1. ADME Study PK SDTM/ADAM And Graph
Fan Lin is a senior manager of the statistical programming at Gilead Sciences. She has about 20 years’ experience in industry, first as an analytical scientist at Smith Kline Beecham, then as a statistical programmer at varied companies. She co-authored several publications on Journal of Medicinal Chemistry.

2. Agenda Introduction: Details: Summary What is ADME?
What is ADME Phase I study?
The challenges in ADME PK study
Details:
ADME study PK process
Steps of resolving the challenges in ADME PK study
Summary

3. Section 1: Introduction

4. What Is ADME?
Pharmacokinetics Basics – Absorption, Distribution, Metabolism and Excretion
The figure is taken from PK/DB – Database for Pharmacokinetic Properties – IFSC/USP (URL=  accessed – April 09, 2011

5. Phase I Studies
Phase I studies are usually conducted on healthy subjects
Different categories of study designs are facilitated to achieve phase I study goals
FIH
SAD
MAD
SAD/MAD
PK/PD
Drug/Drug Interaction (DDI)
Food Effect (FE)
Non-FIH
Bioavailability (BA)
Bioequivalence (BE)
Dose Proportionality
DDI FE
Mass Balance (ADME)
Steady-state QT

6. Introduction of ADME PK Study
To investigate the safety, tolerability and PK/PD of the investigational drug in healthy subjects
Phase 1
Mass balance, to obtain metabolism and excretion of the drug in humans
Usually C14-lableled, conducted as part of clinical development of a drug
ADME
A single dose administration of radio labeled drug in 6-8 healthy male subjects
To access balance, measuring recovery of administered 14C-labeled compound from collected urine, feces and plasma
Design

7. The Challenges in ADME PK Study
Sample Species
PK Tests
CRF Data
Blood
Plasma
Plasma: Blood
Urine
Feces
Derived Urine+Feces
cumulative amount of excrete
cumulative percent of dose administration
Derived records to carry over last available result
Derived cumulative time points

8. Solutions to the Challenges
In Sample Species:
Derived DTYPE=SUM to add urine and feces species
In PK tests:
The LOCF imputation technique was applied in cumulative tests.
In CRF Data:
CRF data time points, 0 to each end time point, was derived in order to merge with concentration file for cumulative time points

9. Section 2: Process And Steps

10. Highlights on Introducing the Process
The work flow from receiving PK concentration file to generating reports in scheme
A summary of the process
The components of the ADME study PK data

11. Scheme of ADME PK Process

12. The Summary of the Scheme
PK concentration-time profiles are received from Bioanalytical Lab
PKMERGE is created using actual sample collection date/time and demographic information from CRF and concentration data from BA lab for Plasma, Whole Blood, Urine and Feces
PKMERGE is used for PK parameter generation by Clinical Pharmacology group and also as a source for PC
PK parameters are calculated using specific software for non-compartmental analysis, such as SAS or WinNonlin, and used as source for SDTM PP dataset
ADSL is updated to contain the PK population flag variables
ADPC and ADPP ADaM datasets are generated by following ADaM IG
PK concentration TFLs are generated from the ADPC and PK parameter outputs are generated from ADPP

13. ADME Study PK Data
Two components in ADME PK: regular cold PK and radio activity PK

14. Highlights of Building the Process
Last Step: Generating Outputs
Step3: Creation of ADaM Datasets
Step2: Creation of SDTM Datasets
Step1: Creation of PKMERGE

15. Step 1: Creation of PKMERGE
Concentration-time profiles are reviewed
WNLCONCN-Sample Concentration Value (WNL): LOCF values kept for PK scientist to review
LOCF

16. Step 1: Creation of PKMERGE (Cont.)
PKMERGE is created
WNLCONCN: BLQs, 0 before first quantifiable concentration, missing after first quantifiable concentration
Actual scheduled end date/time is calculated for interval time points
deviation times(ENDEV) from actual to scheduled are calculated
Served as rawdata to sdtm pc and also as source file provided to PK scientist for PK parameter

17. Step 2: Creation of SDTM Datasets
Important variables in PC and PP
PK parameter
PC domain: one record per analyte per planned time point per time point reference per visit per subject
PP domain: one record per PK parameter per time-concentration profile per modeling method per subject

18. Example of SDTM PC Data Structure
Feces Species:

19. Example of SDTM PP Data Structure
Urine, Plasma and Blood Species:
PK parameter
calculated values

20. Step 3: Creation of ADaM/ADPC
DTYPE
1) LOCF
The reason of N.S was kept in
sample comment variable
Subject discharged on Day 14
LOCF performed after Day 13

21. Step 3: Creation of ADaM/ADPC (Cont.)
DTYPE
2) SUM: handling timepoints
Derived in urine and feces
Calculate first four urine intervals
sum result as 0 to 24 hr
Sum of 24 to 36 and 36 to 48 hr in
Urine as 24 to 48 hr
Now urine and feces have the same
intervals
Adding values in urine and feces
Urine time points
are different

22. Step 3: Creation of ADaM/ADPC (Cont.)
DTYPE
2) SUM: handling no results available
When both urine and feces has N.S.
Sum is set to N.S.
When both urine and feces has BLQ
Sum is set to BLQ
When one has BLQ the other has N.S.

23. Step 3: Creation of ADaM/ADPC (Cont.)
Result
Time point
Reason
Species
Analysis Visit
DTYPE=SUM
DTYPE=SUMLOCF
No sample available after Day 13 due to subject discharged
LOCF was performed on N.S. records, after Day 13

24. Last Step: Generating Reports
Standard PK Outputs
Tables: Include both listing and summary statistics
Figures: PK concentration vs time profile includes individual plot and mean(SD)/median(Q1/Q3) plot
Challenge Outputs
Mean(SD) Cumulative Percent Dose Plot
1) Plot three lines: urine, feces and urine+feces
2) LOCF values were displayed on the plot
3) The number of actual subjects at each visit was presented at the bottom outside the plot area

25. Mean(SD) Cumulative Percent Dose Figure
layout lattice / rows=2 rowweights=( )
LOCF values were plotted
Layout overlay/ … ; blockplot x=visit block=n
endlayout ;
DiscreteLegend "series" / title=" " halign=left VALIGN=top exclude=(" ")
To display No. of Subjects row: 1) create a missing category for it 2) in legend statement exclude this missing category
Actual number of subjects remained

26. Section 3: Summary

27. Conclusions ADME PK study is a single dose study design
Radioactivity PK brings more challenges
The focus of this presentation describes the process flow of ADME PK study, PKMERGE, SDTM, ADaM and PK outputs
The solutions to the challenges were provided
The process described in earlier slides meets industry standard

28. References
PHUSE CSS Development of Standard Script for Analysis and Programming Working Group
Analysis and Displays Associated to Non-Compartmental Pharmacokinetics – With a Focus on Clinical Trials. Final Version March Available
CSS WhitePaper PK final 25March2014.pdf
Lin, F. (2019). ADME Study PK SDTM/ADaM And Graph
AD078.pdf

29. Acknowledgement
I would like to thank Gilead stat programming management team for their supports

30. Organization: Gilead Sciences, Inc.
Contact Information
Name: Fan Lin
Organization: Gilead Sciences, Inc.
Address: 333 Lakeside Drive
City, State ZIP: Foster City, CA 94404