vivo, as rats receiving an intraperitoneal administration of VU0448187 prior to MDZ treatment demonstrated a significant increase in circulating 1- and 4-hydroxy- midazolam (1-OH-MDZ, 4-OH-MDZ) levels compared with rats administered MDZ alone. The discovery of a potent substrate-selective activator of rodent CYP3A with an in vitro to in vivo translation serves to illuminate the impact of increasing intrinsic enzymatic activity of hepatic and extrahepatic CYP3A in rodents, and presents the basis to build models capable of framing the clinical relevance of substrate-dependent heterotropic activation.
Preclinical studies implicate a role for α₁-noradrenergic receptors in the effects of psychostimulants, including 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy"). The present study evaluated the effects of the α₁-noradrenergic receptor antagonist doxazosin on the acute pharmacodynamic and pharmacokinetic response to MDMA in 16 healthy subjects. Doxazosin (8 mg/d) or placebo was administered for 3 days before MDMA (125 mg) or placebo using a randomized, double-blind, placebo-controlled, 4-session, crossover design. Doxazosin reduced MDMA-induced elevations in blood pressure, body temperature, and moderately attenuated positive mood but enhanced tachycardia associated with MDMA. The results indicate that α₁-adrenergic receptors contribute to the acute cardiostimulant and to a minor extent possibly also to the thermogenic and euphoric effects of MDMA in humans.