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De Novo Design of Protein Kinase Inhibitors by in Silico Identification of Hinge Region-Binding Fragments

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† Drug Discovery Unit (DDU), Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Sir James Black Centre, DD1 5EH, U.K.
‡ Department of Chemistry, MSD, Newhouse, Lanarkshire, ML1 5SH, U.K.
§ Fakultät Chemie - Chemische Biologie, Technische Universität Dortmund, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
∥ Institut für Pharmazie und Biochemie, Johannes Gutenberg-Universität Mainz, Staudinger Weg 5, 55128 Mainz, Germany
*E-mail: [email protected]; [email protected]
Cite this: ACS Chem. Biol. 2013, 8, 5, 1044–1052
Publication Date (Web):March 27, 2013
https://doi.org/10.1021/cb300729y

Copyright © 2022 American Chemical Society. This publication is licensed under CC-BY.

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Abstract

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Protein kinases constitute an attractive family of enzyme targets with high relevance to cell and disease biology. Small molecule inhibitors are powerful tools to dissect and elucidate the function of kinases in chemical biology research and to serve as potential starting points for drug discovery. However, the discovery and development of novel inhibitors remains challenging. Here, we describe a structure-based de novo design approach that generates novel, hinge-binding fragments that are synthetically feasible and can be elaborated to small molecule libraries. Starting from commercially available compounds, core fragments were extracted, filtered for pharmacophoric properties compatible with hinge-region binding, and docked into a panel of protein kinases. Fragments with a high consensus score were subsequently short-listed for synthesis. Application of this strategy led to a number of core fragments with no previously reported activity against kinases. Small libraries around the core fragments were synthesized, and representative compounds were tested against a large panel of protein kinases and subjected to co-crystallization experiments. Each of the tested compounds was active against at least one kinase, but not all kinases in the panel were inhibited. A number of compounds showed high ligand efficiencies for therapeutically relevant kinases; among them were MAPKAP-K3, SRPK1, SGK1, TAK1, and GCK for which only few inhibitors are reported in the literature.

Phosphorylation is the most important and widespread covalent modification of proteins. It is used to control enzyme activity in cellular processes and thereby plays a major role in cell signaling and is fundamental to all aspects of cell behavior and organization. (1) Protein kinases catalyze the transfer of the γ-phosphate group from ATP to recognized amino acids of proteins. Kinases have implications for many diseases including cancer, diabetes, and Alzheimer’s disease and constitute the second most exploited group of drug targets with many ongoing drug discovery efforts. (2) Despite the extensive research over the past two decades, selective chemical tools are still needed to dissect the complex nature of kinase regulation. (2, 3)
A wealth of structural information has revealed the general architecture of protein kinases, their binding sites, and complex regulation. (4, 5) The ATP-binding sites of most protein kinases share similar features (Figure 1a). (6, 7) A key recognition motive is the hinge region that forms hydrogen bonds to the adenine moiety of ATP and is targeted by many kinase inhibitors. Often, inhibitors also address one or both of the adjacent hydrophobic pockets I and II. These are more variable between different kinases than the hinge region, and the differences can be exploited to achieve selectivity. (8)

Figure 1

Figure 1. (a) ATP binding site of a typical protein kinase (adapted from ref 7). (b) In silico screening cascade used to design novel kinase inhibitor libraries.

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Kinase inhibitors are commonly discovered by high-throughput, virtual or fragment-based screening, often using compound libraries sourced from commercial suppliers. (9-13) While successful in delivering hit compounds, they have only limited template diversity. In order to tackle this issue, various research groups have developed approaches to expand their libraries with proprietary compounds. (14-20) Libraries that contained compounds with heterocycles, which have the potential to interact with the hinge region of the kinase binding site but no previous reported activity against kinases, were of particular high value. (14-18) A difficulty in expanding the kinase libraries was to assess synthetic feasibility of the suggested compounds, especially if they contained novel cores. (14, 17)
Here, we report on the structure-based de novo design of protein kinase inhibitors. The approach is centered on fragments that have precedence for synthesis but are not commercially available with the required substitution pattern. Libraries around six core fragments without previous reported activity against kinases were synthesized, and selected compounds were screened against a panel of 117 kinases. In addition, the crystal structure of one novel inhibitor in complex with cSrc was determined. Every tested compound was active against at least one kinase. While predicting general activity against kinases on a scaffold level was highly successful, predicting selectivity on a compound level failed. Ligand efficient inhibitors were identified for a number of kinases, which have implications in a range of diseases but for which only a few inhibitors have been reported to date.

Results and Discussion

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Structure-Based Design of Novel Protein Kinase Inhibitor Libraries

An in silico screening cascade was established for the design of novel kinase inhibitor libraries (Figure 1b). This approach consisted of the following four principle steps: core fragment extraction out of commercially available compounds, selection of candidate core fragments, docking of core fragments, and fragment expansion.
A core fragment was defined as a ring system plus the directly attached heteroatom containing functional groups. (11) Starting from over two million compounds, about 84,000 unique core fragments were extracted. In the next step these core fragments were filtered for fragment-like properties, the absence of unwanted functionalities, and limited complexity. The resulting 11,000 core fragments were subsequently filtered using a 3D pharmacophore to remove scaffolds that did not contain a hinge-binding motif (Supplementary Figure S1). About 6,000 core fragments that passed this filter step were docked into the binding sites of a panel of 46 different protein kinases (Supplementary Table S1). These proteins were chosen based on the availability in the MRC Protein Phosphorylation Unit in Dundee for compound testing and access to a crystal structure in the public domain. (21) In order to make the docking scores comparable for the different kinases, they were normalized relative to the best score for any fragment in the active site of each kinase in a similar way as carried out previously. (22) Following this approach the top 100 nonselective core fragments were visually inspected. The vast majority of these were predicted to form two or three hydrogen bonds with the kinase hinge region. Six out of the top scoring core fragments or fragments closely resembling these scaffolds had been co-crystallized with a kinase as part of a larger compound (Figure 2). Comparing the docked poses with the binding modes of these compounds confirmed that they were placed correctly in the binding site in at least one kinase of the docking panel (RMSD of maximum common substructure <2 Å). The novelty of the high-ranking hinge binders was assessed by using the core fragments as substructure queries for ChEMBL (23) and SciFinder (Chemical Abstracts Service, Columbus, OH). It turned out that 73 of the top 100 nonselective core fragments were already reported in the literature as part of known kinase inhibitors. Six novel fragments out of the remaining 27 fragments were short-listed for library enumeration based on synthetic considerations (Figure 3). None of the selected core fragments was available as part of commercial compounds with either the required substitution pattern or the desired diversity at the time of the study.

Figure 2

Figure 2. Docking poses of six high ranking core fragments (green carbon atoms) superimposed on crystallographically determined binding modes of ligands containing the same or a closely related core fragment (cyan carbon atoms). Putative hydrogen bonds to the hinge region are shown as dashed lines. RMSD values are given for the maximum common substructure between core fragment and ligand. (The binding sites are oriented as depicted in Figure 1a.)

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Figure 3

Figure 3. Predicted binding modes with respect to the hinge region for six high-ranking core fragments (A–F) for which binding to protein kinases was not reported in the literature. Only the most frequent binding mode for each core fragment is shown. The substitution points that target the hydrophobic pockets I and II and that have been selected for diversifying the cores are indicated as R1 and R2, respectively. (The binding sites are oriented as depicted in Figure 1a.)

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In the final step, suitable substitution points to attach additional moieties to interact with the hydrophobic pockets I and II (Figure 3) were assigned. For all short-listed core fragments, more than one binding mode was predicted for the kinases in the docking panel. Typically, in the different orientations several hydrogen bonds with the hinge region were formed, but the interacting atoms of the cores differed. It was therefore ensured that after adopting these alternative binding modes the R-groups would still be placed into desired regions of the binding site. The libraries were enumerated using commercially available building blocks. The reaction products were filtered to remove molecules with unwanted functionalities and non-drug-like molecules according to Lipinski’s rule of five. (24) A final selection was made based on diversity by visual inspection and in-house availability of the building blocks. Due to synthetic considerations, libraries containing the core fragments A and F were generated by varying either R1 or R2 (Supplementary Table S2, Figure 3), whereas for core fragments B and D R1 R2 were varied simultaneously. Core fragments C and E contained only one R-group for enumeration. In total, 265 compounds were short-listed for synthesis. All libraries were synthesized using parallel synthesis in a maximum of six steps. To speed up synthesis the synthetic routes were designed to introduce diversity as late as possible (Scheme 1), and 186 compounds representing all six core fragments were successfully prepared (yield 7–98%, purity >90%).

Scheme 1

Scheme 1. a

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Scheme aReagents and conditions: (a) NBS, DCM, 0 °C; (b) ethoxycarbonyl-isothiocyanate, dioxane, rt; (c) NH2OH·HCl, DIPEA, MeOH/EtOH, 60 °C; (d) R1B(OH)2, K3PO4, PCy3, Pd2(dba)3, dioxane/water, MW, 130 °C; (e) R2COOH, PCl3, CH3CN, MW, 150 °C; (f) formamide, 200 °C; (g) Br2, AcOH, rt; (h) R1B(OH)2, K3PO4, PCy3, Pd2(dba)3, dioxane/water, MW, 100 °C; (i) diethylmalonate, 170 °C; (j) POCl3, reflux; (k) HNO3, H2SO4, rt; (l) R1R′1NH, MeOH, MW, 140 °C; (m) Zn, NH4Cl, MeOH, rt; (n) SOCl2, MeOH, 0 °C–rt; (o) Bredereck’s reagent, toluene, 115 °C; (p) 2-amino-3-hydroxy-pyridine, NaOAc, AcOH, 90 °C; (q) N-phenylbis(trifluoromethanesulfonimide), K2CO3, THF, MW, 120 °C; (r) R2B(OH)2, Pd(PPh3)4, 120 °C; (s) TMSI, DCM, rt; (t) 1,2,4-triazol-3-amine, NaOAc, AcOH, 90 °C; (u) 33% HBr–AcOH, 50 °C; (v) R1SO2Cl or R1COCl or R1NCO or R1NCS, DCM, MW, 110°C; (w) 2-aminoimidazole, NaOAc, AcOH, 90 °C; (x) H2, 10% Pd/C, EtOH, H-Cube; (y) R1SO2Cl or R1COCl or R1NCO or R1NCS, DCM, MW, 110 °C; (z) PPA, 120 °C; (aa) HClO4, MeOH, rt.

Inhibition Profiles of 15 Compounds against a Panel of 117 Protein Kinases

A subset of library compounds was tested against a panel of 117 protein kinases as proof of concept study (Figure 4). The compounds were chosen to cover all six core fragments and to be fragment-sized (MW < 300 Da), therefore having a higher probability of inhibiting a kinase compared to more elaborated library compounds with the potential draw-back of having only moderate binding affinity. (25) Any compounds showing ≥75% inhibition at 100 μM were defined as active, between 40 and 75% as moderately active, and below 40% as inactive (Supplementary Table S3). According to these definitions, all assayed compounds were at least moderately active against at least one kinase (Figure 5a). The most selective compounds were E1 and F2, which both inhibited only one kinase with >40% inhibition (MSK1 and MKK1, respectively). The least selective compounds were B1 and A2, which inhibited 63 and 55 different kinases with >40% inhibition, respectively.

Figure 4

Figure 4. Structures of 15 compounds that were screened against a panel of 117 protein kinases. Core fragments are marked in blue.

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Figure 5

Figure 5. (a) Bar chart showing the number of kinases inhibited to at least 40% (yellow bars) or 75% (green bars) based on 15 compounds tested against a panel of 117 kinases. (b) Bar chart showing how many kinases were inhibited by how many compounds to at least 40% (yellow bars) or 75% (green bars) based on 15 compounds tested against a panel of 117 kinases.

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The kinases presented in the kinase panel were not equally inhibited by the 15 compounds selected to present the different core fragments (Figure 5b, Supplementary Tables S3 and S4). Using a ≥40% inhibition cutoff value, 26 kinases (22%) in the panel were not inhibited by any of the tested compounds. When a cutoff of ≥75% inhibition was used, this number increased to 75 (64%).

Potency Determinations for Selected Compounds

Six compounds (A1, A2, B1, C2, D1, and F3) with inhibition of >50% for certain kinases were selected for IC50 determinations (Table 1). Activity for all compounds was confirmed with IC50 values ranging from 4 to 470 μM. Additionally, all compounds were characterized by high ligand efficiencies (>0.30 kcal/mol heavy atom) (26) for at least one protein kinase, with B1 showing the best ligand efficiencies across a range of kinases.
Table 1. IC50 Values and Ligand Efficiencies for Compounds A1, A2, B1, C2, D1, and F3 for Selected Protein Kinases
compoundkinaseIC50 [μM]aLE [kcal/mol heavy atom]
A1CK2180.33
MAPKAP-K3210.33
RSK21960.26
Src970.28
SRPK11140.28
A2Aurora A1880.26
CDK2260.33
CK21310.27
CLK2250.32
FGF-R12520.25
GCK40.38
IGF-1R470.30
TAK1240.32
B1CHK2170.42
GCK60.46
HER4200.41
IGF-1R250.40
Src200.41
TAK1110.43
VEG-FR390.39
YES1210.41
C2GSK-3β250.32
SGK11100.28
D1EPH-B31580.33
FGF-R14690.29
F3PIM1220.38
PIM3140.40
a

Hill slopes range from 0.7 to 1.3.

Confirmation of Binding Mode

The binding mode of B1 in complex with cSrc was determined using X-ray crystallography. In the most frequent binding mode that was generated for core fragment B in the kinases of the docking panel the exocyclic amino group pointed toward the gatekeeper and the nitrogen atom bridging the heterocycles was placed next to the hinge region (Figure 3). This orientation closely matches the one found in the cSrc-B1 complex structure (Figure 6, Supplementary Table S5, rmsd = 0.77 Å for non-hydrogen atoms).

Figure 6

Figure 6. Crystallographically determined binding mode of B1 in complex with cSrc together with electron density map (2Fo – Fc contoured at 1σ). The fragment binds to the hinge region of the kinase domain. The binding site is oriented as depicted in Figure 1a. Putative hydrogen bonds are indicated as dashed lines. PDB code: 4fic.

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Comparison of Predicted and Observed Inhibition Profiles

