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DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 DMD FastThis article Forward. has not been Published copyedited onand Marchformatted. 7, The 2011 final versionas doi:10.1124/dmd.110.037911 may differ from this version. DMD #37911 1

CYP3A TIME DEPENDENT INHIBITION RISK ASSESSMENT

VALIDATED WITH 400 REFERENCE DRUGS

Alfred Zimmerlin, Markus Trunzer and Bernard Faller Downloaded from

Discovery ADME, Novartis Institutes for BioMedical Research, CH-4002, Basel, Switzerland

dmd.aspetjournals.org

Version 16-Feb-2010 at ASPET Journals on September 27, 2021

Copyright 2011 by the American Society for and Experimental Therapeutics. DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 2

Running Title:

CYP3A time dependent inhibition risk assessment

Corresponding Author: Dr. Alfred Zimmerlin

Discovery ADME, Novartis Institutes for BioMedical Research, WSJ-350.3.12, P.O. box,

CH-4002 Basel, Switzerland

Telephone: +41 613243326 Downloaded from Fax: +41 616968583

Email: [email protected]

Text pages: 15 dmd.aspetjournals.org

Tables: 2

Figures: 5

Reference No: 31 / 40 max. at ASPET Journals on September 27, 2021

Abstract word count: 221 / 250 max.

Introduction word count: 644 / 750 max.

Discussion word count: 1500 / 1500 max.

Abbreviations

P450, cytochrome P450; HLM, human microsomes; HPLC, high-performance liquid chromatography; IC50, inhibitor concentration producing 50% inhibition; kobs, inactivation rate constant, KI, inactivation binding constant; kinact, maximum inactivation rate constant;

LC-MS, liquid chromatography-mass spectrometry; NCE, new chemical entity; TDI, time dependent inhibition; WDI, World Drug Index.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 3

Abstract

Although reversible CYP3A inhibition testing is well established to predict the drug to potential of clinical candidates, it is only recently that time dependent inhibition

(TDI) became the focus of drug designers. Failure of several late stage clinical candidates has been attributed to TDI, and this mechanism is also suspected to play a role in liver toxicities often observed in preclinical species. Measurement of inactivation rates (kinact and KI) is technically challenging and a great deal of variability can be found in the literature. In this Downloaded from paper we have evaluated the TDI potential for 400 registered drugs using a high throughput assay format based on determination of inactivation rate (kobs) at a single concentration of test

compound (10 µM). The advantages of this new assay format are highlighted by comparison dmd.aspetjournals.org to data generated using the IC50 shift assay, a current gold standard approach for preliminary

-1 assessment of TDI. Using an empirically defined positive/negative kobs bin of 0.02 min , 4% of registered drugs only were found positive. This proportion increased to more than 20% at ASPET Journals on September 27, 2021 when in-house lead optimization molecules were considered, emphasizing the importance of filtering this property out when selecting promising drug candidates. Finally, it is suggested that the data and technology described here may be a good basis for building structure activity relationships and in silico modeling.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 4

Introduction

Cytochrome P450 are major metabolizing involved in the of a large number of drugs (Liu et al, 2007). Inhibition of cytochrome P450 enzymes by co- administered drugs can lead to overexposure and has been attributed to the withdrawal of several drugs from the market, of which Mibefradil is one example (Krayenbuhl et al, 1999).

A major cytochrome P450 isoform family, CYP3A is particularly important as it is involved in the metabolism of numerous marketed drugs (Rendic 2002). For new chemical entities Downloaded from (NCEs), inhibition of CYP3A is a major drawback. Risk evaluation for this particular aspect is routine in industry since the early 90s with in vitro assays based on recombinant CYP3A

and fluorigenic substrates (Crespi et al, 1997). These assays have evolved to use cytochrome dmd.aspetjournals.org

P450 isoform specific drug substrates and more relevant in vitro systems e.g. 1’- in human liver microsomes, enabling more reliable in vitro-in

vivo extrapolation of results (Foti et al, 2010). An additional complexity for evaluating the at ASPET Journals on September 27, 2021 drug interaction potential of a CYP3A inhibitor resides in the mechanism of inhibition. Time dependent inhibition (TDI) has been described for numerous drugs (Zhou et al, 2005) and is characterized by an increase of inhibitory potency with compound turnover. Potential mechanisms for this include the formation of a tight binding, quasi-irreversible inhibitory metabolite-complex or the inactivation of P450 enzymes by covalent adduct formation

(Murray 2009; VandenBrink and Isoherranen 2010).

TDI often results in more severe drug interactions as restoration of activity generally requires de novo synthesis of the enzyme (Kalgutkar et al, 2007). Additionally, in some cases protein adducts, formed via TDI, may lead to auto-immune reactions and severe liver toxicities

(Pirmohamed et al, 1996). may be such an example where as yet unexplained and rare liver toxicities led to the market withdrawal (Rxlist.com). Having robust assays in place to detect research compounds with these potential liabilities at early stages in drug discovery is extremely important.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 5

The evaluation of time dependent inhibition is one of the most challenging tasks for

P450 enzymologists requiring complex experimental design, a large number of single incubations per compound and complex data analysis. This complexity has been recently reviewed and conceptual errors that may explain the large variation in published results have been described (Ghanbari et al, 2006; Yang et al, 2007). The characteristic parameters for

TDI, kinact, the maximal inactivation rate and KI, the concentration at half kinact and their ratio kinact/KI are typically used for risk evaluation. Attempts to use excerpts of these experiments Downloaded from in order to screen a large number of compounds have been published recently (Atkinson et al,

2005; Lim et al, 2005; Obach et al, 2007; Perloff et al, 2009). The most popular method used

is the IC50-shift method because of its easy setup which is essentially an extension of the dmd.aspetjournals.org classical high throughput testing for reversible inhibition. The relationship between the shifted

IC50 or fold shift in IC50 to kinact or KI requires careful evaluation and is difficult to use for

TDI risk assessment (Burt et al, 2010; Krippendorff et al, 2009). However, Obach et al. have at ASPET Journals on September 27, 2021 recently proposed a risk ranking model based on the shifted IC50 (Obach et al, 2007).

Alternatively single concentration, single time point assays were reported for which % activity remaining or apparent partition ratio was used for risk evaluation (Lim et al, 2005;

Watanabe et al, 2007).

The present study evaluates a new TDI risk evaluation method based on the measurement of the observed first order enzyme inactivation rate constant, kobs, at a relatively high test compound concentration of 10 µM. This method includes the crucial high dilution step important for TDI studies. The kinetic parameters, kinact and KI were measured for 63 known drugs and compared to literature. Inactivation rates at 10 µM were generated for a large set of 400 marketed reference compounds and rank ordered for risk evaluation.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 6

Methods

Reagents and Chemicals

Midazolam was purchased from Sequoia Research Products Ltd. (Pangbourne, United

Kingdom) and D4-1’-hydroxymidazolam from Cerilliant Corp. (Round Rock, TX, USA).

Drugs and Novartis proprietary compounds were obtained from the Novartis compound store as 10 mM stock solutions in Dimethylsulfoxide (DMSO). Their purity and concentration were

β ′ controlled using various techniques. -Nicotinamide dinucleotide 2 -phosphate Downloaded from tetrasodium salt (NADPH), 1’-hydroxymidazolam and all other reagents, chemicals and buffer salts were purchased from Sigma-Aldrich Chemicals (St. Louis, MO, USA) or Fluka dmd.aspetjournals.org (Buchs, Switzerland). Water, formic acid and acetonitrile for LC-MS-analysis were purchased in analytical grade from Merck (Darmstadt, Germany).

