190: Kick-starting Reporting of Pharmacokinetic Results With Simulated Concentration-Time Data

Statement of Purpose: Analysis of pharmacokinetic (PK) data is often rate-limiting in reporting results of clinical trials, due to time required for bioanalytical analysis and calculation of PK parameters. Here a workflow is proposed with initiation of programming and review of PK datasets, analyses, tables, listings and figures using simulated concentration-time data, providing potential for significant time savings in the reporting of PK results.

Description of Methods & Materials: The proposed workflow only requires the Investigator’s Brochure and a live database, though waiting for completion of all PK collections and a test transfer from the bioanalytical laboratory would allow for data that better represents actual study conduct (eg, missing collections, clotted samples without results, etc.). Concentration-time data are simulated using SAS® based on anticipated PK of the analyte(s). Dummy PC and ADPC (including input file for noncompartmental analysis) are created. A dummy noncompartmental PK analysis is run in validated software (eg, Phoenix WinNonlin®), and the output parameters are used to generate dummy PP and ADPP datasets. Dummy PK tables, listings and figures are generated and reviewed for structure and formatting. When the final transfer is available, the concentration datasets are updated and the noncompartmental PK analysis is run using the actual concentration-time data. The output parameters are used as updated source and PP and ADPP are re-run. PK tables, listings and figures are updated and reviewed for content.

Data & Results: In a standard workflow, programming of PK datasets, tables, listings and figures does not begin until after concentration data are received from the bioanalytical laboratory. Since there may be a lag in the bioanalysis compared to availability of the clinical source data, reporting of PK results is often rate-limiting. However, use of dummy datasets; PK analysis; and tables, listings and figures based on simulated concentration-time data allows for much of the work required for reporting of PK results to be completed ahead of receiving concentration data. In particular, this process allows the structure and format of datasets, tables, listings and figures to be programmed and reviewed while waiting for bioanalytical data. Other potential advantages include an opportunity for pharmacokineticist to review the input file, to confirm alignment with requirements for analysis, and potential flexibility in scheduling programming resources across studies. The extent of time savings is dependent on a number of factors, including timing of bioanalytical transfers relative to availability of Investigator’s Brochure, live database and clinical datasets. Dummy datasets may be especially useful for blinded studies (for which bioanalytical data is often not available until after the database is locked) and more complex studies (where analysis requirements may be unique and/or additional summary and analysis outputs may be required).

Interpretation, Conclusion or Significance: Use of simulated concentration-time data can decrease the time from final analytical data to reporting of PK results, by allowing for programming and PK analysis tasks to be initiated and the structure and format of dummy tables, listings and figures to be reviewed prior to receipt of the final analytical data.

Disclosures: