Mind the gap
I was talking with a Program Director the other day about an upcoming regulatory filing. She was rightly proud of the clinical pharmacology work that had been completed, but she was also anxious about possible holes in the package. As we talked, I thought about how gap analyses have changed over the years, particularly since modeling and simulation results have come to play a larger part in the Clinical Pharmacology Summary in NDA submissions.
In my experience, there are 2 types of gaps to consider as you prepare for a regulatory filing. The first gap concerns the standard clinical pharmacology studies that must be done to comply with regulatory requirements. Formal drug–drug interaction studies and the “thorough QT” study are examples. A gap analysis will determine whether all the required studies are finished and reports written. The quality of the data and whether the study designs and data collection schemes meet current scientific and regulatory standards should also be judged.
The second gap is the one that may exist in a dossier between the claims that a company would like to make about a drug and the evidence to support these claims that will be presented to regulatory agencies. Typically, these gaps become obvious while you are assembling evidence to support the rationale and justification for dose selection; the influence of patient characteristics on PK parameters; and the effects of selected concomitant medications that were not addressed in drug–drug interaction studies.
Gaps of the second type are increasingly being addressed using population PK and PK/PD analyses, whereby data are pooled across studies and the relationships between dose and drug concentration and between drug concentration and clinical response, including efficacy and safety biomarkers, are investigated.
A gap analysis in this latter case consists of 2 parts. First, determine how the desired statements and claims about the drug can be properly supported with population PK and PK/PD modeling and simulation. Second, determine if the phase 2 and 3 data are adequate to support the modeling effort by checking the quality and quantity of the sampling schemes for drug concentration and outcome measures.
The nature of the gaps in a development program will inevitably vary from company to company and from one development program to another, but one thing is certain. The sooner one finds the gaps and fills them, the better.