physiologically based pharmacokinetic modelling for first ... · pbpk for ftih club phase i...

PBPK for FTIH

Club Phase I workshop PBPK: A new Paradigm in Drug Development

Neil Miller

4th April 2018

Physiologically Based PharmacoKinetic

Modelling for First-Time-In-Human Studies

Physiologically Based Pharmacokinetic Modelling for First-Time-

In-Human Studies. Strategy & Case Studies.

A strategy for FTIH PBPK modelling focusing on the key

components of the PBPK models (absorption, distribution and

elimination)

• PBPK modelling is used in Pharma for more than the prediction of Drug

Drug Interactions and scaling of pharmacokinetics to children

• Prediction of First-Time-In-Human (FTIH) pharmacokinetics is a popular

application of PBPK

• Strategies for applications of PBPK modelling are required for

standardisation and providing a foundation for acceptance and further

learning• A minimum set of measured inputs is considered necessary for a FTIH prediction

SummaryTake home messages

2

• Scene setting

• Strategy

• Case studies

• Summary

Neil Miller April 2018 Physiologically Based Pharmacokinetic Modelling for First-Time-In-Human Studies.pptx

• Scene setting

➢ Background

• Strategy

➢ The building blocks

• Case studies

➢ Application

• Summary

ContentPBPK for First-Time-In-Human Studies

3Disclaimer: The views expressed in this presentation are those of the presenter and are not those of GlaxoSmithKline


Scene settingPBPK is more than DDI and Paediatric modelling

4

• Scene setting

• Strategy

• Case studies

• Summary


• Main focus = Drug-Drug Interactions and Paediatric PBPK modellingJamei M. Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance. Curr

Pharmacol Rep. 2016; 2: 161–169.

Drug-Drug

InteractionsPaediatrics

Scene settingPBPK is more than DDI and Paediatric modelling

5

• Scene setting

• Strategy

• Case studies

• Summary


• Submissions represent the tip of the PBPK iceberg and PBPK has added

internal value…

PBPK in

submissions

PBPK use within

companies• Higher usage

• Different usage

pattern

• Internal attrition

❖ Modality selection

❖ Route selection

❖ Setting design specification (target product profile)

❖ Predicting drug concentrations in target/off target tissues

❖ Mechanistic understanding of ADMET

❖ Predicting FTIH PK → Enabling dose prediction

❖ Formulation design

❖ Predicting disease state & special population PK

❖ Assessing food effects

❖ etc.

Scene settingThe reason why I am here today

6

• Scene setting

• Strategy

• Case studies

• Summary


• The Pharma GastroPlusTM User Group: share best practices & exchange new ideas

• Webinars

• e.g. Discovery PBPK on 27th March 2018

• Publications

• e.g.

• Planned new publications

1. Physiologically Based Pharmacokinetic Modelling for First-Time-In-Human Studies – Updated

Model Building Strategy with Challenging Industry Case Studies

2. PBPK modelling for Food Effect

Scene settingPharma GastroPlusTM User Group

7

• Scene setting

• Strategy

• Case studies

• Summary


• The PBPK for FTIH publication:

• Strategic expert opinion and review article

• Revised strategy based on the earlier work of Jones et al (2006):

• New developments in PBPK modelling

• QSPR + PBPK

• Focussed on using GastroPlusTM across multiple Pharma companies

• Case studies to highlight important points

Flow diagrams for the key

components of PBPK models

QSPR = Quantitative Structure Property Relationships

• Scene setting

• Strategy

• Case studies

• Summary

StrategyThe key components (each will have flow diagrams)

8


1. QSPR + PBPK

2. Elimination

3. Distribution

4. Oral absorption

5. Uncertainty and variability

A

b

s

o

r

p

t

i

o

n

E

l

i

m

i

n

a

t

i

o

n

D

i

s

t

r

i

b

u

t

i

o

n

FTIH PK Prediction

• Scene setting

• Strategy

• Case studies

• Summary

Strategy1. QSPR + PBPK: First time means first time!

9


• First-Time-In-Human Studies = First time humans are exposed to a new drug

• And the first time you should make your FTIH PK predictions is the first time you

are exposed to a compound!

