Simcyp MPML MechDermA Model - afpt-clubphase1.com
Transcript of Simcyp MPML MechDermA Model - afpt-clubphase1.com
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Mechanistic modelling of dermal
drug absorption using the
Simcyp MPML
MechDermA Model
PBPK SYMPOSIUM 2018
Paris, April 4th 2018
Sebastian Polak
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PBPK Modelling in NDA Submissions Huang et al. J. Pharm Sci. 102(9):2912-23 (2013)
Need for Dermal PBPK Models: Toward Virtual BE of Generic Products
• Low utility in ANDA / Generic Drug Applications
– PBPK models needed for complex products, topical and locally acting
drugs
– Improvements needed for BA/BE Assessment e.g. WS variability
– GDUFA – 7 grants for PBPK model development for non-oral drug
deliveryZhang & Lionberger 2014 CPT; Lionberger 2014, AAPS AM
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Further Enhancements as Part of the USFDA OGD GDUFA Grant
3
Simcyp was awarded FDA OGD grant in September 2014.
’Development and validation of dermal PBPK modelling platform
towards virtual bioequivalence assessment considering population
variability’
The project aims to develop a physiologically-based dermal absorption and
disposition model along with the supporting database of physiology and its
variability for not only the healthy Caucasian volunteers but also special
populations such as paediatric, geriatric, other races such as Asian and diseased
populations.
The new model will also take into account other mechanisms that play an
important role in dermal absorption, such as skin surface pH, dermal hydration,
skin appendages, binding to keratin, and the effect of permeability-modifying
formulation ingredients and drug-physiology interactions
http://www.simcyp.com/News/2014/October/20141023_FDA_Grant.htm?p=1
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Trial
Design
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
Plasma Protein
Systems
Data
Drug
Data
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Advantage of PBPK: Separating Systems & Drug Information
4
(Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012)
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
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© Copyright 2018 Certara, L.P. All rights reserved.
Trial
Design
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
Plasma Protein
Systems
Data
Drug
Data
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Advantage of PBPK: Separating Systems & Drug Information
5
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
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MPML MechDermA Model Structure
6
Formulation
(Aqueous, Non-Aqueous
solution; Gel; Paste; Patch)
DeffSC
lipids
Stratum Corneum (SC)
(Thickness; Diffusivity;
Permeability; Steady
state binding,
Tortuosity)
Viable Epidermis (VE)
(Thickness; Diffusivity;
Permeability; Blood flow)
DeffVE
CLDBlood flow
(Local vasculature)
Dermis (D)
(Thickness; Diffusivity;
Permeability; Non-Specific
Clearance; Blood flow)
Blood flow
(Local vasculature)
Subcutis (S)
(Thickness; Diffusivity;
Permeability; Blood flow)
Blood flow
(Local vasculature)
Muscle (M)
(Thickness; Diffusivity;
Permeability; Blood flow)
DeffD
DeffS
DeffM
Deffveh
Par tition coefficients
(Various media)
DeffSEB
MPML MechDermA model
• Multi Phase Multi Layer model
• Skin appendages – hair/sebum
• SC – mechanistically described
• More physiological parameters
• Partition and diffusion
coefficients – user provided or
calculated (built-in QSAR
models)
• Shape of the absorption skin
defined by the area of
application – no vertical
permeation
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MPML MechDermA – Stratum Corneum
7
DeffSC
Stratum Corneum (SC)
(Thickness; Diffusivity; Permeability; Steady state
binding, Tortuosity)
KpSC:VEH
KpSEBUM:VEH
Non-specific
protein binding
Deff SC lipids
Tortuous pathway
KpVE:SC
Cell permeability
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MPML MechDermA Model – Parameters
Paediatric Population
Healthy NEurCaucasian
Elderly Subjects
Ethnic Population
Diseased Population (psoriasis)
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Trial
Design
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
Plasma Protein
Systems
Data
Drug
Data
