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Chemical and Computational Methods for the Characterization of Covalent Reactive Groups for the Prospective Design of Irreversible Inhibitors Mark E. Flanagan,*,† Joseph A. Abramite,† Dennis P. Anderson,† Ann Aulabaugh,† Upendra P. Dahal,¥ Adam M. Gilbert,† Chao Li,† Justin Montgomery,† Stacey R. Oppenheimer,† Tim Ryder,‡ Brandon P. Schuff,† Daniel P. Uccello,† Gregory S. Walker,‡ Yan Wu,§ Matthew F. Brown,† Jinshan M. Chen,† Matthew M. Hayward,† Mark C. Noe,† R. Scott Obach,‡ Laurence Philippe,† Veerabahu Shanmugasundaram,† Michael J. Shapiro,† Jeremy Starr,† Justin Stroh,† and Ye Che*,† †

Center of Chemistry Innovation and Excellence, and ‡Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Eastern Point Road, Groton, Connecticut 06340, United States S Supporting Information *

ABSTRACT: Interest in drugs that covalently modify their target is driven by the desire for enhanced efficacy that can result from the silencing of enzymatic activity until protein resynthesis can occur, along with the potential for increased selectivity by targeting uniquely positioned nucleophilic residues in the protein. However, covalent approaches carry additional risk for toxicities or hypersensitivity reactions that can result from covalent modification of unintended targets. Here we describe methods for measuring the reactivity of covalent reactive groups (CRGs) with a biologically relevant nucleophile, glutathione (GSH), along with kinetic data for a broad array of electrophiles. We also describe a computational method for predicting electrophilic reactivity, which taken together can be applied to the prospective design of thiol-reactive covalent inhibitors.

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INTRODUCTION Drugs that covalently modify their biological targets are well represented in the pharmacopeia; at present 39 covalent drugs have been approved for a variety of indications ranging from treatment of infectious disease to hyperlipidemia and cancer.1 Many of these drugs contain electrophilic moieties, such as βlactams (i.e., penicillin antibiotics and β-lactamase inhibitors), acetates (i.e., aspirin), β-lactones (i.e., orlistat), carbamates (i.e., rivastigmine), and acrylamides (i.e., afatinib). A number of reviews have highlighted recent and past efforts to achieve safe covalent inhibitor therapeutics.1−4 Covalent inhibitors can possess advantages over their reversible, noncovalent binding counterparts, such as increased biochemical efficiency, longer duration of action, the potential for improved therapeutic index due to lower efficacious dose, and the potential to avoid some drug resistance mechanisms.5 However, covalent protein modification has also been implicated in immunotoxicity and idiosyncratic hypersensitivity reactions, particularly if the covalent inhibitor is highly reactive and/or lacks specificity.6−8 Many of these observations are based on the failure of noncovalent drugs that are given at high doses (>50 mg) and produce reactive metabolites which overwhelm cellular defense mechanisms (such as glutathione conjugation) and generate multiple covalently modified proteins that can serve as immunogenic hapten adducts. Modulating electrophilic reac© XXXX American Chemical Society

tivity, therefore, is an important design consideration for improving covalent inhibitor selectivity and therefore therapeutic index. Recent studies are consistent with this notion, demonstrating the value of incorporating electrophilic reactivity into reversible and irreversible inhibitor design.9,10 A previous study from our group showed that a high rate of compound reactivity with GSH has correlated with high covalent binding burdens in hepatocytes.11 In addition, the extent of covalent binding burden observed in hepatocytes combined with the daily dose of drug are predictive of human hepatotoxicity.12,13 We have also employed the reactivity of covalent inhibitors with GSH to help assess biological target selectivity in cellular proteomes using activity-based protein profiling.8 The potency of a covalent enzyme inhibitor can be defined by its kinact/Ki ratio, which defines the second-order rate constant for covalent modification of the protein (eq 1). Ki

k inact

E + I ⇌ EI ⎯⎯⎯→ E−I

(1)

Attenuating intrinsic electrophile reactivity can be balanced either by increasing nonbonded interactions with the protein Received: September 17, 2014

A

dx.doi.org/10.1021/jm501412a | J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

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that improve Ki or alter the reactivity of the electrophile in situ to preserve the same overall covalent inhibitor reactivity with that specif ic protein. In fact, one can take advantage of covalent inhibition as a strategy for improving selectivity by targeting nucleophilic amino acid side chains that are proximal to the active site of the enzyme, as has been shown with certain cysteine residues proximal to the ATP-binding site of kinases.14 To rationally design electrophiles for covalent inhibitors, one must either measure and/or calculate the intrinsic reactivity of the CRG and then tune this reactivity and noncovalent protein−inhibitor interactions to optimize selective inhibition of the desired protein target. The reactivities of biologically relevant nucleophiles, particularly cysteine, with electrophilic species has been recently reviewed.15 However, many of the electrophiles studied in that work are very reactive (i.e., enones, acrylonitriles, acid halides) and lie outside the practical range required for targeted covalent inhibitors.16−19 For example, there are only a few reports of sulfur nucleophile reactivity with acrylamides that are found in many covalent kinase inhibitors, and data are lacking for numerous other practical electrophiles.2,20,21 Here we report the development of methods to determine pseudo-first-order kinetics for the addition of GSH to a wide range of electrophiles that can be used for designing targeted covalent inhibitors, linkers for biochemical conjugates, and electrophilic handles for chemical biology probes. In addition, we report the development of a computational method to predict the GSH/electrophile kinetic reaction barrier, a measure that we show here correlates well with experimentally determined pseudo-first-order reaction rates.

