Determination of Affinity and Residence Time of Potent Drug-Target

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Determination of affinity and residence time of potent drug-target complexes by label-free biosensing John G Quinn, Keith E Pitts, Micah Steffek, and Mela M Mulvihill J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.7b01829 • Publication Date (Web): 17 May 2018 Downloaded from http://pubs.acs.org on May 18, 2018

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Journal of Medicinal Chemistry

Determination of Affinity and Residence Time of Potent Drug-Target Complexes by Label-free Biosensing John G. Quinn*#, Keith E. Pitts#, Micah Steffek# and Mela M. Mulvihill# #

Biophysical group, Biochemical and Cellular Pharmacology, Genentech, Inc., 1 DNA Way,

South San Francisco, CA 94080 ABSTRACT Prolonged drug-target occupancy has become increasingly important in lead optimization, and biophysical assays that measure residence time are in high demand. Here we report a practical label-free assay methodology that provides kinetic and affinity measurements suitable for most target classes without long pre-incubations and over comparatively short sample contact times. The method, referred to as a “chaser” assay, has been applied to three sets of unrelated kinase/inhibitor panels in order to measure the residence times, where correlation with observed efficacy was suspected. A lower throughput chaser assay measured a residence time of 3.6 days ± 3.4% (95% CI) and provided single digit pM sensitivity. A higher throughput chaser methodology enabled a maximum capacity of 108 compounds in duplicate /day with an upper residence time limit of 9 h given an assay dissociation time of 34 min. Keywords: Chaser injection, residence time, structure kinetic relationship, affinity, surface plasmon resonance, label-free, time-dependent binding.

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INTRODUCTION In-vitro assays1 performed under stead-state conditions routinely provide affinity constants (KD, or Ki for an inhibitor) that inform the structure-activity relationship (SAR) driving drugdiscovery chemistry. However, affinity becomes challenging to measure for so called “tightbinding” ligands, which possess a low dissociation rate constant (kd, or “off-rate” constant), because progress towards equilibrium is inversely proportional to kd, necessitating prohibitively long pre-incubation times. More specifically, residence time (τ), defined as 1/kd, directly specifies the longevity of the affinity complex and a pre-incubation equal to τ is required in order to approach 87% of steady-state occupancy, assuming a ligand concentration equal to KD and pseudo-first-order kinetics. However, kinetic assays allow KD to be measured without preincubation as the ratio of kd to the association rate constant (ka, or “on-rate“ constant), where KD is kd/ka. In addition to facilitating measurement of KD for tight binders, kinetic assays allow exploitation of kinetics, so-called “time-dependent binding”, in order to promote lead optimization via structure-kinetic relationship (SKR)2,3 for improved translation in vivo.4 In particular, residence time-based optimization5, 6 of drug-target complexes is important in vivo but “on-rate”-based optimization7 may be important in cases where microscopic rebinding effects8 prolong target occupancy. Employing measured kinetic rate constants to support in vivo kinetic modeling provides a more realistic prediction of in-vivo occupancy avoiding equilibrium assumptions, which are often incorrectly assumed to apply to open thermodynamic systems. 9

There are many label-based in-vitro assays that can be adapted to provide approximate kinetic measurements but specialized analyzers, such as KinExA, are often needed when analyzing


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“tight-binding” ligands. KinExA, which requires labeling and membrane partitioning, provides both affinity and kinetic analysis of highly potent interactions10 and can be valuable in large molecule characterization but it is not readily applied to SM inhibitors. Recently, it has become possible to determine the kinetics of ligand-receptor binding in live whole cells using bioluminescence resonance energy transfer (BRET). This assay measures the kinetics of target engagement to a receptor expressed as a fusion to NanoLuc luciferase and exploits a cell permeable tracer probe that competes with the test compound.

While this is a powerful

approach, binding to a receptor-NanoLuc fusion may not translate to wild type receptor and other factors such as inhibitor cell permeability, intracellular inhibitor concentration gradients, receptor turnover and cell growth create a chain of coupled kinetic processes that can prevent resolution of receptor binding. Indeed, such cell assays are complementary to direct ligand binding assays and provide additional information for compound prioritization. Activity assays can provide kinetic measurements for enzyme targets in many formats including jump-dilution, as in the Transcreener assay.11 Transcreener is a semi-quantitative assay for nucleotide-dependent enzymes that reports the accumulation of nucleotides (e.g. ADP) by labelled homogenous immunoassay. This microplate-based assay has been widely adopted and can be coupled to a variety of readouts, such as fluorescence polarization, and is readily adapted to high-throughput screening. It provides residence time readings by indirectly reporting nucleotide concentrations via competition with a labelled-nucleotide for binding to an anti-nucleotide antibody. However, the assay is limited to nucleotide-dependent enzymes and considerable restrictions are imposed in order to meet equilibrium jump conditions and to avoid artifacts. In general, label-based activity assays produce higher measurement error and are prone to artifacts12,13 relative to biophysical techniques. Competitive radioassay14 is an exception because the reporter label (i.e.



