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

Pharmacokinetic interaction between the CYP3A4 inhibitor ketoconazole and the hormone drospirenone in combination with ethinylestradiol or estradiol

AIMS

The present study was conducted to investigate the influence of the strong CYP3A4 inhibitor ketoconazole (KTZ) on the pharmacokinetics of drospirenone (DRSP) administered in combination with ethinylestradiol (EE) or estradiol (E2).

METHODS

This was a randomized, multicentre, open label, one way crossover, fixed sequence study with two parallel treatment arms. A group sequential design allowed terminating the study for futility after first study cohort. About 50 healthy young women were randomized 1 : 1 to 'DRSP/EE' or 'DRSP/E2'. Subjects in the 'DRSP/EE' group received DRSP 3 mg/EE 0.02 mg (YAZ®, Bayer) once daily for 21 to 28 days followed by DRSP 3 mg/EE 0.02 mg once daily plus KTZ 200 mg twice daily for 10 days. Subjects in the 'DRSP/E2' group received DRSP 3 mg/E2 1.5 mg (research combination) once daily for 21 to 28 days followed by DRSP 3 mg/E2 1.5 mg once daily plus KTZ 200 mg twice daily for 10 days.

RESULTS

Oral co-administration of DRSP/EE or DRSP/E2 and KTZ resulted in an increase in DRSP exposure (AUC(0,24 h)) in both treatment groups: DRSP/EE group: 2.68-fold DRSP increase (90% CI 2.44, 2.95); DRSP/E2 group: 2.30-fold DRSP increase (90% CI 2.08, 2.54). EE and estrone (metabolite of E2) exposures were increased ~1.4-fold whereas E2 exposure was largely unaffected by KTZ co-administration.

CONCLUSIONS

A moderate pharmacokinetic drug-drug interaction between DRSP and KTZ was demonstrated in this study. No relevant changes of medical concern were detected in the safety data collected in this study.

Modulation of Cytochrome P450 Activity by 18β-Glycyrrhetic Acid and its Consequence on Buspirone Pharmacokinetics in Rats

The aim of this study was to elucidate the inhibition mechanism of 18β-glycyrrhetic acid (GLY) on cytochrome P450 (CYP) activity and in vivo pharmacokinetic consequences of single GLY dose in rats. An in vitro CYP inhibition study in rat liver microsomes (RLM) was conducted using probe substrates for CYPs. Then, an in vivo pharmacokinetics of intravenous and oral buspirone (BUS), a probe substrate for CYP3A, was studied with the concurrent administration of oral GLY in rats. In the in vitro CYP inhibition study, CYP3A was involved in the metabolism of GLY. Moreover, GLY inhibited CYP3A activity with an IC50 of 20.1 ± 10.7 μM via a mixed inhibition mechanism. In the in vivo rat pharmacokinetic study, single oral GLY dose enhanced the area under plasma concentration-time curve (AUC) of intravenous and oral BUS, but the extent of increase in AUC was only minimal (1.12-1.45 fold). These results indicate that GLY can inhibit the in vitro CYP3A-mediated drug metabolism in RLM via a mixed inhibition mechanism. However, the impact of single oral GLY dose on the pharmacokinetics of BUS in rats was limited, showing that GLY could function as merely a weak inhibitor for CYP3A-mediated drug metabolism in vivo. Copyright © 2015 John Wiley & Sons, Ltd.

The effects of antiepileptic inducers in neuropsychopharmacology, a neglected issue. Part I: A summary of the current state for clinicians

The literature on inducers in epilepsy and bipolar disorder is seriously contaminated by false negative findings. This is part i of a comprehensive review on antiepileptic drug (AED) inducers using both mechanistic pharmacological and evidence-based medicine to provide practical recommendations to neurologists and psychiatrists concerning how to control for them. Carbamazepine, phenobarbital and phenytoin, are clinically relevant AED inducers; correction factors were calculated for studied induced drugs. These correction factors are rough simplifications for orienting clinicians, since there is great variability in the population regarding inductive effects. As new information is published, the correction factors may need to be modified. Some of the correction factors are so high that the drugs (e.g., bupropion, quetiapine or lurasidone) should not co-prescribed with potent inducers. Clobazam, eslicarbazepine, felbamate, lamotrigine, oxcarbazepine, rufinamide, topiramate, vigabatrin and valproic acid are grouped as mild inducers which may (i)be inducers only in high doses; (ii)frequently combine with inhibitory properties; and (iii)take months to reach maximum effects or de-induction, definitively longer than the potent inducers. Potent inducers, definitively, and mild inducers, possibly, have relevant effects in the endogenous metabolism of (i)sexual hormones, (ii) vitamin D, (iii)thyroid hormones, (iv)lipid metabolism, and (v)folic acid.

