in vitro - in vivo extrapolation (ivive) transporter scalars · in vitro in vivo extrapolation...
TRANSCRIPT
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In Vitro In Vivo Extrapolation (IVIVE):
Why It Is Not As Easy As You May Think
Amin Rostami
Professor of Systems Pharmacology University of Manchester, UK
&
Chief Scientific Officer & Senior Vice President of R&D
Certara , Princeton, USA
EMA Workshop on MPS - 2017
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Perfusion culture plate incorporates integrated scaffold permitting formation of 3D liver microtissues that resembles the architecture of a liver sinusoid
LiverChipTM Our Experience with MPS: (Ayşe Ufuk, Tom De Bruyn )
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3
Initial LiverChip Optimisation Studies
3
Albumin secretion in hepatocytes cultured in LiverChipTM is stable for up to 9 days of culture time
Urea medium concentrations are higher compared to static 2D cultures Urea synthesis decreases with time
Effect of culture time on enzyme activity was evaluated
CYP2C9 activity stable – no significant difference in tolbutamide depletion and 4OH-tolbutamide formation on day 3-4 and days 6-7
Inter-day variability based on tolbutamide depletion as a marker was
evaluated Approximately 40% variation in tolbutamide clearance was observed
Ufuk et al, manuscript in preparation
Other Team Members: Tom De Bruyn, Michiharu Kageyama, Alex Galetin, Brian Houston, David Hallifax
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IVIVE: Major Input for PBPK (and any other QSP) Models
In Vitro to In Vivo Extrapolation
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PBPK: Typical View = Nothing New!
Nothing New: Teorell, T. Studies on the diffusion effect upon ionic distribution: II. experiments on ionic accumulation. J. Gen. Physiol. 21, 107–122 (1937)
Veno
us B
lood
Arte
rial B
lood
Lung
Adipose Bone Brain Heart
Kidney Muscle
Skin Liver
Spleen Gut Portal Vein
PO IV
Generic Typical View: Describing the C-T profiles based on physiological knowledge of the flows and partitions
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How it is done? Integrating system information
• But we now define uptake/efflux into/out of selected organs as Permeability Limited
• Transport across a membrane is often defined as Perfusion Limited
• Replacement and additional organ
Permeability-limited models are available for the intestine, liver, kidney, brain and lung.
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Adoption in Industrial Scale
MHRA (PSP 2015)
FDA (PSP 2015)
From Academic Nicety to Industrial Necessity
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PD-DDI, Finney, Loewe, Bliss, Hewlett&Plackett., Greco, Vølund...... Response surfaces Generalized Linear models (GLM)
Direct Dose Response
Specific Diseases, .........safety ....QTc...or
Systems Biology X-fertilization ...
Hill, Indirect Physiological Response, Disease Progression, Cell Growth/Death GLM, Survival Analysis, PKPD-DDI
PKPD...
T2
T1
DT2
DT1
T3 DT3
T2G
T1G
DT2G
DT1G
T3G DT3G
DTTEquilibrium binding, specific receptors, Operational Agonism Receptor states, G-Proteins & ternary complexes ... signalling Ion channel kinetic models ...
Receptor binding...in vitro...system response
Network response motifs, Combinatorial targets Hill in genetic regulatory networks
PBPK under the Umbrella of Systems Pharmacology
Multi-Level Hybrid Models: The Framework for Capturing, Retaining & Re-using the Available Systems Knowledge at a Given Time
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Reduction in Traditional Use of Animal
One for Man, Two for Horse, G. Carson, Bramhall House, New York, 1961
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1A1/2 2A6 2C8/9 2C19 2D6 2 E 1 3A4 0
0.5
1
1.5
2
2.5
3
Met
abol
ite
nmol
/(mg/
min
)
1A1/2 2A6 2C8/9 2C19 2D6 2 E 1 3A4 Specific Probe Compound for Human P450 Enzyme
Different P450 Mediated Activities in 4 Species human horse dog cat
Chauret et al., 1997
A major component of PBPK is information on metabolism.