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isotope) does not alter the probes chemical properties relative to the unlabeled probe and performs well in kinetic analysis15. However regulatory and waste disposal complications have restricted its use. Real-time, label-free biophysical techniques, such as surface plasmon resonance (SPR), have become the gold standard16,17 in kinetic analysis because they are quantitative, apply to almost all target classes, and can measure binding/unbinding in real-time and without reporter labels. Assay development is generally faster with less target-dependent customization and with higher reproducibility relative to microplate-based assays. State-of-the-art systems can now address a comprehensive kinetic/affinity range (e.g. ka of 5 x 107 M-1s-1, kd of 10-5 to 5 s-1, KD of < 1 pM)18,19 and have proven well suited to SM applications such as fragment screening,20 kinetic characterization,21 mechanistic assays,22,23 and lead optimization24. It is possible to overcome longstanding throughput/stability limitations of covalent immobilization strategies by applying directed affinity capture, which preserves the activity, orientation and solubility of the target, facilitating challenging applications such as hit finding against GPCRs.25 Essentially any ligandreceptor pair can be analyzed, enabling detection of both orthosteric and allosteric ligands in addition to novel target classes that lack enzyme activity (e.g. protein-protein inhibitors, proteinDNA inhibitors).26 A diverse range of label-free platforms and configurations exist for large molecule applications; however, our review of published literature indicates that only a handful of flow-injection-based systems appear to be suitable for SM applications. For example, biolayer interferometry, currently available as a microplate-based dip-and-read configuration, is well established in providing kinetic assays27 for protein targets and SM analysis capabilities have been demonstrated28 yet there are few publications supporting general SM applications.

SM

applications often require high sensitivity and the latest generation of biosensing instruments


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(e.g. Biacore S200, Creoptics wave29) allow high quality kinetic analysis from an Rmax as low as approximately 1 RU. The recently released biacore 8K30 is also sensitive and injects eight samples in parallel over eight flow cells each housing paired sensing spots thereby providing a capacity of four 384-well microplates per run. Importantly, virtually all analytical technologies are limited by interference from signal drift, especially when measuring over extended periods as is necessary for potent interactions. Here we have developed a chaser assay that overcomes interferences from signal drift to measure long residence times with appropriate throughput suitable for practical SAR/SKR lead optimization. Furthermore, the assay can in principle be implemented in any real-time, label-free platform suitable for SM applications. The chaser assay format exploits the addition of one, or more, chaser probe injections after the analyte dissociation phase in order to measure the fraction of free binding sites made available through dissociation thereby allowing fractional occupancy to be determined. The probe must rapidly saturate (i.e. (ka x Probe concentration)-1 < 60s) free sites that become available through analyte dissociation. Ideally, the residence time of the probe should be low (< 10%) relative to that of the analyte in order to allow multiple sequential chaser readings over time. Optionally, a higher MW probe and/or a dye-labelled probe provides signal amplification thereby reducing the dissociation phase time needed to obtain an acceptable signal-to-noise ratio. Use of a competitive probe to track changes in analyte occupancy expands on current label-free methodologies and this is the first report exploiting this approach for kinetic analyses. Considerable improvements in kinetic/affinity range are enabled by this approach with high impact in routine drug discovery. Here we describe three chaser experiments, using three unrelated kinase targets and a total of twenty nine SM inhibitors associated with ongoing pipeline projects. A single-point chaser assay, using kinase 1 and a set of eight compounds were employed to establish a correlation with a gold



standard SPR approach. In addition, kinase 2 and a separate panel of six compounds were analyzed in a multi-point chaser assay exploiting a reversible competitive probe for measurement of extremely long residence times. Furthermore, the ability to generate a high resolution chaserbased dissociation curve by application of a rapidly reversible probe was assessed using the interaction of biotin-hydrazide with streptavidin as a model system. Finally, a higher throughput single-point chaser assay was demonstrated, exploiting reversible affinity capture for the interaction of another fifteen compounds with kinase 3..

RESULTS AND DISCUSSION Modeling Single-point Chaser Assay. (a) Simulation of Direct Model Fitting. The chaser approach may be implemented as an extension of conventional single cycle kinetics (SCK) wherein the chaser injection is performed after the dissociation phase. The chaser may be the analyte itself, or preferably, a competitive reporter probe where multi-point chaser measurements are possible if the chosen probe dissociates rapidly. A published two-compartment 1:1 interaction model in SCK format was readily adapted to include a chaser injection to generate mock experimental data, containing both drift and random baseline noise (Figure 1). The mock curves were then back-fitted to the same model (Figure 1(b)) and accurate recovery of all interaction parameters was confirmed. This suggests that a direct model fitting approach can be applied to experimental data where drift is well approximated by simple linear or exponential terms in order to resolve kd accurately. The chaser may be an analyte, or a competitive probe, that binds the same target binding site revealing the fraction of free binding sites that have become available through dissociation in the intervening time interval (∆t). (b) Simulation of


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Report Point-based Model Fitting. While uniform baseline drift is assumed in direct model fitting, more complex drift profiles commonly occur over long time intervals and may often be induced by target-dependent conformational changes within the sensing environment. In addition, mismatched referencing can also introduce artefactual baseline drift that accumulates over extended dissociation times. Therefore an alternative approach that disregards the affected dissociation phase was developed which exploits chaser report points in order to provide estimates of kd. Chaser report points are not subject to interference from baseline drift because they are referenced to a baseline immediately before each chaser injection thereby subtracting the drift component as illustrated in Figure 1(c). This is illustrated using an experimentally measured chaser cycle as shown in Figure S3. The chaser injections reported the fraction of available free sites directly from raw response curves, despite high baseline drift (i.e. > 200 RU), allowing estimation of residence time from eqs 2 to 3.



