- Email: [email protected]
Metabolic screening in vitro: metabolic stability, CYP inhibition and induction
Drug Discovery Today: Technologies
Vol. 1, No. 4 2004
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead profiling
Metabolic screening in vitro: metabolic stability, CYP inhibition and induction Robert J. Riley*, Ken Grime Department of Physical and Metabolic Science, AstraZeneca R&D Charnwood, Loughborough, Leics, UK LE11 5RH
Automated, miniaturised assays are now commonplace within Discovery DMPK (drug metabolism and pharmacokinetics). These have evolved considerably since their inception around a decade ago, in-line with both technology and the desire to provide quality data comparable to more traditional analyses. Several formats exist for routine screening of metabolic stability and CYP (cytochrome P450; see Glossary) inhibition and induction, with the major focus being on in vitro systems using human-derived material. Data from other species remain valuable in assessing in vitro–in vivo projections and are pivotal to support safety assessment studies.
Introduction Sub-optimal ADME(T) (absorption, distribution, metabolism, excretion and toxicology) remains a key source of late-stage attrition in drug development [1,2]. Metabolic instability (resulting in rapid clearance) and drug–drug interactions (DDIs) are two major contributors. The majority of early leads are substrates for cytochrome P450 (CYP)-dependent oxidation and most clinical DDIs occur via inhibition or induction of CYP enzymes. Frontline, automated in vitro ADME screening strategies are geared to optimise these parameters. Highthroughput screening strategies and combinatorial chemistry have also served as drivers for automated screens of these properties to be commonplace within DMPK. This increased screening capacity has yielded larger databases and made *Corresponding author: (R.J. Riley) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.008
Section Editors: Han van de Waterbeemd, Christopher Kohl – Pfizer Global Research & Development, Sandwich Laboratories, PDM (Pharmacokinetics, Dynamics and Metabolism), Sandwich, Kent, UK CT13 9NJ Metabolic issues can play a key role in the selection of appropriate clinical candidates and therefore screening of potential liabilities is performed in early discovery. Based on their wide industry experience, Robert Riley and Ken Grime review here technologies used to study metabolic stability as an early indicator for half-life. The metabolism of drugs can also lead to various safety issues. This review therefore in addition looks at technologies to estimate CYP (P450) inhibition and induction, both of which might be a concern for drug–drug interactions in the clinic and in the past have caused withdrawal of marketed drugs.
available more time for discerning scientists to mine data routinely. These advances should provide an arsenal of computational tools from which predictive in silico drug discovery might emerge [2]. This review highlights recent progress in the development of these assays, which have arisen as a result of efforts to align them with technological advances and to project in vitro data to the clinical scenario.
Metabolic stability Several in vitro test systems (hepatocytes, microsomes or recombinant proteins) are available to determine metabolic stability. Optimisation of the metabolic clearance of lead series can perhaps be best accomplished through the discerning combination of in vitro metabolic stability assays and rapid metabolite identification. A typical automated intrinsic clearance (CLINT; see Glossary) assay format is shown in Fig. 1. The automation employed depends on the requirements of the individual lab and varies www.drugdiscoverytoday.com
365
Drug Discovery Today: Technologies | Lead profiling
Glossary ADMET: absorption, metabolism, excretion and toxicity. CLint: intrinsic clearance. This describes the enzyme-catalysed removal of a drug by the system and therefore is not influenced by other physiological determinants of clearance, such as hepatic blood flow. Cin vivo: referred to here as the in vivo concentration of drug entering the liver following absorption into the hepatic portal vein. CLmax: a term for the maximum intrinsic clearance of an enzyme displaying positive cooperativity (e.g. CYP3A4), when the enzyme is fully activated. CYP: cytochrome P450. These enzymes are involved in the oxidative metabolism of a high proportion of marketed drugs. Ion trap: ion trap mass spectrometers allow multiple fragmentations of metabolites, giving rise to a better ‘‘fingerprint’’ for each metabolite. Essentially the set up causes ions to accelerate and decelerate within the trap by altering the rf voltage applied to a ring electrode. Increasing the rf voltage allows ions of increasing m/z to leave the trap. Collisions within the trap result in further fragmentation. Km: the Michaelis–Menten constant describing the affinity of a substrate for an enzyme. The Km is equal to the substrate concentration at which the reaction rate is half maximal. LC–MS–MS: (high performance) liquid chromatography triple quadrupole mass spectrometry. Parent ions of the desired mass/charge (m/z) ratio are isolated and directed from quadrupole 1 (used as a mass filter) into quadruple 2 (collision cell). In the collision cell, the parent ion is fragmented (due to collision with inert gas molecules) to give daughter ions. The daughter ions are allowed to enter quadruple 3. Specific parent and daughter ion pairs (transitions) are highly selective for a given compound of interest. LC–NMR: (high performance) liquid chromatography nuclear magnetic resonance. TOF: time of flight mass spectrometry. This allows accurate mass measurement to 4 decimal places. Ions are accelerated from the ion source to a fixed kinetic energy, then allowed to pass down a field-free flight tube to a detector. The time taken for an ion to reach the detector is proportional to the mass/charge (m/z) ratio.
Vol. 1, No. 4 2004
from more involved machinery, such as the SAGIANTM robotics system (Beckman Coulter, http://www.beckmancoulter.com) and the Tecan Genesis Freedom system, (http://www.tecan.com), which incorporate multiple plate stacking systems and centrifuges that enable complete assays to be performed by the robot, to more simple benchtop machines, such as the Hamilton Microlab Star (http:// www.hamiltoncompany.com) or the Packard MultiPROBE1 II, (http://www.perkinelmer.com). When choosing the type of robot to use, scale and throughput need to be considered [3]. Generic NADPH consumption or O2 biosensor technology [4] never realised their early promise, therefore LC–MS–MS (see Glossary) is now routinely used for sample analysis because this offers selectivity and speed advantages (run times of only 60–90 s). Further time-saving is afforded by cassetting of samples or enabling four sample streams to be input into one mass spectrometer [3]. One example of this is MUXtechnology from Waters Micromass1 (http://www.micromass.co.uk). Powerful software enables LC–MS–MS parameters to be generated for large numbers of compounds. These methods are used to perform automated acquisition and processing (QuanOptimiseTM and MassLynxTM Software from Waters Micromass1 or Analyst1 Software from MDS Sciex, http://www.sciex.com). The processed LC–MS–MS data are automatically imported into Excel to calculate CLint values (based on the half-life the of parent compound). Fitting of the concentration-time data can be semi-automated, with the spreadsheet designed to flag outliers and provide statistical analysis on the quality of the data fit. This makes data review rapid. The data are then closely inspected and several quality checks performed.
Test vehicle considerations Solvent concentrations should be kept to a minimum (1%, v/v). Fig. 2 shows a survey of the effects of the more commonly used solvents (at 1%, v/v, final) on CYP activity. Acetonitrile appears the more favoured solvent, even though it might activate CYP1A2 activity. However, compound solubility for early discovery compounds might be limited even in this vehicle. These effects can be dependent on both enzyme source and probe substrate [5] and each laboratory should assess an appropriate combination in their hands. Comparable data for other enzymes and species other than human are sparse. However, the ability of some phase II enzymes to tolerate higher solvent concentrations than CYPs in microsomes [6], and the exquisite sensitivity of mono-amine oxidase to DMSO, are perhaps worthy of note.
Concentration of assay protein Fig. 1. General paradigm for metabolic stability screening.
366
www.drugdiscoverytoday.com
It is important to minimise the concentration of the metabolising source (e.g. 0.25–1 mg/ml for microsomes and 0.25– 1 106 cells/ml for hepatocytes) and also the incubation
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Fig. 2. Overview of potential effects of commonly used organic solvents (1%, v/v) on CYP activities with recombinant proteins and HLM (red bars, acetonitrile; yellow bars, DMSO blue bars, methanol). Positive and negative values on the y-axis indicate inhibition and activation, respectively.
time to avoid artefacts produced as a result of non-specific binding and non-linear kinetics [7–9].
