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Population based
PBPK-PD models
Iain Gardner
Simcyp
© Copyright 2017 Certara, L.P. All rights reserved.
Overview of talk
• Systems approach to PBPK modelling
• Regulatory use of PBPK models in Pharmaceutical Industry
• Incorporating PD effects into PBPK models
– Drug transporter polymorphisms and statin efficacy
– Simulation of Cisapride/Clarithromycin interaction and QT
prolongation
• EU_TOX_RISK project
• Conclusions
2
© Copyright 2012 Certara, L.P. All rights reserved.
CLuint per
Liver
CLuint per
g Liver
In vitro
system
In vitro
CLuint
Prediction of human PK in virtual individuals
Scaling
Factor
(MPGGL, HPGL)
Liver
weight
Combine in vitro-in vivo extrapolation
(IVIVE) and PBPK approaches
in virtual individuals to predict
drug concentration and effect
Identifying relevant DISTRIBUTION of
values for demographical, biological,
physiological and genetic parameters in
target population & the COVARIATIONS
between the parameters in target
POPULATION
© Copyright 2012 Certara, L.P. All rights reserved.
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
[Plasma Protein]
Systems
Data
Drug
Data
Trial
Design
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) & DDI in population of interest
Mechanistic IVIVE linked PBPK models
Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012
Separating systems & drug information (Systems Pharmacology)
© Copyright 2012 Certara, L.P. All rights reserved.
Typical outputs from a population PBPK simulation (n = 100)
Mean plasma
and tissue
concentrations
adipose
muscle
heart
Variability
between
trials or
individuals
Drug
interactions
AUCratio = 11.4 (10.5 – 12.4)
Cmaxratio = 3.3 (3.1-3.6)
© Copyright 2012 Certara, L.P. All rights reserved.
Parameterisation of PBPK model
• Model can be informed with in vivo data
– Need data, can’t be used prospectively
• Model built from bottom up using in vitro data
– using in vitro-in vivo extrapolation approaches
• To describe profile after extravascular (oral)
administration need to estimate
– rate and extent of absorption
– volume of distribution (Vss)
– clearance
© Copyright 2012 Certara, L.P. All rights reserved.
Data need for IVIVE-PBPK model
• Rate and extent of
absorption
– Solubility
– Permeability
• Physicochemical
parameters
• In vitro cell systems
In silico input/ in vitro input
• Clearance
– In vitro plasma binding
– In vitro BP partitioning
– In vitro rate of metabolism
• Volume of distribution
– Physicochemical
parameters
• LogP, pKa
– In vitro plasma binding
– In vitro Blood:plasma (BP)
partitioning
• Plasma binding, BP
partitioning and metabolism
rates are species specific
– For rat need rat data
– For human need human
data
© Copyright 2012 Certara, L.P. All rights reserved.
How accurate are IVIVE approaches in predicting clearance?
• Two most commonly used systems
– HLM and Human Hepatocytes
• For accurate IVIVE need to have some understanding of the
performance of your in vitro system
– Assay conditions
– Source of liver tissue
– Number of donors used
– Often need to correct for systematic bias (under-prediction)
8
(Hallifax & Houston, 2012, J. Pharm. Sci, 101, 2645)
Hepatocyte (n= 89)
(RMSE = 0.59 r= 0.73)HLM (n= 64)
(RMSE = 0.63; r = 0.81)
© Copyright 2012 Certara, L.P. All rights reserved.
Data generated (n=1) using discovery protocols and used for IVIVE scaling
Clear difference between 2 labs
A – HLM lab 1
B – HLM lab 2
C – rat SSS – fixed exponent
D – rat SSS - Tang
E – NHP LBF method
9
© Copyright 2012 Certara, L.P. All rights reserved.
10
Recent publications on Pharmaceutical regulatory use of PBPK
FDA
EMA
Industry
© Copyright 2012 Certara, L.P. All rights reserved.