To compare the modeling results with the experimental inhibition data, we docked the 15 compounds that were profiled in our kinase panel (Figure 4) into the binding site of the kinases in the docking panel (Supplementary Table S1). The rank obtained for the least favorable scoring inhibitor among the 15 tested compounds for each kinase was compared to the number of identified hits for that target (Supplementary Table S6). If docking performance was perfect, both numbers would be identical (e.g., if two hits were identified for a kinase the ligand with the lowest score should have rank 2 with the other ligand being on rank 1). While for some targets the worst rank corresponded closely to the number of identified hits, for most targets separation between active and inactive compounds was not possible, both when a cutoff value of ≥40% (data not shown) or ≥75% inhibition was used. Similarly, docking failed to predict the selectivity profile of the inhibitors (Supplementary Table S7). For this comparison, the scores for all 15 compounds docked into all 46 protein structures were normalized relative to the best score for any fragment in the active site of each kinase obtained when docking the library of candidate core fragments. Compounds were considered to be predicted as active if their normalized score was ≥1.0 (equal or better than the best score for a core fragment in a particular kinase). At the chosen cutoff level, there was only little agreement between predicted and confirmed activity. For instance, compound B1, the least selective ligand in the panel, was predicted to be active for only nine out of the 31 kinases for which activity was found. In contrast, E1 and F2, the most selective compounds in the panel were predicted to bind to 10 and 21 kinases, respectively. However, neither compound demonstrated activity against these kinases in a biochemical assay. The only compound for which the profile was predicted correctly was B4, which did not inhibit any kinase in the docking panel.
Overall, the chosen de novo design strategy (Figure 1b) was highly successful. Starting with a database of core fragments, six scaffolds for which activity against kinases was not reported previously were identified, and synthetic routes were developed to prepare focused libraries around each core. All tested compounds were active against at least one kinase (Supplementary Table S3). We attribute this success to the following reasons: (1) Focusing on core fragments that occur in commercially available compounds but are not available with the required substitution pattern ensured that the libraries were synthetically accessible and at the same time novel. (2) Exploiting the wealth of kinase crystal structures when short-listing the cores canceled differences in the kinase binding sites and increased the chances that highly ranked cores would actually bind to a kinase when part of a larger compound.
Evaluating a subset of the library compounds in a large kinase panel confirmed some previous findings but also highlighted differences (Supplementary Table S4). For instance, we deliberately concentrated our synthetic efforts on predicted unselective fragments. It turned out that derivatives of each core displayed distinct selectivity profiles (Figure 5). This observation is in tune with the notion that kinase selectivity often does not stem from the core itself but from its substituents. (13, 27) Further, we observed inhibition of PIM1 and PIM3 (Table 1). These kinases possess an altered hinge binding region incompatible with forming the hydrogen-bonding interactions typically observed in kinase-ligand complexes (Figure 1a). (28) It was speculated that interactions with different amino acids in the binding site require a similar pharmacophore as the interactions with the hinge region that presumably lead to high hit rates of fragment-like kinase inhibitors for these atypical kinases. (13) Our data confirms this hypothesis. However, Posy et al. (27) and Bamborough et al. (29) reported low hit rates for ASK1 (0 and 0.2%, respectively). In contrast, by testing just 15 compounds against this kinase we found two hits containing two different scaffolds. Anastassiadis et al. (30) found no hits for NEK6 and MAPKAP-K3, whereas in our exercise two inhibitors containing the same scaffold and seven inhibitors containing three different scaffolds were discovered, respectively. One of the MAPKAP-K3 inhibitors had an IC50 value of 21 μM and a ligand efficiency of 0.33 kcal/mol heavy atom (Table 1). The previous studies had concluded that these kinases were less tractable. Our data contradict these findings, which is another warning that profiling data can be interpreted only in relation to the chemical space covered by the screening library.
Molecular docking was very successful when predicting binding modes of fragments and general activity against kinases on a core fragment-level but less so when individual compounds for individual kinases were considered. Six high ranking core fragments had been co-crystallized with kinases as part of more elaborated compounds (Figure 2). All of the observed binding modes for the core fragments were among the ones generated in the kinase docking panel. Also, the binding mode of B1 in cSrc was predicted correctly (Figure 6). Remarkably, 73 out of the top 100 ranked core fragments had activity against kinases reported in the literature. This unusually high enrichment was presumably caused by exclusively docking compounds that fulfilled a pharmacophore required for hinge-binding fragments (Supplementary Figure S1a) and by using consensus scoring across a panel of kinase structures (Supplementary Table S1). This success provided confidence that also the remaining high ranking fragments for which no activity against kinases was reported would be suitable hinge-binding scaffolds. At least for the selected core fragments that were chosen for library synthesis and for which examples were tested in a kinase panel this turned out to be the case. When trying to predict the selectivity profiles of the tested library compounds, docking failed to give useful guidance (Supplementary Table S7). This failure is driven by the inability to correctly rank the tested compounds according to their affinity for given kinases (Supplementary Table S6). Small changes, such as adding a nitrogen atom to the heterocycles (E1 vs F1, Figure 4), which change the selectivity of the compounds are not accurately captured by the used scoring function, and furthermore discrimination between more drastic changes, such as ranking structurally unrelated compounds, failed in most cases. In addition, predicting the effect of the subtle differences in the kinase binding sites on compound affinity remains a challenge. (14) In the past, more promising results were obtained by using structural and inhibition data to train predictors. (31-34) However, due to their nature, these approaches do not allow to make predictions for new compound classes or kinases.
A number of compounds were chosen for IC50 determinations against selected kinases (Table 1). All tested compounds were characterized by high ligand efficiencies (≥0.30 kcal/mol heavy atom) for at least one kinase, rendering them in general promising starting points for drug discovery for a range of targets with high medical interest. (26) Among others, compounds with high ligand efficiencies were identified for MAPKAP-K3, SRPK1, SGK1, TAK1, and GCK (Table 1). For all of these kinases less than 30 compounds with affinities ≤100 μM are reported in ChEMBL Kinase SARfari (as of April 2012). (23) They are thought to be targets for the treatment of cancer, systemic inflammation response syndrome, rheumatoid arthritis, other inflammatory diseases, and hypertension. (35-39) The hits identified by our in silico study can serve as starting points to further explore these targets.

Summary and Conclusion

A de novo design approach for novel kinase inhibitors was established that was centered on core fragments having precedence for synthesis but no reported activity against kinases. Using a wide range of methods at the interface of chemistry and biology, e.g., structure-based design, chemical synthesis, X-ray crystallography and biological profiling, the approach was validated. Six small molecule libraries were synthesized. Selected compounds were profiled in a large kinase panel, and dose–response curves were determined. In addition, the binding mode of one compound was confirmed using X-ray crystallography. Whereas predicting binding modes and general activity against kinases was very reliable, correctly predicting selectivity solely based on docking to crystal structures failed. Nevertheless, this study demonstrated the overall success of the chosen design strategy. In addition, a number of inhibitors with good binding efficiency were identified for a range of kinases with therapeutic relevance and can now serve as starting points for chemical tools to further explore these targets.

Methods

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Core Fragment Extraction

Core fragments were derived from an in-house database containing more than two million commercially available compounds using a method described previously. (11) In brief, core fragments were defined as ring atoms plus the atoms of directly attached polar functional groups. Polar functional groups were specified as polar hetero atoms (S, N, P, or O) and polar hetero atoms double or triple bonded to carbon atoms or linked to other polar hetero atoms or carbonyl groups. Functional groups linking ring atoms were added to both resulting scaffolds. Fragments containing only carbon atoms or carbon atoms and aromatic sulfur or oxygen atoms were disregarded.

Fragment Selection

Three filters were used to eliminate inappropriate or undesirable scaffolds from the generated database of core fragments. First, fragments that contained unwanted functionalities as described previously were removed. (11) Second, fragments that did not comply with a modified “Rule of Three” were removed from the database. (40) Here, only ring fragments with MW < 300 Da, number of hydrogen bond donors ≤3, number of hydrogen bond acceptors ≤6, and clogP ≤ 3 were retained. Additionally, core fragments with the total charge ≤ −1 or ≥1 were removed. Finally, compounds containing more than two fused rings or more than six rotatable bonds were rejected.

Pharmacophore Search

The 3D pharmacophore search was carried out using the UNITY tool available with the SYBYL-X 1.0 package (Tripos Inc.). The default file sln3d_macros.def that contained the definition of predefined feature types (macros) such as hydrogen-bond acceptor or hydrogen-bond donor was modified to also account for C–H···O hydrogen bonds as often observed in protein kinases (Supplementary Figure S1b). Otherwise, default settings were used when converting the filtered core fragments to a 3D UNITY database.
The pharmacophore was generated based on the crystal structure of CDK2 in complex with an imidazole piperazine inhibitor (PDB code 2w05). The resulting pharmacophore model was made up of four features (Supplementary Figure S1a). One hydrogen-bond acceptor and two hydrogen-bond donor features (radius 0.6 Å) were defined to interact with the hinge region. To consider the directionality of the hydrogen bonds, the features were linked with their hydrogen-bond binding partners in the protein. The aromatic feature (radius 1.5 Å) was added to take into account that the vast majority of protein kinase inhibitors contain an aromatic ring system that is placed within the adenine binding region. (14) To fulfill the pharmacophore, all hits were required to contain the aromatic feature and two out of the possible three hydrogen-bonding features.

Receptor Preparation

Crystal structures of protein kinases that were available in the MRC Protein Phosphorylation Unit, University of Dundee, for profiling at the beginning of the study were downloaded from the PDB (Supplementary Table S1). All crystal structures representing these kinases exhibit the DFG-in conformation. (6) The structures were aligned with CDK2 (PDB code 2w05) using the hinge region as matching atoms. Hydrogen atoms of polar groups were added using MOLOC (Gerber Molecular Design), and their positions were minimized with the MAB force field as implemented in MOLOC. (41) Subsequently, all nonprotein atoms were removed from the crystal structures. Compounds were placed into the binding site and scored using DOCK 3.5.54. (42, 43) The required sphere set to define the region with a low dielectric constant was composed of the bound ligands’ atoms. If necessary, the set was manually modified to cover the entire buried part of binding site. The sphere set used as matching points for docking was manually generated by placing matching atoms into the adenine binding region close to the hinge region. The same set was used for all protein kinases. To favor interactions between ligand and hinge region, partial charges of the carbonyl oxygen atoms in the amide backbone of the hinge region were decreased by 0.4, and the partial charge of the amide hydrogen atom was increased by 0.4 (Figure S1a). The total charge was then balanced by distributing the charge difference among the remaining hinge region atoms. Grids to store information about excluded volumes, van der Waals potential, and ligand desolvation were calculated as described previously. (44, 45)

Ligand Preparation

Tautomers, stereoisomers, and protonation states were enumerated and converted to a suitable database format as described previously. (44)

Docking Protocol

Multiple conformations and orientations of each ligand were docked into the kinase binding sites using DOCK 3.5.54. (42, 43) Ligand and receptor overlap bins were set to 0.4 Å. Distance tolerance for matching ligand atoms to receptor was set to 1.2 Å. All fragments were docked into the ATP binding pocket, and each docking orientation was filtered for steric fit. Only fragments that were able to pass the steric fit filter were scored for electrostatic, van der Waals, and desolvation energies. The best scoring conformation and representative (tautomer, protonation state) of each compound was used for final ranking.
To compare fragments between different kinases, the originally calculated energy scores were normalized by the formula Sijnorm = Sij/Sjbest where Sijnorm is the normalized score for fragment i in the active site of kinase j, Sij is the original score, and Sjbest is the best score for any ligand in the active site of the kinase j. All negative Sijnorm scores were set to zero. Thus, Sijnorm = 1 if the fragment scores best of all docked fragments for a particular kinase, and Sijnorm = 0 if it scores worst.

Library Enumeration

The reagents were selected through a web interface based on Pipeline Pilot (Accelrys Software Inc.). Only in-house reagents that were commercially available were used for the enumeration process. Enumeration of libraries was performed using the “core plus R-groups” method from Pipeline Pilot which was again presented through a customized web interface. All reaction products were filtered to remove molecules with unwanted functionalities, >5 hydrogen-bond donors, >10 hydrogen bond acceptors, molecular weight >500 Da, clogP > 5, and polar surface area >140 Å. Finally, all remaining reaction products were docked into the binding site of three kinases (CSK, RSK1, and GSK3β) to exclude those compounds which did not sterically fit into a typical ATP binding site.

Chemistry

All synthesized compounds had a purity of greater than 90% (measured on analytical HPLC-MS system). The synthetic details for the library compounds together with M+ and 1H NMR data to confirm compound identity and purity are listed in the Supporting Information.

Kinase Assay

Selected compounds were screened against a panel of mammalian kinases routinely run in the MRC Protein Phosphorylation Unit at the University of Dundee (www.kinase-screen.mrc.ac.uk). (21) Compounds were supplied in DMSO and screened in duplicates at 100 μM concentration using a radioactive (33P-ATP) filter-binding-assay. For hit validation and all subsequent IC50 determinations, selected compounds were solubilized in DMSO at a top concentration of 51 mM and serially diluted to achieve 10-point titration of final assay concentrations from 1 mM to 30 nM. All IC50 determination assays were done in duplicate and the IC50 values were calculated using Prism (GraphPad Software, Inc.). All biochemical assays were run below the Kmapp for the ATP for each enzyme allowing comparison of inhibition across the panel.

Crystallization and Structure Determination of cSrc-B1

Fragment B1 was co-crystallized with cSrc using conditions similar to those previously reported by Michalczyk et al. (46) Diffraction data of the cSrc-B1 complex crystals were collected at the PX10SA beamline of the Swiss Light Source (PSI, Villingen, Switzerland) to a resolution of 2.5 Å, using wavelengths close to 1 Å. The data set was processed and refined as described previously. (46) Detailed data, refinement, and Ramachandran statistics are provided in Supplementary Table S5.

Supporting Information

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Additional tables with percent inhibition data for the discussed compounds, hit rates in various kinase screens, and data collection and refinement details for the crystal structure of cSrc-B1; a figure of the pharmacophore used for virtual screening; the synthetic routes to the described compounds; NMR spectra of key compounds. This material is available free of charge via the Internet at http://pubs.acs.org.

Accession Codes

The crystal structure of cSrc in complex with B1 has been deposited into the PDB with the code 4fic.

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
    • Paul G. Wyatt - Drug Discovery Unit (DDU), Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Sir James Black Centre, DD1 5EH, U.K.;  Email: [email protected] [email protected]
    • Ruth Brenk - Institut für Pharmazie und Biochemie, Johannes Gutenberg-Universität Mainz, Staudinger Weg 5, 55128 Mainz, Germany;  Email: [email protected] [email protected]
  • Authors
    • Robert Urich - Drug Discovery Unit (DDU), Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Sir James Black Centre, DD1 5EH, U.K.
    • Grant Wishart - Department of Chemistry, MSD, Newhouse, Lanarkshire, ML1 5SH, U.K.
    • Michael Kiczun - Department of Chemistry, MSD, Newhouse, Lanarkshire, ML1 5SH, U.K.
    • André Richters - Fakultät Chemie - Chemische Biologie, Technische Universität Dortmund, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
    • Naomi Tidten-Luksch - Drug Discovery Unit (DDU), Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Sir James Black Centre, DD1 5EH, U.K.
    • Daniel Rauh - Fakultät Chemie - Chemische Biologie, Technische Universität Dortmund, Otto-Hahn-Straße 6, 44227 Dortmund, Germany
    • Brad Sherborne - Department of Chemistry, MSD, Newhouse, Lanarkshire, ML1 5SH, U.K.
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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This work was supported by the Welcome Trust (WT083481) and through a CASE studentship for R.U., jointly funded by MSD&Schering-Plough and the Biotechnology and Biological Sciences Research Council. N.T.-L. was supported by a fellowship from the German Academic Exchange Service (DAAD). We thank OpenEye for free software licenses, J. Downward (University of Dundee) for administration of the workstations, A. Hinton (MSD&Schering-Plough, Newhouse, U.K.) for help with Pipeline Pilot, and the staff in the MRC Protein Phosphorylation Unit at the University of Dundee for carrying out the kinase assays. D.R. is grateful for funds by the German Federal Ministry for Education and Research, which supported this work through the German National Genome Research Network-Plus (NGFNPlus) (Grant No. BMBF 01GS08104).

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This article references 46 other publications.

