Shirley M. Tsunoda

Dietary effects on drug metabolism and transport.

Metabolic food-drug interactions occur when the consumption of a particular food modulates the activity of a drug-metabolising enzyme system, resulting in an alteration of the pharmacokinetics of drugs metabolised by that system. A number of these interactions have been reported. Foods that contain complex mixtures of phytochemicals, such as fruits, vegetables, herbs, spices and teas, have the greatest potential to induce or inhibit the activity of drug-metabolising enzymes, although dietary macroconstituents (i.e. total protein, fat and carbohydrate ratios, and total energy intake) can also have effects. Particularly large interactions may result from the consumption of herbal dietary supplements.Cytochrome P450 (CYP) 3A4 appears to be especially sensitive to dietary effects, as demonstrated by reports of potentially clinically important interactions involving orally administered drugs that are substrates of this enzyme. For example, interactions of grapefruit juice with cyclosporin and felodipine, St John’s wort with cyclosporin and indinavir, and red wine with cyclosporin, have the potential to require dosage adjustment to maintain drug concentrations within their therapeutic windows. The susceptibility of CYP3A4 to modulation by food constituents may be related to its high level of expression in the intestine, as well as its broad substrate specificity. Reported ethnic differences in the activity of this enzyme may be partly due to dietary factors.Food-drug interactions involving CYP1A2, CYP2E1, glucuronosyltransferases and glutathione S-transferases have also been documented, although most of these interactions are modest in magnitude and clinically relevant only for drugs that have a narrow therapeutic range. Recently, interactions involving drug transporters, including P-glycoprotein and the organic anion transporting polypeptide, have also been identified. Further research is needed to determine the scope, magnitude and clinical importance of food effects on drug metabolism and transport.

Red wine decreases cyclosporine bioavailability

Many commonly ingested substances such as grapefruit juice and Hypericum perforatum (St Johns wort) have been found to interact with important therapeutic agents such as cyclosporine (INN, ciclosporin). The mechanism for these interactions is thought to involve modulation of the activity of the drug‐metabolizing enzyme cytochrome P4503A4 (CYP3A4) and/or the drug transport protein P‐glycoprotein. In vitro data suggest that red wine may interact with CYP3A4 substrates such as cyclosporine.

Evaluation of in vivo P-glycoprotein phenotyping probes: a need for validation.

Drug transporters are involved in clinically relevant drug-drug interactions. P-glycoprotein (P-gp) is an efflux transporter that displays genetic polymorphism. Phenotyping permits evaluation of real-time, in vivo P-gp activity and P-gp-mediated drug-drug interactions. Digoxin, fexofenadine, talinolol and quinidine are commonly used probe drugs for P-gp phenotyping. Although current regulatory guidance documents highlight methodologies for evaluating transporter-based drug-drug interactions, whether current probe drugs are suitable for phenotyping has not been established, and validation criteria are lacking. This review proposes validation criteria and evaluates P-gp probes to determine probe suitability. Based on these criteria, digoxin, fexofenadine, talinolol and quinidine have limitations to their use and are not recommended for P-gp phenotyping.

Limited Sampling Strategy to Predict AUC of the CYP3A Phenotyping Probe Midazolam in Adults: Application to Various Assay Techniques

Midazolam clearance is used to phenotype hepatic CYP3A activity but requires multiple plasma samples following a single intravenous dose. The authors evaluated the use of a limited sampling scheme, using different assay techniques to determine the reproducibility of such a strategy in estimating midazolam AUC. Seventy‐three healthy adults received midazolam as a single intravenous bolus dose. At least eight plasma samples were collected from each subject and were assayed using either LC/MS/MS or electron capture gas chromatography. Eleven subjects were randomly selected for the training set using stepwise linear regression to determine relationships between midazolam plasma concentrations and AUC. Validation of the predictive equations was done using the remaining 62 subjects. Mean percent error (MPE), mean absolute error (MAE), and root mean square error (RMSE) were calculated to determine bias and precision. Based on the training set, five models were generated with coefficients of determination ranging from 0.87 to 0.95. Validation showed that MPE, MAE, and RMSE values were acceptable for three of the models. Int rasubject reproducibility was good. In addition, training set data from one institution were able to predict data from the other two institutions using other assay techniques. Minimized plasma sampling may provide a simpler method for estimating midazolam AUC for CYP3A phenotyping. A limited sampling strategy is more convenient and cost‐effective than standard sampling strategies and is applicable to more than one assay technique.

