Yoshiyuki Ohno

General Framework for the Quantitative Prediction of CYP3A4-Mediated Oral Drug Interactions Based on the AUC Increase by Coadministration of Standard Drugs

BackgroundCytochrome P450 (CYP) 3A4 is the most prevalent metabolising enzyme in the human liver and is also a target for various drug interactions of significant clinical concern. Even though there are numerous reports regarding drug interactions involving CYP3A4, it is far from easy to estimate all potential interactions, since too many drugs are metabolised by CYP3A4. For this reason, a comprehensive framework for the prediction of CYP3A4-mediated drug interactions would be of considerable clinical importance.ObjectiveThe objective of this study was to provide a robust and practical method for the prediction of drug interactions mediated by CYP3A4 using minimal in vivo information from drug-interaction studies, which are often carried out early in the course of drug development.Data sourcesThe analysis was based on 113 drug-interaction studies reported in 78 published articles over the period 1983–2006. The articles were used if they contained sufficient information about drug interactions. Information on drug names, doses and the magnitude of the increase in the area under the concentration-time curve (AUC) were collected.MethodsThe ratio of the contribution of CYP3A4 to oral clearance (CRCYP3A4) was calculated for 14 substrates (midazolam, alprazolam, buspirone, cerivastatin, atorvastatin, ciclosporin, felodipine, lovastatin, nifedipine, nisoldipine, simvastatin, triazolam, zolpidem and telithromycin) based on AUC increases observed in interaction studies with itraconazole or ketoconazole. Similarly, the time-averaged apparent inhibition ratio of CYP3A4 (IRCYP3A4) was calculated for 18 inhibitors (ketoconazole, voriconazole, itraconazole, telithromycin, clarithromycin, saquinavir, nefazodone, erythromycin, diltiazem, fluconazole, verapamil, cimetidine, ranitidine, roxithromycin, fluvoxamine, azithromycin, gatifloxacin and fluoxetine) primarily based on AUC increases observed in drug-interaction studies with midazolam. The increases in the AUC of a substrate associated with coadministration of an inhibitor were estimated using the equation 1/(1 - CRCYP3A4 · IRCYP3A4), based on pharmacokinetic considerations.ResultsThe proposed method enabled predictions of the AUC increase by interactions with any combination of these substrates and inhibitors (total 251 matches). In order to validate the reliability of the method, the AUC increases in 60 additional studies were analysed. The method successfully predicted AUC increases within 67–150% of the observed increase for 50 studies (83%) and within 50–200% for 57 studies (95%). Midazolam is the most reliable standard substrate for evaluation of the in vivo inhibition of CYP3A4. The present analysis suggests that simvastatin, lovastatin and buspirone can be used as alternatives. To evaluate the in vivo contribution of CYP3A4, ketoconazole or itraconazole is the selective inhibitor of choice.ConclusionThis method is applicable to (i) prioritise clinical trials for investigating drug interactions during the course of drug development and (ii) predict the clinical significance of unknown drug interactions. If a drug-interaction study is carefully designed using appropriate standard drugs, significant interactions involving CYP3A4 will not be missed. In addition, the extent of CYP3A4-mediated interactions between many other drugs can be predicted using the current method.

General Framework for the Prediction of Oral Drug Interactions Caused by CYP3A4 Induction from In Vivo Information

