Physiologically based pharmacokinetic modelling of … web...Agenda Page 2 PBPK modelling of biologics • April 2017 • Overview: Physiologically Based Pharmacokinetic (PBPK) Modelling

19 - 21 April 2017 Christoph Niederalt

QuanTI Closing Conference

Physiologically based pharmacokinetic modelling of biologics

Agenda

PBPK modelling of biologics • April 2017 Page 2

• Overview:

Physiologically Based Pharmacokinetic (PBPK) Modelling

• Application Examples

• Albuferon:

Testing hypotheses on relevant mechanisms

• Glucose-Insulin model

Physiologically-based PK modeling


Wikipedia:

Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling

technique for predicting the absorption, distribution, metabolism and excretion (ADME) of

synthetic or natural chemical substances in humans and other animal species.

PBPK Applications


PK-Sim for Biologics Organ Representation


Plasma

Enz 1 Enz N

blood

Trans 1 Trans N

…

Blood Flow

Lymph

Flow …

convection

diffusion

cells

Endosome Lysosome

Tissue

Cells

Interstitial

Space

Protection from Catabolism by Binding to FcRn


• The neonatal Fc receptor for

IgG (FcRn) is expressed by

endothelial cells and circulating

monocytes.

• Ligands: IgG and albumin

(binding at distinct sites)

• Ligands bound to FcRn in acidic

endosomal compartment

recycle back into circulation

extended half-life

figure taken from D. C. Roopenian and S. Akilesh,

Nature reviews immunology, 7, 715, (2007)

Generic PBPK Model for Biologics Protection from Catabolism by Binding to FcRn


• The FcRn sub-model implemented in PK-Sim® was developed starting from

the model published by Garg and Balthasar#

• Competition with endogenous IgG is taken into account

# A. Garg and J.B. Balthasar. J. Pharmacokinet. Pharmacodyn. 34, 687-709, (2007)

vascular space

interstitial space

endosomal space

cellular space

CL IgG + FcRn IgG-FcRn

Kd (pH 6.0)

blood cells

IgG + FcRn IgG-FcRn Kd (pH 7.4)

IgG + FcRn IgG-FcRn Kd (pH 7.4)

Generic PBPK Model for Biologics Example: Model Development


IgG antibody (7E3) in wild-type and FcRn-knockout mice

Experimental data are taken from

A. Garg and J.B. Balthasar.

J. Pharmacokinet. Pharmacodyn. 34,

687-709, (2007)

Example: Albuferon Case Study


Albuferon is an Interferon-a–Albumin fusion protein

co-developed by Human Genome Sciences and Novartis as hepatitis C drug.

Crucial Questions prior to

First-in-Human:

• What Albuferon dose will

be appropriate (safe & efficacious)?

• Does albumin-FcRn binding

prolong half-life as expected?

• Is IFN-a effect at target

similar to “naked” IFN-a?

figure taken from

http://www.hgsi.com/albuferona-albinterferon-alfa-2b.html

Factors Determining Half-Life of Albuferon


Lysosomal

degradation and

protection from

degradation by FcRn

Using fusion proteins

containing the Fc

fragment of IgG or

albumin is one

approach to half-life

extension

Target mediated

deposition and

degradation in tissue

and blood cells

(effect related)

Model for IFNAR Mediated PK


Representation of target mediated deposition and clearance

• reversible binding of drug to IFNAR

• irreversible internalization of drug-receptor complex (drug as well as receptor

are degraded once they are internalized)

D

R Cm

Ci

Intracellular

receptor

binding

internali-

zation

lysis

Interstitial

IFNAR Mediated PK – Monkey


model fit to IFN-b and comparison with IFN-a data (different dosages)

Effect of receptor mediated

deposition and clearance is

seen for the two lower-doses

of IFN-b

Experimental data:

D.E. Mager et al., J. Pharmacol. Exp. Ther. 306 (1), 262-270 (2003); J.M. Collins et al., Cancer Drug Deliv. 2 (4), 247-253 (1985).

evaluation

with IFN-a

Intermediate Summary


Model representing lysosomal degradation and FcRn salvage established

Model for IFNAR mediated deposition and clearance established

Next step:

Integration of FcRn and TDM model components and prediction of fusion protein

PK

Prediction for Albuferon (Monkey)


Prediction under the assumption that Kd(Albuferon-FcRn) = Kd(Albumin-FcRn)

• FcRn mediated protection from catabolism

is well represented

• Model without target mediated deposition &

clearance outperforms integrated model

Experimental data:

Pfizer & B.L. Osborn et al., J. Pharmacol. Exp. Ther. 303 (2), 540-548 (2002).