Human Liver Microsomes

Pools of human liver microsomes (HLM, catalog number: 457081) from 50 individual at ASPET Journals on September 27, 2021 donors (lot number: 82087) were obtained from BD Biosciences (Woburn, MA, USA). The microsomes were characterized by the vendor with regard to the levels of enzyme-selective marker activities (CYP1A2, phenacetin O-deethylation; CYP2A6, coumarin 7-hydroxylation;

CYP2B6, (S)-mephenytoin N-demethylation; CYP2C8, 6α-hydroxylation;

CYP2C9, diclofenac 4’-hydroxylation; CYP2C19, (S)-mephenytoin 4’-hydroxylation;

CYP2D6, 1’-hydroxylation; CYP2E1, chlorzoxazone 6-hydroxylation; CYP3A4, testosterone 6β-hydroxylation; CYP4A11, lauric acid 12-hydroxylation; flavin-containing monooxygenase (FMO), methyl p-tolyl sulfide oxidation).

Time dependent Inhibition

To determine kinact and KI, six test compound concentrations (from 2.5 to 50 µM final concentration) were obtained by dispensing 2.5 to 50 nL of 10 mM stock solutions using a

Labcyte ECHO 520 acoustic dispenser (Labcyte, Bucher Biotec, Basel, Switzerland) into an

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 7 empty 96 well plate shortly before starting the incubation by the addition of 10 µL of 0.5 mg/mL human liver microsomes and 1 mM NADPH in 50 mM sodium phosphate buffer (pH

7.5, pre-warmed at 37°C) using a Multidrop Combi (Thermo Fisher Electron Corporation,

Vantaa, Finland). DMSO concentration was adjusted to 0.5% in all incubations. For screening experiments, only 10 nL of the stock solutions of test compounds were dispensed for a 10 µM final concentration and a DMSO content of 0.1%. The plates were then pre-incubated at 37°C in an incubator (ELMI Skyline DTS-4, LTF Labortechnik, Wasserburg, Germany) for 0, 10, Downloaded from 20 and 30 min and the residual CYP3A activity was determined by the addition of 90 µL (10 fold dilution) of 10 µM midazolam, 0.06 µM D4-1’-hydroxy-midazolam (internal standard)

and 1 mM NADPH in 50 mM sodium phosphate buffer (pH 7.5, pre-warmed at 37°C) using dmd.aspetjournals.org the Multidrop. Adding the internal standard D4-1’-hydroxy-midazolam to the incubation rather than to the stop solution was found to improve assay robustness. The plates were incubated for 8 additional minutes before being stopped by the addition of one volume (100 at ASPET Journals on September 27, 2021

µL) of ice cold acetonitrile. Stopped incubation plates were stored at -20°C overnight and centrifuged at 5000 x g for 35 min at 4°C to pellet precipitated material. Aliquots of the supernatants were analyzed for 1’-hydroxymidazolam and D4-1’-hydroxymidazolam by liquid chromatography mass spectrometry (LC-MS). Under these conditions, the inactivation rate of 10 µM verapamil was measured 80 times (20 assays) over a period of 6 month and did not deviate significantly from the value of 0.073 min-1 (CV=8%, min=0.060 min-1, max=0.086 min-1).

Reversible Inhibition

Dilution ranges of test compounds were obtained by dispensing 2.5 to 100 nL of 10 mM test article stock solutions in DMSO directly into the wells of a 384-well microplate by acoustic dispensing (0.5, 1, 2, 3, 5, 10, 15 and 20 µM final concentrations). For the definition of enzyme activity range (0-100%), 16 wells were filled with 100 nL DMSO only (100%) and

16 wells with 100 nL of 5 mM (0% activity, 5 µM final concentration). 10 nL of

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 8 a 10 mM midazolam (10 mM in DMSO, 2 µM final concentration) and 5 nL D4-1’- hydroxymidazolam (0.28 mM in DMSO, 0.028 µM final concentration) were added as substrate and internal standard into every well of the 384 microplate. All wells were supplemented to 100 nL with pure DMSO using the ECHO 520 (0.2 % final DMSO content).

The incubation was started by the addition of 50 µL of a mix of 0.05 mg/mL human liver microsomes and 1 mM NADPH dissolved in 50 mM sodium phosphate buffer (pH 7.5, pre- warmed at 37°C) using a Multidrop Combi Reagent Dispenser (Thermo Fisher Electron Downloaded from Corporation, Vantaa, Finland). The plate was immediately placed in an incubator (ELMI

Skyline DTS-4, LTF Labortechnik, Wasserburg, Germany) and incubated for 10 min before

stopping by the addition of 50 µL ice-cold acetonitrile containing 1 µM as dmd.aspetjournals.org additional internal standard for LC-MS-analysis

LC-MS analysis:

Analysis of samples was performed on a high performance liquid chromatography– at ASPET Journals on September 27, 2021 tandem mass spectrometry (LC-MS) system consisting of a TSQ Quantum Discovery Max mass spectrometer controlled by QuickQuan 2.0 and equipped and with an electrospray ion source (Ion Max electrospray interface) from Thermo Fisher Scientific (Reinach,

Switzerland), a CTC-HTS Pal autosampler (CTC Analytics, Zwingen, Switzerland) with a sample cooling unit (10°C) and a Rheos pumps model 2000 (Thermo Fisher Scientific,

Switzerland). Samples were separated on a Phenomenex PolarRP column (2.1 x 50 mm, 3.5

µm) protected by a guard-column (2 x 4 mm) containing the same material (provided by

Brechbühler AG, Schlieren, Switzerland) using an isocratic mobile phase of water/acetonitrile

(65:35) containing 0.1 % formic acid at a flow rate of 400 µL/min for 2 min. The injection volume was 20 µL and the first 0.5 minutes of eluent were diverted to waste to protect the ion source from salts and polar impurities. 1’-hydroxymidazolam and D4-1’-hydroxymidazolam were detected in positive ion mode by selective reaction monitoring using the mass transition of 342 to 324 and 346 to 328 respectively at a collision energy of 20 eV.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 9

Data Analysis

For time-dependent experiments, CYP3A enzyme activity in human liver microsomes was determined using the turnover of midazolam to 1’-hydroxymidazolam calculated by the ratio of 1’-hydroxymidazolam to the internal standard D4-1’-hydroxymidazolam. The ratios were transformed to percentage active enzyme remaining (initial activity E0 = 100%) and plotted over the pre-incubation time. The first order inactivation rate constant kobs was calculated by

XLfit 4.3.2 (ID Business Solutions Ltd., Guildford, Surrey, UK) using model 503 with a Downloaded from range of 80% (a) and a background value (b) of 20%. When inactivation was strong, these values allowed the best estimate of initial slopes without the need for manual exclusion of the dmd.aspetjournals.org 30 min data point. Individual kobs values for test articles were corrected by the kobs value of the control containing DMSO only. The percentage of reversible inhibition (%inh-rev) was calculated by the ratio of 1’-hydroxymidazolam to D4-1’-hydroxymidazolam at pre-

incubation time 0 min in relation to the ratio of the control containing DMSO only. In case of at ASPET Journals on September 27, 2021 a strong reversible inhibition (%inh-rev > 50%) no kobs values were calculated.

In full time-dependent inhibition experiments, the Michaels-Menten relationship were used for the determination of the maximal inactivation rate constant kinact and the irreversible inhibition constant KI. The calculated kobs values were plotted over the inhibitor concentration and kinact and KI were calculated by non-linear regression using XLfit model 250.