The effects of menopause and hormone replacement therapies on prednisolone and erythromycin pharmacokinetics

The pharmacokinetics of oral and intravenous prednisolone and intravenous erythromycin were examined in premenopausal women, postmenopausal women not undergoing hormone replacement therapy, postmenopausal women undergoing estrogen replacement therapy, and postmenopausal women undergoing estrogen and progestin replacement therapy. The unbound clearance of prednisolone was significantly lower in postmenopausal women (11.6 ± 2.3 ml/min/kg) than in premenopausal women (16.6 ± 3.5 ml/min/kg). A comparable difference was also observed in total clearance and in half‐life. The bioavailability and volume of distribution of prednisolone were unaffected by menopausal status. Hormone replacement therapies did not significantly affect prednisolone pharmacokinetics. In contrast to prednisolone elimination, erythromycin elimination, as measured by the erythromycin breath test, was not significantly affected by either menopausal status or hormone replacement therapy. In addition, there was no correlation between prednisolone clearance and the erythromycin breath test result. Although cytochrome P450 3A4 (CYP3A4) has been implicated in steroid hormone metabolism, these results suggest that another enzyme system, which is decreased in menopause (rather than simply an age effect), is also involved in prednisolone metabolism.

Assessment of oral midazolam limited sampling strategies to predict area under the concentration time curve (AUC) during cytochrome P450 (CYP) 3A baseline, inhibition and induction or activation.

UNLABELLED A previous study reported a 2- and 3-timepoint limited sampling strategy (LSS) model accurately predicted oral midazolam area under the concentration time curve (AUC), and thus cytochrome P450 (CYP) 3A activity. OBJECTIVE This study evaluated whether the LSS models predict midazolam AUC during CYP3A baseline, inhibition and induction/activation. MATERIALS AND METHODS Plasma midazolam concentrations from 106 healthy adults from 6 published studies were obtained where oral midazolam was co-administered alone or with ketoconazole, double-strength grapefruit juice, Ginkgo biloba extract, pleconaril, or rifampin. Observed and predicted midazolam AUCs were determined. Bias and precision of the LSS models were determined. RESULTS Contrasting results were observed for the 2- and 3-timepoint LSS models in accurately predicting midazolam AUC during baseline CYP3A conditions. With the exception of 1 study (single dose, double-strength grapefruit juice), the 2- and 3-timepoint LSS models did not accurately predict midazolam AUC during conditions of CYP3A inhibition and induction/activation. CONCLUSION The previously reported 2- and 3-timepoint oral midazolam LSS models are not applicable to the evaluated conditions of CYP3A baseline, inhibition, and induction/ activation.

Evaluation of intravenous midazolam limited sampling models to determine area under the concentration time curve during cytochrome P450 3A baseline, inhibition and induction or activation.

OBJECTIVE This study evaluated if previously published limited sampling models (LSMs) accurately predict midazolam area under the concentration time curve (AUC) during cytochrome P450 (CYP) 3A baseline, inhibition and induction/activation. MATERIALS AND METHODS Plasma midazolam concentrations (n = 108) were obtained where intravenous midazolam was co-administered alone or concomitantly with ketoconazole, itraconazole, aprepitant, rifampin, or pleconaril. Observed AUC was calculated using noncompartmental analysis. Predicted AUC was calculated from the LSMs. Bias and precision were determined by percent mean prediction error (%MPE), percent mean absolute error (%MAE), and percent root mean squared error (%RMSE). RESULTS Contrasting results were observed for LSMs in predicting CYP3A baseline activity, with the majority of studies resulting in unacceptable bias and precision. During CYP3A inhibition, unacceptable bias and precision were observed from single- and 2-time point LSMs. %MAE and %RMSE values exceeded acceptable limits during CYP3A induction with rifampin. Contrasting results were observed with pleconaril. CONCLUSION The contrasting results during CYP3A baseline and induction/activation, as well as the unacceptable bias and precision during CYP3A inhibition, limits the widespread use of the previously published LSMs.

Limited sampling strategy of partial area under the concentration-time curves to estimate midazolam systemic clearance for cytochrome P450 3A phenotyping.

