Quantitative Interpretation of Intracellular Drug Binding and Kinetics

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Quantitative Interpretation of Intracellular Drug Binding and Kinetics Using the Cellular Thermal Shift Assay Brinton Seashore-Ludlow, Hanna Axelsson, Helena Almqvist, Björn Dahlgren, Mats Jonsson, and Thomas Lundbäck Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.8b01057 • Publication Date (Web): 12 Nov 2018 Downloaded from http://pubs.acs.org on November 14, 2018

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Biochemistry

Quantitative Interpretation of Intracellular Drug Binding and Kinetics Using the Cellular Thermal Shift Assay Brinton Seashore-Ludlow†,‡, Hanna Axelsson†,‡, Helena Almqvist†,‡, Björn Dahlgren§, Mats Jonsson§ and Thomas Lundbäck†,‡,¶,* † Chemical Biology Consortium Sweden, Science for Life Laboratories, Karolinska Institutet, SE-171 65 Solna, Sweden ‡ Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-171 65 Solna, Sweden § Department of Chemistry, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden ¶ Mechanistic Biology & Profiling, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden

KEYWORDS: Target engagement, drug discovery, pharmacological validation

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ABSTRACT: Evidence of physical interaction with the target protein is essential in the development of chemical probes and drugs. The cellular thermal shift assay (CETSA) allows evaluation of drug binding in live cells, but lacks a framework to support quantitative interpretations and comparisons with functional data. We outline an experimental platform for such analysis using human kinase p38α. Systematic variations to the assay´s characteristic heat challenge demonstrate an apparent loss of compound potency with increasing duration or temperature, in line with expectations from established literature for thermal shift assays. Importantly data for five structurally diverse inhibitors can be quantitatively explained using a simple model of linked equilibria and published binding parameters. The platform further distinguishes between ligand mechanisms, and allows for quantitative comparisons of drug binding affinities and kinetics in live cells and lysates. We believe this work has broad implications in the appropriate use of CETSA for target and compound validation.

INTRODUCTION Clinical efficacy is attained when drugs reach and modulate the activity of disease-relevant target molecules1-3. Hence drug discovery efforts commonly start with determination of compound binding to such targets in biochemical assays, while the physiological relevance of these interactions is examined in parallel using functional cellular assays. In this process it is essential to understand and distinguish between target and off-target mediated contributions to the observed functional effects. Establishing clear links between target binding and amelioration of disease phenotype is critical for pharmacological validation of a given target protein, as well as the approach taken to modulate its activity4-6.


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Although comparisons between biochemical and cellular assays appear relatively straightforward, they are often hampered by shifts in potency across these model systems7-10. The reasons for these shifts are well established and include factors that influence intracellular availability of the compounds, i.e. serum protein binding, cell permeability and efflux, as well as competition for the intended binding sites with endogenous metabolites. The magnitude of these shifts naturally varies between compounds, thus complicating comparisons of structure-activity relationships. Use of cellular target engagement assays, where the physical drug-target interaction is measured in the same cellular setting as the functional responses, can help explain and bridge these gaps. Experimental determination of drug binding has, however, been difficult to achieve in live cells, although several new technologies have recently emerged11, 12. One of these is the cellular thermal shift assay (CETSA), which relies on ligand-induced changes in the thermal stability of the target protein13, 14. Similar to conventional melting temperature (Tm) shift assays this stabilization is measured as a persistent presence of native protein with elevated temperatures, while non-liganded protein denatures and irreversibly aggregates. The key distinguishing factor for CETSA is that the heat challenge is achieved within the context of live cells or tissues, such that aggregation of thermally denatured protein is facilitated by the prior existence of exposed hydrophobic surfaces. In practice the ligand-induced stabilization is observed either by monitoring a shift in protein aggregation temperature (Tagg), or by estimating the ligand concentration needed to achieve stabilization at a single elevated temperature. We refer to the latter format as isothermal doseresponse fingerprints (ITDRFCETSA) to signify their known dependence on assay conditions13, 14. Unfortunately this limitation of observed potency values is commonly neglected and direct comparisons with functional cellular readouts are often reported without appropriate consideration


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of temperature induced rearrangements in equilibria. Such practices may lead to misleading conclusions on the pharmacological relevance of the measured target engagement, especially when the experiments are performed on single compounds and conditions. This is particularly important in case destabilization is observed, since the simplest explanation of such behavior is that the ligand binds preferentially to a denatured state of the target protein and thus promotes aggregation. To improve on this decision making we must learn from already established knowledge in the field of conventional Tm shift assays, including considerations of the thermodynamic and kinetic profiles for compounds and their target proteins15-17. To illustrate the importance of these considerations we developed a cost-effective, microplatebased CETSA for the human kinase p38α (also known as mitogen-activated kinase 14 or MAPK14) based on AlphaScreen technology. This assay platform allows systematic investigation of variations on the magnitude and length of the transient heating on the observed ITDRFCETSA responses. We present experimental data for six structurally distinct inhibitors at 10 different heat pulse temperatures and with seven different heating times in cell lysates. For most inhibitors, the ITDRFCETSA is highly dependent on the magnitude and duration of the heat challenge, i.e. the results are in good agreement with expectations from simple models of linked equilibria and irreversible aggregation. These results emphasize why direct comparisons between CETSA responses at a single condition and compound potencies measured at physiological temperatures should be avoided. Further development of these models, and importantly their application to additional model systems, is much needed to realize the full potential of CETSA and generalize these results across protein classes.


