Drug Discovery in the Kinase Inhibitory Field using the Nested Chemical
Library TM technology
György Kéria,b*, Zsolt Székelyhidia, Péter Bánhegyia, Zoltán Vargaa, Bálint-Hegymegi
Barakonyia, Csaba Szántai-Kisa, Doris Hafenbradld#, Bert Klebld#, Gerhard Mullerd#, Axel
Ullriche, Dániel Erősb, Zoltán Horváthb, Zoltán Greffb, Jenő Marosfalvib, János Patób, István
Szabadkaib, Ildikó Szilágyib, Zsolt Szegedib, István Vargab, Frigyes Wáczeka, László Őrfia,b,c
a
Peptide Biochemistry Res. Group of Hung. Acad. Sci. and Cooperative Research Centre,
Semmelweis University, Rippl-Rónai u. 37., Budapest 1062, Hungary
b
c
Vichem Chemie Research Ltd., Herman Otto u. 15., Budapest 1022, Hungary
Department of Pharmaceutical Chemistry, Semmelweis University, Hőgyes Endre u. 9.,
Budapest 1092, Hungary
d
#
Axxima
Pharmaceuticals
AG,
Max-Lebsche-Platz
32,
Munich
81377,
Germany
now GPC Biotech AG, Max-Lebsche-Platz 32, D-81377 Munich, Germany
e
Max Planck Institute, Department of Biochemistry, D-82152, Am Klopferspitz 18/A,
Martinsried , Germany
*
Corresponding author: Tel.:+36-1-487-2080; fax: +36-1-487-2081; e-mail: keri@vichem.hu
Abstract
Kinase inhibitors are in the front line of modern drug research where mostly three technologies
are used for hit and lead finding: HTS of random libraries, 3D design based on X-ray data, and
focused libraries around limited number of new cores. Our novel Nested Chemical Library
(NCL) technology is based on a knowledge base approach where focused libraries around
1
selected cores are used to generate a pharmacophore model. NCL was designed on the platform
of a diverse kinase inhibitory library organized around 97 core structures. We have established a
unique proprietary kinase inhibitory chemistry around these core structures with small focused
sublibraries around each core. All of the compounds in our NCL library are stored in a big
unified SQL (Structured Query Language) database along with their measured and calculated
physicochemical and ADME and toxicity (ADMET) properties, together with thousands of
molecular descriptors calculated for each compound. Drug-likeness of all the compounds can be
visualized with the widely accepted calculated Lipinski parameters. Biochemical kinase
inhibitory on selected cloned kinase enzymes for a few hundred compound sets from NCL can
provide enough biological data for rational computerized design of new analogues based on our
pharmacophore
model
generating
3DNET4WTM
QSPAR
(Quantitative
Structure-
Property/Activity Relationships) approach. Using this pharmacophore modelling approach and
the ADMET filters we can preselect the synthesizable compounds for hit and lead optimisation.
Starting from there and integrating the information from QSPAR high quality leads can be
generated within a small number of optimization cycles.
Introduction
In recent years, dramatic developments in molecular biology and protein chemistry have greatly
enhanced our understanding of the molecular pathomechanisms of various diseases. As a result,
traditional concepts of drug design that apply trial-and-error approaches are being increasingly
superseded by novel strategies. These are based on the knowledge of molecular
pathomechanisms related to the disorders of inter- and intracellular signalling.1 More than 80
2
percent of diseases with known molecular pathomechanisms involve communication
abnormalities, including cancer, infectious diseases, arteriosclerosis, arthritis, neurodegenerative
disorders, etc. False proliferation signals, occupied signaling channels, cell-injury, inflammation,
autoimmune reactions are potential factors in the etiology of these diseases.2 Signal transduction
therapy endeavours to repair signaling defects involved in cell communication. A
communication system works properly if the shared information is relevant and the individual
components of the system contribute adequate responses. Communication is prerequisite to the
maintenance and development of living systems and any malfunction of intra and intercellular
communication leads to disease. Normal cells that duly perform their physiological functions do
not send or receive false messages and are securely controlled by the messages of the
communication network. They are even willing to “commit suicide” (apoptosis) for the sake of
the host organism. This situation is entirely different with diseased cells. Cancer cells, for
example, generate a false, mimicked proliferation signal for themselves (via oncogenes and other
genomic changes) and these results in their uncontrolled growth, while the so called survival
factors inhibit the programmed cell death mechanism.3,4
In recent years many validated target molecules have been discovered in various pathological
states especially in cancer and many more are expected in the near future. The human genome is
reported to contain about 32,000 genes that express an estimated 250,000 to 300,000 proteins.