Figure 1. Principle of chaser injection. (a) Simulated SCK binding response curve using a twocompartment 1:1 model (Supplemental sections S1 and S2) where the analyte association phase (0 to 500 s) is composed of 4 serial injections (indicated by the stepping profile apparent in the association phase) with 3-fold increases in concentration up to a maximum of 100 nM. The interaction parameters were ka of 1 x 106 M-1s-1, kd of 5 x 10-5 s-1, Rmax of 18 RU and kt of 5 x 107 RU M-1 s-1. The dissociation phase time (500 s to 3000 s) is indicated by the time interval (∆t). The dotted curves (red) are replicate curves simulated with added drift (± 3 x 104 RU s-1). After the time interval (3000 s), a saturating concentration of chaser compound produces a response that represents the fraction of free sites (Rfree) that have become available. Thus the bound analyte response after ∆t is given as R = Rmax - Rfree and fractional occupancy is R = (Rmax - Rfree)/ Rmax. (b) Simulated curves (black) from (a) with baseline noise added to 0.03 RU (rmsd) where all parameters were back-fitted locally to a two-compartment model (superimposed red curves). All fitted kinetic parameters were returned with high accuracy (i.e. < 0.2% of the true value) and with high statistical confidence (i.e. < 0.3% (SEF)) while the fit quality was also high (i.e. χ2 was < 0.1% of Rmax for all fitted curves). (c) Two-compartment model simulation comparing the effect of negative drift (i.e. -1 x 10-4 RU/s) on conventional binding curves (black) and a multipoint chaser curve (blue with circles) where both curves were defined by identical simulation parameters. The chaser curve was constructed by fitting over a set of ten chaser report points assumed to be generated by serial injection of a rapidly reversible competitive chaser probe. The probe was assumed to saturate the surface while dissociating fully in the time interval between chaser injections, thereby reporting Rfree (indicated by vertical line connecting triangle-to-circle) at regular time intervals, and was plotted as compound occupancy in terms of response (i.e. Rmax x R, (circles)).


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Single-point Chaser Assay. Compounds were run as singletons suitable for higher throughput SAR/SKR rank order yet the assay may be performed with replicates (as in Figure 5) when accurate kinetic constants are required. A single-point chaser assay, using kinase 1 and a set of eight compounds were analyzed and the resulting data established a correlation with conventional SPR model fitting, the gold standard in label-free residence time measurement. Conventional two-compartment model fitting to association and dissociation phases are shown in Figure 2 (a) & (b), respectively. The simplicity and speed at which ka can be measured contrasts with kd estimation principally because the analyte concentration can be selected to achieve saturation in a short (< 60 s) time interval while producing a response that is well resolved from drift. State-of-the-art label-free biosensors possess low baseline noise (< ± 0.015 RU (rmsd)) and replicate curves are almost perfectly superimposed (i.e. residuals approaching the baseline noise). This combined with a high tolerance for baseline drift allowed estimation of kd from extremely low fractional dissociation (i.e. high residual occupancy), facilitating increased throughput. Fractional dissociation for the eight compounds tested ranged from 13% to 90% after approximately 3 h. In addition to conventional modeling, single-point chaser report points were independently measured for each compound and analyzed as outlined in Figure S3. Estimates of kd from direct modeling and from chaser report point analysis were in good agreement as shown in Table 1. An estimated Pearson correlation coefficient (r) of 0.99 (Figure S4) establishes high correspondence between the report point-based chaser approach and conventional direct model fitting, albeit using singleton analysis for a small panel of compounds.



Figure 2. Two-compartment 1:1 model fit to double referenced binding curves. (a) Typical association phase curve (black) obtained by injecting 500 nM compound and fit (red) to eq S1. (b) Dissociation phase model fit (red curves) of the coupled dissociation phase equation set (i.e. eqs S2 and S3) to normalized (i.e. 100 x R/Rmax) dissociation phase curves (black curves) for eight compounds run as singletons over separate sets of paired target-coated sensing spots. Values of ka and kt returned from the association phase fit were held constant when fitting the dissociation phase. When averaged all ka values were estimated to within ± 20% (SEM) with an average χ2 of 2.3% of Rmax. Similarly kd values were estimated with low error (i.e. ± 3.2% (95% CI)) from well fit curves (i.e. average χ2 of < 2% of Rmax for all curves).

