Forensic Pharmacometrics: Part 1 – Data Assembly
Introduction: Pharmacometric modeling and simulation (M&S) is moving from merely describing pharmacokinetic (PK) and pharmacodynamic (PD) phenomena to informing critical drug development and regulatory decision-making milestones. As M&S results become integral to a program’s outcome, the consequences of lapses in data assembly and analytic result quality can jeopardize the role of pharmacometrics in contributing to the transition to model-based drug development. There are few standards available to define measures of acceptability and suggest strategies for assessing the “fit for purpose” of analysis datasets or model building efforts. While there is often attention paid to documenting the amount of data deleted from the analysis datasets and the reasons for such deletion, less attention has been paid to embedding proactive quality assurance activities into the data assembly process. These quality assurance activities might include, for example, a review of programming logic and coding as well as the assumptions used to re-create dosing histories.
- Describe a case study of a forensic assessment of analysis-ready datasets performed as part of a due diligence effort in preparing for re-purposing data to support future development program efforts and regulatory filings
- Describe methods used in the forensic assessment that identified problems and errors in the previously constructed datasets and propose proactive quality assurance activities
Methods: The due diligence effort incorporated a systematic review of the various data elements to identify and focus efforts only on those data warranting correction and rebuild. A series of quality assurance checks comparing the analysis-ready datasets to the source data files were developed by both data programmers and scientists addressing their individual areas of expertise regarding quality assurance. Three teams, operating in parallel and consisting of a scientist and a data programmer, were constituted to focus on different aspects of the PK and PK/PD datasets and modeling. A senior scientist, supported by a medical writing team, served as the overall integrator of efforts and point of contact for various groups within the Sponsor organization. A review of the previously prepared technical reports was used to identify the assumptions and strategies that went into the original data assembly and model-building efforts.
Results: The forensic analysis of the analysis-ready datasets revealed a mismatch in demographic data with corresponding dosing and PK data in a large percentage of patients and systematic errors in the creation of dosing histories, including improper use of NONMEM®-derived data items (ADDL) and incorrect dose amounts in subsets of patients across studies. The descriptions of patient disposition and data deletions in the technical reports were insufficient in supplying reasons or rationale for the programming logic errors discovered. A summary of the forensic assessment findings was presented to the Sponsor. Given the extent of the issues with the data and the likelihood of significant impact on the modeling results, senior management charged the data assessment teams with re-creating the analysis-ready datasets, re-running the models, and re-writing the technical reports within 4 weeks. Subsequently, the data assessment teams were also charged with a strategic review and update of derivative documents including the proposed product label, the Clinical Pharmacology Summary, and the data definition documentation.
Conclusions: This forensic assessment of a completed analysis demonstrates a gap that currently exists in defining the criteria for judging the quality of data assembly efforts along with the comprehensiveness of data programming, technical report, and other supportive work product documentation. Strategies for this assessment can be used as a basis for independent validation of pharmacometric work products prior to use in critical decision-making activities, as well as in the development of standards for quality assurance activities during the execution of a pharmacometric analysis
American Conference on Pharmacometrics (ACoP), Mashantucket, Connecticut, October 2009
By Thaddeus H. Grasela, Jill Fiedler-Kelly, Darcy Hitchcock, Elizabeth A. Ludwig, Julie Passarell