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V2, A2, C2
V1, A1, C1
V3, A3, C3
V4, A4, C4
Physico-chemical drug properties and human CNS system characteristics: determinants of CNS PK at different
locations in human CNS
Elizabeth CM de Lange Professor in Predictive Pharmacology, LACDR,
Leiden University, The Netherlands [email protected]
BB
B
Drug Dosing
Brain PK Trans-duction EFFECT
Homeostatic feedback
Plasma PK
Trans-duction
Homeostatic feedback
E
2
Prediction of CNS drug effect in human
2
Drug concentrations at the site of action drives the effect of the drug
Which concentration in the human brain is most representative to the
brain target site concentration?
blood
What CNS sites in human are accessible to obtain
information about brain PK?
target
CSF CSF
3 De Lange. Utility of CSF in translational neuroscience. JPKPD. 2013
Prediction of CNS PK in human
Differences in rate of pharmacokinetic processes Differences in sizes of physiological compartments
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BB
B
Drug Dosing
Brain PK Trans-duction EFFECT
Homeostatic feedback
Plasma PK
Trans-duction
Homeostatic feedback
E B
BB
Drug Dosing
Brain PK Trans-
duction EFFEC
T
Homeostatic feedback
Plasma PK
Trans-d
uction
Homeostatic
feedback
E
Prediction of CNS PK in human
Systems parameters: Blood flow
Barrier permeabilities
Transporter/ enzyme function
Volumes (intra- / extracellular)
Blood / tissue pH
Capillary surface area
Receptor density
Signal transduction
Homeostatic feedback
Drug characteristics: Molecular weight
LogP / logD
pKa / charge at pH 7.4
PSA (polar surface area)
H-bond donor / acceptor
P-gp / MRP (etc) substrate
Receptor affinity
etc
Pharmacokinetics • Plasma kinetics • Barrier transport
• Intractissue distribution
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Drug versus system properties
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V2, A2, C2
V1, A1, C1
V3, A3, C3
V4, A4, C4
Towards a comprehensive physiology-based pharmacokinetic (PBPK) CNS model
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Cerebral blood
BCSFB BBB
CSF
, Cis
tern
a M
agn
a
CSF
, Lat
eral
Ven
tric
les
CSF
, Su
bar
ach
no
idal
Epen
dym
al
ce
ll la
yer
Cerebral blood
BCSFB BBB
BrainECF
BrainECF
CSF
, lu
mb
ar
Metabolism Faciliated /active transport
Diffusion Fluid flow
Physiological brain compartments, flows, membranes, active transporters,
metabolic enzymes, subcellular compartments, pH values, targets
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BRAIN CELLS
Brain Cells
CNS properties
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Drug 1 Drug 2 Drug 3 Drug 4 Drug 5 Drug 6 Drug 7 Drug 8
Molecular weight
Lipophilicity
pKa
Polar Surface Area
H-bond donor
H-bond acceptor
Pgp substrate
Transporter X substrate
Drug physico-chemical properties
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V2, A2, C2
V1, A1, C1
V3, A3, C3
V4, A4, C4
Experimental approach
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Cerebral blood
BCSFB BBB
CSF
, Cis
tern
a M
agn
a
CSF
, Lat
eral
Ven
tric
les
CSF
, Su
bar
ach
no
idal
Epen
dym
al
ce
ll la
yer
Cerebral blood
BCSFB BBB
BrainECF
BrainECF
CSF
, lu
mb
ar
Metabolism Faciliated /active transport
Diffusion Fluid flow
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BRAIN CELLS
Brain Cells
Experimental approach
Animal experiment Animal PK profiles
Translation to human model
Validation on human data
2.9 l
0.175 ml/min
0.4 ml/min
240 ml
22.5 ml
7.5 ml
22.5 ml
90 ml
0.4 ml/min
0.4 ml/min
0.4 ml/min
Animal PBPK model
0.2 ul/min
2.2 ul/min
10.6 ml
290 ul
50 ul
17 ul
50 ul
180 ul
2.2 ul/min
2.2 ul/min
2.2 ul/min
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Experimental approach
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Drug 1 Drug 2 Drug 3 Drug 4 Drug 5 Drug 6 Drug 7 Drug 8
Molecular weight
Lipophilicity
pKa
Polar Surface Area
H-bond donor
H-bond acceptor
Pgp substrate
Transporter X substrate
Extending in vivo data on multiple drugs
10.6 ml
Plasma
Periphery 1
