Jessie Au, Pharm.D., Ph.D.


Guill Wientjes, Ph.D.

Principal Scientist

Au and Wientjes have used computational modeling in their research to address complex biological problems that either could not be measured experimentally due to limitations of the available technologies (e.g., measuring drug effects on intended molecular targets) or the nature of the problem (e.g., outcomes of clinical trials in patients with diverse biological or physical characteristics). In the early 1990s, they demonstrated the successful use of computational modeling to predict the drug transfer from on top of the skin to the blood. The U.S. Food and Drug Administration considers modeling-informed drug development as a critical component in a New Drug Application as well as to demonstrate bioequivalence of generic drug formulations. Another noteworthy example is their work on using computational modeling to predict drug delivery and exposure at the target tumor sites and consequently the patient response (and hence predict the clinical trial outcome). The treatment is the intravesical MMC therapy of non-muscle invading bladder cancer discussed above. Au and Wientjes have undertaken a series of preclinical and clinical pharmacologic studies that enable them to establish and confirm the mathematical models to depict the spatial- and time-dependent delivery of a therapeutic to different locations of bladder tissues during intravesical chemotherapy. They then obtained tumors from human patients and determined the range of drug concentration/delivery needed to produce therapeutic benefits. By comparing the extent of drug delivery and the range of effective drug exposure using additional computer simulations, they established that a therapeutic outcome can be improved substantially by changing the treatment conditions (dose, dosing volume, residual urine volume, urine production rate, urine pH value). The computation results also indicated: (a) marginal improvement by changing individual parameters, such that a trial proving the effect of each individual parameter would require enrolling thousands to tens of thousands of patients and would not have been possible due to high costs and limited patient availability, and (b) simultaneous changes in five parameters would yield significant clinical benefits such that a trial required only 230 patients to demonstrate statistically significant benefits. Au and Wientjes, with their surgical oncologist collaborators (in 14 academic centers in the United States, Canada, and Europe) used these computational results to challenge the conventional clinical study paradigm of changing one parameter at a time and instead made simultaneous changes of five parameters in the randomized phase III trial. The trial results are closely aligned with the model predictions. This model-informed phase III trial design, which took place in early 1990s, is a clear example of the successful use of quantitative systems pharmacology. Au and Wientjes are now developing new models to identify the suitable clinical trial protocols in their development of the drug-loaded, tumor-penetrating microparticles in patients with peritoneal cancer.