European Regulatory Viewpoint on PBPK in Support of ...€¦ · European Regulatory Viewpoint on...

European Regulatory Viewpoint on PBPK in

Support of Regulatory Decision Making

Susan Cole1,4, Anna Nordmark2,6, Ine Rusten3,5,6, Theresa Shepard1,6

2014 MISG New Technologies Forum on PBPK, June 30th, London

Affiliations: Medicines and Healthcare products Regulatory Agency, London, UK (1), Medical

Products Agency, Uppsala, Sweden (2), The Norwegian Medicines Agency, Oslo, Norway (3),

EMA Pharmacokinetics Working Party (4), EMA Paediatric Committee (5), EMA Modelling and

Simulation Working Group (6)

Disclaimer

The views expressed in this presentation are those

of the speaker, and are not necessarily those of

MHRA, MPA, NOMA or EMA.

30th June 2014

PBPK seen as a valuable tool: Encouraged in numerous European guidelines

“…Physiological based pharmacokinetic models may

be used as a tool… .” (Hepatic impairment guideline)

“PK/PD modelling techniques, using age appropriate and validated biomarkers,

need to be considered to find the optimal dose. … physiologically based

pharmacokinetic models to predict PK characteristics in the neonatal population

may be considered if appropriate.” (Medicinal products in term and preterm neonates)

“PBPK simulations may be used to evaluate the in vivo relevance of competitive or

mechanism based inhibition (MBI) observed in vitro… . If the results of the simulation

with appropriate sensitivity analyses are negative and the modelling is acceptable, no

in vivo study is required,” (DDI guideline)

“For some enzyme systems, where well validated in silico PBPK models

have been developed, these can be used to predict pharmacogenetic

differences… . “It may also be acceptable to use PBPK simulations to

predict the interaction effect in the subpopulation if the simulation is

qualified for this purpose.”(Use of pharmacogenetic methodologies)

3

PBPK seen as a valuable tool: Important potential value for benefit:risk decisions

From EMA-EFPIA Modelling and Simulation Workshop, December 2011

PBPK provides a mechanistic basis to reduce/quantify uncertainty in

extrapolation and to identify “at risk” populations.

Extrapolation - always a component of

benefit:risk decisions and can be an

important contributor to uncertainty.

Examples: elderly, polypharmacy (DDI),

critically ill, obese patients, paediatric, ethnic

groups (pharmacogenetics), … .

4

PBPK in European Procedures: When are regulatory decisions based on M&S made?

Drug development and model building

Learning and confirming

Preclinical Phase I Phase IIb Phase III Registration/ Labelling

(MAA/SmPC)

Phase IIa Phase IV

Continuum of learn/confirm/predict at each decision point

M&S M&S M&S M&S M&S

Uncertainty Confidence in drug and disease

Adapted from Lalonde RL et al., Model-based drug development. Clin Pharmacol Ther 2007;82:21-32

First presented at the EMA/EFPIA Modelling and Simulation Workshop, 2011

Anytime Scientific Advice

Clinical Trial Applications (some National Agencies), Qualification of Novel Methodologies

Early Paediatric Investigation Plan

Late MAA + post-lic.

M&S: modelling and simulation

MAA: marketing authorisation application

SmPC: summary of product characteristics 5

European regulatory experience: PBPK in Paediatric Investigation Plans

• Mandatory procedure,

binding decision on

development plan

• To support “paediatric use” in

all subsets of paediatric

population

• Data on efficacy, safety and

age-appropriate formulation

• M&S to support dose

selection, study design,

analysis plan

Analysis technique

Descriptive analyses

73 (90.1%) summary statistics including confidence intervals; graphics; summaries of PK or PD parameters

PK modelling 41 (50.6%) fixed effect or population PK models

PK-PD modelling

17 (21.0%) including exposure-response, PK-response models

Dose-response modelling

10 (12.3%) including dose-PD (eg, ANCOVA model), dose-toxicity, dose-PK-PD models

Physiologically-based PK modelling 3 (3.7%)

Dose-exposure modelling 3 (3.7%)

Other 22 (27.2%) Formal hypothesis testing on efficacy or PD endpoints; non-parametric

time-to-event analyses; other types of models not captured above

Hampson et al, Bridging the gaps: A review of dose-investigations in paediatric

investigation plans. Br J Clin Pharmacol. 2014 Apr 10. doi: 10.1111/bcp.12402.