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MATERIALS AND METHODS Reagents. Compounds were obtained from the following vendors: AMG548 (Tocris Biosciences, product number: 3920), SB203580 (Tocris Biosciences, product number: 1202), RWJ67657 (Tocris Biosciences, product number: 2999), BIRB796 (Selleckchem, product number: S1574), PH-797804 (Selleckchem, product number: S2726), and LY2228820 (Selleckchem, product number: S1494). HL-60 cells (ATCC® CCL-240™) were grown in RPMI 1640 (SigmaAldrich, product number R8758) supplemented with 10% FBS (SigmaAldrich, product number P9665), 2 mM glutamine and 100 units mL-1 penicillin-streptomycin (SigmaAldrich, product number P4333). Details on antibodies are listed below and in Table S1. Details on microplates and AlphaScreen reagents are provided below in the descriptions of the CETSA experiments. In lysate CETSA experiments. Serial dilutions of compound solutions in DMSO (prepared on an Agilent Bravo automated liquid handling platform) were acoustically transferred to an assay ready plate (Greiner PP plate, product number 784201) and diluted with HEPES buffer (pH 7.4) supplemented with 150 mM NaCl and 0.01% Tween-20 using a Multidrop Combi (Thermo Fisher Scientific). Six µL of the diluted compound solutions were dispensed to 384-well PCR plates (Biorad, product number HSP3821) using the Bravo. Cell lysates were prepared by pelleting, washing, and resuspending HL-60 cells in HEPES buffer (pH 7.4) supplemented with 150 mM NaCl at a concentration of 6.6 million cells mL-1. The cells were lyzed by repeated freeze-thawing of this suspension three times and the resulting lysates were aliquoted and frozen at -80°C for later use. Prior to an experiment the lysate was thawed and centrifuged at 2000 rpm for 5 minutes. Six µL of lysate was dispensed to each well of the PCR plates containing the compound solutions using a MultiDrop Combi. Plates were sealed (PlateLoc, Agilent), and incubated at 37°C for 30


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minutes. The PCR plates were then heated at the indicated temperature for the indicated time and then cooled to 20°C (ProFlex PCR System, Thermo Fisher Scientific). The heated plates were next centrifuged at 3700 rpm for 15 minutes at 4°C. 3 µL of the heated and centrifuged lysates were then transferred to an AlphaScreen compatible plate (Proxiplate plus, PerkinElmer, product number 6008280) using the Bravo. Six µL per well of a detection mix was added to the detection plate using a MultiDrop Combi. The detection mix was prepared in 1X AlphaLisa Immunoassay buffer (PerkinElmer, product number AL000F) and final concentrations of the reagents were: rabbit acceptor beads (10 µg mL-1, PerkinElmer, product number AL104C), mouse donor beads (10 µg mL-1, PerkinElmer, product number AS104D), rabbit anti-p38α antibody (0.2 nM, Abcam, product number ab170099), mouse anti-p38α (0.8 nM, Abcam, product number ab31828), and 0.05% SDS. Plates were incubated at room temperature overnight and then detected on an EnVision plate reader (PerkinElmer) using a protocol for AlphaScreen readings. Live cell CETSA. Serial dilutions of the compound solutions were acoustically transferred to assay ready plates as described above and diluted with serum-free medium using a Multidrop Combi. 5 µL of the diluted compound solutions were dispensed to 384-well PCR plates as described above. A cell suspension was prepared by pelleting, washing twice, and resuspending HL-60 cells in serum-free media at a concentration of 6.6 million cells mL-1. 5 µL of the cell suspension was dispensed to each well of the PCR plates containing the compound solutions. Plates were sealed (PlateLoc, Agilent), and incubated at 37°C for 30 minutes. The PCR plates were then heated and subsequently cooled as described above. 10 µL of 3X lysis buffer (Alpha SureFire® Ultra™ 5X lysis buffer, PerkinElmer, product number ALSU-LB) was added to each well and the plates were sealed and placed at -80°C until detection to facilitate cell lysis. After thawing the lysates were mixed 10 times and then 3 µL of the lysate was transferred to an


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AlphaScreen compatible plate as described above. Six µL per well of a detection mix was added to the detection plate using a MultiDrop. The detection mix was added and plates were read after an overnight incubation as described above. Processing of CETSA data. Raw AlphaScreen signals were transformed to represent % stabilization of the p38α protein using the equation: 100 * ((assay signal - background signal) / (maximal signal - background signal)). For plates heated to 48-54°C the background signal was defined as the average of the signal observed for the lowest concentration of each specific compound, while the maximal signal was defined as the average of the signal observed for the highest concentration of each specific compound. For plates heated to 55.5-61.5°C the background signal was defined as the average of the signal for the DMSO control wells on the specific plate and the maximal signal was defined as the average signal of the highest concentration of all compounds from the temperature range 48-52.5°C (Figure S1). For LY2228820 these latter values were also used for the plates heated to 52.5 and 54°C. For the heat duration studies the background was defined as the average of the signal in the DMSO control wells for each plate and the maximal signal was defined as the average of the signal observed for the AMG548 (100 µM) control wells for each plate. For the 20 minutes time point the average of all AMG548 wells from the plates 110 minutes was used. Models and data analysis. Initial data analysis was done following import of the raw data from the Envision reader (available in Table S2) into GraphPad Prism for normalization, analysis and visualization. As the applied readout (AlphaScreen) does not differentiate between liganded (NL) and unliganded (N) forms of the target protein the sum of these species were used for illustrations, following normalization of data as outlined in Figure S1. In short, what is illustrated is the fraction of stabilized protein that would be unfolded in the absence of ligand. Best-fits of the


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concentration responses were obtained by applying a sigmoidal binding curve model with variable slope. The statistical details of the experiments are available in each figure legend with the number of replicates n meaning the number of independent data points, i.e. results from a separate well in the microplates. What is illustrated in each figure is in the majority of cases the mean and standard deviation (S.D.) of four independent replicates, but for some of the assay development data in the Supporting Information there are fewer replicates (never less than two). The kinetic relaxation model, as described in detail in the main text, was based on exclusive binding to a single binding site on the native protein coupled to protein denaturation. Ligand depletion and binding to the unfolded protein or aggregates were not considered. Binding parameters and rate constants were based on literature values and extrapolated to the different temperatures based on published enthalpies and heat capacities, assuming constant heat capacity changes that allow extrapolation into the temperature range of interest (48-61.5°C). The temperature dependence of the rate constants were assumed to follow the Eyring equation in the investigated temperature interval (Figure S2). The below set of ordinary differential equations was then solved numerically, using an open source software package pyodesys18, in parallel to determine the rates of changes in free ligand (L) concentration as well as liganded (NL), unliganded (N), unfolded (U), and aggregated (A) forms of the target protein:

𝑑𝑁 = ― 𝑘𝑢 N[t] + 𝑘𝑓 U[t] ― 𝑘𝑜𝑛 N[t] L[t] + 𝑘𝑜𝑓𝑓 NL[t] 𝑑𝑡 𝑑𝑈 = 𝑘𝑢 N[t] ― 𝑘𝑓 U[t] ― 𝑘𝑎𝑔𝑔 U[t] 𝑑𝑡 𝑑𝑁𝐿 = 𝑘𝑜𝑛 N[t] L[t] ― 𝑘𝑜𝑓𝑓 NL[t] 𝑑𝑡 𝑑𝐿 = ―𝑘𝑜𝑛 N[t] L[t] + 𝑘𝑜𝑓𝑓 NL[t] 𝑑𝑡


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Biochemistry

𝑑𝐴 = 𝑘𝑎𝑔𝑔 U[t] 𝑑𝑡

Best-fit parameters were found by minimizing the mean absolute deviations between experiment and model prediction. The thermally induced relaxation assumed instant heating and cooling in the PCR machine, with starting concentrations calculated based on the prior establishment of equilibrium during the pre-incubation at 37°C. In addition refolding was assumed to be negligible upon termination of heating, i.e. during the cooling and subsequent sample handling prior to detection. This assumption has little practical consequence unless kagg  ku, such that a significant population of denatured protein builds up. At this stage the model also disregards the inclusion of an initial lag or nucleation phase, as motivated by the presence of nucleating aggregates formed as a consequence of thermal unfolding and aggregation of less thermostable proteins. These assumptions represent considerable limitations in this simplified model, but as discussed in the Supporting Information they are likely better addressed first in simpler model systems than dilute cell lysates.

RESULTS AND DISCUSSION CETSA was introduced in 2013 as a novel means to establish evidence of physical target engagement in cell lysates, live cells and tissues13. It has now become a standard for demonstration of the pharmacological importance of a broad range of different target proteins from multiple target classes19. However, solid pharmacological validation requires a quantitative understanding of the relation between functional cell responses and observed target engagement. Such comparisons are still largely missing, partly because most studies are limited in throughput and because most studies fail to consider the rapid re-equilibration that occurs upon heating. Collectively these


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limitations call for improved experimental routines allowing for translation of observed CETSA responses to affinities. Theoretical considerations. CETSA requires heating to elevated temperatures, naturally resulting in rearrangement of established equilibria. Methods accounting for such heat induced reequilibration, and how this depends on the thermodynamics of ligand binding and protein unfolding, are well established for Tm shift assays on isolated proteins16, 17. This knowledge must be appropriately adapted to the context of CETSA experiments before comparisons are made with functional readouts at physiological temperatures20. Aside from the more complex sample matrix, a key factor distinguishing CETSA from conventional Tm shift assays is the use of a transient heat pulse to denature and irreversibly aggregate non-stabilized proteins (Figure 1). The heat challenge (often 3 minutes) occurs on a short time scale relative to those applied in Tm shift experiments, where care is taken to ensure kinetic equilibrium throughout the melting transition21,

22.

Three minutes is also short in

comparison to the 1h applied in isothermal denaturation experiments, in which the kinetics of protein denaturation is continuously monitored following a rise in temperature23-25. Given the shorter time frame for equilibration we expect a more frequent appearance of “frozen” equilibria15, i.e. in some instances the CETSA results will be more reflective of the equilibrium established during the pre-incubation than the expected shifted equilibrium at the elevated temperature20. Any efforts to quantify CETSA data must therefore apply a suitable kinetic model that accounts for system relaxation in response to transient heating.


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Figure 1. Molecular events triggered by the transient heat challenge in CETSA experiments. The simplified model to the right is based on exclusive ligand binding to the native protein, which is linked to a two-state protein unfolding event and subsequent irreversible aggregation of the unfolded protein. The model omits the ligand depletion that occurs in complex sample matrices and the direct denaturation of the liganded protein. 1) The pre-incubation serves to establish equilibrium binding at 37C. 2) A rapid temperature increase induces a relaxation of the system towards a new ligand binding equilibrium, with a relaxation time governed by the rate constants for ligand association and dissociation. The new equilibrium reflects whether the interaction at 37C is enthalpically or entropically driven, resulting in a loss or gain of complex, respectively. 3) Temperature-induced unfolding results in irreversible protein aggregation over time, driven by the abundance of exposed hydrophobic surfaces in the heated cell lysate or intact cells. The relaxation time towards an irreversibly aggregated state depends on the rates of protein unfolding and aggregation. Depending on the relation between the rates of unfolding and subsequent aggregation the illustrated three-state model can sometimes be reduced to an irreversible two-state model as illustrated by the dashed arrow. 4) The cooling results in relaxation towards a new equilibrium at


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room temperature. Remaining levels of unfolded protein either refolds or proceeds to irreversible aggregates. CETSA measurements are based on lasting levels of soluble protein, whether liganded or not, at room temperature. Letters denote: N = native protein, L = free ligand, NL = ligand-bound protein, U = unfolded protein, and A = aggregated protein.

