Ruth Hyland

DRUG-DRUG INTERACTIONS FOR UDP-GLUCURONOSYLTRANSFERASE SUBSTRATES: A PHARMACOKINETIC EXPLANATION FOR TYPICALLY OBSERVED LOW EXPOSURE (AUCI/AUC) RATIOS

Glucuronidation is a listed clearance mechanism for 1 in 10 of the top 200 prescribed drugs. The objective of this article is to encourage those studying ligand interactions with UDP-glucuronosyltransferases (UGTs) to adequately consider the potential consequences of in vitro UGT inhibition in humans. Spurred on by interest in developing potent and selective inhibitors for improved confidence around UGT reaction phenotyping, and the increased availability of recombinant forms of human UGTs, several recent studies have reported in vitro inhibition of UGT enzymes. In some cases, the observed potency of UGT inhibitors in vitro has been interpreted as having potential relevance in humans via pharmacokinetic drug-drug interactions. Although there are reported examples of clinically relevant drug-drug interactions for UGT substrates, exposure increases of the aglycone are rarely greater than 100% in the presence of an inhibitor relative to its absence (i.e., AUCi/AUC ≤2). This small magnitude in change is in contrast to drugs primarily cleared by cytochrome P450 enzymes, where exposures have been reported to increase as much as 35-fold on coadministration with an inhibitor (e.g., ketoconazole inhibition of CYP3A4-catalyzed terfenadine metabolism). In this article the evidence for purported clinical relevance of potent in vitro inhibition of UGT enzymes will be assessed, taking the following into account: in vitro data on the enzymology of glucuronide formation from aglycone, pharmacokinetic principles based on empirical data for inhibition of metabolism, and clinical data on the pharmacokinetic drug-drug interactions of drugs primarily cleared by glucuronidation.

Comparison of Different Algorithms for Predicting Clinical Drug-Drug Interactions, Based on the Use of CYP3A4 in Vitro Data: Predictions of Compounds as Precipitants of Interaction

Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for “steady-state” and “time-based” approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

Human cytochrome P450s: selectivity and measurement in vivo

This review considers the human cytochrome P450 (CYP) enzymes responsible for the metabolism of drugs. This area has generated considerable interest over the past 10 or so years due to the identi® cation of the major human isoforms and some rationalization of the reasons for their selectivity towards various substrate classes (Smith and Jones 1992). At the same time, the pharmacokinetic behaviour of these same substrates has been investigated and intimately linked to the relative in vivo expression of each isoform. Thus a new chemical structure can be rapidly assessed as to which isoform(s) is important in its metabolism and then predictions made as to its likely in vivo performance particularly with regard to its variability. The knowledge has come from studying the pharmacokinetics of compounds which are metabolized by speci® c isoforms. Historically, the in vivo formation has been generated before the isozyme responsible for metabolism has been identi® ed, although such examples will in future became an increasing rarity. It is the purpose of this review to cover developments in these two intimately linked areas: why a compound is metabolized by a particular isoform (substrate± structure activity relationships, SSAR) and how we assess the expression of that isoform in vivo.

Targeted Quantitative Proteomics for the Analysis of 14 UGT1As and -2Bs in Human Liver Using NanoUPLC–MS/MS with Selected Reaction Monitoring

Targeted quantitative proteomics using heavy isotope dilution techniques is increasingly being utilized to quantify proteins, including UGT enzymes, in biological matrices. Here we present a multiplexed method using nanoLC-MS/MS and multiple reaction monitoring (MRM) to quantify 14 UGT1As and UGT2Bs in liver matrices. Where feasible, we employ two or more proteotypic peptides per protein, with only four proteins quantified with only one proteotypic peptide. We apply the method to analysis of a library of 60 human liver microsome (HLM) and matching S9 samples. Ten of the UGT isoforms could be detected in liver, and the expression of each was consistent with mRNA expression reported in the literature. UGT2B17 was unusual in that ∼30% of liver microsomes had no or little (<0.5 pmol/mg protein) content, consistent with a known common polymorphism. Liver S9 UGT concentrations were approximately 10-15% those of microsomes. The method was robust, precise, and reproducible and provides novel UGT expression data in human liver that will benefit rational approaches to evaluate metabolism in drug development.