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Advantage of PBPK: Separating Systems & Drug Information
9
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
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Intra-individual Variability
• Eight different locations
1. Forehead
2. Face (cheek)
3. Volar Forearm
4. Dorsal Forearm
5. Upper Arm
6. Lower Leg
7. Thigh
8. Back
10
• Various structural
elements
1. Skin surface
2. Stratum corneum
3. Viable epidermis
4. Dermis
5. Subcutis
6. Muscle
7. Hair
• Various parameters
1. Skin temperature
2. Skin surface pH
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Intra-individual Variability
• Eight different locations
1. Forehead
2. Face (cheek)
3. Volar Forearm
4. Dorsal Forearm
5. Upper Arm
6. Lower Leg
7. Thigh
8. Back
11
• Various structural
elements
1. Skin surface
2. Stratum corneum
3. Viable epidermis
4. Dermis
5. Subcutis
6. Muscle
7. Hair
• Various parameters
1. Number of layers
2. Corneocyte pH
3. Corneocyte size
4. Fraction of p/w/l
5. Tortuosity
6. Lipids fluidity/th
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Intra-individual Variability
• Eight different locations
1. Forehead
2. Face (cheek)
3. Volar Forearm
4. Dorsal Forearm
5. Upper Arm
6. Lower Leg
7. Thigh
8. Back
12
• Various structural
elements
1. Skin surface
2. Stratum corneum
3. Viable epidermis
4. Dermis
5. Subcutis
6. Muscle
7. Hair
• Various parameters
1. Thickness
2. Blood flow
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Intra-individual Variability
• Eight different locations
1. Forehead
2. Face (cheek)
3. Volar Forearm
4. Dorsal Forearm
5. Upper Arm
6. Lower Leg
7. Thigh
8. Back
13
• Various structural
elements
1. Skin surface
2. Stratum corneum
3. Viable epidermis
4. Dermis
5. Subcutis
6. Muscle
7. Hair
• Various parameters
1. Hair follicle
diameter
2. Hair shaft
diameter
3. Hair follicle density
4. Sebum fluidity
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Trial
Design
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
Plasma Protein
Systems
Data
Drug
Data
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Advantage of PBPK: Separating systems & drug information
14
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
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• Phys-chem
1. MW
2. Density
3. LogP
4. LogD
5. pKa
• Partition coefficients1. Klip/w
2. Kvw
3. KSC/VE
4. …..
• Diffusion coefficients1. Dveh
2. Dlip
3. Dve
4. ….
Drug Related Parameters
• ADME parameters
1. BP / fu
2. CL
3. ….• Cell membrane
permeability1. Pcell
• Protein binding
and ionization1. fuSC
2. fni
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Drug Related Parameters – QSAR Based Calculation
Combination of
literature derived:
Kretsos 2008
and in-house developed
models
𝐾𝑆𝐶:𝑉𝐸
=𝐾𝑙𝑖𝑝:𝑣
0.7 ∗ 0.68+0.32𝑓𝑢
+ 0.025 ∗ 𝑓𝑛𝑖,𝑉𝐸 ∗ 𝐾𝑜:𝑤
fusc = 1 – (EXP(logKNernst)/(1+EXP(logKNernst))
where:
logKNernst = (ln(HBA+4.824))^(ln(abs(logP)))
• Extensive literature search to find best possible QSAR models to predict drug
related parameters
Dermal Absorption Model can be run with most parameters predicted using QSAR from
physico-chemical properties (MW, LogP/D, HBD, HBA, pKa)
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Drug Related Parameters
• All parameters can be also provided manually (from in vitro and in vivo
studies)
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Formulation Data – Solution
18
𝐷𝑣𝑒ℎ ൗ𝑐𝑚2
ℎ =3600 ∗ 8.2∗ 10−8 ∗ 𝑇
𝜂𝑣𝑒ℎ ∗ 𝑉𝐴1/3 1 +
3 ∗ 𝑉𝐵𝑉𝐴
2/3
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Formulation Data – Emulsion
19
• Diffusion of drug is modelled
• Oil droplets movement neglected
• During storage and application,
drug distribution is assumed to be
at the thermodynamic equilibrium
Yotsuyangi et al. 1973, JPS, 62(1), 41
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Formulation Data – Suspension/Paste/Patch
20
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Formulation Data – Patch
21
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Trial
Design
Age
Weight
Tissue Volumes
Tissue Composition
Cardiac Output
Tissue Blood Flows
Plasma Protein
Systems
Data
Drug
Data
Dose
Administration route
Frequency
Co-administered drugs
Populations
Prediction of drug PK (PD) in population of interest
Mechanistic IVIVE linked PBPK models