consumption of GSH by oxidation, background instability, or reaction with water. Method A (MS). The generation of MS data took advantage of a ReactArray Liquid Handling system (Gilson 215SW). The automation provided by the ReactArray system allowed for simultaneous monitoring of multiple reactions. MS injections were made at predetermined time points, which were then used to plot the consumption of starting material (CRG) as a function of time. The MS method and use of the ReactArray system also allowed for the simultaneous monitoring of product formation. The resulting crossover plots provided a convenient check to rule out starting material consumption as a result of other factors, such as decomposition (an example crossover plot is supplied in the Supporting Information (SI). The ReactArray system also made possible the monitoring of reactions at higher temperature (60 °C) for evaluation of particularly unreactive CRGs. Method B (NMR). NMR spectra were collected on a Bruker 600 MHz NMR (Bruker BioSpin Corporation, Billerica, MA) using 5 mm (ID) NMR tubes at 37 °C. Typically, 1D spectra were recorded using a presaturation pulse sequence (zgpr) with a sweep width of 7500 Hz and a total recycle time of 7 s. Each acquisition consisted of 2 dummy scans followed by 32 scans for an elapsed time of 5.75 min. Most analyses consisted of 200 successive acquisitions for a total elapsed time of 19 h. Consumption of starting material (CRG) was determined by monitoring the disappearance of diagnostic signals in the NMR spectra as a function of time. A kinetic barrier method was employed for computational prediction of thiol reactivity. As acrylamides are the most commonly used CRGs found in targeted covalent inhibitors of kinases, a quantum chemical approach was undertaken to predict the electrophilic reactivity of various acrylamides toward sulfur-containing nucleophiles in terms of reaction half-lives (t1/2) through transition-state modeling. In the chemical reaction between GSH or other protein thiols with electrophilic compounds, the reactive, nucleophilic species are often the negatively charged thiolate anions, with an additional lone pair and excess charge. These are considerably better nucleophiles than their protonated counterparts. Under experimental conditions the rate constant increases with pH, reflecting the greater amount of thiolate as the reactive species.22−26 To this end, methanethiolate (MeS−) was used as a computational surrogate for the thiolate ion GS−, focusing on intrinsic kinetic reaction barriers and a Boltzmann weighting to account for conformational degrees of freedom. Reactivity is being considered in the kinetic sense, that is, how well the TS structure in Figure 1 will be stabilized through resonance and other structural/electronic factors. Thus, we approximate the activation free energy of the studied conjugate reactions by calculating the free energy difference between the transitionstate structure and the precursor molecules.26,27 The kinetic reaction barrier (ΔG⧧), determined at room temperature (298.15 K), calculated as the free energy difference between the transition-state structure and the precursor molecules, was used for evaluating relative reactivity. Most of the acrylamides under analysis have several nonequivalent local minima as ground-state conformations, which are populated at a given temperature according to a Boltzmann distribution. For example, the rotation of the single bond connecting both double bonds in acrylamides leads to two different ground-state conformations, s-cis and s-trans, respectively. Starting with each of these ground-state conformers often results in the

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RESULTS AND DISCUSSION Determination of rate information for select CRGs was conducted by reaction with GSH using two methods, which used, as the primary sources of detection, either mass spectrometry (MS, method A) or nuclear magnetic resonance spectroscopy (NMR, method B). In both cases, consumption of starting material (CRG) was detected and plotted as the natural log to check for linearity and pseudo-first-order reaction kinetics, kpseudo1st. The rate information [t1/2, kpseudo1st] for the reaction in question was then calculated according to eqs 2 and 3.18 ln([electrophile]) = −k pseudo1st × t + ln([electrophile0]) (2)

t1/2 =

0.693 (60 × k pseudo1st)

(3)

Reactions were conducted with 1 mM electrophile (CRG), 10 mM GSH in 100 mM phosphate buffer, pH 7.4 and 37 °C, in the presence of an organic cosolvent [10% acetonitrile (ACN) or dimethylacetamide (DMA)] unless otherwise noted. These conditions of concentration and temperature were designed primarily for convenience to accurately measure the kinetics in an automated manner. For both methods (described in more detail below), the setup of equipment and degassing of reaction media efficiently excluded molecular oxygen from the reactions such that background oxidation of GSH to the disulfide was not a significant contributor to reaction progression (or a lack thereof). To ensure this, a GSH control reaction was routinely run in the absence of a CRG to check for B



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concentration, cysteine pKa, and other factors can vary greatly even within a gene family, such as kinases.29,30 Consequently, coming up with one representative set of conditions generally predictive of (enzymatic) cysteine reactivity is not feasible. However, what this study does provide are internally consistent methods for determining the intrinsic reactivity of CRGs and data for a collection of these molecules that taken together can be used to define an appropriate target zone of reactivity.31 Recognizing the potential for different sensitivity of electrophiles to various analytical techniques, we developed the two distinct detection methods described above. Mass spectrometric detection (method A) was preferred due to its easy interface with the automated ReactArray liquid handling system. However, in certain cases where ionization was inadequate for quantitation, appropriate NMR signals were generally detectable such that quantitation of the electrophile concentration could be performed by this method. The two methods (A and B) were generally in good agreement. To examine assay reproducibility, a subset of CRGs was studied; measured half-lives (t1/2) and standard deviations appear in Table 1. Excellent reproducibility was observed for Figure 1. Calculated kinetic reaction barriers for acrylamides reacted with MeSH. The kinetic reaction barrier is calculated as the free energy difference between the transition-state structure and the precursor molecules and results in a high squared correlation coefficient (R2 = 0.915, n = 16) for the quantum chemical prediction of t1/2 at 37 °C, thus enabling an in silico screening of electrophilic reactivity of acrylamides for rational design of targeted irreversible inhibitors: filled circles, arylacrylamides; empty circles, alkylacrylamides; empty triangle, 1,3-dimethyl-1H-pyrrole-2,5-dione.

Table 1. Reproducibility and Cosolvent Effects compd 3 7 9 10 12

mean t1/2 (h) (triplicate) in DMA 1.18 4.86 11.60 16.56 26.53

± ± ± ± ±

mean t1/2 (h) (triplicate) in ACN

0.03 0.12 1.05 2.00 1.42

0.75 3.97 7.95 14.72 18.63

± ± ± ± ±

0.04 0.49 1.16 0.26 3.09

Reactions run in the presence of 10% cosolvent.

corresponding transition-state structure. This has been taken into account in deriving the Boltzmann-weighted, effective kinetic reaction barriers reported in Table 2.27 To better understand the relative reactivity of CRGs utilized in covalent drug design, we used GSH (10 mM) as a model nucleophile and subjected each of the electrophiles studied to kinetic analysis of electrophile disappearance under pseudofirst-order conditions (Figure 2, example reaction). GSH was chosen, because it is a relevant biological nucleophile that deactivates electrophiles in vivo. While serum levels of GSH are typically quite low, intracellular concentrations can be 2 mM or higher.28 Additionally, its properties of low volatility and good water solubility facilitated experimental design. It is important to note that the setup of these reactions and the choice of GSH as the nucleophile was not necessarily meant to accurately simulate the local environment that exists within enzyme active sites. These local conditions of ionic strength, effective

the CRGs studied as indicated by the percentage relative standard deviation (PRSD) of the measurements. In most cases, PRSD values were less than 15%, except for compound 12 whose PSRD was 17% with acetonitrile as the cosolvent. To study the effects of the organic cosolvent, the same set of CRGs was evaluated with DMA and ACN; these results are also reported in Table 1. These data indicate that reactions run with ACN as cosolvent were consistently faster; therefore, to be internally consistent, all of the CRGs studied herein were evaluated with ACN as cosolvent. The choice of CRGs used in these studies spans functional groups typically used in covalent inhibitors, such as acrylamides, as well as less commonly used electrophiles, such as cyanamides (which react with nucleophiles via the Pinner reaction), SnAr substrates, and α,β-unsaturated sulfonamides. Because acryl-

Figure 2. Example reaction and standard conditions. Standard protocol illustrating reaction of glutathione (GSH, 10 mM) with an example covalent reactive group (CRG, 1 mM). Reaction progression and related rate data are determined from the consumption of CRG as monitored by mass spectrometry (method A) or NMR (method B). The natural log of CRG consumption was then used to determine rate information (t1/2 and kpseudo1st). Reactions were run in the presence of 10% organic cosolvent acetonitrile (ACN) or dimethylacetamide (DMA) for convenience and improved solubility. C



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Table 2. aAcrylamide Reactivity: Trends and SAR

a

All data collected at 37 °C. bCalculated kinetic reaction barrier values.

amides have been used extensively for this purpose and are relatively straightforward to incorporate into most drug molecules, significant effort was made to assemble a comprehensive set of these electrophiles to gain a better understand of the structure−activity relationships (SAR) associated with the olefinic portion of these CRGs, as well as the effects of various N-substitutions. The data collected for a selection of acrylamide CRGs appears in Table 2 in order of decreasing reactivity. In general, the rank-ordering of reactivity observed is intuitive based on first-principles of soft nucleophile−electrophile reactivity. However, the absolute differences measured were in some cases interesting: for example, the magnitude of N-alkyl or N-aryl substituent effects on reactivity (e.g., 4 vs 6; 9 vs 14). Importantly, the reactivity trends observed with these acrylamides illustrate the potential to significantly modulate reactivity within an electrophile class by modifying nonconjugated substituents. At the outset of our study, experimental reactivity data associated with these fine structural changes were not well represented in the literature.18

During the course of our studies we found that our measured data correlated very well with predicted values generated using density functional theory (DFT) calculations at the Becke, three-parameter, Lee−Yang−Parr (B3LYP)/6-311+G(d,p) level of theory (R2 = 0.915; Figure 1).32 Therefore, we show that these subtle differences in reactivity for acrylamides are accurately predicted using in silico methods, enabling their use in prospective covalent inhibitor design. Unexpectedly, efforts to use 13C NMR chemical shifts were not as predictive as anticipated (correlations for 13C NMR chemical shifts as a function of t1/2 are supplied in the SI), indicating that this method of measuring electronic characteristics of electrophilic carbon atoms inadequately replicates actual rate data. One unanticipated result that came from this data set was the magnitude of effect on reactivity associated with various acrylamide olefin substitutions. Incorporating a methyl group on the β-position of an acrylamide was expected to attenuate reactivity through a combination of inductive and steric effects. However, addition of an α-methyl group, where steric effects at D



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the thiol nucleophile through general base catalysis.36 This mechanism requires the amine substituent to be in the free-base form, which in some cases may be facilitated by the local environment of an enzyme active site. Under these conditions, the β-aminomethyl group overcomes its own steric effect by virtue of the general base catalysis. The glutathione reactivity data for 21−23 is consistent with this reaction mechanism, wherein electrophilic reactivity correlates with the predicted basicity of the acrylamide’s aminomethyl substituent relative to the pH of the assay buffer (pH = 7.4). Specifically, greater presence of the amine conjugate base leads to enhanced reactivity relative to the β-alkyl substituted acrylamides 24 and 25. Consequently, armed with specific information about the environment of the active site being targeted, one could potentially use amine pKa relative to the local pH of the binding site to tune CRG reactivity within this chemotype. Because it may not always be practical or desirable to use an acrylamide CRG in covalent inhibitor or biochemical conjugate design, we also evaluated a number of nonacrylamide CRGs, a selection of which appears in Table 4. These CRGs include electrophiles which participate in Sn2, Pinner, and SnAr reactions as well as α,β-unsaturated sulfonamides. Similar to the acrylamides, it is possible to tune reactivity through a combination of steric and inductive effects for each electrophile chemotype. It is important to point out that, because the kinetic barrier calculations take into consideration the transition state of the reaction in question, it is not always possible to use this method to make direct comparisons (predictions) across different reaction mechanism classes. Consequently, there is a very necessary role for measuring covalent reactivity across electrophile classes to make cross-class comparisons. Finally, we evaluated a number of highly unreactive CRGs at elevated temperature (60 °C). All of the compounds appearing in Table 5 reacted too slowly at 37 °C to be practically measured (t1/2 > 60 h). However, at 60 °C measurable half-lives could be obtained for many of these examples. While such electrophiles might be impractical for designing linkers for biochemical conjugations, they could, in theory, represent an ideal case for designing exquisitely selective covalent inhibitors where nonbonded interactions between the inhibitor and the protein are optimized to accelerate the reaction.

the electrophilic carbon atom should be less significant, had a prominent effect on reactivity (Figure 3, 18−20). This

Figure 3. Effects of α-substitution on acrylamide reactivity. Inductive effects and potentially other factors can greatly influence the reactivity of α-substituted acrylamides. Series of compounds with equivalent Rgroups at the meta-position.

observation could be purely a manifestation of the inductive effect associated with the methyl group. However, it might also reflect a rotation in the carbonyl−α-carbon bond [C(CO)− Cα] caused by buttressing of the methyl group with the acrylamide N−H (e.g., Figure 3, 20). DFT calculations for 17 and 20 suggest that the s-cis and s-trans conformers were the only two minimum energy conformations for both reactants. The s-cis conformation is 1.52 kcal/mol more stable for 17, while the s-trans conformation is 0.69 kcal/mol more favorable for 20. This is consistent with earlier small-molecule X-ray studies.33,34 Interestingly, addition of either a hydroxyl or methoxy group to the α-methyl substituent (Figure 3, 18 and 19, respectively) greatly increased reactivity. These observations could be consistent with either hypothesis, because addition of these groups would not only enhance reactivity by virtue of their inductive effects but also might be expected to increase reactivity through improved orbital alignment resulting from intramolecular hydrogen bonding as illustrated in Figure 3 (representative trasition-state strucutures for 17−20 shown in Figure S4). Another reactivity trend was seen with acrylamides containing a β-aminomethyl substituent (Table 3). There are a number of literature reports that suggest an advantage of having a basic amine at this position in covalent drug design, as it may accelerate the reaction with cysteine by deprotonating

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Table 3. β-Aminomethyl-Substituted Acrylamides

CONCLUSIONS In summary, we developed two complementary experimental methods for determining intrinsic rate data for a range of CRGs and drug molecules. We have reported this data for a catalog of CRGs in an internally consistent manner, allowing one to not only compare the rank ordering of reactivity of these moieties but also to examine absolute differences that exist due to sometimes subtle structural and/or electronic changes. We have also compared our results with those generated through in silico methods. This revealed that values determined for the kinetic barrier from quantum chemical calculations of the energy difference between carbanion intermediates and the corresponding precursor ground state exhibited the best correlation with experimental results. In aggregate, this information can be used to prospectively tune and optimize a CRG for a “target zone of reactivity,” potentially complementing the many other factors that must be considered in pursuing a targeted covalent inhibitor approach. While it may never be possible to completely eliminate the potential risks and other complications associated with covalent drugs, by applying a strategy that incorporates intrinsic reactivity as an integral

All data collected at pH 7.4 and 37 °C. bACD pKa predictor. cPercent conjugate base present at pH = 7.4 based on predicted pKa using the Henderson−Hasselbalch equation.35 a

E



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Table 4. Non-acrylamide CRGs

Table 5. Examples of Highly Unreactive CRGs Evaluated at 60 °Ca

a All CRGs shown were unreactive (t1/2 > 60 h) at 37 °C. All CRGs were evaluated using method A at 60 °C.

a

Agilent 1100 high performance liquid chromatograph (HPLC)/MS for online analysis if desired, and the ReactArray control software to automatically set up, sample, and quench the reactions and prepare analytical samples for LCMS over the course of reaction based on the script that was written. The temperature setting for all reactions was 37 °C. All the stock solutions and solvents were bubbled with nitrogen for at least 6 h prior to use. All reactions were run under nitrogen. A 250 μL amount of 10.0 mM solution of electrophile in an organic solvent (ACN or DMA) was manually transferred to a reaction vial. A 250 μL amount of 2.0 mM solution of indoprofen (used as internal standard in MS analysis) in the same organic solvent was automatically transferred to the vial using the liquid handler system of ReactArray. A 4.50 mL amount of 11.1 mM solution of glutathione in 100 mM potassium phosphate buffer pH 7.4 was automatically transferred to the vial using ReactArray’s liquid handler system. The reactions were initiated upon addition of GSH. After the above operations were performed, each reaction vessel contained 1 mM electrophile, 0.10 mM indoprofen, and 10 mM GSH in 100 mM potassium phosphate buffer:organic solvent, 90:10, in a total volume of 5 mL. The software script was written such that the liquid handler system automatically sampled each reaction mixture for a total of 7 h, with a 1 h interval, by taking out 100 μL of the reaction mixture and diluting with 900 μL of deionized (DI) water. Control reactions were run in the absence of glutathione in 100 mM potassium phosphate buffer at pH 7.4. The reactants, products, and the internal standard were separated by liquid chromatography (LC) using an Atlantis T3 column (3.0 × 75 mm, 3 μm) and quantified by the MS detector. The LC mobile phase consisted of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in methanol). Liquid chromatography was initiated with 95% solvent A and was held for 2 min followed by linear increase to bring the mobile phase to 95% solvent B during 6 min. The 95% solvent B mobile phase was held for 2 min and then rapidly and linearly decreased to 95% solvent A during 0.1 min. At the end of the run, the 95% solvent A was held for 2.9 min making the overall run time 13 min. The pseudo-first-order rate constants were determined by plotting the natural log of the consumption of electrophile as a function of time. The negative slope of the straight line is the pseudo-first-order rate constant. Method B: NMR Method To Determine Kinetic Parameters. The electrophile stock was initially prepared as a 200 mM solution in dimethyl sulfoxide DMSO-d6. A 2.0 mM electrophile standard solution was prepared by taking a 20 μL aliquot of the concentrated stock solution and diluting it 100 fold into a 100 mM potassium phosphate buffer, pH = 7.4 (90/10, H2O/D2O). A 20 mM GSH solution was prepared in 100 mM phosphate buffer, pH = 7.4 (90/10, H2O/D2O). A 0.5 mL aliquot of the 2 mM electrophile solution was placed in a 5 mm outside diameter (OD) NMR tube. The 2 mM solution was

All data collected at 37 °C.

parameter in design it may be possible to at least minimize these issues while still being able to leverage the advantages that covalent drugs can offer. We have begun to apply this strategy to our internal drug discovery programs with encouraging results. Specific applications and additional studies evaluating the reactivity of CRGs with other biologically relevant nucleophiles will be reported in due course.

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EXPERIMENTAL SECTION

Method A: ReactArray Method To Determine Kinetic Parameters. Reactions were carried out with a ReactArray workstation, which consists of a reactivate rack with individual temperatureand stirring-controlled reaction vessels, a solvent rack with 130 mL solvent bottles, a reagent rack with 40 mL vials, an analytical rack with 2 mL liquid chromatography mass spectrometry (LCMS) vials, a Gilson 215SW liquid handler with a 402 dual syringe dilutor, a single syringe, an injection module for automatic sample injection into an F