Interspecies Differences in Metabolising Enzymes
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Modelling and Simulation A related area in modernizing clinical trials has been the development and application of quantitative pharmacometric predictive models to support regulatory decision making. Modeling and simulation (M/S) tools for drug exposure and its response have been useful in both pre- and postmarket settings when questions related to safety and efficacy of therapeutic products arise. Some recent examples where M/S has served as a useful predictive tool include dose selection for pivotal trials, dosing in select populations such as pediatrics, optimization of dose and dosing regimen in a subset patient population, prediction of efficacy and dosing in an unstudied patient population in clinical trials, characterizing exposure and dose-related QT interval prolongation, and using physiologically based pharmacokinetic
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Path to Success in
Using PBPK-IVIVE and Virtual Humans
Path (I) Refining In Vitro Tests for Quantitative IVIVE Path (II) Providing & Integrating System Information Path (III) Transparent Methods and Case Examples Path (IV) Showing Value & Re-Engineering Practices
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Recombinantly expressed system (rhCYP)
Human Liver Microsomes (HLM)
Hepatocytes
Intact cells containing full complement of drug metabolising enzymes
Fractionation & Isolation of Enriched Organelles
The Debate at the Time: Which In Vitro System to Use?
Infrequent supply / cost Donor variability
Differences in intrinsic activity between rhCYP & HLM
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How Representative Is My HLM/Hepatocyte?
- High degree of lot-to-lot consistency for CYP and UGT activity
- Representation of
the “average patient” and known CYP polymorphisms
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[dA/dt] (pmol/min/pmol CYP isoform)
(nmol/ml) [S]
Determination of Intrinsic Clearance: Right Units
In vitro system
rhCYP
HLM
HHEP
In Vitro CLuint
μl / min / per functional unit of system
[dA/dt] (pmol/min/mg microsomal protein)
(nmol/ml) [S]
[dA/dt] (pmol/min/106 cells)
(nmol/ml) [S]
[S] = Free Substrate Concentration
[S] << Km (hopefully!)
inc
int int fu
CL CLu =
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In vitro system
rhCYP
HLM
HHEP
In vitro CLuint
µL.min-1
pmol P450 isoform
µL.min-1
mg mic protein
CLuint per g Liver
µL.min-1
106 cells
Applying Appropriate Scaling Factors in Human IVIVE
CLuint per Liver
Scaling Factor 1
Scaling Factor 2
X Liver Weight
X
X
X
HPGL
pmol P450 isoform
mg mic protein X MPPGL
MPPGL
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Literature Values: Human Microsomal Protein per Gram of Liver
Barter et al. (2007) Current Drug Metabolism Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: Reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver
1970
Schoene et al. [36]
1972 ; 35
Galetin et al., [19]
2004 ; 40
1980 1990 2000 2010
Pelkonen et al. [34]
1973 ; 35
Pelkonen et al. [35]
1974; 36
Beaune [53]
1982 (19) Baarnhielm et al.,[14]
1986 ; 77 Knaak et al., [25]
1993 ; 7 Iwatsubo et al., [5]
1997a ; 52.5
Lipscomb et al., [29]
1998 ; 21
Lipscomb et al., [30]
2003 ; 56
Wilson et al., [42]
2003 ; 33
Hakooz et al., [20]
2006 ; 40
Barter et al., In preparation ; 29
Howgate et al., [9]
2006 ; 33
Obach et al., [32]
1997 ; 45
Houston, [11]
1994 ; 45
Carlile et al., [18]
1999 ; 50
Walker et al., [41]
1996 ; 45
Uchaipichat et al., [70]
2006 ; 45 Li et al., [28]
2003 ; 45
Kuperman et al., [26]
1994 ; 45
Soars et al., [39]
2002 ; 45
Bayliss et al., [16]
1999 ; No Values
Le Goff et al., [27]
2002 ; 45
Rat MPPGL
Reports on Assessing Human MPPGL
Some Reports Predicting Human Hepatic Clearance
No Reference?
Mohutsky et al.,[72]
2006 ; 45
Anderson et al., [4]
2001 ; 45
Lu et al.,[71]
2006 ; 45
Pelkonen, [78]
1999 ; 77
Boase & Miners [79]
2002 ; 45
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Literature Values: Number of Human Hepatocytes per Gram of Liver
Arias,[13] 1988; 120
Bayliss et al.,[17]
1990; 120
Iwatsubo et al.,[5]
1997a; 120 Bachman et al.,[15]
2003; 120
Zuegge et al.,[7]
2001; 120
McGinnity et al.,[31]
2004; 120 Szakacs et al.,[40]
2001; 135
1970 1980 1990 2000 2010
Lipscomb et al., [29]
1998; 116 Wilson et al., [42]
2003; 107 Barter et al.,
In preparation; 86
Reports on Assessing Human HPGL (106 cells/g)
Kuperman et al. [26]
1994 ; 120 Bayliss et al., [16]
1999 ; 120
Some Reports Predicting Human Hepatic Clearance
No Reference?
