Kinase profiling in early stage drug discovery: sorting things out

Drug Discovery Today: Technologies

Vol. 18, No. nullC 2015

Editors-in-Chief Kelvin Lam – Simplex Pharma Advisors, Inc., Boston, MA, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY

TODAY

TECHNOLOGIES

Profiling used in lead optimization and drug discovery

Kinase profiling in early stage drug discovery: sorting things out Olivier Defert*, Sandro Boland Amakem Therapeutics N.V. Agoralaan Abis, 3590 Diepenbeek, Belgium

Protein kinases represent one of the largest superfamilies of drugable targets and a major research area for both the pharmaceutical industry and academic

Section editor: Haiching Ma – Reaction Biology Corporation, Malvern, PA 19355, USA.

groups. This has resulted in the emergence of numerous screening technologies and services dedicated to kinase profiling. In spite of this plentiful offering, the field is not without its own pitfalls, as the profusion of reported conditions and data can ultimately complicate interpretation of project results. Here, we discuss how kinase profiling was used in our early stage drug discovery efforts, from the perspective of a smaller biotech relying largely on assay outsourcing. Introduction Protein kinases (EC 2.7.10, EC 2.7.11 and EC 2.7.12) are enzymes transferring a phosphate group from ATP to Ser, Thr or Tyr residues in a peptide substrate [1]. With over 500 representatives in the human genome [2] and a number of clinically relevant mutants identified, they represent, next to G protein-coupled receptors, one of the largest superfamilies of targets in the human genome, and an essential component of numerous signal transduction cascades [3]. Since the approval and success of Imatinib (Gleevec1) in 2001, the field of kinase inhibitors has grown and now represents a major area of interest in drug discovery and development. To date, 30 kinase inhibitors have been approved by the FDA (this number including three natural-product-based macrolides inhibiting mTOR) [4]. Two kinase inhibitors, Palbociclib [5] and Lenvatinib [6], have already been approved in 2015. *Corresponding author.: O. Defert ([email protected]) 1740-6749/$ ß 2015 Elsevier Ltd. All rights reserved.

Historically, kinase inhibitors have been associated with treatment of various cancers and this is reflected by the high proportion of oncology indications for FDA-approved kinase inhibitors [4]. Nonetheless, it is worth mentioning that the Rho-kinase (ROCK) inhibitor Fasudil (HA1077) [7] is used in Japan since 1995 for the treatment of cerebral vasospasm. More recently, other non-cancer indications have been slowly surfacing, with the approval of Tofacitinib [8] (JAK 1/3; 2012) for treatment of rheumatoid arthritis and Nintedanib (FGFR, PDGFR, VEGFR, Flt3; 2014) for treatment of idiopathic pulmonary fibrosis [9,10]. Further, the ROCK inhibitor Ripasudil (K-115) has been approved in Japan as an intraocular pressure (IOP) lowering agent for treatment of glaucoma and is being investigated (Phase-II) for treatment of diabetic retinopathy [11]. The rise of protein kinases and protein kinase inhibitors as a major research area in both industry and academic groups has been paralleled by the emergence of screening technologies and services dedicated to the evaluation and profiling of candidate compounds. The field is now being considered as a mature market, wherein over a dozen major contract research organizations (CROs) compete not only in price, but also in terms of kinome coverage of selectivity panels, screening technology and turnaround time [12,13]. Often, this compound screening offer is complemented by further services, for example, cell-based assays, determination of association and dissociation kinetics, competition studies or mode of action studies; which further differentiate those CROs from

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Drug Discovery Today: Technologies | Profiling used in lead optimization and drug discovery

each other. With such a vast offering, one would intuitively expect compound evaluation in early kinase drug discovery to process smoothly. Yet, the area is not without its own pitfalls, which can complicate or mislead medicinal chemistry efforts.