The IC50 values were calculated by plotting the ratio of 1’-hydroxymidazolam to D4-1’- hydroxymidazolam against the inhibitor concentration in the incubation using XLfit model

205. Range and background were set to 100% and 0% using the DMSO control (DMSO) and the ketoconazole fully inhibited control.

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 10

Results

Experimental design

Evidence suggests that experimental design for the characterization of TDI is a major contributor to data variability in published results. Several authors devoted full papers with extensive data reviews in their discussion and propose several important guidelines for TDI incubations (Ghanbari et al, 2006; Yang et al, 2007). In addition to following these guidelines, a particular innovation of our method is the use of an acoustic dispenser device. Non-contact Downloaded from dispensing of nanoliters of compound stock solutions in DMSO allows the use of very small incubation volumes (10 µL) while keeping DMSO concentrations to a minimum (0.1%). This

low volume allows for a high dilution (1:10) of the pre-incubate and final stop in the same dmd.aspetjournals.org vessel of a standard 96 well microtiter plate. This simplified protocol minimizes errors introduced by successive sampling and dispensing steps used in most described TDI

protocols. at ASPET Journals on September 27, 2021

Experimental conditions were chosen to ascertain proper characterization of TDI and following the general rules described by Ghanbari et al. (Ghanbari et al, 2006): (i) the inactivator is diluted ten-fold after pre-incubation, (ii) the length of the incubation time is short and the probe substrate concentration high (approximately 5 fold Km), (iii) the residual activity is corrected by a control with no inhibitor and (iv) initial slope values of residual activity versus time were used to derive the inactivation rate kobs. Since DMSO is known to impact midazolam hydroxylation activity by CYP3A4 (Nishiya et al, 2010), it was maintained to a minimum (0.1%). For full kinetic studies, a range of 8 concentrations up to 50 µM of test compound was used (including DMSO control) at 4 pre-incubation times (0, 5, 10 and 30 min). For some drugs the range of concentrations was reduced to avoid excessive reversible inhibition during the incubation step.

From the time and concentration dependent loss of enzyme activity curves (Figure

1A), an initial inactivation rate (kobs) was calculated which, when plotted against

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 11 concentration (Figure 1B), enabled the experimental determination of the maximal inactivation rate (kinact) and the concentration at half kinact (KI). For the time dependent inhibition screen, an excerpt of the full kinetic studies was chosen were a single test compound concentration of 10 µM in the pre-incubation was tested.

Time- and concentration dependent inactivation

Table 1 shows the comparison of kinetic parameters obtained for 21 drugs with data from literature references using similar conditions (pooled human liver microsomes and Downloaded from midazolam as marker substrate for CYP3A). Although individual differences can be noted

(, raloxifene and ticlopidine) the overall concordance is good considering that

there is already a large variability among published values. For some compounds our dmd.aspetjournals.org experimental design did not allow to measure KI and kinact because either the kinact (azamulin, nicardipine and ) or the KI (, and ThioTEPA) were too high.

No TDI effect was observed for and under the conditions used. at ASPET Journals on September 27, 2021

Table 2 compiles the TDI kinetic parameters measured for 63 drugs known or suspected to be time dependent inhibitors. The standard errors given in the table are a measure of the goodness of fit and indicate which parameter is best described by the dataset (kinact or

KI). Some drugs, although clearly time dependent inactivators were very potent reversible inhibitors which precluded proper evaluation of their kinetic parameters for TDI (azamulin, mibefradil, nicardipine and ritonavir). Khellin and isradipine were the only compounds where kinact was clearly not reached at the highest concentration investigated (50 µM). For 16 additional drugs we could not detect significant TDI in the concentration range tested i.e. up to 50 µM (, azithromycin, , , , diclofenac, , , fluoxetine, flutamide, isoniazid, , paroxetine, pulegone,

ThioTEPA and ).

Risk evaluation

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 12

The kinact/KI ratio is used throughout the literature (Obach et al, 2007) to evaluate TDI risk because this term combines both the intrinsic capacity of a compound to reduce enzyme activity (kinact) and the concentration at which this occurs (KI).

As described in previous studies (Obach et al, 2007) we found that the shifted IC50

2 correlates well (R = 0.77) with kinact/KI for the 40 drugs in our test set for which a numerical value for the shifted IC50 could be obtained (Figure 2B, Table 2). Using the kobs at 10 µM a

2 similar correlation (R = 0.74) to the kinact/KI ratio could be obtained (Figure 2A, Table 2) but Downloaded from for all drugs, were a kinact and KI is available (57).

Time dependent inhibition screen and compound ranking

Figure 3 shows the inactivation rate (kobs) measured for 400 drugs (Supplemental dmd.aspetjournals.org

Table S1) and around 4000 randomly selected NCEs from Novartis drug discovery programs.

For world drug index (WDI) drugs the assay has been adapted to be able to measure kobs

-1 values higher than 0.1 min with accuracy. The kobs value of the DMSO control was 0.004 at ASPET Journals on September 27, 2021 min-1, i.e. at 37°C, 10% of the midazolam hydroxylase activity of human liver microsomes was spontaneously lost after 30 minutes of incubation. Interestingly the kobs values for test compounds increased continuously from values similar to the DMSO control to the high values for known potent TDI drugs like . Only about 18% of the test compounds have a kobs similar to the DMSO control. From the shape of the curve in Figure 3 and from positive drug benchmarks, a positive/negative bin border was set at a kobs value of

0.02 min-1. Using this binning scheme, the percentage of TDI positive drugs is very low with only 4% (Figure 4). A similar proportion (3%) of drugs showed high reversible inhibition (% reversible inhibition > 50%). The proportion of positive NCEs among a recent random set of around 4000 was much higher (23%). Those showing high reversible inhibition were also more abundant (9%).

Interplay with reversible inhibition

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 13

High and reversible inhibitory potency of HIV protease inhibitors means that maximal

TDI inactivation rates could not be reached without strong interference from reversible inhibition. However, as expected from the experimental design, all drugs/in-house compounds showing more than 50% competitive inhibition in this assay (3% of drugs and 9% of in-house compounds) were also shown to have IC50 values lower than 1 µM in the reversible inhibition assay when midazolam was at its Km concentration (Figure 5). Interestingly however up to

71% of TDI positive NCEs were not flagged in the reversible inhibition assay (IC50 < 1 µM, Downloaded from data not shown). About 24% had no warning at all (IC50 > 10 µM). This proportion increased to approximately 83% and 38% with registered drugs. Among negatives the same pattern can

be seen. Only 2% of the TDI negative drugs showed high reversible inhibition and only about dmd.aspetjournals.org

16% had IC50 values lower than 10 µM. Again this proportion increased to 7% and 38% for

NCEs.

Discussion at ASPET Journals on September 27, 2021

The systematic and detailed investigation of TDI of P450 enzymes and its inclusion in drug interaction prediction algorithms in early drug development stages is relatively recent

(Obach et al, 2007).

Kinetic parameters for a total of 63 drugs were measured to provide benchmarks for compound risk assessment (Table 2). When comparing the TDI parameters measured with those published using similar test systems, a good concordance was found (Table 1). The variability seen between our data and literature was similar to the variability seen between different literature references. All of the known strong positive TDI compounds often used as benchmarks were also found to be positive in our test system (e.g. troleandomycin, verapamil and ).

