U.S. patent application number 17/006264 was filed with the patent office on 2021-04-01 for methods for the prediction of a personalized esa-dose in the treatment of anemia.
The applicant listed for this patent is ALBERT-LUDWIGS-UNIVERSITAT FREIBURG, DEUTSCHES KREBSFORSCHUNGSZENTRUM STIFTUNG DES OFFENTLICHEN RECHTS. Invention is credited to Michael Jarsch, Ursula Klingmueller, Andreas Raue, Agustin Rodriguez Gonzalez, Max Schelker, Marcel Schilling, Bernhard Steiert, Jens Timmer.
Application Number | 20210093696 17/006264 |
Document ID | / |
Family ID | 1000005273729 |
Filed Date | 2021-04-01 |
![](/patent/app/20210093696/US20210093696A1-20210401-D00001.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00002.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00003.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00004.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00005.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00006.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00007.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00008.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00009.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00010.png)
![](/patent/app/20210093696/US20210093696A1-20210401-D00011.png)
View All Diagrams
United States Patent
Application |
20210093696 |
Kind Code |
A1 |
Rodriguez Gonzalez; Agustin ;
et al. |
April 1, 2021 |
METHODS FOR THE PREDICTION OF A PERSONALIZED ESA-DOSE IN THE
TREATMENT OF ANEMIA
Abstract
An integrative pharmacokinetic/pharmacodynamics (PK/PD) ESA-EpoR
mathematical model calculates the binding behavior of
erythropoiesis stimulating agents (ESA). The invention provides
methods for the determining of ESA binding sites in cells or
patients suffering from anemia. Knowing the amount of ESA binding
sites enables the clinical practitioner to optimize the dosage
regimen during a treatment of anemia, in particular in patients
suffering from a cancerous disease. Further provided are methods
for screening ESAs which have a higher specificity for cells
strongly expressing the EPO receptor such as colony forming
units-erythroid (CFU-E) cells, and not to cells with a low level of
EPO receptor cell surface expression, which is the case in cancer
cells. Also provided is a computer implemented method, comprising
the use of the mathematical model of the invention.
Inventors: |
Rodriguez Gonzalez; Agustin;
(Heidelberg, DE) ; Schilling; Marcel; (Heidelberg,
DE) ; Klingmueller; Ursula; (Heidelberg, DE) ;
Raue; Andreas; (Cambridge, MA) ; Schelker; Max;
(Berlin, DE) ; Timmer; Jens; (Freidburg, DE)
; Jarsch; Michael; (Bad Heilbrunn, DE) ; Steiert;
Bernhard; (Freiburg, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DEUTSCHES KREBSFORSCHUNGSZENTRUM STIFTUNG DES OFFENTLICHEN
RECHTS
ALBERT-LUDWIGS-UNIVERSITAT FREIBURG |
Heidelberg
Freiburg |
|
DE
DE |
|
|
Family ID: |
1000005273729 |
Appl. No.: |
17/006264 |
Filed: |
August 28, 2020 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15319073 |
Dec 15, 2016 |
10796799 |
|
|
PCT/EP2015/063775 |
Jun 18, 2015 |
|
|
|
17006264 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/20 20180101;
G01N 33/80 20130101; G01N 2800/222 20130101; G01N 33/6893 20130101;
G16B 5/00 20190201; A61K 38/1816 20130101; G01N 33/5044 20130101;
G16H 20/17 20180101; G01N 33/721 20130101; G16H 50/50 20180101;
A61K 9/0019 20130101 |
International
Class: |
A61K 38/18 20060101
A61K038/18; G01N 33/50 20060101 G01N033/50; G01N 33/68 20060101
G01N033/68; G01N 33/80 20060101 G01N033/80; G16H 50/50 20060101
G16H050/50; G16B 5/00 20060101 G16B005/00; G16H 20/17 20060101
G16H020/17; G16H 50/20 20060101 G16H050/20; A61K 9/00 20060101
A61K009/00; G01N 33/72 20060101 G01N033/72 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 18, 2014 |
EP |
14173054.9 |
Claims
1. A method for determining a dosage of an Erythropoiesis
Stimulating Agent (ESA) that is sufficient for treating anemia in a
patient, the method comprising the steps of: a) Calculating a
degradation of hemoglobin per time for the patient from a
hemoglobin concentration of the patient from at least two separate
time points, b) Determining a present hemoglobin concentration of
the patient from a concentration of hemoglobin from a recent blood
sample obtained from the patient, c) Calculating an ESA dosage
based on the degradation of hemoglobin per time and the present
hemoglobin concentration to treat anemia in the patient; and d)
Administering the ESA dosage to the patient to thereby treat anemia
in the patient.
2. (canceled)
3. The method according to claim 1, wherein the hemoglobin
concentration of the patient from at least two separate time points
is determined by measuring the hemoglobin concentrations in blood
samples obtained from the patient from at least two different time
points, or from a past anemia treatment history of the patient.
4. The method of claim 1, further including the step of: Monitoring
the hemoglobin concentration of the patient over time after the
administration of the ESA dosage.
5. The method of claim 1, wherein the administration is a
subcutaneous or intravenous injection.
6. The method of to claim 4, wherein the hemoglobin concentration
of the patient is monitored by obtaining a blood sample from the
patient.
7. The method of claim 1, further including the steps of: a)
Monitoring the clearance of said ESA dosage from a serum in said
patient, b) Calculating from the clearance of said ESA dosage in
said patient the number of initial ESA binding sites present in
said patient using a non-linear dynamic pharmacokinetic (PK)
ESA-EPO-R pathway model, and c) Adjusting the ESA dosage
administered to the patient in accordance with the number of ESA
binding sites.
8. (canceled)
9. (canceled)
10. The method of claim 7, wherein the ESA dosage is administered
subcutaneously, and wherein the non-linear dynamic pharmacokinetic
(PK) ESA-EPO-R pathway model considers clearance of the
administered ESA in a blood compartment, transport of the
administered ESA from an interstitial compartment into the blood
compartment, and clearance of the ESA in the interstitial
compartment.
11. The method of claim 1, wherein the ESA dosage is selected from
the group of an Epoetin alfa dosage, an Epoetin beta dosage, an
erythropoiesis stimulating protein dosage and a Continuous
erythropoietin receptor activator dosage.
12. The method of claim 7, wherein said non-linear dynamic
pharmacokinetic (PK) ESA-EPO-R pathway model is based on a system
of the ordinary differential equations (ODE): d [ ESASC ] dt = -
ksc clear [ ESASC ] / ( k sc_clear _sat + [ ESASC ] ) - ksc_out [
ESASC ] ( 2.1 . ) d [ ESA ] dt = k sc out [ ESASC ] - k clear [ ESA
] - k on [ ESA ] [ EpoR ] + k off [ ESAEpoR ] + k ex [ ESAEpoRi ] (
2.2 . ) d [ EpoR ] dt = - k on [ ESA ] [ EpoR ] + k off [ ESAEpoR ]
+ k t B max - k t [ EpoR ] + k ex [ ESAEpoRi ] ( 2.3 . ) d [
ESAEpoR ] dt = k on [ ESA ] [ EpoR ] - k off [ ESAEpoR ] - k e [
ESAEpoR ] ( 2.4 . ) d [ ESAEpoRi ] dt = k e [ ESAEpoR ] - k ex [
ESAEpoRi ] - k di [ ESAEpoRi ] - k de [ ESAEpoRi ] ( 2.5 . ) d [
dESAi ] dt = k di [ ESAEpoRi ] ( 2.6 . ) d [ dESAe ] dt = k de [
ESAEpoRi ] , ( 2.7 . ) ##EQU00004## where, ESA is
Erythropoiesis-stimulating agent in medium/blood, EpoR is
Erythropoietin receptor, ESA EpoR is a complex of ESA bound to EpoR
on the cell surface, ESAEpoR.sub.i is an internalized complex of
ESA bound to EpoR, dESA.sub.i is intracellular degraded ESA,
dESA.sub.e is extracellular degraded ESA, ESA.sub.SC is ESA in the
subcutaneous compartment, k.sub.sc_clear is ESA clearance in the
subcutaneous compartment, k.sub.sc_clear_sat is saturation of ESA
clearance in the subcutaneous compartment, K.sub.sc_out is an ESA
transportation constant to the blood compartment, k.sub.clear is an
ESA clearance constant in the blood compartment, k.sub.on is an
ESA-EpoR association rate/on-rate, k.sub.off is an ESA-EpoR
dissociation rate/off-rate, k.sub.t is a ligand-independent
receptor turnover rate, k.sub.e is an ESA-EpoR complex
internalization constant, k.sub.ex is an ESA and EpoR recycling
constant, k.sub.di is an intracellular ESA degradation constant,
k.sub.de is an extracellular ESA degradation constant, and wherein
B.sub.max is the number of initial ESA binding sites per cell/per
patient.
13. A method for identifying an Erythropoiesis Stimulating Agent
(ESA) having a specific activity for cells with a high cell surface
expression of Erythropoietin-receptor (EPO-R), comprising the steps
of: a) Obtaining the half maximal effective concentrations (EC50)
of a candidate ESA and a reference ESA for EPO-R activation in a
first cell, b) Obtaining the EPO-R activation induced by the
candidate ESA and the reference ESA at their respective EC50 as
obtained in (a) in a second cell, wherein said second cell is
characterized by a significantly lower cell surface expression of
EPO-R compared to the first cell, wherein a decreased activation of
EPO-R in said second cell by the candidate ESA compared to the
activation of EPO-R in said second cell by the reference ESA, is
indicative for the specificity of said candidate ESA for cells with
a strong cell surface expression of EPO-R.
14. The method according to claim 13, wherein said reference ESA is
Epoetin beta.
15. The method according to claim 13, wherein the method is an
in-vitro or an in-silico method.
16. The method according to claim 15, wherein the method is the
in-silico method and wherein said EPO-R activation is calculated
with a non-linear dynamic ESA-EPO-R pathway model.
17. The method according to claim 16, wherein said non-linear
dynamic ESAEPO-R pathway model is based on the following ODE: d [
ESA ] dt = - k on [ ESA ] [ EpoR ] + k off [ ESAEpoR ] + k ex [
ESAEpoRi ] ( 1.1 . ) d [ EpoR ] dt = - k on [ ESA ] [ EpoR ] + k
off [ ESAEpoR ] + k t B max - k t [ EpoR ] + k ex [ ESAEpoRi ] (
1.2 . ) d [ ESAEpoR ] dt = k on [ ESA ] [ EpoR ] - k off [ ESAEpoR
] - k e [ ESAEpoR ] ( 1.3 . ) d [ ESAEpoRi ] dt = k e [ ESAEpoR ] -
k ex [ ESAEpoRi ] - k di [ ESAEpoRi ] - k de [ ESAEpoRi ] ( 1.4 . )
d [ dESAi ] dt = k di [ ESAEpoRi ] ( 1.5 . ) d [ dESAe ] dt = k de
[ ESAEpoRi ] , wherin B max is the number of initial ESA binding
sites ( 1.6 . ) ##EQU00005## where, ESA is
Erythropoiesis-stimulating agent in medium/blood, EpoR is
Erythropoietin receptor, ESA EpoR is a complex of ESA bound to EpoR
on the cell surface, ESAEpoR.sub.i is an internalized complex of
ESA bound to EpoR, dESA.sub.i is intracellular degraded ESA,
dESA.sub.e is extracellular degraded ESA, ESA.sub.SC is ESA in the
subcutaneous compartment, k.sub.sc_clear is ESA clearance in the
subcutaneous compartment, k.sub.sc_clear sat is saturation of ESA
clearance in the subcutaneous compartment, K.sub.sc_out is an ESA
transportation constant to the blood compartment, k.sub.clear is an
ESA clearance constant in the blood compartment, k.sub.on is an
ESA-EpoR association rate/on-rate, k.sub.off is an ESA-EpoR
dissociation rate/off-rate, k.sub.t is a ligand-independent
receptor turnover rate, k.sub.e is an ESA-EpoR complex
internalization constant, k.sub.ex is an ESA and EpoR recycling
constant, k.sub.di is an intracellular ESA degradation constant,
k.sub.de is an extracellular ESA degradation constant.
18. The method according to claim 15, wherein the method is the
in-vitro method, and wherein said first cell is a cell ectopically
expressing EPO-R, such as H838-EpoR, and/or wherein said second
cell is not ectopically expressing EPO-R, such as H838.
19. A computer implemented method for assessing the number of ESA
binding sites in a cell, or a an organism, the method comprising
(a) Obtaining the depletion rate of an ESA in said cell or the
organism, (b) Calculating the amount of ESA binding sites in said
cell or organism based on the depletion rate of the ESA using a
non-linear dynamic EPO-R pathway model.
20. The method according to claim 19, wherein said organism is a
patient or wherein said cell is a cell endogenously expressing the
EPO-R receptor, and wherein the cell is a red blood cell precursor
cell, or a tumor cell, or a cell ectopically expressing EPO-R.
21. The method according to claim 19, wherein the patient is a
human patient, and wherein step (a) constitutes the obtaining the
depletion rate of an ESA as acquired in a serum sample of a patient
at a time point subsequent to the administration of an initial ESA
dose to said patient, and step (b) constitutes calculating the
amount of ESA binding sites based on the depletion rate of the ESA
using a non-linear dynamic pharmacokinetic (PK) ESA-EPO-R pathway
model.