Kinase profiling in the early stage drug discovery process There have been several reviews dissecting the principles of early discovery e.g. 14–16. Basically, the role of kinase profiling at this stage does not fundamentally differ from the role of a primary screen in other research areas, as binding affinity, inhibitory concentration or % inhibition values are still being used as a major, but not unique, parameter in compound selection and optimization. However, the high number of profiling possibilities that are readily available allows the easy introduction of additional steps in the hit-to-lead optimization process. Such options include (and are not limited to) on-target assays in secondary readout or in cell lysate, kinase panels, reversibility studies, evaluation at multiple assay conditions, competition studies, reversibility studies or kinetic studies. Amakem was incorporated in 2010. With about a dozen research scientists, our perspective is actually comparable to many academic groups. For such young and/or smaller biopharma with chemical matter available, services proposed by CROs represent a considerable help, as they eliminate the need and constraints of primary assay development and allow resources to be concentrated on more specialized assays. Further, the diversity of proposed assay formats and technologies allows cherry picking of the setup(s) and condition(s) that best reflect the project goals and philosophy. In addition, a single technology does not represent the ultimate response to all issues encountered over kinase-related projects; and while there is competition between CROs, those technologies are better seen as complementary [12,13]. Our main project focused on ROCK, a Ser/Thr protein kinase from the AGC family, for which potent inhibitors that were exclusively Type-I (ATP-competitive) had already been

reported [17]. We were therefore in search of a primary screening assay that allowed characterization of low nanomolar or sub-nanomolar compounds, as well as an easy assessment of ATP competition and therefore opted for the gold standard radiometric assay measuring phosphorylation of a peptide substrate [18]. A different project might have resulted in a different choice. For instance, type-II inhibitors of ABL1, such as Imatinib, preferentially bind a non-phosphorylated, inactive state of the kinase [19,20], making screening assays based on ATP consumption or phosphate transfer less appropriate (Fig. 1). In our internal projects, kinase screening & profiling was used on six different instances:  A primary on-target screen allowing a first selection of active compounds. This was run at low ATP concentration (1 mM), so that measured IC50 values approximate Kd.  An optional re-screen at higher ATP concentration (200 mM), which was concomitant with the first line of functional assays and stability assays. This screen provided a first assessment of ATP competition and was particularly useful in order to compare the on-target activity of potent compounds (IC50 in primary screen lower than formal enzyme concentration).  A parallel re-screen involving different assay conditions and readout, so to check consistency of on-target activity.  A formal mode of action study (reversibility, substrate competition. . .) that was run for limited candidates.  A complete selectivity panel, which was run for candidates considered of particular interest after functional assays and early pharmacokinetics studies.  Follow-up IC50 determinations for off-target activities.

On-target activity: keep landmarks in sight! Using on-target activity (e.g. IC50, Ki or Kd values) as one of the primary guides for compound optimization in early stage

Y-27632

Fasudil

O H

H N

N

N H

NH2 O

ROCK1 (nM) 46

ROCK2 (nM) 61

Ref

Type

IC50

[26]

IC50

IC50

871

245

[27]

IC50

IC50 IC50

-

54 800

[28] [25]

IC50 IC50

Ki

25

-

[27]

Ki

140

-

Ki

220

300

Type

O S N

NH

Ref

ROCK2 (nM) 400

[26]

10700 430

660

[30] [31]

IC50

3162

1349

[27]

[24]

Ki

32

50

[32]

[23]

Ki

330

-

[24]

ROCK1 (nM) 660 180

[29]

Drug Discovery Today: Technologies

Figure 1. Variation in reported on-target activities of the ROCK inhibitors Y-27632 and Fasudil.

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Drug Discovery Today: Technologies | Profiling used in lead optimization and drug discovery

