Houda Hachad

New Antiepileptic Drugs: Review on Drug Interactions

During the Past decade, nine new antiepileptic drugs (AEDs) namely, Felbamate, Gabapentin, Levetiracetam, Lamotrigine, Oxcarbazepine, Tiagabine, Topiramate, Vigabatrin and Zonisamide have been marketed worldwide. The introduction of these drugs increased appreciably the number of therapeutic combinations used in the treatment of epilepsy and with it, the risk of drug interactions. In general, these newer antiepileptic drugs exhibit a lower potential for drug interactions than the classic AEDs, like phenytoin, carbamazepine and valproic acid, mostly because of their pharmacokinetic characteristics. For example, vigabatrin, levetiracetam and gabapentin, exhibit few or no interactions with other AEDs. Felbamate, tiagabine, topiramate and zonisamide are sensitive to induction by known anticonvulsants with inducing effects but are less vulnerable to inhibition by common drug inhibitors. Felbamate, topiramate and oxcarbazepine are mild inducers and may affect the disposition of oral contraceptives with a risk of failure of contraception. These drugs also inhibit CYP2C19 and may affect the disposition of phenytoin. Lamotrigine is eliminated mostly by glucuronidation and is susceptible to inhibition by valproic acid and induction by classic AEDs such as phenytoin, carbamazepine, phenobarbital and primidone.

Are Circulating Metabolites Important in Drug–Drug Interactions?: Quantitative Analysis of Risk Prediction and Inhibitory Potency

The potential of metabolites to contribute to drug–drug interactions (DDIs) is not well defined. The aim of this study was to determine the quantitative role of circulating metabolites in inhibitory DDIs in vivo. The area under the plasma concentration–time curve (AUC) data related to at least one circulating metabolite was available for 71% of the 102 inhibitor drugs identified. Of the 80 metabolites characterized at steady state, 78% had AUCs >10% of that of the parent drug. A comparison of the inhibitor concentration/inhibition constant ([I]/Ki) ratios of metabolites and the respective parent drugs showed that 17 of the 21 (80%) reversible inhibitors studied had metabolites that were likely to contribute to in vivo DDIs, with some metabolites predicted to have inhibitory effects greater than those of the parent drug. The in vivo drug interaction risks associated with amiodarone, bupropion, and sertraline could be identified from in vitro data only, when data pertaining to metabolites were included in the predictions. In conclusion, cytochrome P450 (CYP) inhibitors often have circulating metabolites that contribute to clinically observed CYP inhibition.

Qualitative Analysis of the Role of Metabolites in Inhibitory Drug-Drug Interactions: Literature Evaluation Based on the Metabolism and Transport Drug Interaction Database

Guidance from the Food and Drug Administration on drug interaction studies does not include a specific section on contributions of metabolites to observed inhibitory drug-drug interactions, and the quantitative role of drug metabolites in inhibitory drug-drug interactions is not presently known. The current work was undertaken to evaluate what fraction of inhibitors of common drug-metabolizing enzymes [cytochrome P450 (P450) 1A2, 2E1, 2D6, 2C9, 2C19, 2C8, 2B6, and 3A4] have circulating metabolites that may contribute to observed in vivo interactions. A literature analysis was conducted using the Metabolism and Transport Drug Interaction Database to identify all precipitants (i.e., inhibitors) that cause more than a 20% increase in the area under the plasma concentration-time curve (AUC) of marker substrates. The database, PubMed, and product labels were then used to determine whether circulating metabolites were present after administration of these inhibitors. Of the total of 129 inhibitors identified, 106 were confirmed to have metabolites that circulate in plasma. An additional 14 inhibitors were identified that are extensively metabolized but whose metabolites either have not been identified or have not been investigated. Hence, only 7% of the inhibitors did not have circulating metabolites. Of the 21 potent inhibitors (>or=5-fold increase in AUC) currently known, 17 had circulating metabolites, and the remaining four were all extensively metabolized. On the basis of available in vitro data, 24 of all of the inhibitors were mechanism-based inactivators of P450 enzymes, while 105 were characterized as reversible inhibitors. In vitro evaluation of inhibition potential was conducted for only 32% of the circulating metabolites of the inhibitors. In conclusion, circulating metabolites are often present with inhibitors of P450 enzymes, suggesting a need for increased efforts to characterize the inhibitory potency of metabolites of candidate drugs and for newer models for in vitro to in vivo extrapolations.