Test compound concentration One of the most critical experimental variables is the concentration of test compound used during the incubation. Early in drug discovery, circulating concentrations (CIN VIVO; see Glossary) that are probably associated with a projected efficacious dose in man can only be estimated. For most compounds, KM (see Glossary) is likely to be >Cin vivo. Therefore, the initial substrate concentration chosen should be 1– 3 mM (probably to be Km). When a more informed estimate of Cin vivo becomes available, the substrate concentration could be reassessed and should be justified scientifically. If Km < Cin vivo, then the substrate concentration chosen should be Cin vivo. Despite some shortcomings (e.g. less sensitivity than the measurement of specific metabolite appearance), monitoring the depletion of the test compound (at a single low-substrate concentration (S), where S Km; Fig. 3) with time is the approach of choice to determine CLint in a discovery setting. As total substrate depletion is measured, rates most likely represent multiple reactions, rather than single biotransformations. Data from the authors’ laboratory have been used to validate this approach for a variety of in vitro metabolising systems [10]. Several CYP enzymes, most notably CYP3A4 and CYP2C9, demonstrate atypical, non-Michaelis–Menten kinetics, which indicate auto-activation of the enzyme [11]. This phenomenon has been observed with recombinant CYPs, microsomes and hepatocytes [12,13]. In this instance, the kinetic analysis for in vitro–in vivo scaling requires further
interpretation. The use of CLmax, the maximum CLint, has been proposed but examples of any clinical relevance of this term are perhaps limited to a few compounds. Nevertheless, initial screening at several concentrations is recommended for CYP substrates (Fig. 3).
Test systems Microsomes and hepatocytes In vivo hepatic metabolic clearance can be estimated from the turnover or CLint derived from in vitro tissue preparations, such as hepatocytes, particularly for compounds undergoing CYP-dependent oxidation [14]. Many labora-
Fig. 3. Relationship between substrate concentration (S), CLint defined by the Michaelis–Menten equation (full line) and Clmax described using the Hill equation (dotted line). Values for Vmax, Km and S50 are 200 pmol/ min/mg and 15 mM, respectively, for the simulations shown.
www.drugdiscoverytoday.com
367
Drug Discovery Today: Technologies | Lead profiling
tories use hepatic microsomes rather than hepatocytes to determine CLint [15,16] because they are a more flexible system with which to study oxidative biotransformations in terms of ease of preparation from many species, long-term storage and availability. However, hepatocytes provide the most physiologically relevant model with which to measure qualitative and quantitative aspects of hepatic metabolism because they contain the full complement of enzymes a compound is probably to encounter during firstpass metabolism. Additionally, interactions with transporter proteins present in hepatocyte membranes can be key determinants of hepatic clearance. Hepatocytes have therefore become the cellular system of choice given the limitations of hepatic tissue slices for quantitative studies. Rat hepatocytes and human liver microsomes (HLM) are used routinely metabolic stability screens. Human hepatocytes are used for more optimised compounds and the data are used to estimate human clearance and dose [17]. The in vivo clearance of a compound or series of compounds might not always be predicted well from in vitro data. Cross-species in vitro–in vivo scaling can be useful in evaluating how well human hepatocyte CLint can predict human clearance in vivo [18]. Concerns over the longer-term availability of freshly isolated human hepatocytes and their de-differentiation in culture has prompted the search for sustainable sources. Three main strategies have been considered: (i) immortalisation of primary hepatocytes; (ii) manipulation of culture conditions to select liver-specific function in cell lines; and (iii) transdifferentiation of human precursor cells to hepatocytes. Some success has been achieved using immortalising genes, in that stable expression of several enzymes has been accomplished through several passages of culture [19]. Modification of cell culture conditions to manipulate the expression of relevant proteins in proliferative hepatic cell lines has also yielded some success. Although instability of the lines is a common problem [19], cells can be cryopreserved at a stage when relevant functionally active proteins are expressed and then thawed and plated for use. Hematopoietic bone marrow mesenchymal stem cells are capable of generating many different types of tissue cells, including adult human hepatocytes [20]. There has been some optimism that manipulation of the hepatic progenitor cells could provide a supply of human hepatocytes. However, transplantation research has demonstrated that the differentiation of stem cells into hepatocytes is at best a highly inefficient process and can only be achieved when cell fusion (to host liver cells) occurs (i.e. differentiation into adult hepatocytes does not occur) [21]. It appears unlikely that stem cells will provide useful ADME tools in the near future. 368
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
Given the complexity of the systems, it is perhaps not surprising they have not yet delivered their ultimate goal: a stable cell line expressing differentiated functions similar to primary hepatocytes pertaining to metabolism, transport and enzyme induction. Meanwhile, techniques have been perfected whereby hepatocytes can be isolated from small liver resections. Together with recent advances in cryopreservation technology, this might improve the flexibility and access to human cells. Further validation of such tools is emerging [22].
Recombinant enzymes Recent trends have seen a move towards ‘‘frontloading’’ recombinant CYPs, either as artificial HLM [23] or in CYP reaction phenotyping [24]. Armed with this information, one can also attempt to apply existing tenets of structureactivity relationships (SARs) for individual enzymes, which might not be immediately obvious from hepatocyte or microsomal data. These data coupled with some kind of ‘‘decision tree’’ [25] could provide a useful starting point for such analyses and might offer the potential to eliminate leads with unfavourable CYP profiles [10]. This knowledge can influence compound selection and optimisation and provides valuable insight into species differences, contribution of the gut to first-pass metabolism and possible intersubject variability anticipated in clinical studies. Early information about polymorphic metabolism coupled with increased awareness of functional polymorphisms in drug targets could also provide an opportunity for individualisation of drug-dosing regimens in the future [26]. Such analysis could provide a means of progressing compounds, which might have once been eliminated from the drug development pipeline. There can be little doubt that these approaches are most advanced for the CYP enzyme family. However, ongoing efforts with other major protein families should culminate in similar methodology becoming available for these in the not-too-distant future (see Outstanding issues). Metabolic stability data are important in predicting in vivo clearance. However, metabolite identification data can also be vital for defining the most metabolically labile sites in a compound of interest. The most common MS techniques involved are time-of-flight (TOF; see Glossary) and ION TRAP (see Glossary). The high level of automation required is supported by powerful software designed to search extensive databases to identify potential metabolites from the masses found in the sample (e.g. MetaboLynxTM software from WatersTM, http://www.micromass.co.uk). LC–MS–MS can then be performed automatically to confirm the identity of the metabolites. Column size is an important factor in obtaining improved sensitivity and capillary columns (1 mm 15 cm) are now used routinely used. LC–NMR (see Glossary) is used
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Table 1. Some considerations in the selection of higher throughput CYP assay format Technology 1
Technology 2
Technology 3
Name of specific type of technology
CYP inhibition (Radiometric)
CYP inhibition (Fluorescence)
CYP inhibition (LC–MS–MS)
Names of specific technologies with associated companies and company websites
Radiolabelled probes substrates from Amersham Biosciences (http://www.amershambiosciences.com)
High-throughput P450 inhibition kits (http://www.gentest.com) Microfluidics (http://www.tecan.com)
Substrates and metabolites from Sigma-Aldrich (http://www.sigmaaldrich.com) and Ultrafine (http://www.ultrafine.co.uk)
Pros
Robust No interference from test inhibitor or incubation matrix Substrates are drugs (cf. fluorescent probes) Limited data processing Good signal to noise
Very rapid analysis with limited data processing. Technology most suited to high-throughput screening
Substrates are drugs (cf. fluorescent probes) Good signal to noise
Cons
Medium-throughput + radioactivity
Poor signal to noise is an issue with some probe substrates Some probes very sensitive to inhibition by DMSO Probes do not bear structural resemblance or have physicochemical properties of typical drugs
Medium-throughput LC–MS system failures/troublshooting LC–MS time is at a premium in discovery laboratorys
References
[32]
See Related articles
[30]
when the exact site of modification is ambiguous from the LC–MS–MS data.
DDI screening CYP inhibition Inhibition of CYP activity can be manifested as dramatic changes in pharmacokinetics (PK), adverse drug reactions and, in extreme cases, fatalities [27]. This awareness has coincided with the generation of a battery of automated screens for CYP inhibition. When considering the major hepatic CYPs responsible for oxidative drug metabolism, it could be argued that only the key drug metabolising CYPs should be considered early in drug discovery (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4). Assays use a common analytical end-point: liquid scintillation counting of radioactivity liberated during site-specific metabolism [28]; selective analysis of fluorescent metabolites [29] mass spectrometry [30]. The precise choice of substrate probe depends on several key considerations: throughput requirement, potential analytical interference [31], clinical experience with probe substrates and cost of analysis (Table 1). Recombinant CYPs (rCYPs) often serve as the metabolising system because, in addition to the more obvious selectivity advantages, their use keeps the total protein concentration low, minimising non-specific binding. The influence of protein concentration on the apparent Ki or IC50 can be profound for lipophilic CYP3A4 inhibitors, such as itraconazole
and ketoconazole; the apparent IC50 for ketoconazole can easily vary between 0.005 and 0.3 mM [10]. For CYP3A4, the different binding modes or sites within the enzyme’s active site can also confound inter-assay comparisons (Fig. 4). It is now commonplace to use several different substrates for CYP3A4. The choice of buffer (and strength) might influence enzyme activity but is typically 0.1 M phosphate or a Tris-
Fig. 4. An example of differential effects on prototypic CYP3A/5 substrate oxidations in HLM. Abbreviations: MDZ 10 -OH, midazolam 1-hydroxylation; MDZ 40 -OH, midazolam 4-hydroxylation; NIF, nifedipine oxidation; TST, testosterone 6b-hydroxylation.
