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Mechanistic Oral Absorption Modelling, An update on cross-industry activities

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Presented by Neil Parrott, Pharmaceutical Sciences

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Mechanistic Oral Absorption Modelling, An update on cross-industry activities

  1. 1. Mechanistic Oral Absorption Modelling An update on cross-industry activities Neil Parrott, Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Center Basel 1 Paris, April, 2019
  2. 2. Roche Group Roche pRED is one of three fully independent research hubs 2
  3. 3. Overview • Food Effects – Review Recent Activities • Predicting the Effect of Acid Reducing Agents – A Roche Case Study • Future Directions 3
  4. 4. 4 Confidence in MAM : Industry
  5. 5. Confidence in MAM : Regulators 5 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”.
  6. 6. Confidence in MAM : Regulators 6 “The large knowledge gaps in product, API, and physiology hinder the ability of PBPK to prospectively predict the food effect” 48 food effect predictions ~50% within 1.25-fold 75% within 2-fold
  7. 7. Regulatory Guidance Prior to 2019 7 Food effect bioavailability studies are needed to support global filings of NDAs
  8. 8. Regulatory Guidance 2019 8 No mention of MAM A missed opportunity to encourage this to streamline and enhance food effect assessments. May effectively discourage sponsors from investing in this approach Mentions possible consideration of BCS category to waive FE studies specifically for BCS1 without high first pass metabolism.
  9. 9. New from the GastroPlus User Group 9 Journal of Pharmaceutical Sciences Volume 108, Issue 1, January 2019, Pages 592-602
  10. 10. New from the GastroPlus User Group 10 • Consideration of molecule type to set level of confidence • Workflow with standardized inputs and a model validation step with clinical data in one prandial state • Model must be validated against clinical food effect data before prediction of food effect (e.g., for new formulations, API polymorphs, or change of dose) • Mechanistic predictions of food effect could substitute for unnecessary clinical studies during late-stage clinical development or life cycle management
  11. 11. PBPK Food Effect Working Group January 2018 – Dec 2019 Chair – Arian Emami Riedmaier (AbbVie) Co-chair – Neil Parrott (Roche) Goals • Assess performance of mechanistic model predictions of food effect using a consistent strategy and input data • Provide an industry best practice, categorizing molecules according to prediction confidence
  12. 12. Timeline - 2019 Jan Feb Mar Apr May Jun Jul Aug Sep NovOct Dec • Evaluate modeling outcome and progress • Finalize any outstanding modeling work • Compile information on modeling success based on criteria Manuscript compilation and writing • Post-modeling evaluation (e.g. sensitivity analysis) • Reach out to regulatory authorities for input Review and submit manuscript
  13. 13. The European Network on Understanding Gastrointestinal Absorption-related Processes • COST ACTION CA16205 • SPRING MEETING Sofia, 12-13 Feb 2019 • Presentations available at https://gbiomed.kuleuven.be/english/research/ 50000715/50000716/ungap
  14. 14. Predicting the Effect of Acid Reducing Agents • pH-dependent DDI may occur in the stomach when a poorly soluble weakly basic drug with pH dependent solubility is co-administered with an acid reducing agent (ARA) e.g. proton pump inhibitor (PPI), histamine 2 receptor antagonist (H2RA) or antacid • Many weakly basic compounds show reduced exposure (Cmax and AUC) which can lead to significant impact on efficacy of these compounds
  15. 15. PBBM for effect of acid reducing agents
  16. 16. MAM for ARA • Data on pH-dependent solubility can be integrated • Measured data on the physiological changes due to ARA can be integrated – PPIs increases fasting gastric pH from ~1.3 to ~4.5 – Postprandial gastric pH increases from ~4.5 to ~6.5 – Decreased gastric emptying rate FastedFed 4 - 56.5 1.8