CSFSAS
CSFCM
CSFTFV
CSFLV
Brain
ECF
QDIFF
QDIFF
Deep brain
Periphery 2
QDIFF
QDIFF
CLPL-ECF
QPL-PER1 QPL-PER2
CLE
QDIFF
CLCSF_PL
QECF_ICF
Generic drug translational model (for 9 drugs with distinctive phys-chem properties)
Individual drug translational models
Adaptation of the model
Yamamoto. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharm Res. 2017
Yamamoto et al, A generic multi-compartmental CNS distribution model structure for 9 drugs allows prediction of human brain target site concentrations. Pharm Res, 2017
Prediction of the data
Plasma Better Brain ECF Worse Brain ECF
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Adult TBI patients: morphine
Drug Data source Data condition Human plasma Human brainECF Human CSF
Morphine Bouw et al.. 2001; Ederoth et, al. 2003
healthy with TBI 2 individuals 2 individuals
Prediction of CNS PK in human
Patient 1 (focal TBI)
plasma brainECF plasma brainECF plasma brainECF
Patient 2 (Focal TBI) Patient 4 (Focal TBI)
16 Patient 5 (Focal TBI, only 2 blood samples) Patient 6 (Diffuse TBI)
Pedriatic TBI patients: morphine
Prediction of CNS PK in human
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BrainECF CSFLV CSFCM CSFSAS
Yamamoto et al. Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations.. AAPS J. 2017
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BrainECF CSFLV CSFCM CSFSAS
Yamamoto et al. Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations.. AAPS J. 2017
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BrainECF CSFLV CSFCM CSFSAS
Relation between drug concentrations and their time course in brainECF, CSF in lateral ventricles, CSF in cisterna Magna, and CSF in lumbar region are • Drug dependent • Species dependent • Time dependent
Animal experiment Animal PK profiles
Translation to human model
Human prediction
0.2 ml/min
2.2 ml/min
10.6 ml
290 ml
50 ml
17 ml
50 ml
180 ml
2.2 ml/min
2.2 ml/min
2.2 ml/min
Animal PBPK model
100
1000
10000
100000
0 120 240 360 480 600 720
Pre
dic
ted
Hu
man
Ace
tam
ino
ph
en
Co
nce
ntr
atio
n (
ng/
ml)
Time (min)
Plasma observed
CSF (SAS) observed
Plasma predicted
SAS (CSF) predicted
Brain ECF predicted
LV
CM
2.9 l
0.175 ml/min
0.4 ml/min
240 ml
22.5 ml
7.5 ml
22.5 ml
90 ml
0.4 ml/min
0.4 ml/min
0.4 ml/min
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Human prediction without in vivo data?
21 21
Comprehensive full PBPK CNS model
Yamamoto et al. Predicting PK profiles in multiple CNS compartments using a comprehensive PBPK model. CPT PSP 2017
22 22
Neutral compound
Zwitterionic compound
Neutral P-gp substrate
Acidic compound
Basic compounds
Basic P-gp substrates
Model prediction and actual data
Yamamoto et al. Predicting PK profiles in multiple CNS compartments using a comprehensive PBPK model. CPT PSP 2017
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Model prediction and actual data
Yamamoto et al. Prediction of human CNS PK using a PBPK modeling approach. Eur J Pharm Sci. 2017
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Simulations – systems changes
Yamamoto et al. Prediction of human CNS PK using a PBPK modeling approach. Eur J Pharm Sci. 2017
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Phenytoin epileptic patients
Yamamoto et al. Prediction of human CNS PK using a PBPK modeling approach. Eur J Pharm Sci. 2017
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The comprehensive CNS full PBPK drug distribution model is able to adequately predict PK in different CNS compartments in rats and human Relations between PK in brainECF, CSF in lateral ventricles, cisterna Magna and the subarachnoidal space (lumbar region) are
• Drug dependent • Species dependent • Time dependent
Mathematical modeling can substantially aid in understanding of PK at different locations in the brain. This provides the basis for further understanding of influence of drug-target binding kinetics and impact of disease conditions, and also paves the way for better understanding and prediction of CNS drug effects.
