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Simulating Oral Absorption with PK-Sim®
Methodology and Application Examples
Stefan Willmann, Andrea N. Edginton, Walter Schmitt, Marcus Kleine-Besten, and Jennifer B. Dressman
Presentation Overview
PK-Sim®’s Absorption Model
Application Examples
Example 1
Example 2
Example 3
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
PK-Sim® Model-Structure
Real Integrated Whole Body Modelcomprising:
Fully integrated GI-tractBiliary tract, enables enterohepaticcyclingMost important organsFor each organ:
• metabolizing pathways• different active transporter types
(influx, efflux, Pgp-like)
Capability for treating even very sophisticated problems.
VEN
OU
S B
LOO
D P
OO
L
AR
TER
IAL
BLO
OD
PO
OL
PANCREAS
SPLEEN
PORTAL VEIN
LIVER
KIDNEY
LUNG
DOSEi.v.
p.o.
GALL BLADDER
STOMACH
WALL
SMALL INTESTINE
WALL
LARGE INTESTINE
WALL
TESTESHEART
BRAINFAT
BONESKIN
MUSCLE
VEN
OU
S B
LOO
D P
OO
L
AR
TER
IAL
BLO
OD
PO
OL
PANCREAS
SPLEEN
PORTAL VEIN
LIVER
KIDNEY
LUNG
DOSEi.v.
p.o.
GALL BLADDER
STOMACH
WALL
SMALL INTESTINE
WALL
LARGE INTESTINE
WALL
TESTESHEART
BRAINFAT
BONESKIN
MUSCLE
Willmann et al., Biosilico 1(4), 121-124 (2003)
PK-Sim® Model-Structure
Real Integrated Whole Body Modelcomprising:
Fully integrated GI-tractBiliary tract, enables enterohepaticcyclingMost important organsFor each organ:
• metabolizing pathways• different active transporter types
(influx, efflux, Pgp-like)
Capability for treating even very sophisticated problems.
VEN
OU
S B
LOO
D P
OO
L
AR
TER
IAL
BLO
OD
PO
OL
PANCREAS
SPLEEN
PORTAL VEIN
LIVER
KIDNEY
LUNG
DOSEi.v.
p.o.
GALL BLADDER
STOMACH
WALL
SMALL INTESTINE
WALL
LARGE INTESTINE
WALL
TESTESHEART
BRAINFAT
BONESKIN
MUSCLE
VEN
OU
S B
LOO
D P
OO
L
AR
TER
IAL
BLO
OD
PO
OL
PANCREAS
SPLEEN
PORTAL VEIN
LIVER
KIDNEY
LUNG
DOSEi.v.
p.o.
GALL BLADDER
STOMACH
WALL
SMALL INTESTINE
WALL
LARGE INTESTINE
WALL
TESTESHEART
BRAINFAT
BONESKIN
MUSCLE
Willmann et al., Biosilico 1(4), 121-124 (2003)
PK-Sim®‘s Input Parameters
Lipophilicity (LogMA preferred)
Plasma Protein Binding
(alternatively unbound fraction)
(effective) Molweight
pKa values for acids/bases
Solubility vs. pH table
Plasma Clearance hepatic/renal
(alternatively in vitro metaboli-
sation rates, Km & Vmax, …)
Transit Function (for rat): Graphical Representation
Simulation of Intestinal Transit & Absorption
Continuous one-compartment model for small intestine:• Spatially varying properties (effective surface area, pH)• GI transit described as plug-flow with dispersion
Principle
Willmann et al., Pharm. Res. 20, 1766-1771 (2003)
Model for Passive Absorption
Model for the intestinalpermeability coefficient was build using a data set of 126marketed compounds with nosolubility limitation at thera-peutic doses.
An excellent fit was obtained.
All outliers are known to besubstrates active transporters.