PBPK Impact on New Drug Approvals
Revatio (Sildenafil)
Pulmonary Arterial
Hypertension
Xarelto (Rivaroxaban)
Deep Vein
Thrombosis and
Pulmonary Embolism
Edurant (Rilpivirine)
HIV infection
Iclusig (Ponatinib)
Chronic Myeloid
Leukemia
Olysio
(Simerprevir)
Hepatitis C
Opsumit (Macitentan)
Pulmonary Arterial
Hypertension
Imbruvia (Ibrutinib)
Mantle Cell Lymphoma and
Chronic Lymphocytic
Leukemia
Movantik (Naloxegol)
Opioid Induced
Constipation
Cerdelga(Eliglustat)
Gaucher DiseaseJevtana (Cabazitaxel)
Prostate Cancer
Zykadia (Ceritinbi)
Metastatic Non-Small
Cell Lung Cancer
Bosulif (Bosutinib)
Chronic Myelogenous
Leukemia
Lynparza (Olaparib)
Advanced Ovarian
Cancer
Farydak (Panobinostat)
Multiple myeloma Lenvima (Lenvatinib)
Thyroid cancer
Odozmo (Sonidegib)
Basal Cell Carcinoma
Tagrisso
(Osimertinib)
Metastatic NSCLC
Cotellic (Cobimetinib)
Metastatic Melanoma
Alecensa (Alectinib)
Non Small Cell Lung Cancer
Aristada (Aripiprazolel)
Schizophrenia
10 fast track,
breakthrough,
priority or
accelerated
approvals
• 12 – oncology
• 3 – pulmonary
• 2 – anti-viral
• 4 – orphan
• 1 – gastro
• 1 - CNS
Almost 100 label
claims informed
by PBPK,
including DDI,
absorption, ethnic
bridging,
formulation
© Copyright 2017 Certara, L.P. All rights reserved. 12
Prospective DDI prediction - inhibition
Wagner et al., Clinical Pharmacokinet, 2015
15 substrate models submitted by 9 sponsors; mainly CYP3A4 metabolised
© Copyright 2017 Certara, L.P. All rights reserved. 13
Prospective DDI prediction - induction
Wagner et al., Clinical Pharmacokinet, 2015
11 substrate models submitted by 6 sponsors; mainly CYP3A4 metabolised.
Four inducers were used: rifampicin, efavirenz, carbamazepine, rifabutin
In some cases, Indmax for rifampicin was increased from 8 to 11.5-fold (Xu et al.,
DMD, 2011)
© Copyright 2017 Certara, L.P. All rights reserved.
Rosuvastatin PBPK/PD : Concn at the site of action
Full PBPK modelPermeability-Limited Liver
model
Mevalonic acid
turnover model
• Published PKPD model for the effect of rosuvastatin on cholesterol
synthesis modified to use unbound concentration in liver intracellular
water (liver CuIW) predicted by the PBPK model as the driving
concentration for the PD response instead of plasma concentration. Jamei et al. 2014; Aoyama et al. 2010
© Copyright 2017 Certara, L.P. All rights reserved.
c.521T>C polymorphism associated with reduced OATP1B1 activity,
resulting in increased plasma rosuvastatin concentration.
Parameter estimation used to obtain OATP1B1 CLint,T using plasma
concentration data stratified by genotype (Pasanen et al., 2007).
Genotype OATP1B1 CLint,T
(µL/min/million cells)
c.521TT 126
c.521TC 30
c.521CC 0
Rosuvastatin PBPK/PD: Effect of OATP1B1 genotype
c.521TT c.521TC c.521CC
Rose et al. 2014
© Copyright 2017 Certara, L.P. All rights reserved.
Rosuvastatin PBPK/PD: Effect of OATP1B1 genotype
OATP1B1
genotype
Plasma AUC0-∞h
(ng/ml.h)
Liver CuIW AUC0-∞h
(ng/ml.h)
PD AUEC relative to baseline
(%): Plasma input
PD AUEC relative to baseline
(%): Liver CuIW input
c.521TT 35.0 120 35.1 36.2
c.521TC 56.9 (63%) 114 (-5.7%) 45.9 (30%) 35.1 (-3.1%)
c.521CC 73.6 (111%) 109 (-9.6%) 50.7 (35%) 34.1 (-5.8%)
OATP1B1 c.521T>C associated with a 2.6% lower fractional LDL-C reduction per allele in
>3000 patients treated with rosuvastatin daily (Chasman et al., 2012).