Pharmacokinetic interaction of intravenous fentanyl with ketoconazole

Fentanyl is primarily metabolized by CYP3A, but has also been suggested to act as a weak inhibitor of CYP3A. We investigated the influence of CYP3A inhibition by ketoconazole on the pharmacokinetics of intravenously administered fentanyl and the effect of fentanyl on CYP3A activity. A prospective, open-label, randomized, monocentre, crossover study was conducted in 16 healthy volunteers. They received fentanyl alone (5 microgram per kilogram) or fentanyl plus ketoconazole (200 milligram orally B.I.D. over 2 days). Naloxone (2 × 0.2 milligram i.v.) was given simultaneously with fentanyl to mitigate any opioid effect. Midazolam was administered as a CYP3A probe drug. Fentanyl and its metabolites were quantified by LC/MS/MS in blood and urine samples obtained over 24 hour. Exposure of fentanyl (AUC0- ∞ ) was significantly increased to 133% and systemic clearance was reduced to 78% by ketoconazole, norfentanyl formation was significantly delayed and partial metabolic clearance decreased to 18%. Fentanyl had no influence on midazolam exposure and CYP3A activity whereas ketoconazole decreased CYP3A activity to 13%. Although fentanyl N-dealkylation is substantially inhibited by ketoconazole, exposure of fentanyl itself increased by one third only. Clinically fentanyl dosage adjustments may become necessary when ketoconazole or other strong CYP3A inhibitors are given simultaneously. Fentanyl itself does not influence CYP3A activity.

Prediction of Metabolic Interactions With Oxycodone via CYP2D6 and CYP3A Inhibition Using a Physiologically Based Pharmacokinetic Model

Evaluation of a potential risk of metabolic drug–drug interactions (DDI) is of high importance in the clinical setting. In this study, a physiologically based pharmacokinetic (PBPK) model was developed for oxycodone and its two primary metabolites, oxymorphone and noroxycodone, in order to assess different DDI scenarios using published in vitro and in vivo data. Once developed and refined, the model was able to simulate pharmacokinetics of the three compounds and the DDI extent in case of coadministration with an inhibitor, as well as the oxymorphone concentration variation between CYP2D6 extensive metabolizers (EM) and poor metabolizers (PM). The reliability of the model was tested against published clinical studies monitoring different inhibitors and dose regimens, and all predicted area under the concentration–time curve (AUC) ratios were within the twofold acceptance range. This approach represents a strategy to evaluate the impact of coadministration of different CYP inhibitors using mechanistic incorporation of drug-dependent and system-dependent available in vitro and in vivo data.

The SAR development of substituted purine derivatives as selective CB2 agonists for the treatment of chronic pain

Osteoarthritis (OA) and the associated joint pain are highly prevalent and a leading cause of disability. We have previously reported the identification of a series of purines as selective CB2 agonists and the identification of compound 1 as a clinical candidate for the treatment of joint pain. In this article we describe the further SAR development of the purine scaffold leading to the discovery of compound 6 as a potent, CNS penetrating CB2 agonist with high selectivity for CB2 over CB1 and oral efficacy in animal models of chronic OA pain.

Reliability and extension of quantitative prediction of CYP3A4-mediated drug interactions based on clinical data

An approach was proposed in 2007 for quantitative predictions of cytochrome P450 (CYP)3A4-mediated drug-drug interactions. It is based on two characteristic parameters: the contribution ratio (CR; i.e., the fraction of victim drug clearance by CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when given with the inhibitor and the AUC of the victim drug when it is given alone. So far, these parameters were established for 21 substrates and 17 inhibitors. The goals of our study were to test the assumption of substrate independence of the potency of inhibitors in vivo and to estimate the CR and IR for an extended list of substrates and inhibitors of CYP3A4. The assumption of independence of IRs from the substrate was evaluated on a set of eight victim drugs and eight inhibitors. Forty-four AUC ratios were available. This assumption was rejected in four cases, but it did not result in more than a twofold error in AUC ratio predictions. The extended list of substrates and inhibitors was defined by a thorough literature search. Fifty-nine AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 37 substrates and 25 inhibitors, respectively. The mean prediction error of the ratios was 0.02, while the mean absolute prediction error was 0.58. Predictive distributions for 917 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far.

A semiphysiological population pharmacokinetic model for dynamic inhibition of liver and gut wall cytochrome P450 3A by voriconazole

BACKGROUND

Accurate predictions of cytochrome P450 (CYP) 3A-mediated drug-drug interactions (DDIs) account for dynamic changes of CYP3A activity at both major expression sites (liver and gut wall) by considering the full pharmacokinetic profile of the perpetrator and the substrate. Physiological-based in vitro-in vivo extrapolation models have become of increasing interest. However, due to discrepancies between the predicted and observed magnitude of DDIs, the role of models fully based on in vivo data is still essential.