Willmann et al., J. Med. Chem. 47, 4022-4031 (2004)
]s/cm[MWD
MWCMAMWBMW
MAMWA)MA,MW(P
arparacellullartranscellu
int
44 344 2144444 344444 21
γ−γ−
γ−
β−α−
β−α−
++
+= ]s/cm[
MWDMWC
MAMWBMWMAMWA)MA,MW(P
arparacellullartranscellu
int
44 344 2144444 344444 21
γ−γ−
γ−
β−α−
β−α−
++
+=
Intestinal permeability is based on an empirical equation*:
•D. Leahy et al. in Novel Drug Delivery and Its Therapeuthic Application (1989)
Presentation Overview
PK-Sim®’s Absorption Model
Application Examples
Example 1: Dissolution Limited Absorption
Example 2
Example 3
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Compound X (from ongoing BHC development project)
• neutral• medium lipophilicity (LogMA = 2.2)• MW ~ 450 g/mol• high permeability (Caco2, animal models)• low aqueous solubility
• Human study data:
• single dose under fasted conditions • solution: 5 and 10 mg• IR tablet: 1.25, 5, 10, 15, 20, 30, 40, 60, and 80 mg
(mean particle diameter of IR tablet formulation: 3 µm)
Properties
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Model for Dissolution-Limited Absorption
Add-on module to dynamically simulate dissolution of poly-disperse spherical particles
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
0 1 2 3 4 5 6 70 %
20 %
40 %
60 %
80 %
100 %
3 µm Particles
1.25mg 2.50mg 5.00mg 10.0mg 20.0mg 30.0mg 60.0mg 80.0mg
Frac
tion
Dis
solv
ed
Time [h]
Simulation of Dose Dependent Exposure
0 %
20 %
40 %
60 %
80 %
100 %
0 20 40 60 80 1000
2
4
6
8
10
Mea
sure
d AU
Cno
rm [k
g h
/ L]
Dose [mg]
Experiment Tablet Solution
Simulation Tablet Solution
Sim
ulat
ed F
abs
0 2 4 6 8 10 120
50
100
150
200
250
300
350
400
IR Tablet: 1.25mg 5.00mg 10.0mg 15.0mg 20.0mg 30.0mg 40.0mg 60.0mg 80.0mg
Pla
sma
Con
cent
ratio
n [µ
g/L]
Time [h]
0 2 4 6 8 10 120
100
200
300
400
Solution: 5.00mg 10.0mg
Pla
sma
Con
cent
ratio
n [µ
g/L]
Time [h]
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Presentation Overview
PK-Sim®’s Absorption Model
Application Examples
Example 1
Example 2: Cimetidine PK Variability
Example 3
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
What About Inter-Individual Variability ?
?
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
What About Inter-Individual Variability ?
?
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Age Dependence and Variability of Physiologicaland Anatomical Parameters
Data for the age dependence of physiological parametersrelevant for PBPK modellingsuch as
- body weight, body height, body massindex,
- organ weights, blood flow rates,- tissue composition (water, lipid, and
protein content)- fractions vascular, interstitial and
intracellular
and their variability and cross-correlation were collected in a comprehensive literature search.
Modelling Inter-Individual Variability in PK-Sim®
with the add-on module “PK-Pop“
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Available Information: - in vitro dissolution profiles of IRTagamet® tablets plus threeexperimental CR formulations(400 mg cimetidine)
- in vivo data from 12 malevolunteers
- PK profiles after iv admin. insame individuals (clearance distribution !)
