Two applications (glucose-insulin and disease-progression) and the methods that we needed to use to compute these designs.

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1 Two applications (glucose-insulin and disease-progression) and the methods that we needed to use to compute these designs. Andrew C. Hooker, Joakim Nyberg, Stefanie Henning, Hanna Silber and Mats O. Karlsson Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Uppsala University

Hooker, Joakim Nyberg, Stefanie Henning, Hanna Silber and Mats O.

2 Model based drug development Model & Priors Experiment Design Trial Simulation Trial 2

3 Limitations to making optimal design clinically relevant 1. Optimization only done on sample times in most cases 2. Must assume a model structure. 3. Point estimates of model parameters needed 3

4 Making optimal design more clinically relevant Sample time optimization Theory does not stop us from optimization of other design parameters Dose Covariates Number of samples/group Number of individuals/group Infusion start/stop/duration Start/stop times of studies Wash out period length Etc. Foracchia, M., Hooker, A., Vicini, P. and Ruggeri, A., POPED, a software for optimal experiment design in population kinetics. Comput Methods Programs Biomed,

start/stop/duration Start/stop times of studies Wash out period length Etc. Foracchia, M., Hooker, A., Vicini, P.

5 Optimizing the IVGTT using optimal experimental design Hanna Silber, Joakim Nyberg, Andrew C. Hooker and Mats O. Karlsson Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Uppsala University

Karlsson Division of Pharmacokinetics and Drug Therapy

6 Background / objective Background Provocation experiments are performed in order to study the glucose insulin system The experiments are highly standardized and based on empiric design. Often rich in sampling Aim To evaluate different design aspects of the insulin modified IVGTT for the patient population using the optimal design software PopED 6

7 Insulin dose Glucose dose What can we optimize on? 450 IVGTT Glucose Insulin Hot Glucose mg/dl Sampling times Hot glucose needed? Stop time of infusion 50 Start time of infusion Infusion length time (min) 7

8 The glucose-insulin model for the T2DM patient population Silber et. al. JCP,

9 Practical considerations Complex model 3 submodels (glucose, insulin, hot glucose) 25 parameters 30 observations Long run times design reduction necessary Reduce sampling scheme (10 observations) FO method (for now) Optimize one design aspect at the time 9

times design reduction necessary Reduce sampling scheme (10

10 Constraints on glucose concentrations Effect of changing the insulin dose Glucose concentration 54 or 120 mg/dl at all times Time 10

11 Constraints on glucose concentrations Effect of changing the glucose dose Glucose concentration 360 mg/dl 30 minutes after glucose dose Time 11

12 Results 350 SSD, 54mg/dl 300 RSD, 54mg/dl SSD, 120mg/dl 250 RSD, 120mg/dl Optimization scenario 12 Insulin dose Infusion Start Infusion length Insulin Dose Start/stop inf Glucose dose Insulin dose + Glucose Dose Sampling times % Efficiency

Insulin dose Infusion Start Infusion length Insulin Dose Start/stop

13 Summary and conclusion It is possible to improve on the design of the insulin modified IVGTT Changing the insulin dose has the greatest impact on the efficiency of the design These type of provocation experiments can be improved by the use of population modeling and optimal experimental design 13

on the efficiency of the design These type of provocation experiments can

14 Limitations to making optimal design clinically relevant 1. Optimization only done on sample times in most cases 2. Must assume a model structure. 3. Point estimates of model parameters needed 14

15 Application of Optimal Design for Disease Progression Studies Stefanie Hennig, Sebastian Ueckert, Joakim Nyberg, Andrew Hooker, Mats O. Karlsson Division of Pharmacokinetics and Drug Therapy Department of Pharmaceutical Biosciences Uppsala University

16 Disease Progression Disease status (S) of degenerative diseases (Alzheimer's, Parkinson, osteoporosis) worsens over time Rate of deterioration determined by natural rate of disease progression and the effect of drug treatment Drug treatment in these diseases should slow down disease progression not just relieve clinical symptoms Disease progression studies are performed to obtain information on the effect of drugs for the long term prognosis on a disease Chan, Holford. Annu Rev Pharmacol Toxicol 2001;41: Holford. PAGE

should slow down disease progression not just relieve clinical symptoms Disease progression studies are performed to obtain

