Much of the investigation at Ordway Research Institute (ORI) examines intervention in pathologic processes with small molecules. Part of the activity is in finding novel targets to allow new drugs to be found. Another area of activity is in screening of diverse, drug-like libraries of compounds for these targets with the end of identifying lead compounds for further optimization. Yet another area of interest is in identifying doses and schedules of drugs that will achieve the desired goal of generating therapeutic effectiveness while engendering only a small probability of producing a drug concentration-related toxicity.

It is in this latter area that the Mathematical Modeling Core relates to many of the activities within ORI. The core spans the activities related to anti-infective therapeutics, oncolytic therapy and central nervous system intervention.

In order to fashion exposure-response relationships, it is necessary to relate some measure of drug exposure to the effect that a drug produces. The effect of the drug can be measured in an in vitro setting (Hollow Fiber Core), in an animal system (see, for example, Emerging Infections and Pharmacodynamics [PD] Laboratory), or in humans.

This laboratory has been a leader in the development and validation of optimal sampling theory, so that system information-rich sampling times are identified for study. Once the data are obtained, population pharmacokinetic modeling techniques are applied in each of these settings, with Maximal A-posteriori Probability (MAP) Bayesian estimation employed to estimate drug exposure in specific instances. The estimated drug exposures are then linked to the effect by one of a number of different techniques. In some instances (e.g., prevention of resistance) the drug exposures and the effect are all estimated simultaneously by virtue of large models (e.g., 3-5 parallel inhomogeneous differential equations of 13-17 parameters) in a population analysis. In other instances, the MAP-Bayesian estimates are used as an independent variable in logistic regression analyses when the endpoint is dichotomous or in a Cox Proportional Hazards model when the endpoint is the time to an event. Other approaches are also possible.

Once an exposure-response relationship is identified and a target is decided upon (e.g., a large enough drug exposure to suppress resistance), it is important to ascertain whether a specific drug dose will reliably obtain the desired goal when a large population of patients is considered. This laboratory pioneered the use of Monte Carlo simulation to evaluate drug doses for robustness of target attainment. This new technique has been used for dose assessment and has been adopted by the National Committee for Clinical Laboratory Standards (NCCLS) as the method of choice for determining susceptibility breakpoints.

In these ways, the Mathematical Modeling Core takes the data emanating from the laboratories in ORI and optimizes the utility of this data to provide insight into the best use of drugs to help protect our patients.

Mathematical Modeling Core Director

George L. Drusano, M.D.

Back to top