Experimental model choice. Systematic investigations of the impact of the thermodynamics and kinetics of ligand binding and protein unfolding on CETSA responses require a robust assay platform for high throughput parallel measurements. Aside from the previous demonstration of microplate-based CETSA feasibility, our choice of p38α as model system was based on preexisting knowledge of the denaturation thermodynamics, including sizeable ligand-induced shifts26-30. There is also literature data on the binding kinetics26, 29, 31-35, as well as temperature dependence thereof31,

33,

for multiple inhibitors of distinct structural classes. We included

inhibitors (Table 1) covering different ligand binding modes, i.e. occupying teardrop, linear or extended binding pockets in or at the ATP binding site36, and a broad range of binding kinetics in our study. Equipped with a suitable model system we sought to methodically explore variations to the heat challenge over both length and temperature for a total of 17 perturbations. To isolate effects associated with variations to the heat pulse, i.e. the system relaxation in response to heating, we applied uniform conditions for the compound pre-incubation step. Furthermore, to limit the impact of confounding equilibria, such as differences between compounds in cell membrane permeability, binding to extracellular proteins, competitive interactions with endogenous metabolites, and ligand depletion we performed initial experiments in cell lysates.


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Development and validation of 384-well microplate CETSAs for p38α. We previously reported on a 96-well microplate-based CETSA for human p38α in live HL-60 cells based on AlphaScreen detection14. This assay used 600,000 cells per well and surplus amounts of detection reagents, making it prohibitively expensive for the scale of our planned studies. To improve throughput and cost we established new 384-well compatible CETSAs for both lysates and live cells, including their application on a set of p38α inhibitors7. The assay development involved selection of an antibody pair directed to different epitopes of native p38α37, 38, adoption of detection buffer conditions to suppress ligand-induced signal perturbation, and titrations of cell numbers and reagents (Figures S3 & S4). Final assay conditions were based on 33,000 HL-60 cells per well and included a significant reduction in detection reagents and thus assay cost (Table S3). Variation of the heat pulse temperature. We first examined how concentration responses in CETSA experiments varies with the choice of experimental temperature. Our expectations from the model of linked equilibria (Figure 1) was a right-shifting of the ITDRFCETSA with increasing temperature, i.e. more ligand is required to shift the equilibrium to the native protein states (N + NL) at higher temperatures20. Such behavior was recently demonstrated for the AMG548 inhibitor in a CETSA for p38α tagged with NanoLuc39. Concentration series of six inhibitors in HL-60 cell lysates were heated to ten different temperatures, ranging from the experimentally established Tagg for p38α in these lysates up to 61.5°C. Normalized data are shown for SB203580 in Figure 2a (we also provide the raw data in Table S2 to facilitate alternative normalization and analysis schemes). As expected we observe right-shifted curves with increasing temperature, in agreement with previous observations for AMG548, demonstrating that re-equilibration occurs on a rapid timescale in comparison to the three-minute heat pulse. Similar overall behavior is observed for


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four out of five additional inhibitors (Figure S5), while the results for BIRB796 are treated in detail below.

a

b log(ITDRFCETSA) (M)

100

% stabilization

80 60 40 20 0 -10

-8

-6

-4

-3 -4 -5



 

-6

  

-7

 

-8 -9 0.00298

0.00302

log[cmpd] (M)

0.00306

0.00310

-1

1/T (K )

c 100

% stabilization

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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80 60 40 20 0 -10

-8

-6

-4

log[cmpd] (M)

Figure 2. Isothermal dose-response fingerprints as a function of temperature. (a) Normalized ITDRFCETSA responses for SB203580 following heating for 3 minutes at 48 (), 49.5 (), 51 (), 52.5 (), 54 (), 55.5 (), 57 (), 58.5 (), 60 (), and 61.5°C (). The normalization procedure is described in Figure S1. Data are presented as the mean ± SD (n = 4). Dashed lines represent best fits to a sigmoidal binding curve model with variable slope. Solid lines represent best fits to a kinetic relaxation model as described in the text. (b) Van´t Hoff plot with best-fit ITDRFCETSA values to the sigmoidal model as a function of the reciprocal of the heat pulse temperature for SB203580 (), RWJ67657 (), PH-797804 (), BIRB796 (), AMG548 (), and LY222820 (). Linear regression resulted in composite Van´t Hoff enthalpies of -526, -623,


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-566, -161, -630, and -590 kJ mol-1. (c) Normalized ITDRFCETSA responses for BIRB796, with symbols and details as described in (a).

Importantly the expected right-shifting is not unique to this model system, as it has been reported also for the binding of panobinostat to HDAC1, HDAC2 and phenylalanine hydroxylase40, SMYD3 inhibitors41 and lactate dehydrogenase42. Collectively these data illustrate why absolute comparisons with results from functional assays are inappropriate unless heat induced reequilibration is accounted for, i.e. the equilibrium moves away from that established during the pre-incubation and deviations increase as the temperature moves away from Tagg. Accordingly, observation of matching values between CETSA data obtained at a single experimental condition with those observed in functional assay can be coincidental and should not be interpreted as evidence of target mediated pharmacology without further comparative studies. Comparisons of rank-order are justified41, 42, but care must be exercised when comparing compounds with different binding thermodynamics and kinetics (see below). We wanted to understand to what extent the observed right-shifting of apparent potency curves agrees with predictions from the simplified model of linked equilibria. We first applied a basic sigmoidal binding curve model as commonly used in the CETSA literature (Figure 2a). Best-fit ITDRFCETSA values based on this model are presented as Van´t Hoff plots in Figure 2b. For five out of six inhibitors the apparent potency changes over four orders of magnitude in the investigated 13.5°C temperature interval. As with conventional Tm shift assays the temperature dependence is expected to be largely governed by the protein unfolding enthalpy (HU)15-17, i.e. the temperature dependence of the folding of each protein dictates how much ligand is required to shift the equilibrium back to the folded state. Here these five inhibitors demonstrate similar Van´t Hoff plot


