Presented by Neil Miller

1. F.I.H 4 F.I.H
Paris PBPK SYMPOSIUM 2019
Neil Miller
4th April 2019
Faith In Holistic modelling for
First-In-Human PK

2. • Have faith in using PBPK modelling for predicting First-In-Human (FIH)
pharmacokinetics
• Use everything that we/you know by connecting the parts
• See failures as opportunities to learn as “In a complex world, failure is
inevitable.”
Matthew Syed author of Black Box Thinking: The Surprising Truth About Success
Summary
Take home messages
2
• Scene setting
• Components
• Case studies
• Summary
Neil Miller April 2019 FIH 4 FIH.pptx

3. • Scene setting
 Belief, parts and interconnection
• The parts: optimising components of a PBPK prediction
 Flow diagrams/decision trees
• Case studies: connecting the parts
 Applications
• Summary
Content
Faith In Holistic modelling for First-In-Human PK (for PO dosing)
3Disclaimer: The views expressed in this presentation are those of the presenter and are not those of GlaxoSmithKline
Neil Miller April 2019 FIH 4 FIH.pptx

4. • Holistic: characterized by the belief that the parts of something are
intimately interconnected and explicable only by reference to the whole
Scene setting
Definition of Holistic
4
Neil Miller April 2019 FIH 4 FIH.pptx
Proof that the
approach
works
Components:
1. Oral Absorption
2. Distribution
3. Metabolism & Elimination
Platform for
integration/interconnecting
of the parts = PBPK
• Scene setting
• Components
• Case studies
• Summary

5. Scene setting
Proof that PBPK works for F.I.H PK predictions
5
Neil Miller April 2019 FIH 4 FIH.pptx
“The simulation results using
PBPK were shown to be superior
to those obtained via traditional
one compartment analyses. In
many cases, this difference was
statistically significant.”
“Our prospective human PK
prediction methods yielded good
prediction results.”
• Scene setting
• Components
• Case studies
• Summary

6. Scene setting
Proof that PBPK works for F.I.H PK predictions
6
Neil Miller April 2019 FIH 4 FIH.pptx
“In the majority of cases, PBPK
gave more accurate predictions of
pharmacokinetic parameters and
plasma concentration-time profiles
than the Dedrick approach.”
“This evaluation demonstrates that
PBPK models can lead to
reasonable predictions of human
pharmacokinetics.”
• Scene setting
• Components
• Case studies
• Summary

7. Scene setting
Parts of a PBPK F.I.H PK prediction
7
Neil Miller April 2019 FIH 4 FIH.pptx
• Oral Absorption: Examining the multifactorial process driving the fraction
of drug that reaches the systemic circulation is critical
• Distribution: Understanding tissue distribution is essential for PBPK
modelling, and has been facilitated by mechanistic equations
• Metabolism and Elimination: PBPK modelling requires quantitative
understanding of the main mechanism(s) of drug clearance
• Scene setting
• Components
• Case studies
• Summary

8. Scene setting
PBPK = Platform for integration of the parts
8
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary

9. Components
PBPK Modelling for FIH Predictions
9
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary

10. Components
Oral Absorption
10
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
ASF absorption scale factors, BSSR bile salt solubilisation ratio, MPT mean precipitation time
a Other processes-transporters: efflux transporters can be incorporated in GastroPlus models with
a simple method (e.g. adjusting permeability based on preclinical observations or in-vitro data) to
more complex methods (e.g. specifically incorporating effects of transporters)
1Sutton SC. Role of physiological intestinal water in oral absorption. AAPS J. 2009;11(2):277–85.
2Kesisoglou F. Use of preclinical dog studies and absorption modelling to facilitate late stage formulation bridging for a BCS II drug candidate. AAPS PharmSciTech. 2014;15(1):20–8.

11. Components
Distribution
11
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
BPR blood/plasma ratio, Fup fraction unbound in plasma, Kp tissue-to-plasma partition
coefficient, SpecPStc specific in-vivo diffusional clearance per millilitre of tissue cell volume
1Lukacova V, Parrott N, Lavé T, Fraczkiewicz G, Bolger M, Woltosz W. General approach to calculation of tissue:plasma partition coefficients for physiologically based pharmacokinetic (PBPK) modeling. AAPS National Annual Meeting and Exposition; 16–20 Nov 2008; Atlanta (GA).
2Xia B, Heimbach T, Lin TH, He H, Wang Y, Tan E. Novel physiologically based pharmacokinetic modeling of patupilone forhuman pharmacokinetic predictions. Cancer Chemother Pharmacol. 2012;69(6):1567–82.
3De Buck SS, Sinha VK, Fenu LA, Nijsen MJ, Mackie CE, Gilissen RA. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos. 2007;35(10):1766–80.
4Samant TS, Lukacova V, Schmidt S. Development and qualification of physiologically based pharmacokinetic models for drugs with atypical distribution behavior: a desipramine case study. CPT Pharmacometrics Syst Pharmacol. 2017;6(5):315–21.