6

European regulatory experience: PBPK in EMA Scientific Advice Procedures

• Optional procedure, written

advice, not binding

– Prospective input on

development

– Focus on development strategies

(not pre-evaluation of MAA data)

• Advice provided by SAWP (chair =

Rob Hemmings)

• MSWG opinion when M&S

involved (chair = Terry Shepard)

• Highly encourage for M&S with high

regulatory impact

59 procedures referred to MSWG in 2013

5 involving PBPK

(1 adult, 4 paediatric)

CHMP: Committee on Human Medicinal Products

SAWP: Scientific Advice Working Party

MSWG: Modelling and Simulation Working Group

Note: Can also seek advice directly with national

authorities (e.g. MHRA, MPA, NOMA, etc)

MSWG report and work plan:

http://www.ema.europa.eu/ema/index.jsp?curl=pages/contacts/CHMP/people_listing_000122.jsp&mid=WC0b01ac058063f485 7

http://www.ema.europa.eu/ema/index.jsp?curl=pages/contacts/CHMP/people_listing_000122.jsp&mid=WC0b01ac058063f485

http://www.ema.europa.eu/ema/index.jsp?curl=pages/contacts/CHMP/people_listing_000122.jsp&mid=WC0b01ac058063f485

European regulatory experience: PBPK in Marketing Authorisation Applications

During

MAA?

Drug as enzyme substrate +

Drug as enzyme perpetrator +

Transporter-based: substrate or perpetrator +

Organ impairment

(Hepatic and renal)-

Paediatrics +

Pregnancy, ethnicity, geriatrics, obesity,

disease states-

Biopharmaceutics

Food effect, formulation change, pH effect

(including DDIs on gastric pH), other dose

route

+

Miscellaneous Tissue concentration +

Drug-drug

interactions

Specific

populations

Potential Applications

8

European regulatory experience: PBPK in Marketing Authorisation Applications (cont’d)

Example SmPC text:

“In the presence of ketoconazole 400 mg once daily, …, exposure to …

increased approximately 2-fold. The predicted increase was approximately

3-fold in the presence of ketoconazole 200 mg twice daily using

physiologically based pharmacokinetic (PBPK) modelling. The uncertainties

of such modelling should be considered.”

Example Decision

27-fold increase in AUC when co-administered with

200 mg ketoconazole BID. Used to predict impact of

moderate and weak CYP3A inhibitors.

Did not agree that model was sufficiently

verified. Asked to do in vivo interaction study

with weak inhibitor.

Waiver of in vivo study for CYP inhibitor, not

meeting in vitro criteria.

Agreed that in vivo study unnecessary and lack

of interaction could be included in SmPC

Worst case evaluation when study not ideal (400

mg ketoconazole QD for substrate with long half-

life).

Avoided need for a repeated DDI study.

Simulated maximum interaction included in

SmPC.

Simulation of DDI with simultaneous inhibition of

two CYP pathways to support contradindication.In progress.

Waiver of in vivo study for inhibitor of drug

transporters.In progress, but unlikely to be endorsed.

9

Our approach to regulatory

review

Question submitted as part of feedback:

“I would appreciate a discussion on how the PBPK submission will be reviewed by the HA. Will

reviewers be hands-on experienced modellers? Will there be any possibility for discussion/clarification

of the model or follow up questions/requests for additional simulations? Any chance to supply model

files as is done with FDA?”

10

European philosophy:

1. All regulatory documents should be constructed with clarity of explanations to allow

high quality, efficient assessment. a. Emphasis on how supports quality, safety and efficacy for benefit:risk decision

b. Applies equally to 1°, 2° documents (MAA, response to questions; briefing documents, list of issues)

c. Applies equally to M&S (including PBPK)

2. Reviewers a. Process ensures that high impact M&S seen by relevant experts (e.g. MSWG)

b. PBPK guideline will prompt assessor training (expected 2015/16)

c. Many complementary skill sets are involved in review of PIPs, scientific advice, MAA module

2.7.2 (e.g. plausibility of assumptions, therapeutic context, clinical implications of uncertainty).

