Population based PBPK-PD models Gardner...CLAR ERYTH ERYTH ITZ KTZ FLUC FLUO PAR Terfenadine +Inhibitor observed 21 34 39 41 82 12.5 7 5 predicted 14 26 25 22 46 27 9 9 Predicted and

Population based

PBPK-PD models

Iain Gardner

Simcyp

© Copyright 2017 Certara, L.P. All rights reserved.

Overview of talk

• Systems approach to PBPK modelling

• Regulatory use of PBPK models in Pharmaceutical Industry

• Incorporating PD effects into PBPK models

– Drug transporter polymorphisms and statin efficacy

– Simulation of Cisapride/Clarithromycin interaction and QT

prolongation

• EU_TOX_RISK project

• Conclusions

2


CLuint per

Liver

CLuint per

g Liver

In vitro

system

In vitro

CLuint

Prediction of human PK in virtual individuals

Scaling

Factor

(MPGGL, HPGL)

Liver

weight

Combine in vitro-in vivo extrapolation

(IVIVE) and PBPK approaches

in virtual individuals to predict

drug concentration and effect

Identifying relevant DISTRIBUTION of

values for demographical, biological,

physiological and genetic parameters in

target population & the COVARIATIONS

between the parameters in target

POPULATION


Age

Weight

Tissue Volumes

Tissue Composition

Cardiac Output

Tissue Blood Flows

[Plasma Protein]

Systems

Data

Drug

Data

Trial

Design

MW

LogP

pKa

Protein binding

BP ratio

In vitro Metabolism

Permeability

Solubility

Dose

Administration route

Frequency

Co-administered drugs

Populations

Prediction of drug PK (PD) & DDI in population of interest

Mechanistic IVIVE linked PBPK models

Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012

Separating systems & drug information (Systems Pharmacology)

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=fQ2w6O47GwQ52M&tbnid=KOoP8nkRh8vYwM:&ved=0CAUQjRw&url=http://pharmacologycorner.com/pharmacology-elderly-pharmacokinetics-polypharmacy-pp/&ei=ZN1CUu_kHqKV0AWfnYH4Bg&bvm=bv.53077864,d.d2k&psig=AFQjCNGFNTs620RkkQifIRl6BWV5RV0lIQ&ust=1380200120166102

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=fQ2w6O47GwQ52M&tbnid=KOoP8nkRh8vYwM:&ved=0CAUQjRw&url=http://pharmacologycorner.com/pharmacology-elderly-pharmacokinetics-polypharmacy-pp/&ei=ZN1CUu_kHqKV0AWfnYH4Bg&bvm=bv.53077864,d.d2k&psig=AFQjCNGFNTs620RkkQifIRl6BWV5RV0lIQ&ust=1380200120166102


Typical outputs from a population PBPK simulation (n = 100)

Mean plasma

and tissue

concentrations

adipose

muscle

heart

Variability

between

trials or

individuals

Drug

interactions

AUCratio = 11.4 (10.5 – 12.4)

Cmaxratio = 3.3 (3.1-3.6)


Parameterisation of PBPK model

• Model can be informed with in vivo data

– Need data, can’t be used prospectively

• Model built from bottom up using in vitro data

– using in vitro-in vivo extrapolation approaches

• To describe profile after extravascular (oral)

administration need to estimate

– rate and extent of absorption

– volume of distribution (Vss)

– clearance


Data need for IVIVE-PBPK model

• Rate and extent of

absorption

– Solubility

– Permeability

• Physicochemical

parameters

• In vitro cell systems

In silico input/ in vitro input

• Clearance

– In vitro plasma binding

– In vitro BP partitioning

– In vitro rate of metabolism

• Volume of distribution

– Physicochemical

parameters

• LogP, pKa

– In vitro plasma binding

– In vitro Blood:plasma (BP)

partitioning

• Plasma binding, BP

partitioning and metabolism

rates are species specific

– For rat need rat data

– For human need human

data


How accurate are IVIVE approaches in predicting clearance?