Table 1. Estimated kinetic interaction constants from conventional kinetic analysis (as described in Figure 2) and single-point chaser analysis from chaser injections recorded in a separate assay run. Compounds 6 and 8 are characterized by relatively high kd values causing the chaser response to equal Rmax. In principle, the dissociation phase time may be adjusted to accommodate these short residence time compounds but in practice this is unnecessary as interference from


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drift is unlikely over short time intervals and conventional SPR analysis performs well in this case. ka (x 105)

Direct kd (x 10-4)

Chaser kd (x 10-4)

Direct KD

Chaser KD

(M-1 s-1)

(s-1)

(s-1)

(pM)

(pM)

Compd 1

63.1

0.580

0.32

9

5

Compd 2

12.9

1.54

1.45

119

112

Compd 3

4.3

2.00

2.16

462

499

Compd 4

7.9

1.40

1.45

178

185

Compd 5

5.3

2.10

2.36

397

446

Compd 6

4.3

2.46

NA

568

NA

Compd 7

2.2

0.36

0.17

166

76

Compd 8

6.2

6.80

NA

1102

NA

ID

Multi-point Chaser Assay. Extremely tightly bound compounds are defined by long residence times and a multi-point chaser assay exploiting a reversible competitive probe was applied. Kinase 2 and a panel of six compounds were analyzed. A reversible competitive chaser probe allowed a series of chaser measurements to be recorded as shown in Figure 3. The probe was selected to allow rapid (preferably < 60 s) saturation of kinase 2 binding sites and rapid dissociation (preferably < 60 s) in order to allow another chaser measurements to be performed in rapid succession. Chaser report points were referenced to baseline immediately before each



chaser injection thereby subtracting any baseline drift that may have been present. Consistent with simulations (Figure 1 (c)), the high quality of the resulting data gave low residuals (< 2%) when fit to data recorded over 12 hours. Time-dependent changes in occupancy were modeled using a conventional exponential decay model (eq S2) and the fitted kinetic parameters are shown in Table 2. Interestingly, the reported confidence intervals show that these compounds do rank order approaching τ ≤ 4.9 days indicating that relatively short dissociation times (∆t 1 Hz) multi-point chaser readings. Such higher resolution multi-point chaser curves might be applied in general kinetic studies where (a) drift prevents conventional analyses, (b) analyte Rmax may be limiting, (c) complex binding such as conformational changes occur,32 (d) orthogonal verification of conventional analyses. While we have focused on SM applications in this report, the chaser aproach may also be applied to kinetic characterization of almost any affinity interaction.



EXPERIMENTAL SECTION Materials. Assays were conducted using a Biacore 8K and Biacore T200 (GE Healthcare BioSciences AB, SE-751 84, Uppsala, Sweden). All reagent coupling kits and sensors were from GE Healthcare. All reagents were from Sigma-Aldrich (3300 S 2nd St, St. Louis, MO 63118, USA) unless otherwise stated. All three kinases were expressed recombinantly and purified in-house. Kinase 1 and kinase 2 possess a terminal biotin added as a post-translational modification33 and indicated by the suffix NAvi. Kinase 3 was expressed with an N-terminal hexahistidine affinity tag indicated by the suffix His. The kinases and test compounds are from currently active projects and are therefore not disclosed. All experiments were performed using a standard kinase buffer (i.e. 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid, (HEPES), pH 7.5, 0.15 M sodium chloride, 10 mM magnesium chloride, 0.1 % (w/v) carboxymethylated dextran, MW of 3500, (CM dextran), 0.2 % (w/v) polyethyleneglycol, MW of 3500, (PEG) (Spectrum chemical MFG corp, Cadera, CA 90248, USA), 0.2 mM tris(2-carboxyethyl)phosphine (TCEP). All 29 tested compounds were from internal Genentech collections and were confirmed to be > 95% pure by mass spectroscopy analysis. Data Analysis and Statistical Methods. Chaser calculations were performed using Microsoft Excel. Microsoft Excel and Biaevaluation (GE Healthcare Bio-Sciences AB) were employed for data processing. Model fitting was performed using Biaevaluation (GE Healthcare Bio-Sciences AB) and Graphpad Prism version 6 (GraphPad Software, Inc. 7825 Fay Avenue, Suite 230, La Jolla, CA, 92037, USA). Curve fitting programs enable fitting of binding-interaction data to interaction models by non-linear regression and the associated statistical methods to confirm goodness of fit and confidence in parameter estimates are well established.34 Statistical parameters such as confidence intervals (CI) and standard error of the fit (SEF) are used to report