Quantitative Structure Property Relationships (QSPR) predictions from structure

Pieces of the jigsaw puzzle

PBPKpKa

Peff

Fup

• Scene setting

• Strategy

• Case studies

• Summary

Strategy1. QSPR + PBPK: First time means first time!

10


• Why…

QSPR + PBPK → guide thinking

QSPR + PBPK → identify and/or prioritise key experiments

FTIH

Drug Discovery

• Scene setting

• Strategy

• Case studies

• Summary

Strategy2. Elimination

11


• Original:

Jones HM et al. A Novel Strategy for Physiologically Based Predictions of

Human Pharmacokinetics. Clin Pharmacokinet 2006; 45 (5): 511-542

• Referenced ECCS

• Hepatic metabolism: suggested

additional in vitro experiments to

establish an IVIVC for CL or use of

empirical scaling factors

• Renal elimination: If GFR*Fup not

predictive utilise preclinical PK data

• Removed no-go for PBPK if biliary

elimination, suggested use of

preclinical PK data or additional in

vitro experiments

ECCS = Extended Clearance Classification System; IVIVC = In Vitro In Vivo Correlation; GFR = Glomerular Filtration Rate ; Fup = Fraction unbound in plasma

• Scene setting

• Strategy

• Case studies

• Summary

Strategy3. Distribution

12


• Original:



• Requirement to measure inputs for

tissue partition equations

• Removed no-go for PBPK, further

investigation required

• Manipulation of BPR for basic

compounds to reflect potential

lysosomal partitioning

• Accounting for systematic error in

tissue partition predictions

• Use of permeability-limited tissue

models

BPR = Blood/Plasma ratio

• Scene setting

• Strategy

• Case studies

• Summary

Strategy4. Absorption

13


• Original:



• Requirement to measure solubility

(aqueous and biorelevant)

• Requirement to measure in vitro

permeability and establish a

correlation with in vivo Peff

• Use IV data for systemic disposition

• Manipulation of BSE SR and/or MPT

• Dynamic GI tract fluid volumes

• Use particle size distribution

• Verify ASF model using preclinical

PK data

BSE SR = Bile Salt Effect Solubilization Ratio; MPT = Mean Precipitation Time; GI = Gastrointestinal; ASF = Absorption Scale Factors;

• Scene setting

• Strategy

• Case studies

• Summary

Strategy5. Uncertainty and variability

14


➢ There is always uncertainty in the compound specific inputs and variability in

the physiology of the population (animals and humans)

All measurements have a degree of uncertainty

regardless of precision and accuracy. This is

caused by two factors, the limitation of the

measuring instrument (systematic error) and the

skill of the experimenter making the

measurements (random error).

Variability is the extent to which data points in a

statistical distribution or data set diverge from

the average, or mean, value as well as the extent

to which these data points differ from each

other.

Case study 1Ranitidine

15


• Scene setting

• Strategy

• Case studies

• Summary

• Ranitidine is used to treat:

• Intestinal and stomach ulcers

• Gastroesophageal reflux disease

• Conditions where your stomach makes too much acid

• “Looking back to move forwards”:

• Early example of rational drug-design (blockbuster launched in the early 1980s)

• How does PBPK and FTIH strategy fare when applied to Ranitidine today?

Case study 1Ranitidine: QSPR + PBPK

16


• Scene setting

• Strategy

• Case studies

• Summary

• ADMET Predictor v8.5:• Basic compound (S+ pKa = 7.85)

• Not very lipophilic (S+ logP = 0.66)

• Adequate solubility (S+ aqueous solubility = 0.24mg/mL)

• Adequate permeability (S+ Peff = 1.24 x 104cm/s)

• ECCS = renal elimination (QSPR) metabolism (Query)

• ADMET_Risk = 3.0

• ADMET_Code = CYP2C19 and CYP2D6

• PBPK:

• Platform to examine how the pieces fit together

• Use it to guide thinking and drive experimentation

Pieces of the jigsaw puzzle

PBPK

pKa

Peff

Fup

Varma MV et al. Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS). Pharm Res.

2015;32(12):3785-802

Case study 1Ranitidine: PBPK as a microscope

17


• Scene setting

• Strategy

• Case studies

• Summary

• Avoid the temptation to focus on the plasma profile and instead explore:

Rapid dissolution & no precipitation

Fa = 95%

Fg = 78%

Fh = 7%

Absorption throughout the GI tract

Simulated line,

no observed data!