17 Natural History DS NH = S 0 + α 0 t α 0 S 0 17

18 Symptomatic treatment Effects DS dt = S s + α 0 t Offset (symptomatic, S) drug effect 18

19 Protective treatment Effects DS dt = S 0 + α p t Disease modifying (protective, P) drug effect 19

20 Disease modifying + symptomatic effects DS DT = S S + α P t Disease modifying + offset (protective+ symptomatic, PS) drug effect 20

21 Treatment periods S0 + α0t t < t1 DS = SS + αp( t - t ) t < t < t S0 + α0( t - t2) t > t Delayed start period Treatment period Wash-out period t 1 = Start time t 2 = Stop time 21

22 Aim Demonstrate an application of optimal design optimizing period lengths (delayed start, treatment, wash-out) for DP studies Determine optimal start time and stop time of treatment for separate models Determine efficiency loss if no observations taken after stopping the treatment (during washout) Characterize drug effects across different mechanisms and magnitudes for model discrimination using uncertainty on parameter values (ED-optimality) 22

23 Design Parameters flexible start/stop time of treatment Parameters: Baseline S 0 = 100 Slope α 0 = 2 Baseline symptomatic effect S s = 90 Slope protective effect α p = 0.2 BSV on all parameters = 30% Residual error additive = 10 proportional = 22% Parameter Uncertainty (EDdesign) Slope of disease progression, α 0 =15% Design: Total Study Period (t): 12 Observations (n): 13 evenly spread [0-12] h fixed Number of patients: 200 (1 Group same design) Treatment Period: flexible Optimizations performed using PopED v.2 23

24 Results Flexible start and stop time Number of observations during different study periods Model Before Treatment Number of observations (%) During Treatment After Treatment Protective 0% 50% 50% Symptomatic 20% 50% 30% Protective+Symptomatic 10% 40% 50% 24

25 Results No Washout Period Efficiency of designs decreased between 10-40% per parameter Number of observations (%) Model Before Treatment During Treatment After Treatment Protective 46% 54% 0% Symptomatic 8% 92% 0% Protective+Symptomatic 54% 46% 0% 25

26 Results Model Discrimination Results of comparison from 100 simulation from true model and estimations using the true model and 2 alternative models Design: Combined models design Based on significant reduction in OFV True Test % True model accepted Symptomatic Model Protective Model Protective+Symptomatic Model Protective 100 P+S 91 Symptomatic 100 P+S 100 Symptomatic 100 Protective

27 Effect Discrimination Assumed a uniform distribution of P and S effect between 0% and 100% of the total effect Find an ED optimal design Simulate and estimate 9 different scenarios from the indefinite number of combinations using NONMEM VI Look at the 95% confidence region around the estimates 27

28 Results - Effect Discrimination Start Time 1.07 Stop Time 6.11 OFV 3.49e11 Start start time Time stop time Stop Time 28

29 Results - Effect Discrimination Simulation and Estimation 1000 Simulation of 9 different effect scenarios 0% P 100% S 2 25% P 75% S 0% P 0% S P a p 50% P 50% S % P90 S % P 100% S S 0% S 29

30 Results - Effect Discrimination Confidence Regions P25/S75 P75/S25 P100/S Symptomatic effect Baseline (S S ) P25/S25 P0/S0 P50/S50 P100/S0 P75/S75 P0/S Slope for protective effect (α P ) parameter estimate true parameter value estimated parameter mean P S protective effect symptomatic effect 95 % confidence region 30

31 Results - Effect Discrimination CR OD for full effect range versus OD on a specific effect (P50%S50%) ap SS 31

32 Conclusion for disease progression example Efficiency of the design increases if we have washout observations Optimization for a uniform distribution of effects showed good performance CR spanning large parts of the parameter range made differentiating between some close effects impossible 32

33 Conclusions 1. Optimization on other design variables needed and can be more informative than sample times 2. If model structure not known this uncertainty should be taken into account a. Multiple model structures b. Distributions around assumed parameter values 3. Design constraints on the dependent variable useful 33

SOFTWARE FOR OPTIMAL DESIGN IN POPULATION PKPD: A COMPARISON

SOFTWARE FOR OPTIMAL DESIGN IN POPULATION PKPD: A COMPARISON France Mentré 1, Stephen Duffull 2, Ivelina Gueorguieva 3, Andy Hooker 4, Sergei Leonov 5, Kayode Ogungbenro 6, Sylvie Retout 1 1. INSERM U738,