drug discovery is intuitively justifiable. Nonetheless a couple of pitfalls should be avoided at this stage. The question of benchmarking with respect to known inhibitors is a common one, in view of the profusion of screening assay technologies and conditions in literature. While it is commonly assumed that the different techniques will yield comparable results, on-target activities reported for the same compound can in some instances vary widely. The choice of assay conditions (e.g. peptide substrate, enzyme concentration or substrate concentration) and technology can affect the activity readout. Binding of some inhibitors depends on the activation state of the kinase [19,20], which can lead to a misestimation of compound potency if the wrong activation state or screening technology is used. In this context, a number of screening technologies are specifically adapted for the discovery of nonATP site kinase inhibitors [21]. Even less obvious factors can influence on-target activity, such as construct design. An interesting example is provided by MAPKAPK2, wherein construct length affected the binding affinity of ATP analogues and Staurosporine by an order of magnitude or more, while the constructs had comparable enzymatic activity after their activation by p38a [22]. Such effect does not result from a readout artefact, but represent a real change in the thermodynamic properties of the resulting construct. Those factors can complicate comparison of isolated IC50 or Kd values unless sufficient reference compounds are directly included in the assay. Examples of such discrepancies are provided by the ROCK inhibitors Y-27632 [23] and Fasudil [7,24]. Those compounds are moderately potent, but are often used as reference due to their reported selectivity [25]. For both compounds, and against both ROCK isoforms, reported IC50 values vary by over 10-fold across literature [23–32] (Table 1). While some degree of inter-assay variability can be anticipated for ATP-competitive kinase inhibitors, those differences are not necessarily justified by the difference in ATP concentrations between assays. The spread in on-target activity only gets larger when other types of values (Ki, Kd) are taken in consideration. As can be expected, such differences are not limited to those reference compounds. In an internal exercise aiming to assess two ROCK2 assays provided by distinct CROs (here referred to as CRO1 & CRO2), we compared the IC50 values of 141 ROCK inhibitors, spanning different chemical series and over 3 log units of on-target activity (Fig. 2). Both assays involved a radiometric readout and were carried out in presence of 1 mM ATP. Overall, there was good correlation between assays (R = 0.86, with a slope of 1.0 for the linear fit). However, an important shift in on-target potency was observed (intercept of 1.6 pIC50 unit). When evaluated at CRO1, some proposed chemical variations seemed to result in a marked drop in potency, when compared to structurally related compounds described in literature [29]. Meanwhile, IC50 values from CRO2 suggested that the same compounds e54

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Table 1. Activity of 12 reference ROCK inhibitors in the CRO2 assay (1 mM ATP) and comparison to initial literature data. Compound

IC50 CRO2a (nM)

Original value (nM)

Ref

b

[24]

Fasudil

R1: 103 R2: 103

R: 330 (Ki)

H-1152

R1: 2.0 R2: 2.3

R: 1.6 (Ki)b

[33]

Y-27632

R1: 61 R2: 54

R1: 220 (Ki)

[23]

R2: 300 (Ki)

Y-39983

R1: 0.2 R2: 3.2

R2: 3.6 (IC50)

[34]

GSK-429286

R1: 2.3 R2: 1.6

R1: 14 (IC50)

[35]

GSK-269962

R1: 0.2 R2: 0.4

R1: 1.6 (IC50) R2: 1.4 (IC50)

[36]

SB-772077-B

R1: 2.5 R2: 5.8

R1: 5.6 (IC50) R2: 6.0 (IC50)

[36]

TC-S 7001

R1: 1.2 R2: 1.1

R1: 0.6 (IC50) R2: 1.1 (IC50)

[37]

SR3677

R1: 9.2 R2: 2.1

R1: 56 (IC50) R2: 3 (IC50)

[38]

KD-025

R1: 5900 R2: 93

R1: >20 000 (IC50) R2: 105 (IC50)

[39]

RKI-1447

R1: 0.2 R2: <0.1

R1: 15 (IC50) R2: 6.2 (IC50)

[40]

JMC-Cpd5

R1: <0.1 R2: <0.1

R1: <1 (IC50) R2: <1 (IC50)

[41]

a All compounds tested in presence of 1 mM ATP. Each value is the average of at least two experiments. b ROCK1/ROCK2 unspecified in original publication. R1: ROCK1. R2: ROCK2. R: ROCK (unspecified).