www.drugdiscoverytoday.com
369
Drug Discovery Today: Technologies | Lead profiling
based system. The effects of solvents have already been discussed. Interestingly, the fluorogenic probe reaction 7-benzyloxy-4-(trifluoromethyl) coumarin (BFC) O-debenzylation appears uniquely sensitive to DMSO at concentrations 0.2%, v/v. The co-incubation format of most assays is such that mechanism-based irreversible inhibition cannot be readily monitored. Irreversible inactivation is observed in CYP-catalysed reactions with reasonable frequency, possibly due to the reactivity of high energy oxygenated intermediates. The classical robust method for determining the extent of mechanism-based inhibition and the kinetic constants involved has been described by Mayhew et al. [32]. Some laboratories have used fluorescent substrates to track the inhibitor complex in real-time as a higher throughput alternative [33]. As with reversible CYP inhibition, attempts to apply kinetic constants derived from in vitro mechanismbased inhibition assays in quantitative predictions of in vivo interactions have shown promise, albeit with limited datasets [34]. However, even the most potent irreversible inhibitors might not show dramatic effects on the PK of co-administered drugs (Outstanding issues). A list of substrates for the major human hepatic CYP isoforms for in vitro and in vivo drug–drug interaction experiments has been recommended [35]. A range of [O- or Nmethyl-14C]-substrates can be employed, which liberate [14C]-formaldehyde as the product of enzyme-specific oxidative demethylation [28]. Because of the concerns around the extensive use of radioactivity, fluorescent probes have also been assessed [29]. Availability of specific fluorescent probes and recombinant CYP systems have accelerated the development of robust high-throughput assays in miniaturized 384-well form [36]; indeed 1536w format could also be feasible. This increasing capacity and resulting lower cost per sample might present pharmaceutical companies with the ability to screen the whole or part of a compound bank for quick classification or development of more predictive in silico models (Outstanding issues). Although these fluorogenic substrates have use in the early phases of drug discovery [30], recent experiences have challenged their suitability in driving structure– activity relationships and making in vivo projections. Enhanced MS sensitivity has recently provided assays using individual substrates with rCYPs [30] or HLM [37]. Substrates used can also be used as probes in vivo and represent ‘‘drug space’’. Monolithic silica technology and ‘‘ultrafast’’ chromatography might afford even further reductions in analysis time, which might again enhance throughput [38]. This laboratory now has a range of radiometric, fluorescent and MS-based assays. At each phase of development, these assays were cross-validated to assess inter-assay consistency and so on, which is key to analysis from appropriate databases. 370
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
Perhaps the most up to the minute technology in CYP inhibition assays is microfluidics. With wide-ranging applications in biochemistry and molecular biology [39], microfluidics is now being used in high-throughput ADME studies (including serum albumin and alpha glycoprotein binding, Pglycoprotein interaction and CYP inhibition). The LabCD ADMETTM System (Tecan, http://www.tecan.com) combines microscale fluid paths, reaction chambers and passive valves on a disposable disc that operate on an existing Tecan platform. Assays can be configured to use less than 10 mL of each reagent, with fluid being moved along pathways by capillary action and centrifugal forces via disc spinning. This technology offers potential benefits in cost and assay reproducibility, thereby minimising cross-site variability in data.
CYP induction DDIs arising through enzyme induction are less significant compared with enzyme inhibition and their impact on clinical PK can be difficult to detect against background intersubject variability [10]. Hepatic enzyme induction commonly observed in species used in safety assessment studies often has little relevance for man given marked inter-species differences in induction response [40]. It could be argued that the exposures required for clinically significant hepatic enzyme induction are probably to be well in excess of those required for therapeutic efficacy with newer therapies that exhibit nanomolar potency for their target. However, the role of the pregnane X receptor (PXR) in the regulation of other genes involved in xenobiotic metabolism, including CYP2B6, CYP2C8, CYP2C9, GSTA2 and MDR1, as well as genes critical to bile acid metabolism and homeostasis, should not be overlooked. Indeed, the discovery of potent ligands of human PXR [41], and the role of transporter proteins in the hepatic uptake and accumulation of PXR ligands, such as rifampicin, support regular screening for human enzyme induction in vitro. The discovery that many compounds induce drug-metabolizing enzymes through the interaction with nuclear hormone receptors [42] has prompted the establishment of enhanced throughput systems using reporter gene technology for PXR and the constitutive androstane receptor (CAR) as well as the Ah receptor. These might be used once transcriptional activation has been established as the mechanism of induction in cultured human hepatocytes for a lead series (Outstanding issues). Consideration should also be given to the host cell line (usually HepG2 or HuH7), which might demonstrate variable expression of uptake and efflux proteins and respond differently to external stimuli. Clearly, such differences could confound interpretation of data, thereby affecting comparisons between species and systems and extrapolation to the in vivo setting. Luo et al. [41] have also presented data that emphasise the importance of understanding the pros and cons of the test
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Links
Related articles
Hamilton Microlab Star (http://www.hamiltoncompany.com) Packard MultiPROBE1 II (http://www.perkinelmer.com) SAGIANTM robotics system supplied by Beckman Coulter (http:// www.beckmancoulter.com) Tecan instruments (http://www.tecan.com) Waters Micromass1 (http://www.micromass.co.uk) MDS Sciex (http://www.sciex.com) CYPEX (http://www.cypex.co.uk) Gentest (http://www.gentest.com)
Gombar V.K. et al. (2003) Role of ADME characteristics in drug discovery and their in silico evaluation: in silico screening of chemicals for their metabolic stability. Curr. Top. Med. Chem. 3, 1205–1225 Eddershaw, P.J. et al. (2000) ADME/PK as a part of a rational approach to Drug Discovery. Drug Discov. Today 5, 409–414 Jenkins, K.M. et al. (2004) Automated high throughput ADME assays for metabolic stability and cytochrome P450 inhibition profiling of combinatorial libraries. J. Pharm. Biomed. Anal. 34, 989–1004 Cohen, L.H. et al. (2003) In vitro drug interactions of cytochrome P450: an evaluation of fluorogenic to conventional substrates. Drug Metab. Dispos. 31, 1005–1015
system for enzyme induction together with the analytical endpoint. Differential exposure to parent compound or active metabolites might also affect the interpretation of data in different test systems [43].
Conclusions Various scales of automation are available to cater for the requirement of medium- to high-throughput ADME studies. The automation revolution in DMPK has been driven by advances in combinatorial chemistry, parallel synthesis and high-thoughput screening methods for target affinity. Automated in vitro metabolic stability and DDI studies provide large amounts of data that improve understanding and that, in turn, should make available in silico models. The ultimate aim of these must be to predict in vivo disposition (Outstanding issues). However, automation is no substitute for scientific robustness or integrity and should not encourage complacency. Attention to detail and provision of high quality data is paramount. Important considerations include the use of low protein (enzyme) concentrations and reasonable incubation times to avoid misinterpretation of results. Appropriate substrate concentrations should be used in metabolic stability assays. Even in early discovery, it is important to understand, within a chemical series, the in vitro kinetics (Km, auto- or hetero-activation, solvent effects on reaction velocities) and, as a result, choose appropriate incubation conditions. Obviously, it is important that automated assays have the flexibility to encompass this information. The usefulness of in vitro data in estimating clearance in vivo is widely acknowledged. However, there is still a need to refine existing strategies. For example, recombinant CYP data could be used to evaluate inter-subject variability and the impact of CYP polymorphisms. Projections of the extent of in vivo interaction, made using in vitro Ki estimates, are promising. Re-analysis of external datasets using Ki values determined in-house has confirmed the promise of this approach. These datasets are limited and there is still much to be done in terms of generating substantial databases of in vitro and clinical data together with the incorporation of information on inter-subject and popu-
lation variability. Considerations of mechanism-based inhibition, the extent of in vitro and in vivo binding and uptake into hepatocytes (of inhibitor and substrate) must be considered if ‘‘false-negatives’’ are to be avoided. In vitro inhibition studies using isolated human hepatocytes could be used to address some of these issues.