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confidence in parameter estimates. The 95% confidence interval associated with a fitted parameter specifies an interval where there is a 95% probability that the true population value lies inside the interval, which we specify as a percentage of the mean e.g. 1 ± 1% (95% CI). The SEF for a parameter is also a measure of parameter confidence and tends to be a lower value as it is not constrained to a 95% probability margin. These parameters are a measure of the information content of the data and specify the degree to which the curves define the parameter value from the fit. The goodness of fit between a model curve and an experimental curve is usually described by χ2 when the number of data points is high. χ2 is the square of the averaged residual response difference and approaches the baseline noise for the best fits. We express χ2 as a relative percentage of Rmax in order to normalize for different analytes and surface capacities. When the number of data points is low it is more common to describe the goodness of fit by the regression coefficient, R2. Beyond curve fitting we use the standard error of the mean (SEM) and root-mean-square deviation (rmsd) to report variation in the mean value of a population. Simulations and Data Analysis. Commercially available instrumentation allows multiple primary injection formats including multi-cycle kinetics35, SCK36 and gradient injection37. Each of these formats generates binding curves composed of association/dissociation phases that can be fit with a two-compartment model38 estimating Rmax, the analyte saturation response, ka, kd, and the mass transport constant (kt). The model can be fit as an analytic39 or more commonly by numerical integration of a coupled set of ordinary differential equations combined with nonlinear squares curve fitting as described in the Supporting Material (e.g., “Section S1 in the Supporting Information”)). kt is usually a fitted parameter but can be pre-calculated40 assuming the dextran hydrogel does not contribute to transport limitation,



(

k t = 10 9.Mw.1.4. F .D 2 / W .h 2

)

1/ 3

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eq 1

where F is the flow rate (m3 s-1), h is the height (m), W is the width (m) of the flow cell, D is the analyte diffusion coefficient (m2 s-1), and MW is the analyte molecular weight (Da). MW is usually known allowing D to be estimated from the Stokes-Einstein-Sutherland equation41. Application of the two-compartment model extends the measurable kinetic range but breaks down when kt 5 x 107 M-1s-1). The fractional analyte occupancy (R) remaining after ∆t can be estimated from a single chaser response point allowing the apparent t1/2 to be calculated from eq 2. t1/ 2 = −(Ln(2).∆t ) / Ln(R )

eq 2

The apparent t1/2 may be corrected for mass transport limitation and expressed in terms of kd using the following equation. k d = (Ln (2) / t1/ 2 )(1 + δ .(1 − b0 / 2.Ln (2) ))

eq 3

where b0 is the fractional occupancy at start of dissociation phase also b0 ≈ Rmax when the surface is pre-saturated with a high affinity analyte. δ is the surface reaction flux relative to kt eq 4

δ t = k a .Rmax / k t


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It is preferable to saturate the target-coated surface during primary analyte injection(s) in order to unambiguously define Rmax. A second paired target-coated surface that has not been exposed to primary analyte (i.e. fully unoccupied) reports Rmax during the chaser injection (Figure S3(d)). The Rmax recorded in this way accounts for any loss in binding activity of target that may have occurred during the time interval (∆t). Accurate double referencing is essential in conventional kinetic analyses as any residual drift will increase fitting error. However, a probe injection that saturates rapidly obviates the need for double referencing because paired baseline/chaser report points may be recorded in rapid succession, effectively minimizing interferences and allowing chaser analysis from raw curves exhibiting significant drift (e.g. Figure S3(a)).

Conventional and Chaser Kinetic Assay. A series S SA sensor chip was installed into a Biacore 8K with analysis temperature set at 20 °C primed into running buffer (i.e. kinase buffer with 0.0001 % (w/v) n-dodecyl β-D-maltoside (Anatrace Products, LLC, Maumee, Ohio, USA)). A Biacore 8K assay provided chaser responses for eight compounds previously analyzed by conventional kinetics. 747 nM Kinase-1-NAvi (MW of 40 kDa) in running buffer was injected over all sensing spots, yielding approximately 2500 RU kinase-1 bound. Each compound (500 nM) was injected over spot 1 of each respective sensing channel for 1 min at flow rate of 50 µL/min, promoting rapid saturation. Bound compound was allowed dissociate for 3.13 h before re-injecting each respective compound over both sensing spots in order to provide a chaser injection response. In this case the chaser injections were performed in SCK format where five serial 4-fold dilutions from 500 nM were injected in series for 60 s at 100 µL/min. Replicates were not performed in this SAR/SKR run. For chaser kinetic analysis, chaser report points were taken after surface saturation relative to a baseline report point before the chaser SCK phase. For



conventional kinetic analysis, data was imported into Biaevaluation version 2.0.4, referenced and then fit to a two-compartment 1:1 interaction model thereby estimating both ka and kd. Multi-Point Chaser Assay. (a) Kinase-2-Inhibitor Residence Time. A series S SA (streptavidin) chip was inserted into a Biacore 8K (GE Health Sciences). The instrument was primed into running buffer (i.e. containing 50 mM Tris pH 7.5 instead of HEPES and containing 4 % (v/v) glycerol, 0.005 % (v/v) Tween-20, 0.5 mM TCEP, and 2 % (v/v) dimethylsulfoxide (DMSO). Kinase-2-NAvi (MW of 31 kDa) was captured to yield 2900 RU on spot 2 and both spots were subsequently blocked by injecting 100 µg/mL EZ-Link™ Amine-PEG2-Biotin (Life Technologies, 850 Lincoln Centre Dr, Foster City, CA 94404). Analyte samples were diluted to a final concentration of 5 µM and injected for 6 min to ensure saturation, with a 10 min dissociation time. The chaser compound (MW of 557 Da) was diluted to 4 µM in running buffer and injected for 30 s with a 60 s dissociation time. The chaser was injected prior to each analyte exposure, and also at 1030 s, 11740 s, 22440 s, 33146 s, and 43568 s after analyte exposure. Each of these sequential chaser injection responses can be used to estimate residence time independently and can therefore be considered replicates recorded at different time points. (b) Streptavidin-biotin Hydrazide Residence Time. A SA (streptavidin) sensor chip was installed into a Biacore 8K with analysis temperature set at 37 °C. The system was primed into running buffer (i.e. 20 mM phosphate buffer saline, pH 7.5, containing 0.15 M sodium chloride). Spot 1 of all eight channels was saturated by injecting 200 nM biotin-hydrazide for 100 s at 20 µL/min. 250 µM 4’-hydroxyazobenzene-2-carboxylic acid in running buffer was injected for 20 s at 50 µL/min providing a chaser injection.