The increased number of proteins might be explained by through alternative splicing and post
transcriptional and translational modifications and regulations. About 20-25 percent of the
druggable genome are kinases involved in signal transduction, but at this stage only 4 kinase
inhibitors are in clinical practice, which means that the field still offers a lot of options for drug
discovery. One of the first proof of concept drugs for signal transduction therapy in cancer was
3
Gleevec – an inhibitor of Bcr-Abl kinase – launched in May 2002.5,
6
Gleevec treats chronic
myeloid leukemia (CML) with 90-per-cent efficacy. According to our present knowledge almost
200 compounds with kinase inhibitory activity are in preclinical and clinical development against
more than 50 kinase targets for signal transduction therapy (the current clinical pipeline contains
53 small molecule kinase inhibitors)7 this definitely indicates the significance influence of this
area in drug research.8, 9 The most important small molecule synthetic tyrosine kinase inhibitors
which have been launched or are in late stage clinical development for cancers are Gleevec, the
epidermal growth factor receptor tyrosine kinase inhibitors: Iressa, Tarceva and Lapatinib, the BRaf inhibitor BAY-43-9006 and the VEGF kinase inhibitor, SU11248. Several agents targeting
other tyrosine kinases implicated in cancer, are in preclinical development.10 Our team
contributed significantly to the development of SU101 which has reached Phase III. Clinical
trials and to the predecessor oxindols of SU11248, one of the most promising clinical candidates
in cancer trials, which addresses inhibition of the tyrosine kinases PDGF-R, Flt-3, VEGF-R,
and c-kit11, 12, 13
Hit and lead finding strategies against validated targets can be achieved via high throughput
screening of huge combinatorial chemistry libraries or via rational drug discovery.
Theoretically combinatorial chemistry can result in extreme numbers (10100) of planned
compounds. This number can be reduced by applying pertinent restrictions (i.e. exclusion of
larger molecules and using Lipinski’s rules); however, approximately 1050 molecules remain
after applying such filters, which means that combinatorial chemistry still means as much as
shooting in the dark. This confirms the conclusion that testing random libraries for hit and lead
finding cannot be efficient and rational drug design is thus the preferred scenario for kinase
inhibitor drug discovery.14 The scheme of rational drug design is illustrated in Figure 1.
4
Figure 1. Scheme of rational drug design (HTS: High Throughput Screening, RDD: Rational Drug Design).
Originally, the term rational drug design was used for the planning of molecules, based on the
three-dimensional structure of targets. In the meantime, however, the concept of rational drug
design has been expanded and now it means the whole complex process including
pathomechanism-based target selection, target validation, structural biology, molecular
modelling, structure-activity relationships, pharmacophore-based compound selection and
pharmacological optimisation.15
Materials and Methods
Vichem Chemie Research Ltd has developed a unique hit and lead finding method based on its
Nested Chemical LibraryTM (NCL) technology (Figure 2.) The NCL was designed on the
platform of our up-to-date knowledge base what we have built up from the knowledge we
5
accumulated from our experience in the last 15 years of kinase inhibitory chemistry. Our library
is organized around 97 core structures and we have generated small focused sublibraries around
each core.
CVL: Chemical Validation Library
EVL: Extended Validation Library
ML: Master Library
Figure 2. Nested Chemical Library™ (NCL)
The Chemical Validation Library (CVL) includes ~300 compounds around 97 selected core
structures with proven kinase inhibitory activity on various kinase targets arising from work
published in the current literature and patent(s) (applications). (Figure 3 shows 10 representative
core structures)
6
N
N
N
N
1.
6.
36.
N
N
16.
N
29
N
N
N
N
N
O
13.
N
N
N
N
N
N
37.
38.
O
54.
86.
Figure 3. NCL is clustered around 97 core structures, 10 representative structures are shown
EVL is an extension of the CVL and covers almost 700 compounds based on the following
criteria: to increase the number of target kinases against which we have inhibitors available
(currently we synthesized inhibitors for 83 different kinases) and to include more analogues
around the proven kinase inhibitory leads from the CVL. The Master Library (ML) includes
novel compounds and analogue structures around CVL and EVL, extended by an additional ~ 10
000 compounds. We have a large series of new and patentable compounds in ML with a lot of
space for further chemical and pharmacological optimisation.