Outstanding issues Significant progress has been made in understanding and reducing oxidative (phase I) metabolism. Compounds so designed might be substrates for phase II enzymes and transporter proteins, which in turn could lead to extra-hepatic or non-metabolic clearance. Our aspirations now must be to understand these issues. Projections of the extent of in vivo interaction, made using in vitro Ki, are promising. However, in vitro data are largely focused on five CYPs. It might be prudent to expand this to CYPs that have gained in importance as understanding has increased (2B6, 2C8, 3A5). In addition to blood binding of inhibitor and substrate, tissue distribution and active uptake might influence the intra-hepatic free concentration. Identification of the key processes and transporter proteins responsible for these phenomena and their incorporation into in vitro screens should permit a more accurate assessment of the in vivo effects of CYP inhibition observed in in vitro screening. The use of animal models to validate these processes should not be overlooked. More attention is also now being paid to irreversible (mechanismbased) inhibition. There is, however, a need for greater understanding of how to interpret the in vitro data and of the toxicological consequences. Cultured human hepatocytes are viewed as the gold-standard for investigating the potential for drug–drug interactions arising through enzyme induction. In addition, reporter gene assays are widely used for screening purposes. Differences between the models and interpretation of the data must be considered carefully to extrapolate to in vivo effectively. Several ongoing industry–academia collaborations should provide valuable databases together with validated in silico models with which to predict drug–drug interactions and the relative risk in specific patient populations. This should help prioritise early clinical interaction studies. The future must focus further on reducing costs as well as the time taken to get a drug to the market. A key to this aim is the availability of high-quality desktop in silico models to reliably predict ADMET properties from molecular structure. This might take significant investment, but is warranted if the reward is robust, predictive tools.
www.drugdiscoverytoday.com
371
Drug Discovery Today: Technologies | Lead profiling
References 1 Hodgson, J. (2001) ADMET – turning chemicals into drugs. Nat. Biotechnol. 19, 722–726 2 Davis, A.M. and Riley, R.J. (2004) Predictive ADMET studies, the challenges and opportunities. Curr. Opin. Chem. Biol. 8, 378–386 3 Kassel, D.B. (2004) Applications of high throughput ADME in drug discovery. Curr. Opin. Chem. Biol. 8, 339–345 4 Joseph, S. et al. (2003) An amperometric biosensor with human CYP3A4 as a novel drug screening tool. Biochem. Pharmacol. 65, 1817–1826 5 Tang, C. et al. (2000) Substrate-dependent effect of acetonitrile on human liver microsomal cytochrome P450 2C9 (CYP2C9) activity. Drug Metab. Dispos. 28, 567–572 6 Uchaipichat, V. (2004) Human UDP-glucuronosyltransferases: isoform selectivity and kinetics of 4-methylumbelliferone and 1-naphthol glucuronidation, effects of organic solvents, and inhibition by diclofenac and probenecid. Drug Metab. Dispos. 32, 413–423 7 Forti, R.S. and Fisher, M.B. (2004) Impact of incubation conditions on bufuralol human clearance predictions: enzyme lability and nonspecific binding. Drug Metab. Dispos. 32, 295–304 8 Obach, R.S. (1999) Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab. Dispos. 27, 1350–1359 9 Austin, R.P. et al. (2002) The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metab. Dispos. 30, 1497–1503 10 Riley, R.J. (2001) The potential pharmacological and toxicological impact of P450 screening. Curr. Opin. Drug Discov. Dev. 4, 45–54 11 Houston, J.B. and Galetin, A. (2003) Progress towards prediction of human pharmacokinetic parameters from in vitro technologies. Drug Metab. Rev. 35, 393–415 12 Witherow, L.E. and Houston, J.B. (1999) Sigmoidal kinetics of CYP3A substrates: an approach for scaling dextromethorphan metabolism in hepatic microsomes and isolated hepatocytes to predict in vivo clearance in the rat. J. Pharmacol. Exp. Ther. 290, 58–65 13 Ngui, J.S. et al. (2000) Cytochrome P450 3A4-mediated interaction of diclofenac and quinidine. Drug Metab. Dispos. 28, 1043–1050 14 Houston, J.B. and Carlile, D.J. (1997) Prediction of hepatic clearance from microsomes, hepatocytes and liver slices. Drug Metab. Rev. 29, 891–922 15 Obach, R.S. et al. (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 283, 46–58 16 Clarke, S.E. and Jeffrey, P. (2001) Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica 31, 591– 598 17 Riley, R.J. et al. (2002) The influence of DMPK as an integrated partner in modern drug discovery. Curr. Drug Metab. 3, 527–550 18 Naritomi, Y. et al. (2003) Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab. Dispos. 31, 580–588 19 Anderson, K. et al. (1998) Cytotoxicity of xenobiotics and expression of glutathione-S-transferase in immortalised rat hepatocyte cell lines. Hum. Exp. Toxicol. 17, 131–137 20 Jiang, Y. (2002) Pluripotency of mesenchymal stem cells derived from adult marrow. Nature 418, 41–49 21 Fausto, N. (2004) Liver regeneration and repair: hepatocytes, progenitor cells and stem cells. Hepatology 39, 1477–1487 22 McGinnity, D.F. et al. (2004) Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab. Dispos. 32, 1247–1253
372
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
23 Eddershaw, P.J. and Dickins, M. (1999) Advances in in vitro drug metabolism screening. Pharm. Sci. Technol. Today 2, 13–19 24 McGinnity, D.F. et al. (2000) Automated definition of the enzymology of drug oxidation by the major human drug metabolising cytochromes P450 (CYPs). Drug Metab. Dispos. 28, 1327–1334 25 Lewis, D.F. (2000) On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics: towards the prediction of human P450 substrate specificity and metabolism. Biochem. Pharmacol. 60, 293–306 26 Ingelman-Sundberg, M. et al. (1999) Polymorphic human cytochrome P450 enzymes: an opportunity for individualised drug treatment. Trends Pharm. Sci. 20, 342–349 27 Jankel, C. and Fitterman, L.K. (1993) Epidemiology of drug–drug interactions as a cause of hospital admissions. Drug Saf. 9, 51–59 28 Moody, G.C. et al. (1999) Fully automated analysis of activities catalysed by the major human liver cytochrome P450 (CYP) enzymes: assessment of human CYP inhibition potential. Xenobiotica 29, 53–75 29 Crespi, C.L. et al. (1998) High throughput screening for inhibition of cytochrome P450 metabolism. Med. Chem. Res. 8, 457–471 30 Weaver, R. et al. (2003) Cytochrome P450 inhibition using recombinant proteins and mass spectrometry/multiple reaction monitoring technology in a cassette. Drug Metab. Dispos. 32, 1–12 31 Zou, L. et al. (2002) Effects of intrinsic fluorescence and quenching on fluorescence-based screening of natural products. Phytomedicine 9, 263– 267 32 Mayhew, B.S. et al. (2000) An in vitro model for predicting in vivo inhibition of cytochrome P4503A4 by metabolic intermediate complex formation. Drug Metab. Dispos. 28, 1031–1037 33 Yamamoto, T. et al. (2004) High-throughput screening for the assessment of time-dependent inhibitions of new drug candidates on recombinant CYP2D6 and CYP3A4 using a single concentration method. Xenobiotica 34, 87–101 34 Ito, K. et al. (1998) Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes. Metab. Dispos. 31, 945–954 35 Tucker, G.T. et al. (2001) Optimizing drug development: strategies to assess drug metabolism/transporter interaction potential – toward a consensus. Pharm. Res. 18, 1071–1080 36 Kariv, I. et al. (2001) Development of a miniaturized 384-well high throughput screen for the detection of substrates of cytochrome P450 2D6 and 3A4 metabolism. J. Biomol. Screen. 6, 91–99 37 Walsky, R.L. and Obach, R.S. (2004) Validated assays for human cytochrome P450 activities. Drug Metab. Dispos. 32, 647–660 38 Peng, S.X. et al. (2003) High-throughput cytochrome P450 inhibition assays by ultrafast gradient liquid chromatography with tandem mass spectrometry using monolithic columns. Rapid Commun. Mass Spectrom. 17, 509–518 39 Fitzgerald, D.A. (2001) Making every nanoliter count. The Scientist 15, 29–31 40 Lu, C. and Li, A.P. (2001) Species comparison in P450 induction: effects of dexamethasone, omeprazole, and rifampin on P450 isoforms 1A and 3A in primary cultured hepatocytes from man, Sprague–Dawley rat, minipig and beagle dog. Chem. Biol. Interact. 134, 271–281 41 Luo, G. et al. (2002) CYP3A4 induction by drugs: correlation between a pregnane X receptor reporter gene assay and CYP3A4 expression in human hepatocytes. Drug Metab. Dispos. 30, 795–804 42 Waxman, D.J. (1999) P450 gene induction by structurally diverse xenochemicals: central role of nuclear receptors CAR, PXR and PPAR. Arch. Biochem. Biophys. 369, 11–23 43 Riley, R.J. and Kenna, J.G. (2004) Cellular models for ADMET predictions and evaluation of drug–drug interactions. Curr. Opin. Drug Discov. Dev. 7, 86–99
Vol. 1, No. 4 2004
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Lead profiling
Metabolic screening in vitro: metabolic stability, CYP inhibition and induction Robert J. Riley*, Ken Grime Department of Physical and Metabolic Science, AstraZeneca R&D Charnwood, Loughborough, Leics, UK LE11 5RH
Automated, miniaturised assays are now commonplace within Discovery DMPK (drug metabolism and pharmacokinetics). These have evolved considerably since their inception around a decade ago, in-line with both technology and the desire to provide quality data comparable to more traditional analyses. Several formats exist for routine screening of metabolic stability and CYP (cytochrome P450; see Glossary) inhibition and induction, with the major focus being on in vitro systems using human-derived material. Data from other species remain valuable in assessing in vitro–in vivo projections and are pivotal to support safety assessment studies.