Forty eight replicate chaser injections were

performed over three separate sensing channels in parallel during a 12.5 h period and curves


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containing air spikes were removed. Chaser report points were taken after surface saturation relative to the baseline before injection. Single-Point Chaser Assay with Affinity Capture. A series S NTA (Nitrilotriacetic acid)sensor chip was installed in a Biacore 8K with analysis temperature set to 20 °C and primed into running buffer (i.e. containing 25 mM HEPES, pH 7.5, 0.1% (w/v) polyethyleneglycol and 0.05% (w/v) carboxymethylated dextran. 4.6 µM kinase-3-His (MW of 50 kDa) was preincubated with 6.66 µM of weakly bound compound (proprietary in-house compounds) for 10 min and then diluted to a final concentration of 200 nM in running buffer. Each binding regeneration cycle was composed of 6 injections as follows. Pre-incubated kinase-3-His solution was injected over all spots for 180 s at flow rate 10 µL/min. After 5 minutes stabilization, the test compound was injected for 60 s at 100 µL/min over spot 1. Thirty minutes subsequently, 1 µM chaser probe (i.e. proprietary kinase inhibitor with covalently linked dye) was injected for 60 s over all spots at 60 µL/min. The surface was regenerated by injecting 0.35 M ethylenediaminetetraacetate (EDTA) containing 0.1 mg/ml protease K and 20 % (v/v) desorb 1 (GE Health Sciences) for 80 s. 25 mM sodium hydroxide followed by 1 mM nickel chloride (GE Health Sciences) was injected over all channels in series. The eight probe sample configuration of the Biacore 8k allowed the above binding-regeneration cycle sequence to be performed as eight parallel replicates for each of the fifteen compounds tested.



ASSOCIATED CONTENT Supporting Information Supporting information referred from the manuscript is available free of charge on the ACS Publication website at DOI: General two-compartment 1:1 interaction model and a special case version suitable for analysis of SCK. A Figure describing implementation of the chaser approach and a correlation plot comparing kd as measured using the chaser approach to replicate measurements using a conventional direct label-free method. AUTHOR INFORMATION Corresponding Author *J.G.Q.: Phone +1 650 225 4408; E-mail, [email protected]. ORCID John G. Quinn: 0000-0002-4664-6232 Notes The authors declare no competing financial interest. ACKNOWLEDGEMENTS This work was funded by Genentech Inc. ABBREVIATIONS USED


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SM, small molecule; SPR, surface plasmon resonance; RU, response unit; SCK , single cycle kinetics; SKR, structure kinetic relationship; SCK, single cycle kinetics; BRET, bioluminescence resonance energy transfer; h, hours; s, seconds; ka, association rate constant; kd, dissociation rate constant; dissociation affinity constants, KD ; τ, residence time; t1/2 , half life; Rmax , response at saturation; R = fractional occupancy, Rfree, response equivalent to fraction of free sites; 95% CI, 95% confidence limit; nM, nanomolar; pM, picomolar; fM, femotomolar; χ2, Chi squared; SEM, rmsd, R2, regression coefficient; r, Pearson correlation coefficient; M, moles; M-1s-1, inverse mole seconds; s-1, inverse seconds; min, minutes; NTA, Nitrilotriacetic acid; HEPES, 4-(2hydroxyethyl)-1-piperazineethanesulfonic acid; SEM, standard error of the mean; SEF, Standard error of the fitted parameter; CL, Confidence limit; [3H]biotin, triturated biotin; 4’hydroxyazobenzene-2-carboxylic acid, HABA; PEG, polyethyleneglycol; TCEP, tris(2carboxyethyl)phosphine; SA, Streptavidin; CM dextran, carboxymethyated dextran.

REFERENCES

(1) Hulme, E.C.; Trevethick, M.A. Ligand Binding Assays at Equilibrium: Validation and Interpretation. Br. J. Pharmacol. 2010, 161, 1219–1237. (2) Vilums, M.; Zweemer, A. J. M.; Yu, Z.; de Vries, H.; Hillger, J. M.; Wapenaar, H.; Bollen, I. A.E.; Barmare, F.; Gross, R.; Clemens, J.; Krenitsky, P.; Brussee, J.; Stamos, D.; Saunders, J.; Hitman, L. H.; IJzerman, A. P. Structure–kinetic Relationships - An Overlooked Parameter in Hit-to-Lead Optimization: A Case of Cyclopentylamines as Chemokine Receptor 2 antagonists. J. Med. Chem. 2013, 56, 7706– 7714.