Population Simulation:- 100 virtual individuals (age, weight and height matched) with varyinggastric emptying time (15–45 min.) and small intestinal transit time (2,5–
5,5 h)
Example: Cimetidine
0 1 2 3 4 5 6
0 %
20 %
40 %
60 %
80 %
100 %
TAGAMET EUDR75 EUDR15 EUDR26C
imet
idin
e R
elea
se
Time [h]
Exp. Data from: Jantratid et al., Clin. Pharmacokin. (2006;45(4):385-99)
FaSSIF @ pH 6.5
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Example: Cimetidine
(unpublished data, Marcus Kleine-Besten, Univ. Frankfurt)
0 1 2 3 4 5 6
0 %
20 %
40 %
60 %
80 %
100 %
TAGAMET EUDR75 EUDR15 EUDR26C
imet
idin
e R
elea
se
Time [h]
In vitro release profile:
Time [h]0 2 4 6 8 10 12
Pla
sma
Con
cent
ratio
n [m
g/L]
0
1
2
3
4
5
Mean5% and 95%Min/Max
Tagamet® tablets
Example: Cimetidine
(unpublished data, Marcus Kleine-Besten, Univ. Frankfurt)
0 1 2 3 4 5 6
0 %
20 %
40 %
60 %
80 %
100 %
TAGAMET EUDR75 EUDR15 EUDR26C
imet
idin
e R
elea
se
Time [h]
In vitro release profile:
Time [h]0 2 4 6 8 10 12
Pla
sma
Con
cent
ratio
n [m
g/L]
0
1
2
3
4
5
Mean
Min/Max5% and 95%
Eudragit 7,5 %
Example: Cimetidine
(unpublished data, Marcus Kleine-Besten, Univ. Frankfurt)
0 1 2 3 4 5 6
0 %
20 %
40 %
60 %
80 %
100 %
TAGAMET EUDR75 EUDR15 EUDR26C
imet
idin
e R
elea
se
Time [h]
In vitro release profile:
Time [h]0 2 4 6 8 10 12
Pla
sma
Con
cent
ratio
n [m
g/L]
0
1
2
3
4
5
Mean5% and 95%Min/Max
Eudragit 15 %
Example: Cimetidine
(unpublished data, Marcus Kleine-Besten, Univ. Frankfurt)
0 1 2 3 4 5 6
0 %
20 %
40 %
60 %
80 %
100 %
TAGAMET EUDR75 EUDR15 EUDR26C
imet
idin
e R
elea
se
Time [h]
In vitro release profile:
Time [h]0 2 4 6 8 10 12
Pla
sma
Con
cent
ratio
n [m
g/L]
0
1
2
3
4
5
Mean5% and 95% Min/Max
Eudragit 26 %
Presentation Overview
PK-Sim®’s Absorption Model
Application Examples
Example 1
Example 2
Example 3: Simulations in Children
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Special Populations: Children
Food & Drug Administration (FDA) and European authorities pressuring industry to perform clinical trials in paediatric patients
Increase access to treatments for children and reduce off-label, unlicensed treatment
50% of all drugs administered in hospitals are not properly licensed for use in children*
Adverse drug reactions due to dosing errors and use in non-labeled ages - from Dec 2001-2004, 820 serious ADRs occurred due to off label use with 130 being fatal (reported)**
* Conroy et al. Survey of unlicensed and off label drug use in paediatric wards in European countries. British Medical Journal 320:79-82. (2000)** European Medicines Agency. Evidence of harm from off label or unlicensed medicines in children. EMEA/11207/04. (2004)
Scaling PBPK Models to Children
PBPK Modelling in children requires knowledge about the
physiological and clearance differences relative to adults.
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Scaling PBPK Models to Children
PBPK Modelling in children requires knowledge about the
physiological and clearance differences relative to adults.
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Children are not just small adults!
Scaling PBPK Models to Children
PBPK Modelling in children requires knowledge about the
physiological and clearance differences relative to adults.