Several drugs had not been described as positive CYP3A4 TDIs to our knowledge: , , , isradipine, motesanib, , nimodipine, , , ruboxistaurin and . As most of them show also significant

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 14 reversible inhibition of CYP3A4 (Table 2), it is likely that clinical relevance has been clarified by specific clinical drug interaction trials. Therapeutic concentrations of the isradipine were for example shown to have little effect on exposure in humans (Backman et al, 1999).

Full kinetic characterization of time dependent inhibition necessitates a complex experimental set up, requires a laborious workflow and numerous data points need to be collected and analyzed. When capacity is a limiting factor or in order to reduce the number of Downloaded from candidates for full characterization, a surrogate of the full experiment or a screening approach is useful. Obach et al (Obach et al, 2007) found a correlation between kinact/KI with the shifted

IC50 obtained after pre-incubation of the test compound for 30 minutes. The composite dmd.aspetjournals.org parameter kinact/KI has been used and validated to estimate clinical drug interaction risk by these authors. We were able to reproduce this correlation with our method using a large set of drugs (Figure 2A). In addition we also found a good correlation between the inactivation rate at ASPET Journals on September 27, 2021 at 10 µM (kobs) and kinact/KI (Figure 2B). Disadvantages of using the IC50 shift assay for TDI risk assessment versus our method include (i) a numerical value of the shifted IC50 is not always attainable without repeating the experiment and fine-tuning, (ii) the magnitude of the shift may depend on the dilution factor, (iii) strong reversible inhibition may interfere more dramatically and mask TDI and (iv) the presence of the substrate and its binding properties may influence the binding mode of the test compound and TDI mechanism. The two last arguments being particularly relevant in experiments where dilution of the pre-incubate is smaller (2 to 5 fold; Atkinson et al, 2005). In addition, measuring kobs was also less resource- intensive as it was possible to determine kobs accurately using only four pre-incubation time points whereas proper IC50 determination requires 6 to 8 concentrations spanning over 2 orders of magnitude at least.

The choice of a concentration of 10 µM in the pre-incubation step was found to be a good compromise between solubility limitations of test compounds and their likelihood to reach

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 15 this concentration level in human blood at therapeutic doses. The known exceptions to this are macrolide . Erythromycin is a known time-dependent inhibitor with clinical relevance, steady state therapeutic plasma concentrations reach values approaching KI (27

µM) and cause an increase in the AUC for Midazolam after co-administration (Ito et al,

2003). However, most modern drugs are highly efficacious and rarely exceed micromolar blood concentrations to exert their therapeutic effect.

CYP3A inactivation rates at 10 µM have been determined for 400 drugs Downloaded from (Supplemental Table S1) and about 4000 Novartis proprietary compounds. Most compounds showed some level of TDI with only about 18% behaving similar to the DMSO control. The

loss of enzyme activity for so many compounds suggests that it may be due to other dmd.aspetjournals.org mechanisms than reactive or tight binding of metabolites. Uncoupling of the catalytic cycle

(futile cycling) induced by binding, turnover of compounds and associated generation of reactive oxygen species maybe an additional mechanism for TDI (Narasimhulu 2007). Our at ASPET Journals on September 27, 2021 method (based on kobs at 10 µM), correlates well with other methods for TDI risk assessment.

Therefore this method is a valid and robust approach for TDI risk assessment and is a reliable approach to prioritize research compounds for mechanistic kinact and KI determination.

-1 We have chosen a binary categorization scheme with a kobs value of 0.020 min as the lower limit for positive compounds. This threshold allows one to flag weaker TDI compounds like

-1 as positive. Azithromycin is clearly negative with a kobs of 0.002 min . This is consistent with their published relative interaction potency in vivo (Ito et al, 2003). Examples of drugs with high KI, which are appropriately flagged as positive using this assay, include , and . Despite a kinact/KI ratio suggesting poor risk for erythromycin (Table 2), it is a clinically relevant TDI because of its very high therapeutic systemic concentrations. In our assay erythromycin is measured with a kobs of 0.008 ± 0.003 min-1 (Supplemental Table S1) and is therefore considered as negative. This example emphasizes that if high therapeutic concentrations are expected the conditions of the assay

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 16 described here should be adapted (higher pre-incubate concentration) or the bin border reduced accordingly.

The set of drugs used for this study was artificially enriched with all known TDI drugs. When an unbiased random drug set was tested, the proportion of TDI positive compounds with a kobs value greater than 0.02 min-1 was only 4% (Figure 3). In contrast, the proportion of positive

NCEs among the 4000 pre-clinical candidates tested is much higher (23%). Among the NCEs

-1 found with kobs > 0.02 min (TDI positive) more than 95% confirmed as positives in follow- Downloaded from up in vitro mechanistic studies (data not shown). The proportion of compounds showing high reversible inhibition also increased from 3% to 9%. Interestingly about 35% of the drugs and dmd.aspetjournals.org 24% of the NCEs which are TDI positive showed no inhibition (IC50 > 10 µM) in the standard assay for reversible inhibition, suggesting that TDI testing is required for full evaluation of the drug to drug interaction risk. Conversely, very few TDI negative drugs (2%) and NCEs (7%)

showed strong competitive CYP3A inhibition (IC50 < 1 µM) suggesting that this TDI screen at ASPET Journals on September 27, 2021 has a stronger filtering capacity than the reversible inhibition assay. More than half of the TDI negative drugs with strong reversible inhibition were azole , which are well known for their potent CYP3A4 reversible inhibition. Despite strong reversible inhibition,

Itraconazole could be identified as a TDI in a mechanistic study (Table 2). However, because

Itraconazole metabolites are known to be also strong inhibitors of CYP3A4 (Isoherranen et al,

2004) it cannot be excluded that TDI results from their formation and not from inactivation of the enzyme. TDI evaluation was not done systematically in earlier times of drug discovery; the low proportion of marketed drugs with TDI suggests that positives were filtered out through their indirect effects or in late stage drug interaction clinical trials.

A rather poorly described aspect of TDI of CYP3A is the potential selectivity difference between CYP3A4 and CYP3A5. Midazolam hydroxylation is known to be catalyzed by both isoform and inhibitors have been shown to exhibit differential potencies on

DMD Fast Forward. Published on March 7, 2011 as DOI: 10.1124/dmd.110.037911 This article has not been copyedited and formatted. The final version may differ from this version. DMD #37911 17 both isoforms (McConn et al, 2004). If significant turnover subsists in vivo due to a lower sensitivity towards CYP3A5 it may complicate considerably the prediction of CYP3A TDI in clinical outcomes. However, in preliminary experiments using human liver microsomes lacking the CYP3A5 isoform, we did not observe major differences in the TDI of benchmark inhibitors like troleandomycin, nelfinavir, raloxifene, ruboxistaurin, tofoisipam or nefazodone. More experiments are needed to understand the impact of CYP3A5 on inactivation rates of midazolam hydroxylation. Downloaded from

Reversible inhibition has been shown to be substrate dependent and it is recommended to use a second substrate, such as testosterone or , in addition to midazolam.

Because TDI parameters obtained with the dilution method do not include the competition dmd.aspetjournals.org aspect they are likely to be independent of the substrate used (Watanabe et al, 2007).

The discrimination between TDI positives forming a metabolic intermediate (MI) complex versus those being authentic mechanism-based inactivators (MBI) would be of great at ASPET Journals on September 27, 2021 interest as the clinical risk is higher for the later (Lim et al, 2005; Kalgutkar et al, 2007).

Combining the high throughput screen described here with recently validated methods to distinguish MI from MBI appears accessible and is currently under investigation.