(3) Xia, L.; Burger, W. A. C.; van Veldhoven, J. P. D.; Kuiper, B. J.; van Duijl, T. T.; Lenselink, E. B.; Paasman, E.; Heitman, L. H.; IJzerman, A. P. Structure-affinity Relationships and Structure–kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A3 Receptor Antagonists. J. Med. Chem. 2017, 60, 7555– 7568.

(4) Tummino, P.J.; Copeland R.A. Residence Time of Receptor-Ligand Complexes and its Effect on Biological Function, Biochem. 2008, 44,5481-5492. (5) Copeland, R. A. The Drug–Target Residence Time Model: A 10-year Retrospective. Nature 2016, 15, 87–95. (6) Tonge, P.J. Drug-Target Kinetics in Drug Discovery. 2018, ACS Chem Neurosci. 9, 29-39. (7) Schoop, A; Fabian, D. On-rate-Based Optimization of Structure-kinetic Relationship- Surfing the Kinetic Map. Drug Disc. Today : Technologies. 2015, 17, 9-15. (8) Vauquelin, G. Effects of Target Binding Kinetics On In-Vivo Drug Efficacy: koff, kon and Rebinding. Br. J. Pharmacol. 2016, 173, 2319–2334. (9) Vauquelin, G.; Van Liefde I.; Swinney, D.C. On the Different Experimental Manifestations of Two-state ‘Induced-fit’ Binding of Drugs to Their Cellular Targets. Br. J. Pharmacol. 2016, 173, 1268–1285. (10) Drake, A.W.; Myszka, D.G; Klakampa. S.L. Characterizing High-affinity Antigen/Antibody Complexes by Kinetic- and Equilibrium-based Methods. Anal. Biochem. 2004, 328, 35–43. (11) Kumar, M.; Lowery, R. G. A High-Throughput Method for Measuring Drug Residence Time Using the Transcreener ADP Assay. SLAS Discov, 2017, 22, 915–922.


Page 26 of 32

Page 27 of 32 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


(12) McGovern, S.L.; Caselli, E; Grigorieff, N.; Shoichet, B.K. A Common Mechanism Underlying Promiscuous Inhibitors from Virtual and High-throughput Screening. J. Med Chem. 2002, 45, 1712–1722. (13) Shoichet, B. K. Interpreting Steep Dose-response Curves in Early Inhibitor Discovery. J Med Chem. 2006, 49, 7274–7277. (14) Xia, L.; Burger, W. A. C.; van Veldhoven, J. P. D.; Kuiper, B. J.; van Duijl, T. T.; Lenselink, E. B.; Paasman, E.; Heitman, L. H.; IJzerman, A. P. Structure-affinity Relationships and Structure–kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A3 Receptor Antagonists. J. Med. Chem. 2017, 60, 7555– 7568.

(15) Motulsky, H. J.; Lawrence, C. M. The Kinetics of Competitive Radioligand Binding Predicted by the Law of Mass Action. Mol. Pharm. 1984, 25, 1-9. (16) Poda, S. B.; Kobayashi, M.; Nachane, R.; Menon, V,; Gandhi, A. S.; Budac, D. P.; Li, G.; Campbell, B. M.; Tagmose, L. Development of a Surface Plasmon Resonance Assay for the Characterization of Small-Molecule Binding Kinetics and Mechanism of Binding to Kynurenine 3-Monooxygenase. Assay Drug Dev. Technol. 2015, 13, 466–475. (17) Danlin, Y.; Singh, A.; Wu, H.; Kroe-Barrett, R. Comparison of Biosensor Platforms in the Evaluation of High Affinity Antibody-antigen Binding Kinetics. Anal. Biochem. 2016, 508, 7896. (18) GE Healthcare Life Sciences. Biacore T200. Available at https://www.gelifesciences.com/en/us/shop/protein-analysis/spr-label-freeanalysis/systems/biacore-s200-p-05541 (accessed March 19, 2018).