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
-> mechanistic model for clearance scaling
(Edginton et al., Clin. Pharmacokin. (in press 2006))
Simulating Oral Absorption in Children
Example: Oral administration of 10 mg/kg Ciprofloxacin in
children
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
Simulating Drug Absorption: Summary
The rate and extent of oral drug absorption can be well
simulated based on simple physico-chemical input parameters
and a detailed description of the GI physiology
Physiology-based simulations of drug absorption can be used
to make predictions in the early development phase
for hypothesis testing in later development stages
to aid formulation development
to study sub-populations (e.g. children, elderly, diseases)
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
The PK-Sim®-TeamBTS-Systems Biology & Computational Solutions- Dr. Andrea Edginton- Karsten Höhn- Marcus Kleine-Besten- Dr. Jörg Lippert- Dr. Walter Schmitt- Michael Sevestre- Juri Solodenko- Wolfgang Weiss- Dr. Stefan Willmann
Bayer-internal Cooperations- PK groups of BHC-Pharma
(Dr. G. Ahr, Dr. W. Mück, Dr.H.Stass and colleagues)
External Cooperations- Prof. Jenny Dressman,
Uni Frankfurt- NIMBUS Biotechnology, Leipzig- Physiomics plc, UK
Simulating Drug AbsorptionDr. Stefan Willmann, PAGE 2006
B A C K U P S L I D E S
Relevant Physiological Parameters: Summary
(Physiological data collected in collaboration with Prof. Dressman, Frankfurt, data for dogs and mice not shown)
human rat
Simulating Drug AbsorptionDr. Stefan Willmann, EDAN 2006
Transit Function (for rat): Graphical Representation
Simulation of Intestinal Transit & Absorption
Willmann et al., Pharm. Res. 20, 1766-1771 (2003)
substance-specific properties physiological properties
( ) )!S)t,z(C()z(A)]t(C)t,z(C[Pdzd
dtdz)t,z(Md
intlumeneffpvlumenintpv
2
≤−=
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20Age (y)
Rel
. Int
rinsi
c C
lear
ance
Sum
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20Age (y)
Rel
. Int
rinsi
c C
lear
ance
Scaling of Intrinsic Clearances
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20
Age (y)
Rel
. Int
rinsi
c C
lear
ance
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0 5 10 15 20
Age (y)
Rel
. Int
rinsi
c C
lear
ance
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20
Age (y)
Rel
. Int
rinsi
c C
lear
ance
Renal
CYP 3A4
CYP 1A2
20 %
30 %
50 %
Hypothetical Compound
26 %
23 %
51 %
Validation of Clearance Scaling
R2 = 0.91R2 = 0.910.01
0.1
1
10
100
0.01 0.1 1 10 100
Training compounds Alfentanil Midazolam Gentamicin Isepamicin Caffeine Ropivacaine Morphine Lorazepam
Test compounds Buprenorphine Lidocaine Ciprofloxacin Paracetamol Theophylline Fentanyl
Observed Clearance [ml/min/kg]
Pre
dict
ed C
lear
ance
[ml/m
in/k
g]
One Pathway
Multiple Pathways0.01
0.1
1
10
100
0.01 0.1 1 10 100
R2 = 0.5246Q2 = 0.7977(all: 0.6137)
Clearance in Children [ml/min/kg]
Adu
lt C
lear
ance
[ml/m
in/k
g]
R2 = 0.61R2 = 0.61
Using adult clearances would lead to much worse prediction!
Calculation of Partition Coefficients
tissuewaterwaterprotein
tissueproteinwaterlipid
tissuelipidwatertissue fKfKfK +∗+∗= ///
Steady state organ/plasma partition coefficients
K = Partition Coefficient (Kprotein/water = HSA binding in case of plasma and calculated from Lipophilicity in all other cases)
f = Volume fraction
waterplasma
watertissue
plasma
tissueplasmatissue K
KCCK
/
// == watertissueK /= .fu
Calculation of Distribution Dynamic
Permeability x Surface-Area Products
Fick’s First Law: )( 21 CCAdDJ −⋅⋅= C2
C1
D
d
