Vincent Buchheit, Nicolas Frey
Hoffmann-La Roche, Basel, Switzerland
Objectives: Highlight the importance of cleaned PK data, on the results of a population PK analysis, using a real case example
Methods: The PK data from one phase III study and three phases II studies, from an oncology drug, have been pool together in order to study the sources and correlates of variability in drug concentrations among individuals. The most important data issues, such as covariate outliers, infusion rate physiologically impossible have been fixed. From this pooled dataset, we generated a second dataset where additional PK data issues, mainly identified using a previous developed PK model with a Bayesian feedback analysis, haven been corrected. The base and final models, developed from the cleaned dataset, have then been run with the non-fully cleaned dataset and modeling results were compared.
Results: Results will be shared during the poster session.
Conclusions: Data collected during a clinical trial will never be 100% accurate. The author believes that the effort (time and resources) spent on exploring data and fixing data issues bring quality and efficiency to the modelling flow. [1]The use of Model Based Drug Development is more and more advocated in the pharmaceutical industry, sometimes to answer questions such as "What is the best dose?", or "What is the best dose regimen?" The matter of data quality is essential to help responding to those questions.
References:
[1] Buchheit, V. et al., Efficient quality review for modeling input dataset, PAGE 20 (2011) Abstr 2041 [www.page-meeting.org/?abstract=2041]
Reference: PAGE 22 () Abstr 2749 [www.page-meeting.org/?abstract=2749]
Poster: Covariate/Variability Model Building