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Present Addresses

placed in a Bruker 600 MHz NMR spectrometer (Bruker BioSpin Corporation, Billerica, MA) controlled with Topspin V2 equipped with a 5 mm 19F-selective (SEF) probe. The probe temperature was set to 37 °C. The NMR signal was locked on D2O, and the instrument was tuned and matched prior to shimming. The standard was then removed and diluted 1:1 using 0.5 mL of 20 mM GSH, resulting in a final solution with an electrophile concentration of 1 mM and a GSH concentration of 10 mM. The NMR tube was capped, mixed, and returned to the NMR spectrometer. Typically, 1D spectra were recorded using a presaturation pulse sequence provided by Bruker (zgpr) with a sweep width of 7500 Hz and a total recycle time of 7 s. The resulting time-averaged free induction decays were transformed using an exponential line broadening of 1 Hz to enhance signal-tonoise. Each acquisition consisted of 2 dummy scans followed by 32 scans for a total acquisition time of 5.8 min. Most analyses consisted of 200 successive acquisitions every 1 h for a total elapsed time of 19 h. Pseudo-first-order rate constants were determined by plotting the natural log of the consumption of electrophile, as defined by the area of a given resonance from an electrophile vs time. Molecular Mechanics and DFT Calculations. Monte Carlo conformational searches of the ground states of reactants were performed with MacroModel software37 in water using the OPLS2005 force field38 and the GB/SA implicit solvation model.39 All conformations within 5 kcal/mol of the lowest energy conformer estimated by molecular mechanics calculations were retained for the subsequent DFT geometry optimization. Full geometry optimizations and frequency analyses of DFT calculations were carried out in implicit aqueous solution at the B3LYP/6-311+G(d,p) level of theory using the Gaussian 09 software.40 A scale factor of 0.9877 was used to correct zero-point vibrational energy41 and implicit solvation using the SMD polarizable continuum model of Cramer and Thrular.42 Transition-state optimizations were conducted for additions of a simple model thiolate nucleophile (MeS−) to the electrophilic βcarbon of every ground-state conformer identified.26 The nature of stationary points was checked by means of frequency calculations, and transition states were further verified by intrinsic reaction coordinate (IRC) calculations. Further, more accurate single point energies in solution using SMD at the level of B3LYP/6-311++G(3df,3pd) theory were obtained for both of the reactants and transition states. Compound Preparation and Characterization. Reagents and solvents were obtained from commercial sources unless otherwise noted. Routine 1H NMR and 13C NMR spectra for CRG preparation were recorded on either Varian Inova 400 or 500 MHz spectrometers. Low resolution mass spectral data were collected using a Waters Micromass ZMD (electrospray ionization, chromatography on a Varian Polaris 5 C-18 column with acetonitrile and 0.1% formic acid aqueous gradient eluant). All CRGs were acquired from commercial sources or through standard synthetic methods. All reactions were run under nitrogen. The glutathione used in these studies was purchased from Aldrich. All compounds (CRGs) were determined to be ≥95% pure by HPLC using the method described in method A.

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¥

Celgene Corp., 86 Morris Ave., Summit, NJ 07901. University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92023-0412. §

Author Contributions

This manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was funded by Pfizer, Inc. The authors thank Drs. Mark Bunnage, Depak Dalvie, John Kath, Chris O’Donnell, Atli Thorarensen, Anthony Wood, and Prof. E. J. Corey for many helpful conversations and for their critical evaluation of this manuscript.

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ABBREVIATIONS USED CRG, covalent reactive group; GSH, glutathione; NMR, nuclear magnetic resonance; zgpr, presaturation pulse sequence; MS, mass spectrometry; ACN, acetonitrile; DMA, dimethylacetamide; DFT, density functional theory; SMD, solvation model based on full solute electron density; IRC, intrinsic reaction coordinate; PRSD, percentage relative standard deviation; SAR, structure−activity relationships; B3LYP, Becke, three-parameter, Lee−Yang−Parr; ACD, ACD/Laboratories; LCMS, liquid chromatography mass spectrometry; HPLC, high performance liquid chromatography; DI, deionized; LC, liquid chromategraphy; DMSO, dimethyl sulfoxide; OD, outside diameter; SEF, 19F selective; OPLS, optimized potentials for liquid simulations

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ASSOCIATED CONTENT

* Supporting Information S

Additional experimental details, including details around other in silico methods employed, 13C NMR chemical shift acquisition, and correlations of α- and β-carbon 13C NMR chemical shifts for acrylamide-containing CRGs with experimentally determined half-lives. Sample ReactArray (MS) data for compound 8 and representative trasition-state structures for 17−20. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