)( 21 CCAdDKJ −⋅⋅=
PAtissue ∼ Lipophilicity . MW-α . Atissue
Volume of Distribution
0 5 10 15 20 25 300
5
10
15
20
25
30
VD
ss m
easu
red
VDss calculated
Beta Blockers
Doxorubicin
Prediction of Partition Parameters
0.01
0.10
1
10
100
0.01 0.10 1.00 10.00 100.00
Measured
Cal
cula
ted
actively influxed
actively effluxed
Validation
Brain/Plasma Partition-CoefficientFraction Unbound in Plasma
0,0
0,2
0,4
0,6
0,8
1,0
0,0 0,2 0,4 0,6 0,8 1,0
Measured
Cal
cula
ted
0,0
0,2
0,4
0,6
0,8
1,0
0,0 0,2 0,4 0,6 0,8 1,0
Measured
Cal
cula
ted
RatHuman
Keldenich et al., oral presentation at LogP2004 Symposium, Zürich (2004)
Organ/Plasma Partition CoefficientsMore Examples
Willmann et al., Poster presentation at LogP2004 Symposium, Zürich (2004)
Membrane Affinity
Partitioning into Phospholipid-
Membrane
Chains
Headgroups
MA = Cmembrane / CwaterMA = Cmembrane / Cwater
Polar compound Lipophilic compound
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
+-
Cmembrane
Cwater
Membrane Affinity vs. logKow
Relationship logKow / logMA nonlinear
logMA (Mw)
pH-Dependence different from logKow
Example: Basic compound
Austin et al. , Fisons PharmaceuticalsI. logP Symposium, Lausanne 1995
Gobas et al. , J. Pharm. Sci 77, 265 (1988)
General Model Results
Assumption: Dissolution is not the rate limiting step for absorption
Simulating Drug AbsorptionDr. Stefan Willmann, EDAN 2006
General Model Results
Region A: no solubility limitation
Region B: dose-dependentabsorption
Region C: Fabs < 1 %(solubility-limited)
Region D:Fabs < 1 %(permeability-limited)
FDA-Suggestion: (M/S) = 250 ml
“Minimum Acceptable Solubility“ *)
*) W. Curatolo (Pfizer) Pharm. Sci. Technol. Today 1(9), 387-393 (1998)
Simulating Drug AbsorptionDr. Stefan Willmann, EDAN 2006
Example: Cimetidine
0
2
4
6
8
10
12
30 40 50 60 70 80 90 100 110 120 130 140 150
No.
Liver [ml/ min]
Plasma clearance
0
2
4
6
8
10
12
58 60 62 64 66 68 70 72 74 76 78 80
No.
kg
Weight
0
2
4
6
8
10
152 154 156 158 160 162 164 166 168 170 172 174 176
No.
cm
Height
0
2
4
6
8
180 200 220 240 260 280 300 320 340 360
No.
GI-Tract [min]
Intestina l T ransit T ime
0
2
4
6
8
10
0 10 20 30 40 50 60
No.
GI-Tract [min]
Gastric Emptying T ime
Simulating Drug AbsorptionDr. Stefan Willmann, EDAN 2006
Model Application: Assessment of EHC
Example: Model compound:
weak base with pKa = 8.5,
LogMA = 4,2, MW = 520,
Solubility = 250 mg/L at pH 6.5,
dose = 25 mg, subject to EHC
0 2 4 6 8 10 121
10
100
Simulating Drug AbsorptionDr. Stefan Willmann, EDAN 2006
Plas
ma
Con
cent
ratio
n [m
g/L]
Time [h]
GBE
Model Application: Assessment of EHC
Example: Model compound:
weak base with pKa = 8.5,
LogMA = 4,2, MW = 520,
Solubility = 250 mg/L at pH 6.5,
dose = 25 mg, subject to EHC
Population Simulation (N=25) 0 2 4 6 8 10 121
10
100
Pla
sma
Con
cent
ratio
n [m
g/L]
Time [h]
0
1
2
3
180 190 200 210 220 230 240 250 260 270 280 290 300
No.
min
EHC Lag T ime
0
1
2
3
4
500 600 700 800 900 1000 1100
No.
Liver [ml/ min]
Plasma clearance
0
1
2
3
4
500 600 700 800 900 1000 1100
No.
Gallbladder [ml/ min]
Plasma clearance
Model Application: Assessment of EHC
Example: Model compound:
weak base with pKa = 8.5,
LogMA = 4,2, MW = 520,
Solubility = 250 mg/L at pH 6.5,
dose = 25 mg, subject to EHC
Population Simulation (N=25)
0
1
2
3
180 190 200 210 220 230 240 250 260 270 280 290 300
No.
min
EHC Lag T ime
0
1
2
3
4
500 600 700 800 900 1000 1100
No.
Liver [ml/ min]
Plasma clearance
0
1
2
3
4
500 600 700 800 900 1000 1100
No.
Gallbladder [ml/ min]
Plasma clearance
0 1 2 3 4 5 6 7 8 9 10 11 121
10
100
Pla
sma
Con
cent
ratio
n [m
g/L]
Time [h]