(337cr) Mathematical Modeling of Diabetic Kidney Disease Progression to Elucidate Lack of Efficacy of Pharmacological Agents.

My research interests are in using mathematical and computational methods to elucidate the mechanism of disease progression to enable the discovery and development of novel therapeutics. I am particularly interested in using ordinary differential equations and agent-based models to better understand disease mechanisms.

Current Research:

Diabetic kidney disease (DKD) is a growing health problem that affects a significant portion of the global population. Therapeutic treatments for glomerular fibrosis, tissue damage that occurs in the kidneys of diabetics, are lacking. Therapeutic treatments such as glucose control, aminoguanidine, and alagebrium have been proposed clinically but are insufficient therapeutic approaches due to their lack of efficacy.

To determine the lack of therapeutic efficacy, we developed a mechanistic systems biology model of glomerular fibrosis in diabetes from a previous model of interstitial fibrosis. We first verified and extended the model mechanism using in vitro and in vivo studies of glomerular fibrosis, then we gathered and collated quantitative data from the literature of glomerular fibrosis in diabetes and finally used the data to re-estimate model parameters such that the model was an accurate representation of glomerular fibrosis in diabetes.

Using our systems biology model, we identified key biomolecules, such as advanced glycation end products and matrix proteins modified due to high blood glucose, as responsible for the lack of therapeutic efficacy when glucose control or aminoguanidine is applied. Our model predicts the active breakdown of advanced glycation end products would be the most efficacious treatment approach. Our model also helps explain the lack of efficacy of alagebrium, which is due to its inability to reduce the expression of the key intermediate molecule, transforming growth factor – β.

Overall, our model illuminates a mechanistic understanding of the disease etiology of glomerular fibrosis in diabetes to enable us to provide mechanistic explanations for the lack of efficacy of different pharmacological agents for treating glomerular fibrosis. This understanding can enable the development of therapeutics that are more efficacious at treating kidney damage in diabetes.

Future interests:

I’m interested in working in quantitative systems pharmacology (QSP) groups to aid in the drug discovery and development process. For instance, developing models to improve clinical outcomes from pre-clinical studies by predicting therapeutic efficacy across species or enabling the determination of dosage regimens. From my research projects, I have acquired knowledge in the process of model development for biological systems and expertise in using programming languages such as Python and Matlab. I have gained exposure to individual and population PK/PD modeling in SimBiology and Monolix from workshops and learned statistical modeling of high-throughput experimental data from a bioinformatics course. I was given the opportunity to make use of these skills to expand a QSP model for a global pharmaceutical company.