Evidence-based guideline: Antiepileptic drug selection for people with HIV/AIDS Report of the Quality Standards Subcommittee of the American Academy of Neurology and the Ad Hoc Task Force of the Commission on Therapeutic Strategies of the International League Against Epilepsy

Objective: To develop guidelines for selection of antiepileptic drugs (AEDs) among people with HIV/AIDS. Methods: The literature was systematically reviewed to assess the global burden of relevant comorbid entities, to determine the number of patients who potentially utilize AEDs and antiretroviral agents (ARVs), and to address AED-ARV interactions. Results and Recommendations: AED-ARV administration may be indicated in up to 55% of people taking ARVs. Patients receiving phenytoin may require a lopinavir/ritonavir dosage increase of ∼50% to maintain unchanged serum concentrations (Level C). Patients receiving valproic acid may require a zidovudine dosage reduction to maintain unchanged serum zidovudine concentrations (Level C). Coadministration of valproic acid and efavirenz may not require efavirenz dosage adjustment (Level C). Patients receiving ritonavir/atazanavir may require a lamotrigine dosage increase of ∼50% to maintain unchanged lamotrigine serum concentrations (Level C). Coadministration of raltegravir/atazanavir and lamotrigine may not require lamotrigine dosage adjustment (Level C). Coadministration of raltegravir and midazolam may not require midazolam dosage adjustment (Level C). Patients may be counseled that it is unclear whether dosage adjustment is necessary when other AEDs and ARVs are combined (Level U). It may be important to avoid enzyme-inducing AEDs in people on ARV regimens that include protease inhibitors or nonnucleoside reverse transcriptase inhibitors, as pharmacokinetic interactions may result in virologic failure, which has clinical implications for disease progression and development of ARV resistance. If such regimens are required for seizure control, patients may be monitored through pharmacokinetic assessments to ensure efficacy of the ARV regimen (Level C).

Antiepileptic drug selection for people with HIV/AIDS: Evidence-based guidelines from the ILAE and AAN

A joint panel of the American Academy of Neurology (AAN) and the International League Against Epilepsy (ILAE) convened to develop guidelines for selection of antiepileptic drugs (AEDs) among people with HIV/AIDS. The literature was systematically reviewed to assess the global burden of relevant comorbid entities, to determine the number of patients who potentially utilize AEDs and antiretroviral agents (ARVs), and to address AED–ARV interactions. Key findings from this literature search included the following: AED–ARV administration may be indicated in up to 55% of people taking ARVs. Patients receiving phenytoin may require a lopinavir/ritonavir dosage increase of approximately 50% to maintain unchanged serum concentrations (Level C). Patients receiving valproic acid may require a zidovudine dosage reduction to maintain unchanged serum zidovudine concentrations (Level C). Coadministration of valproic acid and efavirenz may not require efavirenz dosage adjustment (Level C). Patients receiving ritonavir/atazanavir may require a lamotrigine dosage increase of approximately 50% to maintain unchanged lamotrigine serum concentrations (Level C). Coadministration of raltegravir/atazanavir and lamotrigine may not require lamotrigine dosage adjustment (Level C). Coadministration of raltegravir and midazolam may not require midazolam dosage adjustment (Level C). Patients may be counseled that it is unclear whether dosage adjustment is necessary when other AEDs and ARVs are combined (Level U). It may be important to avoid enzyme‐inducing AEDs in people on ARV regimens that include protease inhibitors or nonnucleoside reverse transcriptase inhibitors because pharmacokinetic interactions may result in virologic failure, which has clinical implications for disease progression and development of ARV resistance. If such regimens are required for seizure control, patients may be monitored through pharmacokinetic assessments to ensure efficacy of the ARV regimen (Level C).

