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D-optimal Adaptive Bridging Studies in Pharmacokinetics

D-optimal Adaptive Bridging Studies in Pharmacokinetics. Lee-Kien Foo Stephen Duffull. Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand. Bridging Studies. A concept for extrapolating information gathered from a clinical study in

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D-optimal Adaptive Bridging Studies in Pharmacokinetics

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  1. D-optimal Adaptive Bridging Studies in Pharmacokinetics Lee-Kien Foo Stephen Duffull Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand

  2. Bridging Studies • A concept for extrapolating information gathered from a clinical study in an original region to a new region • One of the purposes is to assess the dose exposure relationship in the new region

  3. Optimal Adaptive Design Initial design Collect data Refine design Fit a model Optimize design

  4. Aim To develop an optimal adaptive design method for bridging studies (D-optimal ABS) that can be applied to pharmacokinetics

  5. This Talk • D-optimal ABS • Simulation studies • Results • Discussion

  6. D-optimal ABS • Information from a prior population • Adaptive D-optimal design • Combine information in the process • Learn about the target population

  7. D-optimal ABS Collect data from prior population Initial design Fit the model D-optimal design

  8. D-optimal ABS Collect data from prior population Initial design Fit the model Collect batch data from target population D-optimal design

  9. D-optimal ABS Collect data from prior population Initial design Reduce data proportionally Pool data Fit the model Collect batch data from target population D-optimal design

  10. D-optimal ABS Collect data from prior population Initial design Reduce data proportionally Pool data Fit the model Collect batch data from target population D-optimal design

  11. D-optimal ABS • Iteration b • Estimation: • Population Fisher information matrix:

  12. Simulation Studies • Simulation studies were performed for two different scenarios • Carried out in MATLAB and NONMEM, called from MATLAB, is used for estimation • POPT is used to find the D-optimal design • 100 D-optimal ABS were simulated for each scenario • The relative percentage difference of the estimated parameter values from the true parameter values (%RE) were used to assess the performance of the 100 D-optimal ABS

  13. Simulation Study 1: Context • Bridging study from adult patients to paediatric patients • Dose schedule is based on mg/kg • Paediatric patients parameter values were scaled allometrically • Drug is a small molecule and given orally • Bateman pharmacokinetic model

  14. Simulation Study 1 • Bateman pharmacokinetic model

  15. Simulation Study 1 • Bateman pharmacokinetic model Linear accumulation:

  16. Simulation Study 1

  17. Simulation Study 1 - %RE(Theta)

  18. Simulation Study 2: Context • Bridging study from normal weight adult patients to obese adult patients • Drug is a large molecule and given subcutaneously • Delayed absorption in obese patients • Transit compartment model

  19. Simulation Study 2 • Transit compartment model

  20. Simulation Study 2 • Transit compartment model

  21. Simulation Study 2

  22. Simulation Study 2 • Linear accumulation • Geometric accumulation • Number of batches:

  23. Simulation Study 2 Geometric Accumulation - %RE(Theta)

  24. Simulation Study 2Geometric Accumulation - %RE(Omega)

  25. Simulation Study 2Geometric Accumulation with Mixture Model Estimation - %RE(Omega)

  26. Final Iteration - %RE(Theta)

  27. Discussion • Both linear and geometric accumulation D-optimal ABS provides reasonable parameter estimates in the last iteration • D-optimal ABS is a potentially useful method for bridging studies • Need to test on batch size

  28. Acknowledgements • Professor Stephen Duffull • School of Pharmacy • University of Otago • PAGE • Pharsight student sponsorship • Modelling and Simulation lab members

  29. Simulation Study 2Linear Accumulation - %RE(Theta)

  30. Simulation Study 2Linear Accumulation - %RE(Omega)

  31. Simulation Study 2 Linear Accumulation with Mixture Model Estimation

  32. Simulation Study 2 Linear Accumulation with Mixture Model Estimation

  33. Simulation Study 2 Geometric Accumulation with Mixture Model Estimation

  34. Simulation Study 2 Geometric Accumulation with Mixture Model Estimation

  35. Simulation Study 23 Batches Linear - %RE(Theta)

  36. Simulation Study 23 Batches Linear - %RE(Omega)

  37. Simulation Study 23 Batches Geometric - %RE(Theta)

  38. Simulation Study 23 Batches Geometric - %RE(Omega)

  39. Final Iteration - %RE(Omega)

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