22-24. (canceled)
25. A method for estimating the biological activity of an ESA,
comprising the steps of: Calculating the occupancy of the EPO
receptor on human CFU-E cells in response to a range of ESA
concentrations using the non-linear dynamic pharmacokinetic (PK)
ESA-EPO-R pathway model, a) Calculating the area under the curve
for the ESA from the resultant of step (a) as a measure for EPO
receptor occupancy of the ESA, b) Calculating the concentration of
the ESA for which the half maximum occupancy of the EPO receptor is
reached to obtain an EC50.sub.ESA, c) Compare the EC50.sub.ESA with
a predetermined EC50.sub.EPOalfa or EC50.sub.EPObeta, Wherein the
difference between the EC50.sub.ESA compared to the predetermined
EC50.sub.EPOalfa or EC50.sub.EPObeta correlates with the difference
of the biological activity of the ESA compared with the biological
activity of EPO alfa or EPO beta.
26. The method according to claim 25, wherein the EC50.sub.EPOalfa
or EC50.sub.EPObeta are predetermined by performing in addition
steps (a) to (c) with EPO alfa or EPO beta as the ESA to obtain in
step (c) the EC50.sub.EPOalfa or EC50.sub.EPObeta.
27-31. (canceled)
Description
RELATED APPLICATIONS
[0001] This Application is a Continuation of application Ser. No.
15/319,073, now U.S. Pat. No. 10,796,799, filed on Dec. 15, 2016,
which is the U.S. National Stage of International Application
PCT/EP15/63775, filed Jun. 18, 2015, which designates the U.S.,
published in English, and claims priority under 35 U.S.C.
.sctn..sctn. 119 or 365(c) to European Application No. 14173054.9,
filed on Jun. 18, 2014 in the European Patent Office. The entire
teachings of the above applications are incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] The present invention pertains to the use of an Integrative
pharmacokinetic/pharmacodynamics (PK/PD) ESA-EpoR mathematical
model for calculating the binding behaviour of erythropoiesis
stimulating agents (ESA). The invention provides methods for the
determining of ESA binding sites in cells or patients suffering
from anemia. Knowing the amount of ESA binding sites enables the
clinical practitioner to optimize the dosage regimen during a
treatment of anemia, in particular in patients suffering from a
cancerous disease. Further provided are methods for screening ESAs
which have a higher specificity for cells strongly expressing the
EPO receptor such as colony forming units-erythroid (CFU-E) cells,
and not to cells with a low level of EPO receptor cell surface
expression, which is the case in cancer cells. Also provided is a
computer implemented method, comprising the use of the mathematical
model of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1A are plots showing depletion of Epo alfa and Epo beta
by murine BaF3 cells stably expressing the murine EpoR (BaF3-mEpoR)
whereas parental BaF3 cells had no impact underscoring the
specificity of the assay.
[0004] FIG. 1B are plots of depletion by BaF3 cells stably
expressing the human EpoR (BaF3-hEpoR) or parental BaF3 cells.
[0005] FIG. 1C shows the K.sub.D of the different ESA to the
respective association and dissociation rates, revealing that the
association of NESP and CERA is much slower compared to Epo alpha
and Epo beta whereas the dissociation rate is enhanced.
[0006] FIG. 2A is a histogram of NSCLC cell lines H838, H1299, A549
and H1944, with H838 and H1299 indicating moderate expression
levels of EpoR mRNA and A549 indicating low levels of EpoR
mRNA.
[0007] FIG. 2B are gels indicating enrichment by
immunoprecipitation and detection by immunoblotting that reveals
the presence of the EpoR protein in H838 and H1299 cell lines and
at very low levels in A549 cell line, whereas it was absent in
H1944 cell line.
[0008] FIG. 2C are plots showing the binding properties of the EpoR
expressed in the NSCLC cell lines H838, H1299, A549 and H1944, with
Epo beta being depleted by the NSCLC cell lines harboring a
functional EpoR, but not by the EpoR negative NSCLC cell line
H1944.
[0009] FIG. 3A is a plot of time-resolved analysis of Epo beta
depletion revealing rapid reduction of Epo beta from the
supernatants of hCFU-E but not of hHSC that lack the EpoR.
[0010] FIG. 3B is a plot of hEpoR in H838 (H838-hEpoR) showing by
enrichment using immunoprecipitation and immunoblotting that
expression of EpoR was highly increased and the phosphorylated EpoR
was substantially elevated.
[0011] FIG. 3C are gels of depletion experiments and model-based
analysis revealing binding properties rather similar to hCFU-E
establishing the H838-hEpoR cell line as suitable model system to
examine the impact of different ESA on cells harboring high levels
of the EpoR as observed in the hematopoietic system versus cells
expressing low levels as in the tumor context.
[0012] FIG. 4A plot the results of stimulations that were performed
for different ESA concentrations and predicted the EC.sub.50 for
both Epo beta and CERA in cells with high EpoR levels.
[0013] FIG. 4B indicate that as cells deplete less Epo beta, Epo
beta results in stronger activation than CERA in cells with low
levels of EpoR.
[0014] FIG. 5A plots pharmacokinetic behavior of increasing CERA
concentrations in healthy volunteers, with circles showing the mean
values of CERA concentrations in serum, and solid lines
representing the trajectories predicted for the CERA clearance for
the given concentrations and the experimental data.
[0015] FIG. 5B plotspharmacokinetic behavior of increasing CERA
concentrations in NSCLC patients in stage III or IV, with circles
showing the mean values of CERA concentrations in serum, and solid
lines representing the trajectories predicted for the CERA
clearance for the given concentrations and the experimental
data.
[0016] FIG. 5C is a histogram indicating the number of CFU-E cells
for each cancer patient, shown as a relative comparison of CERA
clearance capability (% of CFU-E) of NSCLC patients and healthy
subjects, and showing a high patient-to-patient variability.
[0017] FIG. 6A shows an ESA-EpoR in vitro trafficking model.
[0018] FIG. 6B is an ESA-EpoR in vivo PK/PD model showing the
correlation between the individual patient histories with the PK/PD
data and these ones with the number of CFU-E per patients, and
these ones with the outcome of the ESA treatment.
[0019] FIG. 6C is an ESA-EpoR in vivo PK/PD model, including the
additional reactions of the production of Hb by active ESA-EPO-R
signalling, and the patient specific degradation of Hb.
[0020] FIG. 7 are plots of STAT5 phosphorylation in response to
stimulation with Epo beta or CERA in H838 and hCFU-E cells,
indicating that the activation of EpoR signaling by CERA is less
effective in cells with low levels of the EpoR such as NSCLC cells
(FIG. 7 left panel) compared to cells with higher levels of the
EpoR like hCFU-E (FIG. 7 right panel).
[0021] FIG. 8A is an integrative PK/PD ESA-EpoR model that
describes all NSCLC patient data sets.
[0022] FIG. 8B is an integrative PK/PD ESA-EpoR model that
describes a patient data set involving NSCLC patient ID:2101
(clinical trial CSR NA17101).
[0023] FIG. 8C is an integrative PK/PD ESA-EpoR model that
describes a patient data set involving healthy subject ID:25
(clinical trial WP16422).
[0024] FIG. 8D is a histogram estimating the number of ESA-binding
sites for individual cancer patients, demonstrating that the
distribution of the estimated KHb_deg parameter differs widely in
healthy subjects and NSCLC patients (FIG. 8D left panel), and
showing a high patient-to-patient variability and a very different
distribution from the healthy subjects (FIG. 8D right panel).
[0025] FIG. 9A present the results of CERA treatment simulations
based on the patient-specific parameters in three NSCLC
patients
[0026] FIG. 9B presents the results of an integrative PK/PD
ESA-EpoR mathematical model that predicted a systematic overdosing
of a large fraction of NSCLC IIIB-IV patients treated within the
EMEA-recommended ESA guidelines for anemia in cancer.
[0027] FIG. 9C presents the results of an integrative PK/PD
ESA-EpoR mathematical model that optimizes the ESA dosing and
scheduling to achieve a hematological response within the limits of
the ESAs guidelines for most of the NSCLC IIB-IV patients,
minimizing the risk of overdosing.
[0028] FIG. 9D represent a prediction for all ESA regimens required
to effectively treat all the NSCLC IIIB-IV patients of the CSR
NA17101 clinical trial.
DESCRIPTION
[0029] Lung carcinoma is the most frequent cause of death in cancer
with 1.59 million of deaths in 2012, of which 80% were diagnosed as
Non-Small Cell Lung Carcinoma (NSCLC). Most of the patients are
diagnosed in a stage IIIB or IV and treated with a combination of
platinum compounds and taxanes, gemcitabine or vinorelbine as a
first line of treatment. In lung carcinoma there is a high
prevalence of anemia ([Hb].ltoreq.11 g/dL), ranging from 50% to
70%, although in advanced stages it could reach up to 90%. The
anemic grade depends on the therapy, tumor stage and duration of
the disease. Cancer related anemia reduces the quality of life
(Cella et al, 2004) and it is considered a risk factor for
mortality in cancer patients (Caro 2001). Furthermore, it has been
reported that anemia affects the outcome of the anticancer therapy,
diminishing the chemotherapy response in NSCLC patients (Albain
1991, MacRae 2002 and Robnett 2002).
[0030] The etiology of anemia in cancer is complex due to the
multifactorial causes such as deficiencies in vitamin B12 and folic
acid, bleeding, haemolysis, inflammatory cytokines secreted in the
tumor context and reduction in the iron uptake (Weiss and Goodnough
N.Engl. J Med 2005) are some of the causal origins of cancer
related anemia. In addition, platinum-based chemotherapy inhibits
the renal production of Epo and exerts myelosuppresion what
increases the anemia (Groopman 1999, Kosmidis 2005, Ludwig
2004).
[0031] Currently, there are two available therapeutic approaches
for the management of anemia in cancer patients: homologous red
blood cells (RBC) transfusions or administration of Erythropoiesis
Stimulating Agents (ESAs). The first option has an immediate but
transient improvement of anemia. The disadvantages of the RBC
transfusions are: the potential risk of infectious agent
transmission, immunosuppression, hemolysis, allergic reactions, a
non-sustained relief of anemia symptoms and the risk of
transfusion-related acute lung injury (Klein 2007). Furthermore,
the clinical demand of transfusions in lung carcinoma is higher in
NSCLC than in other cancers (Barret-Lee 2000), with 40% requiring
at least one transfusion, and 22% requiring more than one (Langer
2002, Barret-Lee, Estrin 1999, Skillings 1999).
[0032] The second therapeutic alternative is based on the
administration of ESAs. This approach increases and sustains the
haemoglobin (Hb) levels, reduces the likelihood of RBC
transfusions, and improves the quality of life (Meta-analisis
cochrane: Tonia, Melter, The Cochrane library 2012). ESA treatments
increase the red blood cell (RBC) production by specific activation
of erythropoiesis receptor (EpoR) of erythrocytic progenitors in
the bone marrow (Egrie 1986, 2003) (Wu, Liu Lodish, Cell 1995).
However, this treatment is not effective in 30% to 40% of patients.
The reasons underlying this failure are not yet defined, but
different ESA administration protocols showed a significant
reduction of such a large portion of patients (Hirsh 2007)
suggesting the need of further protocol-optimization in managing
NSCLC related anemia. Another disadvantage of the ESA treatments is
that conflicting reports on the improvement in tumor response and
survival were published. The use of ESAs in cancer is restricted by
label on the settings of only cancer and only radiotherapy
(Metanalisis Aapro 2012) (11 Meta-analyses 2010). This restriction
implemented by the national authorities was based on the outcomes
of the ENHANCE, DAHANCA-10, EPO-CAN-20 and AMG20010103 studies, in
which found that ESA treatments increases cancer disease
progression, thromboembolic events and mortality (Henke 2003
"ENHANCE", Overgaard 2007 "DAHANCA-10", Wright et al. J Clin Oncol
2007 "EPO-CAN-20" and Smith et al 2008 "AMG 20010103") (Metanalisis
Aapro 2012).
[0033] Also reported was an increment of mortality in the ESA
treated patients in the chemotherapy setting (Leyland-Jones 2005
"BEST"), (Hedenus 2003 "AMG 20000161"), (Thomas 2008) and "PREPARE"
(Untch 2011a, 2011b), (IV:Katodritou 2008) (Bennett 2008, Glaspy
2010), but several studies contradicted the previous results
reporting no significant difference in mortality (Pirker et al
2008), (Moebus 2010) (Engert 2010), nor significant impact on the
disease progression (Warner 2004, Reed 2005, Bohlius 2009, Gupta
2009, Ludwig 2009, Nagel 2011, Hershman 2009, Nitz 2011, Machtay
2007 and Glaspy et al 2010). There are also other studies that
reported an increment of therapy effect by ESA treatment (Hadland
2009), and an increment of survival benefit as well (Littlewood
2001, Vansteenkiste 2002, and Delarue 2013).
[0034] These contradicting reports aimed to perform meta-analyses
of the different trials. All meta-analyses reported that ESAs
treatments reduce the transfusions requirements but still there are
some contradicting findings regarding the mortality risk of ESA
treatments in chemotherapy settings. (Bennett 2009, Bohlius 2009,
Tonelli 2009, Hedenus 2005, Boogaerts 2006, Seidenfeld 2006, Ludwig
2009, Aapro 2009b, Glaspy 2010, Tonia et al Cochrane 2012). The
reasons for such variability of conclusions might be due to
differences in the study designs, heterogeneity of the treated
patients, the varying ESA dose regimens and data analysis.
[0035] Since the first safety issues about ESA treatments were
reported in 2003, several groups worked in the hypothesis of a
functional EpoR in tumor context, as the logical mechanism exerting
the tumor progression under ESA treatments in anemic cancer
patients. In tumor tissue and carcinoma cell lines, EpoR mRNA
expression levels were detected but very low in comparison with
erythroid progenitors. The results were reproduced at protein level
by western blot, immunohistochemistry, and in an animal model.