  1. 1
    Cohen, P. (2002) Protein kinases—the major drug targets of the twenty-first century? Nat. Rev. Drug. Discovery 1, 309– 315
    [Crossref], [PubMed], [CAS], Google Scholar
    1
    Timeline: Protein kinases - the major drug targets of the twenty-first century?
    Cohen, Philip
    Nature Reviews Drug Discovery (2002), 1 (4), 309-315CODEN: NRDDAG ISSN:. (Nature Publishing Group)
    A review. Protein phosphorylation regulates most aspects of cell life, whereas abnormal phosphorylation is a cause or consequence of disease. A growing interest in developing orally active protein-kinase inhibitors has recently culminated in the approval of the first of these drugs for clin. use. Protein kinases have now become the second most important group of drug targets, after G-protein-coupled receptors. Here, I give a personal view of some of the most important advances that have shaped this field.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XivVGmtr8%253D&md5=67d2fbc11a55b5980ee0692f7bc13cc9
  2. 2
    Cohen, P. and Alessi, D. R. (2013) Kinase Drug Discovery - What’s Next in the Field? ACS Chem. Biol. 8, 96– 104
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    2
    Kinase Drug Discovery - What's Next in the Field?
    Cohen, Philip; Alessi, Dario R.
    ACS Chemical Biology (2013), 8 (1), 96-104CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
    A review. Over the past 15 years protein kinases have become the pharmaceutical industry's most important class of drug target in the field of cancer. Some 20 drugs that target kinases have been approved for clin. use over the past decade, and hundreds more are undergoing clin. trials. However, the recent approval of the first protein kinase inhibitors for the treatment of inflammatory diseases, coupled with an enhanced understanding of the signaling networks that control the immune system, suggests that there will be a surge of interest in this area over the next 10 years. In this connection, we discuss opportunities for targeting protein kinases in the MyD88 signaling network for the development of drugs to treat chronic inflammatory and autoimmune diseases. Activating mutations in protein kinases underlie many other diseases and conditions, and we also discuss why the protein kinases SPAK/OSR1 and LRRK2 have recently become interesting targets for the treatment of hypertension and Parkinson's disease, resp., and the progress that has been made in developing LRRK2 inhibitors. Finally we suggest that more focus on the identification of inhibitors of kinase activation, rather than kinase activity, may pay dividends in identifying exquisitely specific inhibitors of signal transduction cascades, and we also highlight "pseudo-kinases" as an attractive and unexplored area for drug development that merits much more attention in the years to come.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjvF2q&md5=5bf2ced4163b6ae70e604b2916a6a068
  3. 3
    Knapp, S., Arruda, P., Blagg, J., Burley, S., Drewry, D. H., Edwards, A., Fabbro, D., Gillespie, P., Gray, N. S., Kuster, B., Lackey, K. E., Mazzafera, P., Tomkinson, N. C., Willson, T. M., Workman, P., and Zuercher, W. J. (2013) A public-private partnership to unlock the untargeted kinome Nat. Chem. Biol. 9, 3– 6
    [Crossref], [PubMed], [CAS], Google Scholar
    3
    A public-private partnership to unlock the untargeted kinome
    Knapp, Stefan; Arruda, Paulo; Blagg, Julian; Burley, Stephen; Drewry, David H.; Edwards, Aled; Fabbro, Doriano; Gillespie, Paul; Gray, Nathanael S.; Kuster, Bernhard; Lackey, Karen E.; Mazzafera, Paulo; Tomkinson, Nicholas C. O.; Willson, Timothy M.; Workman, Paul; Zuercher, William J.
    Nature Chemical Biology (2013), 9 (1), 3-6CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    Chem. probes are urgently needed to functionally annotate hitherto-untargeted kinases and stimulate new drug discovery efforts to address unmet medical needs. The size of the human kinome combined with the high cost assocd. with probe generation severely limits access to new probes. We propose a large-scale public-private partnership as a new approach that offers economies of scale, minimized redundancy and sharing of risk and cost.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvVekur%252FF&md5=1f6c13ec461f96201838934cb95012c0
  4. 4
    Rabiller, M., Getlik, M., Klüter, S., Richters, A., Tückmantel, S., Simard, J. R., and Rauh, D. (2010) Proteus in the world of proteins: conformational changes in protein kinases Arch. Pharm. (Weinheim) 343, 193– 206
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  5. 5
    Eswaran, J. and Knapp, S. (2010) Insights into protein kinase regulation and inhibition by large scale structural comparison Biochem. Biophys. Acta, Proteins Proteomics 1804, 429– 432
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  6. 6
    Liao, J. J. (2007) Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors J. Med. Chem. 50, 409– 424
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    6
    Molecular Recognition of Protein Kinase Binding Pockets for Design of Potent and Selective Kinase Inhibitors
    Liao, Jeffrey Jie-Lou
    Journal of Medicinal Chemistry (2007), 50 (3), 409-424CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A review. Many kinase inhibitors have failed in preclin. or clin. development because of the lack of such selectivity that induces intolerable side effects, mostly because the catalytic cleft is highly conserved in sequence and conformation. Systematic anal. of the crystal structures of protein kinases can offer insight into the design of highly selective kinase inhibitors to overcome these effects.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXlt1Cmsg%253D%253D&md5=77542d3ba589bd834a0d2b97a24eea05
  7. 7
    Traxler, P. (1998) Tyrosine kinase inhibitors in cancer treatment (part II) Expert Opin. Ther. Pat. 8, 1599– 1625
    [Crossref], [CAS], Google Scholar
    7
    Tyrosine kinase inhibitors in cancer treatment (part II)
    Traxler, Peter
    Expert Opinion on Therapeutic Patents (1998), 8 (12), 1599-1625CODEN: EOTPEG; ISSN:1354-3776. (Ashley Publications)
    A review with 169 refs. In the last few years, enormous progress in the field of signal transduction inhibition has been made. Many companies have entered the field. Along with the epidermal growth factor receptor (EGFR) tyrosine kinase, many other tyrosine kinases have been identified as interesting targets for drug discovery projects. X-ray data of more than 40 crystal structures of protein kinases, in most cases complexed with an inhibitor, have been published. Pharmacophore models for the binding of inhibitors in the ATP-binding site of protein kinases have been developed that are generally applicable, enabling the rational design of tyrosine as well as serine/threonine kinase inhibitors. It has been proven by numerous examples that the ATP-binding of protein kinases is an exciting target for the design of anticancer drugs. In many cases, it has also been demonstrated that through rational design it is possible to modify a lead structure in such a way that inhibitors with an altered selectivity profile are obtained. Chem. optimization of several lead structures led to development candidates with potent in vitro and in vivo activity fulfilling the pharmacodynamic, pharmacokinetic, toxicol. and tech. (synthesis, formulation) requirements for a clin. candidate. Currently, there are seven tyrosine kinase inhibitors in early phases of clin. trials. In addn., several candidates are close to entering Phase I trials this year or at the beginning of next year. It is expected that pos. results from clin. trials will greatly contribute to the clin. proof of concept of the value of signal transduction inhibition and will greatly stimulate further research in this area. This review is a continuation of a review with the same title of last year and summarizes published patent literature and related publications between 1997 and Sept. 1998.
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  8. 8
    Fang, Z., Grütter, C., and Rauh, D. (2013) Strategies for the selective regulation of kinases with allosteric modulators: exploiting exclusive structural features ACS Chem. Biol. 8, 58– 70
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    8
    Strategies for the Selective Regulation of Kinases with Allosteric Modulators: Exploiting Exclusive Structural Features
    Fang, Zhizhou; Gruetter, Christian; Rauh, Daniel
    ACS Chemical Biology (2013), 8 (1), 58-70CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
    A review. The modulation of kinase function has become an important goal in modern drug discovery and chem. biol. research. In cancer-targeted therapies, kinase inhibitors have been experiencing an upsurge, which can be measured by the increasing no. of kinase inhibitors approved by the FDA in recent years. However, lack of efficacy, limited selectivity, and the emergence of acquired drug resistance still represent major bottlenecks in the clinic and challenge inhibitor development. Most known kinase inhibitors target the active kinase and are ATP competitive. A second class of small org. mols., which address remote sites of the kinase and stabilize enzymically inactive conformations, is rapidly moving to the forefront of kinase inhibitor research. Such allosteric modulators bind to sites that are less conserved across the kinome and only accessible upon conformational changes. These mols. are therefore thought to provide various advantages such as higher selectivity and extended drug target residence times. This review highlights various strategies that have been developed to utilizing exclusive structural features of kinases and thereby modulating their activity allosterically.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvVCls7bN&md5=50864096bf66229ff74482a9b56847d6
  9. 9
    Stahura, F. L., Xue, L., Godden, J. W., and Bajorath, J. (1999) Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP-binding site of protein kinases J. Mol. Graph. Model. 17 (1–9) 51– 52
    [CAS], Google Scholar
    9
    Color atlas
    Stahura, Florence L.; Xue, Ling; Godden, Jeffrey W.; Bajorath, Jurgen
    Journal of Molecular Graphics & Modelling (1999), 17 (1), 51-54CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Science Inc.)
    There is no expanded citation for this reference.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXhtV2rsg%253D%253D&md5=1586f4dbe73277b9b58b9a8fa2dfdee9
  10. 10
    Prien, O. (2005) Target-family-oriented focused libraries for kinases--conceptual design aspects and commercial availability ChemBioChem 6, 500– 505
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  11. 11
    Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., and Wyatt, P. G. (2008) Lessons learnt from assembling screening libraries for drug discovery for neglected diseases ChemMedChem 3, 435– 444
    [Crossref], [PubMed], [CAS], Google Scholar
    11
    Lessons learned from assembling screening libraries for drug discovery for neglected diseases
    Brenk, Ruth; Schipani, Alessandro; James, Daniel; Krasowski, Agata; Gilbert, Ian Hugh; Frearson, Julie; Wyatt, Paul Graham
    ChemMedChem (2008), 3 (3), 435-444CODEN: CHEMGX; ISSN:1860-7179. (Wiley-VCH Verlag GmbH & Co. KGaA)
    To enable the establishment of a drug discovery operation for neglected diseases, out of 2.3 million com. available compds. 222 552 compds. were selected for an in silico library, 57 438 for a diverse general screening library, and 1 697 compds. for a focused kinase set. Compiling these libraries required a robust strategy for compd. selection. Rules for unwanted groups were defined and selection criteria to enrich for lead-like compds. which facilitate straightforward structure-activity relationship exploration were established. Further, a literature and patent review was undertaken to ext. key recognition elements of kinase inhibitors ("core fragments") to assemble a focused library for hit discovery for kinases. Computational and exptl. characterization of the general screening library revealed that the selected compds. (1) span a broad range of lead-like space, (2) show a high degree of structural integrity and purity, and (3) demonstrate appropriate soly. for the purposes of biochem. screening. The implications of this study for compd. selection, esp. in an academic environment with limited resources, are considered.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXlt1Krtb4%253D&md5=b804d0ac8766e954391798e24aa7fbff
  12. 12
    Decornez, H., Gulyas-Forro, A., Papp, A., Szabo, M., Sarmay, G., Hajdu, I., Cseh, S., Dorman, G., and Kitchen, D. B. (2009) Design, selection, and evaluation of a general kinase-focused library ChemMedChem 4, 1273– 1278
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Bamborough, P., Brown, M. J., Christopher, J. A., Chung, C. W., and Mellor, G. W. (2011) Selectivity of kinase inhibitor fragments J. Med. Chem. 54, 5131– 5143
    [ACS Full Text ACS Full Text], Google Scholar
    There is no corresponding record for this reference.
  14. 14
    Akritopoulou-Zanze, I. and Hajduk, P. J. (2009) Kinase-targeted libraries: the design and synthesis of novel, potent, and selective kinase inhibitors Drug Discovery Today 14, 291– 297
    [Crossref], [PubMed], Google Scholar
    There is no corresponding record for this reference.
  15. 15
    Erickson, J. A., Mader, M. M., Watson, I. A., Webster, Y. W., Higgs, R. E., Bell, M. A., and Vieth, M. (2010) Structure-guided expansion of kinase fragment libraries driven by support vector machine models Biochem. Biophys. Acta, Proteins Proteomics 1804, 642– 652
    [Crossref], [CAS], Google Scholar
    15
    Structure-guided expansion of kinase fragment libraries driven by support vector machine models
    Erickson, Jon A.; Mader, Mary M.; Watson, Ian A.; Webster, Yue W.; Higgs, Richard E.; Bell, Michael A.; Vieth, Michal
    Biochimica et Biophysica Acta, Proteins and Proteomics (2010), 1804 (3), 642-652CODEN: BBAPBW; ISSN:1570-9639. (Elsevier B. V.)
    This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amt. of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addn., a broad diversity of X-ray structure information is needed for binding mode prediction. This is important for scaffold and substituent site selection. Borrowing from FBDD, the process involves fragmentation of known actives, proposition of binding mode hypotheses for the fragments, and model-driven recombination using a pharmacophore derived from known kinase inhibitor structures. The support vector machine method, using Merck atom pair derived fingerprint descriptors, was used to build models from activity from 6 kinase assays. These models were qualified prospectively by selecting and testing compds. from the internal compd. collection. Overall hit and enrichment rates of 82% and 2.5%, resp., qualified the models for use in library design. Using the process, 7 novel libraries were designed, synthesized and tested against these same 6 kinases. The results showed excellent results, yielding a 92% hit rate for the 179 compds. that made up the 7 libraries. The results of one library designed to include known literature compds., as well as an anal. of overall substituent frequency, are discussed.
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  16. 16
    Vieth, M., Erickson, J., Wang, J., Webster, Y., Mader, M., Higgs, R., and Watson, I. (2009) Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches J. Med. Chem. 52, 6456– 6466
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    16
    Kinase Inhibitor Data Modeling and de Novo Inhibitor Design with Fragment Approaches
    Vieth, Michal; Erickson, Jon; Wang, Jibo; Webster, Yue; Mader, Mary; Higgs, Richard; Watson, Ian
    Journal of Medicinal Chemistry (2009), 52 (20), 6456-6466CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A reconstructive approach based on computational fragmentation of existing inhibitors and validated kinase potency models to recombine and create "de novo" kinase inhibitor small mol. libraries is described. The screening results from model selected mols. from the corporate database and seven computationally derived small mol. libraries were used to evaluate this approach. Specifically, 1895 model selected database mols. were screened at 20 μM in six kinase assays and yielded an overall hit rate of 84%. These models were then used in the de novo design of seven chem. libraries consisting of 20-50 compds. each. Then 179 compds. from synthesized libraries were tested against these six kinases with an overall hit rate of 92%. Comparing predicted and obsd. selectivity profiles serves to highlight the strengths and limitations of the methodol., while anal. of functional group contributions from the libraries suggest general principles governing binding of ATP competitive compds.
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  17. 17
    Kettle, J. G. and Ward, R. A. (2010) Toward the comprehensive systematic enumeration and synthesis of novel kinase inhibitors based on a 4-anilinoquinazoline binding mode J. Chem. Inf. Model. 50, 525– 533
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    17
    Toward the Comprehensive Systematic Enumeration and Synthesis of Novel Kinase Inhibitors Based on a 4-Anilinoquinazoline Binding Mode
    Kettle, Jason G.; Ward, Richard A.
    Journal of Chemical Information and Modeling (2010), 50 (4), 525-533CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    There are currently eight small-mol. kinase inhibitors approved as cancer treatments, and a significantly larger no. of compds. are in the earlier stages of clin. development. Although kinase inhibitors are most commonly developed in a cancer setting, other disease areas have been targeted. The vast majority of reported kinase small-mol. inhibitors contain functionalities that interact with the ATP binding site of the kinase. The 4-anilinoquinazolines have previously been reported as potent epidermal growth factor receptor (EGFR) inhibitors, binding at the hinge' region of the ATP site. Subsequently, this chem. series has been optimized against a no. of different kinases including Src and Aurora B. Here, we detail the computational enumeration of ring systems that have the ability to make comparable interactions to the 4-anilinoquinazoline core. These were prioritized by computational, medicinal, and synthetic chem. input, and a no. of libraries were subsequently synthesized (no prepn. shown).
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  18. 18
    Baldwin, I., Bamborough, P., Haslam, C. G., Hunjan, S. S., Longstaff, T., Mooney, C. J., Patel, S., Quinn, J., and Somers, D. O. (2008) Kinase array design, back to front: biaryl amides Bioorg. Med. Chem. Lett. 18, 5285– 5289
    [Crossref], [PubMed], Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Deanda, F., Stewart, E. L., Reno, M. J., and Drewry, D. H. (2008) Kinase-targeted library design through the application of the PharmPrint methodology J. Chem. Inf. Model. 48, 2395– 2403
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    19
    Kinase-Targeted Library Design through the Application of the PharmPrint Methodology
    Deanda, Felix; Stewart, Eugene L.; Reno, Michael J.; Drewry, David H.
    Journal of Chemical Information and Modeling (2008), 48 (12), 2395-2403CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    The PharmPrint methodol., as modified and implemented by Deanda and Stewart, was prospectively evaluated for use as a virtual high-throughput screening tool by applying it to the design of target-focused arrays. To this end, PharmPrint quant. structure-activity relationship (QSAR) models for the prediction of AKT1, Aurora-A, and ROCK1 inhibition were constructed and used to virtually screen two large combinatorial libraries. Based on predicted activities, an Aurora-A targeted array and a ROCK1 targeted array were designed and synthesized. One control group per designed array was also synthesized to assess the enrichment levels achieved by the QSAR models. For the Aurora-A targeted array, the hit rate, against the intended target, was 42.9%, whereas that of the control group was 0%. Thus, the enrichment level achieved by the Aurora-A QSAR model was incalculable. For the ROCK1 targeted array, the hit rate against the intended target was 30.6%, whereas that of the control group was 5.10%, making the enrichment level achieved by the ROCK1 QSAR model 6-fold above control. Clearly, these results support the use of the PharmPrint methodol. as a virtual screening tool for the design of kinase-targeted arrays.