were equipotent with their literature counterparts. Resynthesis of those reported analogues, which were not commercially available and comparison of selected molecules at a third CRO using a different assay technology ultimately validated the proposed molecular variations, and led to the selection of CRO2 for further profiling. In view of the inter-assay variability, comparing the ontarget activity of candidates to reference compounds evaluated under identical assay conditions should be a logical step. In a further exercise, we compared IC50 values for a selection of 12 well-known ROCK inhibitors from literature. All compounds were evaluated at CRO2 using the kinase Hotspot assay technology. An ATP concentration of 1 mM was used, so that IC50 values approach Ki as much as possible. While results are essentially in line with literature data [23,24,34– 41], differences can nonetheless be found (Table 1). The most striking finding concerns RKI-1447, which appeared moderately potent (IC50 of 14.5 and 6.2 nM vs. ROCK1 and ROCK2, respectively) under the primary assay conditions used in the original publication [40], but clearly appears sub-nanomolar

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Drug Discovery Today: Technologies | Profiling used in lead optimization and drug discovery

10 H N

9

pIC50 ROCK2 - CRO2

N

N

N H

8

IC50 – CRO1: 3500 nM IC50 – CRO2: 26 nM IC50 – Lit.[29]: 32 nM

H N

7

N

N

O

N H

6

O

5

H N N

4

IC50 – CRO1: 370 nM IC50 – CRO2: 6.3 nM IC50 – Lit.[29]: 20 nM

4

5

6

7

8

9

10

N H

N

O

New molecular variation IC50 – CRO1: 320 nM IC50 – CRO2: 3.4 nM

pIC50 ROCK2 - CRO1

Drug Discovery Today: Technologies

Figure 2. Comparison of observed pIC50 values against ROCK2, for a selection of 141 ROCK inhibitors. While good correlation is observed between assays provided by two independent CROs, an important shift in observed on-target activity is observed (1.6 pIC50 unit). As a result, proposed molecular variations seemed to result in decreased activity when evaluated at CRO1. Resynthesis and evaluation of the appropriate, non-commercial analogues from literature ultimately validated the proposed variations.

under the CRO2 setup; a fact which was also noted by its discoverers when performing a limited selectivity panel [42]. This difference in apparent potency (>50-fold for both ROCK1 and ROCK2) cannot be fully explained by the difference in ATP concentrations between assays (15 mM for ROCK1 and 50 mM for ROCK2, instead of 1 mM). Similarly, GSK-269962 appears slightly more potent (8–10 fold) in the present assay than in the original publication (ATP concentration of 1 mM in both assays) [36]. In spite of such variations between assay formats, it is still common that the activity of an emerging compound series is reported without comparison to common reference compounds, or that comparison is only done on basis of literature data. Providing such data can even be a hurdle. For instance, when publishing results on ROCK inhibitors, we twice received as feedback that a 56 nM IC50 for Y-27632 appeared overly optimistic.

Mode of action studies In line with the sheer number of protein kinases in the human genome, a huge diversity of protein kinase inhibitors have been reported in literature, and have been categorized into five major classes [21]. Type I inhibitors simply occupy the ATP-binding site of a kinase. Type II inhibitors bind to a pocket that is adjacent to the ATP-binding site, and is only accessible in the inactive conformation of a kinase (DFG-out). Nonetheless, Type-II inhibitors frequently occupy part of the ATP-binding site, and can remain ATP-competitive. Type-III inhibitors bind totally outside of the ATP-binding site, in a nearby cleft that is used by the protein substrate. Those inhibitors can be non-competitive or uncompetitive with respect to ATP. Type-IV inhibitors bind to regions that are located even further from the ATP-binding site, and regulate

