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The finalised EMA guideline and latest experience of PBPK models in Regulatory Submissions

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The finalised EMA guideline and latest experience of PBPK models in Regulatory Submissions

  1. 1. The finalised EMA guideline and latest experience of PBPK models in Regulatory submissions Paola Coppola, Pharmacokinetics Assessor - MHRA PBPK Symposium - Paris 4th April 2019
  2. 2. 2 Disclaimer The views expressed in this presentation are those of the speaker, and are not necessarily those of MHRA or EMA.
  3. 3. 3 Overview PBPK model evaluation in EU EMA PBPK guideline New areas of interest in Regulatory submissions
  4. 4. 4 European Commission EMA COMP HMPCCHMP CVMP PDCO CAT PKWP MSWPSAWP BSWP PRAC CHMP: Committee for Human Products for Medicinal Use PDCO: Paediatric Committee SAWP: Scientific Advice Working Party MSWP: Modelling and Simulation Working Party EWG: Extrapolation Working Party BSWP: Biostatistics Working Party PKWP: Pharmacokinetics Working Party Other WPs EWPG The European Regulatory System
  5. 5. 5 EMA PBPK Guideline Jan 2017 End of public consultation Oct 2018 Agreed by PKWP and MSWP Dec 2018 Adopted by CHMP Jul 2019 It comes into effect
  6. 6. 6 EMA PBPK Guideline To describe the expected content of PBPK modelling and simulation reports included in regulatory submissions, such as applications for authorisation of medicinal products, paediatric investigation plans and clinical trial applications. This includes the documentation needed to support the qualification of PBPK platform for the intended use and the evaluation of the drug model. The guideline applies to commercially available platforms and to in-house built platforms. AIM
  7. 7. 7 Regulatory impact decision The regulatory impact is directly linked to the risk to the patient in case the modelling predictions or assumptions lead to erroneous regulatory decisions. The impact of a simulation also depends on how much weight of evidence the PBPK simulation will have in a certain scenario, the therapeutic context and the resulting treatment recommendations. Regulatory impact decision Examples High All changes to SmPC PBPK model in place of clinical data Extrapolation outside the studied area Medium Paediatric dose to be confirmed by clinical data Low Dose selection for FIHQualification requirements
  8. 8. 8 PBPK: platform qualification Qualification may be obtained via: • Regulatory submission (specific to this, subsequent needs re-evaluation) • CHMP qualification procedure (can be cited in future applications) https://www.ema.europa.eu/en/human-regulatory/research-development/scientific-advice-protocol-assistance/qualification-novel-methodologies-medicine-development • Published papers if the included validation dataset is sufficiently current and described in sufficient detail to allow a thorough understanding of the data by regulators Emphasis is not on what is required per se but how it should be reported to allow confidence in the submitted model
  9. 9. 9 PBPK: platform qualification Pre-specified data set: - Selection criteria for the drugs and the in vitro and in vivo parameters should be described - compounds with similar ADME characteristics to that of the intended use - a range of PK characteristics that could influence the outcome should be covered (e.g. 8 to 10 compounds may be sufficient) - additional drugs included in the qualification data set should not be used in the platform building The process of establishing confidence in a PBPK platform to simulate a certain scenario, in a specific context, on the basis of scientific principles and ability to predict a large dataset of independent data thereby showing the platforms ability to predict a certain purpose. In the context of PBPK model, qualification is purpose and platform version specific.
  10. 10. 10 PBPK: platform qualification Appendix 1 PBPK guideline Qualification of the ability to quantify the effects on investigational drugs being victim of drug interaction Qualification of the ability to detect investigational drugs as perpetrators of drug interaction Simulation of exposure in paediatric population
  11. 11. 11 PBPK: predictive performance The process of establishing confidence in the drug model. The reliability is assessed on the basis of how well important characteristics of the drug model has been tested against in vivo PK data and whether adequate sensitivity and uncertainty analyses have been conducted to support the models ability to provide reliable predictions. Evaluation of the drug model when the investigational drug is a victim drug Evaluation of the drug model when the investigational drug is a perpetrator Simulation of exposure in an alternate population Appendix 2 PBPK guideline
  12. 12. 12 Regulatory submission 2016 review Model purposes: • DDI (ca. 75%*) • Better understanding of PK, role of enzymes/transporters • Dose recommendations • Food effect • Effect of polymorphisms/ethnic differences • PK in special population (renal/hepatic impairment) • Comparison between strengths/formulations *In many cases there is more than one purpose
