Translation from mouse to human of pharmacokinetic ...
Transcript of Translation from mouse to human of pharmacokinetic ...
Translation from mouse to human of pharmacokinetic-pharmacodynamic modelling of biomarker response –learnings from the AstraZeneca Oncology portfolio
Rhys Owen Jones3rd ICPAD Workshop – Amsterdam November 8th & 9th
Improving Phase II success rate5Rs framework and application of PKPD to predict human target engagement
• Right Tissue: Demonstrate adequate
exposure, and PKPD pre-clinically and
clinically in order to
• Build confidence molecule has PKPD
properties to reach sufficient levels of target
engagement (TE) to viably test the clinical
hypothesis
• Predict a PD active dose & establish a dose /
schedule model to help guide clinical study
design and input to set criteria for PoM
• Benchmark emerging data from dose
escalation studies against pre-clinical model
5Rs
framework
Pre-clinical data and predictive pharmacokinetic-pharmacodynamic
modelling is the cornerstone to predict human doseWhat is the evidence for pre-clinical data being adequate to predict human dose?
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Predicted human exposure
Good understanding of translation of model
systems to predict human PK
PK is well predicted
Predicted human active dose
Translation of pre-clinical model systems to humanin vivo drug potency for
target engagement
Limited demonstration on how well we do this
Predicted human dose for optimal efficacy
Limited validated pre-clinical model systems
available
Attrition rate in late phases continues to be high, often
due to lack of efficacy
DosePlasma /
Tissue PKTarget
OccupancyTarget
EngagementPathway
ModulationPhenotypic Response
Efficacy /
Toxicity
Pre-clinical data and predictive pharmacokinetic-pharmacodynamic
modelling is the cornerstone to predict human doseWhat is the evidence for pre-clinical data being adequate to predict human dose?
4
Predicted human exposure
Good understanding of translation of model
systems to predict human PK
PK is well predicted
Predicted human active dose
Translation of pre-clinical model systems to humanin vivo drug potency for
target engagement
Limited demonstration on how well we do this
Predicted human dose for optimal efficacy
Limited validated pre-clinical model systems
available
Attrition rate in late phases continues to be high, often
due to lack of efficacy
DosePlasma /
Tissue PKTarget
OccupancyTarget
EngagementPathway
ModulationPhenotypic Response
Efficacy /
Toxicity
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What comparison can we make comparing mouse to
human PKPD?
PK
Surrogate PD
Tumour PD
PK
Surrogate PD
Tumour PDDetailed articulation to define
PKPD relationship
Rarely sufficient data to build
PKPD relationship
Less emphasis & rarely
explored
When biologically feasible often
offers greater depth of data to
build PKPD relationship
Fully characterised Fully characterised
Minimal requirements
• Availability of biomarker pre-clinically and clinically
• Tissue and assays may differ mouse to patient
• Quantitative pre-clinical models and minimally, clinical data to overlay
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What comparison can we make comparing mouse to
human PKPD?
Tumour PD Surrogate PD
Case example 1
• Exposure vs. biomarker modulation
explored in mouse tumour (CDX
model) and human PBMC
• Datasets provide comparisons of
derived EC50
• Good agreement between mouse and
human
Mouse tumour EC50 = 10 nM Human PBMC EC50 = 8 nM
0
20
40
60
80
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120
140
160
0 4 8 12 16 20 24
pSe
r2 (
%b
asel
ine
at T
=0)
Time (hours)
Patient101 Patient106
Patient110 Patient111
Case example 2: Early insight builds confidence in model based approach to
prioritise dose / schedule
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• PK and pProtein (in PBMC) data for 4 patients available to assess pre-clinical (mouse CDX
model) to clinical translation of PKPD relationship
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200
250
300
0 4 8 12 16 20 24
Pla
sma
con
cen
trat
ion
(n
g/m
L)
Time (hours)
Patient101
Patient106
Patient110
Patient111
PK pProtein (PBMCs)
0
20
40
60
80
100
10 100 1000 10000
pSe
r2 (
%b
asel
ine
at T
=0)
Plasma concentration (ng/mL)
Patient101
Patient106
Patient110
Patient111
pProtein vs [plasma]
Lines show individual model fit
to patient PKPreclinical PD model used to
predict pProtein timecourse (lines)
in patients
IC50 from xenograft model
IC50 from
fitting to
clinical
data
IC50 curve from CDX studies
consistent with IC50 curve derived
by fitting model to clinical data
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Case example 3: Clinical data and benchmarking against pre-clinical
requirements for efficacy suggest insufficient target engagement at a tolerated
dose
Biomarker EC50
Mouse (tumour) 15 nM
Human (PRP) 72 nM
• Clinical PD explored in
platelet rich plasma (PRP)
assay
• High degree of variability
observed across patients
• Population mean EC50 in
patients 5-fold higher than
mouse tumour
Phospho protein
Simulation of biomarker time course for
suppression on repeat dosing mouse and human
Human
Mouse
Patient exposure vs. response
relationship for biomarker in PRP
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Case example 4: Inadequate clinical PD to derive quantitative clinical relationship
• Exposure vs. response defined in mouse across 3 cell-line xenograft models (one shown).