OBJECTIVE

The primary objective of this study was to develop a coupled dynamic model for the interaction of the CYP3A inhibitor voriconazole and the prototypical CYP3A substrate midazolam.

METHODS

Raw concentration data were obtained from a DDI study. Ten subjects were given either no pretreatment (control) or voriconazole twice daily orally. Midazolam was given either intravenously or orally after the last voriconazole dose and during control phases. Data analysis was performed by the population pharmacokinetic approach using non-linear mixed effects modelling (NONMEM 7.2.0). Model evaluation was performed using visual predictive checks and bootstrap analysis.

RESULTS

A semiphysiological model was able to describe the pharmacokinetics of midazolam, its major metabolite and voriconazole simultaneously. By considering the temporal disposition of all three substances in the liver and gut wall, a time-varying CYP3A inhibition process was implemented. Only the incorporation of hypothetical enzyme site compartments resulted in an adequate fit, suggesting a sustained inhibitory effect through accumulation. Novel key features of this analysis are the identification of (1) an apparent sustained inhibitory effect by voriconazole due to a proposed quasi accumulation at the enzyme site, (2) a significantly reduced inhibitory potency of intravenous voriconazole for oral substrates, (3) voriconazole as a likely uridine diphosphate glucuronosyltransferase (UGT) 2B inhibitor and (4) considerable sources of interindividual variability.

CONCLUSION

The proposed semiphysiological modelling approach generated a mechanistic description of the complex DDI occurring at major CYP3A expression sites and thus may serve as a powerful tool to maximise information acquired from clinical DDI studies. The model has been shown to draw precise and accurate predictions. Therefore, simulations based on this kind of models may be used for various clinical scenarios to improve pharmacotherapy.

Determination of the most influential sources of variability in tacrolimus trough blood concentrations in adult liver transplant recipients: a bottom-up approach

Tacrolimus, an immunosuppressant drug, presents a narrow therapeutic window and a large pharmacokinetic variability with poor correlation between drug dosing regimen and blood concentration. The objective was to identify predictive factors influencing tacrolimus trough concentrations (C0) using a bottom-up approach. A physiologically based pharmacokinetic (PBPK) model of tacrolimus was proposed, taking into account the body weight, the proportion of fat (P(fat)), hematocrit, lipid fraction of organs, typical intrinsic clearance (CLi(typ)), CYP3A5 genotype of liver donor, plasma unbound fraction of tacrolimus (fu(p)), and concomitant drugs (CYP3A4 inhibitors). For the evaluation of the PBPK model, mean C0 and concentrations 2 h after oral dose of tacrolimus were compared with those from 66 liver transplant recipients included in a multicentric pharmacokinetic study and were found very close. Tacrolimus concentration profiles were simulated in a virtual population defined by a set of covariate values similar to those from the real population. The sensitivity of tacrolimus C0 with respect to each covariate has been tested to identify the most influential ones. With the range of covariate values tested, the impact of each covariate on tacrolimus C0 may be ranked as follows: fu(p), CLi(typ), bioavailability, body weight, hematocrit, CYP3A5 polymorphism, P(fat), and CYP3A4 inhibitory drug-drug interactions. Values for initial dosing regimen of tacrolimus in order to reach a C0 of 10 ng/ml at day 5 (assuming a constant dosing schedule) as a function of CYP3A5 donor genotype and patient's hematocrit and body weight are proposed.

Pharmacokinetic drug-drug interactions between 1,4-dihydropyridine calcium channel blockers and statins: factors determining interaction strength and relevant clinical risk management

Background

Coadministration of 1,4-dihydropyridine calcium channel blockers (DHP-CCBs) with statins (or 3-hydroxy-3-methylglutaryl-coenzyme A [HMG-CoA] reductase inhibitors) is common for patients with hypercholesterolemia and hypertension. To reduce the risk of myopathy, in 2011, the US Food and Drug Administration (FDA) Drug Safety Communication set a new dose limitation for simvastatin, for patients taking simvastatin concomitantly with amlodipine. However, there is no such dose limitation for atorvastatin for patients receiving amlodipine. The combination pill formulation of amlodipine/atorvastatin is available on the market. There been no systematic review of the pharmacokinetic drug–drug interaction (DDI) profile of DHP-CCBs with statins, the underlying mechanisms for DDIs of different degree, or the corresponding management of clinical risk.

Methods

The relevant literature was identified by performing a PubMed search, covering the period from January 1987 to September 2013. Studies in the field of drug metabolism and pharmacokinetics that described DDIs between DHP-CCB and statin or that directly compared the degree of DDIs associated with cytochrome P450 (CYP)3A4-metabolized statins or DHP-CCBs were included. The full text of each article was critically reviewed, and data interpretation was performed.