12. Components
Metabolism and Elimination
12
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
CLR renal clearance, CLR,u unbound renal clearance, ECCS Extended Clearance Classification System,
Fup fraction unbound in plasma, GFR glomerular filtration rate, IVIVE in-vitro in-vivo extrapolation
1Mathialagan S, Piotrowski MA, Tess DA, Feng B, Litchfield J, Varma MV. Quantitative prediction of human renal clearance and drug-drug interactions of organic anion transporter substrates using in vitro transport data: a relative activity factor approach. Drug Metab Dispos. 2017;45(4):409–17.
2Scotcher D, Jones C, Rostami-Hodjegan A, Galetin A. Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance. Eur J Pharm Sci. 2016;94:59–71.
3Kimoto E, Bi YA, Kosa RE, Tremaine LM, Varma MVS. Hepatobiliary clearance prediction: species scaling from monkey, dog, and rat, and in vitro-in vivo extrapolation of sandwich-cultured human hepatocytes using 17 drugs. J Pharm Sci. 2017;106(9):2795–804.

13. Case studies: connecting the parts
Applications of the flow diagrams
13
Neil Miller April 2019 FIH 4 FIH.pptx
• Looking at real-world examples of how PBPK was applied for FIH PK
• Compounds with challenging properties
• Accuracy of predictions often under the spotlight, but support to internal
decision making and opportunities to learn must not be over looked
• Scene setting
• Components
• Case studies
• Summary

14. Case studies: Compound 1
Applications of the flow diagrams
14
Neil Miller April 2019 FIH 4 FIH.pptx
• Physchem = neutral and highly lipophilic (clogP >5)
• Plasma free fraction & aqueous solubility too low for accurate quantification
 These properties meant it was challenging to verify an IVIVE
• Preclinical in vitro and in vivo pharmacology promising
 Extrapolation of pharmacokinetics to estimate a clinical dose was conducted
• Scene setting
• Components
• Case studies
• Summary

15. Case studies: Compound 1
Applications of the flow diagrams
15
Neil Miller April 2019 FIH 4 FIH.pptx
• Predicting Oral Absorption was challenging due to low solubility:
• Scene setting
• Components
• Case studies
• Summary
Solubility was enhanced
in biorelevant media
Preclinical data showed
decreasing bioavailability
with increasing dose with
an influence of formulation
and feeding status
The percentage water in
small and large intestine
compartments was reduced
from 40 & 10% to 10 & 0.1%
respectively
Particle size:
1μm for nano-suspension
80μm for tablets
Physiological model parameter
changes are not recommended best
practice, but there is considerable
uncertainty and ongoing debate over
the relevant parameterization of
intestinal water volumes!
Due to the predicted food effect, the first clinical
study was conducted in the fed state

16. Case studies: Compound 1
Applications of the flow diagrams
16
Neil Miller April 2019 FIH 4 FIH.pptx
• Distribution:
• Scene setting
• Components
• Case studies
• Summary
Predicted volumes using
the Lukacova method
based on the
physicochemical
properties and assuming a
Fup of 0.1% for all
species, were in
reasonable agreement
with the observed data

17. Case studies: Compound 1
Applications of the flow diagrams
17
Neil Miller April 2019 FIH 4 FIH.pptx
• Metabolism & Elimination:
• Scene setting
• Components
• Case studies
• Summary
Metabolically cleared
based on in vitro data
Scaling of in vitro data to
predict clearance should
account for differences in
binding in vitro and in vivo,
but binding could not be
measured. Assuming that
in vitro free fraction was
equivalent to Fup resulted
in a large overprediction of
clearance in the rat
Human clearance was
predicted using an
empirical scaling factor
derived from the rat which
was verified by scaling of
hepatocyte intrinsic
clearances measured in
dog and monkey