2C19

2D6

3A41A2 But recall ECCS (QSPR) predicts renal elimination!

Case study 1Ranitidine: What if scenarios

18


• Scene setting

• Strategy

• Case studies

• Summary

• Target tissue = GI tract so good that Fa is essentially complete, but systemic

exposure could have safety implications

Clearance Distribution

Predicted passive distribution

Note: Predicted Pgp substrate

(60% confidence) so potentially

over estimates

BPR Brain Kp

0.78 0.95

0.55 0.95

2.00 3.61

Case study 1Ranitidine: Modelling pre-clinical data

19


• Scene setting

• Strategy

• Case studies

• Summary

• Pre-clinical verification = learnings that inform human prediction

• Dog PK data found in the literature:

Eddershaw PJ et al. Absorption and disposition of ranitidine

hydrochloride in rat and dog. XENOBIOTICA, 1996, VOL. 26, NO. 9, 947-956


20


• Scene setting

• Strategy

• Case studies

• Summary

• Clearance:

• Dog CLP was moderate (10.4 mL/min/kg) with CLR (2.7 mL/min/kg) accounting for about

30% of total clearance

• Assuming BPR = 1 then clearance is 14% LBF (Dog LBF = 56 mL/min/kg)

• Assuming Fup = 85% then Fup * GFR is around 5 mL/min/kg (Dog GFR = 6.1 mL/min/kg)

• Actual CLr is 52% Fup * GFR

➢ Human CLH = 7.7 x 18/56 = 2.5 mL/min/kg (Human LBF = 18 mL/min/kg)

➢ Human CLR = 0.85 x 1.8 x 0.52 = 0.8 mL/min/kg (Human GFR = 1.8 mL/min/kg)

CLp = Plasma Clearance; CLR = Renal Clearance; BPR = Blood/Plasma ratio; Fup = Fraction unbound in plasma

• Distribution (using observed clearance):

• Lukacova Kp method

• logP = 0.66, Base pKa = 7.85, BPR = 1.02 (Rat APv8.5) and Fup = 85% (Human DrugBank)


21


• Scene setting

• Strategy

• Case studies

• Summary

Simulated line

Observed data

Preclinical verification

of Kp method


22


• Scene setting

• Strategy

• Case studies

• Summary

• Oral absorption (using compartmental PK model for systemic disposition):• Solubility of ranitidine hydrochloride in water is 660 mg/mL Journal of Pharmaceutical Sciences Vol. 94, No. 8, August 2005

• Liver FPE = 7.7 mL/min/kg / 56 mL/min/kg * 100 = 14% (assuming CLP = CLB)

• Dog in vivo Peff fitted at 0.5945 x 104 cm/s

Rsq = 0.915

Preclinical verification

of ACAT model

Dog permeability likely

to be higher than human

Cmax, tmax and AUC0-t ≤ 2.0-fold

In paper, mean from n=5:

Cmax = 502 ng/mL

tmax = 2.2 h

AUC0-t = 2237 ng-h/mL

• Peff = Fitted dog in vivo Peff and Lukacova Kp method (BPR = 1.02)

Case study 1Ranitidine: PBPK FTIH PO PK prediction

23


• Scene setting

• Strategy

• Case studies

• Summary

Van Hecken AM et al. RANITIDINE: SINGLE DOSE PHARMACOKINETICS AND

ABSOLUTE BIOAVAILABILITY IN MAN. Br. J. clin. Pharmac. (1982), 14, 195-200

Mass in urine under predicted

• Fitting CLR (10 L/h) to the urine data…

Case study 1Ranitidine: Refining the human PBPK PO PK model

24


• Scene setting

• Strategy

• Case studies

• Summary



Human

Clearance still under

predicted

Mass in urine

adequately predicted

• Fitting Liver CL (15 L/h) and CLR (14 L/h)…

Case study 1Ranitidine: Refining the human PBPK PO PK model

25


• Scene setting

• Strategy

• Case studies

• Summary



Human

Adequate prediction

Mass in urine

adequately predicted

• Recall that dog permeability likely

to be higher than human

• Attempts to build an adequate

model by lowering Peff and

optimizing clearance were

unsuccessful (missed the upswing

in plasma concentrations)