These findings were however questioned by other groups due to the
use of unspecific antibodies in some of the studies, the lack of
signaling activation upon ESA stimulation, absence of EpoR in
biopsies or the non-effect of ESAs treatment in tumor animal
models. In the positive cases for specific EpoR expression at
transcript and protein levels, EpoR levels were ranging from 10- up
to 1000-fold lower than in Epo responsive cell lines, or by
overexpression of receptor or in erythroid progenitors. This low
level of EpoR expression in non-erythroid cells is an intrinsic
liability of any experimental approach to study on EpoR presence
and functionality upon ESA stimulations in tumor cells.
Furthermore, the radioactive Epo-binding assay is one of the most
sensitive approaches at the time of revealing ESA and EpoR binding
behavior on the cellular surface. It has been reported that lower
levels than 50 receptors per cell makes the measurements unreliable
(Um 2007). The very low expression of EpoR in the tumor cell lines
and tissue in addition to wide used of unspecific antibodies have
constituted so far the "Achilles heel" of the functional studies of
EpoR in a tumor context.
[0036] The characterization and prediction of an effective and
safer ESA treatment of anemia in cancer and chemotherapy setting
constitutes a complex question. The outcome is influenced by the
dynamic interplay of many components, and it has to be addressed
from multiple angles, which requires quantitative experimental
studies at different levels. These different perspectives go from
molecular studies of EpoR activation in a single cell to the study
of ESAs pharmacokinetic (PK) and pharmacodynamics (PD) in carcinoma
patients. Due to the complexity, non-linear relationship and
involvement of multiple scales, this requires a Systems Biology
approach that combines experimental data generation and
mathematical modeling. The inventors focused in NSCLC, due to its
high impact in the populations and the high prevalence of anemia.
This would also simplify the heterogeneity of the outcomes in the
ESA treatments and avoid any effect by the different underlying
malignancies (11:24,32). Due to the wide variation of responses to
ESA treatments in NSCLC patients, the inventors used individual
patient data, in order to standardize and harmonize outcomes across
the clinical trial. This approach will allow us to identify and
correlate defined patient populations with hematological responses.
The inventors also performed model-based predictions of the minimal
personal effective ESA concentration (MPEC) in order to avoid the
transient overdosing, which is suspected to be associated with
thrombovascular events and potential EpoR activation in tumor
context.
[0037] Mathematical modeling of biological systems has become a
widely used approach to better understand the system behavior as a
whole rather than observing isolated parts (Kitano, 2002). The
rapid development of quantitative molecular biology (Cox and Mann,
2011) enables to calibrate mathematical models to experimental data
and therefore to generate model predictions. Established approaches
for modeling and parameter estimation are publicly available in
software tools like SBML-PET, COPASI or PottersWheel (Zi and Klipp,
2006; Hoops et al., 2006; Maiwald and Timmer, 2008).
[0038] In view of the unsolved questions regarding the use of ESA
in the treatment of anemia, in particular in the context of a
cancer patient, it was an objective of the present invention to
provide novel means and methods to assess the optimal dosage of ESA
in a patient and thereby to avoid over- or under dosing.
Furthermore the invention intends to provide diagnostic tools to
supply the clinical practitioner with additional information about
the anaemic status of a patient before preparing a treatment
plan.
[0039] In one aspect the above problem is solved by a method for
determining the dosage of an Erythropoiesis Stimulating Agent (ESA)
that is sufficient for treating anemia in a patient, the method
comprising the steps of (a) Calculating from the hemoglobin
concentration of the patient from at least two separate time points
the patient's individual hemoglobin degradation rate (degradation
of hemoglobin per time), (b) Determining the concentration of
hemoglobin from a recent blood sample obtained from the patient
(the patient's present hemoglobin concentration), and (c)
Calculating based on the patient's hemoglobin degradation rate and
the patient's present hemoglobin concentration the ESA dosage
sufficient for treating the anemia in the patient. The method is
preferably performed in-vitro.
[0040] The step of calculating the ESA dosage is preferably
performed using the non-linear dynamic pharmacokinetic (PK)
hemoglobin (Hb) ESA-EPO-R pathway model as described in detail
herein below.
[0041] In context of the herein described invention the hemoglobin
concentration of the patient (or subject, terms which are used
herein as synonyms) is preferably determined through blood samples
taken from the patient. Methods for calculating the haemoglobin
concentrations are well known in the art. Alternatively, since most
anemia patients have a treatment history where haemoglobin
concentrations were determined at multiple time points, the
patients hemoglobin degradation rate may be calculated from these
values taken from the individual patient's medical file.
[0042] Another aspect of the invention pertains to an ESA for use
in the treatment of anemia of a patient, wherein the treatment
comprises, [0043] (a) Calculating an ESA dosage according to a
method of any of claims 1 to 3, [0044] (b) Administering to the
patient an ESA dosage as calculated in (a), [0045] (c) Optionally,
monitoring the patient's hemoglobin concentration over time after
the administration in (b), [0046] (d) Optionally, repeating step
(a) and (d).
[0047] The administration is preferably a subcutaneous
injection.
[0048] The above problem is solved in a further aspect by an
Erythropoiesis Stimulating Agent (ESA) for use in the personalized
treatment of anemia, the treatment comprising the steps of [0049]
(a) Administration of a (preferably clinically safe) dose of an ESA
to an individual patient suffering from anemia, [0050] (b)
Monitoring the clearance of said ESA from the serum in said
patient, [0051] (c) Calculating from the clearance of said ESA in
said patient the number of initial ESA binding sites present in
said patient using a non-linear dynamic pharmacokinetic (PK)
ESA-EPO-R pathway model, and [0052] (d) Adjusting the individual
dosage of said ESA for said treatment in accordance with the number
of ESA binding sites calculated in (c), [0053] (e) Optionally,
repeating steps (b) to (d).
[0054] In an alternative aspect, the invention may relate to an
Erythropoiesis Stimulating Agent (ESA) for use in the personalized
treatment of anemia, the treatment comprising the steps of [0055]
(a) Administration of a (preferably clinically safe dose) of an ESA
to an individual patient suffering from anemia and determining the
level of Hb at the time the ESA is administered, [0056] (b)
Monitoring the concentration of Hb in said patient, [0057] (c)
Calculating from change of concentration of Hb in said patient the
number of initial ESA binding sites present in said patient using a
non-linear dynamic pharmacokinetic (PK) hemoglobin (Hb) ESA-EPO-R
pathway model, and [0058] (d) Adjusting the individual dosage of
said ESA for said treatment in accordance with the number of ESA
binding sites calculated in (c), [0059] (e) Optionally, repeating
steps (b) to (d).
[0060] As an alternative embodiment the individual dosage of said
ESA is calculated on the basis of the patient's hemoglobin
degradation rate. Surprisingly it could be shown in context of the
present invention that each patient has a specific hemoglobin
degradation rate which correlates with the clinical development of
anemia in the patient. Therefore the present invention discloses an
ESA for the treatment of anemia in a patient wherein the ESA dosage
using the herein described non-linear dynamic pharmacokinetic (PK)
hemoglobin (Hb) ESAEPO-R pathway model on basis of a predetermined
hemoglobin degradation rate, the specific biding properties of the
used ESA in the treatment (for example the EC.sub.50 of EPOR
occupancy by the ESA) and the present hemoglobin concentration at
the time the treatment is started. Hence an embodiment pertains to
an ESA for use in the treatment of anemia in a patient, wherein the
treatment comprises the initial determination of the patient's
hemoglobin degradation rate.
[0061] The hemoglobin degradation rate may either be determined by
measuring hemoglobin concentrations in the patient at several time
points, for example in an ESA naive or ESA receiving patient, or
using the patient's previous treatment history. In accordance with
the herein described mathematical model the specific
characteristics of the ESA to be used in therapy, for example CERA,
are used for determining the ESA dosage.
[0062] Based in the initial experiments in vitro (ESA depletion
experiments) as described in the example section, the mathematical
model as disclosed describes the binding properties of each ESA:
the association rate "k.sub.on" and the dissociation rate
"k.sub.off" (the dissociation constant "K.sub.D" is defined as
koff/kon). Based in the binding properties of each ESA, the herein
disclosed model can calculate the integral occupancy of the EpoR on
human CFU-E for 60 minutes. The EC.sub.50 (ESA concentration
required to obtain half-maximum EpoR occupancy) is calculated for
each ESA and this correlates with the ESA activity in hCFU-E. In
the integrative non-linear dynamic pharmacokinetic (PK) hemoglobin
(Hb) ESA-EPO-R pathway model, the integral occupancy of the
ESA-EpoR is linked to Hb production. The amount of ESA-EpoR is,
among all the other parameters, depending on the k.sub.on and the
k.sub.off rate of the specific ESA. Based on the ESA depletion
experiments, the mathematical model calculates k.sub.on and
k.sub.off for each ESA. This data can be used (i) to calculate
EC.sub.50 values for each ESA and (ii) calculate Hb values based on
ESA injections. Thereby, the using the non-linear dynamic
pharmacokinetic (PK) hemoglobin (Hb) ESA-EPO-R pathway model of the
invention, the ESA dosage for achieving a production of hemoglobin
in the anemia patient that is sufficient to alleviate the anemia
can be calculated.
[0063] The term "anemia" in context of the herein described
invention shall refer to a condition wherein the red blood cells
are reduced. Anemia is typically diagnosed on a complete blood
count. Apart from reporting the number of red blood cells and the
hemoglobin level, the automatic counters also measure the size of
the red blood cells by flow cytometry, which is an important tool
in distinguishing between the causes of anemia. Examination of a
stained blood smear using a microscope can also be helpful, and it
is sometimes a necessity in regions of the world where automated
analysis is less accessible. In modern counters, four parameters
(RBC count, hemoglobin concentration, MCV and RDW) are measured,
allowing others (hematocrit, MCH and MCHC) to be calculated, and
compared to values adjusted for age and sex. Some counters estimate
hematocrit from direct measurements. In the context of the present
invention anemia is present if an individual has a hemoglobin (Hb)
concentration of less than 14 g/dL, more preferably of less than 12
g/dL, most preferably of less than 11 g/dL.
[0064] In certain embodiments of the invention the anemia to be
treated in accordance with the described methods is an anemia that
has developed according to any possible cause or disease. This
includes all types of cancer, all inflammation-associated anemia
(chronic infection disease, autoimmune or rheumatologic disorders
and any other illnesses or treatments that results in anemia based
on reduced endogenous Epo production, inefficient eryhtropoiesis or
increased desruction of red blood cells). Furthermore, and
particularly preferred, is that the anemia is caused by chronic
kidney disease (CKD), myelodysplastic syndrome (MDS), or is anemia
associated to myelofibrosis, anemia in context of HIV, aplastic
anemias, anemia in premature infants, non-severe aplastic anemia,
anemia in beta thalassemia, anemia in sickle cell disease and ESA
erythropoiesis stimulation after allogeneic hematopoietic stem cell
transplantation.
[0065] The inventors of the present invention surprisingly
discovered that a mathematical model describing the EPO-EPO-R
signaling pathway in a cell can be adapted to predict the behavior
of not only ESAs in a cell, but also of the dynamics of ESAs
administered to a patient.
[0066] Initially the model is able to describe at cellular level
the activity of the different ESAs based in the affinity of each
ESA (time of EpoR occupancy). This activity corresponds to the
EPO-R activation by ESA binding to the EPO receptor. This
activation of the EPO-R will induce the proliferation and
maturation of the erythropogenitors, the main cellular population
on the body that express EpoR into erythrocytes. For the present
invention the initial core model that describes the EpoR activation
at cellular level by ESA was extended in order to be used in a
physiological situation in an organism, in particular a human
patient. Clearance of an administered ESA in the blood compartment,
transport of an subcutaneous administered ESA into the blood
compartment and saturable clearance of the ESA in the interstitial
compartment were added to the initial model. This extended version
of the initial ESA-EPO-R model was surprisingly able to describe
the published pharmacokinetic (PK) and pharmacodynamics (PD)
experimental data of each ESA as shown in the examples. The
inventors could characterize induced anemia by cancer and
chemotherapy in individual patients at colony forming unit of
erythroids (CFU-E), the progenitors of the erythroids. It was
observed that patients in the same cancer type and disease stage
(FIG. 5c) show different numbers of CFU-E. This explains the
different ESA treatment outcomes observed in patients-40% of the
NSCLC patients do not respond to ESA treatment in the current
approved posology (protocol to treat anemic patients with cancer).
Lower levels of CFU-E means lower levels of response to ESA
treatments and it correlated with the individual outcomes at
hemoglobin levels (Hb).
[0067] In the context of the invention which is described in the
following, the mathematical models are all based on the basic
findings as published and publically accessible in the publication
Becker V et al., Science. 2010 Jun. 11; 328(5984):1404-8. This
reference is incorporated in its entirety, for the purpose of
understanding the application of the mathematical models in the
present invention. The models used in context of the present
invention were adjusted to answer the respective questions of the
herein disclosed invention. In this respect the term "non-linear
dynamic EPO-EPO-R pathway model" shall refer to the model as
published by the above Becker V et al. 2010 reference. The term
"non-linear dynamic ESA-EPO-R pathway model" shall refer to an new
version of the non-linear dynamic EPO-EPO-R pathway model, which
describes the binding/dissociation dynamics of ESAs to the EPO-R on
a cellular level. The term "non-linear dynamic pharmacokinetic
ESA-EPO-R pathway model" shall refer to the non-linear dynamic
ESA-EPO-R pathway model which is adjusted to the situation in an
organism, in particular a human patient. The basic rationales for
the models disclosed herein are provided in the Materials and
Methods section of the present application. Thus itis a preferred
embodiment that the non-linear dynamic pharmacokinetic (PK)
ESAEPO-R pathway model considers clearance of the administered ESA
in the blood compartment, transport of the administered ESA from
the interstitial compartment into the blood compartment, and
clearance of the ESA in the interstitial compartment.