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slopes (526-590 kJ mol-1), comparing well within a factor of two with the reported HU of 353452 kJ mol-1 for murine and human p38α26, 27. We thus concluded that the observed right-shifting of ITDRFCETSA curves could be broadly explained by HU. The higher numbers likely reflect the additional contribution from exothermic binding events, as experimentally verified using calorimetry for two of these27, 43. Further analysis of these data based on a kinetic relaxation model are described in a later paragraph. It should be emphasized that the linear Van´t Hoff plots as shown in Figure 2b cannot be used for further extrapolation to 37C, as the slopes will change gradually and not include the contribution from the protein folding event well below the Tagg. Importantly, as observed for additional model systems41, 42, 44, the rank order of five out of six inhibitors remains the same in the investigated temperature interval. We observed a distinctly different behavior for the sixth compound BIRB796, with smaller apparent potency shifts across the same temperature range (Figures 2b & 2c). Aside from the different binding mode, in which an Asp-Phe-Gly motif on the activation loop of p38α is displaced to expose a hydrophobic pocket for ligand binding, this inhibitor stands out with a several orders of magnitude slower off-rate (Table 1). The observed behavior of BIRB796 is referred to as a “frozen equilibrium”15, indicating that there is insufficient time for ligand dissociation during the transient heat pulse, such that the measurement is partly reflective of the equilibrium as established during the pre-incubation period. This behavior has a strong impact on the relative rank order of BIRB796, which appears most potent at all temperatures except those close to Tagg. While this demonstrates how CETSA can be employed for mechanistic differentiation between compounds, it also shows how experiments must be performed at multiple conditions before quantitative comparisons between compounds or with functional readouts are made.


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Variation of the heat pulse length. Given the reliance on irreversible aggregation for CETSA measurements we next sought to probe how the observed stabilization varies with the length of the heat challenge. We went into these experiments assuming that the relaxation times towards protein unfolding and subsequent irreversible aggregation would be important determinants of the response, expecting an apparent loss of ligand potency over time as more protein unfolds and is driven out of solution (Figure 1). To test this hypothesis, we pre-incubated HL-60 lysates with compounds at 37°C and then exposed the samples to a 52.5°C heat pulse with durations ranging from 15 s to 20 min. Normalized ITDRFCETSA responses for incubations with SB203580 are shown in Figure 3a and similar behaviors are reported for AMG548, RWJ67657, PH-797804, and LY222820 in Figure S5. We observed a continuous and several orders of magnitude right-shift of apparent potencies with heating time for these inhibitors, demonstrating that re-equilibration occurs continuously on this time scale. These observations are in line with our expectations and observations from variations of the heat pulse temperature. A key practical finding was that the heating time could be reduced six-fold from the original three minutes13 to 30s, while maintaining a sufficient assay to background signal ratio. This reduction in heating time naturally limits temperature-induced changes in equilibria, as illustrated by the smaller right-shifting of response curves. We envision that future technology improvements will focus on further reductions of heating time as this will also minimize undesirable impacts on cell permeability, which must also be accounted for prior to comparisons with functional readouts.


17

Biochemistry

a


120

% stabilization

100 80 60 40 20 0

-5 

-6

 

-7 

-8

-8

-6

-4

-0.5

0.0

log[cmpd] (M)

c



-9

-10

0.5

1.0

Time (min)

d

120

100

[p38 forms] (%)

100

% stabilization

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 42

80 60 40 20 0

50

0

-10

-8

-6

-4

0

log[cmpd] (M)

60

120

180

240

300

Time (s)

Figure 3. Isothermal dose-response fingerprints as a function of heating time. (a) Normalized ITDRFCETSA responses for SB203580 following a transient heating time of 15 s (), 30s (), 1 min (), 3 min (), 6 min (), 10 min (), and 20 min (). Data are presented as the mean ± SD (n = 4). Fitted solid lines are based on a kinetic relaxation model as described in the text. (b) Best-fit ITDRFCETSA values based on a sigmoidal binding curve model with variable slope as a function of heat pulse length for SB203580 (), RWJ67657 (), PH-797804 (), BIRB796 (), AMG548 (), and LY222820 (). (c) Normalized ITDRFCETSA responses for BIRB796, with symbols and details as described in (a). (d) Simulated curves for the system relaxation as a function of heat duration at 52.5°C. The lines denote the complex with SB203580 (), native protein (), unfolded protein (), and aggregated protein () at four ligand concentrations (solid lines 0.1 nM; dotted lines 0.4 µM; dashed/dotted lines 1.6 µM; dashed lines 6.2 µM). The thin vertical lines


18

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illustrate five out of seven experimental heating times (15s, 30s, 1min, 3min, 5min), demonstrating how the prevalence of the four different states changes over time at the different ligand concentrations. Equilibrium parameters were from Table 1 (as listed for SB203580), koff=4.7 s-1 was obtained from the Eyring plot in Figure S2, and ku=0.1 s-1 and kagg=0.05 s-1 were used for the unfolding and aggregation, respectively.

While these five inhibitors behaved in qualitative agreement with our expectations and ranked in the same order at all heating times (Figure 3b), BIRB796 stands out again with a persistent stability of the ligand-protein complex until the heating time approaches 10-20 minutes (Figure 3c). As already mentioned this behavior is referred to as a “frozen equilibrium”15, i.e. the shorter time points are not sufficient to allow re-equilibration of the protein-ligand interaction. Mechanistically we interpret these data to say that the slow dissociation of pre-formed and kinetically stable protein-ligand complexes (NL) is a prerequisite (at 52.5C) for subsequent unfolding and aggregation of the less stable unliganded protein (N). This also means that the use of longer heating times results in an overestimation of the relative potency of BIRB796 in comparison to the other inhibitors (Figure 3b), for which re-equilibration is faster. Quantitative analysis based on a kinetic relaxation model. We next sought to probe to what extent the simplified model in Figure 1 can quantitatively explain the time and temperaturedependent re-equilibrations across all six inhibitors. Such efforts require the application of numerical approaches to identify parallel solutions to an appropriate set of differential equations that describe the system45. A set of five differential equations were derived from the model in Figure 1 and applied herein as described in detail in the Materials and Methods section. As many parameters are required to simultaneously describe the thermodynamics and kinetics of inhibitor