Rose et al. 2014
© Copyright 2017 Certara, L.P. All rights reserved.
Some Drug withdrawals due to QT prolongation
17
Courtesy of Dr. Norman Stockbridge
QSAR
in vitro (hERG)
in vitro cardiac
cells
in vitro Purkinje
fibers
ex vivo heart
in vivo animals
FIH
TQT
© Copyright 2017 Certara, L.P. All rights reserved.
How is TdP risk likely to be assessed in the future?
18
© Copyright 2017 Certara, L.P. All rights reserved. 19
Linking PBPK and cardiac safety simulation
Human heart left ventricular cell model
Multiple ion channels
(Ikr, Ca, K Na channels)
Accounts for differences in cell physiology in epicardium,
mid myocardium and endocardium
Inter-individual differences accounted for
Metabolic and contractility effects (electro-mechanical
coupling) also considered
O’Hara and Rudy PLoS computational Biology 7, 2011
ten Tusscher et al. Am J Physiol Heart Circ Physiol. 2004, 286(4)
ten Tusscher et al. Phys Med Biol. 2006, 51(23)
© Copyright 2017 Certara, L.P. All rights reserved.
QT effects of cisapride +/- clarithromycin: PBPK-PD Model inputs
• PBPK model
• Compound file developed for
Cisapride
o CYP 3A4 substrate
• Default Clarithromycin compound
file
o Mechanism-based CYP 3A4
inhibition
• Cardiac effect model
o Individual free plasma and
heart concentrations
o HERG affinity
o in silico/in vitro
o Other ion channel affinityo in silico/in vitro
o ten Tusscher human
ventricular cardiomyocyte
model
• Endpoints
o Pseudo ECG
o QTcB
20
• Healthy volunteer population
– 1 trial of 12 subjects
– Simulations matched for gender and age range
• Group 1
– Cisapride 10mg QDS for 10 days
– Clarithromycin 500mg BD on days 6-10
• Group 2
– Clarithromycin 500mg BD for 10 days
– Cisapride 10mg QDS on days 6-10
Frequent blood samples and ECG
measurements on day 5 and day 10 (Van Haarst, 1998, CPT, 64, 542-546)
© Copyright 2017 Certara, L.P. All rights reserved.
Prediction of plasma concentrations
OBS PRED
Cmax
(ng/ml)
2800±
700
2988±
1144
AUC
(ng×h/ml)
17200±
4100
21737±
11583
CLARITHROMYCINCISAPRIDE +
CLARITHROMYCINCISAPRIDE
OBS PRED
Cmax
(ng/ml)
51 ±
12
58 ±
22
AUC
(ng×h/ml)
834 ±
260
1124 ±
455
OBS PRED
Cmax
(ng/ml)
140±
22
137±
57
AUC
(ng×h/ml)
2635±
396
2886±
1293
© Copyright 2017 Certara, L.P. All rights reserved.
QTc prolongation by cisapride +/- clarithromycin
OBSERVED PREDICTED
average QTcB
(ms)
406 389 CISAPRIDE
419 405CISAPRIDE +
CLARITHROMYCIN
free plasma concentration
© Copyright 2017 Certara, L.P. All rights reserved.
QTc prolongation by cisapride +/- clarithromycin
OBSERVED PREDICTED
average QTcB
(ms)
406 400 CISAPRIDE
419 420CISAPRIDE +
CLARITHROMYCIN
free heart tissue concentration
Both drugs prolong QT interval and this is accounted for in the model
Predicted QTcB value for cisapride + clarithromycin is 414 ms if only PK change
is accounted for
© Copyright 2017 Certara, L.P. All rights reserved. 24
Simulation of QTc changes with terfenadine
ΔQTc [ms]INHIBITOR
CLAR ERYTH ERYTH ITZ KTZ FLUC FLUO PAR
Terfenadine
+Inhibitor
observed 21 34 39 41 82 12.5 7 5
predicted 14 26 25 22 46 27 9 9
Predicted and observed changes of QTc for terfenadine alone
and after addition of metabolic inhibitor.