[0068] The basic application of the mathematical methods as
required by the herein described inventive methods is standard to
the person of skill in the field of systems biology. Using the
information as provided by the present patent application, the
person of skill in view also of the Becker V et al. 2010
publication can perform the necessary steps to work the
invention.
[0069] For the present disclosure the following variables,
constants and acronyms are used:
TABLE-US-00001 TABLE 1 Acronyms CFU-E Colony forming unit-erythroid
NSCLC Non-small cell lung carcinoma Hb Hemoglobin RBC Red blood
cells Epo Erythropoietin EpoR Erythropoietin receptor PK
Pharmacokinetics PD Pharmacodynamics MEPC Minimal Personal
Effective ESA Concentration CKD Chronic kidney disease MDS
Myelodysplastic syndrome NESP Novel erythropoiesis stimulating
protein CERA Continuous erythropoietin receptor activator STAT5
Signal transducer and activator of transcription 5 EC50
Half-maximal effective concentrations ODE Ordinary differential
equation U Units
TABLE-US-00002 TABLE 2 Variables ESA Erythropoiesis-stimulating
agent in medium / blood Epo Erythropoietin EpoR Erythropoietin
receptor ESAEpoR Complex of ESA bound to EpoR on the cell surface
ESAEpoR.sub.i Internalized complex of ESA bound to EpoR dESA.sub.i
Intracellular degraded ESA dESA.sub.e Extracelullar degraded ESA
ESA.sub.sc ESA in the subcutaneous compartment Hb Hemoglobin in
blood
TABLE-US-00003 TABLE 3 Kinetic constants k.sub.sc_clear ESA
clearance constant in the subcutaneous compartment
k.sub.sc_clear_sat Saturation of ESA clearance in subcutaneous
compartment k.sub.sc_out ESA transportation constant to the blood
compartment k.sub.clear ESA clearance constant in the blood
compartment k.sub.on ESA-EpoR association rate/on-rate k.sub.off
ESA-EpoR dissociation rate/off-rate K.sub.D ESA-EpoR dissociation
constant (k.sub.off/k.sub.on) k.sub.t Ligand-independent receptor
turnover rate B.sub.max Number of ESA binding sites per cell/per
patient k.sub.e ESA-EpoR complex internalization constant k.sub.ex
ESA and EpoR recycling constant k.sub.di Intracellular ESA
degradation constant k.sub.de Extracellular ESA degradation
constant k.sub.Hb_pro Hemoglobin production constant by the
ESA-EpoR complex K.sub.Hb_deg Hemoglobin degradation constant (net
loss of hemoglobin)
[0070] The models disclosed in the present application are based on
the following ordinary differential equations with reference to
FIG. 6. This model describes the following reaction scheme which is
based on prior biological knowledge. The ESA binds reversibly
(k.sub.on respectively k.sub.off) to the Epo receptor (EPO-R) which
is exposed on the cell surface. Thereby, the ESA-receptor complex
gets activated and can induce phosphorylation of downstream
signaling molecules like STAT5. The ESA-receptor complex is then
internalized (k.sub.e) into intracellular receptor pools where ESA
is either exported (k.sub.ex) or degraded (k.sub.de and k.sub.di)
and the receptor can translocate back to the membrane (k.sub.ex).
In addition, a ligand independent turnover (k.sub.t) of EpoR
ensures that the cell is sensitive for a broad range of ligand
concentrations. In the equations [ ] denote concentrations of the
respective components. These are, EpoR or EPO-R is the EPO
receptor, ESAEpoR is the complex of ESA bound to the EPO-R.
ESAEpoR.sub.i is the internalized complex. dESA is degraded ESA,
either cell-internally (dESAi) or extracellular (dESAe). The
equations are:
d [ ESA ] dt = - k on [ ESA ] [ EpoR ] + k off [ ESAEpoR ] + k ex [
ESAEpoRi ] ( 1.1 ) d [ EpoR ] dt = - k on [ ESA ] [ EpoR ] + k off
[ ESAEpoR ] + k t B max - k t [ EpoR ] + k ex [ ESAEpoRi ] ( 1.2 )
d [ ESAEpoR ] dt = k on [ ESA ] [ EpoR ] - k off [ ESAEpoR ] - k e
[ ESAEpoR ] ( 1.3 ) d [ ESAEpoRi ] dt = k e [ ESAEpoR ] - k ex [
ESAEpoRi ] - k di [ ESAEpoRi ] - k de [ ESAEpoRi ] ( 1.4 ) d [
dESAi ] dt = k di [ ESAEpoRi ] ( 1.5 ) d [ dESAe ] dt = k de [
ESAEpoRi ] . ( 1.6 ) ##EQU00001##
[0071] For the model simulating the in-vivo patient situation this
model is extended resulting in system of seven coupled ordinary
differential equations (ODE). The expanded model in figure (6b)
describes the situation including the blood and interstitium
compartments. Intravenous ESA is either cleared in the blood
compartment (k.sub.clear) or binds to the EPO-R (k.sub.on,
k.sub.off). Subcutaneous applied ESA (ESA.sub.SC) is transported to
the blood compartment (k.sub.sc_out) or saturable cleared in the
interstitial compartment (k.sub.sc_clear_sat). The non-linear
dynamic pharmacokinetic ESA-EPO-R pathway model:
d [ ESA SC ] dt = - k sc_clear [ ESA SC ] ( k sc_clear _sat + [ ESA
SC ] ) - k sc_ou t [ ESA SC ] ( 2.1 . ) d [ ESA ] dt = k sc out [
ESA SC ] - k clear [ ESA ] - k on [ ESA ] [ EpoR ] + k off [
ESAEpoR ] + k ex [ ESAEpoRi ] ( 2.2 . ) d [ EpoR ] dt = - k on [
ESA ] [ EpoR ] + k off [ ESAEpoR ] + k t B max - k t [ EpoR ] + k
ex [ ESAEpoRi ] ( 2.3 . ) d [ ESAEpoR ] dt = k on [ ESA ] [ EpoR ]
- k off [ ESAEpoR ] - k e [ ESAEpoR ] ( 2.4 . ) d [ ESAEpoRi ] dt =
k e [ ESAEpoR ] - k ex [ ESAEpoRi ] - k di [ ESAEpoRi ] - k de [
ESAEpoRi ] ( 2.5 . ) d [ dESAi ] dt = k di [ ESAEpoRi ] ( 2.6 . ) d
[ dESAe ] dt = k de [ ESAEpoRi ] . ( 2.7 . ) ##EQU00002##
[0072] Since the amount of hemoglobin (Hb) in a patients serum is
directly correlated to the activity of ESA-EPO-R system, the
invention may instead of determining the concentration of the ESA
after initial administration of the ESA as a function of time,
determine the Hb concentration, which is a standard parameter
observed during anemia treatment. In this embodiment, the above
model comprises the additional reactions of the production of Hb by
the activated ESA-EPO-R (k.sub.Hb_pro) and the patient specific
degradation of Hb (k.sub.Hb_deg).
[0073] In this case the model includes the additional ODE:
d [ H b ] d L = k H b p r o [ ESAEpoR ] - k H b deg [ Hb ] ( 2.8 .
) ##EQU00003##
[0074] For both models the dissociation constant of K.sub.D is
defined as
K.sub.D=k.sub.off/k.sub.on (3.1)
[0075] In these models B.sub.max is the initial number of binding
sites for ESA.
[0076] Further explanation of the equations is provided in the
example section and FIG. 6.
[0077] The values for the respective concentrations of elements and
the all constants used in the above equations can be determined
experimentally using, for example, a method known to the skilled
person or the methods provided herein below in the example
section.
[0078] The object of the present invention is solved in an
additional aspect by an Erythropoiesis Stimulating Agent (ESA) for
use in a method of diagnosing the anemic status in a patient, the
method comprising the steps of [0079] (a) Administering to said
patient a clinically safe dosis of an ESA, [0080] (b) Assessing the
clearance of the administered ESA in the serum of said patient over
time, [0081] (c) Calculating from the clearance of said ESA using a
non-linear dynamic pharmacokinetic (PK) ESA-EPO-R pathway model the
amount of ESA binding sites in said patient, which is predictive
for the anemic status of the patient.
[0082] In accordance with the present invention, a clinically safe
dose of an ESA is a dose approved by the authorities for the
treatment of anemia.
[0083] In the herein described methods clearance rate of an ESA in
the serum of a patient is determined. Preferably, and this holds
true for all aspects and embodiments as described herein, the
clearance rate (or change of concentration) of said ESA is
determined based on the initial dose of ESA administered to a
patient. Subsequent to the initial ESA administration, samples
obtained from a patient can be analyzed for the remaining ESA
concentration for at least one time point subsequent to the initial
ESA treatment. Ideally, the ESA concentration is observed over
several time points, for example 1 to 6 weeks, preferably 1 to 3
weeks, and includes at least 2, preferably 5, more preferably 7 to
10 independent measurements of ESA concentration at different time
points. An example for an observation plan would be the
administration of the ESA at day 0, and the subsequent measuring of
the ESA concentration in the patient at days 1, 2, 3, 5, 7, 10 and
14. This may be adjusted depending on the clinical scenario. For
the alternative embodiment of the invention regarding the
calculation of initial ESA binding sites based on the observation
of the change of Hb concentration in a patient, the same principle
is applied.
[0084] In a certain embodiment of the invention the ESA is any ESA
known to the skilled person, which includes in particular EPO
biosimilars, but is preferably selected from the group of Epoetin
alfa, Epoetin beta, Novel erythropoiesis stimulating protein (NESP)
and Continuous erythropoietin receptor activator (CERA). CERA is
preferred for the herein described invention.
[0085] The problem of the invention is also solved by a method for
monitoring anemia in a patient who received at an earlier time
point a dose of an ESA, comprising the steps of [0086] (a)
Providing a serum sample from a patient suffering from anemia who
received at an earlier time point a dose of an ESA, [0087] (b)
Measuring the concentration of hemoglobin in said sample, [0088]
(c) Calculating the amount of ESA binding sites based on the
hemoglobin concentration in said sample using a non-linear dynamic
pharmacokinetic (PK) ESA-EPO-R pathway model, wherein the amount of
ESA binding sites indicates the anemic status of a patient.
[0089] Preferable the calculation is further based on the initial
ESA dose, and the initial Hb concentration in the patient at the
time the ESA was administered.
[0090] In context of the here described invention a patient is
preferably a patient that is suffering from anemia in the context
of a cancer disease, the cancer disease preferably being a lung
cancer such as non-small cell lung cancer (NSCLC).
[0091] In preferred embodiments the non-linear dynamic
pharmacokinetic (PK) ESA-EPO-R pathway model is based on a system
of the ordinary differential equations (ODE) as described above. In
this context the invention seeks to obtain the initial number of
ESA binding sites, which is B.sub.max. B.sub.max is therefore
predictive for or an approximation of the colony forming
unitserythroid (CFU-E).
[0092] Another aspect of the invention pertains to a method for
identifying an Erythropoiesis Stimulating Agent (ESA) having a
specific activity for cells with a high cell surface expression of
Erythropoietin-receptor (EPO-R), comprising the steps of [0093] (a)
Obtaining the half maximal effective concentrations (EC50) of a
candidate ESA and a reference ESA for EPO-R activation in a first
cell, [0094] (b) Obtaining the EPO-R activation induced by the
candidate ESA and the reference ESA at their respective EC50 as
obtained in (a) in a second cell, wherein said second cell is
characterized by a significantly lower cell surface expression of
EPO-R compared to the first cell, wherein a decreased activation of
EPO-R in said second cell by the candidate ESA compared to the
activation of EPO-R in said second cell by the reference ESA, is
indicative for the specificity of said candidate ESA for cells with
a strong cell surface expression of EPO-R.
[0095] The above method may be performed solely in-silico or
in-vitro. Preferably Epoetin alfa or beta are selected as reference
ESA. However also other ESA which have similar characteristics,
which can be derived from performing the inventive method, can be
used as reference ESA.
[0096] Preferred is however that the method is an in-silico method
and that said EPO-R activation is calculated with a non-linear
dynamic ESA-EPO-R pathway model, more preferably according to the
equations as described above. The EPO-R activation is preferable
the integral of ESA bound to the EPO receptor ([ESAEPO-R]), for
example for the first 60 minutes after stimulation. The time frame
is however not essential to obtain the activation of the EPO
signaling.
[0097] Preferably the calculation of the EPO-R activation in
context of the above in-silico method comprises the input or the
obtaining of the dissociation constant KD for at least the
candidate ESA, and predicting the EPO-R activation over a period of
time according to a non-linear dynamic ESA-EPO-R pathway model.
[0098] The ESA identified by the method is specific for cells
expressing high amount of cell surface EPO-R and therefore, this
ESA is characterized by being specific for colony forming
unit-erythroid (CFU-E) cells. Cells having a low cell surface
expression of EPO-R are in context of the present invention tumor
cells, such as lung cancer tumor cells, in particular non-small
cell lung cancer cells.
[0099] For performing the method in-vitro, it may be preferred that
said first cell is a cell ectopically expressing EPO-R, such as
H838-EpoR, and/or wherein said second cell is not ectopically
expressing EPO-R, such as H838.
[0100] The problem of the invention is additionally solved by a
computer implemented method for predicting or assessing the number
of colony forming units-erythroid (CFU-E) or an approximation
thereof, in a patient, wherein the patient has received an
administration of an ESA at an earlier first point of time, the
method comprising the steps of: [0101] (a) Obtaining the initial
administered ESA dose, [0102] (b) Obtaining the concentration of
said ESA in a serum sample of said patient at at least one second
time point after the initial administration of said ESA to said
patient. [0103] (c) Determining the concentration rate of said ESA
as a function of time in said patient [0104] (d) Calculating based
on a non-linear pharmacokinetic (PK) ESA-EPO-R model and the
concentration rate of said ESA in said patient the initial number
of ESA binding sites in said patient, wherein the initial number of
ESA binding sites in said patient is predictive for the number of
CFU-E in said patient.