Introduction Sub-optimal ADME(T) (absorption, distribution, metabolism, excretion and toxicology) remains a key source of late-stage attrition in drug development [1,2]. Metabolic instability (resulting in rapid clearance) and drug–drug interactions (DDIs) are two major contributors. The majority of early leads are substrates for cytochrome P450 (CYP)-dependent oxidation and most clinical DDIs occur via inhibition or induction of CYP enzymes. Frontline, automated in vitro ADME screening strategies are geared to optimise these parameters. Highthroughput screening strategies and combinatorial chemistry have also served as drivers for automated screens of these properties to be commonplace within DMPK. This increased screening capacity has yielded larger databases and made *Corresponding author: (R.J. Riley) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.008
Section Editors: Han van de Waterbeemd, Christopher Kohl – Pfizer Global Research & Development, Sandwich Laboratories, PDM (Pharmacokinetics, Dynamics and Metabolism), Sandwich, Kent, UK CT13 9NJ Metabolic issues can play a key role in the selection of appropriate clinical candidates and therefore screening of potential liabilities is performed in early discovery. Based on their wide industry experience, Robert Riley and Ken Grime review here technologies used to study metabolic stability as an early indicator for half-life. The metabolism of drugs can also lead to various safety issues. This review therefore in addition looks at technologies to estimate CYP (P450) inhibition and induction, both of which might be a concern for drug–drug interactions in the clinic and in the past have caused withdrawal of marketed drugs.
available more time for discerning scientists to mine data routinely. These advances should provide an arsenal of computational tools from which predictive in silico drug discovery might emerge [2]. This review highlights recent progress in the development of these assays, which have arisen as a result of efforts to align them with technological advances and to project in vitro data to the clinical scenario.
Metabolic stability Several in vitro test systems (hepatocytes, microsomes or recombinant proteins) are available to determine metabolic stability. Optimisation of the metabolic clearance of lead series can perhaps be best accomplished through the discerning combination of in vitro metabolic stability assays and rapid metabolite identification. A typical automated intrinsic clearance (CLINT; see Glossary) assay format is shown in Fig. 1. The automation employed depends on the requirements of the individual lab and varies www.drugdiscoverytoday.com
365
Drug Discovery Today: Technologies | Lead profiling
Glossary ADMET: absorption, metabolism, excretion and toxicity. CLint: intrinsic clearance. This describes the enzyme-catalysed removal of a drug by the system and therefore is not influenced by other physiological determinants of clearance, such as hepatic blood flow. Cin vivo: referred to here as the in vivo concentration of drug entering the liver following absorption into the hepatic portal vein. CLmax: a term for the maximum intrinsic clearance of an enzyme displaying positive cooperativity (e.g. CYP3A4), when the enzyme is fully activated. CYP: cytochrome P450. These enzymes are involved in the oxidative metabolism of a high proportion of marketed drugs. Ion trap: ion trap mass spectrometers allow multiple fragmentations of metabolites, giving rise to a better ‘‘fingerprint’’ for each metabolite. Essentially the set up causes ions to accelerate and decelerate within the trap by altering the rf voltage applied to a ring electrode. Increasing the rf voltage allows ions of increasing m/z to leave the trap. Collisions within the trap result in further fragmentation. Km: the Michaelis–Menten constant describing the affinity of a substrate for an enzyme. The Km is equal to the substrate concentration at which the reaction rate is half maximal. LC–MS–MS: (high performance) liquid chromatography triple quadrupole mass spectrometry. Parent ions of the desired mass/charge (m/z) ratio are isolated and directed from quadrupole 1 (used as a mass filter) into quadruple 2 (collision cell). In the collision cell, the parent ion is fragmented (due to collision with inert gas molecules) to give daughter ions. The daughter ions are allowed to enter quadruple 3. Specific parent and daughter ion pairs (transitions) are highly selective for a given compound of interest. LC–NMR: (high performance) liquid chromatography nuclear magnetic resonance. TOF: time of flight mass spectrometry. This allows accurate mass measurement to 4 decimal places. Ions are accelerated from the ion source to a fixed kinetic energy, then allowed to pass down a field-free flight tube to a detector. The time taken for an ion to reach the detector is proportional to the mass/charge (m/z) ratio.
Vol. 1, No. 4 2004
from more involved machinery, such as the SAGIANTM robotics system (Beckman Coulter, http://www.beckmancoulter.com) and the Tecan Genesis Freedom system, (http://www.tecan.com), which incorporate multiple plate stacking systems and centrifuges that enable complete assays to be performed by the robot, to more simple benchtop machines, such as the Hamilton Microlab Star (http:// www.hamiltoncompany.com) or the Packard MultiPROBE1 II, (http://www.perkinelmer.com). When choosing the type of robot to use, scale and throughput need to be considered [3]. Generic NADPH consumption or O2 biosensor technology [4] never realised their early promise, therefore LC–MS–MS (see Glossary) is now routinely used for sample analysis because this offers selectivity and speed advantages (run times of only 60–90 s). Further time-saving is afforded by cassetting of samples or enabling four sample streams to be input into one mass spectrometer [3]. One example of this is MUXtechnology from Waters Micromass1 (http://www.micromass.co.uk). Powerful software enables LC–MS–MS parameters to be generated for large numbers of compounds. These methods are used to perform automated acquisition and processing (QuanOptimiseTM and MassLynxTM Software from Waters Micromass1 or Analyst1 Software from MDS Sciex, http://www.sciex.com). The processed LC–MS–MS data are automatically imported into Excel to calculate CLint values (based on the half-life the of parent compound). Fitting of the concentration-time data can be semi-automated, with the spreadsheet designed to flag outliers and provide statistical analysis on the quality of the data fit. This makes data review rapid. The data are then closely inspected and several quality checks performed.
Test vehicle considerations Solvent concentrations should be kept to a minimum (1%, v/v). Fig. 2 shows a survey of the effects of the more commonly used solvents (at 1%, v/v, final) on CYP activity. Acetonitrile appears the more favoured solvent, even though it might activate CYP1A2 activity. However, compound solubility for early discovery compounds might be limited even in this vehicle. These effects can be dependent on both enzyme source and probe substrate [5] and each laboratory should assess an appropriate combination in their hands. Comparable data for other enzymes and species other than human are sparse. However, the ability of some phase II enzymes to tolerate higher solvent concentrations than CYPs in microsomes [6], and the exquisite sensitivity of mono-amine oxidase to DMSO, are perhaps worthy of note.
Concentration of assay protein Fig. 1. General paradigm for metabolic stability screening.
366
www.drugdiscoverytoday.com
It is important to minimise the concentration of the metabolising source (e.g. 0.25–1 mg/ml for microsomes and 0.25– 1 106 cells/ml for hepatocytes) and also the incubation
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Fig. 2. Overview of potential effects of commonly used organic solvents (1%, v/v) on CYP activities with recombinant proteins and HLM (red bars, acetonitrile; yellow bars, DMSO blue bars, methanol). Positive and negative values on the y-axis indicate inhibition and activation, respectively.
time to avoid artefacts produced as a result of non-specific binding and non-linear kinetics [7–9].
Test compound concentration One of the most critical experimental variables is the concentration of test compound used during the incubation. Early in drug discovery, circulating concentrations (CIN VIVO; see Glossary) that are probably associated with a projected efficacious dose in man can only be estimated. For most compounds, KM (see Glossary) is likely to be >Cin vivo. Therefore, the initial substrate concentration chosen should be 1– 3 mM (probably to be Km). When a more informed estimate of Cin vivo becomes available, the substrate concentration could be reassessed and should be justified scientifically. If Km < Cin vivo, then the substrate concentration chosen should be Cin vivo. Despite some shortcomings (e.g. less sensitivity than the measurement of specific metabolite appearance), monitoring the depletion of the test compound (at a single low-substrate concentration (S), where S Km; Fig. 3) with time is the approach of choice to determine CLint in a discovery setting. As total substrate depletion is measured, rates most likely represent multiple reactions, rather than single biotransformations. Data from the authors’ laboratory have been used to validate this approach for a variety of in vitro metabolising systems [10]. Several CYP enzymes, most notably CYP3A4 and CYP2C9, demonstrate atypical, non-Michaelis–Menten kinetics, which indicate auto-activation of the enzyme [11]. This phenomenon has been observed with recombinant CYPs, microsomes and hepatocytes [12,13]. In this instance, the kinetic analysis for in vitro–in vivo scaling requires further
interpretation. The use of CLmax, the maximum CLint, has been proposed but examples of any clinical relevance of this term are perhaps limited to a few compounds. Nevertheless, initial screening at several concentrations is recommended for CYP substrates (Fig. 3).