Maraviroc: in vitro assessment of drug–drug interaction potential

AIMS To characterize the cytochrome P450 enzyme(s) responsible for the N-dealkylation of maraviroc in vitro, and predict the extent of clinical drug-drug interactions (DDIs). METHODS Human liver and recombinant CYP microsomes were used to identify the CYP enzyme responsible for maraviroc N-dealkylation. Studies comprised enzyme kinetics and evaluation of the effects of specific CYP inhibitors. In vitro data were then used as inputs for simulation of DDIs with ketoconazole, ritonavir, saquinavir and atazanvir, using the Simcyptrade mark population-based absorption, distribution, metabolism and elimination (ADME) simulator. Study designs for simulations mirrored those actually used in the clinic. RESULTS Maraviroc was metabolized to its N-dealkylated product via a single CYP enzyme characterized by a K(m) of 21 microM and V(max) of 0.45 pmol pmol(-1) min(-1) in human liver microsomes and was inhibited by ketoconazole (CYP3A4 inhibitor). In a panel of recombinant CYP enzymes, CYP3A4 was identified as the major CYP responsible for maraviroc metabolism. Using recombinant CYP3A4, N-dealkylation was characterized by a K(m) of 13 microM and a V(max) of 3 pmol pmol(-1) CYP min(-1). Simulations therefore focused on the effect of CYP3A4 inhibitors on maraviroc pharmacokinetics. The simulated median AUC ratios were in good agreement with observed clinical changes (within twofold in all cases), although, in general, there was a trend for overprediction in the magnitude of the DDI. CONCLUSION Maraviroc is a substrate for CYP3A4, and exposure will therefore be modulated by CYP3A4 inhibitors. Simcyptrade mark has successfully simulated the extent of clinical interactions with CYP3A4 inhibitors, further validating this software as a good predictor of CYP-based DDIs.

Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions. AIMS The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODS The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP. RESULTS When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONS The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

Optimized Assays for Human UDP-Glucuronosyltransferase (UGT) Activities: Altered Alamethicin Concentration and Utility to Screen for UGT Inhibitors

The measurement of the effect of new chemical entities on human UDP-glucuronosyltransferase (UGT) marker activities using in vitro experimentation represents an important experimental approach in drug development to guide clinical drug-interaction study designs or support claims that no in vivo interaction will occur. Selective high-performance liquid chromatography-tandem mass spectrometry functional assays of authentic glucuronides for five major hepatic UGT probe substrates were developed: β-estradiol-3-glucuronide (UGT1A1), trifluoperazine-N-glucuronide (UGT1A4), 5-hydroxytryptophol-O-glucuronide (UGT1A6), propofol-O-glucuronide (UGT1A9), and zidovudine-5′-glucuronide (UGT2B7). High analytical sensitivity permitted characterization of enzyme kinetic parameters at low human liver microsomal and recombinant UGT protein concentration (0.025 mg/ml), which led to a new recommended optimal universal alamethicin activation concentration of 10 μg/ml for microsomes. Alamethicin was not required for recombinant UGT incubations. Apparent enzyme kinetic parameters, particularly for UGT1A1 and UGT1A4, were affected by nonspecific binding. Unbound intrinsic clearance for UGT1A9 and UGT2B7 increased significantly after addition of 2% bovine serum albumin, with minimal changes for UGT1A1, UGT1A4, and UGT1A6. Eleven potential UGT and cytochrome P450 inhibitors were evaluated as UGT inhibitors, resulting in observation of nonselective UGT inhibition by chrysin, mefenamic acid, silibinin, tangeretin, ketoconazole, itraconazole, ritonavir, and verapamil. The pan-cytochrome P450 inhibitor, 1-aminobenzotriazole, minimally inhibited UGT activities and may be useful in reaction phenotyping of mixed UGT and cytochrome P450 substrates. These methods should prove useful in the routine assessments of the potential for new drug candidates to elicit pharmacokinetic drug interactions via inhibition of human UGT activities and the identification of UGT enzyme-selective chemical inhibitors.

Reaction Phenotyping in Drug Discovery: Moving Forward with Confidence?