[0105] An alternative aspect provides a computer implemented method
for predicting or assessing the number of colony forming
unit-erythroid (CFU-E) or an approximation thereof, in a patient,
wherein the patient has received an administration of an ESA at an
earlier first point of time, the method comprising the steps of:
[0106] (a) Obtaining the hemoglobin (Hb) concentration in said
patient at the time point of the initial ESA administration, [0107]
(b) Obtaining the concentration of Hb in said patient at at least
one second time point after the initial administration of said ESA
to said patient. [0108] (c) Determining the change in Hb in said
patient as a function of time, [0109] (d) Calculating based on the
change of Hb in said patient using a non-linear pharmacokinetic
(PK) ESA-EPO-R model the initial number of ESA binding sites in
said patient, wherein the initial number of ESA binding sites in
said patient is predictive for the number of CFU-E in said
patient.
[0110] Another aspect of the invention then relates to a computer
implemented method for predicting the amount of initial ESA binding
sites in a patient, the method comprising the steps of: obtaining
the clearance rate of an ESA after initial administration of said
ESA to a patient as a function of serum concentration of the ESA of
time, calculating based on a non-linear pathway model the number of
initial ESA binding sites (Bmax). Preferably the non-linear pathway
model is a non-linear dynamic PK ESA-EPO-R pathway model.
[0111] The computer implemented method for assessing the number of
ESA binding sites in a cell, or a an organism, may alternatively
comprise the steps of [0112] (a) In vitro determination of the
clearance rate of an ESA in said cell or organism at at least one
time point subsequent to the addition/administration of an initial
ESA dose to said cell or organism, [0113] (b) Calculating the
amount of ESA binding sites in said cell or organism based on the
clearance rate of the ESA using a non-linear dynamic EPO-R pathway
model.
[0114] However, preferred is the above method wherein said organism
is a patient, preferably a human patient, or wherein said cell is a
cell endogenously expressing the EPO-R receptor, such as a red
blood cell precursor cell, or a tumor cell.
[0115] Preferably said organism is a human patient. In this
scenario step (a) constitutes the in vitro determination of the
clearance rate of an ESA in a serum sample of a patient at a time
point subsequent to the administration of an initial ESA dose to
said patient, and step (b) constitutes calculating the amount of
ESA binding sites based on the clearance rate of the ESA using a
non-linear dynamic EPO-R pathway model.
[0116] In a preferred embodiment of the invention the computer
implemented method requires for the calculating step (b) as input
the clearance rate of an ESA in said cell or organism as a function
of ESA concentration over time as determined in (a), and a
dissociation constant K.sub.D that is specific for the ESA
added/administered to said cell or organism.
[0117] Yet another aspect of the invention provides a
computer-readable storage medium having computer-executable
instructions stored, that, when executed, cause a computer to
perform a computer implemented method according to the present
invention.
[0118] In preferred embodiments of all aspects of the invention the
K.sub.D of the ESA is about 16 pM for Epoetin alfa, about 17 pM for
Epoetin beta, about 789 pM for NESP and about 982 pM for CERA.
[0119] In a further aspect of the present invention there is
provided an Erythropoiesis Stimulating Agent (ESA) for use in the
treatment of anemia, the treatment comprising the steps of [0120]
(a) Obtaining the level of hemoglobin in a patient suffering from
anemia, [0121] (b) Calculating from the level of hemoglobin (Hb) in
said patient the number of initial ESA binding sites present in
said patient using a non-linear dynamic Hb ESA-EPO-R pathway model,
and [0122] (c) Determining a therapeutically effective dosage of an
ESA for use in a treatment of anemia in said patient based on the
number of initial ESA binding sites in said patient as calculated
in (b).
[0123] The non-linear dynamic Hb ESA-EPO-R pathway model used in
this aspect takes into account the additional reactions of the
production of Hb based on the active ESA-EPO-R complex and a
patients individual Hb degradation.
[0124] The term "treatment" as used herein covers any treatment of
a disease or condition (e. g., anemia) in a mammal, particularly a
human, and includes: (i) preventing the disease or condition from
occurring in a subject which may be predisposed to the disease but
has not yet been diagnosed as having it; (ii) inhibiting the
disease or condition, i. e. arresting its development; or (iii)
relieving the disease or condition, i. e. causing its regression or
the amelioration of its symptoms.
[0125] As used herein, the term "therapeutically effective amount"
refers to that amount of a polymer-modified synthetic
erythropoiesis stimulating protein which, when administered to a
mammal in need thereof, is sufficient to effect treatment (as
defined above), for example, as inducer of red cell production, an
anti-anemia agent, etc. The amount that constitutes a
"therapeutically effective amount" will vary depending on the ESA,
the condition or disease and its severity, and the patient to be
treated, its weight, age, gender, etc., but may be determined
routinely by one of ordinary skill in the art with regard to
contemporary knowledge and to this disclosure.
[0126] Administration of the ESA of the invention may be performed
via any accepted systemic or local route known for the respective
ESA, for example, via parenteral, oral (particularly for infant
formulations), intravenous, nasal, bronchial inhalation (i. e.,
aerosol formulation), transdermal or topical routes, in the form of
solid, semi-solid or liquid or. aerosol dosage forms, such as, for
example, tablets, pills, capsules, powders, liquids, solutions,
emulsion, injectables, suspensions, suppositories, aerosols or the
like. The erythropoiesis stimulating agents of the invention can
also be administered in sustained or controlled release dosage
forms, including depot injections, osmotic pumps, pills,
transdermal (including electrotransport) patches, and the like, for
the prolonged administration of the polypeptide at a predetermined
rate, preferably in unit dosage forms suitable for single
administration of precise dosages. The compositions will include a
conventional pharmaceutical carrier or excipient and a protein
antagonist or agonist of the invention and, in addition, may
include other medicinal agents, pharmaceutical agents, carriers,
adjuvants, etc. Carriers can be selected from the various oils,
including those of petroleum, animal, vegetable or synthetic
origin, for example, peanut oil, soybean oil, mineral oil, sesame
oil, and the like. Water, saline, aqueous dextrose, and glycols are
preferred liquid carriers, particularly for injectable solutions.
Suitable pharmaceutical carriers include starch, cellulose, talc,
glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk,
silica gel, magnesium stearate, sodium stearate, glycerol
monostearate, sodium chloride, dried skim milk, glycerol, propylene
glycol, water, ethanol, and the like. Other suitable pharmaceutical
carriers and their formulations are described in "Remington's
Pharmaceutical Sciences" by E. W. Martin.
[0127] The inventors furthermore discovered the mathematical model
of the invention can be used to determine the biological activity
of an ESA candidate compound. Hence there is also provided a method
for estimating the biological activity of an ESA, comprising the
steps of: [0128] (a) Calculating the occupancy of the EPO receptor
on human CFU-E cells in response to a range of ESA concentrations
using the non-linear dynamic pharmacokinetic (PK) ESA-EPO-R pathway
model, [0129] (b) Calculating the area under the curve for the ESA
from the resultant of step (a) as a measure for EPO receptor
occupancy of the ESA, [0130] (c) Calculating the concentration of
the ESA for which the half maximum occupancy of the EPO receptor is
reached to obtain an EC50ESA, [0131] (d) Compare the EC50ESA with a
predetermined EC50EPOalfa or EC50EPOalfa,
[0132] Wherein the difference between the EC50ESA compared to the
predetermined EC50EPOalfa or EC50EPOalfa correlates with the
difference of the biological activity of the ESA when compared with
the biological activity of EPO alfa or EPO beta.
[0133] The biological activity of the ESA or EPO is preferably
provided in Units (U) per .mu.g and described the ability of the
ESA to induce blood cell proliferation.
[0134] The EC50EPOalfa or EC50EPOalfa may be predetermined by
performing in addition steps (a) to (c) of the aforementioned
method using EPO alfa or EPO beta as "ESA" to obtain in step (c)
the values for EC50EPOalfa or EC50EPOalfa. The biological activity
of EPO alfa or EPO beta is well known. Alternatively, other ESAs
for which the biological activity is known may be used as a
reference.
[0135] In this aspect the non-linear dynamic pharmacokinetic (PK)
ESA-EPO-R pathway model as described herein is used.
[0136] Another aspect of the invention further An Erythropoiesis
Stimulating Agent (ESA) for use in the treatment of anemia in a
subject, the treatment comprising the steps of [0137] (a)
Determining or providing hemoglobin concentrations in the subject
from at least two separate time points and calculating therefrom a
subject specific hemoglobin degradation rate, [0138] (b)
Determining the present hemoglobin concentration in the subject,
[0139] (c) Calculating from the subject specific hemoglobin
degradation rate and the hemoglobin concentration in the subject
the dosage of an ESA sufficient to treat the anemia in the subject
using a non-linear dynamic pharmacokinetic (PK) hemoglobin (Hb)
ESA-EPO-R pathway model, and [0140] (d) Administering to the
subject the calculated dosage of the ESA as determined in (c),
[0141] (e) Optionally, monitoring the hemoglobin concentration in
the subject after administration of the ESA and adjusting the next
dosage of the ESA by repeating steps (b) to (d).
[0142] The invention also pertains to a computer implemented method
for determining an ESA dosage for an anemia treatment in a subject,
the method comprising the steps of [0143] (a) providing at least
two separate hemoglobin concentrations of the subject before the
treatment, [0144] (b) calculating from the hemoglobin
concentrations in (a) a subject specific hemoglobin degradation
rate, [0145] (c) calculating from the subject specific hemoglobin
degradation rate as deter-mined in [0146] (b) and from a present
hemoglobin concentration in the patient, an ESA dosage using a
non-linear dynamic pharmacokinetic (PK) hemoglobin (Hb) ESA-EPO-R
pathway model.
[0147] The method may further comprise repeating step (c) for
obtaining an adjusted next ESA dosage.
[0148] Finally there is provided a method for the stratification of
an anemia patient receiving ESA treatment, the method comprising
the determination of a patient specific hemoglobin degradation rate
by monitoring hemoglobin concentration in the patient over time and
calculating therefrom the patient specific hemoglobin degradation
rate and, wherein an increased hemoglobin degradation rate in the
patient compared to a reference value indicates a decreased
response to the ESA treatment, and wherein an increased hemoglobin
degradation rate in the patient compared to a reference value
indicates that the patient is overdosed.
[0149] The present invention will now be further described in the
following examples with reference to the accompanying figures and
sequences, nevertheless, without being limited thereto. For the
purposes of the present invention, all references as cited herein
are incorporated by reference in their entireties. In the
Figures:
[0150] FIG. 1: Characterization of ESA binding properties based on
the determination of ligand depletion and the ESA-EpoR mathematical
model. Parental BaF3 cells (BaF3) and BaF3 stably expressing the
murine EpoR (BaF3-mEpoR) were incubated with 100 .rho.M Epo alfa or
100 .rho.M Epo beta. At the indicated times the supernatant was
removed and the concentration of Epo was quantified by an ELISA
assay. Based on this data the association rate k.sub.on, the
dissociation rate k.sub.off and the number of ESA binding sides at
the cellular surface (B.sub.max) were estimated by the ESA-EpoR
mathematical model and the ESA-specific dissociation constant
K.sub.D (k.sub.off/k.sub.on) was calculated. (a) BaF3 cells and
BaF3 stably expressing the human EpoR (BaF3-hEpoR) were incubated
with Epo alfa, Epo beta, NESP and CERA. At the indicated times the
supernatant was removed and the concentration of Epo was quantified
by an ELISA assay. Based on this data the association rate
k.sub.on, the dissociation rate k.sub.off and the number of ESA
binding sides at the cellular surface (B.sub.max) were estimated by
the ESA-EpoR mathematical model and the ESA-specific dissociation
constant K.sub.D (k.sub.off/k.sub.on) was calculated. (b) Predicted
by the ESA-EpoR mathematical model for each ESA the association
rate k.sub.on was plotted against the dissociation rate k.sub.off.
The calculated ESA-specific dissociation constant K.sub.D for the
hEpoR is indicated by symbols. Shaded areas around the symbols
indicate the confidence interval of the K.sub.D
(k.sub.off/k.sub.on). The heatmap displays the values of the
K.sub.D.
[0151] FIG. 2: Presence of a functional EpoR on human lung cancer
cell lines. (a) Total mRNA was extracted from the NSCLC cell lines
H838, H1299, A549 and H1944 and the expression of the EpoR mRNA was
determined by qRT-PCR. The EpoR mRNA expression in H838 cells was
used as reference. (b) BaF3 cells and BaF3-hEpoR as well as the
indicated NSCLC cell lines were stimulated with 10 U/ml of Epo beta
for 10 min or were left untreated and were lysed. The abundance of
the phosphorylated EpoR (pEpoR) and the total EpoR was determined
by immunoprecipitation (IP) and quantitative immunoblotting (IB).
The experiment was performed in biological triplicates and one
representative immunoblot is shown. (c) The NSCLC cell lines H838,
H1299, A549 and H1944 were stimulated with 4 pM of Epo beta and the
Epo depletion kinetics was determined by an ELISA assay up to 8000
min incubation time. The ESA-EpoR mathematical model was employed
to describe the depletion kinetics in all analyzed NSCLC cell lines
and to determine the number of ESA binding sites/cell
(B.sub.max).
