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Generating Robust PK/PD Data Sets

by Scientific Writing Team

While no one sets out to conduct a clinical study that will not meet its objectives, studies commonly fail to meet their objectives or fail to reach their full potential by generating a less than optimal data set.  This is particularly evident in clinical pharmacology investigations that involve pharmacokinetic (PK) and/or pharmacodynamic (PD) analyses.

As consultants with a broad background and expertise in clinical pharmacology, we have observed a number of common pitfalls in the planning, conduct, and analysis of studies with a PK and/or PD (PK/PD) component that can result in a sub-optimal data set.

These pitfalls can then have a significant impact on future decisions within drug development programs.  Obtaining robust PK/PD data sets is our focus at Nuventra by providing intelligent solutions and ideas that can help maximize the potential of clinical pharmacology studies and help close the gap between the desire to have a study meet its objectives and actually meeting those objectives.

As expected in the current environment of escalating costs and shrinking R&D budgets, companies are looking to do more with less.  The cost of conducting clinical pharmacology studies is quite often a key driver of many decisions regarding study planning and execution.  Rightfully so, it makes sense that studies should be designed and executed to answer the most salient questions with minimal R&D spend and a minimal number of subjects.  Furthermore, it makes sense to get everything out of a clinical study to justify the time and money spent and the impact on the development program.  However, one specific cost that is often not considered is the “opportunity cost” of lost or sub-optimal data.

The opportunity cost of a sub-optimal data set can be quite high if, for example, a study needs to be repeated or clarity was not gained regarding dose selection for future studies.  Overall, our extensive experience with clinical pharmacology has paid dividends for our clients in decreasing the opportunity cost of conducting drug development.

For example, Nuventra was engaged by a client during the conduct of an IV infusion study and we quickly discovered that the PK sampling schedule was not optimal. The CRU was collecting a blood sample 15 to 30 minutes prior to end of a 1.5 hour infusion. Obtaining a blood sample for PK analysis as close to discontinuation of infusion is important as this is typically the Cmax and Tmax for the drug. Drug concentrations will likely decrease rapidly after the infusion is stopped and if samples are taken too early or too late relative to infusion termination then the concentration versus time profile will not be as precise as it could be, which leads to less than optimal PK parameter estimation. If Nuventra had not identified the issues then the client’s PK results would not have been accurate.