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

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Presentation transcript:

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand 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 an original region to a new region One of the purposes is to assess the dose exposure relationship in the new region

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Optimal Adaptive Design Collect data Fit a model Optimize design Initial design Refine design

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Aim To develop an optimal adaptive design method for bridging studies (D-optimal ABS) that can be applied to pharmacokinetics

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand This Talk D-optimal ABS Simulation studies Results Discussion

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Information from a prior population Adaptive D-optimal design Combine information in the process Learn about the target population

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Fit the model D-optimal design Initial design Collect data from prior population

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Fit the model D-optimal design Initial design Collect batch data from target population Collect data from prior population

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Reduce data proportionally Pool data Fit the model D-optimal design Initial design Collect batch data from target population Collect data from prior population

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Reduce data proportionally Pool data Fit the model D-optimal design Initial design Collect batch data from target population Collect data from prior population

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand D-optimal ABS Iteration b –Estimation: –Population Fisher information matrix:

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand 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

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand 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

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 1 Bateman pharmacokinetic model

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 1 Bateman pharmacokinetic model Linear accumulation: CLVka CL V ka

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 1

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 1 - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand 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

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Transit compartment model

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Transit compartment model CLVMTTN CL V MTT N

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Linear accumulation Geometric accumulation Number of batches:

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Geometric Accumulation - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Geometric Accumulation - %RE(Omega)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Geometric Accumulation with Mixture Model Estimation - %RE(Omega)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Final Iteration - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand 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

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Acknowledgements Professor Stephen Duffull School of Pharmacy University of Otago PAGE Pharsight student sponsorship Modelling and Simulation lab members

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Linear Accumulation - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Linear Accumulation - %RE(Omega)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Linear Accumulation with Mixture Model Estimation

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Linear Accumulation with Mixture Model Estimation

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Geometric Accumulation with Mixture Model Estimation

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 Geometric Accumulation with Mixture Model Estimation

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 3 Batches Linear - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 3 Batches Linear - %RE(Omega)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 3 Batches Geometric - %RE(Theta)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Simulation Study 2 3 Batches Geometric - %RE(Omega)

Modelling and Simulation Lab, School of Pharmacy, University of Otago, New Zealand Final Iteration - %RE(Omega)