Results

There were three circumstances related to pharmacokinetic DDIs in the combined use of DHP-CCB and statin: 1) statin is comedicated as the precipitant drug (pravastatin–nimodipine and lovastatin–nicardipine); 2) statin is comedicated as the object drug (isradipine–lovastatin, lacidipine–simvastatin, amlodipine–simvastatin, benidipine-simvastatin, azelnidipine– simvastatin, lercanidipine–simvastatin, and amlodipine–atorvastatin); and 3) mutual interactions (lercanidipine–fluvastatin). Simvastatin has an extensive first-pass effect in the intestinal wall, whereas atorvastatin has a smaller intestinal first-pass effect. The interaction with simvastatin seems mainly driven by CYP3A4 inhibition at the intestinal level, whereas the interaction with atorvastatin is more due to hepatic CYP3A4 inhibition. The interaction of CYP3A4 inhibitor with simvastatin has been more pronounced compared with atorvastatin. From the current data, atorvastatin seems to be a safer CYP3A4-statin for comedication with DHP-CCB. There is no convincing evidence that amlodipine is an unusual DHP-CCB, either as a precipitant drug or as an object drug, from the perspective of CYP3A4-mediated drug metabolism. Amlodipine may have interactions with CYP3A5 in addition to CYP3A4, which may explain its particular characteristics in comparison with other DHP-CCBs. The degree of DDIs between the DHP-CCB and statin and the clinical outcome depends on many factors, such as the kind of statin, physicochemical proprieties of the DHP-CCB, the dose of either the precipitant drug or the object drug, the sex of the patient (eg, isradipine–lovastatin), route of drug administration (eg, oral versus intravenous nicardipine–lovastatin), the administration schedule (eg, nonconcurrent dosing method versus concurrent dosing method), and the pharmacogenetic status (eg, CYP3A5-nonexpressers versus CYP3A5-expressers).

Conclusion

Clinical professionals should enhance risk management regarding the combination use of two classes of drugs by increasing their awareness of the potential changes in therapeutic efficacy and adverse drug reactions, by rationally prescribing alternatives, by paying attention to dose adjustment and the administration schedule, and by review of the appropriateness of physician orders. Further study is needed – the DDIs between DHP-CCBs and statins have not all been studied in humans, from either a pharmacokinetic or a clinical perspective; also, the strength of the different pharmacokinetic interactions of DHP-CCBs with statins should be addressed by systematic investigations.

Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data

Induction of cytochrome P450 3A4 (CYP3A4) expression is often implicated in clinically relevant drug-drug interactions (DDI), as metabolism catalyzed by this enzyme is the dominant route of elimination for many drugs. Although several DDI models have been proposed, none have comprehensively considered the effects of enzyme transcription/translation dynamics on induction-based DDI. Rifampicin is a well-known CYP3A4 inducer, and is commonly used as a positive control for evaluating the CYP3A4 induction potential of test compounds. Herein, we report the compilation of in vitro induction data for CYP3A4 by rifampicin in human hepatocytes, and the transcription/translation model developed for this enzyme using an extended least squares method that can account for inherent inter-individual variability. We also developed physiologically based pharmacokinetic (PBPK) models for the CYP3A4 inducer and CYP3A4 substrates. Finally, we demonstrated that rifampicin-induced DDI can be predicted with reasonable accuracy, and that a static model can be used to simulate DDI once the blood concentration of the inducer reaches a steady state following repeated dosing. This dynamic PBPK-based DDI model was implemented on a new multi-hierarchical physiology simulation platform named PhysioDesigner.

Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach

Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug-genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.

In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates

We present a unified quantitative approach to predict the in vivo alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug-drug interactions (DDI). An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of -0.15 and -0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.

Interaction between Red Yeast Rice and CYP450 Enzymes/P-Glycoprotein and Its Implication for the Clinical Pharmacokinetics of Lovastatin

Red yeast rice (RYR) can reduce cholesterol through its active component, lovastatin. This study was to investigate the pharmacokinetic properties of lovastatin in RYR products and potential RYR-drug interactions. Extracts of three registered RYR products (LipoCol Forte, Cholestin, and Xuezhikang) were more effective than pure lovastatin in inhibiting the activities of cytochrome P450 enzymes and P-glycoprotein. Among CYP450 enzymes, RYR showed the highest inhibition on CYP1A2 and CYP2C19, with comparable inhibitory potencies to the corresponding typical inhibitors. In healthy volunteers taking the RYR product LipoCol Forte, the pharmacokinetic properties of lovastatin and lovastatin acid were linear in the dose range of 1 to 4 capsules taken as a single dose and no significant accumulation was observed after multiple dosing. Concomitant use of one LipoCol Forte capsule with nifedipine did not change the pharmacokinetics of nifedipine. Yet, concomitant use of gemfibrozil with LipoCol Forte resulted in a significant increase in the plasma concentration of lovastatin acid. These findings suggest that the use of RYR products may not have effects on the pharmacokinetics of concomitant comedications despite their effects to inhibit the activities of CYP450 enzymes and P-gp, whereas gemfibrozil affects the pharmacokinetics of lovastatin acid when used concomitantly with RYR products.