AbstractBackground: Induction of cytochrome P450 (CYP) 3A4 potentially reduces the blood concentrations of substrate drugs to less than one-tenth, which results in ineffective pharmacotherapy. Although the prediction of drug-drug interactions (DDIs) that are mediated by induction of CYP3A4 has been performed mainly on the basis of in vitro information, such methods have met with limited success in terms of their accuracy and applicability. Therefore, a realistic method for the prediction of CYP3A4-mediated inductive DDIs is of major clinical importance. Objective: The objective of the present study was to construct a robust and accurate method for the prediction of CYP3A4-mediated inductive DDIs. Such a method was developed on the basis of the principle applied for prediction of inhibitory DDIs in a previous report. A unique character of this principle is that the extent of alterations in the area under the plasma concentration-time curve (AUC) is predicted on the basis of in vivo information from minimal clinical studies without using in vitro data. Methods: The analysis is based on 42 DDI studies in humans reported in 37 published articles over the period 1983–2007. Kinetic analysis revealed that the reduction in the AUC of a substrate of CYP3A4 produced by consecutive administration of an inducer of CYP3A4 could be approximated by the equation 1/(1 + CRCYP3A4 · ICCYP3A4), where CRCYP3A4 is the ratio of the apparent contribution of CYP3A4 to the oral clearance of a substrate and ICCYP3A4 is the apparent increase in clearance of a substrate produced by induction of CYP3A4. Using this equation, the ICCYP3A4 was calculated for seven inducers (bosentan, carbamazepine, efavirenz, phenytoin, pioglitazone, rifampicin [rifampin], and St John’s wort [hypericum]) on the basis of the reduction in the AUC of a coadministered standard substrate of CYP3A4, such as simvastatin, in ten DDI studies. The CRCYP3A4 was calculated for 22 substrates on the basis of the previously reported method from inhibitory DDI studies using a potent CYP3A4 inhibitor such as itraconazole or ketoconazole. Results: The proposed method enabled the prediction of AUC reduction by CYP3A4 induction with any combination of these substrates and inducers (total 154 matches). To assess the accuracy of the prediction, the AUC reductions in 32 studies were analysed. We found that the magnitude of the deviation between the mean values of the observed and predicted AUCs of all substrate drugs was <20% of the AUCs of the respective substrate drugs before administration of the inducers. In addition, rifampicin was found to be the most potent inducer among the compounds analysed in the present study, with an ICCYP3A4 value of 7.7, followed by phenytoin and carbamazepine, with values of 4.7 and 3.0, respectively. The ICCYP3A4 values of the other CYP3A4 inducers analysed in the present study were approximately 1 or less, which suggests that the AUCs of coadministered drugs may not be reduced to less than approximately half, even if the drug is metabolized solely by CYP3A4. Conclusion: By using the method reported in the present study, the susceptibilities of a substrate drug of CYP3A4 to inductive DDIs can be predicted quantitatively. It was indicated that coadministration of rifampicin, Phenytoin and carbamazepine may reduce plasma AUCs to less than half for a broad range of CYP3A4 substrate drugs, with CRCYP3A4 values greater than 0.13, 0.21 and 0.33, respectively.

Prediction of pharmacokinetic drug-drug interaction caused by changes in cytochrome P450 activity using in vivo information

The aim of the present paper was to present an overview of the current status of the methods used to predict the magnitude of pharmacokinetic drug-drug interactions (DDIs) which are caused by apparent changes in cytochrome P450 (CYP) activity with an emphasis on a method using in vivo information. In addition, more than a hundred representative CYP substrates, inhibitor and inducer drugs involved in significant pharmacokinetic DDIs were selected from the literature and are listed. Although the magnitude of DDIs has been conventionally predicted based on in vitro experiments, their predictability is restricted occasionally due to several difficulties, including a precise determination of the unbound inhibitor concentrations at the enzyme site and a reliable in vitro measurement of the inhibition constant (K(i)). Alternatively, a simple method has been recently proposed for the prediction of the magnitude of DDIs based on information fully available from in vivo clinical studies. The new in vivo-based method would be applicable to the adjustment of dose regimens in actual pharmacotherapy situations although it requires a prior clinical study for the prediction. In this review, theoretical and quantitative relationships between the in vivo- and the in vitro-based prediction methods are considered. One of the interesting outcomes of the consideration is that the K(i)-normalized dose (dose/in vitro K(i)) of larger than approximately 20L (2-200L, when variability is considered) may be a pragmatic index which predicts significant in vivo DDIs. In the last part of the article, the relevance of the inclusion of the in vivo-based method into the process of new drug development is discussed for good prediction of in vivo DDIs.