(1) Singh, J.; Petter, R. C.; Baillie, T. A.; Whitty, A. The resurgence of covalent drugs. Nat. Rev. Drug Discovery 2011, 10, 307−317. (2) Noe, M. C.; Gilbert, A. M. Targeted covalent enzyme inhibitors. In Annual Reports in Medicinal Chemistry; Manoj, C. D., Ed.; Academic Press: New York, 2012; Vol. 47, Chapt. 27, pp 413−439. (3) Kalgutkar, A. S.; Dalvie, D. K. Drug discovery for a new generation of covalent drugs. Expert Opin. Drug Discovery 2012, 7, 561−581. (4) Potashman, M. H.; Duggan, M. E. Covalent modifiers: An orthogonal approach to drug design. J. Med. Chem. 2009, 52, 1231− 1246. (5) Johnson, D. S.; Weerapana, E.; Cravatt, B. F. Strategies for discovering and derisking covalent, irreversible enzyme inhibitors. Future Med. Chem. 2010, 2, 949−964. (6) Uetrecht, J. Immune-mediated adverse drug reactions. Chem. Res. Toxicol. 2009, 22, 24−34. (7) Zhang, X.; Liu, F.; Chen, X.; Zhu, X.; Uetrecht, J. Involvement of the immune system in idiosyncratic drug reactions. Drug Metab. Pharmacokinet. 2011, 26, 47−59. (8) Lanning, B. R.; Whitby, L. R.; Dix, M. M.; Douhan, J.; Gilbert, A. M.; Hett, E. C.; Johnson, T. O.; Joslyn, C.; Kath, J. C.; Niessen, S.; Roberts, L. R.; Schnute, M. E.; Wang, C.; Hulce, J. J.; Wei, B.; Whiteley, L. O.; Hayward, M. M.; Cravatt, B. F. A road map to evaluate the proteome-wide selectivity of covalent kinase inhibitors. Nat. Chem. Biol. 2014, 10, 760−767. (9) Krishnan, S.; Miller, R. M.; Tian, B.; Mullins, R. D.; Jacobson, M. P.; Taunton, J. Design of reversible, cysteine-targeted Michael acceptors guided by kinetic and computational analysis. J. Am. Chem. Soc. 2014, 136, 12624−12630.

AUTHOR INFORMATION

Corresponding Authors

*E-mail: mark.e.flanagan@pfizer.com. *E-mail: ye.che@pfizer.com. G



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(10) Jost, C.; Nitsche, C.; Scholz, T.; Roux, L.; Klein, C. D. Promiscuity and selectivity in covalent enzyme inhibition: A systematic study of electrophilic fragments. J. Med. Chem. 2014, 57, 7590−7599. (11) Dahal, U. P.; Obach, R. S.; Gilbert, A. M. Benchmarking in vitro covalent binding burden as a tool to assess potential toxicity caused by nonspecific covalent binding of covalent drugs. Chem. Res. Toxicol. 2013, 26, 1739−1745. (12) Bauman, J. N.; Kelly, J. M.; Tripathy, S.; Zhao, S. X.; Lam, W. W.; Kalgutkar, A. S.; Obach, R. S. Can in vitro metabolism-dependent covalent binding data distinguish hepatotoxic from nonhepatotoxic drugs? An analysis using human hepatocytes and liver S-9 fraction. Chem. Res. Toxicol. 2009, 22, 332−340. (13) Nakayama, S.; Atsumi, R.; Takakusa, H.; Kobayashi, Y.; Kurihara, A.; Nagai, Y.; Nakai, D.; Okazaki, O. A zone classification system for risk assessment of idiosyncratic drug toxicity using daily dose and covalent binding. Drug Metab. Dispos. 2009, 37, 1970−1977. (14) Leproult, E.; Barluenga, S.; Moras, D.; Wurtz, J.-M.; Winssinger, N. Cysteine mapping in conformationally distinct kinase nucleotide binding sites: Application to the design of selective covalent inhibitors. J. Med. Chem. 2011, 54, 1347−1355. (15) Schwöbel, J. A. H.; Koleva, Y. K.; Enoch, S. J.; Bajot, F.; Hewitt, M.; Madden, J. C.; Roberts, D. W.; Schultz, T. W.; Cronin, M. T. D. Measurement and estimation of electrophilic reactivity for predictive toxicology. Chem. Rev. 2011, 111, 2562−2596. (16) Esterbauer, H.; Zollner, H.; Scholz, N. Reaction of glutathione with conjugated carbonyls. Z. Naturforsch., C: J. Biosci. 1975, 30c, 466− 473. (17) Potter, D. W.; Tran, T.-B. Rates of ethyl acrylate binding to glutathione and protein. Toxicol. Lett. 1992, 62, 275−285. (18) Böhme, A.; Thaens, D.; Paschke, A.; Schüürmann, G. Kinetic glutathione chemoassay to quantify thiol reactivity of organic electrophiles: Application to α,β-unsaturated ketones, acrylates, and propiolates. Chem. Res. Toxicol. 2009, 22, 742−750. (19) Schwobel, J. A. H.; Wondrousch, D.; Koleva, Y. K.; Madden, J. C.; Cronin, M. T. D.; Schuurmann, G. Prediction of Michael-type acceptor reactivity toward glutathione. Chem. Res. Toxicol. 2010, 23, 1576−1585. (20) Barf, T.; Kaptein, A. Irreversible protein kinase inhibitors: Balancing the benefits and risks. J. Med. Chem. 2012, 55, 6243−6262. (21) Ward, R. A.; Anderton, M. J.; Ashton, S.; Bethel, P. A.; Box, M.; Butterworth, S.; Colclough, N.; Chorley, C. G.; Chuaqui, C.; Cross, D. A. E.; Dakin, L. A.; Debreczeni, J. E.; Eberlein, C.; Finlay, M. R. V.; Hill, G. B.; Grist, M.; Klinowska, T. C. M.; Lane, C.; Martin, S.; Orme, J. P.; Smith, P.; Wang, F.; Waring, M. J. Structure- and reactivity-based development of covalent inhibitors of the activating and gatekeeper mutant forms of the epidermal growth factor receptor (EGFR). J. Med. Chem. 2013, 56, 7025−7048. (22) Friedman, M.; Cavins, J. F.; Wall, J. S. Relative nucleophilic reactivities of amino groups and mercaptide ions in addition reactions with α,β-unsaturated compounds. J. Am. Chem. Soc. 1965, 87, 3672− 3682. (23) Lutolf, M. P.; Tirelli, N.; Cerritelli, S.; Cavalli, L.; Hubbell, J. A. Systematic modulation of Michael-type reactivity of thiols through the use of charged amino acids. Bioconjugate Chem. 2001, 12, 1051−1056. (24) Ferrer-Sueta, G.; Manta, B.; Botti, H.; Radi, R.; Trujillo, M.; Denicola, A. Factors affecting protein thiol reactivity and specificity in peroxide reduction. Chem. Res. Toxicol. 2011, 24, 434−450. (25) Marino, S. M.; Gladyshev, V. N. Analysis and functional prediction of reactive cysteine residues. J. Biol. Chem. 2012, 287, 4419−4425. (26) Krenske, E. H.; Petter, R. C.; Zhu, Z.; Houk, K. N. Transition states and energetics of nucleophilic additions of thiols to substituted α,β-unsaturated ketones: Substituent effects involve enone stabilization, product branching, and solvation. J. Org. Chem. 2011, 76, 5074− 5081. (27) Mulliner, D.; Wondrousch, D.; Schueuermann, G. Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations. Org. Biomol. Chem. 2011, 9, 8400−8412.