Methods for the quantitative evaluation and prediction of CYP enzyme induction using human in vitro systems

IMPORTANCE OF THE FIELD

For successful drug development, it is important to investigate the potency of candidate drugs causing drug-drug interactions (DDI) during the early stages of development. The most common mechanisms of DDIs are the inhibition and induction of CYP enzymes. Therefore, it is important to develop co.mpounds with lower potencies for CYP enzyme induction.

AREAS COVERED IN THIS REVIEW

The aim of the present paper is to present an overview of the current knowledge of CYP induction mechanisms, particularly focusing on the transcriptional gene activation mediated by pregnane X receptor, aryl hydrocarbon receptor and constitutive androstane receptor. The adoptable options of in vitro assay methods for evaluating CYP induction are also summarized. Finally, we introduce a method for the quantitative prediction of CYP3A4 induction considering the turnover of CYP3A4 mRNA and protein in hepatocytes based on the data obtained from a reporter gene assay.

WHAT THE READER WILL GAIN

In order to predict in vivo CYP enzyme induction quantitatively based on in vitro information, an understanding of the physiological induction mechanisms and the features of each in vitro assay system is essential. We also present the estimation method of in vivo CYP induction potency of each compound based on the in vitro data which are routinely obtained but not necessarily utilized maximally in pharmaceutical companies.

TAKE HOME MESSAGE

It is desirable to select compounds with lower potencies for the inductive effect. For this purpose, an accurate prioritization procedure to evaluate the induction potency of each compound in a quantitative manner considering the pharmacologically effective concentration of each compound is necessary.

Analysis of cytochrome P450 gene polymorphism in a lupus nephritis patient in whom tacrolimus blood concentration was markedly elevated after administration of azole antifungal agents

WHAT IS KNOWN AND OBJECTIVE

Both itraconazole (ITCZ) and voriconazole (VCZ) are potent inhibitors of cytochrome P450 (CYP) 3A, and their effects have been reported to be equal. However, ITCZ is metabolized by CYP3A, whereas VCZ is mainly metabolized by CYP2C9 and CYP2C19 and only partially by CYP3A. We experienced the case of a patient who showed a 5-fold increase in trough levels of tacrolimus (FK) level after switching from ITCZ to VCZ. Our objective is to discuss the mechanism of the increase drug-drug interaction in terms of serum concentration of the azole drugs and patient pharmacogenomics.

CASE SUMMARY

A 53-year-old woman was treated with FK (1 mg/day) for lupus nephritis. Because fungal infection was suspected, she received ITCZ (100 mg/day). When ITCZ was replaced with VCZ (400 mg/day), the blood concentration of FK increased markedly from 6·1 to 34·2 ng/mL. During coadministration with FK, the levels of ITCZ and VCZ were 135·5 ng/mL and 5·5 μg/mL, respectively, with the VCZ level around 3-fold higher than the previously reported level (1·4-1·8 μg/mL). Her CYP genotypes were CYP2C19*1/*2, CYP3A4*1/*1 and CYP3A5*3/*3.

WHAT IS NEW AND CONCLUSION

The patient was a CYP2C19 intermediate metabolizer (IM) and deficient in CYP3A5. The increase in plasma VCZ level appears to have been at least in part, associated with the CYP2C19 IM phenotype. One possible explanation for the marked increase in blood FK concentration was increased inhibition of CYP3A because of the impaired metabolism and subsequent increased plasma concentration of VCZ. This case shows that the severity of drug interactions may be influenced by metabolic gene polymorphism.

Impact of obesity on drug metabolism and elimination in adults and children

The prevalence of obesity in adults and children is rapidly increasing across the world. Several general (patho)physiological alterations associated with obesity have been described, but the specific impact of these alterations on drug metabolism and elimination and its consequences for drug dosing remains largely unknown. In order to broaden our knowledge of this area, we have reviewed and summarized clinical studies that reported clearance values of drugs in both obese and non-obese patients. Studies were classified according to their most important metabolic or elimination pathway. This resulted in a structured review of the impact of obesity on metabolic and elimination processes, including phase I metabolism, phase II metabolism, liver blood flow, glomerular filtration and tubular processes. This literature study shows that the influence of obesity on drug metabolism and elimination greatly differs per specific metabolic or elimination pathway. Clearance of cytochrome P450 (CYP) 3A4 substrates is lower in obese as compared with non-obese patients. In contrast, clearance of drugs primarily metabolized by uridine diphosphate glucuronosyltransferase (UGT), glomerular filtration and/or tubular-mediated mechanisms, xanthine oxidase, N-acetyltransferase or CYP2E1 appears higher in obese versus non-obese patients. Additionally, in obese patients, trends indicating higher clearance values were seen for drugs metabolized via CYP1A2, CYP2C9, CYP2C19 and CYP2D6, while studies on high-extraction-ratio drugs showed somewhat inconclusive results. Very limited information is available in obese children, which prevents a direct comparison between data obtained in obese children and obese adults. Future clinical studies, especially in children, adolescents and morbidly obese individuals, are needed to extend our knowledge in this clinically important area of adult and paediatric clinical pharmacology.