Typically, novel and potential kinase targets can be validated chemically by using the
Chemical Validation Library compounds16,
17, 18
. CVL compounds are tested in biochemical
assay for activity against a kinase of interest. Next, the selected and active inhibitors of a
particular kinase are applied to therapeutically relevant cellular assays in order to confirm the
role of the kinase activity in this biological model. The validated hits from the CVL will also
represent a good starting point for extended biological screening. Biochemical activity data
obtained for the selected compound sets from NCL can provide enough biological data for
7
rational computerized design of new analogues based on our Pharmacophore model generating
3DNET4WTM QSPAR approach.19,20,21 The program builds abstract pharmacophore (QSAR)
models from validated, significant descriptors selected by sequential or genetic algorithms.
Enhanced MLR (Multiple Linear Regression), PLS (Partial Least Squares), ANN (Artificial
Neural Network), LLM (Linear Learning Machine) methods are optional and can be combined
with PCA (Principal Component Analysis). Pharmacophore models are validated by external
validation, the validation set is not known for the program. New analogues are designed on the
basis of bioisosteric changes using validated standard reaction schemes. New potential hit
molecules are filtered by their ADMET properties, drug-likeness and patentability. Thousands of
molecular descriptors are calculated for hit compound structures and fed with 3DNT4WTM
QSPAR program system.
In the hit and lead optimisation work interesting compounds are selected by our pharmacophore
model and the ADMET filters. This in silico selection subsequently becomes synthesized. The
new compounds are then tested for biological activity, physicochemical parameters are being
calculated and measured . These data are fed back into the model for continuous improvement
and optimization. If we have good and reliable biochemical kinase assays available, we can
generate a lead compound against a particular target molecule within approximately five
chemical optimization cycles.
For optimisation of the hit and lead molecules we apply two methods: focused and diverse
optimisation. If the hit compound is patentable we apply focused optimisation: generating
various possible derivatives around the selected leads with traditional medicinal chemistry
approach. In case of the hit compound is not patentable we generate diverse molecular library
with virtual screening, pre-selecting them with the pharmacophore model. The new compounds
8
are synthesised with solution or solid-phase synthesis, validated with HPLC-MS and NMR and
tested in biological assay. The biological results are again fed back into the pharmacophore
model to accomplish the lead optimisation process. In summary, approximately five compound
optimization cycles are needed with this approach to generate
a series of sufficiently
characterized lead molecules against a kinase target. (Figure 4.). 22, 23, 24, 25, 26
Figure 4. Hit / Lead Finding Strategy
In Table 1 we present a series of validated kinase target molecules and the IC50 values of our best
hit compounds (without disclosing the structure but indicating the core structure). In table 2 we
present the hit rate, which we found using a series of compounds from our CVL and EVL library
against various kinases.
9
Table 1. Targets and inhibitors according to Core structures
Number of
Target
Indica-tions
tested
Hit compounds
Core
AX3359 IC50 = 25 pM
13.
AX12257 IC50 = 8 pM
58.
AX13237 IC50 = 3 nM
16.
AX9041 IC50 =10 nM
4.
AX13234 IC50 = 20 nM
4.
AX13985 IC50 = 77 nM
89.
AX11466 IC50 = 13 nM
32.
compounds
EGF-R
PDGF-R
Cancer
Cancer,
1796
580
gliomas
VEGF-R
RIP(RIPK-1)
Oncology
Inflammation
340
724
related
diseases
RICK
Viral
(RIPK-2)
infections
(HCMV),
AX11443 IC50 = 16 nM
664
AX8652 IC50 = 40 nM
AX7015 IC50 = 40 nM
Inflammation
related
diseases
10
29.
UL-97
Viral
927
AX11922 IC50 = 0.4 nM
4.
AX7100 IC50 = 13 nM
25.
AX39010 IC50 = 7 nM
85.20,27
infections
(HCMV)
PknG
SRPK-1
M.
627
Tuberculosis
AX38833 IC50 = 15 nM
infection
AX38824 IC50 = 15 nM
Oncology
432
AX2926 IC50 = 8 nM
16.
AX8757 IC50 = 43 nM
Akt
Oncology
349
AX971 IC50 = 33 nM
8.
AX9385 IC50 = 1.1 nM
ROCK-2
Oncology,
325
neural
AX6115 IC50 = 0.2 mM
1.
