News

Metrum Research Group is pleased to announce the appointment of Bill Knebel, Pharm.D., Ph.D. as President of the biomedical modeling and simulation services firm.

by Tim Bergsma

Already widely received in the finance community, cloud computing is gaining acceptance in the pharmaceutical industry as well. At this year’s American Conference on Pharmacometrics, Metrum Research Group staff presented a poster on the topic, and helped lead a related panel discussion.

For new developers, getting a package ready for building and submitting to CRAN is an expletive-filled, head-scratching experience to say the least. Trying to figure out the basics of what goes in depends and what goes in imports is a lost afternoon most of us would like back. Once that is understood, filling in relevant information to each field is a mundane task even for a well polished package developer. The out-of-the-box roxygen skeleton supplied by RStudio gives the bare bones road map of what should be part of function documentation:

An article in the recent issue of Heptaology illustrates an insightful application of hepatitis C viral dynamic modeling and clinical pharmacology. Guedj et al. (Hepatology 2012 55: 1030-1037) present an HCV dynamic model of viral load versus time during mericitabine therapy. Rather than seeing the clear bi-phasic viral load profile we see with interferon and the protease inhibitors (with characteristic sharp initial drop in viral load), the viral load decline during mericitabine therapy is more complicated: either a slower bi-phasic profile or mono-phasic profile. The authors propose an interesting modeling solution to gain insight into mericitabine’s antiviral activity.

Recently, I was asked to speak at this year’s PaSiPhIC conference about different approaches to meta-analysis. As I was putting together my presentation, I began to wonder: How can we best leverage traditional meta-analysis methods in a model-based drug development framework?

The primary rationale for model-based meta-analysis (MBMA) is to improve decision-making by better leveraging prior information from multiple sources. Decision-makers generally attempt to consider such prior information, but it is usually done in a relatively qualitative manner, and each individual decision-maker is usually aware of only a subset of the prior information. MBMA seeks to make the process more quantitative and comprehensive. The process and results of MBMA may be made visible (aka transparent) to the decision-makers. The end result is that the decision-makers are better informed, and they can contribute their knowledge to the modeling process leading to better, more trusted models and model-based inferences.

I recently returned from the 2013 PAGE meeting in Glasgow. As usual, the scientific presentations were some of the best in the field of pharmacometrics. At this year’s meeting I was offered an opportunity to present some of our recent thoughts about model-based drug development in oncology.