Presented by Paola Coppola

1. The finalised EMA guideline and latest experience of PBPK models
in Regulatory submissions
Paola Coppola, Pharmacokinetics Assessor - MHRA
PBPK Symposium - Paris 4th April 2019

2. 2
Disclaimer
The views expressed in this presentation are those of the speaker,
and are not necessarily those of MHRA or EMA.

3. 3
Overview
PBPK model evaluation in EU
EMA PBPK guideline
New areas of interest in Regulatory
submissions

4. 4
European Commission
EMA
COMP HMPCCHMP CVMP PDCO CAT
PKWP
MSWPSAWP
BSWP
PRAC
CHMP: Committee for Human Products for Medicinal Use
PDCO: Paediatric Committee
SAWP: Scientific Advice Working Party
MSWP: Modelling and Simulation Working Party
EWG: Extrapolation Working Party
BSWP: Biostatistics Working Party
PKWP: Pharmacokinetics Working Party
Other WPs
EWPG
The European Regulatory System

5. 5
EMA PBPK Guideline
Jan 2017
End of public
consultation
Oct 2018
Agreed by PKWP and MSWP
Dec 2018
Adopted by CHMP
Jul 2019
It comes into effect

6. 6
EMA PBPK Guideline
To describe the expected content of PBPK modelling and simulation
reports included in regulatory submissions, such as applications for
authorisation of medicinal products, paediatric investigation plans and
clinical trial applications. This includes the documentation needed to
support the qualification of PBPK platform for the intended use and the
evaluation of the drug model. The guideline applies to commercially
available platforms and to in-house built platforms.
AIM

7. 7
Regulatory impact decision
The regulatory impact is directly linked to the risk to the patient in case the modelling
predictions or assumptions lead to erroneous regulatory decisions.
The impact of a simulation also depends on how much weight of evidence the PBPK
simulation will have in a certain scenario, the therapeutic context and the resulting treatment
recommendations.
Regulatory
impact decision
Examples
High All changes to SmPC
PBPK model in place of clinical data
Extrapolation outside the studied area
Medium Paediatric dose to be confirmed by clinical
data
Low Dose selection for FIHQualification requirements

8. 8
PBPK: platform qualification
Qualification may be obtained via:
• Regulatory submission
(specific to this, subsequent needs re-evaluation)
• CHMP qualification procedure
(can be cited in future applications) https://www.ema.europa.eu/en/human-regulatory/research-development/scientific-advice-protocol-assistance/qualification-novel-methodologies-medicine-development
• Published papers
if the included validation dataset is sufficiently current and described in sufficient detail to allow a thorough understanding of
the data by regulators
Emphasis is not on what is required per se but how it should be reported to allow confidence in the
submitted model

9. 9
PBPK: platform qualification
Pre-specified data set:
- Selection criteria for the drugs and the in vitro and in vivo parameters should be described
- compounds with similar ADME characteristics to that of the intended use
- a range of PK characteristics that could influence the outcome should be covered (e.g. 8 to
10 compounds may be sufficient)
- additional drugs included in the qualification data set should not be used in the platform
building
The process of establishing confidence in a PBPK platform to simulate a certain scenario, in a
specific context, on the basis of scientific principles and ability to predict a large dataset of
independent data thereby showing the platforms ability to predict a certain purpose.
In the context of PBPK model, qualification is purpose and platform version specific.

10. 10
PBPK: platform qualification
Appendix 1
PBPK guideline
Qualification of the ability to quantify the effects on investigational drugs being victim of
drug interaction
Qualification of the ability to detect investigational drugs as perpetrators of drug
interaction
Simulation of exposure in paediatric population

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PBPK: predictive performance
The process of establishing confidence in the drug model. The reliability is assessed on the
basis of how well important characteristics of the drug model has been tested against in vivo PK
data and whether adequate sensitivity and uncertainty analyses have been conducted to
support the models ability to provide reliable predictions.
Evaluation of the drug model when the investigational drug is a victim drug
Evaluation of the drug model when the investigational drug is a perpetrator
Simulation of exposure in an alternate population
Appendix 2
PBPK guideline

12. 12
Regulatory submission
2016 review
Model purposes:
• DDI (ca. 75%*)
• Better understanding of PK, role of
enzymes/transporters
• Dose recommendations
• Food effect
• Effect of polymorphisms/ethnic differences
• PK in special population (renal/hepatic impairment)
• Comparison between strengths/formulations
*In many cases there is more than one purpose

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Regulatory submission
Post 2016
increasing number of procedures and SA
Different applications:
• Special populations - paediatrics, pregnancy
• Diseases states- Oncology,
Gastroenterology
• More focus on PD end point
• Biopharmaceutical Applications
Additional purposes:
Still DDIs, but also UGTs and Transporters as
clearance pathway
Limited experience in locally acting products
Model discussed at MSWP in 2016*
*2016 Activity report of the Modelling and simulation working group (MSWG)