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  20. 20
    Lowrie, J. F., Delisle, R. K., Hobbs, D. W., and Diller, D. J. (2004) The different strategies for designing GPCR and kinase targeted libraries Comb. Chem. High Throughput Screen. 7, 495– 510
    [Crossref], [PubMed], [CAS], Google Scholar
    20
    The different strategies for designing GPCR and kinase targeted libraries
    Lowrie, J. F.; Delisle, R. K.; Hobbs, D. W.; Diller, D. J.
    Combinatorial Chemistry and High Throughput Screening (2004), 7 (5), 495-510CODEN: CCHSFU; ISSN:1386-2073. (Bentham Science Publishers Ltd.)
    A review. In recent years the trend in combinatorial library design has shifted to include target class focusing along with diversity and drug-likeness criteria. In this manuscript we review the computational tools available for target class library design and highlight the areas where they have proven useful in our work. The protein kinase family is used to illustrated structure-based target class focused library design, and the G-protein coupled receptor (GPCR) family is used to illustrate ligand-based target class focused library design. Most of the tools discussed are those designed for libraries targeted to a single protein and are simply applied "brute-force" to a large no. of targets within the family. The tools that have proven to be the most useful in our work are those that can ext. trends from the computational data such as docking and clustering or data mining large amts. of structure activity or high throughput screening data. Finally, areas where improvements are needed in the computational tools available for target class focusing are highlighted. These areas include tools to ext. the relevant patterns from all available information for a family of targets, tools to efficiently apply models for all targets in the family rather than just a small subset, mining tools to ext. the relevant information from the computational absorption, distribution, metab., excretion and toxicity (ADMET) and targeting data, and tools to allow interactive exploration of the virtual space around a library to facilitate the selection of the library that best suits the needs of the design team.
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  21. 21
    Bain, J., Plater, L., Elliott, M., Shpiro, N., Hastie, C. J., McLauchlan, H., Klevernic, I., Arthur, J. S., Alessi, D. R., and Cohen, P. (2007) The selectivity of protein kinase inhibitors: a further update Biochem. J. 408, 297– 315
    [Crossref], [PubMed], [CAS], Google Scholar
    21
    The selectivity of protein kinase inhibitors: a further update
    Bain, Jenny; Plater, Lorna; Elliott, Matt; Shpiro, Natalia; Hastie, C. James; McLauchlan, Hilary; Klevernic, Iva; Arthur, J. Simon C.; Alessi, Dario R.; Cohen, Philip
    Biochemical Journal (2007), 408 (3), 297-315CODEN: BIJOAK; ISSN:0264-6021. (Portland Press Ltd.)
    The specificities of 65 compds. reported to be relatively specific inhibitors of protein kinases have been profiled against a panel of 70-80 protein kinases. On the basis of this information, the effects of compds. that we have studied in cells and other data in the literature, we recommend the use of the following small-mol. inhibitors: SB 203580/SB202190 and BIRB 0796 to be used in parallel to assess the physiol. roles of p38 MAPK (mitogen-activated protein kinase) isoforms, PI-103 and wortmannin to be used in parallel to inhibit phosphatidylinositol (phosphoinositide) 3-kinases, PP1 or PP2 to be used in parallel with Src-I1 (Src inhibitor-1) to inhibit Src family members; PD 184352 or PD 0325901 to inhibit MKK1 (MAPK kinase-1) or MKK1 plus MKK5, Akt-I-1/2 to inhibit the activation of PKB (protein kinase B/Akt), rapamycin to inhibit TORC1 [mTOR (mammalian target of rapamycin)-raptor (regulatory assocd. protein of mTOR) complex], CT 99021 to inhibit GSK3 (glycogen synthase kinase 3), BI-D1870 and SL0101 or FMK (fluoromethylketone) to be used in parallel to inhibit RSK (ribosomal S6 kinase), D4476 to inhibit CK1 (casein kinase 1), VX680 to inhibit Aurora kinases, and roscovitine as a pan-CDK (cyclin-dependent kinase) inhibitor. We have also identified harmine as a potent and specific inhibitor of DYRK1A (dual-specificity tyrosine-phosphorylated and -regulated kinase 1A) in vitro. The results have further emphasized the need for considerable caution in using small-mol. inhibitors of protein kinases to assess the physiol. roles of these enzymes. Despite being used widely, many of the compds. that we analyzed were too non-specific for useful conclusions to be made, other than to exclude the involvement of particular protein kinases in cellular processes.
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  22. 22
    Gozalbes, R., Simon, L., Froloff, N., Sartori, E., Monteils, C., and Baudelle, R. (2008) Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries J. Med. Chem. 51, 3124– 3132
    [ACS Full Text ACS Full Text], Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., and Overington, J. P. (2012) ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 40, D1100– 1107
    [Crossref], [PubMed], [CAS], Google Scholar
    23
    ChEMBL: a large-scale bioactivity database for drug discovery
    Gaulton, Anna; Bellis, Louisa J.; Bento, A. Patricia; Chambers, Jon; Davies, Mark; Hersey, Anne; Light, Yvonne; McGlinchey, Shaun; Michalovich, David; Al-Lazikani, Bissan; Overington, John P.
    Nucleic Acids Research (2012), 40 (D1), D1100-D1107CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
    ChEMBL is an Open Data database contg. binding, functional and ADMET information for a large no. of drug-like bioactive compds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chem. biol. and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compds. and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.
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  24. 24
    Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 23, 3– 25
    [Crossref], [CAS], Google Scholar
    24
    Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
    Lipinski, Christopher A.; Lombardo, Franco; Dominy, Beryl W.; Feeney, Paul J.
    Advanced Drug Delivery Reviews (1997), 23 (1-3), 3-25CODEN: ADDREP; ISSN:0169-409X. (Elsevier)
    A review with 50 refs. Exptl. and computational approaches to est. soly. and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the mol. wt. (MWT) is >500 and the calcd. Log P (CLogP) is >5. Computational methodol. for the rule-based Moriguchi Log P (MLogP) calcn. is described. Turbidimetric soly. measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric soly. than leads in the pre-HTS era. In the development setting, soly. calcns. focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with exptl. thermodn. soly. measurements.
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  25. 25
    Hajduk, P. J. and Greer, J. (2007) A decade of fragment-based drug design: strategic advances and lessons learned Nat. Rev. Drug Discovery 6, 211– 219
    [Crossref], [PubMed], [CAS], Google Scholar
    25
    A decade of fragment-based drug design: strategic advances and lessons learned
    Hajduk, Philip J.; Greer, Jonathan
    Nature Reviews Drug Discovery (2007), 6 (3), 211-219CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
    A review. Since the early 1990s, several technol. and scientific advances - such as combinatorial chem., high-throughput screening and the sequencing of the human genome - have been heralded as remedies to the problems facing the pharmaceutical industry. The use of these technologies in some form is now well established at most pharmaceutical companies; however, the return on investment in terms of marketed products has not met expectations. Fragment-based drug design is another tool for drug discovery that has emerged in the past decade. Here, we describe the development and evolution of fragment-based drug design, analyze the role that this approach can have in combination with other discovery technologies and highlight the impact that fragment-based methods have made in progressing new medicines into the clinic.
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  26. 26
    Hopkins, A. L., Groom, C. R., and Alex, A. (2004) Ligand efficiency: a useful metric for lead selection Drug Discovery Today 9, 430– 431
    [Crossref], [PubMed], [CAS], Google Scholar
    26
    Ligand efficiency: a useful metric for lead selection
    Hopkins Andrew L; Groom Colin R; Alex Alexander
    Drug discovery today (2004), 9 (10), 430-1 ISSN:1359-6446.
    There is no expanded citation for this reference.
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  27. 27
    Posy, S. L., Hermsmeier, M. A., Vaccaro, W., Ott, K. H., Todderud, G., Lippy, J. S., Trainor, G. L., Loughney, D. A., and Johnson, S. R. (2011) Trends in kinase selectivity: insights for target class-focused library screening J. Med. Chem. 54, 54– 66
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    27
    Trends in Kinase Selectivity: Insights for Target Class-Focused Library Screening
    Posy, Shana L.; Hermsmeier, Mark A.; Vaccaro, Wayne; Ott, Karl-Heinz; Todderud, Gordon; Lippy, Jonathan S.; Trainor, George L.; Loughney, Deborah A.; Johnson, Stephen R.
    Journal of Medicinal Chemistry (2011), 54 (1), 54-66CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    A kinome-wide selectivity screen of >20000 compds. with a rich representation of many structural classes has been completed. Anal. of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compds. are mostly similar in their physicochem. characteristics, we identify specific features that are present more frequently in compds. that bind to many kinases. Our results support a scaffold-oriented approach for building compd. collections to screen kinase targets.
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  28. 28
    Jacobs, M. D., Black, J., Futer, O., Swenson, L., Hare, B., Fleming, M., and Saxena, K. (2005) Pim-1 ligand-bound structures reveal the mechanism of serine/threonine kinase inhibition by LY294002 J. Biol. Chem. 280, 13728– 13734
    [Crossref], [PubMed], Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Bamborough, P., Drewry, D., Harper, G., Smith, G. K., and Schneider, K. (2008) Assessment of chemical coverage of kinome space and its implications for kinase drug discovery J. Med. Chem. 51, 7898– 7914
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    29
    Assessment of Chemical Coverage of Kinome Space and Its Implications for Kinase Drug Discovery
    Bamborough, Paul; Drewry, David; Harper, Gavin; Smith, Gary K.; Schneider, Klaus
    Journal of Medicinal Chemistry (2008), 51 (24), 7898-7914CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    More than 500 compds. chosen to represent kinase inhibitor space have been screened against a panel of over 200 protein kinases. Significant results include the identification of hits against new kinases including PIM1 and MPSK1, and the expansion of the inhibition profiles of several literature compds. A detailed anal. of the data through the use of affinity fingerprints has produced findings with implications for biol. target selection, the choice of tool compds. for target validation, and lead discovery and optimization. In a detailed examn. of the tyrosine kinases, interesting relationships have been found between targets and compds. Taken together, these results show how broad cross-profiling can provide important insights to assist kinase drug discovery.
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  30. 30
    Anastassiadis, T., Deacon, S. W., Devarajan, K., Ma, H., and Peterson, J. R. (2011) Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity Nat. Biotechnol. 29, 1039– 1045
    [Crossref], [PubMed], [CAS], Google Scholar
    30
    Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity
    Anastassiadis, Theonie; Deacon, Sean W.; Devarajan, Karthik; Ma, Haiching; Peterson, Jeffrey R.
    Nature Biotechnology (2011), 29 (11), 1039-1045CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
    Small-mol. protein kinase inhibitors are widely used to elucidate cellular signaling pathways and are promising therapeutic agents. Owing to evolutionary conservation of the ATP-binding site, most kinase inhibitors that target this site promiscuously inhibit multiple kinases. Interpretation of expts. that use these compds. is confounded by a lack of data on the comprehensive kinase selectivity of most inhibitors. Here we used functional assays to profile the activity of 178 com. available kinase inhibitors against a panel of 300 recombinant protein kinases. Quant. anal. revealed complex and often unexpected interactions between protein kinases and kinase inhibitors, with a wide spectrum of promiscuity. Many off-target interactions occur with seemingly unrelated kinases, revealing how large-scale profiling can identify multitargeted inhibitors of specific, diverse kinases. The results have implications for drug development and provide a resource for selecting compds. to elucidate kinase function and for interpreting the results of expts. involving kinase inhibitors.
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  31. 31
    Anderson, P. C., De Sapio, V., Turner, K. B., Elmer, S. P., Roe, D. C., and Schoeniger, J. S. (2012) Identification of binding specificity-determining features in protein families J. Med. Chem. 55, 1926– 1939
    [ACS Full Text ACS Full Text], Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Subramanian, G. and Sud, M. (2010) Computational Modeling of Kinase Inhibitor Selectivity ACS Med. Chem. Lett. 1, 395– 399
    [ACS Full Text ACS Full Text], Google Scholar
    There is no corresponding record for this reference.
  33. 33
    Martin, E. and Mukherjee, P. (2012) Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome J. Chem. Inf. Model. 52, 156– 170
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    33
    Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome
    Martin, Eric; Mukherjee, Prasenjit
    Journal of Chemical Information and Modeling (2012), 52 (1), 156-170CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    Reliable in silico prediction methods promise many advantages over exptl. high-throughput screening (HTS): vastly lower time and cost, affinity magnitude ests., no requirement for a phys. sample, and a knowledge-driven exploration of chem. space. For the specific case of kinases, given several hundred exptl. IC50 training measurements, the empirically parametrized profile-quant. structure-activity relationship (profile-QSAR) and surrogate AutoShim methods developed at Novartis can predict IC50 with a reliability approaching exptl. HTS. However, in the absence of training data, prediction is much harder. The most common a priori prediction method is docking, which suffers from many limitations: It requires a protein structure, is slow, and cannot predict affinity. Highly accurate profile-QSAR models have now been built for roughly 100 kinases covering most of the kinome. Analyzing correlations among neighboring kinases shows that near neighbors share a high degree of SAR similarity. The novel chemogenomic kinase-kernel method reported here predicts activity for new kinases as a weighted av. of predicted activities from profile-QSAR models for nearby neighbor kinases. Three different factors for weighting the neighbors were evaluated: binding site sequence identity to the kinase neighbors, similarity of the training set for each neighbor model to the compd. being predicted, and accuracy of each neighbor model. Binding site sequence identity was by far most important, followed by chem. similarity. Model quality had almost no relevance. The median R2 = 0.55 for kinase-kernel interpolations on 25% of the data of each set held out from method optimization for 51 kinase assays, approached the accuracy of median R2 = 0.61 for the trained profile-QSAR predictions on the same held out 25% data of each set, far faster and far more accurate than docking. Validation on the full data sets from 18 addnl. kinase assays not part of method optimization studies also showed strong performance with median R2 = 0.48. Genetic algorithm optimization of the binding site residues used to compute binding site sequence identity identified 16 privileged residues from a larger set of 46. These 16 are consistent with the kinase selectivity literature and structural biol., further supporting the scientific validity of the approach. A priori kinase-kernel predictions for 4 million compds. were interpolated from 51 existing profile-QSAR models for the remaining >400 novel kinases, totaling 2 billion activity predictions covering the entire kinome. The method has been successfully applied in two therapeutic projects to generate predictions and select compds. for activity testing.
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  34. 34
    Ma, X. H., Wang, R., Tan, C. Y., Jiang, Y. Y., Lu, T., Rao, H. B., Li, X. Y., Go, M. L., Low, B. C., and Chen, Y. Z. (2010) Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines Mol. Pharm. 7, 1545– 1560
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    34
    Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines
    Ma, X. H.; Wang, R.; Tan, C. Y.; Jiang, Y. Y.; Lu, T.; Rao, H. B.; Li, X. Y.; Go, M. L.; Low, B. C.; Chen, Y. Z.
    Molecular Pharmaceutics (2010), 7 (5), 1545-1560CODEN: MPOHBP; ISSN:1543-8384. (American Chemical Society)
    Multitarget agents have been increasingly explored for enhancing efficacy and reducing counter-target activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equiv. to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compds., 12-175 (0.007%-0.104%) of the 168 K MDDR compds., and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compds. similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.
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  35. 35
    Cohen, P. (2009) Targeting protein kinases for the development of anti-inflammatory drugs Curr. Opin. Cell Biol. 21, 317– 324
    [Crossref], [PubMed], [CAS], Google Scholar
    35
    Targeting protein kinases for the development of anti-inflammatory drugs
    Cohen, Philip
    Current Opinion in Cell Biology (2009), 21 (2), 317-324CODEN: COCBE3; ISSN:0955-0674. (Elsevier B.V.)
    A review. In recent years, protein kinases have become the pharmaceutical industry's most studied class of drug target, and some 10 protein kinase inhibitors have so far been approved for the treatment of cancer. However, whether safe drugs that modulate protein kinase activities can also be developed for the treatment of chronic diseases, where they may need to be taken for decades, is an issue that is still unresolved. A no. of compds. that inhibit the p38α MAPK have entered clin. trials for the treatment of rheumatoid arthritis and psoriasis, but side effects have prevented their progression to Phase III clin. trials. Here the author briefly reviews the potential problems in targeting p38 MAPK and discusses other protein kinases that regulate the innate immune system, such as Tpl2, MAPKAP-K2/3, MSK1/2, and IRAK4, which may be better targets for the treatment of chronic inflammatory diseases, and NIK, which is an attractive target for the treatment of multiple myeloma, a late stage B-cell malignancy.
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  36. 36
    Sakurai, H., Miyoshi, H., Toriumi, W., and Sugita, T. (1999) Functional interactions of transforming growth factor beta-activated kinase 1 with IkappaB kinases to stimulate NF-kappaB activation J. Biol. Chem. 274, 10641– 10648
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  37. 37
    Zhong, J., Gavrilescu, L. C., Molnar, A., Murray, L., Garafalo, S., Kehrl, J. H., Simon, A. R., Van Etten, R. A., and Kyriakis, J. M. (2009) GCK is essential to systemic inflammation and pattern recognition receptor signaling to JNK and p38 Proc. Natl. Acad. Sci. U.S.A. 106, 4372– 4377
