Using Model Reduction to Bridge the QSP-Pharmacometrics Divide
One of the biggest challenges—and, hence, the biggest opportunity for quantitative systems pharmacology (QSP)—is drug attrition in Phase 2 clinical trials. Investigational medicines are usually tested for the first time in patients in Phase 2 clinical trials. This is the point when many drug programs fail. In fact, approximately 80 percent of new drugs that move into Phase 2 fail. The major reason for this failure is that the drug doesn’t show efficacy or is not safe.
QSP is a relatively new discipline with enormous potential to improve pharma R&D productivity. It provides a framework for constructing mechanistic, mathematical models of drug action in silico. This integrative discipline incorporates pharmacometric, pharmacokinetic/pharmacodynamic (PK/PD), and physiologically-based PK (PBPK) approaches with systems biology models of biological and biochemical processes. Such models can inform the mechanisms of drug efficacy and safety, as well as confirm the ‘drugability’ of proposed targets.
Describing biological systems at this level of detail invokes the issue of model complexity. QSP models are generally too large to be validated or fit in a traditional sense and they can become intractable to standard methods of analysis or even to the modeler’s own intuition. Model reduction can alleviate these issues of complexity by eliminating portions of a system that have minimal effect upon the outputs or time-scales of interest. Such approaches yield simplified models that still provide accurate predictions. By shrinking a model’s parameter space, increasing computational speed and reducing the number of modeled state-variables, reduction techniques can help bridge the gap between pre-clinical and clinical QSP applications.
In this webinar, Dr. Tom Snowden demonstrated: why reduction methods are a potent and necessary tool in the modeler’s arsenal; how reduction methods can be applied to QSP models; and, how model reduction can be used to extract scientific and business insights from complex models.