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Physiological Based Biopharmaceutics Modelling in industrial practice. Current applications and a vision for the future


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Presented by Xavier Pepin

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Physiological Based Biopharmaceutics Modelling in industrial practice. Current applications and a vision for the future

  1. 1. Physiological Based Biopharmaceutics Modelling in industrial practice. Current applications and a vision for the future Xavier Pepin PBPK symposium 2019 04 April 2019
  2. 2. What to evaluate with PBBM tools ? 2 • Are two batches anticipated to be bioequivalent ? • Acceptable product specifications, acceptable content of excipients • Edge of failure for Critical Material Attributes and Critical Process Parameters Operating range Safe space CMA1 or CPP1 CMA2orCPP2 Knowledge space Edge of failure Clinical reference Size of the safe space Justified specifications Regulatory flexibility
  3. 3. Why do we need biomarkers of physiology ? Complex models PBPK virtual trials may “match” observed variability in PK Cannot define with certainty the parameters responsible for this variability Within and between subject variability Volumes, pH, transit times, enzyme expression levels…etc Availability and cost of some biomarkers IV-microdose, pH, transit times, volume drank during study, enzyme expression… Human permeability (co-dose a reference compound) 3
  4. 4. Calquence ® Added value of stomach pH biomarker on the interpretation of pharmacokinetic data and to link in vitro dissolution to in vivo PK
  5. 5. Calquence® (acalabrutinib): Selective, irreversible BTK inhibitor • Approved in the US in Oct 2018 for the treatment of adults with mantle cell lymphoma (MCL) who have received at least one prior treatment for their cancer.
  6. 6. Biopharmaceutical properties of acalabrutinib ● Biopharm properties ● pKa in the physiological range: 3.5(B), 5.8 (B) ● Log P: 2 ● Intrinsic solubility = 50 ug/mL @ pH 9 ● Estimated human Peff = ~ 5-8 10-4 cm/sec ● Limited impact of bile salts on solubility ● Acid base reaction at the surface increasing surface pH during dissolution
  7. 7. Assessment of In Vivo Absorption Rate Q=80% @ 20 min ensures full and complete release prior to stomach emptying High permeability- 100% absorption Absorption = 100% Gut extraction = 50% Liver extraction = 50% Bioavailability= 25% • ACE-HV-009 : absolute BA study. Deconvolution with Qgut model • ACE-HV-004 + HV112 : deconvolution of in vivo absorption • MDCK-MDR1 data  Peff=5 10-4 cm/s
  8. 8. Assessment of Precipitation Risk Acalabrutinib solution vs paracetamol in TIM-1 Acalabrutinib capsules during pH shift Acalabrutinib capsule vs cyclodextrin solution in dog• In normal pH conditions, acalabrutinib shows BCS class I like properties
  9. 9. Determination of a clinically relevant discrimination dissolution method Proposed Method Potential back-up
  10. 10. SmartPills® Temp pH Pressure
  11. 11. Preclinical dog study ● Fasted beagle dogs ● 2 batches with different dissolution characteristics in vitro ● Repeat administration (n=2) of the two 100 mg formulations ● Coadministration with SmartPill® 603 605 In acidic conditions, no difference in exposure Stomach pH main contributor to variability
  12. 12. Human volunteers received the same batch of 100 mg capsule + SmartPill® in two periods pHdecreases pHincreases Human SmartPill study Within subject variability partly explained by stomach pH GastroPlus sensitivity analysis on n=8 population
  13. 13. Delayed gastric emptying for some subjects Human SmartPill study Drinking event during study has probably flushed the drug from the stomach No drink allowed D D D
  14. 14. PBPK Modelling Strategy
  15. 15. Integration of in vitro data into PBPK • Fit of Product Particle Size Distribution (P-PSD) and upload in G+ as an input for each batch of DP In vivo dissolution is calculated on the basis of local pH and volumes using “DP particle size” Surface pH different from bulk below pKas
  16. 16. Validation of P-PSD = For each DP bacth
  17. 17. Model Validation • Independent studies (ACE-HV-112 and ACE-HV-004) • Two formulations (early and late) • Normal stomach pH and PPI pre-treatment • Dosing with orange drink and grapefruit juice (CYP3A4 inhibition) Model able to reproduce meaningful in vivo dissolution Impact of formulation and physiology well captured
  18. 18. Model Use FP000180 C657/1 FP000264 C657/2 FP000298 C657/3 Measured surface area vs. calculated surface area Closed = laser Open = P-PSD
  19. 19. Model Use – PBPK model -> PSA Particle limit of 270 µm from PBPK model Safetymargin
  20. 20. Decision tree for integration of in vitro data in PBPK
  21. 21. 21 A vision for the future of PBBM Run a pilot clinical study with different drug products. Build a mechanistic absorption model to explain observed exposure Introduction of biomarkers : Understand subject variability and reduce it (system parameters) Move from statistical approach of PK data to mechanistic understanding of individual data Move from mechanistic understanding of populations to personalized medicine Mechanistic integration of dissolution (P-PSD, Z-factor...) The right biomarker for the right drug : pH, transit, bile salt, enzyme expression etc Use Individual disposition parameters Run virtual cross-over BE trials to define edge of failure of CMA and CPP on larger populations with right variability of key parameters
  22. 22. Acknowledgements • Natalie Sanderson • Andrea Moir • Ric Barker • Alex Blanazs • James Mann • The rest of the Calquence® project team at Acerta Pharma and AZ • Cecile Krejsa • Marilee Andrew • Greg Slatter • Tim Ingallinera
  23. 23. Confidentiality Notice This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 1 Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, CB2 0AA, UK, T: +44(0)203 749 5000, 23
  24. 24. Model Use Allows a meaningful API PSD specification to be set
  25. 25. Wettability of drugs 25 Limiting contact angle for spherical particle spontaneous wetting due to gravitation 1 g, ρS = 1.2 g/mL r θ Fg Size of particles determines spontaneous wetting Small particles wet less easily than large particles D . Horter , J . B . Dressman / Advanced Drug Delivery Reviews 4 6 ( 2 0 0 1 ) 7 5 – 87 Wettability Poor wettable material Highly wettable material