Our approach to regulatory

review

Question submitted as part of feedback:

“I would appreciate a discussion on how the PBPK submission will be reviewed by the HA. Will

reviewers be hands-on experienced modellers? Will there be any possibility for discussion/clarification

of the model or follow up questions/requests for additional simulations? Any chance to supply model

files as is done with FDA?”

11

European philosophy (cont’d):

3. Model files a. Seen as best practice to request these

b. Used to provide confidence that the company conclusions can be endorsed or to inform

remaining uncertainties to be addressed through questions to the company

4. Discussion/clarification a. Integral part of EMA qualification procedure

b. Integral part of national scientific advice, not guaranteed in EMA scientific advice. More likely

if highlight PBPK within questions

c. For MAA, request clarification TC (e.g. Day 120)

Three concepts applied to PBPK

modelling and regulatory review

Value

Uncertainty (opposite: confidence)

Regulatory

Impact

12

Medium impact

High impact

Low impact

Impact o

n re

gula

tory

decis

ion

+++ Scientific Advice, Supporting Documentation, Regulatory Scrutiny

++ Scientific Advice, Supporting Documentation, Regulatory Scrutiny

+ Scientific Advice, Supporting Documentation, Regulatory Scrutiny

Framework for M&S in Regulatory Review According to impact on regulatory decision

From EMA-EFPIA Modelling and Simulation Workshop, December 2011

Justify

Describe

Replace

13

Regulatory impact framework: PBPK applications

To support waiver of an in vivo study

for substrate of CYP enzymes.

To support waiver of an in vivo study

for inhibitor of CYP enzymes.

To predict optimal doses in different

age and weight categories of

children. To support SmPC statements

regarding the need to adjust dosage

for drug combinations not tested.

To provide quantitative evidence of

the plausibility of mechanisms

important for the disposition of the

drug.

High

High

Medium to High

High

Low to Medium

Key point: Regulatory Impact ≠ Value

14

IQ White Paper: Level of confidence in PBPK according to application

Application Confidence Comments

P450 + passive processes High -Moderate Intestinal metabolism challenging

Non-P450 + passive processes

Moderate -Low Hepatocytes predictive for some non-P450 processes. Expression levels & scaling factors

unclear.

Clearance/absorption by active

transport

Low Activity scaling factors poorly understood. Interplay of transporters and metabolic enzymes

challenging.

Reversible CYP inhibition or

induction alone High -Moderate

Accurate fm when non-P450 involved challenging. IV CL and mass balance not available at

early stages. Must account for experimental variability in Ki.

Time dependent CYP inhibition Moderate -Low Trend to over-prediction from in vitro data

Combined reversible, TDI &

induction

Low Difficult to evaluate mechanisms

Involving active transport Low to Moderate Predicting transport inhibition possible but intracellular concentrations challenging

B(DD)CS I drugs High No significant limitations or challenges in fasted or fed states

B(DD)CS II drugs

Moderate Need to ensure that in vitro data and / or in vivo models for solubility, dissolution and

precipitation are relevant for human

B(DD)CS III drugs Low

B(DD)CS IV drugs Low

Paediatrics, ethnic variations,

smokers, pregnancy, obese, elderly

Moderate -Low Abundance of enzymes and transporters limited or lacking. Changes in gut physiology

limited.

Organ impairment (renal and

hepatic)

Low Limited verification vs clinical data. Impact of renal/hepatic impairment on CYP expression

and transporter activities not fully clear.

Absorption, food effect & formulation prediction

Special population PK prediction

Preclinical & Clinical PK prediction

DDI prediction

Complex interplay between multiple factors including transporter interactions

15

IQ White Paper: Level of confidence in PBPK according to application

Application Confidence Comments

P450 + passive processes High -Moderate Intestinal metabolism challenging

Non-P450 + passive processes

Moderate -Low Hepatocytes predictive for some non-P450 processes. Expression levels & scaling factors

unclear.

Clearance/absorption by active

transport

Low Activity scaling factors poorly understood. Interplay of transporters and metabolic enzymes

challenging.

Reversible CYP inhibition or

induction alone High -Moderate

Accurate fm when non-P450 involved challenging. IV CL and mass balance not available at

early stages. Must account for experimental variability in Ki.

