Data Standards for Pharmacometric Analysis Data Sets Neelima Thanneer Bristol-Myers Squibb 1

Source data issues and Standard Rules for Imputation As far as possible imputations should be avoided, but they may be necessary particularly for Population PK datasets as complete dosing history is generally not available in source data. Dose date/time imputation typically occurs in below scenarios: IV dosing where every dose date, time should be recorded Oral doses where interval dosing is recorded

Standard Rules for Imputation Dose clock time imputation for CRF designs where every single dose date and time is captured If a dose date is available but time is missing: If a trough sample was taken on the same day, use the trough time as the dose time (if IV use start time of infusion). One can add 5 min If no troughs were taken, use the previous dose time. For BID/TID adjust the imputation based on frequency. If the first dose time is missing then impute using day 1 lab time or impute based on post first dose sample time. For BID/TID adjust the imputation based on frequency. The imputed clock time records should be flagged Account for missing doses/dose interruptions based on the number of tablets or comment in text like variable

Standard Rules for Imputation Infusion Duration Imputation If infusion stop time is available but infusion start time is missing, the protocol defined duration (e.g. 1 hour or 30 min) is used to determine the start of infusion and vice versa if stop time is not available. If both infusion start and stop times are missing on day 1, pre- dose sample time or end of infusion sample time is used along with nominal infusion duration to determine the start of infusion time The imputed date/time records should be flagged

Standard Rules for Imputation When CRF is designed to capture interval doses with start and stop dates recorded and only dose times relative to PK are recorded Variables ADDL and II are derived to capture the not recorded doses ADDL: Number of additional doses exactly like the current one II: Interdose Interval If the dose time relative to PK samples are not recorded then impute using the IV imputation rules ADDL needs to be adjusted based on the recorded time deviations.

Influencing CRF Design CRF’s are not consistently designed to capture the information need for Pharmacometric analysis For BID and TID CRF should capture the prior 2 and 3 dose times relative to PK. First dose time should be collected Dose interuptions for BID and TID doses should indicate which doses are missing Pharmacometricians and programmers can influence the CRF design by getting involved at the design stage. For oral studies where patient takes drug at home CRF should be designed to collect the dose date time relative to the PK sample drawn.

Population PK Data Standards Initiative Sponsored by International Society of Pharmacometrics (ISoP) Group of around 20 enthusiastic pharmacometricians and data programmers has come together at the beginning of 2016 to address the outlined situation and work on its improvement. With the sponsorship of ISoP, the group envisions the development of PPK data standards for interchange and analysis. Some of the benefits of the work will directly reflect on improved: Consistency and efficiency in pharmacometric datasets Quality (fewer errors, enabling development of open-source tools for automatic data checking) Enabling development of open-source tools for automatic graphical exploration Regulatory compliance and audit readiness (enabling development of open-source tools for data submission) Population PK analyses are most commonly included component of regulatory filings but the datasets for such analyses are prepared and documented inconsistently

Objective: Standardized datasets used directly by analysis software Source data Standardized dataset SDTM Population PK standard dataset Analysis ADaM NONMEM, Monolix, R, Stan, Julia, etc can use standardized datasets directly Already the case for R and some other tools Common variable names Common structure Will always look the same Suitable for collaboration Typically SAS datasets Radivojevic, et al. ACoP 2016

What we have done Draft standard for input data Classified by information group Source dataset and variable name Required input from the pharmacometrician? CDISC naming conventions and length requirements Imputation and derivation guidance Controlled terminology General comments

What we have done Current status Path to CDISC data standards for pharmacometrics hand-over of solid draft data structure review by CDISC teams and feasibility assessment implementation guide Preparing a “Perspectives” article to outline our thinking and objectives

For any other stakeholders Next steps Scripts for further conversion into tool dialects Once the standard is established, capture the deliverable in a “White paper” Longer term: Start expanding to PKPD data Develop and publish automation scripts For this initiative to succeed, it must make things faster, easier and more efficient For modelers For programmers For any other stakeholders