AX7425 IC50 = 0.7 mM
inflammation
and
regeneration
Table 2. Number of tested compounds on kinase panel
Target
Tested
Active
Target
Tested
Active
Abl
77
10
PDGFR
54
2
11
Akt
547
37
RICKK
664
64
C-Met
22
2
PKCa
195
14
CDK-1
249
14
PKnG
7435
165
CLK-1
206
41
Rip
724
146
CLK -2
209
19
ROCK-2
325
23
CLK -3
209
6
Src
75
4
InsR
250
9
SRPK-1
432
137
p38
64
7
SRPK-2
582
77
P70 S6K
209
14
UL97
927
442
Results and Discussion
Signal transduction therapy is in the front line of modern drug research and kinases represent
these days the most important target molecule family. More than 50 kinases have been identified
as therapeutically relevant target molecules in various pathological cases including cancer,
atherosclerosis, infectious diseases, neurodegenerative disorders and inflammatory diseases.
Because cellular signaling occurs via network signaling in several cases a multiple target
approach has to be considered. In cancer a series of kinases act like survival factors thus
providing primary targets for drug research. There is a good chance to treat various cancers with
potent compounds against the most important oncogenic signaling pathways . Furthermore
molecular diagnostic techniques serve to analyse the oncogenic pathways in the particular patient
in the a so-called personalized therapy where each patient finally would receive a cocktail of the
relevant inhibitors for his disease. Since kinases seem to have a major role in oncogenic
12
signaling the perspective is that we should have good approved drugs for the most important
oncogenic kinases.
As a general strategy for hit and lead finding against a novel kinase target we prefer to screen
focused libraries or to determine the 3D structures of the target kinase and perform docking
experiments. Our approach is to utilize our hit finding library and using these assay data and
calculating large series molecular descriptors for QSAR and pharmacophore model generation.
During our hit and lead optimisation process we utilize (and continuously improve) this
pharmacophore for pre-selection of the synthesizable compounds. We also use various ADME
filtering models we generated based on experimentally determined parameters (from literature
and from our own resources) to pre-select the synthesizable compounds not only for activity but
also for drug likeliness predictions. Thus we can significantly reduce the number of to be
synthesized compounds for a lead optimisation process and can come up with a promising lead
candidate within approximately 5 optimization cycles.
Acknowledgements
References
1.
2.
3.
Hanash S: Disease proteomics. Nature 2003; 422: 226-232.
Levitzki A: Signal transduction therapy- a novel-approach to disease management.
European Journal of Biochemistry 1994; 226: 1-13.
Hollósy, Ferenc; Mészáros, György; Bökönyi, Gyöngyi; Idei, Miklós; Seprődi, Attila;
Szende, Béla; Kéri, György: Cytostatic, cytotoxic and protein tyrosine kinase inhibitory
activity of ursolic acid in A431 human tumor cells. Anticancer Research 2000; 20(6B),
4563-4570
13
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Kéri Gy.; Rácz G.; Magyar K.; Örfi L.; Horváth A.; Schwáb R.; Hegymegi B B; Szende
B: Pro-apoptotic and anti-apoptotic molecules affecting pathways of signal transduction.
Annals of the New York Academy of Sciences (2003 Dec), 1010 109-12.
Deininger M.W., Goldman J.M., Lydon N., Melo J.V: The tyrosine kinase inhibitor
CGP57148B selectively inhibits the growth of BCR-ABL-positive cells. Blood 1997; 90:
3691-3698.
Goldman J.M: Tyrosine-kinase inhibition in treatment of chronic myeloid leukaemia.
Lancet 2000; 355: 1031-1032.
Klebl B., G. Müller, 2005 Experimental Opinion on Therapeutic Targets (manuscript
submitted)
Dancey, J., Sausville, E. A: Issues and progress with protein kinase inhibitors for cancer
treatment. Nature 2003; 2: 296-313.
Kolibaba K.S., Druker B.J: Protein tyrosine kinases and cancer. Biochim. Biophys. Acta
1997; 1333: F217-F248.
Laird AD, Cherrington JM: Small molecule tyrosine kinase inhibitors: clinical
development of anticancer agents.Expert Opin Investig Drugs. 2003 Jan;12(1):51-64.
Strawn L.M., McMahon G., App H., Schreck R., Kuchler W.R., Longhi M.P., Hui T.H.,
Tang C., Levitzki A., Gazit A., Chen I., Kéri Gy., Őrfi L., Risau W., Flamme I., Ullrich
A., Hirth K.P., Shawver L.K: Flk-1 as a Target for Tumor Growth Inhibition. Cancer
Research 1996; 56: 3540-3545.