• Two most commonly used systems

– HLM and Human Hepatocytes

• For accurate IVIVE need to have some understanding of the

performance of your in vitro system

– Assay conditions

– Source of liver tissue

– Number of donors used

– Often need to correct for systematic bias (under-prediction)

8

(Hallifax & Houston, 2012, J. Pharm. Sci, 101, 2645)

Hepatocyte (n= 89)

(RMSE = 0.59 r= 0.73)HLM (n= 64)

(RMSE = 0.63; r = 0.81)


Data generated (n=1) using discovery protocols and used for IVIVE scaling

Clear difference between 2 labs

A – HLM lab 1

B – HLM lab 2

C – rat SSS – fixed exponent

D – rat SSS - Tang

E – NHP LBF method

9


10

Recent publications on Pharmaceutical regulatory use of PBPK

FDA

EMA

Industry


PBPK Impact on New Drug Approvals

Revatio (Sildenafil)

Pulmonary Arterial

Hypertension

Xarelto (Rivaroxaban)

Deep Vein

Thrombosis and

Pulmonary Embolism

Edurant (Rilpivirine)

HIV infection

Iclusig (Ponatinib)

Chronic Myeloid

Leukemia

Olysio

(Simerprevir)

Hepatitis C

Opsumit (Macitentan)

Pulmonary Arterial

Hypertension

Imbruvia (Ibrutinib)

Mantle Cell Lymphoma and

Chronic Lymphocytic

Leukemia

Movantik (Naloxegol)

Opioid Induced

Constipation

Cerdelga(Eliglustat)

Gaucher DiseaseJevtana (Cabazitaxel)

Prostate Cancer

Zykadia (Ceritinbi)

Metastatic Non-Small

Cell Lung Cancer

Bosulif (Bosutinib)

Chronic Myelogenous

Leukemia

Lynparza (Olaparib)

Advanced Ovarian

Cancer

Farydak (Panobinostat)

Multiple myeloma Lenvima (Lenvatinib)

Thyroid cancer

Odozmo (Sonidegib)

Basal Cell Carcinoma

Tagrisso

(Osimertinib)

Metastatic NSCLC

Cotellic (Cobimetinib)

Metastatic Melanoma

Alecensa (Alectinib)

Non Small Cell Lung Cancer

Aristada (Aripiprazolel)

Schizophrenia

10 fast track,

breakthrough,

priority or

accelerated

approvals

• 12 – oncology

• 3 – pulmonary

• 2 – anti-viral

• 4 – orphan

• 1 – gastro

• 1 - CNS

Almost 100 label

claims informed

by PBPK,

including DDI,

absorption, ethnic

bridging,

formulation

© Copyright 2017 Certara, L.P. All rights reserved. 12

Prospective DDI prediction - inhibition

Wagner et al., Clinical Pharmacokinet, 2015

15 substrate models submitted by 9 sponsors; mainly CYP3A4 metabolised


Prospective DDI prediction - induction

Wagner et al., Clinical Pharmacokinet, 2015

11 substrate models submitted by 6 sponsors; mainly CYP3A4 metabolised.

Four inducers were used: rifampicin, efavirenz, carbamazepine, rifabutin

In some cases, Indmax for rifampicin was increased from 8 to 11.5-fold (Xu et al.,

DMD, 2011)


Rosuvastatin PBPK/PD : Concn at the site of action

Full PBPK modelPermeability-Limited Liver

model

Mevalonic acid

turnover model

• Published PKPD model for the effect of rosuvastatin on cholesterol

synthesis modified to use unbound concentration in liver intracellular

water (liver CuIW) predicted by the PBPK model as the driving

concentration for the PD response instead of plasma concentration. Jamei et al. 2014; Aoyama et al. 2010


c.521T>C polymorphism associated with reduced OATP1B1 activity,

resulting in increased plasma rosuvastatin concentration.

Parameter estimation used to obtain OATP1B1 CLint,T using plasma

concentration data stratified by genotype (Pasanen et al., 2007).

