Absorption modelling: a brief history, emerging trends and path forward

Adam DarwichDepartment of Biomedical Engineering and Health Systems

C

t

oral concentration-time profile

absorption

% ofdose

t

input

2

physiologically-based pharmacokinetic (PBPK)

absorption modelling

drug-specific properties

physicochemical properties,

in vitro assay data

formulation-specific

properties

solubility, dissolution,

particle radius etc.

systems properties

physiology, morphology

systems ODE model

integrating parameter data

to predict oral drug exposure.

- provides framework for combining physiological and in vitro

data to predict in vivo drug exposure

- less reliable for describing individual data and variability

compared to population pharmacokinetics

- useful for extrapolation, in vitro to in vivo, between species,

special populations, drug-drug interactions and

formulation effects

+ +

3

C

t

stomach

RR

R R-R-

R-

small intestine

colon

R R-

R-OH

R-OH

gastricemptying

smallintestinaltransit colonic

transit

metabolism

biliaryexcretion

metabolism

intestinalblood flow

hepaticblood flow

disintegration

dissolution,precipitation

bile

fluid dynamics,

regional pH

passive absorption,active efflux/uptake

fluid dynamics,

regional pH

passive absorption,active efflux/uptake

Degradation(chemical/bacterial)

Variabilityfood effectsconcomitant fluid volumegastrointestinal transitfluid volumesgastrointestinal pHenzyme abundancestransporter abundancesdrug/formulation/disease interactions...

oralformulation

ionisation

4

mechanistic absorption modelling: a brief history

Goodacre &

Murray 1981

• no method for combining in vitro solubility and

permeability to get an overall prediction of in vivo

absorption

• drug dissolution and permeation considered in

plug of luminal fluid transiting through the small

intestine

• provided semi-quantitative assessment of oral

absorption

• several assumed systems parameters where data

was missing

5

Goodacre &

Murray 1981

the mixing-tank model

• Considering poorly soluble drugs and formulation

effects (particle radius)

• Dissolution model approaches the current state

Dressman 1984/6

mechanistic absorption modelling: a brief history

Dokoumentzidis and Macheras 2006 6

Goodacre &

Murray 1981

Dressman 1984/6

Oberle &

Amidon 1987

• Physiological flow model for simulating

multiple peaks due to food effects

• Intestinal model closer to physiology

• Dividing the gastrointestinal tract into four

segments with individual pH, fluid volumes,

transit times

mechanistic absorption modelling: a brief history

7

Goodacre &

Murray 1981

the compartmental absorption and transit model

• Dividing the small intestine into seven equal

segments to more accurately describe intestinal

transit of drug

• Intestinal transit approaches current status

Dressman 1984/6

Oberle &

Amidon 1987

Hintz &

Johnson 1989

Yu &

Amidon 1996

mechanistic absorption modelling: a brief history

8

Goodacre &

Murray 1981

Dressman 1984/6

Oberle &

Amidon 1987

Hintz &

Johnson 1989

Yu &

Amidon 1996

Ito 1999

Cong 2000

Agoram 2001

Willman 2003/4

Sugano 2008/9

Jamei 2009

Sjögren 2013

mechanistic absorption modelling: a brief history

the advanced CAT (ACAT) model

• accounting for:

release from formulation, pH dependent

solubility, precipitation, regional

permeability, transporters, metabolism.

• In principle, representative of current

models

9

2000 Parrott & Lavé

2006a Jones, et al.

2007 De Buck, et al.

2011a-e Poulin, et al.