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Page 20 of 42

binding, protein unfolding and aggregation, and their respective temperature dependencies, we had to adopt established parameters from the literature to obtain stable numerical solutions. This seemed appropriate not only from the perspective of keeping the number of floated parameters to a minimum, but also for the purpose of understanding how well parameters derived from studies on isolated proteins translate to the more complex live cell settings. The identification and qualification of relevant literature data is described in detail in the Supporting Information, including a summary of all parameters in its Table S4. Based on the assessment for SB203580, which has been extensively studied in multiple labs, we first simulated how the system moves away from the pre-established equilibrium at 37C in response to the heat pulse challenge (Figure 3d). The model describes an early buildup of unfolded protein (U), the extent of which depends on the relative rates of unfolding (ku) and aggregation (kagg), which over time leads to aggregated protein (A). This sequence of events can be prevented by titration of SB203580 to µM concentrations, such that the protein-ligand complex remains the dominating species throughout this time interval. Visual inspection of the simulated data in Figure 3d shows that more ligand is required to retain the protein in a soluble form (N or NL) at the later time points, i.e. the model predicts a right-shifting of potency curves over time, in qualitative agreement with our experimental observations. With the underlying model assumption of selective binding to the native state, i.e. with no residual compound binding to denatured or aggregated p38α, the parameters for protein unfolding and aggregation are the same for all six compounds. We first obtained best-fit estimates of the rate of unfolding (ku) and its temperature dependence, i.e. the activation enthalpy of unfolding (∆HU‡ ), for p38α based on the well-established data set for the SB203580 inhibitor. This means all parameters for the binding event and the thermodynamics of protein unfolding were restrained to


20

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Biochemistry

literature values to avoid unjustified overfitting. The fitting was done simultaneously to both data sets for SB203580, i.e. with variations to both the heat pulse temperature and length. As discussed in detail in the Supporting Information, ku was assumed to be rate-limiting for the sequential protein unfolding and aggregation events, i.e. kagg>>ku, effectively reducing these two coupled events to a single two-state irreversible event45, 46 (the dotted line in Figure 1). As illustrated in Figures 2a and 3a best-fit estimates of ku at 0.01 s-1 and ∆HU‡ of 364 kJ mol-1 could quantitatively describe the magnitudes of right-shifting in both dimensions. Further improvements of curve fits, including alignments of curve shapes, can be obtained by allowing adjustments to the literature values, but this was outside the scope of this study. With best-fit parameters for protein unfolding in hand we next explored to what extent these could also reproduce the experimental observations for RWJ65657, PH-797804, LY2228820 and AMG548 (Figure S5). The dissociation enthalpies are unknown for the first three of these and were thus fitted for, while keeping all other parameters fixed. Based on the observation of similar slopes in Figure 2b we expected Hd to be similar to that reported for SB203580 and they all came out in a span between 40-61 kJ mol-1 (Table S4). The AMG548 data sets were similarly approached to yield estimates of koff and Hd, both within a reasonable range as defined by the other inhibitors (Table S4). Visual inspection shows broad agreement between experimental data and predictions based on the simple kinetic relaxation model and a common set of parameters for the protein unfolding for all these compounds, both with regards to relative potency ranking and right-shifting of curves. Finally, as illustrated in Figures 2c and 3c, also the frozen equilibrium behavior of BIRB796 can be reasonably explained by the same model. The key distinguishing factor compared to the five other inhibitors is the slow off-rate also at elevated temperatures, which replaces the protein


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Page 22 of 42

unfolding as the rate-limiting step towards aggregated target protein. Importantly, and in contrast to our observations for the other inhibitors, in this case a quantitative agreement with experimental data could only be achieved following a significant adjustment of the published dissociation constant to 4.4 nM. These orders of magnitude discrepancy in apparent affinity, compared to the reported high pM affinity as provided in Table 1, is immediately apparent from visual inspection of both ITDRFCETSA data sets. It remains to be determined whether this is caused by significant ligand depletion in the lysates, whether the p38α form in the HL-60 lysates differs from that used in biophysical experiments on isolated protein or whether previous reports on affinity are compromised by the slow equilibration. Again, improved fits with curve shape can be achieved by allowing modifications to additional parameters, but here our aim was to demonstrate that the oversimplified model and a single set of parameters for the coupled protein unfolding and aggregation can broadly explain the behavior of all six inhibitors. Several adjustments and improvements to the model as outlined in Figure 1 can also be envisioned and the key assumptions and limitations are discussed in detail in the Supporting Information. We note that a similar model, in which preferential binding occurs to the denatured instead of the native state of the target protein, can be attempted to explain destabilization events47. Unfortunately, the critical distinction between what protein states are liganded is not well recognized in the early CETSA literature, where also destabilization is taken to support sound cellular target engagement. More complex models, including e.g. competition with endogenous ligands, can also be evoked to explain destabilization events, but should then be complemented with additional experimental data to support such models. Translation to live cell assays. The ability to understand and interpret aspects of binding affinity and kinetics in dilute cell lysates is valuable, but a more critical issue is whether these findings can


22

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Biochemistry

be translated to live cell assays. Such translation is not trivial given the introduction of multiple additional equilibria. While these play a role in commonly observed cellular potency drop-offs, regardless of the choice of readout for target engagement or functional impact, a specific issue in CETSA is to what extent these are affected by the transient heat pulse. Comparisons of CETSA behavior in live cell assays required a separate assay optimization effort (Figure S3). It seemed logical to avoid prolonged heating of cells to limit influence on cell permeability and a potential skewing of results. Previous efforts have demonstrated that heating up to about 65C for three minutes can be achieved without major impact on the cell13, 14, 41, while recent reports show increased permeability already below 60C39,

42.

For this reason, we are

currently working towards further reductions of heating times, as the alternative washing of cells prior to heating results in a loss of transient interactions.