14. 14
PBPK
New areas of interest in regulatory submissions
UGTs
CYPs
induction
Pregnancy
Locally
applied
products
Model
Disease
Renal function
models in
neonates
(FDA)

15. 15
PBPK - tumour model
Active substance X
PBPK model used to support the proposal for a new posology.
The model was used to predict the tumour target engagement for the active substance X,
showing that with the new proposed posology the tumour target engagement profile is
expected similar to that achieved using the approved posology. All doses showed to
maintain the target engagement above 90% throughout the dosing interval.
Case study

16. 16
PBPK – disease model
Examples of models to investigate the result of drugs
effecting the GI tract:
• Chemotherapy
• GLP1 receptor agonists delaying gastric emptying

17. 17
PBPK: CYPs induction
• Mechanistic static model used to investigate CYP3A4 induction
• Model qualification was considered not sufficient for quantitative CYP 3A4
induction
• Small qualification dataset (only 6 inducers with 4 different substrates,
only 1 shows low levels of induction)
PBPK Model
Missing data
Case study
Drug X potentially inducer of CYP3A4
• 7 clinical studies with CYP3A4 inducers were used in the data set for model
qualification
• PBPK model considered acceptable to exclude clinical significant risk of DDI
with CYP3A4 inducers.
Conclusions

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PBPK: DDIs with UGTs inhibitors
• ATP-uncompetitive, reversible inhibitor of the MEK1 and MEK2
• Inhibits activation of MEK by BRAF and inhibits MEK kinase activity
• Inhibits growth of BRAF V600 mutant melanoma cell lines and demonstrates
antitumour effects in BRAF V600 mutant melanoma animal models.
• Primarily metabolized through UGT1A1 mediated glucuronidation
Case study: Binimetinib
https://www.ema.europa.eu/en/documents/assessment-report/mektovi-epar-public-assessment-report_en.pdf

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DDIs with UGTs inhibitors
• Simulations used to investigate the effect of 400 mg atazanavir (UGT1A1
inhibitor) on the exposure of 45 mg binimetinib
•  predicted similar binimetinib Cmax in the presence or absence of
UGT1A1 inhibitor
 the possible extent of DDIs mediated by UGT1A1 may be minimal
• Risk of DDIs not evaluated in a clinical study
SmPC warning:
UGT1A1 inducers and inhibitors should be co administered with
caution (Section 4.5)
Model
Missing data
Conclusions
Case study: Binimetinib
https://www.ema.europa.eu/en/documents/assessment-report/mektovi-epar-public-assessment-report_en.pdf

20. 20
Effect of pregnancy on ADME
Tasnif Y et al., Pregnancy-related pharmacokinetic changes. Clin Pharmacol Ther. 2016 Jul;100(1):53-62.

21. 21
PBPK model in pregnancy
Xia B. et al. The AAPS Journal, Vol. 15, No. 4, October 2013
Example for renally excreted and CYP3A4 metabolised compounds

22. 22
PBPK model for prediction of foetal exposure
De Sousa Mendes M. et al, Clin Pharmacokinet (2017) 56:537–550

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PBPK in pregnancy: case study
Update the SmPC Sections 4.6 and 5.2
• Major clearance pathway through cytochrome P450
• Expected decreased Cyp activity during pregnancy
• No knowledge in pregnancy
Scope
Background
Risk Expected systemic over-exposure in pregnant patients
Active substance X

24. 24
Model validation with clinical data
Mechanistic PK model developed in non-
pregnant subjects
Clearance accounted
(based on the available in vivo/in vitro/literature data)
Predictability tested
(simulated versus observed exposure)
Simulation of systemic exposure in pregnancy
(upon model validation)
PBPK in pregnancy
Exposure increased over the
gestational weeks
Prediction
outcome
Assumption:
Maternal plasma concentrations are
predicted to gradually increase during
pregnancy due to decreased Cyp activity
Active substance X
Clinical data needed to validate and qualify the model for in vivo prediction of systemic drug exposure in pregnant women.

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PBPK - non-oral dose routes - systemic exposure
• Prediction of systemic exposure- either site of action, or for safety
• Have not seen adequate qualification data sets for any route - yet!
• Not accepted in place of a clinical study to predict systemic exposure
• Have accepted to inform DDIs by the alternate dose route
• Additional uncertainty in predictions to children
Subcutaneous, Intranasal, Topical, Inhaled routes

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PBPK - Locally Acting products
Dermal, ophthalmic, inhaled, intranasal ASCPT session- Washington 2019
Interest in PBPK models for locally acting products
Discussed challenges around qualification/ verification of models

27. 27
New area of interest
ASCPT - Washington 2019
Interest in GFR models incorporating renal function
maturation in neonates and child (FDA)
More data needed to support an optimal model

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Acknowledgments
Susan Cole
Pharmacokinetics Group Lead, MHRA