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.

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

Section 1: Introduction

What Is ADME? Pharmacokinetics Basics – Absorption, Distribution, Metabolism and Excretion The figure is taken from PK/DB – Database for Pharmacokinetic Properties – IFSC/USP (URL= http://miro.ifsc.usp.br/pkdb/) accessed – April 09, 2011

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

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

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

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

Section 2: Process And Steps

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

Scheme of ADME PK Process

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

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

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

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

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

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

Example of SDTM PC Data Structure Feces Species:

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

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

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

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.

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

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

Mean(SD) Cumulative Percent Dose Figure layout lattice / rows=2 rowweights=(0.92 0.08) 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

Section 3: Summary

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

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 Version1.0. 25 March 2014. Available https://phusewili.org/.../PHUSE CSS WhitePaper PK final 25March2014.pdf Lin, F. (2019). ADME Study PK SDTM/ADaM And Graph https://www.pharmasug.org/proceedings/2019/AD/PharmaSUG-2019- AD078.pdf

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

Organization: Gilead Sciences, Inc. Contact Information Name: Fan Lin Organization: Gilead Sciences, Inc. Address: 333 Lakeside Drive City, State ZIP: Foster City, CA 94404 E-mail: Fan.Lin@gilead.com