A Proposal for a Pharmacokinetic Interaction Significance Classification System (PISCS) Based on Predicted Drug Exposure Changes and Its Potential Application to Alert Classifications in Product Labelling

Background and ObjectivePharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK.MethodsA matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body.ResultsAmong 220 combinations of drugs investigated, estimated exposure changes were more than 5-fold for 41 combinations in which ten combinations were not alerted in the product labelling at least in one country; these involved buspirone, nisoldipine and felodipine as substrates, and ketoconazole, voriconazole, telithromycin, clarithromycin and nefazodone as inhibitors. For those drug combinations, the alert classifications were anticipated as potentially inappropriate. In the current product labelling, many intercountry differences were also noted. Considering the relationships between previously observed exposure changes and the current alert classifications, the boundaries between ‘contraindication’ and ‘warning/caution’ were determined as a 7-fold exposure increase for statins and CCBs, and as a 4-fold increase for BZPs. PISCS clearly discriminated these drug combinations in accordance with the determined boundaries. Classifications by PISCS were expected to be valid even for future drugs because the classifications were made by zones, not by designating individual drugs.ConclusionThe present analysis suggested that many current alert classifications were potentially inappropriate especially for drug combinations where pharmacokinetics had not been evaluated. It is expected that PISCS would contribute to constructing a leak-less alerting system for a broad range of pharmacokinetic DDIs. Further validation of PISCS is required in clinical studies with key drug combinations, and its extension to other CYP and metabolizing enzymes remains to be achieved.

Pharmacokinetic and Pharmacodynamic Analysis of Systemic Effect of Topically Applied Timolol Maleate Ophthalmic Gelling Vehicle (Rysmon® TG)

Purpose: The objective of this study was to evaluate the degree of systemic absorption and the systemic side effect after instillation of timolol maleate ophthalmic gelling vehicle in human. Methods: A volunteer study was employed, and a randomized crossover design with the two phases was used. In one phase, the volunteers instilled a single drop of the 0.5% timolol maleate ophthalmic gelling vehicle; in the other phase, the volunteers instilled a single drop of the 0.5% timolol maleate ophthalmic solution. The plasma concentration of timolol and the heart rates were studied during the following 120 min and 60 min, respectively. Results: The area under the blood concentration time curve (AUC) in timolol maleate ophthalmic gelling vehicle was lower than that in timolol maleate ophthalmic solution (p < 0.05). No differences were observed in heart rates between ophthalmic gelling vehicle and ophthalmic solution. The correlation between the calculated occupancy of beta-adrenergic receptors and the systemic side effects after instillation could be successfully analyzed with a pharmacokinetic and pharmacodynamic model, showing the predictability of the model for the systemic side effects of timolol. Conclusions: The result of our analysis clearly shows that timolol maleate ophthalmic gelling vehicle reduced the systemic absorption below that of ophthalmic solution, but the degree in difference of systemic effects was negligible.

Assessment of Drug–Drug Interaction between Warfarin and Aprepitant and Its Effects on PT-INR of Patients Receiving Anticancer Chemotherapy

Aprepitant is a known inducer of CYP2C9, the main warfarin-metabolizing enzyme. Consequently, co-administration of these two drugs may result in reduction of the anticoagulation activity of warfarin. However, the nature and degree of time-dependent changes in prothrombin time international normalized ratio (PT-INR) after aprepitant and warfarin co-treatment in patients receiving anticancer chemotherapy has not been elucidated. We retrospectively examined the changes in warfarin dose, PT-INR, and warfarin sensitivity index (WSI; average of PT-INR value/average of daily warfarin dose) during four weeks, i.e., one week before and three weeks after aprepitant administration. The mean and standard deviation values of WSI for one week before and one, two, and three weeks after the beginning of aprepitant administration were 0.51±0.22 (1.00, n=34), 0.74±0.30 (1.53±0.59, n=30), 0.38±0.15 (0.82±0.22, n=28), and 0.46±0.29 (0.87±0.23, n=24), respectively. Values in parentheses represent relative changes versus WSI of one week before and number of subjects. Although the mean value of WSI significantly increased one week after aprepitant administration compared to that at one week before the administration, it in turn significantly decreased two weeks after compared to one week before (paired t-test, p<0.05 after Bonferoni correction). In patients taking warfarin, PT-INR should be carefully monitored for at least two weeks after the beginning of aprepitant administration because it may fluctuate with both aprepitant and chemotherapy during this period.