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Physiologically Based PharmacoKinetic modeling (PBPK): A new
Paradigm in Drug Development In silico tools to study food-drug
interactions, an Industry Perspective
Neil Parrott, Pharmaceutical Sciences, Roche Pharma Research and
Early Development, Roche Innovation Center Basel
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Paris, April 4th, 2018
http://www.simcyp.com/
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Roche Group Roche pRED is one of three fully independent
research hubs
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What Roche pRED Works On
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Infectious Diseases Effective treatments for life-threatening
infectious diseases
Immunology & Inflammation Differentiated medicines for
patients with immune and inflammatory diseases
Oncology Developing effective cancer therapies
Neuroscience Developing medicines for serious neurological
diseases
Rare Diseases Tackling rare genetic disorders
Ophthalmology Restoring sight
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Overview
• How do Food Effects Impact Development of a Drug ?
• Predictive Tools for Food Effects and their Application in
Pharma
• Physiologically based Food Effect Modeling
• A Roche Case Study
• Future Directions
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Regulatory Guidance
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Food effect bioavailability studies are needed to support global
filings of NDAs
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Food effect throughout Drug Development
• Conducted early in drug development and may be repeated after
formulation change & with market formulation for product
label
• Effect of different doses, meal types or times of drug intake
in relation to a meal may need to be characterized
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Classification (e.g. BCS) In vitro Pre-clinical in vivo
Simple formulation Early FE in Ph1
Optimized Formulation(s) Repeat FE
Market formulation Repeat FE
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The Need to Understand Mechanisms
• Food can alter the absorption through various changes : GI
physiology, stomach emptying time, pH, bile salt concentration
etc…
• Significant optimization efforts may be required & are
effective only if mechanisms are understood
• Tools to predict and understand food effects include in vitro,
in vivo and in silico models
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Tools Predictive of Food Effects
• Drug properties and classification systems
• Biorelevant solubility / dissolution tests
• Pre-clinical models (beagle dog)
• Physiologically based absorption models
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Potential Complexity of Food Effect
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Abuhelwa, A. Y., et al. (2017). "Food, gastrointestinal pH, and
models of oral drug absorption." European Journal of Pharmaceutics
and Biopharmaceutics 112: 234-248.
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Physiologically Based Pharmacokinetics (PBPK)
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A mathematical modeling technique for predicting the absorption,
distribution, metabolism and excretion (ADME) of synthetic or
natural chemical substances in humans and other animal species.
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Small molecule PBPK modeling
stomach duodenum jejunum ileum caecum colon release
dissolution
permeation
Muscle Kidney
Adipose
Brain
Other tissues
Liver Lung
arte
rial
veno
us
ABSORPTION
DISTRIBUTION
METABOLISM
ELIMINATION
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PBPK Modelling in Industry
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In silico
In vitro
First in human, single/multiple ascending dose exposure or
efficacy modeling,
Drug-Drug interaction, Food effect, Formulation/Absorption
modeling
Healthy subjects & patients
Phase I-IV trials Patient trials,
Special populations, Label requirements
Early risk assessment, Early first in human dose
projection, Toxicokinetic dose projection, Early formulation
assessment
Discovery Early Development Late Development
In vivo
‘Learn and confirm’ through data integration
Continuous Model Refinement & Verification
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Solubility Particle size Charge Lipophilicity Formulation
Intestinal fluid volume Intestinal transit times Intestinal pH
Luminal surface area Metabolizing enzyme expression
Physiology
Agoram, B., W.S. Woltosz, and M.B. Bolger,. Adv. Drug Deliv.
Rev., 2001. 50(Supplement 1): p. S41–S67.
Dissolved
Enterocyte
Portal vein
Undissolved
Model parameters include :
Absorption
Drug specific
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Experience at Roche
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A Case Study
• Parrott et al. (2013). "Physiologically Based Pharmacokinetic
Modelling to Predict Single and Multiple Dose Human
Pharmacokinetics of Bitopertin." Clinical Pharmacokinetics 52(8):
673-683.
• Parrott et al. (2014). "Physiologically Based Absorption
Modelling to Predict the Impact of Drug Properties on
Pharmacokinetics of Bitopertin." The AAPS Journal 16(5):
1077-1084.
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Food effect PBPK prediction
Food effect Clinical Study
2nd Food effect Clinical Study
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Biopharmaceutical Properties
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Solubility (mg/mL)
Phosphate buffer pH 7
Fasted state simulating intestinal fluid: (pH=6.5)
Fasted state simulating intestinal fluid&: (pH=6.5)
Fed state simulating intestinal fluid: (pH=5.0)
0.005
0.017
0.025
0.063
Molecular weight 543.5
Ionization constant Neutral
logD at pH 7.4 3.03
Scaled human permeability (10-4 cm/s)
PAMPA
Caco2 1.2
3.5
& Measured for clinical capsules in FaSSIF at 37C Data
became available after EIH prediction
BCS 2 with enhanced solubility in fed state. However model
predicted no food effect on AUC at expected clinical dose of 13
mg
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Physiologically Based Model Prediction
• PBPK developed based on pre-clinical data and used to predict
human pharmacokinetics prior to the first in human studies
• Predicted : CL: 1 mL/min/kg; Vss = 3 L/kg; F% (
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Model Refinement with First Clinical Data
• Improved simulation of profiles by accounting for slightly
increased solubility and permeability
• Additional modification to intestinal water volume in colon to
reduce late absorption
• Model applied to predict food effect at highest anticipated
clinical efficacious dose of 80 mg – very slight increase in Cmax
and AUC with food
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“Bottom-up” prediction
Refined model
6, 50 & 180 mg
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Parameter Sensitivity Analysis
GastroPlus Baseline parameters
Permeability scaled from Caco2 3.5 *10-4 cm/s Solubility in
fasted state simulating fluid 25 ug/mL Particle size 6 um
radius
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Clinical Food Effect & Verification of Prediction
• Studied in 14 healthy volunteers after a high fat/high calorie
breakfast
Fed / fasted (Geomean)
Cmax AUC
Simulated 1.27 1.17 Observed (90%CI)
1.39 (1.21 to 1.59)
1.14 (1.09 to 1.19)
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Further Model Verification – Particle Size Relative BA study
compared 30 mg tablets containing powder prepared with either jet
or hammer milling
JET milled HAMMER milled
Particle radius (µm) 1.8 12.5
N=22 NHVs Relative BA of HAMMER to JET (90% CI) 78% for
AUCinf/dose (72% – 80%) 62% for Cmax/dose (57% – 67%)
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Relevance of In Vitro Testing
• Verify the relevance of in vitro dissolution tests for in vivo
drug performance.