[0152] FIG. 3: H838-EpoR cells can serve as a model for human CFU-E
cells concerning EpoR levels (a) Human hematopoietic stem cells
(hHSC) from cord blood were isolated and differentiated to human
CFU-E (hCFU-E) as described. hCFU-E and hHSC cells that served as
negative control (a) as well as NSCLC cell line H838 stably
transduced with hEpoR (H838-EpoR) (b) were stimulated with 4 pM of
Epo beta and time-resolved analysis of the depletion kinetics was
monitored via ELISA assay over the time period of 200 min
(experimental data-dots). The model could describe the depletion
kinetics (model-solid line) and estimate KD and Bmax values. (c)
Quantitative immunoblot demonstrating overexpression level of human
EpoR in H838-hEpoR cells compared to parental H838. Functionality
of EpoR is shown by Epo-induced phosphorylation of receptor and
JAK2.
[0153] FIG. 4: CERA preferentially activates cells with high EpoR
expression (a) Model based prediction of differential dose response
for EpoR activation in H838-hEpoR by different ESAS (left panel).
Blue and red lines correspond to Epo beta and CERA respectively.
Dashed lines indicated the EC50 of each ESA in the activation of
the erythroprogenitors, 141 .rho.M and 1048 .rho.M for Epo beta and
CERA respectively. Right panel represents the validation of the
model prediction. Epo beta and CERA activates EpoR in a very
different range of concentrations. H838-hEpoR cells were stimulated
during 10 minutes with increasing concentrations of each ESA. Cells
were lysated, EpoR immunoprecipitated and blotted against total and
phosphorylated form. Blue circles represent experimental data upon
Epo beta stimulation. Red circles represent experimental data
corresponding to CERA stimulation. Solid lines are the activation
trajectories predicted by the model. (B) Left panel represents the
model based prediction of the integral EpoR activation by each EC50
during 60 minutes. Area under the curve shows no significant
difference between Epo beta and CERA activation in H838-EpoR, Right
panel shows the model based prediction of the integral EpoR
activation by each EC50 during 60 minutes in H838. In this case the
area under the curve indicates a probable lower activation of EpoR
by CERA in comparison with Epo beta.
[0154] FIG. 5: Differential pharmacokinetic behavior of CERA among
healthy and NSCLC subjects. (a) Pharmacokinetic behavior of
increasing CERA concentrations in healthy volunteers. Colored
circles are the mean values of CERA concentrations in serum,
determined by ELISA assay. Solid lines represent the trajectories
predicted of the CERA clearance for the given concentrations and
the experimental data. (B) Pharmacokinetic behavior of increasing
CERA concentrations in NSCLC patients in stage III or IV. Colored
circles are the mean values of CERA concentrations in serum,
determined by ELISA assay. Solid lines represent the trajectories
predicted of the CERA clearance for the given concentrations and
the experimental data. The different trajectories reported by the
model, describes the experimental data and showed a reduction of
72%16% in the CERA clearance capability of NSCLC patients. (c)
Characterization and relative comparison of CERA clearance
capability (% of CFU-E) of NSCLC patients and healthy subjects. The
dashed line is the 100% clearance capability of CERA, which
represents the normal capability of CERA clearance in healthy
subjects. The pinky bars represent the number of NSCLC patients
with a define % of CERA clearance capability compared to healthy
subjects (individual PK data extracted from Hirsch et al 2007
clinical trial). The plot represents a general reduction of CFU-E
population (% of CERA clearance capability) in NSCLC patients in
comparison in comparison of the mean value in healthy subjects
represented as 100%. It can be also notice different grades of
reduction in the CFU-E population of NSCLC patients.
[0155] FIG. 6: Graphical representation of the basic and
pharmacokinetic/pharmacodynamic mathematical model. (a) the
reactions 1 to 6 are 1:Binding/unbinding of ESA to the Epo receptor
(EpoR). The k.sub.on/k.sub.off rate constants of the
binding/unbinding reaction are ESA specific and can be fully
characterized using the trafficking model and the respective
depletion data. 2: ESA-EpoR complex internalization. 3: Recycling
to the cell membrane and dissociation of the internalized ESA-EpoR
complex. 4: Production/degradation of EpoR at the cell membrane.
The production/degradation reactions are in equilibrium defining a
certain, cell type (a)/patient (b) specific amount of receptors at
the cell surface characterized by Bmax parameter. 5: Degradation of
internalized ESA-EpoR complex. 6: Degradation and release of
internalized ESA-EpoR complex; (b) additional reactions 7 to 9 are
7: Clearance in the blood compartment, 8: Transport into blood
compartment, 9: Saturable clearance in the interstitial
compartment. (c) Calculation of B.sub.max based on the Hb levels
further includes the reactions 10: Production of Hb triggered by
the activated receptor complex, and 11: depletion of Hb in the
blood of an individual.
[0156] FIG. 7: CERA preferentially activates signal transduction in
cells with high EpoR abundance. Quantification of STAT5
phosphorylation in H838 and hCFU-E cells upon Epo beta and CERA
stimulation. H838 (left panel) and hCFU-E (right panel) cells were
stimulated with 1331 pM of Epo beta and 8841 pM of CERA
corresponding to the half-maximal activation of STAT5
phosphorylation in CFU-Es. Measurements of the degree of
phosphorylated STAT5 (symbols) were performed by mass spectrometry.
Solid lines indicate smoothing spline approximations.
[0157] FIG. 8: Individualized pharmacokinetics and pharmacodynamics
in healthy subjects and NSCLC IIIB-IV patients treated with CERA.
(a) Graphical representation of the equations (1 . . . 11) of the
integrative (PK/PD) ESA-EpoR mathematical model using the cell
designer formalism. Hb: hemoglobin, sc: subcutaneous, dESAi:
intracellular degraded ESA; dESAe: extracellular degraded ESA. (b)
The pharmacokinetics and pharmacodynamics of the NSCLC patient
(ID:2101, CSR NA17101 clinical trial) is shown in purple. The
amount and timing of the CERA dose given to this patient is
displayed in the top panel. In the middle panel, the
pharmacokinetics of CERA is indicated. The concentration of CERA in
the blood stream of this patient at different time points is
symbolized by dots and the trajectories of the mathematical model
are indicated by a solid line. In the lower panel the
pharmacodynamics of hemoglobin (Hb) is shown indicating the
experimental measurements by dots and model trajectories by a solid
line. The model predicted ESA binding sites per patient and the Hb
degradation rate are indicated. (c) The pharmacokinetics and
pharmacodynamics of the healthy subject (ID:25, WP16422 clinical
trial) is shown in green. The amount and timing of the CERA dose
given to this individual is shown in the top panel. In the middle
panel the pharmacokinetics of CERA displayed. The CERA
concentration in the blood stream is indicated by dots and the
solid line represents the model trajectory. The pharmacodynamics of
hemoglobin (Hb) is shown in the lower panel. Dots correspond to
experimental data and the solid line represents the model
trajectory. The model predicted ESA binding sites/patient and the
Hb degradation rate is indicated. (d) The distribution of ESA
binding sites per patient and of the hemoglobin degradation rate in
healthy subjects and NSCLC patients. The distribution of the Hb
degradation rate (left panel) and of the ESA binding sites (Bmax)
(right panel) in 88 healthy subjects (green) and 88 NSCLC patients
(purple) is depicted.
[0158] FIG. 9: NSCLC patient stratification and individualized
treatment recommendation by the integrative PK/PD ESA-EpoR
mathematical model. (a) CERA treatment simulations according to the
patient-specific parameters in three patients of the CSR NA17101
clinical trial. Patient 1, 2 and 3 correspond to ID2303, ID1022 and
ID2652 respectively. Upper panels represent the CERA dose and
regimens given to patients based on the current posology for NESP.
Lower panels represent the outcome for the three patients. Dashed
lines correspond to the optimal outcome that can be achieved within
the limits of the current label for ESAs. Solid line represents the
outcome for each patient predicted by the integrative PK/PD
ESA-EpoR mathematical model. Shading represents the confidence
interval of the model prediction for Hb levels. (b) Patient
stratification based on the current ESA posology. The
patient-specific ESA binding sites per patient and the Hb
degradation rates estimated by the integrative PK/PD ESA-EpoR
mathematical model for all patients in the CSR NA17101 clinical
trial are indicated by the symbols. Patient 1, 2 and 3 studied in
(a) are marked with black circles. Overdosed patients are defined
by a Hb increment >2 g/dl in four weeks and/or reaching Hb
levels >13 g/dl and Non-treatable patients are characterized by
no increment of Hb levels during the treatment. (c) Model-based
optimized ESA treatment of patient 1, 2 and 3. The upper panel
represents the dose and regimens that the model recommends for each
patient. The lower panel represents the model predicted treatment
outcome for each patient. Dashed line corresponds to the ideal
outcome based on the current label for ESAs. Solid line represents
the outcome prediction by the model. Shading represents the assumed
confidence interval of the Hb measurement. (d) Stratification of
the 88 NSCLC IIIB-IV patients from the CSR NA17101 clinical trial.
Patient 1, 2 and 3 are marked with a black circle. The lines
indicate the maximal CERA doses required to successfully treat the
respective patients at an interval of three weeks, except for
patients with a very high Hb degradation rate and a high number of
ESA binding sites that require weekly CERA doses.
EXAMPLES
[0159] Materials and Methods
[0160] Plasmids and Reagents.
[0161] Retroviral expression vectors were pMOWS-puro (Ketteler et
al., 2002). The generation of hemagglutinin (HA)-tagged murine Epo
receptor (pMOWS-HA-mEpoR) and of HA-tagged human EpoR
(pMOWS-HA-hEpoR) was performed as described previously (Becker et
al., 2010). Cells were either treated with Epo alfa (Cilag-Jansen),
Epo beta (Roche), NESP (Amgen), or CERA (Roche) at indicated
concentrations.
[0162] Cell Culture and Transfection.
[0163] Human lung adenocarcenoma cell lines A549, H838, H1299,
H1944, H1650, H1975 and H2030 were purchased by ATCC and cultivated
in Dulbecco's modified Eagle's Medium (DMEM, Lonza) supplemented
with 10% fetal calf serum (FCS, Gibco) and 1%
penicillin/streptomycin (Invitrogen). The Phoenix eco and Phoenix
ampho packaging cell lines (Kinsella & Nolan, 1996) were
cultured in DMEM (Gibco) supplemented with 10% FCS and 1%
penicillin/streptomycin. BaF3 cells (Palacios & Steinmetz,
1985) were cultured in RPMI-1640 (Invitrogen) including 10% FCS and
supplemented with 10% WEHI conditioned medium as a source of IL-3.
For the EpoR overexpressing cell lines H838 (H838-hEpoR) and BaF3
(BaF3-mEpoR and BaF3-hEpoR) 1.5 .mu.g/ml puromycin (Sigma) was
added to the respective medium.
[0164] To obtain hCFU-E cells, CD34+ cells were sorted by MACS
(CD34-Multisort Kit, Miltenyi) from umbilical cord blood of healthy
donors after written consent. CD34+ cells were expanded using Stem
Span SFEM II supplemented with Stem Span CC110 (both StemCell
Technology). After seven days of expansion cells were either washed
extensively using IDMEM (Gibco) to remove cytokines and to initiate
differentiation or cells were used for depletion experiments. For
differentiation cells were cultivated in Stem Span SFEM II
supplemented with 10 ng/ml IL-3 (R&D Systems), 50 ng/ml SCF
(R&D Systems) and 6 U/ml Epo alpha (Cilag-Jansen) as published
by Miharada 2006. After 4 days of cultivation in this media hCFU-E
were harvested to perform depletion experiments. All cells were
cultured at 37.degree. C. with 5% CO2 incubation.
[0165] Transfection of Phoenix eco and Phoenix ampho cells was
performed by calcium phosphate precipitation. Transducing
supernatants were generated 24 h after transfection by passing
through a 0.45 m filter and supplemented with 8 g/ml polybrene
(Sigma). Stably transduced BaF3 cells expressing HA-tagged murine
EpoR (BaF3-mEpoR cells) or HA-tagged human EpoR (BaF3-hEpoR cells)
or H838 cells expressing HA-tagged human EpoR (H838-hEpoR cells)
were selected in the presence of 1.5 .mu.g/ml puromycin (Sigma) 48
h after transduction. Surface expression of EpoR in BaF3 and
H838-hEpoR cells was verified by Flow cytometry analysis.
[0166] Flow Cytometry.
[0167] EpoR surface expression was verified by flow cytometry.
Therefore H838-hEpoR cells were gently detached with Cell
Dissociation Solution (Sigma) according to the manufacturer's
instructions. BaF3-EpoR and H838-hEpoR cells were stained with
anti-HA antibody (Roche) diluted 1:40 in 0.3% PBS/BSA for 20 min at
4.degree. C. Followed by washing of cells with 0.3% PBS/BSA and
incubation of secondary Cy5-labeled antibody against rat (Jackson
Immuno Research), diluted 1:100 in 0.3% PBS/BSA, for 20 min at
4.degree. C. in the dark. After washing samples with 0.3% PBS/BSA,
propidium iodide (BD Biosciences) was added to exclude dead cells.
Canto II (BD Bioscience) was used for sample analysis.
[0168] Depletion Experiments and ELISA
[0169] ESA depletion experiments were conducted in NSCLC tumor cell
lines, BaF3, BaF3-mEpoR, BaF3-hEpoR, hCFU-E, hHSC cells. Tumor
cells were seeded in 6 well-plates (TPP 92006) at a cellular
concentration of 4.times.105 cells in 3 ml of proliferating media
(DMEM supplemented with 10% FCS and 1%). Cells were kept at
37.degree. C., 95% H2O and 5% CO2 during three days. On the third
day cells were washed with DMEM (1% penicillin/streptomycin and 1
mg/ml BSA) and left them starving in 1 ml of washing media during
12 hours. Cells were stimulated with Epo alfa/beta within the
indicated times and concentrations of the depletion plots. After
the incubation time, media was recovered and kept at -80.degree. C.
till the conclusion of the experiment, cells were trypsinized and
counted by hemoytometer chamber. Once the experiment was concluded
ESAs concentration was measured by ELISA (Quantikine IVD ELISA Kit,
R&D DEP00).