• Using the in silico Peff…

Case study 1Ranitidine: Without the pre-clinical verification

26


• Scene setting

• Strategy

• Case studies

• Summary



Human

Poor prediction of AUC0-t

Case study 1Ranitidine: Modelling caveats

27


• Scene setting

• Strategy

• Case studies

• Summary

• Compound used to illustrate some key points:

❖ QSPR + PBPK

❖ Pre-clinical verification

• An extensive data search was not conducted and so there are data gaps

• PBPK modelling is driving the generation of certain data:

• Ranitidine is a base and BPR is critical for the prediction of tissue-to-plasma partitioning

• BPR whilst rarely decision making is of high value for PBPK modelling

➢ Quality PBPK models require a defined set of input data…

BPR = Blood/Plasma ratio

Case studiesCompound input data for PBPK models

28


• Scene setting

• Strategy

• Case studies

• Summary

• A minimum set of inputs is considered necessary for a FTIH prediction:• logP or logD7.4

• pKa values

• Fraction unbound in plasma (Fup)

• Blood/Plasma ratio (BPR)

• In vitro CLint (mL/min/g)

• In vivo PK data in preclinical PK species

• Clearance mechanism

• Human effective permeability

• Solubility in aqueous buffers and biorelevant media

• Need a consistent baseline when playing detective

• Large volume of distribution in multiple species:

• QSPR + PBPK = Lipophilic base with distribution driven by binding to tissue acidic

phospholipids

• Variable BPR across species

• Species specific BPR and the Lukacova tissue partition coefficient equation

adequately predicts the volume of distribution in human

Species Observed Plasma Vss

(L/kg)

BPR Predicted Plasma Vss

(L/kg)

Fold

Error Vss

Rat 16.4 1.8 14.7 -1.1

Rabbit 10.4 1.3 7.2 -1.4

Dog 9.4 1.1 5.7 -1.6

Monkey 5.8 0.8 2.0 -2.9

Minipig 4.5 0.9 4.9 +1.1

Human 2.6 0.6 3.0 +1.2


(L/kg)

BPR

Rat 16.4 1.8

Rabbit 10.4 1.3

Dog 9.4 1.1

Monkey 5.8 0.8

Minipig 4.5 0.9

Human 2.6 0.6


(L/kg)

Rat 16.4

Rabbit 10.4

Dog 9.4

Monkey 5.8

Minipig 4.5

Human 2.6

Case study 2Compound X: To emphasize the power of BPR

29


• Scene setting

• Strategy

• Case studies

• Summary

“All animal studies were ethically reviewed and carried out in accordance with Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare and Treatment of Animals.”

“The human biological samples were sourced ethically and their research use was in accord with the terms of the informed consents”

Under

predictions for

early PK

Outlier?

Over

prediction for

later PK

• Why should we have strategies for PBPK modelling:

1. A universal PBPK strategy for specific applications such as FTIH predictions, based on

best practices and experiences from across companies, should increase the confidence

of regulatory agencies in PBPK modelling

2. Consistency in the building of PBPK models:

• There are many parameters which could be randomly adjusted or fitted as part of model building

• PBPK models involve numerous scientific disciplines and therefore models could be built in various

ways depending on the scientist’s background and preferences

• Consistent physiological parameters and scaling factors


30

• Scene setting

• Strategy

• Case studies

• Summary


• PBPK modelling is used in Pharma for more than the prediction of Drug

Drug Interactions and scaling of pharmacokinetics to children

• Prediction of First-Time-In-Human (FTIH) pharmacokinetics is a popular

application of PBPK

• Strategies for applications of PBPK modelling are required for

standardisation and providing a foundation for acceptance and further

learning• A minimum set of measured inputs is considered necessary for a FTIH prediction


31

• Scene setting

• Strategy

• Case studies

• Summary


To validate the PBPK for FTIH PK predictions strategy

within your companies when the manuscript is published

AcknowledgementsI could not have done it on my own

32


• A big thank you to numerous scientists within GlaxoSmithKline

• Co-authors of the FTIH PBPK paper:

• Neil Parrott (Roche)

• Micaela Reddy (Array BioPharma)

• Viera Lukacova (Simulations Plus)

• Aki Heikkinen (Admescope)

physiologically based pharmacokinetic modelling for first ... · pbpk for ftih club phase i...

Documents