An Uncommon Vignette?
An Uncommon Vignette?
The CEO of a pharmaceutical company, tired of late-stage development failures and FDA questioning regarding dose selection, decides to act on the promise of pharmacometrics. “Fix it,” he says to the head of clinical pharmacology, “I don’t care what it takes!” The clinical pharmacologist agrees to take on the challenge and asks for a data programmer and pharmacometrician. Seizing on an opportunity to spearhead an upcoming “end of phase 2” meeting with the FDA, the pharmacologist quickly sketches out his strategy for the modeling activities required for dose selection and justification. He then instructs his programmer to assemble the required dataset using the data from several phase 1 studies and a recently completed phase 2 study. A week later he discovers that the modeling has not begun because the dataset is still not ready. “What is taking so long?” he wonders.
Before too long, the pharmacologist realizes that his ability to drive the “end of phase 2” meeting agenda is in serious jeopardy. “I only had 1 hour to come up with the analysis plan,” he complains to the pharmacometrician, “I thought that you knew how these datasets are usually constructed.” Then, turning to the data programmer, he complains, “It’s only concentration data from a couple of trials. The statisticians are already done with their analysis.” The pharmacometrician and data programmer cast knowing looks at one another and begin to detail the first round of issues they have already faced.
“After we defined the population to be included and spent half a day explaining to the statisticians how we would reconcile the differences between our population and theirs, we realized that the exact dates and times of neither dosing nor blood sampling were ever cleaned, and, as a result, about 50% of the previous doses looked like they were taken after the blood sample was collected instead of before. Once we addressed that issue, we realized that there was no unique identifier on the sample requisition form for one of the studies, so unless we can come up with a strategy that produces something reasonable, we’ll have to exclude the whole study. You mentioned that we’d need a variable to indicate fed/fasted status because there is an effect of food on pharmacokinetics. In the Phase 1 studies, this can be easily defined; but in Phase 2, we can only make assumptions based on the protocol, so you’ll have to let us know how to assign it. For the efficacy endpoint, we can easily find the calculated change from baseline last observation carried forward measure, but you want all of the interim measurements on the raw scale, right? And we haven’t even considered how to handle missing creatinine clearance values yet! Should we go on?”
“Currently,” the clinical pharmacologist might conclude, “it’s a gamble as to whether any modeling and simulation effort can successfully meet the timelines for a major program deliverable. Every project is different. We do the best we can with what we have.” The consequence is that too often submission deadlines are met by means of a quick-and-dirty pulling together of graphics and text that have been cut and pasted from previous reports in order to create a story for the dose selection and justification briefing book. Although pharmacometricians recognize that this quick-and-dirty approach it is not the best, they are sometimes forced to rationalize the effort over the impossibility of the task. This has the development team wondering when and if they will ever understand what pharmacometrics really has to offer. Clearly pharmacometrics must move beyond crisis-mode operations and become fully integrated into the development team’s strategic thinking and tactical planning. This is the next hurdle that must be past on the road to model-based drug development.
Be sure to read the next Pharma of the Future? blog entry: Intro to Pharma of the Future?. And don’t forget to peruse the previous post: Square Pegs in Round Holes?
This vignette originally appeared as part of the following publication:
Grasela TH, et al. Informatics: The fuel for pharmacometric analysis. AAPS J. 2007;9(1):E84-91.