Objective: Intravenous (IV) midazolam is the preferred cytochrome P450 (CYP) 3A probe for phenotyping, with systemic clearance (CL) estimating hepatic CYP3A activity. A limited sampling strategy was conducted to determine whether partial area under the concentration–time curves (AUCs) could reliably estimate midazolam systemic CL during conditions of CYP3A baseline activity, inhibition, and induction/activation. Methods: Midazolam plasma concentrations during CYP3A baseline (n = 93), inhibition (n = 40), and induction/activation (n = 33) were obtained from 7 studies in healthy adults. Noncompartmental analysis determined observed CL (CLobs) and partial AUCs. Linear regression equations were derived from partial AUCs to estimate CL (CLpred) during CYP3A baseline, inhibition, and induction/activation. Preestablished criterion for linear regression analysis was r2 ≥ 0.9. CLpred was compared with CLobs, and relative bias and precision were assessed using percent mean prediction error and percent mean absolute error. Results: During CYP3A baseline and inhibition, all evaluated partial AUCs failed to meet criterion of r2 ≥ 0.9 and/or percent mean absolute error <15%. During CYP3A induction/activation, equations derived from partial AUCs from 0 to 1 hour (AUC0–1), 0 to 2 hours (AUC0–2), and 0 to 4 hours (AUC0–4) were acceptable, with good precision and minimal bias. These equations provided the same conclusions regarding equivalency testing compared with intense sampling. Conclusions: During CYP3A induction/activation, but not baseline or inhibition, midazolam partial AUC0–1, AUC0–2, and AUC0–4 reliably estimated systemic CL and consequently hepatic CYP3A activity in healthy adults.

Evaluation of partial area under the concentration time curve to estimate midazolam apparent oral clearance for cytochrome P450 3A phenotyping.

Abstract Background: Midazolam apparent oral clearance (CLORAL) is used to estimate intestinal and hepatic cytochrome P450 (CYP) 3A activity. A limited sampling approach was performed to access a midazolam partial area under the concentration time curve (AUC) to estimate CLORAL. Methods: Midazolam plasma concentrations from healthy adults were obtained during CYP3A baseline (n=116), inhibition (n=75), and induction or activation (n=66) from seven published studies. Observed CLORAL and partial AUCs of AUC0-2, AUC0-4, AUC0-6, AUC1-2, AUC1-4, AUC2-4, and AUC2-6 were determined by noncompartmental analysis. Subject data were randomly divided into a training set and a validation set. Linear regression equations, derived from partial AUCs, were developed from training set data. Predicted CLORAL was determined from these equations from validation set data. Preset criterion was a coefficient of determination (r2) greater than or equal to 0.9. Bias and precision were evaluated by relative percent mean prediction error (%MPE) and relative percent mean absolute error (%MAE). Results: During CYP3A baseline conditions, all of the evaluated CLORAL equations had unacceptable r2 (range: 0.34–0.86). During CYP3A inhibition, all of the evaluated CLORAL equations had unacceptable %MAE. Acceptable r2, %MPE, and %MAE were observed during CYP3A induction/activation with AUC0-4 (r2=0.99, %MPE=3.9, %MAE=12.5) and AUC1-4 (r2=0.99, %MPE=6%, %MAE=11.1%). The same equations also predicted the extent of CYP3A induction as a lack of equivalence was observed with AUC0-4 and AUC1-4. Conclusions: Midazolam partial AUCs were unable to estimate CYP3A activity during the evaluated baseline and inhibitory conditions. Midazolam CLORAL utilizing a partial AUC0-4 and AUC1-4 was able to estimate CYP3A induction with rifampin and Ginkgo biloba extract.

Interleukin-28B genotype testing to determine response to the combination of pegylated-interferon and ribavirin for the treatment of hepatitis C virus.

Hepatitis C virus (HCV) is a bloodborne infection that is one of the leading causes of liver disease. If left untreated, HCV can lead to cirrhosis, hepatocellular carcinoma, and death. The current standard of care for HCV is a combination of pegylated-interferon (peg-IFN) and ribavirin (RBV) in which the goal of treatment is to decrease complications and death due to HCV. HCV displays genetic polymorphism, where patients with HCV genotype 1 may have higher viral replication rates and are less likely to respond to treatment. These patients require a longer duration of treatment and a higher RBV dose. The interleukin (IL) 28B genotype test is associated with a sustained virologic response (SVR), defined as an undetectable HCV ribonucleic acid (RNA) upon completion of treatment and 24 weeks thereafter.