Simcyp MPML MechDermA Model - afpt-clubphase1.com

Mechanistic modelling of dermal

drug absorption using the

Simcyp MPML

MechDermA Model

PBPK SYMPOSIUM 2018

Paris, April 4th 2018

Sebastian Polak

© Copyright 2018 Certara, L.P. All rights reserved.

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


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

http://www.simcyp.com/News/2014/October/20141023_FDA_Grant.htm?p=1


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


Trial

Design

Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Tissue Blood Flows

Plasma Protein

Systems

Data

Drug

Data

Dose


Frequency


Populations




5

MW

LogP

pKa

Protein binding

BP ratio

In vitro Metabolism

Permeability

Solubility


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)


Permeability; Blood flow)

DeffVE

CLDBlood flow

(Local vasculature)

Dermis (D)


Permeability; Non-Specific

Clearance; Blood flow)

Blood flow

(Local vasculature)

Subcutis (S)



Blood flow

(Local vasculature)

Muscle (M)



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


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


MPML MechDermA Model – Parameters

Paediatric Population

Healthy NEurCaucasian

Elderly Subjects

Ethnic Population

Diseased Population (psoriasis)


Trial

Design

Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Tissue Blood Flows

Plasma Protein

Systems

Data

Drug

Data

Dose


Frequency


Populations




9

MW

LogP

pKa

Protein binding

BP ratio

In vitro Metabolism

Permeability

Solubility


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




1. Forehead

2. Face (cheek)

3. Volar Forearm

4. Dorsal Forearm

5. Upper Arm

6. Lower Leg

7. Thigh

8. Back

11


elements

1. Skin surface

2. Stratum corneum

3. Viable epidermis

4. Dermis

5. Subcutis

6. Muscle

7. Hair


1. Number of layers

2. Corneocyte pH

3. Corneocyte size

4. Fraction of p/w/l

5. Tortuosity

6. Lipids fluidity/th




1. Forehead

2. Face (cheek)

3. Volar Forearm

4. Dorsal Forearm

5. Upper Arm

6. Lower Leg

7. Thigh

8. Back

12


elements

1. Skin surface

2. Stratum corneum

3. Viable epidermis

4. Dermis

5. Subcutis

6. Muscle

7. Hair


1. Thickness

2. Blood flow




1. Forehead

2. Face (cheek)

3. Volar Forearm

4. Dorsal Forearm

5. Upper Arm

6. Lower Leg

7. Thigh

8. Back

13


elements

1. Skin surface

2. Stratum corneum

3. Viable epidermis

4. Dermis

5. Subcutis

6. Muscle

7. Hair


1. Hair follicle

diameter

2. Hair shaft

diameter

3. Hair follicle density

4. Sebum fluidity


Trial

Design

Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Tissue Blood Flows

Plasma Protein

Systems

Data

Drug

Data

Dose


Frequency


Populations



Advantage of PBPK: Separating systems & drug information

14

MW

LogP

pKa

Protein binding

BP ratio

In vitro Metabolism

Permeability

Solubility

© Copyright 2018 Certara, L.P. All rights reserved. 15

• 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


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)


Drug Related Parameters

• All parameters can be also provided manually (from in vitro and in vivo

studies)


Formulation Data – Solution

18

𝐷𝑣𝑒ℎ ൗ𝑐𝑚2

ℎ =3600 ∗ 8.2∗ 10−8 ∗ 𝑇

𝜂𝑣𝑒ℎ ∗ 𝑉𝐴1/3 1 +

3 ∗ 𝑉𝐵𝑉𝐴

2/3


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


Formulation Data – Suspension/Paste/Patch

20


Formulation Data – Patch

21


Trial

Design

Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Tissue Blood Flows

Plasma Protein

Systems

Data

Drug

Data

Dose


Frequency


Populations




22

MW

LogP

pKa

Protein binding

BP ratio

In vitro Metabolism

Permeability

Solubility


Trial Design

23


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


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


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


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


Case Study 2: Virtual Bioequivalence Assessment

Compare Formulations



Compare Formulations Simulate Population Variability



Compare Formulations

Identify clinically-relevant Critical Product Quality Attributes

Simulate Population Variability

© Copyright 2018 Certara, L.P. All rights reserved.31

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


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


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


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.

[email protected]

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Simcyp MPML MechDermA Model - afpt-clubphase1.com