Intelligent Wondering 8211 Investigative Clinical Pharmacology

The Pharma of the Future

Reviews of new drug applications by regulatory authorities worldwide grow ever more rigorous. I think that one reason for this heightened scrutiny is that reviewers have, over the last 10 years, read many submissions containing results of pharmacometric modeling and simulation analyses. With the use of modeling, explorations of the determinants of drug efficacy and safety are more thorough, and the analyses provide solid support for dose recommendations and labeling content. In short, I think that model-based analyses have raised reviewers’ expectations.

The sciences of pharmacokinetics and pharmacodynamics, which are built on the principles of clinical pharmacology, are at the core of pharmacometric modeling and simulation. Pharmacokinetic techniques allow the robust design, analysis, and interpretation of Phase 1 studies from which information about the absorption, distribution, metabolism, and excretion of compounds is uncovered. Pharmacodynamic analyses of data from Phase 2 and 3 efficacy and safety studies yield insights into drug-disease interactions and the role of intrinsic and extrinsic factors in influencing the risk-to-benefit ratio of a drug. For good reason, then, pharmacokinetics and pharmacodynamics combined with biostatistics and mathematics – pharmacometric modeling and simulation – have emerged as important tools in the analysis and interpretation of data in clinical development programs.

As valuable as pharmacometric modeling and simulation are, caution is warranted. Models may be sufficient to guide further inquiry, but may not give the complete story of the drug. For example, queries of the clinical database may show that the only available pharmacokinetic data came from patients who represented a minority of the full dataset. In that case, the model may need to be revised when more data become available. Or, several covariates may be identified in the model, but some of the covariates may not have a clinical effect. Determining the clinical significance of observed changes in pharmacometric behavior is important.

One approach to investigating the clinical significance of pharmacometric findings is to relate these findings to clinical outcomes in all subjects, independent of whether the subjects contributed drug concentration data to the modeling effort. For example, if reduced drug concentrations were found in obese subjects who contributed drug concentration data, an investigation of safety and efficacy outcomes across the range of body weights in the clinical trial database may be warranted.

An analysis of clinical significance requires the ability to rapidly assemble and subset data into relevant groups of interest. This investigative process benefits greatly from a clinical pharmacology sensibility.  Knowledge of the principles underlying pharmacokinetics and pharmacodynamics gives insight into where to look for issues and answers and how to display results for maximum effectiveness.

The regulatory submission and review process is fraught with challenges. Broadly speaking, companies can follow one of two approaches. The first, and in our experience less desirable, is to minimize the exploration of signals in the hope that they will not emerge as review issues. This approach often results in a lengthy list of regulatory questions that must be urgently addressed in exceedingly short time frames. An alternative approach is the comprehensive review of the determinants of safety and efficacy using population pharmacokinetic and pharmacodynamic modeling, with follow up of potentially relevant issues before submission to regulatory authorities.

Are you hooked? Check out the previous Pharma of the Future? blog entry, An omission, a recommendation, and a prediction. Or visit the Pharma of the Future archive (link to another blog site) to catch up with the future of pharmacometrics.