[0170] The experimental setting for the depletion measurements was
different in the suspension cells; BaF3-hEpoR, BaF3-mEpoR, BaF3,
hCFU-E and hHSC. In the transduced BaF3 cells, the experiments were
conducted in between 9-14 days of selection with puromicin (1.5
.mu.g/ml). Cells were washed three times in RPMI by centrifugation
5 minutes at 212.times.g, and starved 3 hours in RPMI (1%
penicillin/streptomycin and BSA 1 mg/ml) at a concentration of
1.times.106 cells/ml. After the starvation period cells were
adjusted to a final concentration of 40.times.106 cells/ml in 350
.mu.l at 37.degree. C. and 900 rpm in a Thermomixer compact of
Eppendorf. Cells were stimulated by ESA during the indicated times
in the plot and centrifuged during 5 minutes, at 4.degree. C. and
2500 rpm. Supernatant was removed and kept at -80.degree. C. ESAs
measurements were performed by ELISA (Quantikine IVD ELISA Kit,
R&D DEP00). ESAs depletion measurements were conducted in the
same way in hCFU-E and hHSC with the only difference of the cell
concentration 30.times.106 cells/ml, and the used media (Stem Span
SFEM II).
[0171] Immunoprecipitation and Quantitative Immunoblotting.
[0172] For analysis of phosphorylated and total proteins human lung
adenocarcenoma cell lines as well as H838-hEpoR cell line were
seeded, cultivated for 72 h, starved for 3 h in DMEM with 1%
penicillin/streptomycin, 2 mM L-glutamine (Gibco) and 1 mg/ml BSA
and then stimulated with Epo beta or CERA at indicated
concentrations for 10 min. Prior to experiments BaF3 cells were
washed and resuspended in serum-depleted RPMI-1640 supplemented
with 1% penicillin/streptomycin and 1 mg/ml BSA and starved for 3
h. Afterwards the cells were harvested and aliquoted in a density
of 20.times.106/ml and stimulated with Epo beta at indicated
concentrations for 10 min.
[0173] The cells were lysed with 1.25.times.NP-40 lysis buffer
(1.25% NP-40, 187.5 mM NaCl, 25 mM Tris pH 7.4, 12.5 mM NaF, 1.25
mM EDTA pH 8.0, 1.25 mM ZnCl2 pH 4.0, 1.25 mM MgCl2, 1.25 mM
Na3VO4, 12.5% glycerol supplemented with aprotinin and AEBSF). The
protein concentrations in lysates were measured using the
colorimetric BCA protein assay kit (Pierce Protein Research
Products). For Immunoprecipitation analysis the lysates (1500-2000
.mu.g protein for lung adenocarcenoma cell lines, 400 .mu.g protein
for BaF3 cells) were supplemented with antibodies to EpoR (R&D,
MAB 307), JAK2 (Upstate) or STAT5A/B (Santa Cruz, C17) and Protein
A sepharose (GE Healthcare) and rotated over night by 4.degree. C.
Immunoprecipitated proteins were separated by 10% SDS-PAGE and
transferred to nitrocellulose membrane (0.2 m pore, Schleicher
& Schuell). For quantification purposes randomized
non-chronological gel loading was performed (Schilling et al.,
2005). For the detection of the phosphorylated proteins the blots
were probed with mAbs specific for phosphotyrosine (pTyr) (Upstate,
clone 4G10) and then with secondary horseradish peroxidase-coupled
anti-mouse antibodies (Dianova). To remove antibodies, membranes
were treated as described previously (Klingmuller et al., 1995) and
subsequently incubated with pAbs for EpoR (Santa Cruz, C-20) and
horseradish peroxidase-coupled anti-rabbit antibodies (Dianova).
Detection was performed using ECL substrate (GE Healthcare).
Immunoblot data were acquired with the CCD camera-based ImageQuant
LAS 4000 (GE Healthcare) and quantification was performed with the
ImageQuant TL version 7.0 software (GE Healthcare).
[0174] mRNA Isolation, cDNA Preparation and qPCR
[0175] For analysis of EpoR expression the cells were lysed and RNA
extraction was performed using RNeasy Mini kit (Qiagen) according
to the supplier's protocol. To obtain cDNA from RNA, the
high-capacity cDNA reverse transcription kit (Applied Biosystems)
was used according to manufacturer's instructions. Quantitative
real-time PCR (qRT-PCR) analysis was performed using LightCycler
480 (Roche applied-Science). Samples were prepared with reagents of
the LightCycler480 Probes Master Kit from Roche applied-Science.
Specific primers were obtained from Eurofins MWG and universal
probes (UPL) for TaqMan quantification of DNA from Roche
applied-Science. Concentrations were normalized using the geometric
mean of 0-glucuronidase (GUSB) and esterase D (ESD). Primers
targeting human EpoR: forward-ttggaggacttggtgtgtttc;
reverse-agcttccatggctcatcct; ESD:
forward-ttagatggacagttactccctgataa;
reverse-ggttgcaatgaagtagtagctatgat; GUSB:
forward-cgccctgcctatctgtattc; reverse-tccccacagggagtgtgtag.
[0176] Mass Spectrometry Analysis.
[0177] Cellular lysate were subjected to IP with a combination of
two STAT5 antibodies, sc-1081 and sc-836 from Santa Cruz
Biotechnology. Two IPs were pooled per lane. Proteins were
separated by a 10% SDS-PAGE (GE Healthcare) in 1.times. Laemmli
buffer (Laemmli 1970). Following coomassie staining with
SimplyBlue.TM. SafeStain (Invitrogen) STAT5 gel bands were excised
at approximately 90 kDa and cut into small pieces (1 mm3). Gel
pieces were destained, reduced with DTT (dithiothreitol, SIGMA),
alkylated with IAA (iodoacetamide, SIGMA) and digested with 0.3
.mu.g trypsin in 100 mM NH4HCO3/5% acetonitrile buffer overnight.
In-house produced one-source peptide/phosphopeptide ratio standards
for STAT5A and STAT5B were added to the digests (Boehm 2014).
Following a four-step peptide extraction performed sequentially
with 100 mM NH4HCO3/5% acetonitrile, acetonitrile, 5% formic acid,
and acetonitrile, the samples were concentrated in a speedvac
(Eppendorf) and desalted with C18 Ziptips (Millipore) using
solutions based on water, acetonitrile and formic acid. Samples
were analyzed by EASY-nLC 1000 (Thermo Scientific) coupled to a Q
Exactive.TM. Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo
Scientific). As precolumn we used Acclaim PepMap 100, 75
.mu.m.times.2 cm, as analytical column we used Acclaim PepMap RSLC
C18, 2 .mu.m, 100 .ANG., 75 .mu.m.times.25 cm. Survey full scan MS
spectra were acquired at resolution R=70,000 and analyzed for the
native and labelled STAT5 peptide and phosphopeptide pairs with
Xcalibur 3.0.63 (Thermo).
[0178] The in vitro trafficking model (FIG. 6a) was extended to a
pharmacokinetic/pharmacodynamics (PK/PD) model (FIG. 6b) by
including blood and interstitium compartments and patient specific
PK data obtained by either intravenous (IV) or subcutaneous (SC)
injections of ESA/CERA. Additionally, the model provides the link
between ESA bound to the EpoR (ESA_EpoR) and haemoglobin levels
(Hb) measured in patients. The model consists of the following
additional reactions: [0179] 7. Clearance in the blood compartment.
[0180] 8. Transport into blood compartment. [0181] 9. Saturable
clearance in the interstitial compartment. [0182] 10. Production of
Hb triggered by the activated receptor complex. [0183] 11. Patient
specific degradation of Hb.
[0184] The reaction rate equations are given by: [0185] 1.
"k.sub.on*ESA*EpoR" and "k.sub.off*ESA_EpoR" [0186] 2.
"k.sub.e*ESA_EpoR" [0187] 3. "k.sub.ex*ESA_EpoR_i" [0188] 4.
"k.sub.t*Bmax" and "k.sub.t*EpoR" [0189] 5. "k.sub.di*ESA_EpoR_i"
[0190] 6. "k.sub.de*ESA_EpoR_i" [0191] 7. "k.sub.clear*ESA" [0192]
8. "k.sub.scout*ESA_SC" [0193] 9.
"k.sub.scclear*ESA_SC/(k.sub.scclearsat+ESA_SC)" [0194] 10.
"k.sub.hb_pro*ESA_EpoR" [0195] 11. "k.sub.hb_deg*Hb"
[0196] Model Calibration
[0197] For calibration of the model parameters, the inventors used
the D2D software package (Raue et al. PloS ONE 2013) in MATLAB
(Release 2012b, The MathWorks, Inc., Natick, Mass., USA). In order
to minimize the distance between the simulated model trajectories
and the measured data, a maximum likelihood approach was applied.
The inventors used a deterministic optimization algorithm combined
with multiple starting points in the high dimensional parameter
space to find the global optimum of the negative log-likelihood. As
the parameter values can range over several orders of magnitude and
are, by its biochemical definition, strictly positive, the
optimization was performed in logarithmized parameter space. To
account for the log-normally distributed measurement noise of
protein time course data (Kreutz et al. Bioinformatics 2007), also
the data were transformed onto the logarithmic scale and an
additive error model was fitted simultaneously with the kinetic
model parameters. (Raue et al. PloS ONE 2013)
[0198] The affinity parameters (k.sub.on, k.sub.off or k.sub.on and
k.sub.D) and the number of binding sites (B.sub.max) were estimated
individually for each experimental condition, i.e. combination of
ESA and cell type, as they depend on the biochemical properties of
the ESA and on the EpoR expression level of the respective cell
type.
[0199] The structural and practical identifiability of the
parameters was analyzed using the profile likelihood approach as
described by Raue et al. (Bioinformatics 2011). Furthermore, this
method enabled the inventors to determine the parameter's
confidence intervals and the uncertainties of the model
predictions.
Example 1: Model Based Determination of ESA Binding Properties
[0200] To assess the role of Epo and Epo derivatives in the context
of lung cancer, it was essential to develop a reliable,
quantitative assay that enables to determine the number of binding
sides per cell and the specific binding properties of different
human ESA (Epo alpha, Epo beta, NESP and CERA). The inventors
utilized our knowledge that rapid ligand depletion is
characteristic for the Epo-EpoR system (Becker et al 2010) and
established a robust ELISA assay to monitor Epo removal from
cellular supernatants.
[0201] As shown in FIG. 1a this enabled us to accurately quantify
the depletion of Epo alfa and Epo beta by murine BaF3 cells stably
expressing the murine EpoR (BaF3-mEpoR) whereas parental BaF3 cells
had no impact underscoring the specificity of the assay. These
quantifications in combination with our dynamic pathway model of
Epo-EpoR interactions (Becker et al 2010) enabled to calculate the
dissociation constant K.sub.D (FIG. 1a) as well as the association
rate k.sub.on, the dissociation rate k.sub.off and the number of
binding sides (B.sub.max) for Epo alfa and Epo beta interaction
with the murine EpoR.
[0202] The estimated B.sub.max was in good agreement with the
results obtained by traditional saturation binding assays using
radioactively labelled ligand, further validating the assay. To
comparatively examine the binding properties of different ESAs for
the human EpoR, the inventors measured ESA depletion by BaF3 cells
stably expressing the human EpoR (BaF3-hEpoR) or parental BaF3
cells (FIG. 1b). The results showed that whereas Epo alpha and Epo
beta are very rapidly depleted, depletion of NESP and CERA is
moderate. The quantitative time-resolved data in combination with
our dynamic pathway model of ligand-receptor interaction enabled us
to calculate that K.sub.D of Epo alpha and Epo beta, respectively,
are with 16 and 17 pM very similar. However, for NESP the model
indicates a K.sub.D of 789 pM and for CERA a K.sub.D of 982 pM
suggesting for both Epo derivatives a much elevated dissociation
constant.
[0203] Relating the K.sub.D of the different ESA to the respective
association and dissociation rates as shown in FIG. 1c reveals that
the association of NESP and CERA is much slower compared to Epo
alpha and Epo beta whereas the dissociation rate is enhanced.
Therefore by combining simple time-resolved quantification of the
concentration of Epo in cell supernatants with our dynamic pathway
model it was possible to reliably determine the binding properties
of ESA and to show that the available ESA differ significantly in
their properties to bind to the human EpoR.
Example 2: Presence of Functional EpoR in NSCLC Cell Lines
[0204] To determine the presence of a functional EpoR in lung
cancer cells, the inventors first screened a panel of NSCLC cell
lines for the presence of EpoR mRNA. Among these we identified
three adenocarcinoma NSCLC cell lines that showed significant
levels of EpoR mRNA transcripts. As depicted in FIG. 2a H838 and
H1299 showed moderate expression levels of EpoR mRNA and A549 low
levels. H1944 represent NSCLC cell lines with levels below the
detection limit (FIG. 2a). Next evaluated was the expression of the
EpoR protein in the four selected NSCLC cell lines as well as its
functionality. Enrichment by immunoprecipitation and detection by
immunoblotting revealed the presence of the EpoR protein in H838
and H1299 and at very low levels in A549, whereas it was absent in
H1944 (FIG. 2b). In line with previous observations the overall
expression level of EpoR protein was very low compared to
BaF3-hEpoR.