Prediction for Albuferon – Adjusted


Excellent description of data if Kd to FcRn is reduced by a factor 2.5 only

Experimental data:


Prediction for Albuferon – Adjusted


Excellent description of data if Kd to FcRn is reduced by a factor 2.5 only

Experimental data:


In contrast to “naked” interferons for

albuferon the impact of IFNAR mediated

deposition and clearance appears to be

negligible!

Half-life prolongation achieved

Effect at target modified?!

• PBPK model to represent the mechanisms relevant for the PK of biologics

available

• PBPK modelling allows the use of prior knowledge for extrapolation as well as

testing assumptions or hypotheses on relevant mechanisms.

Summary Albuferon Example


Example: Glucose-Insulin Model (GIM)


• Diabetes is a major health issue: in 2010, 285 million people with

diabetes worldwide (6.4%); predicted for 2030, 438 million (7.7%)

The REACTION Project Remote Accessability to Diabetes Management and

Therapy in Operational Healthcare Networks

Physiology of the GIM


Schaller et al. 2013

Processes of the GIM – Organ Level


Model Development Tolerance Tests (Mean Model)


Sch

alle

r et a

l. 20

13

www.systems-biology.com www.bayer.com Page 21

Model Development - Clinical Trial (Individualized Model)


0 2 4 6 8 10 12 14 16 0

2

4

6

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Fitte

d C

on

ce

ntr

atio

n [m

mo

l/L

]

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E C

C E

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E C

C E

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E C

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0 2 4 6 8 10 12 14 16 0

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Reference Concentration [mmol/L]

Pre

dic

ted

Co

nce

ntr

atio

n [m

mo

l/L

]

A

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E C

C E

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0

5

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Meal Meal Meal

Fit Subject 117-1

Glu

co

se

[m

mo

l/L

]

Model Data

0

20

40

60

80

100

Insu

lin[m

U/L

]

0 3 6 12 16 21 240

20

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Time [h]

Glu

ca

go

n[p

mo

l/L

]

Meal Meal Meal

Prediction Subject 117-2

0 3 6 12 16 21 24

Time [h]Schaller et al. 2013

Model Applications: T1DM - Automatic Blood Glucose Control


• Initialization with patient data

(physiological parameters,

e.g. weight, height, gender)

• Blood glucose

measurements taken

frequently, stored and

delivered to the controller

• The process works on two

time scales:

• Short: online calculation of

optimal insulin dose based on

recent glucose measurements

• Long: offline “model

adaptation” based on full

measurement data history

Schaller et al. 2016

2 Clinical Trials: Results


1st trial with i.v. blood

glucose

(red, mean ± stdv)

2nd trial with s.c.

continuous glucose

monitoring (blue, mean ±

stdv)

From 1st -> 2nd:

Increased controller

aggressiveness

Slight increase in low

blood glucose incidence

But:

Very high time-in-target

for control using s.c. CGM

Schaller et al. , unpublished

Glu

co

se

Summary and Outlook


Development and validation of a PBPK/PD model of the

glucose-insulin metabolism for healthy individuals and

individuals with T1DM

Coupled PBPK models of glucose, insulin and glucagon

Detailed GI-tract model including an incretin effect

model

Integration of a subcutaneous insulin absorption and

a detailed insulin receptor model

Platform for

Automated blood glucose control in clinical environments

Evaluation of novel diabetes treatment strategies on virtual

diabetes populations

Open-Systems-Pharmacology.org


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Fully transparent open source development

Open development of scientific content and qualification approaches

Repositories for open PBPK and Systems Pharmacology models

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Documents

Physiologically based pharmacokinetic modelling of … web...Agenda Page 2 PBPK modelling of biologics • April 2017 • Overview: Physiologically Based Pharmacokinetic (PBPK) Modelling