23

Biochemistry

a

b

140

120

120

100

100

% stabilization

% stabilization

140

80 60 40 20

80 60 40 20

0

0

-20

-20

-10

-8

-6

-10

-4

-8

c

d

-4

-4

-6

log(ITDRFCETSA)

-5 -6 -7 -8 -9 0.00298

-6

log[cmpd] (M)

log[cmpd] (M)

log(ITDRFCETSA) (M)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 42

-7

-8

-9

0.00302

0.00306

0.00310

-9

-8

-7

-6

log(Kd) (M)

-1

1/T (K )

Figure 4. Normalized ITDRFCETSA as a function of heating temperature for live HL-60 cells. (a) Symbols represent normalized ITDRFCETSA responses for the interaction with AMG548 following heating at 52.5 (), 55.5 (), 58.5 (), and 61.5°C (). Data are presented as the mean ± SD (n = 4). Fitted dotted lines are based on a sigmoidal binding curve model with variable slope. (b) The corresponding data for BIRB796. (c) Van´t Hoff plot with best-fit ITDRFCETSA values as a function of the reciprocal of the heat pulse temperature for AMG548 (,) and BIRB796 (,). Data represented by solid symbols were reproduced from Figure 2b (lysates), while open symbols represent best-fits to live cell data. Linear regression provided r2 coefficients of regression at 0.99 and 0.85 for the live cell data, translating to composite Van´t Hoff enthalpies of -676 and -256 kJ mol-1, respectively. (d) Log-log plot of best-fit ITDRFCETSA values (from lysate experiments), obtained using the sigmoidal binding model with variable slope, with reported dissociation


24

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Biochemistry

constants for isolated recombinant protein (BIRB796 is excluded). The dashed line represents a scenario where these values are equal. The different symbols represent variations to the heating temperature with a three-minute pulse (,,; 48, 49.5, 51°C) versus a shortening of the heating time to 30 s at 52.5°C (). Linear regression yields slopes of 1.03, 1.08, 1.06, and 1.12 with r2 coefficients of regression at 0.98, 0.97, 0.95 and 0.98, respectively. The ITDRFCETSA values at 48°C () are on average 4.4±1.3 fold higher than published Kd values, while a shortening of heating time to 30 s yields an 8.9±1.9 fold loss of apparent potency ().

We focused this study on the two sub-nM binders AMG548 and BIRB796 as they represent different binding modes to the active site of p38α, they showed different CETSA behavior in dilute lysates and given their low nM potencies they allowed comparisons over a broad temperature interval also in a cellular setting (Figures 4a & 4b). As observed in cell lysates the ITDRFCETSA for AMG548 changes over three orders of magnitude when applying heat pulses in the 52.5-61.5C range, whereas smaller changes are observed for BIRB796. This is further illustrated in the form of Van´t Hoff plots (Figure 4c), where the temperature dependencies of best-fit ITDRFCETSA values are compared with those observed in lysates. Similar slopes are observed between the two assay formats. As illustrated in Figure S6 these observations were made across four compounds (PH-797804 and LY222820 were not tested), although the data for SB203580 and RWJ67657 should be used with caution as the results are partially based on incomplete concentration response curves (limited cellular potency). The most straightforward interpretation of these data is that the frozen equilibria for BIRB796 reproduces in the live cell settings, whereas the interaction with p38α largely re-equilibrate during the three minutes heating period for the other compounds. The comparison is likely also influenced by changes to other


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Page 26 of 42

equilibria in the live cell setting, e.g. it could also reflect rapid changes of serum protein binding and membrane permeability. Additional experiments are justified to understand these aspects of cellular bioavailability on CETSA data, but these are outside the scope of the present study.

CONCLUSIONS In conclusion we herein outline an experimental path that allows for quantitative interpretations of CETSA data. Careful considerations of experimental conditions, e.g. by working close to Tagg, by limiting the heating time, and by testing multiple conditions to probe for frozen equilibria, we obtain ITDRFCETSA values that approach reported binding affinities as measured on isolated proteins (Figure 4d). Importantly the ranking of five out of six inhibitors remain the same at all tested conditions, while the outlier behavior of BIRB796 can be explained by a distinctly different binding kinetics. Using a kinetic relaxation model, we further demonstrate good quantitative agreement with published in vitro data for all compounds except BIRB796. In summary, high throughput compatible CETSA formats allow extrapolations to and comparisons with functional cellular data at 37C to draw conclusions of the pharmacological involvement of target proteins and comparisons of SAR as well as detailed mechanistic studies of compound interactions with target proteins in their physiologically relevant cellular settings. All these considerations are fundamental to understanding of pharmacology, thus warranting future studies on additional model systems and in more primary cellular systems.

TABLES Table 1. Nomenclature, chemical structures and literature data on p38α inhibitors in this study Compound

AMG548

SB203580

RWJ67657

BIRB796

PH-797804 LY222882 0


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Biochemistry

N O

N

N

N

HN N H

F

N NH2

N

N N

S O

O

O N

N H

N

N H

O O

N O

OH

O

N

H N

F

Br

F

F

NH2

N O S OH O

HN

N

N

F

O S OH O

Thermodynamics 15-27

0.046a

30

25

ΔHd (kJ mol-1)

50-60b

53

ΔCp,d (kJ mol-1 K-1)

0.6-1.8b

0.36c

Kd (nM) T (°C)

Kinetics T (°C)

n.a.

kon (M-1s-1) koff (s-1) Kd (nM)

0.5

ΔHd,vH (kJ mol-

25

n.a.

23

25

n.a.