A useful tool for drug interaction evaluation: The University of Washington Metabolism and Transport Drug Interaction Database

The Metabolism and Transport Drug Interaction Database (http://www.druginteractioninfo.org) is a web-based research and analysis tool developed in the Department of Pharmaceutics at the University of Washington. The database has the largest manually curated collection of data related to drug interactions in humans. The tool integrates information from the literature, public repositories, reference textbooks, guideline documents, product prescribing labels and clinical review sections of new drug approval (NDA) packages. The databases easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine kinetics information for drug-metabolising enzymes and transporters, to assess the extent of in vivo drug interaction studies, as well as case reports for drugs, therapeutic proteins, food products and herbal derivatives. This review provides a brief description of the database organisation, its search functionalities and examples of use.

Importance of multi-P450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude and prediction from in vitro data

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

Quantitative correlations among CYP3A sensitive substrates and inhibitors: literature analysis.

As a follow-up to the new classification of CYP3A inhibitors, the present work was undertaken to search for quantitative correlations of AUC ratios between sensitive substrates and midazolam (reference). A large set of clinical studies was obtained utilizing the M&T Drug Interaction Database, and recent Product Labels. Linear relationships were found between midazolam and four CYP3A substrates: simvastatin, buspirone, triazolam and eplerenone. Simvastatin and buspirone were consistently more sensitive than midazolam, independent of the inhibitor. Quantitative correlations of AUC ratios between four CYP3A inhibitors (fluconazole, erythromycin, verapamil, diltiazem) and ketoconazole (400 mg/day) were also uncovered. The average potencies of these inhibitors relative to ketoconazole were 27% for erythromycin, 17% for fluconazole and 19% for verapamil.

Prediction of Maximum Exposure in Poor Metabolizers Following Inhibition of Nonpolymorphic Pathways

Marked increases in exposure of some substrates have been noted in poor metabolizers given inhibitors of nonpolymorphic enzymes. Among the small number of clinical trials conducted to investigate this problem, a wide variation in the degree of maximum exposure ratios (area under the curve in poor metabolizers in the presence of inhibitor/area under the curve in extensive metabolizers) among the different substrates has been reported, with some trials reporting profound increases (> tenfold), and others demonstrating less remarkable changes (< twofold). The conduct of such trials raises safety concerns for the trial participants, in addition to other ethical and logistic concerns; therefore, the possibility was investigated that maximum exposure (area under the curve in poor metabolizers in the presence of an inhibitor) could be predicted, and that substrates susceptible to large increases in exposure could be identified. Existing clinical trials were identified by data mining the literature. A theoretical approach was developed to predict maximum exposure in poor metabolizers from studies in extensive metabolizers treated with an inhibitor of the nonpolymorphic pathway. Maximum exposure was predicted in eleven instances and the mean percentage difference between predicted and observed was 11.9%. Substrates with a fraction of substrate dose metabolized by the polymorphic enzyme (fm(POLY)) higher than 75% are at greater risk of exhibiting maximum exposure ratios of more than tenfold.

Relationship Between Extent of Inhibition and Inhibitor Dose: Literature Evaluation Based on the Metabolism and Transport Drug Interaction Database

A comprehensive search of the literature was undertaken using the Metabolism and Transport Drug Interaction Database (http://depts.washington.edu/didbase/) to evaluate the relationship between extent of inhibition and inhibitor dose. The search included reversible and irreversible inhibitors in studies conducted in the period 1966-2003. Only twelve inhibitors met the criterion of the search: study population exposed to more than one dose of inhibitor within a given study design. Six were reversible inhibitors: ciprofloxacin, enoxacin, felbamate, fluconazole, fluvoxamine and ketoconazole. The other six (cimetidine, diltiazem, disulfiram, paroxetine, verapamil and ritonavir) are considered irreversible inhibitors. Most of the AUC/Clearance data available for both types of inhibitors suggested evidence of dose-dependent inhibition. In the case of reversible inhibitors, the evidence of dose-dependent inhibition is consistent with a number of recent studies suggesting the determination of in vivo inhibition constants based on plasma concentration of inhibitor.