Summary
Dirk-Jan van den Berg
Francesco Bellanti
Willem vd Brink
Sinziana Cristea
Meindert Danhof
Nathalie Doorenweerd
Tony Figaji
Janna Geuer
Piet Hein van der Graaf
Margareta Hammarlund-Udenaes
Thomas Hankemeier
Robin Hartman
Sandra den Hoedt
Laura Kervezee
Naomi Ketharanathan
Maaike Labots
Victor Mangas
Ron Mathôt
Nick van Oijen
Shinji Shimizu
Jasper Stevens
Stina Syvanen
Dick Tibboel
Acknowledgements
Willem vd Brink Yumi Yamamoto Joost Westerhout Wilbert de Witte Eric Wong Ursula Rohlwink Enno Wildschut
9th Annual Course on the BBB in Drug Discovery and Development
Leiden, The Netherlands, 15-17 Oct 2018
www.bbbcourses.org
Elizabeth de Lange & Margareta Hammarlund-Udenaes
29
Yamamoto Y, Välitalo PA, Wong YC, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Beukers MW, van den Berg DJ, Hartman RH, Wong YC, Danhof M, Kokkif H, Kokkif M, Meindert Danhof M, van Hasselt JGC, de Lange ECM*. Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach. Eur J Pharm Sci. 2017 Nov 11;112:168-179. doi: 10.1016/j.ejps.2017.11.011. [Epub ahead of print]
De Lange ECM*, van der Brink W, Yamamoto Y, de Witte W, Wong YC. Approaches to optimize CNS availability by novel CNS Drug Discovery. Exp Opin Drug Disc . 2017. Accepted
van den Brink WJ, Elassais-Schaap J, Gonzalez B, Harms A, van der Graaf PH, Hankemeier T, de Lange ECM*. Remoxipride causes multiple pharmacokinetic/pharmacodynamic response patterns in pharmacometabolomics in rats. Eur J Pharm Sci-2017 Accepted
van den Brink WJ, Elassaiss-Schaap J, Gonzalez-Amoros B, Harms AC, van der Graaf PH, Hankemeier T, de Lange ECM*. Multivariate pharmacokinetic/pharmacodynamic (PKPD) analysis with metabolomics shows multiple effects of remoxipride in rats . Eur J Pharm Sci 109C (2017) pp. 431-440
Yamamoto Y, Välitalo PA, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Beukers MW, van den Berg DJ, Hartman RH, Wong YC, Danhof M, van Hasselt JG, de Lange EC*. Predicting drug concentration-time profiles in multiple CNS compartments using a comprehensive physiologically-based pharmacokinetic model. CPT PSP 2017 Sep 11.
De Witte WEA, Vauquelin G, van der Graaf PH, de Lange EC* The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation. Eur J Pharm Sci. 2017 May 12. pii: S0928-0987(17)30252-X.
Kervezee L, van der Berg DJ, Hartman RH, Meijer J, de Lange EC*. Diurnal variation in the pharmacokinetics and brain distribution of morphine. Eur J Pharm Sci. 2017 May 27. pii: S0928-0987(17)30277-4.
Yamamoto Y, Danhof M, de Lange EC*. Microdialysis: the Key to Physiologically Based Model Prediction of Human CNS Target Site Concentrations.. AAPS J. 2017
Erdo-Pázmány F, Denes L, de Lange EC. Age-associated physiological and pathological changes at the blood- brain barrier – A review. J Cereb Blood Flow Metab. 2017 Jan;37(1):4-24
Yamamoto Y, Välitalo PA, van den Berg DJ, Hartman R, van den Brink W, Wong YC, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Bakshi S, Aranzana-Climent V, Marchand S, Dahyot-Fizelier C, Couet W, Danhof M, van Hasselt JG, de Lange EC*. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharm Res. 2017 Feb;34(2):333-351.