The strategy proposed in this study has been shown to be predictive for kinact/KI ratios and offers some specific advantages over previously published methods. Risk evaluation and prioritization can take place at early discovery phases when structural modifications are still possible. Thanks to the large database of robust TDI data, modeling efforts have been started to help chemists in identifying the metabolic spots likely to generate the inactivating molecular entity. As isolation and characterization of these is virtually impossible because of their very nature (reactive intermediate), modeling and structure-property relationship appears to be the most appropriate approach to rationally design drugs with lower risk of irreversible inhibition.

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Acknowledgements

We thank Rowan Stringer, Novartis Institutes for BioMedical Research, Horsham, UK, for careful reading of the manuscript.

Authorship Contributions

Participated in research design: Zimmerlin, and Trunzer.

Conducted experiments: Zimmerlin, and Trunzer.

Contributed new reagents or analytic tools: Zimmerlin, and Trunzer. Downloaded from

Performed data analysis: Zimmerlin, and Trunzer.

Wrote or contributed to the writing of the manuscript: Zimmerlin, Faller, and Trunzer. dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Footnotes:

Address for reprint requests:

Dr. Alfred Zimmerlin,

Novartis Institutes for BioMedical Research, Fabrikstrasse 14, WSJ-350.3.12,

CH-4002 Basel, Switzerland

E-mail: [email protected] Downloaded from dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Legends for figures.

Fig. 1. Typical graphical representation of data resulting from a full characterization of time

dependent inhibition (A: Time and concentration dependent inactivation of CYP3A4

mediated midazolam hydroxylation by verapamil, B: Relationship between the

observed inactivation rate (kobs) and verapamil concentration)

Fig. 2. Relationship between time dependent inhibitory potency and the risk factor ratio

kinact/KI: (A) Data using kobs; (B) Data using shifted IC50. The solid lines are the lines Downloaded from

of best fit. Values for kobs and shifted IC50 can be found in Table 2.

Fig. 3. Graphical representation of test compounds ranked by inactivation rate kobs

Fig. 4. Distribution of TDI positive, negative and strong reversible inhibitors among dmd.aspetjournals.org

registered drugs and recent new chemical entitites from Novartis.

Fig. 5. Overlapp of TDI ranking and reversible inhibition binning. at ASPET Journals on September 27, 2021

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Table 1

Kinetic parameters of CYP3A TDI in HLM and comparison to literature Novartis Literature -1 -1 Drug kinact (min ) KI (µM) kinact (min ) KI (µM) Ref Amprenavir 0.174 1.9 0.450 0.64 (Zhao et al, 2005) Azamulin >0.1 <0.5 0.680 0.17 (Perloff et al, 2009) Azithromycin nTDI >50 0.016 623 (Ito et al, 2003) Clarithromycin 0.058 13.2 0.072 4.5 (Mayhew et al, 2000) 0.019 15.5 (Watanabe et al, 2007) 0.036 41.5 (Ito et al, 2003) 0.063 15.7 (Xu et al, 2009) Diltiazem 0.031 3.6 0.012 4.5 (Obach et al, 2007) 0.050 8.7 (Perloff et al, 2009)

0.012 0.48 (Zhao et al, 2005) Downloaded from 0.015 18.1 (McConn et al, 2004) 0.019 0.67 (Xu et al, 2009) Diltiazem-N-desmethyl 0.043 0.8 0.027 0.77 (Mayhew et al, 2000) Domperidone 0.018 4.7 0.037 12 (Chang et al, 2010) Erythromycin 0.041 26.5 0.025 13.5 (Ito et al, 2003) dmd.aspetjournals.org 0.067 812 (Yamano et al, 2001) 0.072 15.2 (Zhao et al, 2005) 0.022 12.1 (Watanabe et al, 2007) 0.036 10 (Obach et al, 2007) 0.045 10.9 (McConn et al, 2004) 0.017 1.7 (Xu et al, 2009)

Fluoxetine nTDI >50 0.017 5.3 (Mayhew et al, 2000) at ASPET Journals on September 27, 2021 Isoniazid nTDI >50 0.080 228 (Wen et al, 2002) Nicardipine >0.1 <0.5 0.060 1.3 (McConn et al, 2004) Paroxetine nTDI >50 0.011 13 (Obach et al, 2007) Pioglitazone 0.013 13.0 0.011 10.4 (Lim et al, 2005) Raloxifene 0.163 4.3 0.360 18.7 (Zhao et al, 2005) Ritonavir >0.1 <0.5 0.450 0.38 (Obach et al, 2007) Rosiglitazone 0.011 4.4 0.020 11.9 (Lim et al, 2005) ThioTEPA nTDI >50 0.035 300 (Obach et al, 2007) Ticlopidine 0.008 3.5 0.039 77 (Obach et al, 2007) 0.087 10.0 0.034 5 (Lim et al, 2005) Troleandomycin 0.141 0.6 0.065 0.19 (Xu et al, 2009) 0.032 2.4 (Zhao et al, 2005) Verapamil 0.070 2.4 0.028 2.6 (Watanabe et al, 2007) 0.043 1.8 (Obach et al, 2007) 0.023 2 (Perloff et al, 2009) 0.053 3.5 (Xu et al, 2009)

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Table 2

Kinetic parameters of CYP3A TDI (kinact and KI), risk factor ratio(kinact/KI), initial inactivation rate at 10 µM (kobs), IC50 and shifted IC50 for 63 drugs estimated after 10 fold dilution of the pre-incubate with 10 µM Midazolam.

kinact kinact/KI kobs IC50 Shifted -1 -1 Drug (min ) SE KI (µM) SE (mL/min.µmol) (min ) (µM) IC50 (µM) Acetaminophen 0.009 0.001 1.5 0.4 5.7 0.007 >5 >5 0.039 0.001 3.3 0.5 11.5 0.026 >5 0.83 Amprenavir*2 0.174 0.017 1.9 0.3 93.5 0.135 0.26 0.05 Azamulin*2 >0.1 <0.5 0.106 0.07 <0.025 Cilostamide 0.029 0.006 6.0 3.1 4.8 0.014 2.94 1.81 0.049 0.012 21.9 8.9 2.2 0.010 >5 2.40 Clarithromycin 0.058 0.006 13.2 3.2 4.4 0.026 >5 1.14

Clemizole 0.197 0.026 49.7 8.8 4.0 0.031 >5 0.65 Downloaded from 0.014 0.002 5.7 2.3 2.4 0.006 >5 >5 Cyclosporine A 0.033 0.003 3.3 0.8 9.9 0.019 >5 1.48 0.290 0.068 15.9 5.9 18.3 0.112 2.45 0.27 Diltiazem 0.031 0.002 3.6 0.8 8.4 0.022 >5 2.45 Diltiazem-N-desmethyl*2 0.043 0.005 0.8 0.2 51.5 0.032 >0.5 0.19 dmd.aspetjournals.org Dipyridamole 0.039 0.003 5.3 1.0 7.2 0.026 0.89 0.65 Domperidone 0.018 0.002 4.7 1.2 3.9 0.011 >5 >5 Erythromycin 0.041 0.002 26.5 2.4 1.5 0.008 >5 2.30 Estradiol-valerate 0.030 0.006 14.9 5.3 2.0 0.007 >5 8.02 Ethinylestradiol 0.254 0.087 58.6 23.7 4.3 0.030 >5 0.94 0.059 0.006 7.2 1.1 8.2 0.036 0.41 0.39

