Aerial view of the Saban Research Institute building.

Laboratory of Applied Pharmacokinetics and Bioinformatics

Research Topics

  • Personalized medicine
  • Pediatric infectious diseases

Research Overview

When it comes to choosing a dose for a medication, such as an antibiotic or a cancer drug, we can do far better than the current practice of giving the same dose to everyone. We know that each patient has a unique profile of drug concentrations in the blood, and that if we can shape that profile, we can often improve both the effectiveness and safety of the drug. This is one aspect of the rapidly growing field of personalized medicine, which recognizes that one size definitely does not fit all.  
Our laboratory is one of the world leaders in creating models of drug behavior in populations of patients to find the best dose for an individual patient, a dose that is most likely to achieve the desired concentration profile in blood or tissue. Our lab has experts from a wide variety of fields – including medicine, pharmacy, quantitative biology, mathematics, statistics, and engineering – who create software not unlike that which is used to fly airplanes or guide rockets to their targets. The clinician sets the target, and our software helps to determine the dose most likely to reach that target and have the desired therapeutic effect.  
The data we use in our models comes from our own laboratory and clinical studies as well as from collaborators from all over the United States and the world. In addition to drug concentrations and effects, our models can include patient-specific information such as genetic markers, weight, age, sex, or kidney function—all gathered in order to understand and control the patient’s response to therapy as tightly as we can.

  • Does this approach to personalized medicine shorten hospital stays?
  • Can it save lives?
  • Does it reduce bad side effects?
  • Can it lower healthcare costs?

Yes! These results have been demonstrated in studies around the world, in areas of medicine ranging from cancer and infectious diseases to heart disease and epilepsy.

We are also using population data in other unique ways, for example, devising algorithms using an individual’s genetic profile, based on hundreds or thousands of genes, to finely discern their racial and ethnic origins and to map their ancestry to specific geographic locations. We are exploring how this information can be used to better predict how drugs will behave in patients before a specific drug is prescribed. 


  • NIGMS R01 GM068968: Population Pharmacokinetic Modeling and Dual Optimal Control
  • NICHD R01 HD070886: Ontogeny of Voriconazole Pharmacokinetics and Metabolism