For the pharmaceutical industry, one of the challenges in evaluating the risk of future compound attrition at the discovery stage is the successful prediction of the major routes of clearance in humans. For compounds cleared by metabolism, such information will help to avoid the development of compounds that will exhibit large interpatient differences in pharmacokinetics via 1). routes of metabolism catalyzed by functionally polymorphic enzymes and/or 2). clinically significant metabolic drug-drug interactions, in the later stages of development. The degree of intersubject variability that is acceptable for a drug candidate is uncertain in the discovery stage where knowledge of other important factors is limited or unavailable (i.e. therapeutic index, pharmacodynamic variability, etc). Reaction phenotyping is the semi-quantitative in vitro estimation of the relative contributions of specific drug-metabolizing enzymes to the metabolism of a test compound. However, reaction phenotyping in the discovery stage of drug development is complicated by the absence of radiolabelled parent compound or metabolite bioanalytical standards relative to later stages of development. In this commentary, some of the approaches, based on published data, which can be taken to overcome these challenges are discussed. In addition, knowledge of the molecular structure (i.e. specific chemical substituents), physicochemical properties, and routes of clearance in animals can all help in making a successful prediction for the routes of clearance in humans. In combination, the objective of these studies should be to reduce to a minimum the risk of finding significant inter-patient differences in pharmacokinetics at a later stage in development due to significant metabolism by polymorphic enzymes or drug-drug interactions. Consequently, this data should be used to avoid costly late stage attrition.

Development of an in vitro drug–drug interaction assay to simultaneously monitor five cytochrome P450 isoforms and performance assessment using drug library compounds

INTRODUCTION Inhibition of cytochrome P450 (CYP) is a principal mechanism for metabolism-based drug-drug interactions (DDIs). This article describes a robust, high-throughput CYP-mediated DDI assay using a cocktail of 5 clinically relevant probe substrates with quantification by liquid chromatography/tandem mass spectrometry (LC/MS-MS). METHODS The assay consisted of human liver microsomes and a cocktail of probe substrates metabolized by the five major CYP isoforms (tacrine for CYP1A2, diclofenac for CYP2C9, (S)-mephenytoin for CYP2C19, dextromethorphan for CYP2D6 and midazolam for CYP3A4). The assay was fully automated in both 96- and 384-well formats. RESULTS A series of experiments were conducted to define the optimal kinetic parameters and solvent concentrations, as well as, to assess potential reactant and product interference. The assay was validated against known CYP inhibitors (miconazole, sulfaphenazole, ticlopidine, quinidine, ketoconazole, itraconazole, fluoxetine) and evaluated in a screening environment by testing 9494 compounds. DISCUSSION Our findings show that this assay has application in early stage drug discovery to economically, reliably and accurately assess compounds for DDIs.

Selective Mechanism-Based Inactivation of CYP3A4 by CYP3cide (PF-04981517) and Its Utility as an In Vitro Tool for Delineating the Relative Roles of CYP3A4 versus CYP3A5 in the Metabolism of Drugs

CYP3cide (PF-4981517; 1-methyl-3-[1-methyl-5-(4-methylphenyl)-1H-pyrazol-4-yl]-4-[(3S)-3-piperidin-1-ylpyrrolidin-1-yl]-1H-pyrazolo[3,4-d]pyrimidine) is a potent, efficient, and specific time-dependent inactivator of human CYP3A4. When investigating its inhibitory properties, an extreme metabolic inactivation efficiency (kinact/KI) of 3300 to 3800 ml · min−1 · μmol−1 was observed using human liver microsomes from donors of nonfunctioning CYP3A5 (CYP3A5 *3/*3). This observed efficiency equated to an apparent KI between 420 and 480 nM with a maximal inactivation rate (kinact) equal to 1.6 min−1. Similar results were achieved with testosterone, another CYP3A substrate, and other sources of the CYP3A4 enzyme. To further illustrate the abilities of CYP3cide, its partition ratio of inactivation was determined with recombinant CYP3A4. These studies produced a partition ratio approaching unity, thus underscoring the inactivation capacity of CYP3cide. When CYP3cide was tested at a concentration and preincubation time to completely inhibit CYP3A4 in a library of genotyped polymorphic CYP3A5 microsomes, the correlation of the remaining midazolam 1′-hydroxylase activity to CYP3A5 abundance was significant (R2 value equal to 0.51, p value of <0.0001). The work presented here supports these findings by fully characterizing the inhibitory properties and exploring CYP3cides mechanism of action. To aid the researcher, multiple commercially available sources of CYP3cide were established, and a protocol was developed to quantitatively determine CYP3A4 contribution to the metabolism of an investigational compound. Through the establishment of this protocol and the evidence provided here, we believe that CYP3cide is a very useful tool for understanding the relative roles of CYP3A4 versus CYP3A5 and the impact of CYP3A5 genetic polymorphism on a compounds pharmacokinetics.