Challenges and Strategies in Pharmacometric Programming Jing Su Merck & Co., Inc.

PKPD Programming Forum Sub-team under the Statistics and Pharmacometrics Interest Group (SxP) Objectives Establish a community for pharmacometric and pharmacokinetic programmers across industry and academics  Collect and share best practices, challenges and examples for programmers to support pharmacometric and PK analysis Share ideas of training new PK/PD programmers

Deliverables

More about Deliverables Answer questions for: Target selection Compound selection Go/no-go (discovery and clinical) Dose optimization through exposure-response analysis (efficacy and safety) from phase I through III PK characterization including dose recommendations for sub-populations (e.g. renal or hepatic impairment, obese patients, pediatric patients) Immunogenicity evaluations Study Design Internal Decision Making Drug Submission and Label External Publications

PKNCA (ADPC) Data Support PK non-compartmental analysis (NCA) Include: SUBJID DAY PTALD CONC DOSE PKDTC LSDTC 1 100 2017-01-01T07:30:00   1.5 2017-01-01T09:00:00 2017-01-01T08:00:00 2 3 2017-01-01T10:00:00 4 4.2 2017-01-01T12:00:00 12 3.6 2017-01-01T20:00:00 2.7 2017-01-02T07:55:00 6.5 2017-01-02T12:00:00 2017-01-02T08:00:00 3.9 2017-01-03T07:55:00 7.8 2017-01-03T12:00:00 2017-01-03T08:00:00 2017-01-04T07:55:00 8.1 2017-01-04T12:00:00 2017-01-04T08:00:00 5 4.3 2017-01-05T07:55:00 8.2 2017-01-05T12:00:00 2017-01-05T08:00:00 Include: PK concentration PK time Dosing time Dose amount Covariates Derived variables: actual time since first dose and time since last dose prior to the sample, etc.

Time since Last Dose is Crucial to PK Analysis Correct time since last dose prior to PK sample depends on accuracy of both PK sample and dosing date and time Incorrect time since last dose can cause incorrect analysis and result interpretation In this example, correct Cmax occurred at 4hr post dose If the time of the Cmax sample is incorrectly recorded as 6hr post dose, the Cmax value would appear later than expected. If the time of dosing is incorrectly recorded before the actual time, trough sample would become post dose sample whose value would be less than expected, and Cmax will appear later than expected. Correct data point incorrect data point Incorrect dosing time

NONMEM PopPK Data In addition to PKNCA data, include dosing records (EVID=1) NONMEM reserved variables other covariates Ordered by subjid and time CSV file format is required Dosing records: actual dosing date and time, and actual dose amount are needed for every dose event Imputation is required when actual dosing time and dose amount are not collected for every dose by design. For example, oral daily dose late stage studies. SUBJID DAY PTALD CONC ATSFD EVID AMT 1 . 100 1.5 2 3 4 4.2 12 3.6 2.7 24 6.5 28 3.9 48 7.8 52 72 8.1 76

Challenges Source data related Programming process related Cross-functional collaboration Multiple data sources and data formats Individualized PopPK data specification Lack of standard on raw PK data Imputation on missing dosing time Evolving data collection/mapping standards Missing or incorrect time Quick turn-around

Challenges with Source Data Multiple data sources Raw PK concentration data and other clinical data not in the same database No consistent common variables for merging Difficult to check integrity between two databases Lack of standard on raw PK data in terms of file structure and/or variable names Evolving data collection/mapping standards over years Missing or incorrect PK sampling time and/or dosing time, especially for late stage studies

Challenges – Pm Programming Process Cross-functional collaboration: PKPD programmers work with both pharmacometrician and biostatistician. Communicate with pharmacometrician to get analysis/modeling purpose and programming requirements Communicate with biostatistician to identify correct clinical data source based on modeling purpose, and get details about clinical data to ensure consistency between statistical analyses and Pm modeling Individualized NONMEM PopPK data specification Lack of standard and clarity Hard to program, validate, review and share Quick turn-around after DBL Imputation on missing dosing time

Strategies Standardization Quality improvement Automation Standardize source PK data – follow SDTM standards Follow ADaM requirements for PKPD data if possible Develop PopPK data standards – an ISoP initiative Create guidelines on missing time imputation Quality improvement PK reconciliation/cleaning to improve source data quality Streamline analysis/modeling dataset validation process SAS outputs based on pre-defined validation checklist Automation More SAS macros to improve efficiency, reduce validation needs and enforce standardization Build talent and keep experienced Pm programmers

Challenges = Opportunities

Acknowledgments Bret Musser, Regeneron Qi Shen, Merck & Co., Inc. David Turner, Merck & Co., Inc.