Time dependent CYP inhibition Moderate -Low Trend to over-prediction from in vitro data

Combined reversible, TDI &

induction

Low Difficult to evaluate mechanisms

Involving active transport Low to Moderate Predicting transport inhibition possible but intracellular concentrations challenging

B(DD)CS I drugs High No significant limitations or challenges in fasted or fed states

B(DD)CS II drugs

Moderate Need to ensure that in vitro data and / or in vivo models for solubility, dissolution and

precipitation are relevant for human

B(DD)CS III drugs Low

B(DD)CS IV drugs Low

Paediatrics, ethnic variations,

smokers, pregnancy, obese, elderly

Moderate -Low Abundance of enzymes and transporters limited or lacking. Changes in gut physiology

limited.

Organ impairment (renal and

hepatic)

Low Limited verification vs clinical data. Impact of renal/hepatic impairment on CYP expression

and transporter activities not fully clear.

Absorption, food effect & formulation prediction

Special population PK prediction

Preclinical & Clinical PK prediction

DDI prediction

Complex interplay between multiple factors including transporter interactions

16

Add value even where confidence is moderate to low?

Key point: Confidence ≠ Value, but uncertainty (i.e.

lack of confidence) must be managed

Prerequisites for regulatory

applications of PBPK modelling: Verification of drug model

PBPK Model

System model Anatomy

Biology

Physiology

Pathophysiology

Patient/disease extrinsic factors

Drug specific parameters ADME, PK, PD and MOA Metabolism

Active transport/Passive diffusion

Protein binding

Drug-drug interactions

Receptor binding

Weakest link, but important (DDI risk, paediatric ontogeny,

pharmacogenetics, etc)

17

ADME in typical dossier

In vitro metabolism studies • Human microsomes, hepatocytes

• Purified enzymes

• Addition of specific inhibitors

• Transporter interactions

In vivo studies • Excretion balance study (radioactivity in

excreta, metabolic profiling)

Often not integrated in a useful way

In addition • Correlation of in vivo metabolites to in vitro

pathways

• Co-administration of enzyme inhibitors

• Studies in extensive and poor metabolisers

• Other sources: ethnic differences,

extrapolation discrepancies

KEY: Quantitative integration across studies • Scaling of in vitro data to man + verification of in vivo data (with and without inhibitors,

extensive and poor metabolisers, etc)

• If can’t predict → understand drug disposition mechanisms? → confidence for extrapolation

• PBPK with bottom up, top down (+middle out) most reassuring

18

Optimal ADME in dossier

In vitro metabolism studies • Human microsomes, hepatocytes (follow

metabolite formation)

• Purified enzymes

• Addition of specific inhibitors

• Transporter interactions

In vivo studies • Excretion balance study (radioactivity in

excreta, metabolic profiling) design informed

by PBPK*

• With and without inhibitors, extensive and poor

metabolisers, etc

• IV study (dose)

19 * “virtual mass balance study” (Steve Hall, FDA PBPK meeting, March 2014)

Plausible Range: Transparency around uncertainty

Plausible Range

20

1. Description of M&S and role in development

(including regulatory/company impact).

2. M&S Assumptions.

3. Model building methodology and model evaluation.

Simulation methodology and good practices.

4. Issues for discussion in MSWG.

5. Answers to specific M&S questions. Other

comments/questions to SAWP/CHMP/PDCO.

MSWG Template: Highlighting M&S assumptions

21

E F P I A M I D 3 P A G E 2 0 1 4 A L I C A N T E

MID3: Assumptions

Assumptions Justification New/

Established

Testable/

Not-Testable

Test/Approach

to assess

impact

Evaluation

Pharmacological assumptions

Physiological assumptions

Disease assumptions

Data assumptions

Mathematical and statistical assumptions

Part 2:

Document & Reporting

Current Limitations

Good Doc Practice

Assumptions

Components

& Considerations:

Analysis Plan

Simulations Plan

Report

Slide from Scott Marshall, PAGE 2014 22


(MAA/SmPC)

Phase IIa Phase IV





PBPK in European Procedures Opportunities for regulatory review – drug models



Protein binding


Receptor binding

23

Can be considered in any of

Paediatric Investigation

Plan, Scientific Advice, MAA

Recommend/highly recommend to

seek scientific advice for medium/high

regulatory impact applications


(MAA/SmPC)

Phase IIa Phase IV





PBPK in European Procedures Opportunities for regulatory review – system models

System model Anatomy

Biology

Physiology

Pathophysiology

Patient/disease extrinsic factors

Independent of drug, common

across many drugs → can dissociate

from MAA

24

Consider qualification procedure

(specific conditions of use)

Plausible Range for System Models: Transparency around uncertainty

Plausible Range

Similar approach for systems

models with uncertainty? (e.g. plausible range for CYP ontogeny)

Transparency is key!