In vivo information-guided prediction approach for assessing the risks of drug-drug interactions associated with circulating inhibitory metabolites

The in vivo drug-drug interaction (DDI) risks associated with cytochrome P450 inhibitors that have circulating inhibitory metabolites cannot be accurately predicted by conventional in vitro-based methods. A novel approach, in vivo information-guided prediction (IVIP), was recently introduced for CYP3A- and CYP2D6-mediated DDIs. This technique should be applicable to the prediction of DDIs involving other important cytochrome P450 metabolic pathways. Therefore, the aims of this study were to extend the IVIP approach to CYP2C9-mediated DDIs and evaluate the IVIP approach for predicting DDIs associated with inhibitory metabolites. The analysis was based on data from reported DDIs in the literature. The IVIP approach was modified and extended to CYP2C9-mediated DDIs. Thereafter, the IVIP approach was evaluated for predicting the DDI risks of various inhibitors with inhibitory metabolites. Although the data on CYP2C9-mediated DDIs were limited compared with those for CYP3A- and CYP2D6-mediated DDIs, the modified IVIP approach successfully predicted CYP2C9-mediated DDIs. For the external validation set, the prediction accuracy for area under the plasma concentration-time curve (AUC) ratios ranged from 70 to 125%. The accuracy (75-128%) of the IVIP approach in predicting DDI risks of inhibitors with circulating inhibitory metabolites was more accurate than in vitro-based methods (28-805%). The IVIP model accommodates important confounding factors in the prediction of DDIs, which are difficult to handle using in vitro-based methods. In conclusion, the IVIP approach could be used to predict CYP2C9-mediated DDIs and is easily modified to incorporate the additive effect of circulating inhibitory metabolites.

Inhibition of cytochrome P450 3A by clarithromycin uniformly affects the pharmacokinetics and pharmacodynamics of oxycodone in young and elderly volunteers

The aim of this study was to investigate the effect of the cytochrome P450 3A4 inhibitor clarithromycin on the pharmacokinetics and pharmacodynamics of oral oxycodone in young and elderly subjects. Ten young and 10 elderly healthy subjects participated in this placebo-controlled, randomized, 2-phase crossover study. Subjects took clarithromycin 500 mg or placebo twice daily for 5 days. On day 4, subjects ingested an oral dose of 10 mg oxycodone. Plasma concentrations of oxycodone and its oxidative metabolites were measured for 48 hours, and pharmacological response for 12 hours. Clarithromycin decreased the apparent clearance of oxycodone by 53% in young and 48% in elderly subjects (P < 0.001) and prolonged its elimination half-life. The mean area under the plasma concentration-time curve (AUC0-∞) of oxycodone was increased by 2.0-fold (range, 1.3-fold to 2.7-fold) (P < 0.001) in young and 2.3-fold (range, 1.1-fold to 3.8-fold) (P < 0.001) in elderly subjects. The formation of noroxycodone was decreased by 74% in young and 71% in elderly subjects (P < 0.001). The ratio of AUC0-∞ of oxycodone during the clarithromycin phase compared with the one with placebo did not differ between the age groups. Clarithromycin did not alter the pharmacological response to oxycodone. Clarithromycin increased the exposure to oral oxycodone, but the magnitude of this effect was not age related. Although the pharmacological response to oxycodone was not significantly influenced by clarithromycin, dose reductions may be necessary in the most sensitive patients to avoid adverse effects when oxycodone is used concomitantly with clarithromycin.

A quantitative framework and strategies for management and evaluation of metabolic drug-drug interactions in oncology drug development: new molecular entities as object drugs