2-15 x 106

7.1 x 105

8.5 x 104

1.5 x 107

1.2 x 106

0.017-0.18

3.0 x 10-3

8.3 x 10-6

5.8 x 10-2

2.5 x 10-3

12-21

5.0

0.098

3.9

2.1

47

1)

Binding mode Binding moded

Tear drop

Tear drop

Tear drop

Extended

Linear

Tear drop

References PubMed Identity (PMID)

16378500

17996717 9115281

11722559

14561086 11896401

19720877

24356814

11722559 A span is provided when equivalent data are available from12941343 independent studies. No difference is made between murine and human proteins. 15840510 aMeasured in a T shift experiment and extrapolated to the reference temperature based on a m 14715293 representative Cp,d for the compound series. bThe

spans represent data for two isoforms of p3827, including also a protein with a single point mutation. The higher numbers for the heat capacity change are not supported by reported temperature dependencies of binding affinities and rate constants using surface plasmon resonance (SPR) over a broad temperature range33.


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cMeasured

for a close analog and assumed to be representative for the compound series, i.e. including BIRB79629. dBinding

pocket shape in or adjacent to the ATP binding site36.

ASSOCIATED CONTENT Supporting Information. Additional details on the modelling and underlying assumptions are available in the Supporting Information. This material also contains tables and figures with details on reagents and assay development efforts, literature data for the tested compounds, raw data from the tests to facilitate alternative analysis, and additional illustrations of data as described in the main text.

AUTHOR INFORMATION Corresponding Author * [email protected] Author Contributions T.L. conceived the study. B.S.-L., Ha.A, He.A. and T.L. jointly designed the experimental approach. B.S.-L. developed the new AlphaScreen assays for p38α and performed the majority of experiments with help and input from Ha.A. and He.A. B.D., T.L. and M.J. performed the data analysis for the kinetic relaxation model. B.S.-L. and T.L. wrote the manuscript. All authors provided input to the manuscript and approved the final version. Funding Sources


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Biochemistry

Karolinska Institutet, SciLifeLab and the Swedish Research Council (Vetenskapsrådet) are acknowledged for funding of Chemical Biology Consortium Sweden. B.S.-L. acknowledges the Royal Institute of Technology. Notes Competing interests. T.L. is listed as an inventor on US20150133336 - Methods for determining ligand binding to a target protein using a thermal shift assay. T.L. has waived all rights and declares no competing financial interests. All other authors declare no competing interests.

ACKNOWLEDGMENT We are grateful to Geoff Holdgate at AstraZeneca for careful reading and suggestions on improvements of the manuscript content.

ABBREVIATIONS A, aggregated protein; ATP, adenosine-5'-triphosphate; CBCS, Chemical Biology Consortium Sweden; CETSA, cellular thermal shift assay; DMSO, dimethyl sulfoxide; Hd, enthalpy of ligand dissociation; Hd,vH, enthalpy of ligand dissociation derived from Van´t Hoff analysis; ∆HU‡ , activation enthalpy for ligand dissociation; HU, enthalpy of protein unfolding; HEPES, 4(2-hydroxyethyl)-1-piperazineethanesulfonic acid; HL-60, human promyelocytic leukemia cells; ITDRFCETSA, isothermal dose-response fingerprint; kagg, rate constant for protein aggregation; koff, rate constant for ligand dissociation or off-rate; ku, rate constant for protein unfolding; L, free ligand; MAPK14, mitogen-activated kinase 14 (p38α); N, native (folded) protein; NL,


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Page 30 of 42

ligand-bound protein; PCR, polymerase chain reaction; PBS, phosphate buffered saline; SDS, sodium dodecyl sulphate; SPR, surface plasmon resonance; Tagg, thermal aggregation temperature; Tm, thermal melting temperature; TRIS, tris(hydroxymethyl)aminomethane; U, unfolded protein;

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Biochemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

100

six compounds 11 concentrations

50

0 -10

quantitative comparison of binding affinities and kinetics

-8

-6

-4

-8

-6

-4

100

50

7 time points

0 -10

100

100

100

50

50

50

0 -10

CETSA heat pulse

-8

-6

-4

0 -10

-8

-6

-4

0 -10

-8

-6

-4

10 temperatures


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Biochemistry L

KU =

[U] [N]

U

ku kf

Kd =

cooling

heating

1 2 3 → N +L NL ← 4 y°C 5 6 2 3 7 8 x min @ y°C 9 10 11 12 1 4 13 14 heat challenge 15 16 → N +L 17 ← NL 18 37°C 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

N

[N][L] [NL]

koff

kon

kapp

kagg

detection of remaining soluble protein NL A


Biochemistry

a

log(ITDRFCETSA) (M)

100 80 60 40 20 0 -10

-8

-6

-4

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-3 -4 -5

u

u u

-6

u u u

-7

u u

-8 -9 0.00298

0.00302

log[cmpd] (M)

0.00306

1/T (K-1)

c 100

% stabilization

% stabilization

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

b

80 60 40 20 0 -10

-8

-6


log[cmpd] (M)

-4

0.00310

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a


120 100

% stabilization

80 60 40 20 0

-5 u

-6

u

-10

u

-7 u

-8

-8

-6

-0.5

-4

0.0

log[cmpd] (M)

c

0.5

1.0

Time (min)

d

120

100

100

% stabilization

u

-9

[p38a forms] (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Biochemistry

80 60 40 20 0

50

0

-10

-8

-6

log[cmpd] (M)

-4 ACS Paragon Plus Environment

0

60

120

180

Time (s)

240

300

Biochemistry

a

140

120

120

100

100

% stabilization

% stabilization

80 60 40 20

80 60 40 20

0

0

-20

-20

-10

-8

-6

-10

-4

-8

c

d

log(ITDRFCETSA) (M)

-4 -5 -6 -7 -8 -9 0.00298

-6

-4

log[cmpd] (M)

log[cmpd] (M)

-6

log(ITDRFCETSA)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

b

140

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-7

-8

-9

0.00302

0.00306

1/T (K-1)

0.00310 ACS Paragon Plus Environment

-9

-8

-7

log(Kd) (M)

-6

Quantitative Interpretation of Intracellular Drug Binding and Kinetics

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