  17. 17. A Case Study • Erlotinib EGFR inhibitor used to treat patients with locally advanced or metastatic non-small cell lung cancer • Lipophilic with high permeability and low solubility • CYP3A4 & CYP1A2 substrate • The effect of omeprazole and ranitidine on erlotinib has been studied clinically and this modelling was done retrospectively Parameter logP (O/W) 2.7 pka 5.65 fu 0.046 B/P 0.55 Permeability (cm/s) caco-2 33.6x10-6 -> human Peff 4.3x10-4 Buffer solubility of HCl salt at different pH (mg/ml) pH mg/mL 2.5 0.6 3.4 0.32 5 0.0145 6.5 0.0058 Biorelevant solubility at 37°C (mg/ml) Media start pH end pH mg/mL FaSSIF 6.5 6.4 0.0085 FeSSIF 5 5 0.0533
  18. 18. Step 1: Disposition Model • Mean Cp(t) for IV and PO crossover study 150-mg tablet vs 25-mg 30 minute intravenous infusion in 20 healthy mainly female subjects • 2 compartmental model with nonlinear clearance fit gives best fit • Bioavailability estimated with saturable clearance is 59% vs 106% based on a simple non-compartmental analysis Vc/kg= 0.826 L/kg CV= 26% CL2/kg= 0.150 L/h/kg CV= 54% V2/kg= 1.138 L/kg CV= 33% Vmax = 4.47E-4 mg/s CV= 53% Km = 0.232 µg/mL CV= 77% K12 = 0.182 1/h CV= 60% K21 = 0.132 1/h CV= 63% Tlag = 0.228 h CV= 19% Ka = 0.731 1/h CV= 53% F = 59.27 % CV= 19% nonlinear model fit in PKPlus
  19. 19. Step 2: Fasted State Simulation • Vmax and Km transferred to the enzyme table accounting for changed units and free fraction in plasma • Default model simulation over estimates observed Cp(t) • Reduction in %fluid colon improves match 10% fluid in colon 25% absorption from the large intestine. 1% fluid in colon 8% absorption from the large intestine.
  20. 20. Step 3: Fasted State with/without ARA • Stomach pH changed from 1.3 to 4.0 • Gastric transit increased from 0.25h to 0.5hWithout omeprazole Without ranitidine With omeprazole With ranitidine AUCinf omeprazole ranitidine Observed 54% 67% Simulated 51% 60% Sensitivity to gastric pH
  21. 21. Role of MAM in Managing the Effect of ARA • MAM should play a role in integrating physicochemical, in vitro and in vivo data into a mechanistic framework which can yield a fuller understanding of pH dependent DDIs • A bottom-up approach assumes that all relevant factors are captured in the model and that in vitro to in vivo translation is accurate. • Therefore verification of simulations with clinical data is recommended before application to predict untested situations • PBPK simulations should be used to guide study design appropriately and allow exploration of different scenarios (e.g. staggering of dosing with regard to the two interacting drugs) • Collaborative cross-industry efforts are need to build confidence and extend the utility of this approach. Further work is needed to support more detailed models for physiological changes due to different types of ARA and in different populations.
  22. 22. Conclusion • There is wide recognition of the potential for MAM to streamline development of oral formulations • Increased confidence in MAM is needed to expand the impact with the regulators • Collaborative efforts are ongoing to address this and we can confidently expect progress in the near future PBPK in IND/NDA submissions to US FDA OCP from 2008 to 2017 Grimstein et al JPS 2019
  23. 23. Coming in 2019 • FDA hosted workshop to take place in Silver Spring this September • Physiological Based Biopharmaceutics Modeling (PBBM) to Support Pharmaceutical Quality • Day 1 in vitro, • Day 2 model verification • Day 3 case studies. • J. Dressman, Uni Frankfurt • Xavier Pepin, AstraZeneca • FDA • Sandra Suarez, Andrew Babiskin, Poonam Delvadia, Vidula Kolhatkar, Xinyuan Zhang,
  24. 24. Acknowledgements • Colleagues from Roche pRED Pharmaceutical Sciences • Colleagues from the GastroPlus User Group • Colleagues from the IQ Food Effect Working Group 24
  25. 25. Questions raised by FDA • What are the characteristics of drugs that are susceptible to pH-dependent DDIs? Can a stepwise approach be applied to evaluate the interaction potential? • When conducting pH-dependent DDI assessments: – What are the utilities and limitations of different approaches to evaluating DDIs (e.g., in silico, in vitro, and dedicated clinical studies, as well as population pharmacokinetic analyses)? – What are the study design considerations (e.g., study population, choice of ARAs, dosing regimen and administration, and pharmacokinetic sampling) for the in vivo assessments discussed in 2a above? – Can we extrapolate the findings from a clinical DDI study with one ARA drug (a PPI, H2 blocker, or antacid) to anticipate the DDI potential for other ARAs in the same class or in a different class?

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