More effective therapies through mathematical modeling

Transitioning therapies from the discovery process into clinical trials is one of the most unpredictable stages of drug development. Drugs that have been proven safe in earlier studies can have unexpected results when progressed into a clinical trial. As a senior scientist, Dr. Colin Phipps develops mathematical models from preclinical data to more accurately predict performance in a clinical trial and bring more safe and effective therapies to patients.

Tell us about your research…

My research is centered around the development of mechanistic mathematical models that can predict pharmacokinetic-pharmacodynamic (PKPD) relationships in patients. The foundation of this modeling approach is the translation of information, including data from in vitro and in vivo systems, to the clinical scenario in patients.

My research is centered around the development of mechanistic mathematical models that can predict pharmacokinetic-pharmacodynamic (PKPD) relationships in patients.

For novel targets or modalities these models are informed primarily by preclinical data, while for more established areas this information can be supplemented by existing clinical data. My translational modeling work spans across multiple therapeutic areas including neurology, oncology and immunology, and many modalities including antibody drug conjugates and brain-penetrating biologics.

My translational modeling work spans across multiple therapeutic areas including neurology, oncology and immunology, and many modalities including antibody drug conjugates and brain-penetrating biologics.

Can you explain that to a non-scientist?

Data can be generated that establishes how well a potential therapy might work, using some models in the lab. Using these models, we can apply a set of mathematical equations to help us better understand how well that therapy might work for a patient. We can also use these models to figure out the dosing of a medicine – how much and how often a patient should take their medicine to help them feel better.

Using these models, we can apply a set of mathematical equations to help us better understand how well that therapy might work for a patient.

How could it someday impact patient lives?

The primary outcome of my work is a quantitative prediction that allows us to assess whether a therapy can safely achieve efficacy in patients. Patients benefit from these modeling efforts as it ensures that dosing regimens are optimized based on all available information before dosing a single subject. These models also directly improve the probability that only those therapies with the highest potential to provide transformational benefit to patients are advanced into clinical trials.

These models also directly improve the probability that only those therapies with the highest potential to provide transformational benefit to patients are advanced into clinical trials.