Genotype OATP1B1 CLint,T

(µL/min/million cells)

c.521TT 126

c.521TC 30

c.521CC 0

Rosuvastatin PBPK/PD: Effect of OATP1B1 genotype

c.521TT c.521TC c.521CC

Rose et al. 2014


Rosuvastatin PBPK/PD: Effect of OATP1B1 genotype

OATP1B1

genotype

Plasma AUC0-∞h

(ng/ml.h)

Liver CuIW AUC0-∞h

(ng/ml.h)

PD AUEC relative to baseline

(%): Plasma input

PD AUEC relative to baseline

(%): Liver CuIW input

c.521TT 35.0 120 35.1 36.2

c.521TC 56.9 (63%) 114 (-5.7%) 45.9 (30%) 35.1 (-3.1%)

c.521CC 73.6 (111%) 109 (-9.6%) 50.7 (35%) 34.1 (-5.8%)

OATP1B1 c.521T>C associated with a 2.6% lower fractional LDL-C reduction per allele in

>3000 patients treated with rosuvastatin daily (Chasman et al., 2012).

Rose et al. 2014


Some Drug withdrawals due to QT prolongation

17

Courtesy of Dr. Norman Stockbridge

QSAR

in vitro (hERG)

in vitro cardiac

cells

in vitro Purkinje

fibers

ex vivo heart

in vivo animals

FIH

TQT


How is TdP risk likely to be assessed in the future?

18


Linking PBPK and cardiac safety simulation

Human heart left ventricular cell model

Multiple ion channels

(Ikr, Ca, K Na channels)

Accounts for differences in cell physiology in epicardium,

mid myocardium and endocardium

Inter-individual differences accounted for

Metabolic and contractility effects (electro-mechanical

coupling) also considered

O’Hara and Rudy PLoS computational Biology 7, 2011

ten Tusscher et al. Am J Physiol Heart Circ Physiol. 2004, 286(4)

ten Tusscher et al. Phys Med Biol. 2006, 51(23)


QT effects of cisapride +/- clarithromycin: PBPK-PD Model inputs

• PBPK model

• Compound file developed for

Cisapride

o CYP 3A4 substrate

• Default Clarithromycin compound

file

o Mechanism-based CYP 3A4

inhibition

• Cardiac effect model

o Individual free plasma and

heart concentrations

o HERG affinity

o in silico/in vitro

o Other ion channel affinityo in silico/in vitro

o ten Tusscher human

ventricular cardiomyocyte

model

• Endpoints

o Pseudo ECG

o QTcB

20

• Healthy volunteer population

– 1 trial of 12 subjects

– Simulations matched for gender and age range

• Group 1

– Cisapride 10mg QDS for 10 days

– Clarithromycin 500mg BD on days 6-10

• Group 2

– Clarithromycin 500mg BD for 10 days

– Cisapride 10mg QDS on days 6-10

Frequent blood samples and ECG

measurements on day 5 and day 10 (Van Haarst, 1998, CPT, 64, 542-546)


Prediction of plasma concentrations

OBS PRED

Cmax

(ng/ml)

2800±

700

2988±

1144

AUC

(ng×h/ml)

17200±

4100

21737±

11583

CLARITHROMYCINCISAPRIDE +

CLARITHROMYCINCISAPRIDE

OBS PRED

Cmax

(ng/ml)

51 ±

12

58 ±

22

AUC

(ng×h/ml)

834 ±

260

1124 ±

455

OBS PRED

Cmax

(ng/ml)

140±

22

137±

57

AUC

(ng×h/ml)

2635±

396

2886±

1293


QTc prolongation by cisapride +/- clarithromycin

OBSERVED PREDICTED

average QTcB

(ms)

406 389 CISAPRIDE

419 405CISAPRIDE +

CLARITHROMYCIN

free plasma concentration


QTc prolongation by cisapride +/- clarithromycin

OBSERVED PREDICTED

average QTcB

(ms)

406 400 CISAPRIDE

419 420CISAPRIDE +

CLARITHROMYCIN

free heart tissue concentration

Both drugs prolong QT interval and this is accounted for in the model

Predicted QTcB value for cisapride + clarithromycin is 414 ms if only PK change

is accounted for


Simulation of QTc changes with terfenadine

ΔQTc [ms]INHIBITOR

CLAR ERYTH ERYTH ITZ KTZ FLUC FLUO PAR

Terfenadine

+Inhibitor

observed 21 34 39 41 82 12.5 7 5

predicted 14 26 25 22 46 27 9 9

Predicted and observed changes of QTc for terfenadine alone

and after addition of metabolic inhibitor.

Shaded cells – statistically significant differences in Welch t-test

(α=0.05).