• Peripheral blood data from 4 patients available with multiple samples – inadequate to derive IC50
• Data overlaid onto Emax relationships derived from 3 mouse CDX models
Pe
rce
nt p
ho
sp
ho
pro
tein
re
lative
to
to
tal
Plasma concentration ng/ml
Imax = ~72%; IC50 = 100 ng/ml
Exposure vs. response in cell-line 1
Mouse Human
Degree of inhibition seen in 3 patients out of 4 consistent
with that predicted from mouse PKPD model
Clinical PD overlayed onto predictions from multiple mouse CDX models
Case example 5: Using pre-cinical PKPD predictions when clinical PD
is not available during dose escalation
• 2500 simulated tumor PD profiles
created by combining 500 virtual
patient PK profiles with 5 different
mouse PD models (derived from
NSCLC CDX / PDX models).
• Percentage of virtual patient tumours
achieving ≥ 50% PD knockdown was
calculated at each dose level tested
• Modelling used to guide dose
requirement for of ≥ 50% PD
knockdown (POM criteria) in ≥ 50%
patients (when using PDX models)
Combining clinical PK variability and pre-clinical heterogeneity across CDX / PDX
models to predict target engagement
AZD4785
clinical
PopPK model
Simulate 500
virtual patient
PK profiles
at each dose
NCIH358
PKPD
PC9
PKPD
LXFA983
PKPD
H1437
PKPD
LXFA526
PKPD
Heterogeneity in PKPD
relationship (KRAS mRNA KD)
Count %individuals with
>50% maximum Kras KD
Calculate average
across 5 models
Count %individuals with
>50% maximum Kras KD
Count %individuals with
>50% maximum Kras KD
Count %individuals with
>50% maximum Kras KD
Count %individuals with
>50% maximum Kras KD
Variability in PK
BM KD
BM KD
BM KD
BM KD
BM KD
AZDxxxx
40
50
60
70
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100
0 168 336 504
KR
AS
mR
NA
(%
bas
elin
e)
Time (hours)
840 mg dose; 1 hr inf; PopMean PK
H1437 PKPD
PC9 PKPD
NCI-H358 PKPD
LXFA526 PKPD
LXFA983 PKPD
Example: Predicted PD profiles for typical
patient at xx mg dose
Bio
mark
er
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30
40
50
60
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0 200 400 600 800 1000 1200
%P
atie
nts
wit
h m
ax
KR
AS
red
uct
ion
>5
0%
Dose (mg)
LXFA983 PKPD
LXFA526 PKPD
PC9 PKPD
H1437 PKPD
NCI-H358 PKPD
Average across 5models
Relationship between weekly dose and
estimated %patients achieving ≥ 50% PD
knockdownB
iom
ark
er
Retrospective analysis of preclinical predictions of
exposure-target engagement across multiple therapy areas
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0.1
1
10
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Clin
ica
l /
Pre
-clin
icale
xp
osu
re
Compound no
• Oncology, Respiratory,
Inflamation & Autoimmune (RIA)
and Cardiovascular, Renal &
Metabolism (CVRM) therapy
areas covered
• 88 % predicted IC50 from pre-
clinical data / modelling within
2-fold of that observed in the
clinic
• 3 biomarkers had ~10-fold
difference in prediction
compared to observed
Learnings
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• Pre-clinical tools offer a feasible way to quantitively predict human target engagement (TE)
• Enables the use of pre-clinical insights to benchmark (sparse) clinical data in an iterative
and rapid way as data emerges from clinical trials
• Further analysis is required to understand the variability of clinical data and the precision
with which the biomarker IC50 can be estimated – how does this impact the benchmarking /
calibration of a pre-clinical model?
• PD data from surrogate tissues is valuable to calibrate pre-clinical models and to
demonstrate duration of effect relative to PK. Tumour PD remains gold standard for PoM
• PKPD should be explored in multiple mouse CDX & PDX models rather than only the most
sensitive model
• Benchmarking and back-translation of compounds with the same mechanisms and already
in the clinic is a powerful opportunity to calibrate translational PKPD assumptions
Conclusions
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• Builds confidence in the application of pre-clinical data to predict TE, an active dose in
human, and as an input to define G/NG criteria for early clinical trials (POM)
• Success at quantitatively translating PKPD is predicated on a sufficient level of
understanding of the biology, with appropriate pre-clinical models (in vitro, in vivo) that
enable the kinetics and dynamics of drug effects to be explored adequately
• Portfolios continually evolve to novel targets / mode of action with an increasing diversity
of drug modalities – continuous assessment of this kind is necessary
• The attrition rate in the clinic due to lack of efficacy is still significant and attention should
be directed to improve translational approaches that define the extent and duration of TE
required for optimal efficacy in patient populations – downstream pathway BM, cellular
effects
Acknowledgements
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Co-authors: Simon Barry, Andrew Pierce & James Yates
Case example contributors: Frank Gibbons, Douglas Ferguson, Michael
Davies, Elizabeth Harrington, Tammie Yeh, Alexander MacDonald, Tarjinder
Sahota
Cross-TA: Markus Fridén, Rasmus Löfmark-Jansson
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