This article outlines general strategies for the management and evaluation of pharmacokinetic drug-drug interactions (DDIs) resulting from perturbation of clearance of investigational anticancer drug candidates by concomitantly administered agents in a drug development setting, with a focus on drug candidates that cannot be evaluated in first-in-human studies in healthy subjects. A risk level classification is proposed, based on quantitative integration of knowledge derived from preclinical drug-metabolism studies evaluating the projected percentage contribution [f(i)(%)] of individual molecular determinants (e.g. cytochrome P450 isoenzymes) to the overall human clearance of the investigational agent. The following classification is proposed with respect to susceptibility to DDIs with metabolic inhibitors: a projected maximum DDI expected to result in a ≤1.33-fold increase in exposure, representing a low level of risk; a projected maximum DDI expected to result in a >1.33-fold but <2-fold increase in exposure, representing a moderate level of risk; and a projected maximum DDI expected to result in a ≥2-fold increase in exposure, representing a potentially high level of risk. For DDIs with metabolic inducers, the following operational classification is proposed, based on the sum of the percentage contributions of enzymes that are inducible via a common mechanism to the overall clearance of the investigational drug: <<25%, representing a low level of risk; <50%, representing a moderate level of risk; and ≥50%, representing a potentially high level of risk. To ensure patient safety and to minimize bias in determination of the recommended phase II dose (RP2D), it is recommended that strong and moderate inhibitors and inducers of the major contributing enzyme are excluded in phase I dose-escalation studies of high-risk compounds, whereas exclusion of strong inhibitors and inducers of the contributing enzyme(s) is recommended as being sufficient for moderate-risk compounds. For drugs that will be investigated in diseases such as glioblastoma, where there may be relatively frequent use of enzyme-inducing antiepileptic agents (EIAEDs), a separate dose-escalation study in this subpopulation is recommended to define the RP2D. For compounds in the high-risk category, if genetic deficiencies in the activity of the major drug-metabolizing enzyme are known, it is recommended that poor metabolizers be studied separately to define the RP2D for this subpopulation. Whereas concomitant medication exclusion criteria that are utilized in the phase I dose-escalation studies will probably also need to be maintained for high-risk compounds in phase II studies unless the results of a clinical DDI study indicate the absence of a clinically relevant interaction, these exclusion criteria can potentially be relaxed beyond phase I for moderate-risk compounds, if supported by the nature of clinical toxicities and the understanding of the therapeutic index in phase I. Adequately designed clinical DDI studies will not only inform potential relaxation of concomitant medication exclusion criteria in later-phase studies but, importantly, will also inform the development of pharmacokinetically derived dose-modification guidelines for use in clinical practice when coupled with adequate safety monitoring, as illustrated in the prescribing guidance for many recently approved oncology therapeutics.

Inhibition of human cytochrome P450 3A4 by cholesterol

If cholesterol is a substrate of P450 3A4, then it follows that it should also be an inhibitor, particularly in light of the high concentrations found in liver. Heme perturbation spectra indicated a K(d) value of 8 μM for the P450 3A4-cholesterol complex. Cholesterol inhibited the P450 3A4-catalyzed oxidations of nifedipine and quinidine, two prototypic substrates, in liver microsomes and a reconstituted enzyme system with K(i) ∼ 10 μM in an apparently non-competitive manner. The concentration of cholesterol could be elevated 4-6-fold in cultured human hepatocytes by incubation with cholesterol; the level of P450 3A4 and cell viability were not altered under the conditions used. Nifedipine oxidation was inhibited when the cholesterol level was increased. We conclude that cholesterol is both a substrate and an inhibitor of P450 3A4, and a model is presented to explain the kinetic behavior. We propose that the endogenous cholesterol in hepatocytes should be considered in models of prediction of metabolism of drugs and steroids, even in the absence of changes in the concentrations of free cholesterol.

Effect of single and repeat doses of casopitant on the pharmacokinetics of CYP450 3A4 substrates midazolam and nifedipine

AIM

To evaluate the impact of single and repeated doses casopitant on the pharmacokinetics of single dose midazolam and nifedipine (CYP3A substrates) in healthy subjects. The effect on debrisoquine metabolism (CYP2D6 substrate) was also assessed.

METHODS

Three open-label studies were conducted in healthy subjects. In the first study subjects received single dose 50 or 100 mg oral casopitant, single dose 5 mg oral midazolam and single dose 10 mg oral debrisoquine. In the other two studies subjects received repeated doses of 10 mg (study 2), 30, or 120 mg oral casopitant and single doses of 5 mg oral midazolam (study 2) and single doses of 10 mg oral nifedipine (study 3). Plasma concentration-time data were analyzed using standard non-compartmental methods. The effect of casopitant on all probes was assessed using geometric means ratios and corresponding 90% confidence intervals (CIs).

RESULTS

The AUC(0,∞) of midazolam was increased 1.44-fold (90% CI 1.35, 1.54) and 1.52-fold (90% CI 1.41, 1.65) after co-administration with a single dose of 50 or 100 mg casopitant, respectively. Debrisoquine metabolism was unchanged. After 3 days of casopitant administration, midazolam AUC(0,∞) was increased 1.45- (90% CI 1.32, 1.59), 2.02- (90% CI 1.75, 2.32), and 2.67-fold (90% CI 2.18, 3.27) after co-administration with 10, 30 or 120 mg casopitant, respectively. After 14 days of casopitant administration, midazolam AUC(0,∞) was increased 1.51- (90% CI 1.40, 1.63) to 3.49-fold (90% CI 2.98, 4.08). After 3 days of casopitant administration, nifedipine AUC(0,∞) was increased 1.56- (90% CI 1.37, 1.78) and 1.77-fold (90% CI 1.54, 2.04) after co-administration with 30 or 120 mg casopitant, respectively. Similar increases in nifedipine exposure were observed after 14 days of casopitant administration.

