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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


    Paris, April 4th, 2018

  • Roche Group Roche pRED is one of three fully independent research hubs


  • What Roche pRED Works On

    + +

    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


  • 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


  • Regulatory Guidance


    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


    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


  • Tools Predictive of Food Effects

    • Drug properties and classification systems

    • Biorelevant solubility / dissolution tests

    • Pre-clinical models (beagle dog)

    • Physiologically based absorption models


  • Potential Complexity of Food Effect


    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)


    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



    Muscle Kidney



    Other tissues

    Liver Lung

    ar te

    ria l

    ve no






  • PBPK Modelling in Industry


    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


    Agoram, B., W.S. Woltosz, and M.B. Bolger,. Adv. Drug Deliv. Rev., 2001. 50(Supplement 1): p. S41–S67.



    Portal vein


    Model parameters include :


    Drug specific

  • Experience at Roche


  • 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.


    Food effect PBPK prediction

    Food effect Clinical Study

    2nd Food effect Clinical Study

  • Biopharmaceutical Properties


    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)





    Molecular weight 543.5

    Ionization constant Neutral

    logD at pH 7.4 3.03

    Scaled human permeability (10-4 cm/s)


    Caco2 1.2


    & 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


    “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


    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


    Parrott, N. J., et al. (2016). "Physiol