PhRMA

2011 Jones, et al.

2013 Sjögren, et al.

2016 Sjögren, et al.

N drugs |Models | Blindingfa |

28

19

23

18

21

21

12

43

ACAT3.1,

IDEA

IVIVE

ACAT3.3

ACAT5.1 IVIVE

In-house IVIVE

ACAT5.0 IVIVE

GI-Sim IVIVE

IVIVE

ACAT8.0,

ADAM13.1,

GI-Sim

IVIVE

84 In-house Pre.Clin.

FG |Diss. |

In vitro

Form. |

IVIVEIn vitro

In vitro

2006b Jones, et al. 6 ACAT4 IVIVEIn vitro

In vitro

NA

( )

In vitro

(IVIVE)

In vitro

In vitro

In vitro

(IVIVE)

(IVIVE)

2011 Thelen, et al.

2012 Thelen, et al.

2011 Sugano 29 In-house IVIVEIn vitro

2011 Gertz, et al. 12 In-house IVIVEIn vitro IVIVE

8 TA model In silicoIn vitro

8 TA model In silicoIn vitro

( )

ACAT8.5,

ADAM13.2,

GI-Sim

2016a,b

Margolskee, et al.

2016 Darwich, et al.2020 Matsumura,

et al. 15 In-house ( ) In vitro

In vitroACAT9.0,

ADAM15.0,

GI-Sim

IVIVE

IVIVE(IVIVE)2020 Ahmad, et al. 48

10

predicting oral bioavailability

2021-07-02 11Margolskee et al. 2017; Ahmad et al. 2020

PBPK absorption modellingin pharmaceutical R&D

Drug

Discovery

• Lead

Optimisation

Pre-clinical

Development

• Clinical Lead

Selection

• Entry Into Human

Clinical Development

• Phase I

• Phase II

• Phase III

• Early stage

prediction of

absorption

• Explore limitations

using PSA

• Explore

formulations

• Development

of formulations

for toxicology

studies

• Predict oral

pharmacokinetics

in human

• Define clinical

formulation

strategy

• Predict food

effects

• Design extended

release

formulations

• Develop IVIVCs

• Increasing amount

of data

• Increasing model

complexity

• Increasing level of

validation

Adapted from Parrott and Lavé (2008) 12

criteria for success

2021-07-02 13

inherent PK

variability

limited

information

-high

uncertainty

and

bias

information

acceptable degree of error

utility of model as a function of experimental data

2021-07-02 14

global sensitivity analysis

2021-07-02 15Images curtesy of Dr. Nicola Melillo

global sensitivityanalysis

2021-07-02 16Melillo et al. 2019a&b; Melillo and Darwich 2021

Predict-learn-confirm & middle-out approaches

2021-07-02 17Olivares-Morales et al. 2016; Rostami-Hodjegan 2018

Darwich et al. 2011 18

in vivo information – ‘omics

2021-07-02 19Couto et al. 2020; Achour et al. 2021

2021-07-02 20Riethorst et al. 2016; Grimm et al. 2018; Vertzoni et al. 2021

2021-07-02 21Hens et al. 2016

in vitro-in vivo correlation (IVIVC)

2021-07-02 22Margolskee et al. 2016; Patel et al. 2012

virtual bioequivalence

2021-07-02 23Doki et al. 2017

virtual bioequivalence

2021-07-02 24Tsakalozou et al. 2021

Darwich et al. 2012; Darwich et al. 2020

special disease populations

factorial design

02/07/2021 26Zhou et al. 2017

real-world data and evidence

2021-07-02 27Goulooze et al. 2019; Wang et al. 2020

real-world data and evidence

2021-07-02 28Lesko et al. 2019

final thoughts

02/07/2021 29

in vitro

in vivo RCTs

RWD

formu-

lation

drugphysi-

ology

model

acknowledgements

2021-07-02 30

• CAPKR Centre for Applied Pharmacokinetic Research

University of Manchester

• Oral Biopharmaceutics Tools (OrBiTo) Project, Innovative Medicines Initiative

• UNGAP working groups: www.ungap.eu/working-groups

• Logistics and informatics in Healthcare

KTH Royal Institute of Technology

email: darwich@kth.se

URL: http://www.modelling.systems

references

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02/07/2021 31

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*See companion papers for additional information.