Gestodene 0.107 0.012 15.4 2.7 6.9 0.033 >5 0.62 at ASPET Journals on September 27, 2021 Hydrastine 0.130 0.016 20.2 4.2 6.4 0.037 >5 0.63 Isradipine >0.1 >50 0.004 2.74 2.02 Itraconazole*2 0.036 0.014 2.7 6.2 13.0 0.017 <0.25 <0.25 Khellin >0.1 >50 0.030 >5 0.99 Lansoprazole 0.073 0.055 31.8 32.2 2.3 0.012 >5 1.72 Losartan 0.006 0.001 3.3 1.3 1.8 0.003 >5 <0.25 Mianserin*1 0.093 0.003 3.8 0.3 24.8 0.070 0.98 0.12 Mibefradil*2 >0.1 <0.5 0.046 0.16 <0.025 Mifepristone 0.110 0.004 3.6 0.5 30.6 0.078 1.08 <0.25 Montelukast 0.011 0.003 4.9 4.1 2.2 0.002 >5 7.24 Motesanib 0.038 0.003 7.8 1.4 4.8 0.020 0.61 0.47 Naftidrofuryl*1 0.056 0.002 2.4 0.2 23.2 0.045 >1.2 0.27 Nefazodone*1 0.094 0.010 1.0 0.3 96.5 0.066 0.28 0.07 Nelfinavir*1 0.184 0.048 8.8 3.2 20.9 0.092 0.24 0.12 Nicardipine*2 >0.1 <0.5 0.066 0.07 <0.025 Nimesulide 0.014 0.018 25.4 33.4 0.6 0.005 >5 >5 Nimodipine 0.092 0.015 31.3 7.4 2.9 0.022 0.92 0.75 Omeprazole 0.099 0.025 21.7 7.1 4.6 0.021 >5 1.24 *1 0.048 0.001 0.8 0.2 59.0 0.044 1.04 0.08 Oxatomide 0.039 0.001 4.8 0.9 8.2 0.024 >5 1.01 0.009 0.001 4.7 1.3 1.9 0.004 >5 >6 Pioglitazone 0.013 0.003 13.0 4.3 1.0 0.004 >5 >5 *1 0.080 0.003 4.0 0.2 19.8 0.057 >1.2 0.16 0.012 0.003 9.9 4.3 1.2 0.002 2.72 6.02 Propoxyphene 0.034 0.006 15.1 6.7 2.2 0.007 >5 <0.25 Raloxifene 0.163 0.016 4.3 0.8 37.6 0.100 1.32 0.11 Resveratrol 0.120 0.021 46.0 13.1 2.6 0.017 >5 0.87 0.005 0.001 2.9 0.9 1.8 0.003 >5 >5

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Ritonavir >0.1 <0.5 <0.25 <0.25 Rosiglitazone 0.011 0.001 4.4 1.6 2.5 0.006 >5 >5 Ruboxistaurin 0.100 0.006 2.3 0.4 43.5 0.087 2.93 <0.25 Saquinavir*1 0.026 0.007 5.9 2.2 4.4 0.019 0.28 0.32 0.006 0.003 4.6 2.4 1.4 0.003 2.28 >5 SKF-525A 0.097 0.004 1.0 0.3 101 0.084 >5 <0.25 Sorafenib 0.045 0.001 2.4 0.3 18.9 0.037 >5 0.21 Tadalafil 0.228 0.018 27.5 2.8 8.3 0.058 >5 0.40 Terbinafine 0.009 0.003 10.8 4.8 0.8 0.003 >5 >5 Ticlopidine 0.008 0.001 3.5 2.2 2.3 0.002 >5 >5 Tofisopam 0.210 0.009 6.2 0.4 33.9 0.117 0.92 <0.25 Troglitazone 0.087 0.005 10.0 1.4 8.7 0.040 >5 0.51 Troleandomycin*1 0.141 0.006 0.6 0.1 254 0.129 >1.2 <0.05 Verapamil*1 0.070 0.004 2.4 0.3 29.2 0.060 >1 0.22

Zafirlukast 0.208 0.081 32.3 15.5 6.4 0.042 2.88 0.52 Downloaded from *1 tested range 0.5-12 µM *2 tested range 0.25-5 µM

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CYP3A TIME DEPENDENT INHIBITION RISK RANKING VALIDATED WITH 400 DRUG BENCHMARKS Alfred Zimmerlin, Markus Trunzer, and Bernard Faller Drug Metabolism and Disposition /2010/037911

Supplemental Table S1: Initial observed inactivation rates (kobs) at 10 µM and % reversible inhibition for 400 registered drugs.

Non-proprietary name kobs SE %reversible (min-1) inhibition*1 Acecainide 0.000 0.004 <10% Acemetacin 0.006 0.006 <10% Acetaminophen 0.007 0.001 <10% Albendazole 0.003 0.003 <10% Alimemazine 0.004 0.004 <10% Alprazolam 0.011 0.002 <10% Altretamine 0.003 0.002 <10% Amifostine 0.004 0.003 <10% Aminoglutethimide 0.006 0.002 <10% Aminophenazone 0.004 0.003 <10% Amiodarone 0.004 0.003 <10% Amlodipine 0.026 0.002 <10% Amoxapine 0.002 0.005 <10% Ampicillin 0.004 0.002 <10% Amprenavir 0.135 0.019 47% Amrinone 0.008 0.001 <10% Anisindione 0.000 0.004 <10% Aripiprazole 0.004 0.005 <10% Atenolol 0.008 0.001 <10% Azacitidine 0.005 0.002 <10% Azamulin 70% Azithromycin 0.002 0.000 <10% Baclofen 0.003 0.003 <10% Barbital 0.003 0.003 <10% Bendroflumethiazide 0.000 0.001 <10% Benfluorex 0.000 0.005 <10% Benperidol 0.006 0.004 <10% Benzbromarone 0.008 0.003 <10% Benzthiazide 0.000 0.004 <10% Benzydamine 0.003 0.006 <10% Bepridil 0.002 0.003 <10% Berberine 0.000 0.003 <10% Betacarotene 0.004 0.006 <10% Betamipron 0.014 0.002 <10% Bezafibrate 0.001 0.003 <10% Bifemelane 0.006 0.004 <10% Bifonazole 91% Biotin 0.003 0.002 <10% Bisacodyl 0.008 0.006 <10% Bortezomib 0.007 0.008 <10% Bromazepam 0.001 0.002 <10% Bromhexine 0.002 0.006 <10% Bromopride 0.000 0.006 <10% Bromperidol 0.007 0.001 <10% DMD #37911