Test systems Microsomes and hepatocytes In vivo hepatic metabolic clearance can be estimated from the turnover or CLint derived from in vitro tissue preparations, such as hepatocytes, particularly for compounds undergoing CYP-dependent oxidation [14]. Many labora-
Fig. 3. Relationship between substrate concentration (S), CLint defined by the Michaelis–Menten equation (full line) and Clmax described using the Hill equation (dotted line). Values for Vmax, Km and S50 are 200 pmol/ min/mg and 15 mM, respectively, for the simulations shown.
www.drugdiscoverytoday.com
367
Drug Discovery Today: Technologies | Lead profiling
tories use hepatic microsomes rather than hepatocytes to determine CLint [15,16] because they are a more flexible system with which to study oxidative biotransformations in terms of ease of preparation from many species, long-term storage and availability. However, hepatocytes provide the most physiologically relevant model with which to measure qualitative and quantitative aspects of hepatic metabolism because they contain the full complement of enzymes a compound is probably to encounter during firstpass metabolism. Additionally, interactions with transporter proteins present in hepatocyte membranes can be key determinants of hepatic clearance. Hepatocytes have therefore become the cellular system of choice given the limitations of hepatic tissue slices for quantitative studies. Rat hepatocytes and human liver microsomes (HLM) are used routinely metabolic stability screens. Human hepatocytes are used for more optimised compounds and the data are used to estimate human clearance and dose [17]. The in vivo clearance of a compound or series of compounds might not always be predicted well from in vitro data. Cross-species in vitro–in vivo scaling can be useful in evaluating how well human hepatocyte CLint can predict human clearance in vivo [18]. Concerns over the longer-term availability of freshly isolated human hepatocytes and their de-differentiation in culture has prompted the search for sustainable sources. Three main strategies have been considered: (i) immortalisation of primary hepatocytes; (ii) manipulation of culture conditions to select liver-specific function in cell lines; and (iii) transdifferentiation of human precursor cells to hepatocytes. Some success has been achieved using immortalising genes, in that stable expression of several enzymes has been accomplished through several passages of culture [19]. Modification of cell culture conditions to manipulate the expression of relevant proteins in proliferative hepatic cell lines has also yielded some success. Although instability of the lines is a common problem [19], cells can be cryopreserved at a stage when relevant functionally active proteins are expressed and then thawed and plated for use. Hematopoietic bone marrow mesenchymal stem cells are capable of generating many different types of tissue cells, including adult human hepatocytes [20]. There has been some optimism that manipulation of the hepatic progenitor cells could provide a supply of human hepatocytes. However, transplantation research has demonstrated that the differentiation of stem cells into hepatocytes is at best a highly inefficient process and can only be achieved when cell fusion (to host liver cells) occurs (i.e. differentiation into adult hepatocytes does not occur) [21]. It appears unlikely that stem cells will provide useful ADME tools in the near future. 368
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
Given the complexity of the systems, it is perhaps not surprising they have not yet delivered their ultimate goal: a stable cell line expressing differentiated functions similar to primary hepatocytes pertaining to metabolism, transport and enzyme induction. Meanwhile, techniques have been perfected whereby hepatocytes can be isolated from small liver resections. Together with recent advances in cryopreservation technology, this might improve the flexibility and access to human cells. Further validation of such tools is emerging [22].
Recombinant enzymes Recent trends have seen a move towards ‘‘frontloading’’ recombinant CYPs, either as artificial HLM [23] or in CYP reaction phenotyping [24]. Armed with this information, one can also attempt to apply existing tenets of structureactivity relationships (SARs) for individual enzymes, which might not be immediately obvious from hepatocyte or microsomal data. These data coupled with some kind of ‘‘decision tree’’ [25] could provide a useful starting point for such analyses and might offer the potential to eliminate leads with unfavourable CYP profiles [10]. This knowledge can influence compound selection and optimisation and provides valuable insight into species differences, contribution of the gut to first-pass metabolism and possible intersubject variability anticipated in clinical studies. Early information about polymorphic metabolism coupled with increased awareness of functional polymorphisms in drug targets could also provide an opportunity for individualisation of drug-dosing regimens in the future [26]. Such analysis could provide a means of progressing compounds, which might have once been eliminated from the drug development pipeline. There can be little doubt that these approaches are most advanced for the CYP enzyme family. However, ongoing efforts with other major protein families should culminate in similar methodology becoming available for these in the not-too-distant future (see Outstanding issues). Metabolic stability data are important in predicting in vivo clearance. However, metabolite identification data can also be vital for defining the most metabolically labile sites in a compound of interest. The most common MS techniques involved are time-of-flight (TOF; see Glossary) and ION TRAP (see Glossary). The high level of automation required is supported by powerful software designed to search extensive databases to identify potential metabolites from the masses found in the sample (e.g. MetaboLynxTM software from WatersTM, http://www.micromass.co.uk). LC–MS–MS can then be performed automatically to confirm the identity of the metabolites. Column size is an important factor in obtaining improved sensitivity and capillary columns (1 mm 15 cm) are now used routinely used. LC–NMR (see Glossary) is used
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Table 1. Some considerations in the selection of higher throughput CYP assay format Technology 1
Technology 2
Technology 3
Name of specific type of technology
CYP inhibition (Radiometric)
CYP inhibition (Fluorescence)
CYP inhibition (LC–MS–MS)
Names of specific technologies with associated companies and company websites
Radiolabelled probes substrates from Amersham Biosciences (http://www.amershambiosciences.com)
High-throughput P450 inhibition kits (http://www.gentest.com) Microfluidics (http://www.tecan.com)
Substrates and metabolites from Sigma-Aldrich (http://www.sigmaaldrich.com) and Ultrafine (http://www.ultrafine.co.uk)
Pros
Robust No interference from test inhibitor or incubation matrix Substrates are drugs (cf. fluorescent probes) Limited data processing Good signal to noise
Very rapid analysis with limited data processing. Technology most suited to high-throughput screening
Substrates are drugs (cf. fluorescent probes) Good signal to noise
Cons
Medium-throughput + radioactivity
Poor signal to noise is an issue with some probe substrates Some probes very sensitive to inhibition by DMSO Probes do not bear structural resemblance or have physicochemical properties of typical drugs
Medium-throughput LC–MS system failures/troublshooting LC–MS time is at a premium in discovery laboratorys
References
[32]
See Related articles
[30]
when the exact site of modification is ambiguous from the LC–MS–MS data.
DDI screening CYP inhibition Inhibition of CYP activity can be manifested as dramatic changes in pharmacokinetics (PK), adverse drug reactions and, in extreme cases, fatalities [27]. This awareness has coincided with the generation of a battery of automated screens for CYP inhibition. When considering the major hepatic CYPs responsible for oxidative drug metabolism, it could be argued that only the key drug metabolising CYPs should be considered early in drug discovery (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4). Assays use a common analytical end-point: liquid scintillation counting of radioactivity liberated during site-specific metabolism [28]; selective analysis of fluorescent metabolites [29] mass spectrometry [30]. The precise choice of substrate probe depends on several key considerations: throughput requirement, potential analytical interference [31], clinical experience with probe substrates and cost of analysis (Table 1). Recombinant CYPs (rCYPs) often serve as the metabolising system because, in addition to the more obvious selectivity advantages, their use keeps the total protein concentration low, minimising non-specific binding. The influence of protein concentration on the apparent Ki or IC50 can be profound for lipophilic CYP3A4 inhibitors, such as itraconazole
and ketoconazole; the apparent IC50 for ketoconazole can easily vary between 0.005 and 0.3 mM [10]. For CYP3A4, the different binding modes or sites within the enzyme’s active site can also confound inter-assay comparisons (Fig. 4). It is now commonplace to use several different substrates for CYP3A4. The choice of buffer (and strength) might influence enzyme activity but is typically 0.1 M phosphate or a Tris-
Fig. 4. An example of differential effects on prototypic CYP3A/5 substrate oxidations in HLM. Abbreviations: MDZ 10 -OH, midazolam 1-hydroxylation; MDZ 40 -OH, midazolam 4-hydroxylation; NIF, nifedipine oxidation; TST, testosterone 6b-hydroxylation.