18. Case studies: Compound 1
Evaluating the predictions for 25mg tablet in the fed state
18
Neil Miller April 2019 FIH 4 FIH.pptx
• Despite challenging properties, the pharmacokinetics prediction was good:
• Scene setting
• Components
• Case studies
• Summary
Cmax well-predicted
Greater than 2-fold under-
prediction of AUC and t1/2 longer
than expected, but a later
intravenous microdose study
confirmed good predictions of
bioavailability and Vss
Cmax
Vss
Bioavailability
 Decision making: Compound with appropriate pharmacokinetics progressed
 Learning opportunity: Systemic clearance at the lower end of the predicted range and consideration of
uncertainty in clearance would have avoided a protocol amendment to adjust sampling times

19. Case studies: Compound 3
Applications of the flow diagrams
19
Neil Miller April 2019 FIH 4 FIH.pptx
• Physchem = weak acid and highly lipophilic (clogP = 5)
• Low solubility presented a challenge to in vitro assays
• Prospective PBPK modelling was conducted to understand the compound’s
PK properties and support the decision to move the compound into clinical
development
• Scene setting
• Components
• Case studies
• Summary

20. Case studies: Compound 3
Applications of the flow diagrams
20
Neil Miller April 2019 FIH 4 FIH.pptx
• Absorption modelling focused on monkey as formulation similar to clinical:
• Scene setting
• Components
• Case studies
• Summary
FaSSIF solubility critical
for absorption
modelling in monkey
Low solubility in
simulated gastric
fluid and FaSSIF, but
formulation
improved solubility
Improved solubility
and supersaturation
of formulation meant
permeability was
more of a concern in
terms of uncertainty
Intestinal metabolism had to be considered because it is a CYP3A4 substrate in
humans, but due to its relatively high in vivo permeability and metabolic stability,
it was determined that gut extraction would be minimal in human
In vivo permeability
determined to be high
based on PK data in
rat and dog

21. Case studies: Compound 3
Applications of the flow diagrams
21
Neil Miller April 2019 FIH 4 FIH.pptx
• Prediction of distribution challenging as Vss varied 18-fold in animals:
• Scene setting
• Components
• Case studies
• Summary
The Lukacova method
underestimated Vss across all
preclinical species and this
was mainly due to an in silico
predicted acidic pKa value
Sensitivity analysis showed
that removing the acidic
pKa from the calculation
allowed the calculated Vss to
be sensitive to other
parameters
In vitro Fup was <0.1%
across species, but could
not be precisely determined,
and a range of 0.01-0.1%
enabled reasonable Vss
predictions in preclinical
species

22. Case studies: Compound 3
Applications of the flow diagrams
22
Neil Miller April 2019 FIH 4 FIH.pptx
• Metabolism & Elimination:
• Scene setting
• Components
• Case studies
• Summary
Metabolically cleared
based on in vitro data
Predicting clearance for
acidic compounds by
scaling in vitro data is often
challenging, and
consideration of binding is
important
Hepatocytes provided one
estimate of human
clearance
Given the limitations of the
in vitro assays, single-
species scaling based on
monkey was also used for
a range of predicted
human clearance

23. • Reasonably accurate human PK predictions:
Case studies: Compound 3
Evaluating the predictions for 25mg tablet in the fed state
23
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
Clearance and Vss predictions uncertain for this
compound and it seemed they were both linked to Fup
which could not be measured
Therefore, two combinations of parameters were explored:
• Low clearance & Low Vss due to a lower Fup
• High clearance & High Vss due to a higher Fup
 Decision making: Increased confidence that efficacious exposures would be achieved in the clinic
 Learning opportunity: Assessing the in vitro inputs against in vivo PK profiles in preclinical species, and
determining alternative parameters when in vitro data were inconsistent

24. • Have faith in using PBPK modelling for predicting First-In-Human (FIH)
pharmacokinetics
• Use everything that we/you know by connecting the parts
• See failures as opportunities to learn as “In a complex world, failure is
inevitable.”
Matthew Syed author of Black Box Thinking: The Surprising Truth About Success
Summary
Take home messages
24
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary

25. • People stick to simple empirical approaches because they can be right
• People are wary of complex mechanistic approaches because they can be wrong
• Empirical approaches are fixed and if they are wrong there is no room to grow
• Do the same again and hope for a better outcome
• PBPK approaches take into account all of the parts and grow with knowledge
Summary
Final thoughts
25
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
Have Faith In Holistic modelling
for First-In-Human PK

26. Acknowledgements
Team effort
26
Neil Miller April 2019 FIH 4 FIH.pptx
• Scene setting
• Components
• Case studies
• Summary
• Co-authors of the FIH PBPK manuscript:
• Neil Parrott (Roche)
• Micaela Reddy (Array BioPharma)
• Viera Lukacova (Simulations Plus)
• Aki Heikkinen (Admescope)