Bufexamac 0.003 0.002 <10% Bumetanide 0.009 0.001 <10% Buspirone 0.009 0.003 <10% Camostat 0.000 0.000 <10% Carbamazepine 0.000 0.001 <10% Carbinoxamine 0.000 0.001 <10% Cediranib 0.006 0.006 12% Cefradine 0.002 0.003 <10% Chloramphenicol 0.003 0.003 11% Chlorcyclizine 0.000 0.005 <10% Chlorhexidine 0.002 0.004 <10% Chlormidazole 0.013 0.005 <10% Chloropyramine 0.000 0.002 <10% Chlorothiazide 0.002 0.005 <10% Chlorphenamine 0.000 0.004 <10% Chlorquinaldol 0.002 0.002 <10% Chlortalidone 0.002 0.002 <10% Chromocarb 0.004 0.002 <10% Ciglitazone 0.016 0.004 <10% Cilnidipine 0.005 0.004 <10% Cilostamide 0.014 0.002 16% Cilostazol 0.010 0.002 10% Cinalukast 0.003 0.002 22% Cinchocaine 0.000 0.003 <10% Cinnarizine 0.005 0.003 <10% Cisapride 0.011 0.002 <10% Clarithromycin 0.026 0.003 <10% Clemizole 0.031 0.006 15% Clioquinol 0.004 0.002 <10% Clobenzorex 0.008 0.004 13% Clofazimine 0.002 0.001 31% Clofibrate 0.003 0.003 <10% Clomifene 0.001 0.005 13% Clomipramine 0.003 0.006 <10% Clonazepam 0.003 0.003 <10% Clorindione 0.000 0.004 <10% Clotrimazole 95% Clozapine 0.006 0.002 <10% Colchicin 0.000 0.005 <10% Colforsin 0.003 0.001 <10% Cyclosporine A 0.019 0.001 25% Cyproheptadine 0.005 0.004 <10% Cytarabine 0.004 0.003 <10% Deferoxamine 0.000 0.005 10% Delavirdine 0.112 0.002 20% 0.003 0.003 <10% Desoxycortone 0.004 0.003 <10% Dexamethasone 0.002 0.002 <10% Dexfosfoserine 0.003 0.003 <10% Dexoxadrol 0.003 0.005 <10% Diacerein 0.006 0.003 <10% Diazoxide 0.003 0.004 <10% Diclofenac 0.003 0.000 13% Diclofenamide 0.002 0.006 <10% Dicoumarol 0.016 0.001 <10% Diethylstilbestrol 0.003 0.002 <10% DMD #37911

Diflunisal 0.003 0.003 <10% Dihydralazine 0.018 0.003 <10% Diltiazem 0.022 0.003 <10% Diphenadione 0.002 0.003 <10% Diprophylline 0.003 0.002 <10% Dipyridamole 0.026 0.003 33% Disopyramide 0.000 0.002 <10% Disulfiram 0.005 0.020 11% DMSO*2 0.004 0.002 <10% Docebenone 62% Domperidone 0.011 0.000 <10% Donepezil 0.004 0.004 <10% Doxepin 0.004 0.005 <10% Doxylamine 0.001 0.005 <10% Drofenine 0.007 0.006 <10% Droperidol 0.001 0.005 <10% Duloxetine 0.002 0.002 <10% Edetic acid 0.001 0.003 <10% Efavirenz 0.000 0.004 <10% Efloxate 0.001 0.005 <10% Egtazic-acid 0.010 0.003 <10% Eliprodil 0.004 0.001 <10% Enilconazole 91% Enoximone 0.004 0.002 <10% Enprofylline 0.008 0.001 <10% Erlotinib 0.002 0.002 <10% Erythromycin 0.008 0.003 <10% Estradiol 0.004 0.003 <10% Estradiol-valerate 0.007 0.002 <10% Etamivan 0.004 0.002 <10% Ethinylestradiol 0.030 0.005 11% Ethisterone 0.006 0.002 20% Etodolac 0.013 0.002 <10% Etofylline 0.008 0.002 <10% Felbamate 0.016 0.004 <10% Felodipine 0.013 0.003 25% Fenbendazole 0.005 0.001 <10% Fenbufen 0.004 0.004 <10% Fenclonine 0.003 0.002 <10% Fendiline 0.005 0.006 <10% Fenofibrate 0.001 0.003 <10% Fipexide 50% Fluconazole 0.001 0.002 23% Flufenamic-acid 0.002 0.002 <10% Flumazenil 0.004 0.001 <10% Flunarizine 0.003 0.004 <10% Flunitrazepam 0.002 0.002 <10% Fluoxetine 0.002 0.002 <10% Flurbiprofen 0.003 0.003 <10% Fluspirilene 0.010 0.005 <10% Flutamide 0.001 0.001 <10% Fluvastatin 0.002 0.002 <10% Fluvoxamine 0.006 0.001 <10% Frentizole 0.001 0.005 <10% Furosemide 0.002 0.003 <10% Gefitinib 0.014 0.005 <10% DMD #37911

Gemfibrozil 0.003 0.002 <10% Gestodene 0.033 0.006 <10% Gimatecan 0.004 0.001 <10% Glibenclamide 0.006 0.006 <10% Gliclazide 0.002 0.003 <10% Glipizide 0.009 0.002 <10% Glycyclamide 0.000 0.005 <10% Griseofulvin 0.001 0.006 <10% Guaifenesin 0.003 0.003 <10% Haloperidol 0.005 0.003 <10% Hexachlorophene 0.006 0.003 <10% Hexetidine 0.000 0.005 <10% Hydrastine 0.037 0.004 <10% Hydrocortisone 0.004 0.003 <10% Hydroflumethiazide 0.000 0.004 <10% Hydroxybufuralol 0.000 0.002 <10% Hydroxydiclofenac 0.000 0.003 <10% Hydroxyprogesterone 0.006 0.002 <10% Ibudilast 0.003 0.002 <10% Ibuprofen 0.002 0.004 21% Imatinib 0.009 0.006 <10% Imipramine 0.002 0.006 <10% Indometacin 0.003 0.003 <10% Indoprofen 0.004 0.002 <10% Inosine 0.006 0.002 <10% Ipriflavone 0.005 0.002 <10% Irinotecan 0.004 0.002 <10% Isoniazid 0.000 0.002 <10% Isospaglumic-acid 0.008 0.003 <10% Isradipine 0.004 0.002 15% Itraconazole 82% Ketoconazole 95% Khellin 0.030 0.001 <10% Lamotrigine 0.007 0.001 14% Lansoprazole 0.012 0.001 12% Levcromakalim 0.002 0.003 <10% Levosulpiride 0.002 0.002 <10% Lidocaine 0.001 0.003 <10% Linezolid 0.005 0.004 <10% Linopirdine 51% Lorazepam 0.001 0.003 <10% Losartan 0.003 0.001 13% Mazindol 0.002 0.003 <10% Meclozine 0.004 0.002 14% Mefenamic-acid 0.004 0.002 <10% Meloxicam 0.004 0.002 <10% Mephenesin 0.002 0.003 <10% Meprobamate 0.002 0.002 <10% Methocarbamol 0.005 0.000 <10% Metoclopramide 0.006 0.003 <10% Metolazone 0.004 0.002 <10% Mevastatin 0.003 0.003 <10% Mianserin 52% Mibefradil 71% Miconazole 94% 0.078 0.004 25% DMD #37911