[0205] Upon stimulation with Epo as expected the tyrosine
phosphorylated form of the receptor was absent in parental BaF3
cells and H1944, but evident in H838, H1299 and A549 indicating the
presence of a signaling competent, functional EpoR in these three
NSCLC cell lines. To determine the binding properties of the EpoR
expressed in the NSCLC cell lines, the inventors applied the
depletion assay and showed (FIG. 2c) that Epo beta was depleted by
the NSCLC cell lines harboring a functional EpoR, but not by the
EpoR negative NSCLC cell line H1944 (FIG. 1b). However, Epo beta
depletion was much slower compared to BaF3-EpoR cells suggesting
the presence of a significantly lower number of cell surface
receptors. Accordingly, analysis of the time-resolved data with the
dynamic pathway model revealed binding sides ranging from
undetectable to 90 per cell (FIG. 2c and Table 2), yet the
estimated K.sub.D was comparable to the estimates with BaF3-hEpoR.
This shows that ligand depletion and signaling competent receptor
is present on a subset of NSCLC cell lines.
Example 3: EpoR Depletion Kinetics in Cells with High Numbers of
EpoR
[0206] The main target of Epo treatment during anemia are erythroid
progenitor cells at the colony forming units-erythroid (CFU-E)
stage that express high levels of the EpoR. To quantify the cell
surface expression of the EpoR on human CFU-E and characterize the
binding properties, human CD34+ hematopoietic stem cells (hHSC)
were prepared from human umbilical cord blood and differentiated to
human CFU-E (hCFU-E). Time-resolved analysis of Epo beta depletion
revealed rapid reduction of Epo beta from the supernatants of
hCFU-E but not of hHSC that lack the EpoR (FIG. 3a). Model based
analysis showed a K.sub.D comparable to BaF3-hEpoR and a B.sub.max
of 365 binding sites per cell that was one order of magnitude lower
compared to BaF3-hEpoR but one order of magnitude higher in
comparison to the NSCLC cell line H838.
[0207] To examine whether some of the available ESA could have
advantages in the tumor context due to the distinct binding
properties, the inventors aimed at establishing a cell model system
with elevated hEpoR expression levels mimicking the situation in
hCFU-E as hCFU-E are only available at extremely limiting amounts.
The inventors stably expressed the hEpoR in H838 (H838-hEpoR) and
showed by enrichment using immunoprecipitation and immunoblotting
that the expression of the EpoR was highly increased and the
phosphorylated EpoR was substantially elevated (FIG. 3b). Depletion
experiments and model-based analysis revealed binding properties
rather similar to hCFU-E (FIG. 3c) establishing the H838-hEpoR cell
line as suitable model system to examine the impact of different
ESA on cells harboring high levels of the EpoR as observed in the
hematopoietic system versus cells expressing low levels as in the
tumor context.
Example 4: Identification of CERA as an ESA Preferentially
Activating Cells with High EpoR Expression
[0208] To compare the impact of ESA on tumor cells that express low
levels of EpoR versus cells that display elevated EpoR levels such
as H838-EpoR, model simulations were performed. As readout for EpoR
signaling, we calculated the integral of ESA bound to the EpoR
(ESA_EpoR) for the first 60 minutes after stimulation. First these
stimulations were performed for different ESA concentrations and
predicted the EC.sub.50 for both Epo beta and CERA in cells with
high EpoR levels (FIG. 4a). The model predicts that a 10-fold
higher concentration of CERA is required for the same activation.
This model prediction was experimentally validated in H838-EpoR
cells by quantitative immunoblotting against phosphorylated
EpoR.
[0209] Interestingly, the model predicted that the ESA
concentrations that induce the same activation in cells with high
EpoR levels act differently in cells with low levels of EpoR such
as H838. As these cells deplete less Epo beta, Epo beta results in
stronger activation than CERA in cells with low levels of EpoR
(FIG. 4b). Experimentally this model prediction was validated in
H838 cells by quantitative mass spectrometry against phosphorylated
STAT5. Thus, CERA was identified as an ESA preferentially
activating cells with high EpoR expression, such as H838-EpoR and
hCFU-E cells, rather than cells with low EpoR expression, such as
NSCLC cells.
Example 5: Determination of the Number of CFU-E Cells in Healthy
Subjects and NSCLC Patients by an Integrated PK/PD Model
[0210] Having identified CERA as an ESA preferentially acting on
cells with high EpoR levels, we integrated our model with
pharmacokinetic (PK) data to describe CERA dynamics in patients
(the integrative (PK/PD) ESA-EpoR mathematical model; see above).
In a first step, the inventors analyzed mean PK values of CERA in
the serum of healthy subjects (Locatelli et al.) as well as of
NSCLC stage IIIB-IV patients (Hirsh et al). As CERA, which is
pegylated, is not cleared by the kidney, it was hypothesized that
the clearance of CERA in the blood stream is only accomplished by
binding to EpoR and internalization, as seen in the in vitro
experiments. Furthermore, it was assumed that the main difference
between healthy subjects and NSCLC patients in Epo dynamics is the
number of CFU-E cells, which may be reduced by the tumor load and
by the chemotherapy. Indeed, these assumptions were sufficient to
describe the experimental PK data for both healthy subjects (FIG.
5a) and cancer patients (FIG. 5b). Furthermore, the model
determined a decrease of 72% in the average number of CFU-E cells
in the NSCLC stage IIIB-IV patients, resulting in longer clearance
times of CERA.
[0211] Then, the inventors applied the same approach to PK data of
individual NSCLC patients. While the data appears very
heterogeneous, the model could again describe all data sets based
only on different numbers of ESA binding sites, i.e. CFU-E cells.
While ESA binding sites may also be present on other cells, such as
the NSCLC cells, they will not contribute significantly to
clearance of CERA due to their low expression levels. Importantly,
it was possible to determine the number of CFU-E cells for each
cancer patient, showing a high patient-to-patient variability (FIG.
5c).
Example 6: Determination of the Number of CFU-E Cells in Healthy
Subjects and NSCLC Patients Based on the Patient Hemoglobin (Hb)
Levels
[0212] The above model was also able to correlate the hemoglobin
(Hb) increments with the PK/PD data in individualized patient data
sets.
[0213] In particular, the in vitro trafficking model (FIG. 6A) was
extended to a pharmacokinetic/pharmacodynamics (PK/PD) model (FIG.
6B) by including blood and interstitium compartments and patient
specific PK data obtained by either intravenous (IV) or
subcutaneous (SC) injections of ESA/CERA. The PK profiles
correlates with the number of CFU-E and this number with the
recovery of the anemia, indicated by Hb levels. The inventors
established the correlation between the individual patient
histories with the PK profiles and these ones with the number of
CFU-E per patients, and these ones with the outcome of the ESA
treatment (increment of Hb levels). The Hb model includes therefore
the additional reactions of the production of Hb by active
ESA-EPO-R signalling since the ESAEPO-R signalling induces the
maturation of erythrocytes that therefore increases Hb
concentrations (FIG. 6C). Additionally, the model includes the
patient specific degradation of Hb, which is easily determined in
anemic patients, because there Hb status is regularly
monitored.
Example 7: CERA Preferentially Activates Cells with High EpoR
Expression
[0214] We examined the impact of ESA binding properties and of
different ESA binding sites on receptor activation to assess
whether some of the available ESAs could have advantages in the
tumor context. The ESA-EpoR mathematical model predicted that ESA
concentrations that induce the same degree of activation of
signaling in cells with high EpoR abundance act differently in
cells with low levels of the EpoR (FIGS. 4a and 4b). This behavior
was experimentally validated in H838 and hCFU-E cells by mass
spectrometric analysis of STAT5 phosphorylation in response to
stimulation with Epo beta or CERA (FIG. 7). H838 and hCFU-E were
stimulated with 1331 pM of Epo beta or 8841 pM of CERA,
concentrations that correspond to the half-maximal activation of
STAT5 phosphorylation in hCFU-Es. As the ESA-EpoR mathematical
model predicted (FIG. 4), the activation of EpoR signaling by CERA
is less effective in cells with low levels of the EpoR such as
NSCLC cells (FIG. 7 left panel) compared to cells with higher
levels of the EpoR like hCFU-E (FIG. 7 right panel). Thus, we
identify CERA as an ESA preferentially activating erythroid
progenitor cells rather than tumor cells.
Example 8: Integrative PK/PD ESA-EpoR Model-Based Stratification of
NSCLC Patients
[0215] As in example 5, we applied the same approach to the PK/PD
data from individual NSCLC patients (clinical trial CSR NA17101)
and healthy subjects (clinical trial WP16422). Although the patient
data is apparently very heterogeneous, the integrative PK/PD
ESA-EpoR model (FIG. 8a) is able to describe all patient data sets.
Herein we exemplify two individual cases, NSCLC patient ID:2101
(clinical trial CSR NA17101) (FIG. 8b) and healthy subject ID:25
(clinical trial WP16422) (FIG. 8c). The integrative PK/PD ESA-EpoR
model was able to describe the time-course of CERA concentrations
determined in the serum and the corresponding Hb levels measured in
the blood in response to the indicated ESA regimen, (FIGS. 8b and
c). To describe the heterogeneous PK/PD data, we assume that in
addition to the different number of ESA binding sites, already
explained in example 5, the net loss of Hb (KHb_deg) could be
another key difference between healthy subjects and NSCLC patients.
Due to the inflammation associated with cancer, the half-life of
erythrocytes is shortened and could therefore affect the KHb_deg in
particular in cancer patients. Indeed, this assumption was
sufficient to describe the experimental PD data for both cancer
patients and healthy subjects (FIGS. 8b and c lower panels).
[0216] Importantly, we can estimate the number of ESA-binding sites
for individual cancer patients, showing a high patient-to-patient
variability and a very different distribution from the healthy
subjects (FIG. 8d right). Further, the distribution of the
estimated KHb_deg parameter differs widely in healthy subjects and
NSCLC patients (FIG. 8d left panel).
Example 9: Model-Based Treatment Optimization in NSCLC Anemia
[0217] The current guidelines defined by the European Medicines
Agency (EMEA) recommend that the hemoglobin (Hb) response to ESA
treatment of anemia in cancer should neither exceed increments of
Hb.gtoreq.2 g/dl in the following four weeks after the first ESA
dose nor should Hb levels reach higher values than 13 g/dl. These
guidelines recommend doubling the ESA dose if there is no response
to the treatment (Hb increments .ltoreq.1 g/dl in 4 weeks after the
first ESA dose), or reducing the ESA dose by 25% or 50% if the
increment of Hb levels is .gtoreq.2 g/dl after four weeks and/or if
Hb values ranging from 12 g/dl to 13 g/dl are reached. Interruption
of the treatment is mandatory if the Hb value is higher than 13
g/dl. We employed the integrative PK/PD ESA-EpoR mathematical model
to calculated the EC50 (ESA concentration required to obtain
half-maximum EpoR occupancy) for each ESA and determined the CERA
doses that correspond to the current guidelines for NESP.
Considering the EMEA-recommended ESA guidelines, we performed CERA
treatment simulations based on the patient-specific parameters in
three NSCLC patients (FIG. 9a). In the case of Patient 1 (ID:2303
CSR NA17101) the maximum CERA dose (equivalent to maximal NESP dose
in the guidelines) would be given every three weeks (FIG. 9a upper
left panel), and the model predicts no response within the current
ESA guidelines (FIG. 9a lower left panel). In Patient 2 (ID:1022
CSR NA17101) the model predicts a fast hematological response
within the current ESA guidelines (FIG. 9a upper and lower middle
panels). In Patient 3 (ID:2652 CSR NA17101) the model predicts an
interruption of the ESA treatment (FIG. 9a upper right panel) due
to overshooting Hb values in response to the treatment within the
current ESAs guidelines (FIG. 9a lower right panels).
[0218] To understand the impact of the current ESA guidelines in
the NSCLC anemia treatment, 88 patients from the CSR NA17101
clinical trial were plotted based on patient-specific ESA binding
sites and the Hb degradation rates. Patient stratification was
carried out by response prediction within the current
EMEA-recommended ESA guidelines (FIG. 9b). We defined as overdosed
patients that were predicted to have an Hb increment >2 g/dl in
four weeks and/or reaching Hb levels >13 g/dl, such as Patient 3
(ID:2652 CSR NA17101). We defined patients as treatable if they
were predicted to have an Hb increment of .ltoreq.2 g/dl in four
weeks and reach Hb levels of 12 g/dl, such as Patient 2 (ID:1022
CSR NA17101). We defined patients as non-treatable if they are
predicted to have no increment of Hb levels during the treatment,
such as Patient 1 (ID:2303 CSR NA17101). Interestingly, the
integrative PK/PD ESA-EpoR mathematical model predicted a
systematic overdosing of a large fraction of NSCLC IIIB-IV patients
treated within the EMEA-recommended ESA guidelines for anemia in
cancer (FIG. 9b).
[0219] The integrative PK/PD ESA-EpoR mathematical model can
optimize the ESA dosing and scheduling to achieve a hematological
response within the limits of the ESAs guidelines for most of the
NSCLC IIB-IV patients, minimizing the risk of overdosing (FIG. 9c).
For Patient 2 and 3, the model is able to optimize the ESA regimens
(FIG. 9c middle and right upper panel) that result in hematological
responses without compromising the safety limits (FIG. 9c middle
and right lower panels). In the particular case of Patient 1, the
model recommended an ESA regimen beyond the ESA guidelines (FIG. 9c
left upper panel) to achieve a hematological response (FIG. 9c left
lower panel). Finally, we displayed the prediction for all ESA
regimens required to effectively treat all the NSCLC IIIB-IV
patients of the CSR NA17101 clinical trial (FIG. 9d).
* * * * *