Maximizing the Value and Utility of ADaM for Pharmacokinetic Analyses and Reporting: Much More than just ADPC and ADPP

Transcription

1 Maximizing the Value and Utility of ADaM for Pharmacokinetic Analyses and Reporting: Much More than just ADPC and ADPP James R. Johnson, PhD Sr. Principal Biostatistician/Pharmacokineticist 1

2 Some Basic Definitions Pharmacokinetics: Pharmacokinetics is defined as the study of the time course of drug absorption, distribution, metabolism, and excretion (ADME).

3 Some Basic Definitions Pharmacokinetics: Clinical Pharmacokinetics is the application of pharmacokinetic principles to the safe and effective therapeutic management of drugs in an individual patient..

4 Pharmacokinetic Models Noncompartmental Analysis Compartmental Analysis Single Compartment Analysis Multi compartment Analysis Physiological PK Analysis Population PK Analyses (Many model types) Equivalence Models (BE/BA)

5 Most Common PK Analyses Noncompartmental PK analysis is highly dependent on estimation of total drug exposure. Total drug exposure is most often estimated by area under the curve (AUC) methods, with the trapezoidal rule (numerical integration) the most common method. Due to the dependence on the length of 'x' in the trapezoidal rule, the area estimation is highly dependent on the blood/plasma sampling schedule.

6 A simple Example PARAMCD PARAM PARAMTYP AVAL AVALC UNITS ATPT ATPTN ACETAMIN ACETAMINOPHEN ANALYTE 0 BLQ<(50.0) 0 Hour 0 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 0.25 Hour Post Dose 0.25 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 0.5 Hour Post Dose 0.5 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 0.75 Hour Post Dose 0.75 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 1 Hour Post Dose 1 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 1.25 Hour Post Dose 1.25 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 1.5 Hour Post Dose 1.5 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 1.75 Hour Post Dose 1.75 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 2 Hour Post Dose 2 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 2.5 Hour Post Dose 2.5 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 3 Hour Post Dose 3 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 3.5 Hour Post Dose 3.5 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 4 Hour Post Dose 4 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 6 Hour Post Dose 6 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 8 Hour Post Dose 8 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 12 Hour Post Dose 12 ACETAMIN ACETAMINOPHEN ANALYTE ng/ml 16 Hour Post Dose 16 ACETAMIN ACETAMINOPHEN ANALYTE BLQ<(50.0) 24 Hour Post Dose 24

7 Most Common PK Parameters from Noncompartmental Analysis PK Parameter Description (Computation Method) C max t max C Last t Last Peak exposure, Maximum plasma concentration Time from dosing to peak exposure, time to maximum plasma concentration Last quantifiable plasma concentration (last value observed above assay BLOQ) Time of last quantifiable plasma concentration All of these parameters are Observed. Not computed from a Model

8 Observed PK Parameters Concentration (ng/ml) Cmax Clast Tmax (0.5 hours) Sampling Time (Hours) Tlast (16 hours) BLOQ (<50 ng/ml)

9 Most Common PK Parameters from Noncompartmental Analysis PK Parameter Description (Computation Method) λ z AUC 0 t Terminal elimination rate constant (lambda_z) Exposure: Area Under the Plasma Curve from time 0 to the last quantifiable concentration (t). Calculated using the linear trapezoidal rule. AUC 0 inf Exposure: Area Under the Plasma Curve from time 0 extrapolated to infinity. Calculated as follows: where C last is the last quantifiable concentration All of these parameters are Computed Derived from an algorithm or Model

10 Lambda_z :Needed for AUC Predicted Parameters (From WinNonlin) Terminal elimination rate constant (λ z )

11 AUC0 last (Derived from Observed) Concentration (ng/ml) AUC 0-last Sampling Time (Hours)

12 AUC0 inf (Predicted) Concentration (ng/ml) Clast AUC 0-inf Sampling Time (Hours)

13 SDTM and ADaM Originally designed to support the most common type of PK analyses completed Noncompartmental Analyses Equivalence Analyses (BE/BA)

14 Standard SDTM/ADaM Process Map Completed with WinNonlin

15 SDTM.PC Domain

16 SDTM.PP Domain

17 Differences between SDTM.PP and ADPP SDTM.PP is a domain with Derived (computed or Model derived endpoints).so not a lot of differences Added values for: TRTxxP, TRTxxPN APERIOD AVISIT, AVISITN CRITx, CRITxFL Others, maybe!

18 ADPC and ADPP Engineered to Support Standardized NCA Analyses Standard TLF s White paper:

19 ADPC Designed to Support Descriptive Summaries of Concentration Data Analyte: R-Drug X Sampling Time Point Summary Statistic Drug X 25mg (N=30) Drug X 35mg (N=30) Drug X 50mg (N=30) 0.33 Hour N Mean (SD) (7.7793) ( ) ( ) %CV % % % Median Min, Max 7.09, , , Geometric Mean (SE) (2.6702) (2.7166) (2.9343) 0.50 Hour N Mean (SD) (5.1625) (6.9363) (7.8175) %CV % % % Median Min, Max 7.31, , , Geometric Mean (SE) (2.5607) (2.5195) (2.9586) 0.67 Hour N Mean (SD) (5.4584) (6.5824) (7.9771) %CV % % % Median Min, Max 6.58, , , Geometric Mean (SE) (2.5654) (2.4883) (2.9761)