(28) Zhao, H.-M.; Ruan, H.-H.; Li, H.-T. Progress in the research of GSH in cells. Chin. Sci. Bull. 2011, 56, 3057−3063. (29) Liu, Q.; Sabnis, Y.; Zhao, Z.; Zhang, T.; Buhrlage, S. J.; Jones, L. H.; Gray, N. S. Developing irreversible inhibitors of the protein kinase cysteinome. Chem. Biol. (Oxford, U. K.) 2013, 20, 146−159. (30) Taunton, J. Defining the druggable cysteinome. Presented at the 235th ACS National Meeting, New Orleans, LA, Apr 6−10, 2008, ORGN 242. (31) A “target zone of reactivity” is being defined here as a reactivity range that satisfies a specific set of conditions as presented by the problem at hand. (32) The applicability of the DFT calculations are restricted to a specific reaction mechanism (transition state). Thiolate addition to acrylamides was the primary reaction mechanism studied, and the predictive value of the these calculations across this reaction type was very good. (33) Huo, F. J.; Wu, W. W.; Yin, C. X.; Guo, W.; Xia, C. Z.; Yang, P. N-(4-Chlorophenyl)acrylamide. Acta Crystallogr., Sect. E: Struct. Rep. Online 2004, 60, 629−630. (34) Bertolasi, V.; Gilli, P.; Ferretti, V.; Gilli, G. Intermolecular NH···O hydrogen bonds assisted by resonance. Heteroconjugated systems as hydrogen-bond-strengthening functional groups. Acta Crystallogr., Sect. B: Struct. Sci. 1995, B51, 1004−1015. (35) Henderson, L. J. Concerning the relationship between the strength of acids and their capacity to preserve neutrality. Am. J. Physiol. 1908, 21, 173−179. (36) Carmi, C.; Lodola, A.; Rivara, S.; Vacondio, F.; Cavazzoni, A.; Alfieri, R. R.; Ardizzoni, A.; Petronini, P. G.; Mor, M. Epidermal growth factor receptor irreversible inhibitors: Chemical exploration of the cysteine-trap portion. Mini-Rev. Med. Chem. 2011, 11, 1019−1030. (37) MacroModel, version 9.9, Schrödinger, LLC, New York, NY,2012. (38) Banks, J. L.; Beard, H. S.; Cao, Y.; Cho, A. E.; Damm, W.; Farid, R.; Felts, A. K.; Halgren, T. A.; Mainz, D. T.; Maple, J. R.; Murphy, R.; Philipp, D. M.; Repasky, M. P.; Zhang, L. Y.; Berne, B. J.; Friesner, R. A.; Gallicchio, E.; Levy, R. M. Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem. 2005, 26, 1752−1780. (39) Still, W. C.; Tempczyk, A.; Hawley, R. C.; Hendrickson, T. Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 1990, 112, 6127−6129. (40) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö .; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian 09; Gaussian, Inc., Wallingford, CT, 2009. (41) Andersson, M. P.; Uvdal, P. New scale factors for harmonic vibrational frequencies using the B3LYP density functional method with the triple-zeta basis set 6-311+G(d,p). J. Phys. Chem. A 2005, 109 (12), 2937−2941. (42) Marenich, A. V.; Cramer, C. J.; Truhlar, D. G. Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B 2009, 113 (18), 6378−6396.

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