enzymatic activity through allosteric processes. Finally, some examples of Type V inhibitors, which are best defined as bidentate ligands combining two of the former functionalities, have been reported. In parallel, kinase inhibitors can also be classified based on binding kinetics. Two FDA-approved compounds, Afatinib [43] and Ibrutinib [44], behave as irreversible compounds, targeting a cysteine residue present in EGFR, ErbB2 and ErbB4 (Afatinib) or in Btk (Ibrutinib). A number of comparable compounds are, or have been, investigated. While most of them represent type I kinase inhibitors, covalent allosteric inhibitors interacting with the Plekstrin Homology (PH) domain of Akt have recently been reported [45]. Next to those, pseudo irreversible inhibitors can achieve sustained inhibition of a kinase target either through reversible formation of a covalent bond [46], or by optimization of binding/unbinding kinetics, as was for instance done with some p38 inhibitors [47]. This variety of mechanisms and binding modes prompts in turn for further mode of action studies. Structural methods such as XRD or NMR represent an almost ultimate validation of a compound’s mode of action. However, while those techniques are sometimes used very early (e.g. in a fragment-based approach) they are not necessarily accessible (or affordable) in the first steps of a project, especially in the case of smaller biotech or academic groups. Biochemical mode of action studies are therefore of importance, not only during hit to lead optimization, but also following analysis of screening results. It is indeed, at this stage, important to rapidly discriminate between tractable and untractable compounds. Many kinase inhibitors tend to combine flat, aromatic fragments, often resulting in highly conjugated, lipophilic molecules. Absorbance or www.drugdiscoverytoday.com

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autofluorescence, low solubility and aggregate formation are potential pitfalls to be avoided [48–52]. In 2003, a recursive partitioning analysis aiming to identify promiscuous aggregate formers pointed out lipophilicity and conjugation as factors contributing to aggregate formation [53]. Interestingly, a good number of FDA-approved kinase inhibitors display values exceeding the cLogP and conjugation cutoffs defined in this study, suggesting that comparable structures might form aggregates when tested at micromolar concentrations. The ability of compounds displaying perfectly reasonable kinase inhibitors chemotypes to inhibit firefly luciferase has also been documented; with a 6% hit across the GSK published protein kinase inhibitor set [54]. Further, a number of structures that would usually be flagged as pan-assay interferences (PAINs) [55,56] can show up in kinase screening results. In some cases, such structures are nonetheless amenable to further optimization, provided that a sensible, druglike mode of action can be demonstrated. One example of such situation is provided by AZD1208, a pan-Pim kinase inhibitor [57]. AZD1208 contains a thiazolidinone (rhodanine) sub-structure that would typically have it flagged as a PAIN in screening results, or even excluded from screening in the first place. Yet, AZD1208 is a potent (IC50 < 1 nM) and ATP-competitive inhibitor, that demonstrated good kinase selectivity, efficacy in preclinical models of acute myeloid leukemia [57], and was finally amenable to clinical trials. Other examples of thiazolidinone-based Pim inhibitors have been reported in literature and several of them could be cocrystallized with their targets, further demonstrating occupancy of the ATP-binding site. Rhodanine-based inhibitors displaying Type I, ATP-competitive binding have been reported for other kinases, for example, for CDK2 (Fig. 3a) [58]. Another example wherein a rapid assessment of mode of action is of interest is provided by 2-aminothiazoles. This fragment was recently described as favouring promiscuous inhibition [59]. However, it is also a typical substructure among Type I inhibitors and is found not only in the FDAapproved Dasatinib [60], but also in several other kinase inhibitor series, for example, CDK2 inhibitors such as SMS032 (BMS-387032) [61], or GSK3 inhibitors [62] (Fig. 3b). In all three cases, the 2-aminothiazole moiety was demonstrated (XRD) to form critical H-bond interactions with the hinge region of the targeted kinase [60–62]. Again, a rapid assessment of the mode of action appears mandatory when such a structure emerges from a screening campaign. Competition with ATP is an easily solved matter for all kinase assays based on enzymatic activity. IC50 determination at two markedly different ATP concentrations provides a rapid estimate of how inhibitor binding is influenced by ATP, as long as Km for ATP is not too high. In our case, we chose to perform a primary IC50 determination at 1 mM ATP (a low concentration, so that IC50 would approach Ki) and a secondary experiment at 200 mM ATP (a concentration e56

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H N

(a) H N

O

O

O

S

O

O

S N

NH2 O

S

H2N

AZD1208 (Pan-Pim)

O

PDB 2UZO (CDK2)

(b) H N

N

HO

N

N N

N

S

H N

Cl

O

Dasatinib (Bcr-ABL, Src) H N

HN O

H N

N S

SMS-032 (CDK2)