CONCLUSIONS

Casopitant is a dose- and duration-dependent weak to moderate inhibitor of CYP3A.

Impact of ignoring extraction ratio when predicting drug-drug interactions, fraction metabolized, and intestinal first-pass contribution

Many mathematical models for in vitro to in vivo prediction of drug-drug interactions (DDIs) of orally administered victim drugs have been developed. However, to date, none of these models have been applicable to all intravenously administered victim drugs. We derived and conducted a sensitivity/error analysis of a modification to the existing multiple mode interaction prediction model such that it is applicable to all intravenously administered victim drugs. Using this model we showed that ignoring the hepatic extraction ratio (EH) (as low as 0.3) of intravenously administered victim drugs can result in 1) substantial underestimation of f(m, CYPi) (the fraction of hepatic clearance of the victim drug via a given enzymatic pathway) and 2) error in dissecting the contribution of intestinal and hepatic components of DDIs for orally administered drugs. Using this model we describe DDI boundaries (degree of inhibition or induction) at which ignoring the EH of commonly used victim drugs results in ≥30% error in the predicted area under the concentration-time curve (AUC) ratio or contribution of intestinal interaction to a DDI (CYP3A probes only). For the most widely used victim drug midazolam, these boundaries for AUC ratio are net inhibition (I/K(i) or λ/k(deg)) ≥1.3 or fold induction ≥2.1; for intestinal contribution the boundaries are 0.37 and 1.5, respectively. To accurately predict the intravenous AUC ratio, intestinal contribution, or f(m, CYPi) 1) for all induction DDIs irrespective of EH of the victim drug and 2) for modest to potent inhibition DDIs even when the EH is moderate (≥0.3), we propose that our model be used.

Theoretical considerations on quantitative prediction of drug-drug interactions

The prediction of drug-drug interactions (DDIs) associated with change in clearance for metabolism is reviewed, particularly focusing on pharmacokinetic theories for prediction based on in vitro and in vivo observation. First, there is discussion about how quantitative determination of the contribution of major clearance pathways is fundamental for the accurate prediction of DDIs. Secondly, the concentrations of causative drugs at sites of interactions are discussed. Although DDIs have been predicted from in vitro pharmacokinetic parameters based on predicted hepatic unbound concentrations of inhibitors and inducers, there are noticeable discrepancies between predicted and observed magnitudes of these DDIs. To solve these issues, a method for the prediction of unbound hepatic concentration is proposed based on theoretical considerations. Finally, a pharmacokinetic model to describe the intestinal first pass metabolism is considered, particularly focusing on the importance of the Q(gut) model. Although this Q(gut) model was proposed as an empirical model, theoretical considerations suggest that the model is regarded as a physiologically-based pharmacokinetic model that can predict significance of intestinal DDIs. Theoretical considerations proposed in the present article may be helpful for future analysis of 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 OBJECTIVE

Pharmacokinetic 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.

METHODS

A 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.

RESULTS

Among 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 inter-country 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.

CONCLUSION

The 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.

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.

No pharmacokinetic interaction between paliperidone extended-release tablets and trimethoprim in healthy subjects

OBJECTIVE

The effect of trimethoprim, a potent organic cation transport inhibitor, on the pharmacokinetics (PK) of paliperidone extended-release tablets (paliperidone ER), an organic cation mainly eliminated via renal excretion, was assessed.

METHODS

Open-label, two-period, randomized, crossover study in 30 healthy males. Single dose of paliperidone ER 6 mg was administered either alone on day 1 or day 5 during an 8-day treatment period of trimethoprim 200 mg twice daily. Serial blood and urine samples were collected for PK and plasma protein binding of paliperidone and its enantiomers. The 90% confidence interval (CI) of ratios with/without trimethoprim for PK parameters of paliperidone and its enantiomers calculated.

RESULTS

Creatinine clearance decreased from 119 to 102 mL min(-1) with trimethoprim. Addition of trimethoprim increased unbound fraction of paliperidone by 16%, renal clearance by 13%, AUC(infinity) by 9%, and t((1/2)) by 19%. The 90% CIs for ratios with/without trimethoprim were within the 80-125% range for C(max), AUC(last), and renal clearance. For AUC(infinity), 90% CI was 79.37-101.51, marginally below the lower bound of the acceptance range. Paliperidone did not affect steady-state plasma concentrations of trimethoprim.

CONCLUSIONS

No clinically important drug interactions are expected when paliperidone ER is administered with organic cation transport inhibitors.

Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.