Shaded cells – statistically significant differences in Welch t-test
(α=0.05).
CLAR = clarithromycin, ERYTH = erythromycin, ITZ = itraconazole,
KTZ = ketoconazole, FLUC = fluconazole, FLUO = fluoxetine, PAR = paroxetine
An Integrated EUropean ‘Flagship’ Program Driving Mechanism-based Toxicity Testing and Risk Assessment for the 21st Century
• Horizon 2020 funded• 6 year research project
• Kick-off January 2016
• Improved toxicological testing to predict human risk & meet regulatory needs
• Improved toxicological knowledge to enable ‘read across’ approaches
• Commercial exploitation of developed products & services
• Advance international co-operation in the field of predictive toxicology
• Establish human-relevant, in vitro testing strategies aligned along validatedknowledge of AOPs, and implemented in integrated approach for testing andassessment (IATAs) to meet risk assessment purposes
Expected outcomes of EU-ToxRisk:
EU-ToxRisk is the new ’Flagship’ program funded through Horizon 2020
• 38 European partners
• 1 US partner
• Academia & Research Institutes
• Small & Medium-Sized Enterprises
(SMEs)
• Industry (chemical, pharmaceutical,
cosmetic)
• Regulators & other Stakeholders
EU-ToxRisk unites partners from a variety of backgrounds
• 38 European partners
• 1 US partner
• Academia & Research Institutes
• Small & Medium-Sized Enterprises (SMEs)
• Industry (chemical, pharmaceutical, cosmetic)
• Regulators & other Stakeholders
EU-ToxRisk unites partners from a variety of backgrounds
assay throughput
human relevance
coverage
toxicity en
dp
oin
ts
syst
ems
bio
logy
mo
del
ling
Choice of EU-ToxRisk test systems & strategy
• 2D/3D human cells and tissue slice models
• High content imaging (HCI) to organ-on-a-chip
• Several hundred chemicals
• Case studies for strategy optimisation
• Omics data connected to classical endpoints
• AOPs to guide biological read across (RAX)
• Computational toxicology and data basing
• Biokinetics & experimental ADME data
• PBPK combined with multi-scale hazard modelling
EU-ToxRisk Strategic Choices
Workflow to predict in vivo effect from in vitro data
In vitroToxicity testing
biomarker toxicity
Dose/estimated exposure
In vitro/in silicoinputs
Compound/metabolite
biomarker
toxicity
? risk
? risk
In vitro/in vivo translation
Population variabilityUncertainty
Hazard identification
Biokinetics
Need to relate in vitro toxicity results to the actual concentration of the toxic moiety
– Is parent or a metabolite responsible for toxicity?
– Relate effects to actual free concentration of toxic moiety in the system rather than to
the applied (nominal) concentration
• Binding to proteins
• Binding to plastic
• Evaporation
• Metabolism/degradation
• Action of transporters
Measurements of actual concentrations in the in vitro toxicity experiments (WP4)
Mathematical modelling of in vitro experiments will be needed (WP4)
– Predict IV (FP7 project); explored further in this project
• In vitro system may change with time in long term culture
31
Some challenges
Chemical space
– IVIVE methods developed for pharmaceuticals
• Typically want high exposure
– Translation to chemicals, pesticides, cosmetics etc
Route of exposure
– Oral vs dermal vs inhalation
Knowing the “dose”
– Not a problem for pharmaceuticals
– Often not known precisely in different individuals for chemicals
32
Summary
To link in vitro toxicity data with exposure in target organs in humans
– Need to understand biokinetic data from in vitro experiment
– IVIVE and PBPK modeling can give concentration at the site of
action in tissue
- Use physicochemical properties and in vitro data to construct
the compound model
With an understanding (assumption) of in vitro:in vivo toxicity
relationship
– the dose/exposure leading to toxic concentrations in the organ of
interest can be simulated
Need to consider individual variability/uncertainty in simulations
33
Acknowledgements
www.eu-toxrisk.eu
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 681002
Certara
Ciaran FisherOliver HatleyGopal PowarMasoud Jamei
Sebastian Polak
EU-TOXRISK partners