N

S

N

N S

N O

Pierce, 2005, Cpd 7 (GSK3-β) Drug Discovery Today: Technologies

Figure 3. Examples of kinase inhibitors displaying substructures flagged as high risk for promiscuous inhibition, but behaving as regular Type-I kinase inhibitors. (a) Compounds display a thiazolidinone, which is a known pan-assay interference substructure (PAIN). (b) Compounds display a 2-aminothiazole, which was described as presenting a higher risk for promiscuous inhibition. In all cases, a tractable type-I binding mode could be confirmed through crystallographic studies.

higher than most Km values) [41]. Secondary screening at ATP concentrations as high as 5 mM (the high end of ATP concentration in cells) is sometimes used in literature [57]. Regardless of ATP concentration, this configuration remains rather inexpensive, and can be easily run for a relatively high number of candidates, for example, on selected representatives, after clustering results from a screening campaign. A more formal assessment of ATP competition, using the regular double reciprocal plots can in turn be run on more advanced candidates for confirmation. Development of kinase inhibitors with long residence time [63] is currently receiving substantial attention, whether this effect is achieved through covalent bond formation [43–46] or non-bonding interactions [47,63–65]. Next to biophysical techniques such as SPR, a variety of biochemical methods allow assessment of binding kinetics and reversibility [66] and are now an integral part of the CRO offer. Several of those rely on the fact that the inhibition resulting from a reversible inhibitor with fast binding/unbinding kinetics can

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Drug Discovery Today: Technologies | Profiling used in lead optimization and drug discovery

be relieved by dilution. Following progress curves for the phosphorylation reaction before and after dilution therefore allows detection of irreversible or slowly unbinding compounds [64]. Alternatively, IC50 values can be determined at various time points after dilution, as an IC50 shift towards weaker values is expected from reversible inhibitors [66]. In an inverse setup, determination of IC50 after various preincubation times can be used to detect compounds with slow association kinetics [65]. Those dilution and incubation methods have the advantage of being rapidly applicable to most kinases but are mostly appropriate for compounds displaying dissociation rates that are substantially higher than assay duration, which can still be over an hour for assays based on phosphate transfer. For compounds displaying shorter residence time, use of fluorescent analogue or tracer is another possibility. Such methods are now available for a large portion of the kinome, can potentially be run in cell lysate, and allow determination of dissociation half-lives that do not exceed a few minutes [47,65].

Selectivity profiling: disambiguation needed? With over 500 representatives, protein kinases represent one of the largest superfamilies of proteins in the human genome [2]. Per definition, all of them share ATP as endogenous ligand. With the overall protein kinase fold and the generic structure of the ATP-binding site being well conserved across the whole superfamily, selectivity issues will almost invariably be encountered when developing ATP-competitive ligands and those issues can in turn be translated into side effects. Assessing compound selectivity is therefore a key step in the selection of a chemical series from a list of screening hits, and/or in hit to lead optimization. Accordingly, CROs involved in kinase screening offer dedicated kinase selectivity panels, thereby answering the demand for selectivity information [12,13]. While it is almost a dogma that some degree of selectivity is needed for kinase inhibitors, such information is not always readily available, in spite of efforts reported in literature [18,25,67–72]. Actually, the level of selectivity information published in literature remains frustratingly low, in view of the facilities now offered for compound profiling. And it is still commonplace that on-target activity of a compound series is reported without comparison to any other kinase, or against a single kinase that is taken as a generic example for selectivity. Different definitions of a selective inhibitor are found in literature, each of them with their own advantages and bias. Metrics based on percentage inhibition are the most accessible (price-wise) for smaller groups and biotechs, as they require fewer data points for a given kinase panel. Kinase selectivity score [68] simply reports the fraction of kinases inhibited at a pre-defined concentration. While easy to apprehend, this metric depends on an arbitrarily chosen threshold

