Associate Director Bristol Myers Squibb monroe township, New Jersey, United States
Disclosure(s):
Renuka Hegde: No financial relationships to disclose
Objectives: With the release of the CDISC ADaM Population Pharmacokinetic (popPK) Implementation Guide (IG) for the creation of ADPPK datasets and the forthcoming recommendation by the FDA to submit in this format, companies are striving to understand and implement these new standards. Pharmacometric programmers, who may not have experience creating ADaM datasets (a task typically performed by statistical programmers), now face the challenge of ensuring compliance with the IG. This poster aims to serve as a comprehensive guide to help both programmers and modelers ensure that their dataset formats meet the requirements of the analysis software (e.g., NONMEM) while also adhering to the IG standards, and it will present a case study to illustrate these concepts in practice.
Methods: To achieve compliance with the ADPPK IG, comprehensive changes are required throughout the entire workflow, from dataset preparation to final submission. This includes incorporating all required variables and ensuring that the metadata for all variables, including permissible and conditional ones, is accurate and consistent with the IG. Additionally, it is crucial to update internally developed tools and ensure that modelers' files are compatible for seamless integration. The ADPPK dataset differs from typical ADaM datasets due to the unique aspect of integration of multiple studies into a single pooled dataset. To achieve this, additional effort was dedicated to updating and harmonizing historical studies to align with the IG requirements, as well as ensuring consistency with newly created studies.
Results: A single CDISC ADPPK dataset suitable for both modeling and submission was created. Required variables, such as PARAM/PARAMCD and AVAL/AVALC, were included in the popPK dataset. Baseline demographic and physical characteristics, such as RACE and SEX from ADSL, were included in the popPK dataset without modifications. Analysis-specific race (ARACE) was derived from RACE. Analysis-specific grouping variables, like AGEGR1, were created following the IG recommendation. Missing covariate values were kept as missing, and methods for handling analysis software requirements and best practices for imputation will be discussed. Variables in historical studies were mapped to CDISC-compliant names. Model files were updated to handle ADPPK-required variables (e.g., AFRLT, USUBJIDN), character variables and renaming of variables. The permissible and conditional flag variables, FLGREAS and EXCLF, were used in the model file to identify records for exclusion.
Conclusions: An industry-wide standard for popPK datasets ensures high quality and seamless interactions with health authorities. Conformance to the ADPPK IG enables the creation of a single dataset that meets CDISC standards, modeling requirements, and supports automation and tool development. This case study serves as a guide for pharmacometric programmers and modelers in achieving compliance with the IG, thereby enhancing data quality and consistency across studies.
Citations: [1] Clinical Data Interchange Standards Consortium (CDISC). (2021, 29 November). Analysis Data Model (ADaM) Implementation Guide Version 1.3. Retrieved from https://www.cdisc.org/standards/foundational/adam/adam-implementation-guide-v1-3 [2] Clinical Data Interchange Standards Consortium (CDISC). (2023, October 6) Basic Data Structure for ADaM popPK Implementation Guide v1.0. Retrieved from: https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0
Keywords: CDISC standards, pharmacometric programming, population pharmacokinetics