CLAR = clarithromycin, ERYTH = erythromycin, ITZ = itraconazole,

KTZ = ketoconazole, FLUC = fluconazole, FLUO = fluoxetine, PAR = paroxetine

An Integrated EUropean ‘Flagship’ Program Driving Mechanism-based Toxicity Testing and Risk Assessment for the 21st Century

• Horizon 2020 funded• 6 year research project

• Kick-off January 2016

• Improved toxicological testing to predict human risk & meet regulatory needs

• Improved toxicological knowledge to enable ‘read across’ approaches

• Commercial exploitation of developed products & services

• Advance international co-operation in the field of predictive toxicology

• Establish human-relevant, in vitro testing strategies aligned along validatedknowledge of AOPs, and implemented in integrated approach for testing andassessment (IATAs) to meet risk assessment purposes

Expected outcomes of EU-ToxRisk:

EU-ToxRisk is the new ’Flagship’ program funded through Horizon 2020

• 38 European partners

• 1 US partner

• Academia & Research Institutes

• Small & Medium-Sized Enterprises

(SMEs)

• Industry (chemical, pharmaceutical,

cosmetic)

• Regulators & other Stakeholders

EU-ToxRisk unites partners from a variety of backgrounds

• 38 European partners

• 1 US partner

• Academia & Research Institutes

• Small & Medium-Sized Enterprises (SMEs)

• Industry (chemical, pharmaceutical, cosmetic)

• Regulators & other Stakeholders

EU-ToxRisk unites partners from a variety of backgrounds

assay throughput

human relevance

coverage

toxicity en

dp

oin

ts

syst

ems

bio

logy

mo

del

ling

Choice of EU-ToxRisk test systems & strategy

• 2D/3D human cells and tissue slice models

• High content imaging (HCI) to organ-on-a-chip

• Several hundred chemicals

• Case studies for strategy optimisation

• Omics data connected to classical endpoints

• AOPs to guide biological read across (RAX)

• Computational toxicology and data basing

• Biokinetics & experimental ADME data

• PBPK combined with multi-scale hazard modelling

EU-ToxRisk Strategic Choices

Workflow to predict in vivo effect from in vitro data

In vitroToxicity testing

biomarker toxicity

Dose/estimated exposure

In vitro/in silicoinputs

Compound/metabolite

biomarker

toxicity

? risk

? risk

In vitro/in vivo translation

Population variabilityUncertainty

Hazard identification

Biokinetics

Need to relate in vitro toxicity results to the actual concentration of the toxic moiety

– Is parent or a metabolite responsible for toxicity?

– Relate effects to actual free concentration of toxic moiety in the system rather than to

the applied (nominal) concentration

• Binding to proteins

• Binding to plastic

• Evaporation

• Metabolism/degradation

• Action of transporters

Measurements of actual concentrations in the in vitro toxicity experiments (WP4)

Mathematical modelling of in vitro experiments will be needed (WP4)

– Predict IV (FP7 project); explored further in this project

• In vitro system may change with time in long term culture

31

Some challenges

Chemical space

– IVIVE methods developed for pharmaceuticals

• Typically want high exposure

– Translation to chemicals, pesticides, cosmetics etc

Route of exposure

– Oral vs dermal vs inhalation

Knowing the “dose”

– Not a problem for pharmaceuticals

– Often not known precisely in different individuals for chemicals

32

Summary

To link in vitro toxicity data with exposure in target organs in humans

– Need to understand biokinetic data from in vitro experiment

– IVIVE and PBPK modeling can give concentration at the site of

action in tissue

- Use physicochemical properties and in vitro data to construct

the compound model

With an understanding (assumption) of in vitro:in vivo toxicity

relationship

– the dose/exposure leading to toxic concentrations in the organ of

interest can be simulated

Need to consider individual variability/uncertainty in simulations

33

Acknowledgements

www.eu-toxrisk.eu

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 681002

Certara

Ciaran FisherOliver HatleyGopal PowarMasoud Jamei

Sebastian Polak

EU-TOXRISK partners

Documents

Population based PBPK-PD models Gardner...CLAR ERYTH ERYTH ITZ KTZ FLUC FLUO PAR Terfenadine +Inhibitor observed 21 34 39 41 82 12.5 7 5 predicted 14 26 25 22 46 27 9 9 Predicted and