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  38. 38
    Ackermann, T. F., Boini, K. M., Beier, N., Scholz, W., Fuchss, T., and Lang, F. (2011) EMD638683, a novel SGK inhibitor with antihypertensive potency Cell. Physiol. Biochem. 28, 137– 146
    [Crossref], [PubMed], [CAS], Google Scholar
    38
    EMD638683, a Novel SGK Inhibitor with Antihypertensive Potency
    Ackermann, Teresa F.; Boini, Krishna M.; Beier, Norbert; Scholz, Wolfgang; Fuchss, Thomas; Lang, Florian
    Cellular Physiology and Biochemistry (2011), 28 (1), 137-146CODEN: CEPBEW; ISSN:1015-8987. (S. Karger AG)
    The serum- and glucocorticoid-inducible kinase 1 (SGK1) is transcriptionally upregulated by mineralocorticoids and activated by insulin. The kinase enhances renal tubular Na+-reabsorption and accounts for blood pressure increase following high salt diet in mice made hyperinsulinemic by dietary fructose or fat. The present study describes the in vitro and in vivo efficacy of a novel SGK1 inhibitor (EMD638683). EMD638683 was tested in vitro by detn. of SGK1-dependent phosphorylation of NDRG1 (N-Myc downstream-regulated gene 1) in human cervical carcinoma HeLa-cells. In vivo EMD638683 (4460 ppm in chow, i.e. approx. 600 mg/kg/day) was administered to mice drinking tap water or isotonic saline contg. 10% fructose. Blood pressure was detd. by the tail cuff method, and urinary electrolyte (flame photometry) concns. detd. in metabolic cages. In vitro testing disclosed EMD638683 as a SGK1 inhibitor with an IC50 of 3 μM. Within 24 h in vivo EMD638683 treatment significantly decreased blood pressure in fructose/saline-treated mice but not in control animals or in SGK1 knockout mice. EMD638683 failed to alter the blood pressure in SGK1 knockout mice. Following chronic (4 wk) fructose/high salt treatment, addnl. EMD638683 treatment again decreased blood pressure. EMD638683 thus abrogates the salt sensitivity of blood pressure in hyperinsulinism without appreciably affecting blood pressure in the absence of hyperinsulinism. EMD638683 tended to increase fluid intake and urinary excretion of Na+, significantly increased urinary flow rate and significantly decreased body wt. Conclusion: EMD638683 could serve as a template for drugs counteracting hypertension in individuals with type II diabetes and metabolic syndrome.
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  39. 39
    Hayes, G. M., Carrigan, P. E., Beck, A. M., and Miller, L. J. (2006) Targeting the RNA splicing machinery as a novel treatment strategy for pancreatic carcinoma Cancer Res. 66, 3819– 3827
    [Crossref], [PubMed], [CAS], Google Scholar
    39
    Targeting the RNA Splicing Machinery as a Novel Treatment Strategy for Pancreatic Carcinoma
    Hayes, Gregory M.; Carrigan, Patricia E.; Beck, Alison M.; Miller, Laurence J.
    Cancer Research (2006), 66 (7), 3819-3827CODEN: CNREA8; ISSN:0008-5472. (American Association for Cancer Research)
    Aberrant patterns of pre-mRNA splicing have been established for many human malignancies, yet the mechanisms responsible for these tumor-specific changes remain undefined and represent a promising area for therapeutic intervention. Using immunohistochem., we have localized the expression of a central splicing regulator, serine-arginine protein kinase 1 (SRPK1), to the ductular epithelial cells within human pancreas and have further shown its increased expression in tumors of the pancreas, breast, and colon. Small interfering RNA-mediated down-regulation of SRPK1 in pancreatic tumor cell lines resulted in a dose-dependent decrease in proliferative capacity and increase in apoptotic potential. Coordinately, the disruption of SRPK1 expression resulted in enhanced sensitivity of tumor cells to killing by gemcitabine and/or cisplatin. A dose-dependent redn. in the phosphorylation status of specific SR proteins was detected following the down-regulation of SRPK1 and is likely responsible for the obsd. alterations in expression of proteins assocd. with apoptosis and multidrug resistance. These data support SRPK1 as a new, potential target for the treatment of pancreatic ductular cancer that at present remains largely unresponsive to conventional therapies. Furthermore, these results support the development of innovative therapies that target not only specific splice variants arising during tumorigenesis but also the splice regulatory machinery that itself may be abnormal in malignant cells.
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  40. 40
    Congreve, M., Carr, R., Murray, C., and Jhoti, H. (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discovery Today 8, 876– 877
    [Crossref], Google Scholar
    There is no corresponding record for this reference.
  41. 41
    Gerber, P. R. and Müller, K. (1995) MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry J. Comput. Aided Mol. Des. 9, 251– 268
    [Crossref], [PubMed], [CAS], Google Scholar
    41
    MAB, a generally applicable molecular force field for structure modeling in medicinal chemistry
    Gerber, Paul R.; Mueller, Klaus
    Journal of Computer-Aided Molecular Design (1995), 9 (3), 251-68CODEN: JCADEQ; ISSN:0920-654X. (ESCOM)
    The math. formulation, parametrization scheme, and structural results of a new, generally applicable mol. force field are presented. The central features are a scheme for automatic parameter assignments, the consistent united-atom approxn., the absence of atom types other than elements, the replacement of electrostatic terms by geometrical hydrogen-bonding terms, the concomitant lack of a need for partial at. charge assignment and the strict adherence to a finite-range design. As a consequence of omitting all hydrogen atoms, optimal hydrogen-bond patterns are computed dynamically by appropriate network analyses. For a test set of 1589 structures, selected from the Cambridge Structural Database solely on the grounds of a given element list and criteria for high structure refinement, the agreements are on av. 2 pm for bonds, 2° for valence angles and 10 to 20 pm for the root-mean-square deviation of atom positions, depending somewhat on size and flexibility of the structures. More qual. testing of large-scale structural properties of the force field on proteins and DNA oligomers revealed satisfactory performance.
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  42. 42
    Lorber, D. M. and Shoichet, B. K. (1998) Flexible ligand docking using conformational ensembles Protein Sci. 7, 938– 950
    [Crossref], [PubMed], [CAS], Google Scholar
    42
    Flexible ligand docking using conformational ensembles
    Lorber, David M.; Shoichet, Brian K.
    Protein Science (1998), 7 (4), 938-950CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
    Mol. docking algorithms suggest possible structures for mol. complexes. They are used to model biol. function and to discover potential ligands. A present challenge for docking algorithms is the treatment of mol. flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given mol. are pre-calcd. in the same frame of ref., so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same mol. The algorithm was tested using three org. ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those detd. in X-ray crystal structures (RMS values typically less than 1.5 Å). To test the method's usefulness for inhibitor discovery, multi-compd. and multi-conformer databases were screened for compds. known to bind to dihydrofolate reductase and compds. known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those obsd. exptl. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 mols. in one to four CPU days on a workstation.
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  43. 43
    Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., and Shoichet, B. K. (2002) A model binding site for testing scoring functions in molecular docking J. Mol. Biol. 322, 339– 355
    [Crossref], [PubMed], [CAS], Google Scholar
    43
    A Model Binding Site for Testing Scoring Functions in Molecular Docking
    Wei, Binqing Q.; Baase, Walter A.; Weaver, Larry H.; Matthews, Brian W.; Shoichet, Brian K.
    Journal of Molecular Biology (2002), 322 (2), 339-355CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Science Ltd.)
    Prediction of interaction energies between ligands and their receptors remains a major challenge for structure-based inhibitor discovery. Much effort has been devoted to developing scoring schemes that can successfully rank the affinities of a diverse set of possible ligands to a binding site for which the structure is known. To test these scoring functions, well-characterized exptl. systems can be very useful. Here, mutation-created binding sites in T4 lysozyme were used to investigate how the quality of at. charges and solvation energies affects mol. docking. At. charges and solvation energies were calcd. for 172,118 mols. in the Available Chems. Directory using a semi-empirical quantum mech. approach by the program AMSOL. The database was first screened against the apolar cavity site created by the mutation Leu99Ala (L99A). Compared to the electronegativity-based charges that are widely used, the new charges and desolvation energies improved ranking of known apolar ligands, and better distinguished them from more polar isosteres that are not obsd. to bind. To investigate whether the new charges had predictive value, the non-polar residue Met102, which forms part of the binding site, was changed to the polar residue glutamine. The structure of the resulting Leu99 Ala and Met102 Gln double mutant of T4 lysozyme (L99A/M102Q) was detd. and the docking calcn. was repeated for the new site. Seven representative polar mols. that preferentially docked to the polar vs. the apolar binding site were tested exptl. All seven bind to the polar cavity (L99A/M102Q) but do not detectably bind to the apolar cavity (L99A). Five ligand-bound structures of L99A/M102Q were detd. by X-ray crystallog. Docking predictions corresponded to the crystallog. results to within 0.4 A RMSD. Improved treatment of partial at. charges and desolvation energies in database docking appears feasible and leads to better distinction of true ligands. Simple model binding sites, such as L99A and its more polar variants, may find broad use in the development and testing of docking algorithms.
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  44. 44
    Mpamhanga, C. P., Spinks, D., Tulloch, L. B., Shanks, E. J., Robinson, D. A., Collier, I. T., Fairlamb, A. H., Wyatt, P. G., Frearson, J. A., Hunter, W. N., Gilbert, I. H., and Brenk, R. (2009) One scaffold, three binding modes: novel and selective pteridine reductase 1 inhibitors derived from fragment hits discovered by virtual screening J. Med. Chem. 52, 4454– 4465
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    44
    One Scaffold, Three Binding Modes: Novel and Selective Pteridine Reductase 1 Inhibitors Derived from Fragment Hits Discovered by Virtual Screening
    Mpamhanga, Chidochangu P.; Spinks, Daniel; Tulloch, Lindsay B.; Shanks, Emma J.; Robinson, David A.; Collie, Iain T.; Fairlamb, Alan H.; Wyatt, Paul G.; Frearson, Julie A.; Hunter, William N.; Gilbert, Ian H.; Brenk, Ruth
    Journal of Medicinal Chemistry (2009), 52 (14), 4454-4465CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
    The enzyme pteridine reductase 1 (PTR1) is a potential target for new compds. to treat human African trypanosomiasis. A virtual screening campaign for fragments inhibiting PTR1 was carried out. Two novel chem. series were identified contg. aminobenzothiazole and aminobenzimidazole scaffolds, resp. One of the hits (2-amino-5-chlorobenzimidazole) was subjected to crystal structure anal. and a high resoln. crystal structure in complex with PTR1 was obtained, confirming the predicted binding mode. However, the crystal structures of two analogs (2-aminobenzimidazole and 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole) in complex with PTR1 revealed two alternative binding modes. In these complexes, previously unobserved protein movements and water-mediated protein-ligand contacts occurred, which prohibited a correct prediction of the binding modes. On the basis of the alternative binding mode of 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole, derivs. were designed and selective PTR1 inhibitors with low nanomolar potency and favorable physicochem. properties were obtained.
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  45. 45
    Mysinger, M. M. and Shoichet, B. K. (2010) Rapid context-dependent ligand desolvation in molecular docking J. Chem. Inf. Model. 50, 1561– 1573
    [ACS Full Text ACS Full Text], [CAS], Google Scholar
    45
    Rapid Context-Dependent Ligand Desolvation in Molecular Docking
    Mysinger, Michael M.; Shoichet, Brian K.
    Journal of Chemical Information and Modeling (2010), 50 (9), 1561-1573CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
    In structure-based screens for new ligands, a mol. docking algorithm must rapidly score many mols. in multiple configurations, accounting for both the ligand's interactions with receptor and its competing interactions with solvent. Here the authors explore a context-dependent ligand desolvation scoring term for mol. docking. The authors relate the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation and then use this fraction to scale an atom-by-atom decompn. of the full transfer free energy. The fractional desolvation is precomputed on a scoring grid by numerically integrating over the vol. of receptor proximal to a ligand atom, weighted by distance. To test this method's performance, the authors dock ligands vs. property-matched decoys over 40 DUD targets. Context-dependent desolvation better enriches ligands compared to both the raw full transfer free energy penalty and compared to ignoring desolvation altogether, though the improvement is modest. More compellingly, the new method improves docking performance across receptor types. Thus, whereas entirely ignoring desolvation works best for charged sites and overpenalizing with full desolvation works well for neutral sites, the phys. more correct context-dependent ligand desolvation is competitive across both types of targets. The method also reliably discriminates ligands from highly charged mols., where ignoring desolvation performs poorly. Since this context-dependent ligand desolvation may be precalcd., it improves docking reliability with minimal cost to calcn. time and may be readily incorporated into any physics-based docking program.
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  46. 46
    Michalczyk, A., Klüter, S., Rode, H. B., Simard, J. R., Grütter, C., Rabiller, M., and Rauh, D. (2008) Structural insights into how irreversible inhibitors can overcome drug resistance in EGFR Bioorg. Med. Chem. 16, 3482– 3488
    [Crossref], [PubMed], [CAS], Google Scholar
    46
    Structural insights into how irreversible inhibitors can overcome drug resistance in EGFR
    Michalczyk, Anja; Klueter, Sabine; Rode, Haridas B.; Simard, Jeffrey R.; Gruetter, Christian; Rabiller, Matthias; Rauh, Daniel
    Bioorganic & Medicinal Chemistry (2008), 16 (7), 3482-3488CODEN: BMECEP; ISSN:0968-0896. (Elsevier Ltd.)
    Resistance to kinase-targeted cancer drugs has recently been linked to a single point mutation in the ATP binding site of the kinase. In EGFR, the crucial Thr790 gatekeeper residue is mutated to a Met and prevents reversible ATP competitive inhibitors from binding. Irreversible 4-(phenylamino)quinazolines have been shown to overcome this drug resistance and are currently in clin. trials. In order to obtain a detailed structural understanding of how irreversible inhibitors overcome drug resistance, the authors used Src kinase as a model system for drug resistant EGFR-T790M. The authors report the first crystal structure of a drug resistant kinase in complex with an irreversible inhibitor. This 4-(phenylamino)quinazoline inhibits wild type and drug resistant EGFR in vitro at low nM concns. The cocrystal structure of drug resistant cSrc-T338M kinase domain provides the structural basis of this activity.
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  7. Krisna Prak, Janos Kriston-Vizi, A. W. Edith Chan, Christin Luft, Joana R. Costa, Niccolo Pengo, and Robin Ketteler . Benzobisthiazoles Represent a Novel Scaffold for Kinase Inhibitors of CLK Family Members. Biochemistry 2016, 55 (3) , 608-617. https://doi.org/10.1021/acs.biochem.5b01128
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  9. Siti Fatimah Nur Abdul Aziz, Arilla Sri Masayu Abd Rahim, Yahaya M. Normi, Shahrul Ainliah Alang Ahmad, Abu Bakar Salleh. Rational design of mini protein mimicking uricase: Encapsulation in ZIF ‐8 for uric acid detection. Proteins: Structure, Function, and Bioinformatics 2023, 91 (7) , 967-979. https://doi.org/10.1002/prot.26485
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  25. Nivya James, K. Ramanathan. Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins. Applied Biochemistry and Biotechnology 2018, 186 (1) , 85-108. https://doi.org/10.1007/s12010-018-2724-4
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  29. Dilip Narayanan, Osman A. B. S. M. Gani, Franz X. E. Gruber, Richard A. Engh. Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. Journal of Cheminformatics 2017, 9 (1) https://doi.org/10.1186/s13321-017-0229-8
  30. B. Wilding, N. Klempier. Newest Developments in the Preparation of Thieno[2,3- d ]pyrimidines. Organic Preparations and Procedures International 2017, 49 (3) , 183-215. https://doi.org/10.1080/00304948.2017.1320513
  31. Mikhail Krasavin. N-(Hetero)aryl-2-imidazolines: an emerging privileged motif for contemporary drug design. Chemistry of Heterocyclic Compounds 2017, 53 (3) , 240-255. https://doi.org/10.1007/s10593-017-2047-3
  32. G. T. T. Nguyen, G. Erlenkamp, O. Jäck, A. Küberl, M. Bott, F. Fiorani, H. Gohlke, G. Groth. Chalcone-based Selective Inhibitors of a C4 Plant Key Enzyme as Novel Potential Herbicides. Scientific Reports 2016, 6 (1) https://doi.org/10.1038/srep27333
  33. Pakornwit Sarnpitak, Prashant Mujumdar, Paul Taylor, Megan Cross, Mark J. Coster, Alain-Dominique Gorse, Mikhail Krasavin, Andreas Hofmann. Panel docking of small-molecule libraries — Prospects to improve efficiency of lead compound discovery. Biotechnology Advances 2015, 33 (6) , 941-947. https://doi.org/10.1016/j.biotechadv.2015.05.006
  34. Yuanyuan Shan, Jinyun Dong, Xiaoyan Pan, Lin Zhang, Jie Zhang, Yalin Dong, Maoyi Wang. Expanding the structural diversity of Bcr-Abl inhibitors: Dibenzoylpiperazin incorporated with 1H-indazol-3-amine. European Journal of Medicinal Chemistry 2015, 104 , 139-147. https://doi.org/10.1016/j.ejmech.2015.09.034
  35. Prashant Mujumdar, Pakornwit Sarnpitak, Anton Shetnev, Mikhail Dorogov, Mikhail Krasavin. Facile Pd-catalyzed amination of imidazolin-1-yl chloroazines under microwave irradiation: toward a new kinase-inhibitory chemotype. Tetrahedron Letters 2015, 56 (21) , 2827-2831. https://doi.org/10.1016/j.tetlet.2015.04.059
  36. Richa Goel, Vijay Luxami, Kamaldeep Paul. Synthetic approaches and functionalizations of imidazo[1,2-a]pyrimidines: an overview of the decade. RSC Advances 2015, 5 (99) , 81608-81637. https://doi.org/10.1039/C5RA14795F
  37. Norihito Kawashita, Hiroyuki Yamasaki, Tomoyuki Miyao, Kentaro Kawai, Yoshitake Sakae, Takeshi Ishikawa, Kenichi Mori, Shinya Nakamura, Hiromasa Kaneko. A Mini-review on Chemoinformatics Approaches for Drug Discovery. Journal of Computer Aided Chemistry 2015, 16 (0) , 15-29. https://doi.org/10.2751/jcac.16.15
  38. C. Ramakrishnan, A. M. Thangakani, D. Velmurugan, M. Michael Gromiha. Identification of Novel c-Yes Kinase Inhibitors. 2014, 494-500. https://doi.org/10.1007/978-3-319-09330-7_57
  39. Gisbert Schneider, Karl‐Heinz Baringhaus. De Novo Design: From Models to Molecules. 2013, 1-55. https://doi.org/10.1002/9783527677016.ch1
  40. Haoliang Yuan, Wenting Tai, Shihe Hu, Haichun Liu, Yanmin Zhang, Sihui Yao, Ting Ran, Shuai Lu, Zhipeng Ke, Xiao Xiong, Jinxing Xu, Yadong Chen, Tao Lu. Fragment-based strategy for structural optimization in combination with 3D-QSAR. Journal of Computer-Aided Molecular Design 2013, 27 (10) , 897-915. https://doi.org/10.1007/s10822-013-9687-x
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  • Abstract