(i.e. a higher number of inhibited kinases will likely be found at higher concentrations). An inverse correlation has also been found between such selectivity score and potency [69], which can result in the misranking of compounds. Further, weak hits (e.g. 50% inhibition) and strong hits (e.g. 99% inhibition) have the same impact on selectivity score. An alternative version of this metric uses a (still arbitrary) threshold over observed on-target potency (e.g. 10  IC50). [68]. The Gini coefficient [73] takes into account the observed percentage of inhibition, but remains heavily dependent on assay concentration. Metrics based on Ki, Kd or IC50, such as thermodynamics-based partition index [74] or selectivity entropy [75] eliminate concentration-dependency, but require substantially higher data points to be generated and therefore higher resources to be committed. One aspect not readily addressed by the above metrics is kinetic selectivity. Kinase inhibitors with slow off rates/high residence times can achieve prolonged inhibition of their target (or off targets) after compound washout (in vitro) or clearance from systemic circulation (in vivo). Their cellular effects can even be observed at time points that largely exceed the dissociation half-life observed in vitro for the kinaseinhibitor complex [66]. A typical example is Lapatinib, which still affected (85% inhibition) EGFR phosphorylation 96 h after washout, while dissociation half-life for the EGFR-Lapatinib complex is 5 h [64]. Similarly, selectivity aspects of covalent kinase inhibitors, which may not dissociate at all, go beyond usual metrics. Presence of reactive residues for covalent binding is an obvious factor to take into account. However, chemical reactivity of the warhead used in those inhibitors is also of importance, as it influences the concentration at which non-specific covalent bond formation occurs. Further, such non-specific covalent bond formation was also shown to be time-dependent [76]. Besides the differing definitions discussed above and potential concentration dependency of some metrics, the selectivity of candidate compounds is also influenced by additional factors. Indeed, if one admits that on-target activity data can vary depending on assay technology and experimental conditions, one also has to admit that selectivity information can be equally context-dependent. A typical example is that selectivity ratios for ATP-competitive kinase inhibitors will be influenced by ATP concentration and by the respective Km values of off-target kinases, as predicted by the Cheng-Prusoff equation [77] (Fig. 4). Those effects can subsequently ponder the goals one sets for selectivity. In order to correctly identify potential selectivity liabilities, it is therefore appropriate to run the selectivity panel at a sufficiently high compound concentration (e.g. 100  Ki or higher). If ATP concentration can be varied in the assay, screening at low ATP concentrations (Km) will be more sensitive for revealing potential off-target activities, while screening at high (ideally physiological) ATP concentrations has more relevance with www.drugdiscoverytoday.com

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(a)

Kinase 1

Kinase 2

Kinase 3

Kinase 4

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(a)

O

N

5 15

Ki (nM) Km ATP (µM) (b)

50 15

50 10

50 90

N N

1000 K1 800

LX-7101

K2

(c)

IC50 (nM)

K3 600 K4 400

IC50 (200 µM ATP)

LIMK2

1.6

3.6

LIMK1

23.5

NT

ROCK2

13.0

407

N

EC50 Cofilin-PP (Functional LIMK screen)

8 nM

EC50 MLC-PP (Functional ROCK screen)

2300 nM

Cmax in aqueous humor

1500 nM

Cmax in sclera

3300 nM

Drug Discovery Today: Technologies

200

0 0

50

100

150

200

[ATP] (μM)

(c)

NH2

N H

N H

IC50 (1 µM ATP)

(b)

O

O

16

Figure 5. (a) Structure of LX-7101, a LIMK2 inhibitor. (b) IC50 values against LIMKs and ROCK2 suggest better selectivity at high ATP concentrations. (c) Comparison between functional (cell-based) data and PK data following topical administration of LX-7101 as eye drops.

14

Selectivity ratio

12 10 SR K1-2

8 SR K1-3

6 SR K1-4

4 2 0 0

50

100

150

200

[ATP] (μM) Drug Discovery Today: Technologies

Figure 4. Measured selectivity ratios depend on ATP concentration and on the respective Km values of off-target kinases. In this theoretical example, a candidate ligand has a Ki value of 5 nM against its main target (Kinase 1) and displays an apparent selectivity ratio of 10 versus three other kinases, having different Km values (a). Apparent IC50 values increase linearly with ATP concentration, with a slope that is inversely proportional to Km (b). As a result, the initial 10-fold selectivity ratio calculated from Ki (SR = Ki2/Ki1) tends towards SR = (Ki2
Km1)/(Ki1
Km2) at high ATP concentrations, which are more relevant with respect to physiological conditions (c). Achieving 10fold selectivity at high ATP concentration only requires a Ki ratio of 7 in case of Kinase 3; but requires a Ki ratio of 60 in case of Kinase 4.