  13. 13. 13 Regulatory submission Post 2016 increasing number of procedures and SA Different applications: • Special populations - paediatrics, pregnancy • Diseases states- Oncology, Gastroenterology • More focus on PD end point • Biopharmaceutical Applications Additional purposes: Still DDIs, but also UGTs and Transporters as clearance pathway Limited experience in locally acting products Model discussed at MSWP in 2016* *2016 Activity report of the Modelling and simulation working group (MSWG)
  14. 14. 14 PBPK New areas of interest in regulatory submissions UGTs CYPs induction Pregnancy Locally applied products Model Disease Renal function models in neonates (FDA)
  15. 15. 15 PBPK - tumour model Active substance X PBPK model used to support the proposal for a new posology. The model was used to predict the tumour target engagement for the active substance X, showing that with the new proposed posology the tumour target engagement profile is expected similar to that achieved using the approved posology. All doses showed to maintain the target engagement above 90% throughout the dosing interval. Case study
  16. 16. 16 PBPK – disease model Examples of models to investigate the result of drugs effecting the GI tract: • Chemotherapy • GLP1 receptor agonists delaying gastric emptying
  17. 17. 17 PBPK: CYPs induction • Mechanistic static model used to investigate CYP3A4 induction • Model qualification was considered not sufficient for quantitative CYP 3A4 induction • Small qualification dataset (only 6 inducers with 4 different substrates, only 1 shows low levels of induction) PBPK Model Missing data Case study Drug X potentially inducer of CYP3A4 • 7 clinical studies with CYP3A4 inducers were used in the data set for model qualification • PBPK model considered acceptable to exclude clinical significant risk of DDI with CYP3A4 inducers. Conclusions
  18. 18. 18 PBPK: DDIs with UGTs inhibitors • ATP-uncompetitive, reversible inhibitor of the MEK1 and MEK2 • Inhibits activation of MEK by BRAF and inhibits MEK kinase activity • Inhibits growth of BRAF V600 mutant melanoma cell lines and demonstrates antitumour effects in BRAF V600 mutant melanoma animal models. • Primarily metabolized through UGT1A1 mediated glucuronidation Case study: Binimetinib https://www.ema.europa.eu/en/documents/assessment-report/mektovi-epar-public-assessment-report_en.pdf
  19. 19. 19 DDIs with UGTs inhibitors • Simulations used to investigate the effect of 400 mg atazanavir (UGT1A1 inhibitor) on the exposure of 45 mg binimetinib •  predicted similar binimetinib Cmax in the presence or absence of UGT1A1 inhibitor  the possible extent of DDIs mediated by UGT1A1 may be minimal • Risk of DDIs not evaluated in a clinical study SmPC warning: UGT1A1 inducers and inhibitors should be co administered with caution (Section 4.5) Model Missing data Conclusions Case study: Binimetinib https://www.ema.europa.eu/en/documents/assessment-report/mektovi-epar-public-assessment-report_en.pdf
  20. 20. 20 Effect of pregnancy on ADME Tasnif Y et al., Pregnancy-related pharmacokinetic changes. Clin Pharmacol Ther. 2016 Jul;100(1):53-62.
  21. 21. 21 PBPK model in pregnancy Xia B. et al. The AAPS Journal, Vol. 15, No. 4, October 2013 Example for renally excreted and CYP3A4 metabolised compounds
  22. 22. 22 PBPK model for prediction of foetal exposure De Sousa Mendes M. et al, Clin Pharmacokinet (2017) 56:537–550
  23. 23. 23 PBPK in pregnancy: case study Update the SmPC Sections 4.6 and 5.2 • Major clearance pathway through cytochrome P450 • Expected decreased Cyp activity during pregnancy • No knowledge in pregnancy Scope Background Risk Expected systemic over-exposure in pregnant patients Active substance X
  24. 24. 24 Model validation with clinical data Mechanistic PK model developed in non- pregnant subjects Clearance accounted (based on the available in vivo/in vitro/literature data) Predictability tested (simulated versus observed exposure) Simulation of systemic exposure in pregnancy (upon model validation) PBPK in pregnancy Exposure increased over the gestational weeks Prediction outcome Assumption: Maternal plasma concentrations are predicted to gradually increase during pregnancy due to decreased Cyp activity Active substance X Clinical data needed to validate and qualify the model for in vivo prediction of systemic drug exposure in pregnant women.
  25. 25. 25 PBPK - non-oral dose routes - systemic exposure • Prediction of systemic exposure- either site of action, or for safety • Have not seen adequate qualification data sets for any route - yet! • Not accepted in place of a clinical study to predict systemic exposure • Have accepted to inform DDIs by the alternate dose route • Additional uncertainty in predictions to children Subcutaneous, Intranasal, Topical, Inhaled routes
  26. 26. 26 PBPK - Locally Acting products Dermal, ophthalmic, inhaled, intranasal ASCPT session- Washington 2019 Interest in PBPK models for locally acting products Discussed challenges around qualification/ verification of models
  27. 27. 27 New area of interest ASCPT - Washington 2019 Interest in GFR models incorporating renal function maturation in neonates and child (FDA) More data needed to support an optimal model
  28. 28. 28 Acknowledgments Susan Cole Pharmacokinetics Group Lead, MHRA

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