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/

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

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

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.

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.

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

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

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

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

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

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% (

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

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

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)

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%)

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

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

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

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.

Future Outlook

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Confidence in the Industry

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”.

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

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

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