respect to in vivo efficacy and potential adverse side effects [72]. While selectivity metrics provide numerical guidelines for compound optimization, real life situations can be much more complex. In the end, functional activity, pharmacokinetics or pharmacodynamics can overrule theoretical metrics and validate or invalidate early views on compound selectivity. A recent example is provided by LX-7101 (Fig. 5). This LIMK inhibitor (IC50 LIMK2 = 1 nM) was developed as an intraocular pressure (IOP) lowering agent for the treatment of glaucoma [78]. At low ATP concentrations LX-7101 shows clear activity on ROCK2, which is a known target of several IOP-lowering agents, and might be considered poorly selective [78,79]. However, the respective Km values of LIMK2 and e58

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ROCK2 are such that in presence of 200 mM ATP, a much higher difference of activity is found between the two kinases. This difference is somewhat reflected in functional assays, where LX-7101 displays much higher potency against cofilin phosphorylation (functional screen for LIMK activity) than against MLC phosphorylation (functional counter-screen for ROCK activity) [79]. Based on such data, LX-7101 would then be considered as nicely selective versus ROCK. Nonetheless, the levels of LX-7101 observed in vivo after topical administration (1500 nM in aqueous humor, 3300 nM in sclera) approach its EC50 in the MLC phosphorylation counterscreen (2300 nM) [78,79]. In the present case, it is unclear whether or not the observed effects of LX-7101 are solely linked to LIMK inhibition. Discussion regarding selectivity under physiological conditions is obviously not limited to this particular example, but extends to many studies associating the inhibition of a kinase with a particular biological effect. It is indeed quite common that a kinase inhibitor demonstrating selectivity against a limited kinase panel is simply considered as ‘selective’ in subsequent publications. Another common issue is that the effect of some reference inhibitors are studied at concentrations that are way beyond those where selectivity was demonstrated. Some typical examples are, again, provided by Y-27632, which is used as a selective ROCK inhibitor in multiple studies investigating the role of ROCKs in biological processes. Y-27632 indeed possesses relatively good selectivity for ROCK1 and ROCK2 [25]. However, it is equally potent against PRK2, and it has already been demonstrated that some of its biological effects are also mediated through PRK2, rather than solely through ROCKs [80]. Selectivity information for this compound is available at up to 10 mM, a concentration at which it also inhibits novel PKCs [25]. Nonetheless, Y-27632 is used at concentrations as high as

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100 mM [81], or even 1 mM [82]. In view of this, one can only encourage generation (and publication) of a sufficient amount of selectivity data when a novel compound is used to demonstrate the involvement of a kinase target in a biological or physiological process.

Conclusions Protein kinases are well-established components of drug development pipelines, and this has resulted in a large number of CROs offering screening & profiling services and in important amounts of data published in literature. This profusion of data sources provides valuable help for early drug discovery, but also generates a number of potential pitfalls to be kept in mind. Differences in results collected from different assay technologies and conditions can be quite significant, and are not necessarily apparent from literature data, especially when no control compound is included in publications, or when generic values collected under different assay conditions are used as comparison. Selectivity information for reference compounds or structurally related compounds can also be incomplete, or ambiguous. Subsequent experiments simply considering that such compounds are ‘selective’ can in turn be misleading, especially when they are carried out at concentrations that largely exceed those where selectivity was initially assessed. For all those reasons, ‘sorting things out’, through careful benchmarking of candidate compounds and optimal use of complementary technologies, remains necessary even when significant literature data is available. For smaller organizations such as academic groups or young biotechs, this can also mean that a comparatively higher amount of time and resources is spent on such exercise.

Conflict of interest Olivier Defert is shareholder and independent manager of Amakem Therapeutics N.V. Sandro Boland is employed by Amakem Therapeutics N.V.

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