Hirth, K.P., Shawver, L.K., Kéri, Gy., Örfi, L., Székely, I., Haimichael, J., Ullrich, A.,
Lammers, R: Treatment of platelet derived growth factor related disorders such as
cancers U.S. Patent No.: 5990141
Morimoto AM, Tan N, West K, McArthur G, Toner GC, Manning WC, Smolich BD,
Cherrington JM Gene expression profiling of human colon xenograft tumors following
treatment with SU11248, a multitargeted tyrosine kinase inhibitor.
Oncogene. 2004 Feb 26;23(8):1618-26
Beroza P., Villar H.O., Wick M.M., Martin G.R: Chemoproteomics as a basis for post
genomic drug discovery. Drug Discov. Today 2002; 7: 807-814.
Dániel Erős, László Őrfi, György Kéri: Rational Drug design and signal transduction
therapy. Pharmachem 2004; 1.
Őrfi, László; Wáczek, Frigyes; Kövesdi, István; Mészáros, György; Idei, Miklós;
Horváth, Anikó; Hollósy, Ferenc; Mak, Marianna; Szegedi, Zsolt; Szende, Béla; Kéri,
György: New antitumor leads from a peptidomimetic library. Letters in Peptide Science
1999; 6(5-6), 325-333.
Klebl, B. M.: Chemical Kinomics: A Target Gene Family Approach in Chemical
Biology. Drug Discovery Today:Technologies. 2004; 1: 25-34.
Klebl, B. M.; Daub, H.; Kéri, Gy.: Chemical Kinomics. In: Chemogenomics in Drug
Discovery: A Medicinal Chemistry Perspective. Kubinyi H, Müller G (Eds.) WILEYVCH Verlag GmbH & Co. KgaA, Weinheim. 2004; 167-190.
Erős, D.; Kövesdi, I.; Őrfi, L.; Takács-Novák, K.; Acsády, Gy.; Kéri, Gy: Curr. Med.
Chem., 2002; 9, 1819-1829.
Erős, D.; Kéri, Gy.; Kövesdi, I.; Szántai-Kis, Cs.; Mészáros, Gy.; Őrfi, L: Mini-Reviews
in Medicinal Chemistry, 2004; 4, 167-177.
Kövesdi, I.; Kéri, Gy.; Ôrfi, L. WO Patent 02/082329.
14
22.
23.
24.
25.
26.
27.
Koul, Anil; Klebl, Bert; Mueller, Gerhard; Missio, Andrea; Schwab, Wilfried;
Hafenbradl, Doris; Neumann, Lars; Sommer, Marc-Nicola; Mueller, Stefan; Hoppe,
Edmund; Freisleben, Achim; Backes, Alexander; Hartung, Christian; Felber, Beatrice;
Zech, Birgit; Engkvist, Ola; Kéri, György; Őrfi, László; Bánhegyi, Péter; Greff,
Zoltán; Horváth, Zoltán; Varga, Zoltán; Markó, Péter; Pató, János; Szabadkai, István;
Székelyhidi, Zsolt; Wáczek, Frigyes; WO 2005023818 A2
Blencke, Stephanie; Zech, Birgit; Engkvist, Ola; Greff, Zoltán; Őrfi, László; Horváth,
Zoltán; Kéri, György; Ullrich, Axel; Daub, Henrik. Axxima Pharmaceuticals AG,
Munchen, Germany. Chemistry & Biology 2004; 11(5), 691-701.
Godl Klaus, Missio Andrea, Daub Henrik, Stein-Gerlach Matthias, Greff Zoltan. WO
2004013633
Pató, J.; Kéri, Gy.; Örfi, L.; Wáczek, F.; Horváth, Z.; Bánhegyi, P.; Szabadkai, I.;
Marosfalvi, J.; Hegymegi-Barakonyi, B.; Székelyhidi, Zs.; Greff, Z.; Choidas, A.;
Bacher, G.; Daub, H.; Obert, S.; Kurtenbach, A.; Habenberger, P. WO Patent 02/094796
A2, 2002.
Örfi, László; Wáczek, Frigyes; Pató, János; Varga, István; Hegymegi-Barakonyi, Bálint;
Houghten, Richard A.; Kéri, György: Improved, high yield synthesis of 3H-quinazolin-4ones, the key intermediates of recently developed drugs. Current Medicinal Chemistry
(2004; 11(19), 2549-2553.
János Pató, György Kéri, László Örfi, Frigyes Wáczek, Zoltán Horváth, Péter Bánhegyi,
István Szabadkai, Jenő Marosfalvi, Bálint Hegymegi-Barakonyi, Zsolt Székelyhidi,
Zoltán Greff, Axel Choidas, Gerald Bacher, Andrea Missio, Anil Koul; US
2004/0171603 A1
15