25


(MAA/SmPC)

Phase IIa Phase IV





PBPK in European Procedures Opportunities for regulatory review – drug models (cont’d)



Protein binding


Receptor binding

26

May be appropriate to consider

qualification procedure (e.g. model

substrates/inhibitors)

Qualification opinion – published

Qualification advice – not published

Benefits of “PBPK-thinking” in drug

development

1. Forces integration of physicochemical data and in vitro and in vivo ADME data

→ mechanistic understanding

2. Raises red flag when in vivo profiles are not predicted → identify gaps in

understanding of ADME

3. Likely to conduct more informative studies; and not to conduct uninformative

studies

4. Complementary to other M&S approaches

a. Dose selection, optimal study design for unstudied populations (e.g. FIH, paediatrics)

b. Anticipate important/informative covariates

5. “Chain of evidence” (idea from small populations guideline)

6. Build confidence for extrapolation (NCE)

7. Continued development of system models and increased confidence when

systematically applied over many NCEs and many applications

27

Challenges for qualification of PBPK

models

28

Characteristic Consequence

Lack of structural identifiability of system

models.

Very careful optimisation of drug-specific parameter,

hypothesis generation and testing, extensive supportive

data (in vitro, in vivo, pre-clinical).

Sensitivity analyses should include important tissue levels

(organs of elimination, site of action for efficacy and

toxicity)

Plethora of published data for probe

substrates, perpetrators.

Possibility of bias in data selection. Criteria for inclusion

and exclusion of studies (meta-analysis guideline).

System models continually evolve with new

systems data (e.g. transporter expression, GI

pH in children, covariance of CYP enzymes),

new predictive methodologies (e.g. Orbito

project).

Literature citations can never be accepted as adequate

demonstration of predictive performance of current

version of a software. Need mechanism for continuous

demonstration of continued system validity.

System models qualified using proprietary

data.

Possible to use in system qualification data sets for

demonstration of continued system validity of new

software versions? Need creative solution to deal with

this?

Limited systems data for certain processes,

subpopulations (transporter abundance,

ontogeny of enzymes, GI pH in children,

impact of aging)

Limited confidence in predictions. Driver for data

gathering, new methodology to derive systems

parameters, etc. Opportunity for EU projects? (e.g. Orbito)

Identify gap

Experience, expertise

Develop new

methodology

Acceptance

Regulatory standards,

guidelines, practice Cycle of

Innovation

The Future: PBPK in regulatory decision making is evolving

29

Regulatory mechanisms that support

continual evolution of models, while

maintaining documentation of continued

system validity

PBPK Concept paper

Released for public consultation (clarity around expectations for system

model and drug-specific parameters)

Key points

Endorse and support growth of PBPK applications to • provide mechanistic understanding;

• support dose selection, study design;

• inform SmPC for unstudied interactions;

• identify “at risk” populations;

• reduce or waive in vivo studies.

Confidence ≠Value ≠Regulatory Impact

• Where there is value, manage uncertainty and communicate openly → drive regulatory decision (more conservative)

• EFPIA assumption framework useful? • Regulatory impact drives assessment, expectations of

documentation • Seek scientific advice, include PBPK explicitly in questions • Modeller should attend discussion meeting

Maturity of PBPK and regulatory experience →

time is right to develop and communicate standards

Refusal or

Withdrawal

Approval

Benefit

Risk

CHMP Opinion + Annexes (SmPC, Conditions)

Indication Specific Obligations, RMP

Refusal or

Withdrawal

Approval

Benefit

Risk

CHMP Opinion + Annexes (SmPC, Conditions)

Indication Specific Obligations, RMP

30

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