www.drugdiscoverytoday.com
369
Drug Discovery Today: Technologies | Lead profiling
based system. The effects of solvents have already been discussed. Interestingly, the fluorogenic probe reaction 7-benzyloxy-4-(trifluoromethyl) coumarin (BFC) O-debenzylation appears uniquely sensitive to DMSO at concentrations 0.2%, v/v. The co-incubation format of most assays is such that mechanism-based irreversible inhibition cannot be readily monitored. Irreversible inactivation is observed in CYP-catalysed reactions with reasonable frequency, possibly due to the reactivity of high energy oxygenated intermediates. The classical robust method for determining the extent of mechanism-based inhibition and the kinetic constants involved has been described by Mayhew et al. [32]. Some laboratories have used fluorescent substrates to track the inhibitor complex in real-time as a higher throughput alternative [33]. As with reversible CYP inhibition, attempts to apply kinetic constants derived from in vitro mechanismbased inhibition assays in quantitative predictions of in vivo interactions have shown promise, albeit with limited datasets [34]. However, even the most potent irreversible inhibitors might not show dramatic effects on the PK of co-administered drugs (Outstanding issues). A list of substrates for the major human hepatic CYP isoforms for in vitro and in vivo drug–drug interaction experiments has been recommended [35]. A range of [O- or Nmethyl-14C]-substrates can be employed, which liberate [14C]-formaldehyde as the product of enzyme-specific oxidative demethylation [28]. Because of the concerns around the extensive use of radioactivity, fluorescent probes have also been assessed [29]. Availability of specific fluorescent probes and recombinant CYP systems have accelerated the development of robust high-throughput assays in miniaturized 384-well form [36]; indeed 1536w format could also be feasible. This increasing capacity and resulting lower cost per sample might present pharmaceutical companies with the ability to screen the whole or part of a compound bank for quick classification or development of more predictive in silico models (Outstanding issues). Although these fluorogenic substrates have use in the early phases of drug discovery [30], recent experiences have challenged their suitability in driving structure– activity relationships and making in vivo projections. Enhanced MS sensitivity has recently provided assays using individual substrates with rCYPs [30] or HLM [37]. Substrates used can also be used as probes in vivo and represent ‘‘drug space’’. Monolithic silica technology and ‘‘ultrafast’’ chromatography might afford even further reductions in analysis time, which might again enhance throughput [38]. This laboratory now has a range of radiometric, fluorescent and MS-based assays. At each phase of development, these assays were cross-validated to assess inter-assay consistency and so on, which is key to analysis from appropriate databases. 370
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
Perhaps the most up to the minute technology in CYP inhibition assays is microfluidics. With wide-ranging applications in biochemistry and molecular biology [39], microfluidics is now being used in high-throughput ADME studies (including serum albumin and alpha glycoprotein binding, Pglycoprotein interaction and CYP inhibition). The LabCD ADMETTM System (Tecan, http://www.tecan.com) combines microscale fluid paths, reaction chambers and passive valves on a disposable disc that operate on an existing Tecan platform. Assays can be configured to use less than 10 mL of each reagent, with fluid being moved along pathways by capillary action and centrifugal forces via disc spinning. This technology offers potential benefits in cost and assay reproducibility, thereby minimising cross-site variability in data.
CYP induction DDIs arising through enzyme induction are less significant compared with enzyme inhibition and their impact on clinical PK can be difficult to detect against background intersubject variability [10]. Hepatic enzyme induction commonly observed in species used in safety assessment studies often has little relevance for man given marked inter-species differences in induction response [40]. It could be argued that the exposures required for clinically significant hepatic enzyme induction are probably to be well in excess of those required for therapeutic efficacy with newer therapies that exhibit nanomolar potency for their target. However, the role of the pregnane X receptor (PXR) in the regulation of other genes involved in xenobiotic metabolism, including CYP2B6, CYP2C8, CYP2C9, GSTA2 and MDR1, as well as genes critical to bile acid metabolism and homeostasis, should not be overlooked. Indeed, the discovery of potent ligands of human PXR [41], and the role of transporter proteins in the hepatic uptake and accumulation of PXR ligands, such as rifampicin, support regular screening for human enzyme induction in vitro. The discovery that many compounds induce drug-metabolizing enzymes through the interaction with nuclear hormone receptors [42] has prompted the establishment of enhanced throughput systems using reporter gene technology for PXR and the constitutive androstane receptor (CAR) as well as the Ah receptor. These might be used once transcriptional activation has been established as the mechanism of induction in cultured human hepatocytes for a lead series (Outstanding issues). Consideration should also be given to the host cell line (usually HepG2 or HuH7), which might demonstrate variable expression of uptake and efflux proteins and respond differently to external stimuli. Clearly, such differences could confound interpretation of data, thereby affecting comparisons between species and systems and extrapolation to the in vivo setting. Luo et al. [41] have also presented data that emphasise the importance of understanding the pros and cons of the test
Vol. 1, No. 4 2004
Drug Discovery Today: Technologies | Lead profiling
Links
Related articles
Hamilton Microlab Star (http://www.hamiltoncompany.com) Packard MultiPROBE1 II (http://www.perkinelmer.com) SAGIANTM robotics system supplied by Beckman Coulter (http:// www.beckmancoulter.com) Tecan instruments (http://www.tecan.com) Waters Micromass1 (http://www.micromass.co.uk) MDS Sciex (http://www.sciex.com) CYPEX (http://www.cypex.co.uk) Gentest (http://www.gentest.com)
Gombar V.K. et al. (2003) Role of ADME characteristics in drug discovery and their in silico evaluation: in silico screening of chemicals for their metabolic stability. Curr. Top. Med. Chem. 3, 1205–1225 Eddershaw, P.J. et al. (2000) ADME/PK as a part of a rational approach to Drug Discovery. Drug Discov. Today 5, 409–414 Jenkins, K.M. et al. (2004) Automated high throughput ADME assays for metabolic stability and cytochrome P450 inhibition profiling of combinatorial libraries. J. Pharm. Biomed. Anal. 34, 989–1004 Cohen, L.H. et al. (2003) In vitro drug interactions of cytochrome P450: an evaluation of fluorogenic to conventional substrates. Drug Metab. Dispos. 31, 1005–1015
system for enzyme induction together with the analytical endpoint. Differential exposure to parent compound or active metabolites might also affect the interpretation of data in different test systems [43].
Conclusions Various scales of automation are available to cater for the requirement of medium- to high-throughput ADME studies. The automation revolution in DMPK has been driven by advances in combinatorial chemistry, parallel synthesis and high-thoughput screening methods for target affinity. Automated in vitro metabolic stability and DDI studies provide large amounts of data that improve understanding and that, in turn, should make available in silico models. The ultimate aim of these must be to predict in vivo disposition (Outstanding issues). However, automation is no substitute for scientific robustness or integrity and should not encourage complacency. Attention to detail and provision of high quality data is paramount. Important considerations include the use of low protein (enzyme) concentrations and reasonable incubation times to avoid misinterpretation of results. Appropriate substrate concentrations should be used in metabolic stability assays. Even in early discovery, it is important to understand, within a chemical series, the in vitro kinetics (Km, auto- or hetero-activation, solvent effects on reaction velocities) and, as a result, choose appropriate incubation conditions. Obviously, it is important that automated assays have the flexibility to encompass this information. The usefulness of in vitro data in estimating clearance in vivo is widely acknowledged. However, there is still a need to refine existing strategies. For example, recombinant CYP data could be used to evaluate inter-subject variability and the impact of CYP polymorphisms. Projections of the extent of in vivo interaction, made using in vitro Ki estimates, are promising. Re-analysis of external datasets using Ki values determined in-house has confirmed the promise of this approach. These datasets are limited and there is still much to be done in terms of generating substantial databases of in vitro and clinical data together with the incorporation of information on inter-subject and popu-
lation variability. Considerations of mechanism-based inhibition, the extent of in vitro and in vivo binding and uptake into hepatocytes (of inhibitor and substrate) must be considered if ‘‘false-negatives’’ are to be avoided. In vitro inhibition studies using isolated human hepatocytes could be used to address some of these issues.