Milrinone 0.006 0.000 <10% Montelukast 0.002 0.002 22% Motesanib 0.020 0.001 45% 0.003 0.002 <10% Mycophenolic-acid 0.010 0.004 <10% Nabumetone 0.006 0.002 <10% Naftidrofuryl 0.045 0.009 <10% Nandrolone 0.004 0.003 17% N-desmethlydiltiazem 0.032 0.001 <10% Nefazodone 0.066 0.007 50% Nefopam 0.002 0.002 <10% Nelfinavir 71% Nialamide 0.010 0.001 <10% Nicardipine 73% Nicergoline 0.005 0.003 29% Nictindole 89% Nifedipine 0.004 0.003 <10% Niflumic-acid 0.002 0.003 <10% Nimesulide 0.005 0.000 <10% Nimodipine 0.022 0.003 33% Nitrazepam 0.006 0.001 <10% Nitrendipine 0.003 0.004 22% Nonivamide 0.004 0.001 15% 0.015 0.002 <10% Nortriptyline 0.002 0.002 <10% Ofloxacin 0.003 0.003 <10% Olvanil 0.017 0.001 10% Omeprazole 0.021 0.002 <10% Ondansetron 0.044 0.000 49% Orlistat 0.005 0.002 <10% Ornidazole 0.002 0.003 <10% Oxatomide 0.024 0.002 <10% Oxazepam 0.005 0.002 <10% Oxcarbazepine 0.005 0.002 <10% Oxolinic-acid 0.001 0.002 <10% Palmidrol 0.004 0.003 <10% Panobinostat 0.010 0.001 18% Paroxetine 0.000 0.000 18% 0.006 0.001 <10% Pentamidine 0.001 0.011 <10% Pentoxifylline 0.005 0.003 <10% Pentoxyverine 0.005 0.004 <10% Pergolide 0.013 0.002 <10% Perospirone 0.003 0.003 16% Perphenazine 0.014 0.003 <10% Phenobarbital 0.003 0.003 <10% Phenylbutazone 0.004 0.002 <10% Phenytoin 0.004 0.002 <10% Pimethixene 0.003 0.001 <10% Pimozide 0.003 0.003 14% Pindolol 0.002 0.004 <10% Pioglitazone 0.004 0.000 <10% Pipemidic-acid 0.002 0.003 <10% Pirenperone 0.018 0.003 12% Pirenzepine 0.057 0.006 22% Pirfenidone 0.008 0.001 <10% DMD #37911

Piribedil 0.009 0.002 <10% Pirinixic-acid 0.006 0.002 <10% Pramocaine 0.002 0.001 <10% Praziquantel 0.004 0.002 <10% Prazosin 0.006 0.001 <10% Prednisolone 0.002 0.002 <10% Prednisone 0.004 0.002 <10% Prenylamine 0.007 0.003 <10% Primaquine 0.004 0.004 12% Primidone 0.002 0.002 <10% Probenecid 0.004 0.002 <10% Probucol 0.008 0.001 <10% Prochlorperazine 0.008 0.003 <10% Profenamine 0.004 0.002 <10% Progesterone 0.002 0.001 20% Promazine 0.003 0.003 <10% Promethazine 0.002 0.002 <10% Propentofylline 0.004 0.000 <10% Propoxyphene 0.007 0.004 <10% Pulegone 0.005 0.001 <10% Pyrimethamine 0.003 0.003 <10% Quinidine 0.000 0.001 <10% Racepinefrine 0.003 0.003 <10% Raloxifene 0.100 0.014 24% Ramipril 0.008 0.002 <10% Ranitidine 0.001 0.002 <10% Reserpine 0.015 0.002 <10% Resveratrol 0.017 0.002 17% Ribavirin 0.008 0.003 <10% Riluzole 0.003 0.002 <10% Risperidone 0.003 0.001 <10% Ritonavir 97% Rolipram 0.006 0.002 <10% Roquinimex 0.009 0.002 <10% Rosiglitazone 0.006 0.001 10% Ruboxistaurin 0.087 0.005 25% Rufinamide 0.009 0.001 <10% Salbutamol 0.003 0.003 <10% Saquinavir 62% Sertraline 0.012 0.003 <10% Simvastatin 0.003 0.002 18% SKF-525a 0.084 0.018 11% SN-38 0.015 0.005 <10% Sorafenib 0.037 0.006 <10% Sotrastaurin 0.033 0.001 <10% Spiperone 0.008 0.003 <10% Spiroxatrine 0.013 0.001 <10% Succinylsulfathiazole 0.005 0.001 <10% Sulconazole 97% Sulfabenzamide 0.003 0.005 <10% Sulfacetamide 0.014 0.003 <10% Sulfachlorpyridazine 0.003 0.002 <10% Sulfadiazine 0.007 0.002 <10% Sulfadimethoxine 0.003 0.020 <10% Sulfadimidine 0.001 0.007 <10% Sulfadoxine 0.002 0.002 <10% DMD #37911

Sulfafurazole 0.003 0.002 <10% Sulfaguanidine 0.003 0.003 <10% Sulfamerazine 0.005 0.002 <10% Sulfamethizole 0.003 0.002 <10% Sulfamethoxazole 0.001 0.002 <10% Sulfamethoxypyridazine 0.003 0.003 <10% Sulfametoxydiazine 0.003 0.003 <10% Sulfamonomethoxine 0.004 0.002 <10% Sulfaphenazole 0.006 0.002 <10% Sulfapyridine 0.004 0.003 <10% Sulfathiazole 0.003 0.002 11% Sulfinpyrazone 0.004 0.002 <10% Sulfisomidine 0.003 0.010 <10% Sulpiride 0.002 0.003 11% Sunitinib 0.005 0.002 <10% Tadalafil 0.058 0.006 <10% Talniflumate 0.002 0.002 <10% Tamoxifen 0.002 0.000 12% Temazepam 0.003 0.002 11% Terbinafine 0.003 0.002 <10% Terfenadine 0.005 0.001 <10% Tetrabenazine 0.002 0.002 <10% Tetracaine 0.003 0.002 <10% Tetracycline 0.003 0.003 <10% Thalidomide 0.000 0.018 <10% Thioridazine 0.006 0.002 11% Thiotepa 0.002 0.001 <10% Tiabendazole 0.005 0.003 <10% Ticlopidine 0.002 0.001 <10% Tinidazole 0.007 0.002 <10% Tinoridine 0.006 0.002 <10% Tiocarlide 0.005 0.003 <10% Tiotixene 0.003 0.002 16% Tioxidazole 0.001 0.008 12% Tofisopam 0.117 0.002 39% Tolazamide 0.005 0.002 11% Tolbutamide 0.000 0.002 12% 0.010 0.000 <10% Tolnaftate 0.003 0.006 <10% Tomelukast 0.002 0.004 <10% Tracazolate 0.007 0.001 <10% Tranilast 0.002 0.003 <10% Trazodone 0.016 0.001 <10% Tretinoin 0.005 0.003 <10% Triacetin 0.015 0.001 <10% Triamterene 0.001 0.002 <10% Triazolam 0.003 0.003 <10% Trichlormethiazide 0.000 0.001 14% Trifluoperazine 0.010 0.002 13% 0.003 0.005 <10% Trifluridine 0.003 0.003 <10% Trimetazidine 0.002 0.002 14% Trimethobenzamide 0.001 0.002 <10% Trimethoprim 0.001 0.005 <10% Trimetozine 0.004 0.004 <10% Trimipramine 0.002 0.003 14% DMD #37911

Tripelennamine 0.000 0.002 <10% Triprolidine 0.004 0.002 14% Tritiozine 0.003 0.002 11% Troglitazone 0.040 0.001 <10% Troleandomycin 0.129 0.014 26% Tropicamide 0.002 0.003 <10% Urapidil 0.004 0.004 <10% Vandetanib 0.000 0.002 <10% Veralipride 0.000 0.001 <10% Verapamil 0.060 0.007 <10% Vidarabine 0.005 0.002 <10% Vinpocetine 0.006 0.003 <10% Warfarin 0.001 0.001 <10% Xylazine 0.004 0.003 <10% Zafirlukast 0.042 0.002 16% Zalcitabine 0.001 0.002 <10% Zardaverine 0.003 0.003 <10% Zidovudine 0.003 0.002 <10% Zileuton 0.000 0.002 13% Zopiclone 0.004 0.003 <10%

*1 % inhibition of 1’-hydroxymidazolam formation from 10 µM Midazolam by 1 µM test compound. *2 not DMSO corrected