20 ADPC Designed to Support Graphical Displays of Concentration Data Standard Concentration by Time Profile: 150 Subject: (M 18yr 75.4kg 26.4kg/m2 1.9m2 A 50mg) Plasma Concentration (ng/ml)

21 ADPC Designed to Support Graphical Displays of Concentration Data

22 ADPP Designed to Support Descriptive Summaries of PK Parameter Estimates Combines across PK Schedules A and B Analyte: R-Drug X C max (ng/ml) t max (hr) AUClast (hr*ng/ml) AUCinf (hr*ng/ml) AUCextr (%) CL/F (L/hr) t 1/2 (hr) 25 mg Drug X, n=29 Gmean/Median (a) Min, Max 9.28, , , , , , , 6.55 CV (%) 48.2 NA NA 35 mg Drug X, n=29 Gmean/Median (a) Min, Max 7.21, , , , , , , CV (%) 54.5 NA NA 50 mg Drug X, n=30 Gmean/Median (a) Min, Max 12.10, , , , , , , 6.17 CV (%) 41.7 NA NA (a) The geometric mean, gmean, is provided except for t max and t 1/2 where medians are shown NA Not applicable Schedule A Predose, 10min, 20min, 40min, 1.5hr, 2hr, 4.5hr, 8hr Schedule B Predose, 0.5hr, 1hr, 1.5hr, 3hr, 4.5hr, 6hr, 8hr

23 Pharmacokinetic analyses have become more Complex Sparse Samples, Population PK Models, Relationships between parameters.much more. ADPP and ADPC are just the beginning

24 Sparse Samples: Simple Example Week Subject #1 Subject #2 Subject #3 Subject #4 Dosing > Sparse > BLQ<(100) Final > 52 BLQ<(100) BLQ<(100) BLQ<(100) 193

25 Sparse Samples: Simple Example Insufficient Information for individual AUC 4000 Concentration (ug/ml) Subject 1 Subject 2 Subject 3 Subject Sampling Week

26 Sparse Samples: Simple Example Population Elimination Curve Concentration (ug/ml) 1000 Subject 1 Subject 2 Subject 3 Subject 4 Population Sampling Week

27 Population Model Data Population Model #1: Coefficients BETA_ BETA_ BETA_2 4.43E 04 RSQUARE Population Predicted Concentration Applies to All Subjects Does this belong in ADPC? ADPOPPC (No USUBJID Variable) Time Predicted Concentration Time Predicted Concentration

28 Predicted and Residuals Example Scenario USUBJID TRT01PN ATPTN AVAL PRED IPRED IRES IWRES WRES CWRES Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Model Predicted Concentrations from Population Model: Do These belong in ADPC? or ADPCPRED?

29 Predicted and Residuals Example Observed Concentration v Predicted Concentrations 20 Subject: Concentration (ng/ml) Observed Predicted1 Predicted Sampling Time (Hours)

30 Parameter Estimates From Models Multiple Derived PK Parameters from both Observed and Predicted Concentrations USUBJID DOSE PK_Parameter AVAL_EST PRED_EST RESIDUAL RATIO PARAM Lambda_z Analyte_A HL_Lambda_z Analyte_A Tmax Analyte_A Cmax Analyte_A Cmax_D Analyte_A Tlast Analyte_A Clast Analyte_A AUClast Analyte_A AUCall Analyte_A AUCINF_obs Analyte_A AUCINF_D_obs Analyte_A AUC_%Extrap_obs Analyte_A Vz_F_obs Analyte_A Cl_F_obs Analyte_A ADPP or ADPPPRED?

31 Simple Parameter Estimate Example 3 period Crossover Study, Multiple Predicted PK Parameters with ratios of Observed to Predicted Subject 25mg Dose Cmax 25mg Dose Cmax Predicted 25mg Dose Ratio 50mg Dose Cmax 50mg Dose Cmax Predicted 50mg Dose Ratio 100mg Dose Cmax 100mg Dose Cmax Predicted 100mg Dose Ratio ADPP or ADPPRAT?

32 Simple Parameter Estimate Example From Modeling Observed versus Predicted is needed to show goodness of fit of Model 90 Observed versus Predicted Cmax by Dose Predicted Cmax (ng/ml) mg Dose 50 mg Dose 100 mg Dose 20 ADPPRAT Observed Cmax (ng/ml)

33 Recall: Standard SDTM/ADaM Process Map Completed with WinNonlin

34 Much More than Just ADPC and ADPP

35 Documenting Advanced ADaM PK Modeling Datasets Use as many parameters as possible defined for ADPC and ADPP. Extend the datasets and define PRED, RESIDUAL, RATIO variables in modeled datasets, where needed. Use PARAMCD, PARAM to define model elements. ADRG/DEFINE.XML are your friends. Use them to fully document these advanced datasets.

36 Documenting Advanced ADaM PK Modeling Datasets For ADPOPccc ADaM Datasets where NO USUBJID is identified consider: Population predictions may have TRT01P, TRT01PN as the unique record identifier Include ATPT, ATPTN (and nominal time equivalents) PARAMCD, PARAM should mirror code lists used in ADPP, ADPC, or ADPcccccc ADRG is your very good friend. Cross reference the SAP and Population PK Analysis Plan

37 Much More than ADPP or ADPP Conclusions ADaM is very powerful and extensible for the analysis of complex PK Models and Analyses. The full utility and power of the BDS data structure is very useful for advanced PK model derived and predicted parameters. Recommend that NCA analyses NOT be mixed in ADaM datasets with more complex derived endpoints.

38 Thank You!!!!