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    Figure 1

    Figure 1. (a) ATP binding site of a typical protein kinase (adapted from ref 7). (b) In silico screening cascade used to design novel kinase inhibitor libraries.

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    Figure 2

    Figure 2. Docking poses of six high ranking core fragments (green carbon atoms) superimposed on crystallographically determined binding modes of ligands containing the same or a closely related core fragment (cyan carbon atoms). Putative hydrogen bonds to the hinge region are shown as dashed lines. RMSD values are given for the maximum common substructure between core fragment and ligand. (The binding sites are oriented as depicted in Figure 1a.)

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    Figure 3

    Figure 3. Predicted binding modes with respect to the hinge region for six high-ranking core fragments (A–F) for which binding to protein kinases was not reported in the literature. Only the most frequent binding mode for each core fragment is shown. The substitution points that target the hydrophobic pockets I and II and that have been selected for diversifying the cores are indicated as R1 and R2, respectively. (The binding sites are oriented as depicted in Figure 1a.)

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    Scheme 1

    Scheme 1. a

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    Scheme aReagents and conditions: (a) NBS, DCM, 0 °C; (b) ethoxycarbonyl-isothiocyanate, dioxane, rt; (c) NH2OH·HCl, DIPEA, MeOH/EtOH, 60 °C; (d) R1B(OH)2, K3PO4, PCy3, Pd2(dba)3, dioxane/water, MW, 130 °C; (e) R2COOH, PCl3, CH3CN, MW, 150 °C; (f) formamide, 200 °C; (g) Br2, AcOH, rt; (h) R1B(OH)2, K3PO4, PCy3, Pd2(dba)3, dioxane/water, MW, 100 °C; (i) diethylmalonate, 170 °C; (j) POCl3, reflux; (k) HNO3, H2SO4, rt; (l) R1R′1NH, MeOH, MW, 140 °C; (m) Zn, NH4Cl, MeOH, rt; (n) SOCl2, MeOH, 0 °C–rt; (o) Bredereck’s reagent, toluene, 115 °C; (p) 2-amino-3-hydroxy-pyridine, NaOAc, AcOH, 90 °C; (q) N-phenylbis(trifluoromethanesulfonimide), K2CO3, THF, MW, 120 °C; (r) R2B(OH)2, Pd(PPh3)4, 120 °C; (s) TMSI, DCM, rt; (t) 1,2,4-triazol-3-amine, NaOAc, AcOH, 90 °C; (u) 33% HBr–AcOH, 50 °C; (v) R1SO2Cl or R1COCl or R1NCO or R1NCS, DCM, MW, 110°C; (w) 2-aminoimidazole, NaOAc, AcOH, 90 °C; (x) H2, 10% Pd/C, EtOH, H-Cube; (y) R1SO2Cl or R1COCl or R1NCO or R1NCS, DCM, MW, 110 °C; (z) PPA, 120 °C; (aa) HClO4, MeOH, rt.

    Figure 4

    Figure 4. Structures of 15 compounds that were screened against a panel of 117 protein kinases. Core fragments are marked in blue.

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    Figure 5

    Figure 5. (a) Bar chart showing the number of kinases inhibited to at least 40% (yellow bars) or 75% (green bars) based on 15 compounds tested against a panel of 117 kinases. (b) Bar chart showing how many kinases were inhibited by how many compounds to at least 40% (yellow bars) or 75% (green bars) based on 15 compounds tested against a panel of 117 kinases.

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    Figure 6

    Figure 6. Crystallographically determined binding mode of B1 in complex with cSrc together with electron density map (2Fo – Fc contoured at 1σ). The fragment binds to the hinge region of the kinase domain. The binding site is oriented as depicted in Figure 1a. Putative hydrogen bonds are indicated as dashed lines. PDB code: 4fic.

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    This article references 46 other publications.