Soars et al., [39]
2002 ; 120
Naritomi et al.,[77]
2003; 120 Ekins & Obach [80]
2000; 120
Barter et al. (2007) Current Drug Metabolism Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: Reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver
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12.2% 5.0%
3.8%
5.4%
16.5% 0.2%
3.4% 1.9% 14.0%
0.3%
33.0%
4.3% CYP1A2 CYP2A6 CYP2B6 CYP2C8 CYP2C9 CYP2C18 CYP2C19 CYP2D6 CYP2E1 CYP2J2 CYP3A4 CYP3A5
Scaling of rhCYP Data
CYP isoform abundance:
pmols CYP isoform per
mg of microsomal protein
Many groups use Shimada et al. (1994) values Don’t differentiate between Japanese and Caucasian
Calculated weighted means, CVs and tested for homogeneity
Literature review for papers reporting enzyme abundance values – Caucasian population
30-40 papers reviewed; 19-27 used for meta-analysis
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Human Liver Sample
10g
Weight recorded
Perfusion of liver sample with digestion media to produce a homogenous
suspension of hepatocytes
Counting of cells
Incomplete digestion leading to incomplete release of hepatocytes
into suspension
Incomplete recovery of cells following centrifugation
Loss of Hepatocytes
UNDERESTIMATION IN HPGL
Problem 1
Calculation of HPGL
Number of cells Liver weight (g)
Problem 2
Centrifugation @ 50g to isolate
hepatocytes
HPGL Determination: Study by Simcyp Group (Sheffield)
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Hepatocyte Specific Marker:
e.g. Cytochrome P450
HPGL 106 cells per g
CYP450 MEASUREMENT
nmols CYP450 g
nmols CYP450 106 cells
HOMOGENISATION
Hepatocyte Suspension of known cell concentration
(x 106 cells)
Liver tissue sample of known mass (g)
Determination of HPGL: Study by Simcyp Group (Sheffield)
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Known Issues with rhCYP Systems
Variable Km (Nakajima et al., 1999)
Differences in activity per unit enzyme (Crespi, 1995)
NADPH cytochrome P450 reductase
Cytochrome b5
Effect of levels of accessory proteins on activity (Venkatakrishnan et al., 2000)
Differences in microsomal binding
Optimisation of rhCYP systems to mimic conditions observed in human liver (Iwatsubo et al., 1997)
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Background information on the use of ISEFs
Accounting for Differences: ISEF
Application of approach
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i metabolic pathways
j CYP isoforms
amount of microsomal protein per gram of liver
Liver weight MPPGL CLint
(L/h) ×
Km(rhCYPj)i
X Vmax (rhCYPj)i n
1 j
n
1 i
j
× = ∑ ∑
= =
pmol/min/mg microsomal protein
IVIVE Using rhCYP Systems
ISEFji × ×
CYP abundance in the Target Population
(pmol CYPj/mg microsomal protein) Rate of Metabolism
pmol/min/pmol rhCYPj
Youdim et al Br J Clin Pharmacol, 2008
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Same Issues Different Tissue: In Vitro Measurements
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Interpretation of interaction studies should focus not only on mean effect but also the observed and theoretically conceivable extremes.
PBPK Modelling to Assess Patient Variability
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Stats from EMA
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Same when it comes to the use of:
Qualification vs Verification vs Validation
Language Barrier
“Prediction” vs “Retrodiction” vs “Post-diction”
Predict = Pronunciation: /prɪˈdɪkt/ Say or estimate that (a specified thing) will happen in the future or will be a consequence of something Latin origin = 'made known beforehand, declared', from the verb praedicere, from prae- 'beforehand' + dicere 'say'. Postdiction is an explanation after the fact. In skepticism, it is considered an effect of hindsight bias that explains claimed predictions of significant events https://en.wikipedia.org/wiki/Postdiction Retrodiction : is the act of making a "prediction" about the past. https://en.wikipedia.org/wiki/Retrodiction
Predictive = Relating to or having the effect of predicting an event or result
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Level of Evidence = Level of Confidence
Qualification Verification Validation
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Framework for M&S in Regulatory Review
Medium impact
High impact
Low impact
Impact on regulatory decision
+++ Scientific Advice, Supporting Documentation, Regulatory Scrutiny
++ Scientific Advice, Supporting Documentation, Regulatory Scrutiny
+ Scientific Advice, Supporting Documentation, Regulatory Scrutiny
From EMA-EFPIA Modelling and Simulation Workshop, December 2011
Justify
Describe
Replace
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Translation from Animals to Humans
Scotcher et al 2016 AAPS J, in press Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II Mechanistic Models and In Vitro-In Vivo Extrapolation
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Integrating Organs within MPS: Proportionality?
• Replacement and additional organ
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Integrating Organs within MPS: What is the INTENDED USE?