(19) Creoptix AG. Creoptics Wave Data File. Available at http://www.creoptix.com/images/pdf/CreoptixWave_Brochure.pdf (accessed March 19, 2018). (20) Giannetti, A. M. From Experimental Design to Validated Hits a Comprehensive Walkthrough of Fragment Lead Identification using Surface Plasmon Resonance. Methods in Enzymology, 2011, 493, 169–218. (21) Martin, M.; Anscombe, E.; Meschini, E.; Staunton, D.; Geitmann, M.; Danielson, U.; Wang, L.; Vidal, R.; Reuillon, T.; Golding, B.; Newell, D.; Wedge, S.; Noble, M.; Endicott, J.; Griffin, R. Identification and Characterization of an Irreversible Inhibitor of CDK2. Eur. J. Cancer , 2014, 325, 106. (22) Geitmann, M,; Unge, T.; Danielson, U. H. Biosensor-Based Kinetic Characterization of the Interaction between HIV-1 Reverse Transcriptase and Non-nucleoside Inhibitors. J. Med. Chem. 2006, 49, 2367-2374. (23) Abdurakhmanov, E.; Solbak, S. O.; Danielson, U. H. Biophysical Mode-of-Action and Selectivity Analysis of Allosteric Inhibitors of Hepatitis C Virus (HCV) Polymerase. Viruses 2017, 9, 151. (24) Huber, W. A New Strategy for Improved Secondary Screening and Lead Optimization using High-resolution SPR Characterization of Compound–target Interactions. J. Mol. Recognit. 2005, 18, 273–281. (25) Shepherd, C. A.; Hopkins, A. L. Hopkins, Navratilova, I. Fragment Screening by SPR and Advanced Application to GPCRs. Prog. Biophys. and Mol. Bio. 2014, 116, 113-123.


Page 28 of 32

Page 29 of 32 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


(26) Wang, H.; Ramakrishnan, A.; Fletcher, S.; Prochownik, E. V. A Quantitative, Surface Plasmon Resonance-based Approach to Evaluating DNA Binding by the c-Myc Oncoprotein and its Disruption by Small Molecule Inhibitors. J. Bio. Methods, 2015, 2, e18. (27) Kamat, V.; Rafique, A. Designing Binding Kinetic Assay on the Bio-layer Interferometry (BLI) Biosensor to Characterize Antibody-Antigen Interactions. Anal. Biochem. 2017, 536, 1631. (28) PAL Corporation, Small molecule application note. Available at https://www.fortebio.com/interactions/April_2008/small_molecule.html (accessed March 19, 2018). (29) Creoptix AG. Avoiding Mass Transport Creoptix™ Wave. Available at http://www.creoptix.com/images/newapplicationnotes/05_Creoptix_Avoiding-MassTransport_20160502.pdf (accessed March 19, 2018). (30) GE Healthcare Life Sciences. Biacore 8k. Available at https://www.gelifesciences.com/shop/protein-analysis/spr-label-free analysis/systems/biacore8k-p-05540 (accessed March 19, 2018). (31) Piran, U.; Riordan, W. J. Dissociation Rate Constant of the Biotin-streptavidin Complex. J. of Immunol. Methods, 1990, 133, 141-143. (32) Geitmann, M.; Danielson, U. H. Studies of Substrate-induced Conformational Changes in Human Cytomegalovirus Protease using Optical Biosensor Technology. Anal. Biochem. 2004, 332, 203–214. (33) Cronan J.E. Biotinyation of Proteins in Vivo. A Post-translational Modification to Label, Purify, and Study Proteins. J. Biol. Chem. 1990, 265, 10327–10333.



(34) Motulsky, H. J.; Christopoulos, A. Fitting Models to Biological Data using Linear and NonLinear Regression. A Practical Guide to Curve Fitting, 2003, Graphpad Software Inc., San Diego. (35) Myszka, D.G. Improving Biosensor Analysis. J. Mol. Recognit. 1999, 12, 279–284. (36) Karlsson, R.; Katsamba, P. S.; Nordin, H.; Pol, E.; Myszka, D. G. Analyzing a Kinetic Titration Series using Affinity Biosensors. Anal. Biochem. 2016, 349, 136–147. (37) Quinn, J.G. Evaluation of Taylor Dispersion Injections: Determining Kinetic/affinity Interaction Constants and Diffusion Coefficients in Label-free Biosensing. Anal. Biochem. 2012, 421, 401-410. (38) Myszka, D.G.; He, X.; Dembo, M.; Morton, T.A.; Goldstein, B. Extending the Range of Rate Constants Available from Biacore: Interpreting Mass Transport-Influenced Binding Data. 1998, Biophys. J. 75, 583–594. (39) Sigmundsson, K.; Másson, G.; Rice, R.; Beauchemin, N.; Obrink, B. Determination of Active Concentrations and Association and Dissociation Rate Constants of Interacting Biomolecules: An Analytical Solution to the Theory for Kinetic and Mass Transport Limitations in Biosensor Technology and its Experimental Verification. Biochem. 2002, 41, 8263-8276. (40) Schuck, P. Kinetics of Ligand Binding to Receptor Immobilized in a Polymer Matrix, as Detected with an Evanescent Wave Biosensor. 1. A Computer Simulation of the Influence of Mass Transport. Biophys. J. 1996, 70, 1230-1249. (41) Taylor, G. Dispersion of Soluble Matter in Solvent Flowing Slowly Through a Tube. Proc. R. Soc. London, A. 1953, 219, 186-203.


Page 30 of 32

Page 31 of 32 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


(42) Goldstein, B.; Coombs, D.; He, X.; Pineda A.R.; Wofsy, C. The Influence of Transport on the Kinetics of Binding to Surface Receptors: Application to Cells and Biacore. J. Mol. Recognit. 1999, 12, 293–299.



88x41mm (300 x 300 DPI)


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Determination of Affinity and Residence Time of Potent Drug-Target

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