    1. 1
      Cohen, P. (2002) Protein kinases—the major drug targets of the twenty-first century? Nat. Rev. Drug. Discovery 1, 309– 315
      [Crossref], [PubMed], [CAS], Google Scholar
      1
      Timeline: Protein kinases - the major drug targets of the twenty-first century?
      Cohen, Philip
      Nature Reviews Drug Discovery (2002), 1 (4), 309-315CODEN: NRDDAG ISSN:. (Nature Publishing Group)
      A review. Protein phosphorylation regulates most aspects of cell life, whereas abnormal phosphorylation is a cause or consequence of disease. A growing interest in developing orally active protein-kinase inhibitors has recently culminated in the approval of the first of these drugs for clin. use. Protein kinases have now become the second most important group of drug targets, after G-protein-coupled receptors. Here, I give a personal view of some of the most important advances that have shaped this field.
      >> More from SciFinder ®
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    2. 2
      Cohen, P. and Alessi, D. R. (2013) Kinase Drug Discovery - What’s Next in the Field? ACS Chem. Biol. 8, 96– 104
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      2
      Kinase Drug Discovery - What's Next in the Field?
      Cohen, Philip; Alessi, Dario R.
      ACS Chemical Biology (2013), 8 (1), 96-104CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
      A review. Over the past 15 years protein kinases have become the pharmaceutical industry's most important class of drug target in the field of cancer. Some 20 drugs that target kinases have been approved for clin. use over the past decade, and hundreds more are undergoing clin. trials. However, the recent approval of the first protein kinase inhibitors for the treatment of inflammatory diseases, coupled with an enhanced understanding of the signaling networks that control the immune system, suggests that there will be a surge of interest in this area over the next 10 years. In this connection, we discuss opportunities for targeting protein kinases in the MyD88 signaling network for the development of drugs to treat chronic inflammatory and autoimmune diseases. Activating mutations in protein kinases underlie many other diseases and conditions, and we also discuss why the protein kinases SPAK/OSR1 and LRRK2 have recently become interesting targets for the treatment of hypertension and Parkinson's disease, resp., and the progress that has been made in developing LRRK2 inhibitors. Finally we suggest that more focus on the identification of inhibitors of kinase activation, rather than kinase activity, may pay dividends in identifying exquisitely specific inhibitors of signal transduction cascades, and we also highlight "pseudo-kinases" as an attractive and unexplored area for drug development that merits much more attention in the years to come.
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    3. 3
      Knapp, S., Arruda, P., Blagg, J., Burley, S., Drewry, D. H., Edwards, A., Fabbro, D., Gillespie, P., Gray, N. S., Kuster, B., Lackey, K. E., Mazzafera, P., Tomkinson, N. C., Willson, T. M., Workman, P., and Zuercher, W. J. (2013) A public-private partnership to unlock the untargeted kinome Nat. Chem. Biol. 9, 3– 6
      [Crossref], [PubMed], [CAS], Google Scholar
      3
      A public-private partnership to unlock the untargeted kinome
      Knapp, Stefan; Arruda, Paulo; Blagg, Julian; Burley, Stephen; Drewry, David H.; Edwards, Aled; Fabbro, Doriano; Gillespie, Paul; Gray, Nathanael S.; Kuster, Bernhard; Lackey, Karen E.; Mazzafera, Paulo; Tomkinson, Nicholas C. O.; Willson, Timothy M.; Workman, Paul; Zuercher, William J.
      Nature Chemical Biology (2013), 9 (1), 3-6CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      Chem. probes are urgently needed to functionally annotate hitherto-untargeted kinases and stimulate new drug discovery efforts to address unmet medical needs. The size of the human kinome combined with the high cost assocd. with probe generation severely limits access to new probes. We propose a large-scale public-private partnership as a new approach that offers economies of scale, minimized redundancy and sharing of risk and cost.
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    4. 4
      Rabiller, M., Getlik, M., Klüter, S., Richters, A., Tückmantel, S., Simard, J. R., and Rauh, D. (2010) Proteus in the world of proteins: conformational changes in protein kinases Arch. Pharm. (Weinheim) 343, 193– 206
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    5. 5
      Eswaran, J. and Knapp, S. (2010) Insights into protein kinase regulation and inhibition by large scale structural comparison Biochem. Biophys. Acta, Proteins Proteomics 1804, 429– 432
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    6. 6
      Liao, J. J. (2007) Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors J. Med. Chem. 50, 409– 424
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      6
      Molecular Recognition of Protein Kinase Binding Pockets for Design of Potent and Selective Kinase Inhibitors
      Liao, Jeffrey Jie-Lou
      Journal of Medicinal Chemistry (2007), 50 (3), 409-424CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A review. Many kinase inhibitors have failed in preclin. or clin. development because of the lack of such selectivity that induces intolerable side effects, mostly because the catalytic cleft is highly conserved in sequence and conformation. Systematic anal. of the crystal structures of protein kinases can offer insight into the design of highly selective kinase inhibitors to overcome these effects.
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    7. 7
      Traxler, P. (1998) Tyrosine kinase inhibitors in cancer treatment (part II) Expert Opin. Ther. Pat. 8, 1599– 1625
      [Crossref], [CAS], Google Scholar
      7
      Tyrosine kinase inhibitors in cancer treatment (part II)
      Traxler, Peter
      Expert Opinion on Therapeutic Patents (1998), 8 (12), 1599-1625CODEN: EOTPEG; ISSN:1354-3776. (Ashley Publications)
      A review with 169 refs. In the last few years, enormous progress in the field of signal transduction inhibition has been made. Many companies have entered the field. Along with the epidermal growth factor receptor (EGFR) tyrosine kinase, many other tyrosine kinases have been identified as interesting targets for drug discovery projects. X-ray data of more than 40 crystal structures of protein kinases, in most cases complexed with an inhibitor, have been published. Pharmacophore models for the binding of inhibitors in the ATP-binding site of protein kinases have been developed that are generally applicable, enabling the rational design of tyrosine as well as serine/threonine kinase inhibitors. It has been proven by numerous examples that the ATP-binding of protein kinases is an exciting target for the design of anticancer drugs. In many cases, it has also been demonstrated that through rational design it is possible to modify a lead structure in such a way that inhibitors with an altered selectivity profile are obtained. Chem. optimization of several lead structures led to development candidates with potent in vitro and in vivo activity fulfilling the pharmacodynamic, pharmacokinetic, toxicol. and tech. (synthesis, formulation) requirements for a clin. candidate. Currently, there are seven tyrosine kinase inhibitors in early phases of clin. trials. In addn., several candidates are close to entering Phase I trials this year or at the beginning of next year. It is expected that pos. results from clin. trials will greatly contribute to the clin. proof of concept of the value of signal transduction inhibition and will greatly stimulate further research in this area. This review is a continuation of a review with the same title of last year and summarizes published patent literature and related publications between 1997 and Sept. 1998.
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    8. 8
      Fang, Z., Grütter, C., and Rauh, D. (2013) Strategies for the selective regulation of kinases with allosteric modulators: exploiting exclusive structural features ACS Chem. Biol. 8, 58– 70
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      8
      Strategies for the Selective Regulation of Kinases with Allosteric Modulators: Exploiting Exclusive Structural Features
      Fang, Zhizhou; Gruetter, Christian; Rauh, Daniel
      ACS Chemical Biology (2013), 8 (1), 58-70CODEN: ACBCCT; ISSN:1554-8929. (American Chemical Society)
      A review. The modulation of kinase function has become an important goal in modern drug discovery and chem. biol. research. In cancer-targeted therapies, kinase inhibitors have been experiencing an upsurge, which can be measured by the increasing no. of kinase inhibitors approved by the FDA in recent years. However, lack of efficacy, limited selectivity, and the emergence of acquired drug resistance still represent major bottlenecks in the clinic and challenge inhibitor development. Most known kinase inhibitors target the active kinase and are ATP competitive. A second class of small org. mols., which address remote sites of the kinase and stabilize enzymically inactive conformations, is rapidly moving to the forefront of kinase inhibitor research. Such allosteric modulators bind to sites that are less conserved across the kinome and only accessible upon conformational changes. These mols. are therefore thought to provide various advantages such as higher selectivity and extended drug target residence times. This review highlights various strategies that have been developed to utilizing exclusive structural features of kinases and thereby modulating their activity allosterically.
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    9. 9
      Stahura, F. L., Xue, L., Godden, J. W., and Bajorath, J. (1999) Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP-binding site of protein kinases J. Mol. Graph. Model. 17 (1–9) 51– 52
      [CAS], Google Scholar
      9
      Color atlas
      Stahura, Florence L.; Xue, Ling; Godden, Jeffrey W.; Bajorath, Jurgen
      Journal of Molecular Graphics & Modelling (1999), 17 (1), 51-54CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Science Inc.)
      There is no expanded citation for this reference.
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    10. 10
      Prien, O. (2005) Target-family-oriented focused libraries for kinases--conceptual design aspects and commercial availability ChemBioChem 6, 500– 505
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    11. 11
      Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., and Wyatt, P. G. (2008) Lessons learnt from assembling screening libraries for drug discovery for neglected diseases ChemMedChem 3, 435– 444
      [Crossref], [PubMed], [CAS], Google Scholar
      11
      Lessons learned from assembling screening libraries for drug discovery for neglected diseases
      Brenk, Ruth; Schipani, Alessandro; James, Daniel; Krasowski, Agata; Gilbert, Ian Hugh; Frearson, Julie; Wyatt, Paul Graham
      ChemMedChem (2008), 3 (3), 435-444CODEN: CHEMGX; ISSN:1860-7179. (Wiley-VCH Verlag GmbH & Co. KGaA)
      To enable the establishment of a drug discovery operation for neglected diseases, out of 2.3 million com. available compds. 222 552 compds. were selected for an in silico library, 57 438 for a diverse general screening library, and 1 697 compds. for a focused kinase set. Compiling these libraries required a robust strategy for compd. selection. Rules for unwanted groups were defined and selection criteria to enrich for lead-like compds. which facilitate straightforward structure-activity relationship exploration were established. Further, a literature and patent review was undertaken to ext. key recognition elements of kinase inhibitors ("core fragments") to assemble a focused library for hit discovery for kinases. Computational and exptl. characterization of the general screening library revealed that the selected compds. (1) span a broad range of lead-like space, (2) show a high degree of structural integrity and purity, and (3) demonstrate appropriate soly. for the purposes of biochem. screening. The implications of this study for compd. selection, esp. in an academic environment with limited resources, are considered.
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    12. 12
      Decornez, H., Gulyas-Forro, A., Papp, A., Szabo, M., Sarmay, G., Hajdu, I., Cseh, S., Dorman, G., and Kitchen, D. B. (2009) Design, selection, and evaluation of a general kinase-focused library ChemMedChem 4, 1273– 1278
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    13. 13
      Bamborough, P., Brown, M. J., Christopher, J. A., Chung, C. W., and Mellor, G. W. (2011) Selectivity of kinase inhibitor fragments J. Med. Chem. 54, 5131– 5143
      [ACS Full Text ACS Full Text], Google Scholar
      There is no corresponding record for this reference.
    14. 14
      Akritopoulou-Zanze, I. and Hajduk, P. J. (2009) Kinase-targeted libraries: the design and synthesis of novel, potent, and selective kinase inhibitors Drug Discovery Today 14, 291– 297
      [Crossref], [PubMed], Google Scholar
      There is no corresponding record for this reference.
    15. 15
      Erickson, J. A., Mader, M. M., Watson, I. A., Webster, Y. W., Higgs, R. E., Bell, M. A., and Vieth, M. (2010) Structure-guided expansion of kinase fragment libraries driven by support vector machine models Biochem. Biophys. Acta, Proteins Proteomics 1804, 642– 652
      [Crossref], [CAS], Google Scholar
      15
      Structure-guided expansion of kinase fragment libraries driven by support vector machine models
      Erickson, Jon A.; Mader, Mary M.; Watson, Ian A.; Webster, Yue W.; Higgs, Richard E.; Bell, Michael A.; Vieth, Michal
      Biochimica et Biophysica Acta, Proteins and Proteomics (2010), 1804 (3), 642-652CODEN: BBAPBW; ISSN:1570-9639. (Elsevier B. V.)
      This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amt. of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addn., a broad diversity of X-ray structure information is needed for binding mode prediction. This is important for scaffold and substituent site selection. Borrowing from FBDD, the process involves fragmentation of known actives, proposition of binding mode hypotheses for the fragments, and model-driven recombination using a pharmacophore derived from known kinase inhibitor structures. The support vector machine method, using Merck atom pair derived fingerprint descriptors, was used to build models from activity from 6 kinase assays. These models were qualified prospectively by selecting and testing compds. from the internal compd. collection. Overall hit and enrichment rates of 82% and 2.5%, resp., qualified the models for use in library design. Using the process, 7 novel libraries were designed, synthesized and tested against these same 6 kinases. The results showed excellent results, yielding a 92% hit rate for the 179 compds. that made up the 7 libraries. The results of one library designed to include known literature compds., as well as an anal. of overall substituent frequency, are discussed.
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    16. 16
      Vieth, M., Erickson, J., Wang, J., Webster, Y., Mader, M., Higgs, R., and Watson, I. (2009) Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches J. Med. Chem. 52, 6456– 6466
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      16
      Kinase Inhibitor Data Modeling and de Novo Inhibitor Design with Fragment Approaches
      Vieth, Michal; Erickson, Jon; Wang, Jibo; Webster, Yue; Mader, Mary; Higgs, Richard; Watson, Ian
      Journal of Medicinal Chemistry (2009), 52 (20), 6456-6466CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A reconstructive approach based on computational fragmentation of existing inhibitors and validated kinase potency models to recombine and create "de novo" kinase inhibitor small mol. libraries is described. The screening results from model selected mols. from the corporate database and seven computationally derived small mol. libraries were used to evaluate this approach. Specifically, 1895 model selected database mols. were screened at 20 μM in six kinase assays and yielded an overall hit rate of 84%. These models were then used in the de novo design of seven chem. libraries consisting of 20-50 compds. each. Then 179 compds. from synthesized libraries were tested against these six kinases with an overall hit rate of 92%. Comparing predicted and obsd. selectivity profiles serves to highlight the strengths and limitations of the methodol., while anal. of functional group contributions from the libraries suggest general principles governing binding of ATP competitive compds.
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    17. 17
      Kettle, J. G. and Ward, R. A. (2010) Toward the comprehensive systematic enumeration and synthesis of novel kinase inhibitors based on a 4-anilinoquinazoline binding mode J. Chem. Inf. Model. 50, 525– 533
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      17
      Toward the Comprehensive Systematic Enumeration and Synthesis of Novel Kinase Inhibitors Based on a 4-Anilinoquinazoline Binding Mode
      Kettle, Jason G.; Ward, Richard A.
      Journal of Chemical Information and Modeling (2010), 50 (4), 525-533CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      There are currently eight small-mol. kinase inhibitors approved as cancer treatments, and a significantly larger no. of compds. are in the earlier stages of clin. development. Although kinase inhibitors are most commonly developed in a cancer setting, other disease areas have been targeted. The vast majority of reported kinase small-mol. inhibitors contain functionalities that interact with the ATP binding site of the kinase. The 4-anilinoquinazolines have previously been reported as potent epidermal growth factor receptor (EGFR) inhibitors, binding at the hinge' region of the ATP site. Subsequently, this chem. series has been optimized against a no. of different kinases including Src and Aurora B. Here, we detail the computational enumeration of ring systems that have the ability to make comparable interactions to the 4-anilinoquinazoline core. These were prioritized by computational, medicinal, and synthetic chem. input, and a no. of libraries were subsequently synthesized (no prepn. shown).
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    18. 18
      Baldwin, I., Bamborough, P., Haslam, C. G., Hunjan, S. S., Longstaff, T., Mooney, C. J., Patel, S., Quinn, J., and Somers, D. O. (2008) Kinase array design, back to front: biaryl amides Bioorg. Med. Chem. Lett. 18, 5285– 5289
      [Crossref], [PubMed], Google Scholar
      There is no corresponding record for this reference.
    19. 19
      Deanda, F., Stewart, E. L., Reno, M. J., and Drewry, D. H. (2008) Kinase-targeted library design through the application of the PharmPrint methodology J. Chem. Inf. Model. 48, 2395– 2403
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      19
      Kinase-Targeted Library Design through the Application of the PharmPrint Methodology
      Deanda, Felix; Stewart, Eugene L.; Reno, Michael J.; Drewry, David H.
      Journal of Chemical Information and Modeling (2008), 48 (12), 2395-2403CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      The PharmPrint methodol., as modified and implemented by Deanda and Stewart, was prospectively evaluated for use as a virtual high-throughput screening tool by applying it to the design of target-focused arrays. To this end, PharmPrint quant. structure-activity relationship (QSAR) models for the prediction of AKT1, Aurora-A, and ROCK1 inhibition were constructed and used to virtually screen two large combinatorial libraries. Based on predicted activities, an Aurora-A targeted array and a ROCK1 targeted array were designed and synthesized. One control group per designed array was also synthesized to assess the enrichment levels achieved by the QSAR models. For the Aurora-A targeted array, the hit rate, against the intended target, was 42.9%, whereas that of the control group was 0%. Thus, the enrichment level achieved by the Aurora-A QSAR model was incalculable. For the ROCK1 targeted array, the hit rate against the intended target was 30.6%, whereas that of the control group was 5.10%, making the enrichment level achieved by the ROCK1 QSAR model 6-fold above control. Clearly, these results support the use of the PharmPrint methodol. as a virtual screening tool for the design of kinase-targeted arrays.
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    20. 20
      Lowrie, J. F., Delisle, R. K., Hobbs, D. W., and Diller, D. J. (2004) The different strategies for designing GPCR and kinase targeted libraries Comb. Chem. High Throughput Screen. 7, 495– 510
      [Crossref], [PubMed], [CAS], Google Scholar
      20
      The different strategies for designing GPCR and kinase targeted libraries
      Lowrie, J. F.; Delisle, R. K.; Hobbs, D. W.; Diller, D. J.
      Combinatorial Chemistry and High Throughput Screening (2004), 7 (5), 495-510CODEN: CCHSFU; ISSN:1386-2073. (Bentham Science Publishers Ltd.)
      A review. In recent years the trend in combinatorial library design has shifted to include target class focusing along with diversity and drug-likeness criteria. In this manuscript we review the computational tools available for target class library design and highlight the areas where they have proven useful in our work. The protein kinase family is used to illustrated structure-based target class focused library design, and the G-protein coupled receptor (GPCR) family is used to illustrate ligand-based target class focused library design. Most of the tools discussed are those designed for libraries targeted to a single protein and are simply applied "brute-force" to a large no. of targets within the family. The tools that have proven to be the most useful in our work are those that can ext. trends from the computational data such as docking and clustering or data mining large amts. of structure activity or high throughput screening data. Finally, areas where improvements are needed in the computational tools available for target class focusing are highlighted. These areas include tools to ext. the relevant patterns from all available information for a family of targets, tools to efficiently apply models for all targets in the family rather than just a small subset, mining tools to ext. the relevant information from the computational absorption, distribution, metab., excretion and toxicity (ADMET) and targeting data, and tools to allow interactive exploration of the virtual space around a library to facilitate the selection of the library that best suits the needs of the design team.
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    21. 21
      Bain, J., Plater, L., Elliott, M., Shpiro, N., Hastie, C. J., McLauchlan, H., Klevernic, I., Arthur, J. S., Alessi, D. R., and Cohen, P. (2007) The selectivity of protein kinase inhibitors: a further update Biochem. J. 408, 297– 315
      [Crossref], [PubMed], [CAS], Google Scholar
      21
      The selectivity of protein kinase inhibitors: a further update
      Bain, Jenny; Plater, Lorna; Elliott, Matt; Shpiro, Natalia; Hastie, C. James; McLauchlan, Hilary; Klevernic, Iva; Arthur, J. Simon C.; Alessi, Dario R.; Cohen, Philip
      Biochemical Journal (2007), 408 (3), 297-315CODEN: BIJOAK; ISSN:0264-6021. (Portland Press Ltd.)
      The specificities of 65 compds. reported to be relatively specific inhibitors of protein kinases have been profiled against a panel of 70-80 protein kinases. On the basis of this information, the effects of compds. that we have studied in cells and other data in the literature, we recommend the use of the following small-mol. inhibitors: SB 203580/SB202190 and BIRB 0796 to be used in parallel to assess the physiol. roles of p38 MAPK (mitogen-activated protein kinase) isoforms, PI-103 and wortmannin to be used in parallel to inhibit phosphatidylinositol (phosphoinositide) 3-kinases, PP1 or PP2 to be used in parallel with Src-I1 (Src inhibitor-1) to inhibit Src family members; PD 184352 or PD 0325901 to inhibit MKK1 (MAPK kinase-1) or MKK1 plus MKK5, Akt-I-1/2 to inhibit the activation of PKB (protein kinase B/Akt), rapamycin to inhibit TORC1 [mTOR (mammalian target of rapamycin)-raptor (regulatory assocd. protein of mTOR) complex], CT 99021 to inhibit GSK3 (glycogen synthase kinase 3), BI-D1870 and SL0101 or FMK (fluoromethylketone) to be used in parallel to inhibit RSK (ribosomal S6 kinase), D4476 to inhibit CK1 (casein kinase 1), VX680 to inhibit Aurora kinases, and roscovitine as a pan-CDK (cyclin-dependent kinase) inhibitor. We have also identified harmine as a potent and specific inhibitor of DYRK1A (dual-specificity tyrosine-phosphorylated and -regulated kinase 1A) in vitro. The results have further emphasized the need for considerable caution in using small-mol. inhibitors of protein kinases to assess the physiol. roles of these enzymes. Despite being used widely, many of the compds. that we analyzed were too non-specific for useful conclusions to be made, other than to exclude the involvement of particular protein kinases in cellular processes.
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    22. 22
      Gozalbes, R., Simon, L., Froloff, N., Sartori, E., Monteils, C., and Baudelle, R. (2008) Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries J. Med. Chem. 51, 3124– 3132
      [ACS Full Text ACS Full Text], Google Scholar
      There is no corresponding record for this reference.
    23. 23
      Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., and Overington, J. P. (2012) ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 40, D1100– 1107
      [Crossref], [PubMed], [CAS], Google Scholar
      23
      ChEMBL: a large-scale bioactivity database for drug discovery
      Gaulton, Anna; Bellis, Louisa J.; Bento, A. Patricia; Chambers, Jon; Davies, Mark; Hersey, Anne; Light, Yvonne; McGlinchey, Shaun; Michalovich, David; Al-Lazikani, Bissan; Overington, John P.
      Nucleic Acids Research (2012), 40 (D1), D1100-D1107CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      ChEMBL is an Open Data database contg. binding, functional and ADMET information for a large no. of drug-like bioactive compds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chem. biol. and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compds. and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.
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    24. 24
      Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 23, 3– 25
      [Crossref], [CAS], Google Scholar
      24
      Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
      Lipinski, Christopher A.; Lombardo, Franco; Dominy, Beryl W.; Feeney, Paul J.
      Advanced Drug Delivery Reviews (1997), 23 (1-3), 3-25CODEN: ADDREP; ISSN:0169-409X. (Elsevier)
      A review with 50 refs. Exptl. and computational approaches to est. soly. and permeability in discovery and development settings are described. In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the mol. wt. (MWT) is >500 and the calcd. Log P (CLogP) is >5. Computational methodol. for the rule-based Moriguchi Log P (MLogP) calcn. is described. Turbidimetric soly. measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric soly. than leads in the pre-HTS era. In the development setting, soly. calcns. focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with exptl. thermodn. soly. measurements.
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    25. 25
      Hajduk, P. J. and Greer, J. (2007) A decade of fragment-based drug design: strategic advances and lessons learned Nat. Rev. Drug Discovery 6, 211– 219
      [Crossref], [PubMed], [CAS], Google Scholar
      25
      A decade of fragment-based drug design: strategic advances and lessons learned
      Hajduk, Philip J.; Greer, Jonathan
      Nature Reviews Drug Discovery (2007), 6 (3), 211-219CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)
      A review. Since the early 1990s, several technol. and scientific advances - such as combinatorial chem., high-throughput screening and the sequencing of the human genome - have been heralded as remedies to the problems facing the pharmaceutical industry. The use of these technologies in some form is now well established at most pharmaceutical companies; however, the return on investment in terms of marketed products has not met expectations. Fragment-based drug design is another tool for drug discovery that has emerged in the past decade. Here, we describe the development and evolution of fragment-based drug design, analyze the role that this approach can have in combination with other discovery technologies and highlight the impact that fragment-based methods have made in progressing new medicines into the clinic.
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    26. 26