Dissolution test employed SDS in order to achieve sink
conditions in vitro which was
otherwise not possible due to the low solubility
Reasonable IVIVC confirms relevance of dissolution test
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Food Effect Study with Market Formulation
• A film coated tablet was chosen for the market at a dose of 10
mg
• A relative bioavailability study had shown that 10 mg tablets
were equivalent to 10 mg capsules
• We had confidence that the model was capturing the absorption
behavior and had predicted well the food effect at 80 mg
• However, a 2nd food effect study was conducted in view of
regulatory guidance and the lack of a precedent for waiver of a
study based on PBPK modelling
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Results and Simulation
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Cmax (ng/mL) AUC0-inf (ng*h/mL) Tmax (hrs)
Observed1 Simulated Observed1 Simulated Observed2 Simulated
Fasted 78.2 79 2191 2227 3 2.1
Fed 70 69 2217 2229 2 1.7
1Geometric mean of individual observed values 2 median of
individual observed values
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Conclusion to Roche Case Study
• This BCS2 molecule had well behaved PK and the PBPK model
based on in vitro measurements could be verified with multiple
clinical studies
• A 2nd food effect study added minimal value and could be
waived
• This represents a good percentage of development compounds
(BCS 1 & 2) and overall a significant number of studies might
be waived
• However other BCS2 molecules present more challenges e.g.
alectinib
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Parrott, N. J., et al. (2016). "Physiologically Based Absorption
Modeling to Explore the Impact of Food and Gastric pH Changes on
the Pharmacokinetics of Alectinib." The AAPS Journal: 1-11.
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Future Outlook
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Confidence in the Industry
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Confidence in the Regulators
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AAPS webinar Sept 2017. First-In-Class Regulatory PBPK Modeling
Guidelines from both Sides of the Pond – Ping Zhao, Anna Nordmark.
https://www.pathlms.com/aaps/events/643/video_presentations/80736
“Very low confidence” “Not scientifically there yet”.
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Confidence in Regulators
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48 food effect predictions, ~50% within 1.25-fold, 75% within
2-fold The large knowledge gaps in product, API, and physiology
hinder the ability of PBPK to prospectively predict the food
effect
Our analyses have 3 implications: (1) laying out the strategy of
using PBPK to predict food effect (2) identifying key parameters
commonly optimized to better describe food effect and (3) providing
a knowledgebase that can be expanded
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What is needed to Build Confidence
• A consistent workflow with standardized inputs
• Key principles : – Mechanism of food effect must be understood
– Model is validated against clinical food effect data before it
can be
applied to predict future food effect studies (e.g. for new
formulations)
• Publications and cross-industry verification efforts
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IQ PBPK Working Group 2018
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Chair – Arian Emami Riedmaier (AbbVie) Co-chair – Neil Parrott
(Roche) EISAI GSK DSI ROCHE PFIZER VERTEX AGIOS NOVARTIS GENENTECH
TAKEDA MERCK
Group Kick-off: January 2018 – Ends: Dec 2019 Aim: To assess the
predictive performance of mechanistic model prediction of food
effect using a consistent strategy and input data. Highlight cases
with high vs. low confidence and provide an industry best
practice
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Acknowledgements
• Colleagues from Roche pRED Pharmaceutical Sciences
• Colleagues from the GastroPlus User Group
• Colleagues from the IQ Food Effect Working Group
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Physiologically Based PharmacoKinetic modeling (PBPK):�A new
Paradigm in Drug Development��In silico tools to study food-drug
interactions, an Industry Perspective Roche Group�Roche pRED is one
of three fully independent research hubsWhat Roche pRED Works
OnOverviewRegulatory GuidanceFood effect throughout Drug
DevelopmentThe Need to Understand MechanismsTools Predictive of
Food EffectsPotential Complexity of Food EffectPhysiologically
Based Pharmacokinetics (PBPK)Small molecule PBPK modeling�PBPK
Modelling in Industry AbsorptionExperience at RocheA Case
StudyBiopharmaceutical PropertiesPhysiologically Based Model
PredictionModel Refinement with First Clinical DataParameter
Sensitivity AnalysisClinical Food Effect & Verification of
PredictionFurther Model Verification – Particle SizeRelevance of In
Vitro TestingFood Effect Study with Market FormulationResults and
Simulation�Conclusion to Roche Case StudyFuture OutlookConfidence
in the IndustryConfidence in the RegulatorsConfidence in
RegulatorsWhat is needed to Build ConfidenceIQ PBPK Working Group
2018�Acknowledgements