Advantage of PBPK: Separating Systems & Drug Information
22
MW
LogP
pKa
Protein binding
BP ratio
In vitro Metabolism
Permeability
Solubility
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Trial Design
23
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Features Added in V17
• Consider vehicle evaporation
• Sub-dermis diffusion (subcutis and muscle) from topical
application
• Absorption through psoriatic skin (absorption through cracks,
changes in SC morphology and structure, VE, dermis physiology
and blood flow)
• Paediatric skin absorption (ontogenies of skin physiology
parameters)
• Run PK-PD models for local skin exposure
24
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Features Added in V17
• Geriatric skin physiology
• Gender effect on skin physiology
• Asian (Korean, Japanese and Chinese) skin physiology
• Run up to four simultaneous dermal drug absorption at multiple
locations
• Mixed dosing (dermal, oral, IV, inhaled) of same or combination
of drugs
• Concentration in skin after oral dosing – bidirectional model
based on concentration gradients
25
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Performance Verification – 12 Compounds Being Studied (2017)
Abdulla et al. AAPS 2017
Martins et al. EuISSX 2017
Polak et al. PAGE 2017
Patel et al. SF 2016
Patel et al. AAPS 2017
Patel et al. AAPS 2017
Martins et al.
GRC 2017
Masuda et al. CM 2017
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Case Study 1: Impact of Site of Application – Patch
Abdulla et al. 2017 AAPS AM, San Diego US
MPML-MechDermA model has
database of skin physiology at
eight different locations of body
• Forehead
• Face (cheek)
• Volar forearm
• Dorsal forearm
• Upper arm
• Back
• Thigh
• Lower leg
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Case Study 2: Virtual Bioequivalence Assessment
Compare Formulations
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Case Study 2: Virtual Bioequivalence Assessment
Compare Formulations Simulate Population Variability
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Case Study 2: Virtual Bioequivalence Assessment
Compare Formulations
Identify clinically-relevant Critical Product Quality Attributes
Simulate Population Variability
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Case Study 3: Nicotine Patch Multiple Dosing
0
5
10
15
20
25
30
35
0 10 20 30
Nic
oti
ne
pla
sm
a c
on
ce
ntr
ati
on
(n
g/m
L)
Time (h)
0
5
10
15
20
25
30
35
115 125 135 145 155Nic
oti
ne
pla
sm
a c
on
ce
ntr
ati
on
(n
g/m
L)
Time (h)
Patch single dose, day 1 Patch multiple dose, day 7
Martins et al. 2017, GRC Dermal Barrier Conference
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Simcyp IVIVE: Translating In Vitro Permeability to Clinical Situations
IVIVE (In vitro-in vivo extrapolation)
32
In vitro systems data
In vitro Systems parameters
• skin thickness
• pH
• Hydration level
• hair follicle density
API/Formulation parameters
• Diffusion coefficient
• Partitioning
• Keratin binding
• Refine Unknown/uncertain
Simcyp simulatorIn vivo Systems
parameters + variability
• skin thickness
• pH
• hydration
• Hair follicle density
Simcyp MechDermA Model
Simcyp MechDermA Model
Different
subjects,
locations,
populations
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Virtual Bioequivalence – Aciclovir Creams
33
PBPK modelling allows to translate the
in vitro product characterization to in
vivo situations in terms of local and/or
systemic PK and identify impact of
formulation differences on exposure.
Impact of excipient (propylene glycol)
cannot be neglected in simulations.
Patel et al. 2017
AAPS AM
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Acknowledgement
• Simcyp
– Nikunj Patel (lead)
– Frederico Martins
– Farzaneh Salem
– Khaled Abduljalil
– Masoud Jamei
– Sebastian Polak (PI)
34
• FDA
– Susie Zhang
– Sam Raney
– Priyanka Ghosh
– Zhanglin Li
– Eleftheria Tsakalozou
– Liang Zhao
Disclaimer: Funding for the work presented here was made possible, in part, by the Food
and Drug Administration through grant 1U01FD005225-01, views expressed here by the
authors of the work do not reflect the official policies of the Food and Drug Administration;
nor does any mention of trade names, commercial practices, or organization imply
endorsement by the United States Government.