      Hopkins, A. L., Groom, C. R., and Alex, A. (2004) Ligand efficiency: a useful metric for lead selection Drug Discovery Today 9, 430– 431
      [Crossref], [PubMed], [CAS], Google Scholar
      26
      Ligand efficiency: a useful metric for lead selection
      Hopkins Andrew L; Groom Colin R; Alex Alexander
      Drug discovery today (2004), 9 (10), 430-1 ISSN:1359-6446.
      There is no expanded citation for this reference.
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    27. 27
      Posy, S. L., Hermsmeier, M. A., Vaccaro, W., Ott, K. H., Todderud, G., Lippy, J. S., Trainor, G. L., Loughney, D. A., and Johnson, S. R. (2011) Trends in kinase selectivity: insights for target class-focused library screening J. Med. Chem. 54, 54– 66
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      27
      Trends in Kinase Selectivity: Insights for Target Class-Focused Library Screening
      Posy, Shana L.; Hermsmeier, Mark A.; Vaccaro, Wayne; Ott, Karl-Heinz; Todderud, Gordon; Lippy, Jonathan S.; Trainor, George L.; Loughney, Deborah A.; Johnson, Stephen R.
      Journal of Medicinal Chemistry (2011), 54 (1), 54-66CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      A kinome-wide selectivity screen of >20000 compds. with a rich representation of many structural classes has been completed. Anal. of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compds. are mostly similar in their physicochem. characteristics, we identify specific features that are present more frequently in compds. that bind to many kinases. Our results support a scaffold-oriented approach for building compd. collections to screen kinase targets.
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    28. 28
      Jacobs, M. D., Black, J., Futer, O., Swenson, L., Hare, B., Fleming, M., and Saxena, K. (2005) Pim-1 ligand-bound structures reveal the mechanism of serine/threonine kinase inhibition by LY294002 J. Biol. Chem. 280, 13728– 13734
      [Crossref], [PubMed], Google Scholar
      There is no corresponding record for this reference.
    29. 29
      Bamborough, P., Drewry, D., Harper, G., Smith, G. K., and Schneider, K. (2008) Assessment of chemical coverage of kinome space and its implications for kinase drug discovery J. Med. Chem. 51, 7898– 7914
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      29
      Assessment of Chemical Coverage of Kinome Space and Its Implications for Kinase Drug Discovery
      Bamborough, Paul; Drewry, David; Harper, Gavin; Smith, Gary K.; Schneider, Klaus
      Journal of Medicinal Chemistry (2008), 51 (24), 7898-7914CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      More than 500 compds. chosen to represent kinase inhibitor space have been screened against a panel of over 200 protein kinases. Significant results include the identification of hits against new kinases including PIM1 and MPSK1, and the expansion of the inhibition profiles of several literature compds. A detailed anal. of the data through the use of affinity fingerprints has produced findings with implications for biol. target selection, the choice of tool compds. for target validation, and lead discovery and optimization. In a detailed examn. of the tyrosine kinases, interesting relationships have been found between targets and compds. Taken together, these results show how broad cross-profiling can provide important insights to assist kinase drug discovery.
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    30. 30
      Anastassiadis, T., Deacon, S. W., Devarajan, K., Ma, H., and Peterson, J. R. (2011) Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity Nat. Biotechnol. 29, 1039– 1045
      [Crossref], [PubMed], [CAS], Google Scholar
      30
      Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity
      Anastassiadis, Theonie; Deacon, Sean W.; Devarajan, Karthik; Ma, Haiching; Peterson, Jeffrey R.
      Nature Biotechnology (2011), 29 (11), 1039-1045CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
      Small-mol. protein kinase inhibitors are widely used to elucidate cellular signaling pathways and are promising therapeutic agents. Owing to evolutionary conservation of the ATP-binding site, most kinase inhibitors that target this site promiscuously inhibit multiple kinases. Interpretation of expts. that use these compds. is confounded by a lack of data on the comprehensive kinase selectivity of most inhibitors. Here we used functional assays to profile the activity of 178 com. available kinase inhibitors against a panel of 300 recombinant protein kinases. Quant. anal. revealed complex and often unexpected interactions between protein kinases and kinase inhibitors, with a wide spectrum of promiscuity. Many off-target interactions occur with seemingly unrelated kinases, revealing how large-scale profiling can identify multitargeted inhibitors of specific, diverse kinases. The results have implications for drug development and provide a resource for selecting compds. to elucidate kinase function and for interpreting the results of expts. involving kinase inhibitors.
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    31. 31
      Anderson, P. C., De Sapio, V., Turner, K. B., Elmer, S. P., Roe, D. C., and Schoeniger, J. S. (2012) Identification of binding specificity-determining features in protein families J. Med. Chem. 55, 1926– 1939
      [ACS Full Text ACS Full Text], Google Scholar
      There is no corresponding record for this reference.
    32. 32
      Subramanian, G. and Sud, M. (2010) Computational Modeling of Kinase Inhibitor Selectivity ACS Med. Chem. Lett. 1, 395– 399
      [ACS Full Text ACS Full Text], Google Scholar
      There is no corresponding record for this reference.
    33. 33
      Martin, E. and Mukherjee, P. (2012) Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome J. Chem. Inf. Model. 52, 156– 170
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      33
      Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome
      Martin, Eric; Mukherjee, Prasenjit
      Journal of Chemical Information and Modeling (2012), 52 (1), 156-170CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      Reliable in silico prediction methods promise many advantages over exptl. high-throughput screening (HTS): vastly lower time and cost, affinity magnitude ests., no requirement for a phys. sample, and a knowledge-driven exploration of chem. space. For the specific case of kinases, given several hundred exptl. IC50 training measurements, the empirically parametrized profile-quant. structure-activity relationship (profile-QSAR) and surrogate AutoShim methods developed at Novartis can predict IC50 with a reliability approaching exptl. HTS. However, in the absence of training data, prediction is much harder. The most common a priori prediction method is docking, which suffers from many limitations: It requires a protein structure, is slow, and cannot predict affinity. Highly accurate profile-QSAR models have now been built for roughly 100 kinases covering most of the kinome. Analyzing correlations among neighboring kinases shows that near neighbors share a high degree of SAR similarity. The novel chemogenomic kinase-kernel method reported here predicts activity for new kinases as a weighted av. of predicted activities from profile-QSAR models for nearby neighbor kinases. Three different factors for weighting the neighbors were evaluated: binding site sequence identity to the kinase neighbors, similarity of the training set for each neighbor model to the compd. being predicted, and accuracy of each neighbor model. Binding site sequence identity was by far most important, followed by chem. similarity. Model quality had almost no relevance. The median R2 = 0.55 for kinase-kernel interpolations on 25% of the data of each set held out from method optimization for 51 kinase assays, approached the accuracy of median R2 = 0.61 for the trained profile-QSAR predictions on the same held out 25% data of each set, far faster and far more accurate than docking. Validation on the full data sets from 18 addnl. kinase assays not part of method optimization studies also showed strong performance with median R2 = 0.48. Genetic algorithm optimization of the binding site residues used to compute binding site sequence identity identified 16 privileged residues from a larger set of 46. These 16 are consistent with the kinase selectivity literature and structural biol., further supporting the scientific validity of the approach. A priori kinase-kernel predictions for 4 million compds. were interpolated from 51 existing profile-QSAR models for the remaining >400 novel kinases, totaling 2 billion activity predictions covering the entire kinome. The method has been successfully applied in two therapeutic projects to generate predictions and select compds. for activity testing.
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    34. 34
      Ma, X. H., Wang, R., Tan, C. Y., Jiang, Y. Y., Lu, T., Rao, H. B., Li, X. Y., Go, M. L., Low, B. C., and Chen, Y. Z. (2010) Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines Mol. Pharm. 7, 1545– 1560
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      34
      Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines
      Ma, X. H.; Wang, R.; Tan, C. Y.; Jiang, Y. Y.; Lu, T.; Rao, H. B.; Li, X. Y.; Go, M. L.; Low, B. C.; Chen, Y. Z.
      Molecular Pharmaceutics (2010), 7 (5), 1545-1560CODEN: MPOHBP; ISSN:1543-8384. (American Chemical Society)
      Multitarget agents have been increasingly explored for enhancing efficacy and reducing counter-target activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equiv. to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compds., 12-175 (0.007%-0.104%) of the 168 K MDDR compds., and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compds. similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.
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    35. 35
      Cohen, P. (2009) Targeting protein kinases for the development of anti-inflammatory drugs Curr. Opin. Cell Biol. 21, 317– 324
      [Crossref], [PubMed], [CAS], Google Scholar
      35
      Targeting protein kinases for the development of anti-inflammatory drugs
      Cohen, Philip
      Current Opinion in Cell Biology (2009), 21 (2), 317-324CODEN: COCBE3; ISSN:0955-0674. (Elsevier B.V.)
      A review. In recent years, protein kinases have become the pharmaceutical industry's most studied class of drug target, and some 10 protein kinase inhibitors have so far been approved for the treatment of cancer. However, whether safe drugs that modulate protein kinase activities can also be developed for the treatment of chronic diseases, where they may need to be taken for decades, is an issue that is still unresolved. A no. of compds. that inhibit the p38α MAPK have entered clin. trials for the treatment of rheumatoid arthritis and psoriasis, but side effects have prevented their progression to Phase III clin. trials. Here the author briefly reviews the potential problems in targeting p38 MAPK and discusses other protein kinases that regulate the innate immune system, such as Tpl2, MAPKAP-K2/3, MSK1/2, and IRAK4, which may be better targets for the treatment of chronic inflammatory diseases, and NIK, which is an attractive target for the treatment of multiple myeloma, a late stage B-cell malignancy.
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    36. 36
      Sakurai, H., Miyoshi, H., Toriumi, W., and Sugita, T. (1999) Functional interactions of transforming growth factor beta-activated kinase 1 with IkappaB kinases to stimulate NF-kappaB activation J. Biol. Chem. 274, 10641– 10648
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    37. 37
      Zhong, J., Gavrilescu, L. C., Molnar, A., Murray, L., Garafalo, S., Kehrl, J. H., Simon, A. R., Van Etten, R. A., and Kyriakis, J. M. (2009) GCK is essential to systemic inflammation and pattern recognition receptor signaling to JNK and p38 Proc. Natl. Acad. Sci. U.S.A. 106, 4372– 4377
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    38. 38
      Ackermann, T. F., Boini, K. M., Beier, N., Scholz, W., Fuchss, T., and Lang, F. (2011) EMD638683, a novel SGK inhibitor with antihypertensive potency Cell. Physiol. Biochem. 28, 137– 146
      [Crossref], [PubMed], [CAS], Google Scholar
      38
      EMD638683, a Novel SGK Inhibitor with Antihypertensive Potency
      Ackermann, Teresa F.; Boini, Krishna M.; Beier, Norbert; Scholz, Wolfgang; Fuchss, Thomas; Lang, Florian
      Cellular Physiology and Biochemistry (2011), 28 (1), 137-146CODEN: CEPBEW; ISSN:1015-8987. (S. Karger AG)
      The serum- and glucocorticoid-inducible kinase 1 (SGK1) is transcriptionally upregulated by mineralocorticoids and activated by insulin. The kinase enhances renal tubular Na+-reabsorption and accounts for blood pressure increase following high salt diet in mice made hyperinsulinemic by dietary fructose or fat. The present study describes the in vitro and in vivo efficacy of a novel SGK1 inhibitor (EMD638683). EMD638683 was tested in vitro by detn. of SGK1-dependent phosphorylation of NDRG1 (N-Myc downstream-regulated gene 1) in human cervical carcinoma HeLa-cells. In vivo EMD638683 (4460 ppm in chow, i.e. approx. 600 mg/kg/day) was administered to mice drinking tap water or isotonic saline contg. 10% fructose. Blood pressure was detd. by the tail cuff method, and urinary electrolyte (flame photometry) concns. detd. in metabolic cages. In vitro testing disclosed EMD638683 as a SGK1 inhibitor with an IC50 of 3 μM. Within 24 h in vivo EMD638683 treatment significantly decreased blood pressure in fructose/saline-treated mice but not in control animals or in SGK1 knockout mice. EMD638683 failed to alter the blood pressure in SGK1 knockout mice. Following chronic (4 wk) fructose/high salt treatment, addnl. EMD638683 treatment again decreased blood pressure. EMD638683 thus abrogates the salt sensitivity of blood pressure in hyperinsulinism without appreciably affecting blood pressure in the absence of hyperinsulinism. EMD638683 tended to increase fluid intake and urinary excretion of Na+, significantly increased urinary flow rate and significantly decreased body wt. Conclusion: EMD638683 could serve as a template for drugs counteracting hypertension in individuals with type II diabetes and metabolic syndrome.
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    39. 39
      Hayes, G. M., Carrigan, P. E., Beck, A. M., and Miller, L. J. (2006) Targeting the RNA splicing machinery as a novel treatment strategy for pancreatic carcinoma Cancer Res. 66, 3819– 3827
      [Crossref], [PubMed], [CAS], Google Scholar
      39
      Targeting the RNA Splicing Machinery as a Novel Treatment Strategy for Pancreatic Carcinoma
      Hayes, Gregory M.; Carrigan, Patricia E.; Beck, Alison M.; Miller, Laurence J.
      Cancer Research (2006), 66 (7), 3819-3827CODEN: CNREA8; ISSN:0008-5472. (American Association for Cancer Research)
      Aberrant patterns of pre-mRNA splicing have been established for many human malignancies, yet the mechanisms responsible for these tumor-specific changes remain undefined and represent a promising area for therapeutic intervention. Using immunohistochem., we have localized the expression of a central splicing regulator, serine-arginine protein kinase 1 (SRPK1), to the ductular epithelial cells within human pancreas and have further shown its increased expression in tumors of the pancreas, breast, and colon. Small interfering RNA-mediated down-regulation of SRPK1 in pancreatic tumor cell lines resulted in a dose-dependent decrease in proliferative capacity and increase in apoptotic potential. Coordinately, the disruption of SRPK1 expression resulted in enhanced sensitivity of tumor cells to killing by gemcitabine and/or cisplatin. A dose-dependent redn. in the phosphorylation status of specific SR proteins was detected following the down-regulation of SRPK1 and is likely responsible for the obsd. alterations in expression of proteins assocd. with apoptosis and multidrug resistance. These data support SRPK1 as a new, potential target for the treatment of pancreatic ductular cancer that at present remains largely unresponsive to conventional therapies. Furthermore, these results support the development of innovative therapies that target not only specific splice variants arising during tumorigenesis but also the splice regulatory machinery that itself may be abnormal in malignant cells.
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    40. 40
      Congreve, M., Carr, R., Murray, C., and Jhoti, H. (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discovery Today 8, 876– 877
      [Crossref], Google Scholar
      There is no corresponding record for this reference.
    41. 41
      Gerber, P. R. and Müller, K. (1995) MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry J. Comput. Aided Mol. Des. 9, 251– 268
      [Crossref], [PubMed], [CAS], Google Scholar
      41
      MAB, a generally applicable molecular force field for structure modeling in medicinal chemistry
      Gerber, Paul R.; Mueller, Klaus
      Journal of Computer-Aided Molecular Design (1995), 9 (3), 251-68CODEN: JCADEQ; ISSN:0920-654X. (ESCOM)
      The math. formulation, parametrization scheme, and structural results of a new, generally applicable mol. force field are presented. The central features are a scheme for automatic parameter assignments, the consistent united-atom approxn., the absence of atom types other than elements, the replacement of electrostatic terms by geometrical hydrogen-bonding terms, the concomitant lack of a need for partial at. charge assignment and the strict adherence to a finite-range design. As a consequence of omitting all hydrogen atoms, optimal hydrogen-bond patterns are computed dynamically by appropriate network analyses. For a test set of 1589 structures, selected from the Cambridge Structural Database solely on the grounds of a given element list and criteria for high structure refinement, the agreements are on av. 2 pm for bonds, 2° for valence angles and 10 to 20 pm for the root-mean-square deviation of atom positions, depending somewhat on size and flexibility of the structures. More qual. testing of large-scale structural properties of the force field on proteins and DNA oligomers revealed satisfactory performance.
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    42. 42
      Lorber, D. M. and Shoichet, B. K. (1998) Flexible ligand docking using conformational ensembles Protein Sci. 7, 938– 950
      [Crossref], [PubMed], [CAS], Google Scholar
      42
      Flexible ligand docking using conformational ensembles
      Lorber, David M.; Shoichet, Brian K.
      Protein Science (1998), 7 (4), 938-950CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
      Mol. docking algorithms suggest possible structures for mol. complexes. They are used to model biol. function and to discover potential ligands. A present challenge for docking algorithms is the treatment of mol. flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given mol. are pre-calcd. in the same frame of ref., so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same mol. The algorithm was tested using three org. ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those detd. in X-ray crystal structures (RMS values typically less than 1.5 Å). To test the method's usefulness for inhibitor discovery, multi-compd. and multi-conformer databases were screened for compds. known to bind to dihydrofolate reductase and compds. known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those obsd. exptl. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 mols. in one to four CPU days on a workstation.
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    43. 43
      Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., and Shoichet, B. K. (2002) A model binding site for testing scoring functions in molecular docking J. Mol. Biol. 322, 339– 355
      [Crossref], [PubMed], [CAS], Google Scholar
      43
      A Model Binding Site for Testing Scoring Functions in Molecular Docking
      Wei, Binqing Q.; Baase, Walter A.; Weaver, Larry H.; Matthews, Brian W.; Shoichet, Brian K.
      Journal of Molecular Biology (2002), 322 (2), 339-355CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Science Ltd.)
      Prediction of interaction energies between ligands and their receptors remains a major challenge for structure-based inhibitor discovery. Much effort has been devoted to developing scoring schemes that can successfully rank the affinities of a diverse set of possible ligands to a binding site for which the structure is known. To test these scoring functions, well-characterized exptl. systems can be very useful. Here, mutation-created binding sites in T4 lysozyme were used to investigate how the quality of at. charges and solvation energies affects mol. docking. At. charges and solvation energies were calcd. for 172,118 mols. in the Available Chems. Directory using a semi-empirical quantum mech. approach by the program AMSOL. The database was first screened against the apolar cavity site created by the mutation Leu99Ala (L99A). Compared to the electronegativity-based charges that are widely used, the new charges and desolvation energies improved ranking of known apolar ligands, and better distinguished them from more polar isosteres that are not obsd. to bind. To investigate whether the new charges had predictive value, the non-polar residue Met102, which forms part of the binding site, was changed to the polar residue glutamine. The structure of the resulting Leu99 Ala and Met102 Gln double mutant of T4 lysozyme (L99A/M102Q) was detd. and the docking calcn. was repeated for the new site. Seven representative polar mols. that preferentially docked to the polar vs. the apolar binding site were tested exptl. All seven bind to the polar cavity (L99A/M102Q) but do not detectably bind to the apolar cavity (L99A). Five ligand-bound structures of L99A/M102Q were detd. by X-ray crystallog. Docking predictions corresponded to the crystallog. results to within 0.4 A RMSD. Improved treatment of partial at. charges and desolvation energies in database docking appears feasible and leads to better distinction of true ligands. Simple model binding sites, such as L99A and its more polar variants, may find broad use in the development and testing of docking algorithms.
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    44. 44
      Mpamhanga, C. P., Spinks, D., Tulloch, L. B., Shanks, E. J., Robinson, D. A., Collier, I. T., Fairlamb, A. H., Wyatt, P. G., Frearson, J. A., Hunter, W. N., Gilbert, I. H., and Brenk, R. (2009) One scaffold, three binding modes: novel and selective pteridine reductase 1 inhibitors derived from fragment hits discovered by virtual screening J. Med. Chem. 52, 4454– 4465
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      44
      One Scaffold, Three Binding Modes: Novel and Selective Pteridine Reductase 1 Inhibitors Derived from Fragment Hits Discovered by Virtual Screening
      Mpamhanga, Chidochangu P.; Spinks, Daniel; Tulloch, Lindsay B.; Shanks, Emma J.; Robinson, David A.; Collie, Iain T.; Fairlamb, Alan H.; Wyatt, Paul G.; Frearson, Julie A.; Hunter, William N.; Gilbert, Ian H.; Brenk, Ruth
      Journal of Medicinal Chemistry (2009), 52 (14), 4454-4465CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      The enzyme pteridine reductase 1 (PTR1) is a potential target for new compds. to treat human African trypanosomiasis. A virtual screening campaign for fragments inhibiting PTR1 was carried out. Two novel chem. series were identified contg. aminobenzothiazole and aminobenzimidazole scaffolds, resp. One of the hits (2-amino-5-chlorobenzimidazole) was subjected to crystal structure anal. and a high resoln. crystal structure in complex with PTR1 was obtained, confirming the predicted binding mode. However, the crystal structures of two analogs (2-aminobenzimidazole and 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole) in complex with PTR1 revealed two alternative binding modes. In these complexes, previously unobserved protein movements and water-mediated protein-ligand contacts occurred, which prohibited a correct prediction of the binding modes. On the basis of the alternative binding mode of 1-(3,4-dichlorobenzyl)-2-aminobenzimidazole, derivs. were designed and selective PTR1 inhibitors with low nanomolar potency and favorable physicochem. properties were obtained.
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    45. 45
      Mysinger, M. M. and Shoichet, B. K. (2010) Rapid context-dependent ligand desolvation in molecular docking J. Chem. Inf. Model. 50, 1561– 1573
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      45
      Rapid Context-Dependent Ligand Desolvation in Molecular Docking
      Mysinger, Michael M.; Shoichet, Brian K.
      Journal of Chemical Information and Modeling (2010), 50 (9), 1561-1573CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)
      In structure-based screens for new ligands, a mol. docking algorithm must rapidly score many mols. in multiple configurations, accounting for both the ligand's interactions with receptor and its competing interactions with solvent. Here the authors explore a context-dependent ligand desolvation scoring term for mol. docking. The authors relate the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation and then use this fraction to scale an atom-by-atom decompn. of the full transfer free energy. The fractional desolvation is precomputed on a scoring grid by numerically integrating over the vol. of receptor proximal to a ligand atom, weighted by distance. To test this method's performance, the authors dock ligands vs. property-matched decoys over 40 DUD targets. Context-dependent desolvation better enriches ligands compared to both the raw full transfer free energy penalty and compared to ignoring desolvation altogether, though the improvement is modest. More compellingly, the new method improves docking performance across receptor types. Thus, whereas entirely ignoring desolvation works best for charged sites and overpenalizing with full desolvation works well for neutral sites, the phys. more correct context-dependent ligand desolvation is competitive across both types of targets. The method also reliably discriminates ligands from highly charged mols., where ignoring desolvation performs poorly. Since this context-dependent ligand desolvation may be precalcd., it improves docking reliability with minimal cost to calcn. time and may be readily incorporated into any physics-based docking program.
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    46. 46
      Michalczyk, A., Klüter, S., Rode, H. B., Simard, J. R., Grütter, C., Rabiller, M., and Rauh, D. (2008) Structural insights into how irreversible inhibitors can overcome drug resistance in EGFR Bioorg. Med. Chem. 16, 3482– 3488
      [Crossref], [PubMed], [CAS], Google Scholar
      46
      Structural insights into how irreversible inhibitors can overcome drug resistance in EGFR
      Michalczyk, Anja; Klueter, Sabine; Rode, Haridas B.; Simard, Jeffrey R.; Gruetter, Christian; Rabiller, Matthias; Rauh, Daniel
      Bioorganic & Medicinal Chemistry (2008), 16 (7), 3482-3488CODEN: BMECEP; ISSN:0968-0896. (Elsevier Ltd.)
      Resistance to kinase-targeted cancer drugs has recently been linked to a single point mutation in the ATP binding site of the kinase. In EGFR, the crucial Thr790 gatekeeper residue is mutated to a Met and prevents reversible ATP competitive inhibitors from binding. Irreversible 4-(phenylamino)quinazolines have been shown to overcome this drug resistance and are currently in clin. trials. In order to obtain a detailed structural understanding of how irreversible inhibitors overcome drug resistance, the authors used Src kinase as a model system for drug resistant EGFR-T790M. The authors report the first crystal structure of a drug resistant kinase in complex with an irreversible inhibitor. This 4-(phenylamino)quinazoline inhibits wild type and drug resistant EGFR in vitro at low nM concns. The cocrystal structure of drug resistant cSrc-T338M kinase domain provides the structural basis of this activity.
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  • Supporting Information

    Supporting Information

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    Additional tables with percent inhibition data for the discussed compounds, hit rates in various kinase screens, and data collection and refinement details for the crystal structure of cSrc-B1; a figure of the pharmacophore used for virtual screening; the synthetic routes to the described compounds; NMR spectra of key compounds. This material is available free of charge via the Internet at http://pubs.acs.org.

    Accession Codes

    The crystal structure of cSrc in complex with B1 has been deposited into the PDB with the code 4fic.


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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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