Cytochrome P450 (CYP) 3A4 is not only the most abundant isoform in human liver but also metabolizes approximately 60% of the therapeutic drugs. This feature renders CYP3A4 highly susceptible to both reversible and irreversible (mechanism-based) inhibition. The latter is characterized by NADPH-, time- and concentration-dependent enzyme inactivation, occurring when some drugs are converted by CYPs to reactive metabolites. Mechanism-based inactivation of CYP3A4 by drugs can be due to the chemical modification of the heme, the protein, or both as a result of covalent binding of modified heme to the protein. The clinical pharmacokinetic effect of a CYP3A4 inactivator is a function of its KI, kinact and partition ratio and the synthesis rate of new or replacement enzyme. Predicting drug-drug interactions involving CYP3A4 inactivation is possible when proper pharmacokinetic principles are followed. However, the prediction may become difficult, since the clinical outcomes due to CYP3A4 inactivation depend on many factors associated with the enzyme, drugs and the patients. A number of clinically important drugs have been identified to be mechanism-based CYP3A4 inhibitors. These include antibiotics (e.g. erythromycin and isoniazid), anticancer drugs (e.g. tamoxifen), antidepressants (e.g. fluoxetine and midazolam), anti-HIV agents (e.g. ritonavir and delavirdine), antihypertensives (e.g. dihydralazine and verapamil), steroids and their receptor modulators (e.g. gestodene and raloxifene), and some herbal constituents (e.g. bergamottin and glabridin). Compared to reversible inhibition, mechanism-based inhibitors of CYP3A4 more frequently cause unfavorable drug-drug interactions, as the inactivated CYP3A4 has to be replaced by newly synthesized CYP3A4 protein. Most CYP3A4 inactivators are also PgP substrates/inhibitors, confounding the in vitro-in vivo extrapolation. Clinicians should have good knowledge on these CYP3A4 inactivators and avoid their combination use.
"Both of these drug classes are substrates of cytochrome P450 (CYP) isoenzymes 3A4/5 and the drug-transporter, P-glycoprotein (P-gp). These metabolic pathways are also primarily involved in the elimination of 40 to 60% of all marketed drugs and in vivo expression of both CYP3A4/5 and P-gp vary substantially between individuals      . As a result, administration of a drug that is a CYP3A or P-gp substrate/inhibitor to a liver transplant (LT) recipient can lead to dangerously high immunosuppressant blood levels, while intake of CYP3A inducers can predispose to subtherapeutic dosing and rejection  . "
[Show abstract][Hide abstract] ABSTRACT: Studies of boceprevir and telaprevir based antiviral therapy in liver transplant (LT) recipients with hepatitis C genotype 1 infection have demonstrated dramatic increases in tacrolimus, cyclosporine, and mTOR inhibitor exposure. In addition to empiric dose reductions, daily monitoring of immunosuppressant blood levels is required when initiating as well as discontinuing the protease inhibitors to maximize patient safety. Although improved suppression of HCV replication is anticipated, 20 to 40% of treated subjects have required early treatment discontinuation due to various adverse events including anemia (100%), infection (30%), nephrotoxicity (20%) and rejection (5 to 10%). Simeprevir and faldeprevir will likely have improved efficacy and safety profiles but potential drug interactions with other OATP1B1 substrates and unconjugated hyperbilirubinemia are expected. In contrast, sofosbuvir and daclatasvir based antiviral therapy are not expected to lead to clinically significant drug-drug interactions in LT recipients but confirmatory studies are needed. Liver transplant recipients may also be at increased risk of developing drug induced liver injury (DILI). Establishing a diagnosis of DILI in the transplant setting is very difficult with the variable latency, laboratory features and histopathological manifestations of hepatotoxicity associated with a given drug, the need to exclude competing causes of allograft injury, and the lack of an objective and verifiable confirmatory test. Nonetheless, a heightened awareness of the possibility of DILI is warranted in light of the large number of medications used in LT recipients and the potential adverse impact that DILI may have on patient outcomes.
Journal of Hepatology 11/2013; 60(4). DOI:10.1016/j.jhep.2013.11.013 · 11.34 Impact Factor
"where k inact is the maximum rate of enzyme inactivation, K I is the dissociation rate constant of the inhibitor, and I t is the unbound concentration of inhibitor at the enzyme site at time t (Zhou et al., 2004). The rate of change of CYP3A in response to inactivation may be described by eq. "
[Show abstract][Hide abstract] ABSTRACT: The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.
Drug metabolism and disposition: the biological fate of chemicals 11/2009; 38(2):241-8. DOI:10.1124/dmd.109.028746 · 3.25 Impact Factor
"In view that quantitative as well as mechanistic understanding of biomolecular interactions is important for exploration and engineering of biological networks and for the development of novel therapeutics to combat diseases (29,30), kinetic data of biomolecular interactions have been provided in some databases. For instance, BRENDA (31) and SABIO-RK (32) provide kinetic constants of enzymatic activities, DOQCS contains kinetic parameters of simulation models of cellular signaling derived from experimental and other sources (33). "
[Show abstract][Hide abstract] ABSTRACT: Knowledge of the kinetics of biomolecular interactions is important for facilitating the study of cellular processes and underlying molecular events, and is essential for quantitative study and simulation of biological systems. Kinetic Data of Bio-molecular Interaction database (KDBI) has been developed to provide information about experimentally determined kinetic data of protein-protein, protein-nucleic acid, protein-ligand, nucleic acid-ligand binding or reaction events described in the literature. To accommodate increasing demand for studying and simulating biological systems, numerous improvements and updates have been made to KDBI, including new ways to access data by pathway and molecule names, data file in System Biology Markup Language format, more efficient search engine, access to published parameter sets of simulation models of 63 pathways, and 2.3-fold increase of data (19,263 entries of 10,532 distinctive biomolecular binding and 11,954 interaction events, involving 2635 proteins/protein complexes, 847 nucleic acids, 1603 small molecules and 45 multi-step processes). KDBI is publically available at http://bidd.nus.edu.sg/group/kdbi/kdbi.asp.
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