Outstanding issues Significant progress has been made in understanding and reducing oxidative (phase I) metabolism. Compounds so designed might be substrates for phase II enzymes and transporter proteins, which in turn could lead to extra-hepatic or non-metabolic clearance. Our aspirations now must be to understand these issues. Projections of the extent of in vivo interaction, made using in vitro Ki, are promising. However, in vitro data are largely focused on five CYPs. It might be prudent to expand this to CYPs that have gained in importance as understanding has increased (2B6, 2C8, 3A5). In addition to blood binding of inhibitor and substrate, tissue distribution and active uptake might influence the intra-hepatic free concentration. Identification of the key processes and transporter proteins responsible for these phenomena and their incorporation into in vitro screens should permit a more accurate assessment of the in vivo effects of CYP inhibition observed in in vitro screening. The use of animal models to validate these processes should not be overlooked. More attention is also now being paid to irreversible (mechanismbased) inhibition. There is, however, a need for greater understanding of how to interpret the in vitro data and of the toxicological consequences. Cultured human hepatocytes are viewed as the gold-standard for investigating the potential for drug–drug interactions arising through enzyme induction. In addition, reporter gene assays are widely used for screening purposes. Differences between the models and interpretation of the data must be considered carefully to extrapolate to in vivo effectively. Several ongoing industry–academia collaborations should provide valuable databases together with validated in silico models with which to predict drug–drug interactions and the relative risk in specific patient populations. This should help prioritise early clinical interaction studies. The future must focus further on reducing costs as well as the time taken to get a drug to the market. A key to this aim is the availability of high-quality desktop in silico models to reliably predict ADMET properties from molecular structure. This might take significant investment, but is warranted if the reward is robust, predictive tools.
www.drugdiscoverytoday.com
371
Drug Discovery Today: Technologies | Lead profiling
References 1 Hodgson, J. (2001) ADMET – turning chemicals into drugs. Nat. Biotechnol. 19, 722–726 2 Davis, A.M. and Riley, R.J. (2004) Predictive ADMET studies, the challenges and opportunities. Curr. Opin. Chem. Biol. 8, 378–386 3 Kassel, D.B. (2004) Applications of high throughput ADME in drug discovery. Curr. Opin. Chem. Biol. 8, 339–345 4 Joseph, S. et al. (2003) An amperometric biosensor with human CYP3A4 as a novel drug screening tool. Biochem. Pharmacol. 65, 1817–1826 5 Tang, C. et al. (2000) Substrate-dependent effect of acetonitrile on human liver microsomal cytochrome P450 2C9 (CYP2C9) activity. Drug Metab. Dispos. 28, 567–572 6 Uchaipichat, V. (2004) Human UDP-glucuronosyltransferases: isoform selectivity and kinetics of 4-methylumbelliferone and 1-naphthol glucuronidation, effects of organic solvents, and inhibition by diclofenac and probenecid. Drug Metab. Dispos. 32, 413–423 7 Forti, R.S. and Fisher, M.B. (2004) Impact of incubation conditions on bufuralol human clearance predictions: enzyme lability and nonspecific binding. Drug Metab. Dispos. 32, 295–304 8 Obach, R.S. (1999) Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab. Dispos. 27, 1350–1359 9 Austin, R.P. et al. (2002) The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metab. Dispos. 30, 1497–1503 10 Riley, R.J. (2001) The potential pharmacological and toxicological impact of P450 screening. Curr. Opin. Drug Discov. Dev. 4, 45–54 11 Houston, J.B. and Galetin, A. (2003) Progress towards prediction of human pharmacokinetic parameters from in vitro technologies. Drug Metab. Rev. 35, 393–415 12 Witherow, L.E. and Houston, J.B. (1999) Sigmoidal kinetics of CYP3A substrates: an approach for scaling dextromethorphan metabolism in hepatic microsomes and isolated hepatocytes to predict in vivo clearance in the rat. J. Pharmacol. Exp. Ther. 290, 58–65 13 Ngui, J.S. et al. (2000) Cytochrome P450 3A4-mediated interaction of diclofenac and quinidine. Drug Metab. Dispos. 28, 1043–1050 14 Houston, J.B. and Carlile, D.J. (1997) Prediction of hepatic clearance from microsomes, hepatocytes and liver slices. Drug Metab. Rev. 29, 891–922 15 Obach, R.S. et al. (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmacol. Exp. Ther. 283, 46–58 16 Clarke, S.E. and Jeffrey, P. (2001) Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica 31, 591– 598 17 Riley, R.J. et al. (2002) The influence of DMPK as an integrated partner in modern drug discovery. Curr. Drug Metab. 3, 527–550 18 Naritomi, Y. et al. (2003) Utility of hepatocytes in predicting drug metabolism: comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab. Dispos. 31, 580–588 19 Anderson, K. et al. (1998) Cytotoxicity of xenobiotics and expression of glutathione-S-transferase in immortalised rat hepatocyte cell lines. Hum. Exp. Toxicol. 17, 131–137 20 Jiang, Y. (2002) Pluripotency of mesenchymal stem cells derived from adult marrow. Nature 418, 41–49 21 Fausto, N. (2004) Liver regeneration and repair: hepatocytes, progenitor cells and stem cells. Hepatology 39, 1477–1487 22 McGinnity, D.F. et al. (2004) Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab. Dispos. 32, 1247–1253
372
www.drugdiscoverytoday.com
Vol. 1, No. 4 2004
23 Eddershaw, P.J. and Dickins, M. (1999) Advances in in vitro drug metabolism screening. Pharm. Sci. Technol. Today 2, 13–19 24 McGinnity, D.F. et al. (2000) Automated definition of the enzymology of drug oxidation by the major human drug metabolising cytochromes P450 (CYPs). Drug Metab. Dispos. 28, 1327–1334 25 Lewis, D.F. (2000) On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics: towards the prediction of human P450 substrate specificity and metabolism. Biochem. Pharmacol. 60, 293–306 26 Ingelman-Sundberg, M. et al. (1999) Polymorphic human cytochrome P450 enzymes: an opportunity for individualised drug treatment. Trends Pharm. Sci. 20, 342–349 27 Jankel, C. and Fitterman, L.K. (1993) Epidemiology of drug–drug interactions as a cause of hospital admissions. Drug Saf. 9, 51–59 28 Moody, G.C. et al. (1999) Fully automated analysis of activities catalysed by the major human liver cytochrome P450 (CYP) enzymes: assessment of human CYP inhibition potential. Xenobiotica 29, 53–75 29 Crespi, C.L. et al. (1998) High throughput screening for inhibition of cytochrome P450 metabolism. Med. Chem. Res. 8, 457–471 30 Weaver, R. et al. (2003) Cytochrome P450 inhibition using recombinant proteins and mass spectrometry/multiple reaction monitoring technology in a cassette. Drug Metab. Dispos. 32, 1–12 31 Zou, L. et al. (2002) Effects of intrinsic fluorescence and quenching on fluorescence-based screening of natural products. Phytomedicine 9, 263– 267 32 Mayhew, B.S. et al. (2000) An in vitro model for predicting in vivo inhibition of cytochrome P4503A4 by metabolic intermediate complex formation. Drug Metab. Dispos. 28, 1031–1037 33 Yamamoto, T. et al. (2004) High-throughput screening for the assessment of time-dependent inhibitions of new drug candidates on recombinant CYP2D6 and CYP3A4 using a single concentration method. Xenobiotica 34, 87–101 34 Ito, K. et al. (1998) Prediction of the in vivo interaction between midazolam and macrolides based on in vitro studies using human liver microsomes. Metab. Dispos. 31, 945–954 35 Tucker, G.T. et al. (2001) Optimizing drug development: strategies to assess drug metabolism/transporter interaction potential – toward a consensus. Pharm. Res. 18, 1071–1080 36 Kariv, I. et al. (2001) Development of a miniaturized 384-well high throughput screen for the detection of substrates of cytochrome P450 2D6 and 3A4 metabolism. J. Biomol. Screen. 6, 91–99 37 Walsky, R.L. and Obach, R.S. (2004) Validated assays for human cytochrome P450 activities. Drug Metab. Dispos. 32, 647–660 38 Peng, S.X. et al. (2003) High-throughput cytochrome P450 inhibition assays by ultrafast gradient liquid chromatography with tandem mass spectrometry using monolithic columns. Rapid Commun. Mass Spectrom. 17, 509–518 39 Fitzgerald, D.A. (2001) Making every nanoliter count. The Scientist 15, 29–31 40 Lu, C. and Li, A.P. (2001) Species comparison in P450 induction: effects of dexamethasone, omeprazole, and rifampin on P450 isoforms 1A and 3A in primary cultured hepatocytes from man, Sprague–Dawley rat, minipig and beagle dog. Chem. Biol. Interact. 134, 271–281 41 Luo, G. et al. (2002) CYP3A4 induction by drugs: correlation between a pregnane X receptor reporter gene assay and CYP3A4 expression in human hepatocytes. Drug Metab. Dispos. 30, 795–804 42 Waxman, D.J. (1999) P450 gene induction by structurally diverse xenochemicals: central role of nuclear receptors CAR, PXR and PPAR. Arch. Biochem. Biophys. 369, 11–23 43 Riley, R.J. and Kenna, J.G. (2004) Cellular models for ADMET predictions and evaluation of drug–drug interactions. Curr. Opin. Drug Discov. Dev. 7, 86–99